Data Imputation & Classification

This project is based on the Automobile Dataset from Kaggle. The two objectives of this project is:

  1. Data Imputation of missing data

  2. Determination of best classification algorithm using K-Cross validation method

In [1]:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

from IPython.display import Markdown as md
In [2]:
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objects as go
import plotly.express as px
import cufflinks as cf

init_notebook_mode(connected=True)
cf.go_offline()
In [3]:
automobile_df = pd.read_csv('Automobile_data.csv')
automobile_df.head(10)
Out[3]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
0 3 ? alfa-romero gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 13495
1 3 ? alfa-romero gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 16500
2 1 ? alfa-romero gas std two hatchback rwd front 94.5 ... 152 mpfi 2.68 3.47 9.0 154 5000 19 26 16500
3 2 164 audi gas std four sedan fwd front 99.8 ... 109 mpfi 3.19 3.4 10.0 102 5500 24 30 13950
4 2 164 audi gas std four sedan 4wd front 99.4 ... 136 mpfi 3.19 3.4 8.0 115 5500 18 22 17450
5 2 ? audi gas std two sedan fwd front 99.8 ... 136 mpfi 3.19 3.4 8.5 110 5500 19 25 15250
6 1 158 audi gas std four sedan fwd front 105.8 ... 136 mpfi 3.19 3.4 8.5 110 5500 19 25 17710
7 1 ? audi gas std four wagon fwd front 105.8 ... 136 mpfi 3.19 3.4 8.5 110 5500 19 25 18920
8 1 158 audi gas turbo four sedan fwd front 105.8 ... 131 mpfi 3.13 3.4 8.3 140 5500 17 20 23875
9 0 ? audi gas turbo two hatchback 4wd front 99.5 ... 131 mpfi 3.13 3.4 7.0 160 5500 16 22 ?

10 rows × 26 columns

Data Conversion and Imputation

This dataset represents null value with '?'

To start of, let's first replace them with NaN

In [4]:
automobile_df.replace('?', np.NaN, inplace=True)
In [5]:
automobile_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 205 entries, 0 to 204
Data columns (total 26 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   symboling          205 non-null    int64  
 1   normalized-losses  164 non-null    object 
 2   make               205 non-null    object 
 3   fuel-type          205 non-null    object 
 4   aspiration         205 non-null    object 
 5   num-of-doors       203 non-null    object 
 6   body-style         205 non-null    object 
 7   drive-wheels       205 non-null    object 
 8   engine-location    205 non-null    object 
 9   wheel-base         205 non-null    float64
 10  length             205 non-null    float64
 11  width              205 non-null    float64
 12  height             205 non-null    float64
 13  curb-weight        205 non-null    int64  
 14  engine-type        205 non-null    object 
 15  num-of-cylinders   205 non-null    object 
 16  engine-size        205 non-null    int64  
 17  fuel-system        205 non-null    object 
 18  bore               201 non-null    object 
 19  stroke             201 non-null    object 
 20  compression-ratio  205 non-null    float64
 21  horsepower         203 non-null    object 
 22  peak-rpm           203 non-null    object 
 23  city-mpg           205 non-null    int64  
 24  highway-mpg        205 non-null    int64  
 25  price              201 non-null    object 
dtypes: float64(5), int64(5), object(16)
memory usage: 28.9+ KB

Converting some of the object type features to float64

In [6]:
obj_col_list = ['normalized-losses', 'bore', 'stroke', 'horsepower', 'peak-rpm', 'price']

for col in obj_col_list:
    automobile_df[col] = automobile_df[col].astype('float64')
In [7]:
automobile_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 205 entries, 0 to 204
Data columns (total 26 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   symboling          205 non-null    int64  
 1   normalized-losses  164 non-null    float64
 2   make               205 non-null    object 
 3   fuel-type          205 non-null    object 
 4   aspiration         205 non-null    object 
 5   num-of-doors       203 non-null    object 
 6   body-style         205 non-null    object 
 7   drive-wheels       205 non-null    object 
 8   engine-location    205 non-null    object 
 9   wheel-base         205 non-null    float64
 10  length             205 non-null    float64
 11  width              205 non-null    float64
 12  height             205 non-null    float64
 13  curb-weight        205 non-null    int64  
 14  engine-type        205 non-null    object 
 15  num-of-cylinders   205 non-null    object 
 16  engine-size        205 non-null    int64  
 17  fuel-system        205 non-null    object 
 18  bore               201 non-null    float64
 19  stroke             201 non-null    float64
 20  compression-ratio  205 non-null    float64
 21  horsepower         203 non-null    float64
 22  peak-rpm           203 non-null    float64
 23  city-mpg           205 non-null    int64  
 24  highway-mpg        205 non-null    int64  
 25  price              201 non-null    float64
dtypes: float64(11), int64(5), object(10)
memory usage: 33.7+ KB

A heatmap to see the extent of missing data

In [8]:
fig, ax = plt.subplots(figsize=(16,6))
sns.heatmap(automobile_df.isnull(),yticklabels=False,cbar=False,cmap='viridis')
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x14b38b90>

Features with NaN:

In [9]:
automobile_df.columns[automobile_df.isnull().any()].tolist()
Out[9]:
['normalized-losses',
 'num-of-doors',
 'bore',
 'stroke',
 'horsepower',
 'peak-rpm',
 'price']

I will go through the features by one and try to do imputation more accurately based on trend or correlation

If no strong correlation is found, then I will fill the missing blanks with mean or mode

normalized-losses

In [10]:
automobile_df['normalized-losses'] = automobile_df['normalized-losses'].dropna()
In [11]:
automobile_df['normalized-losses'].mean()
Out[11]:
122.0
In [12]:
automobile_df['normalized-losses'].mode()
Out[12]:
0    161.0
dtype: float64
In [13]:
automobile_df['normalized-losses'].median()
Out[13]:
115.0
In [14]:
# This function shows correlation value of selected feature with ALL the features
def show_correlation(sel_feature):
    corr_list = automobile_df[list(automobile_df.columns)].corr()[sel_feature]
    return corr_list.sort_values(ascending=False)
In [15]:
corr_list = show_correlation('normalized-losses')
corr_list
Out[15]:
normalized-losses    1.000000
symboling            0.528667
horsepower           0.295772
peak-rpm             0.264597
price                0.203254
engine-size          0.167365
curb-weight          0.119893
width                0.105073
stroke               0.065627
length               0.023220
bore                -0.036167
wheel-base          -0.074362
compression-ratio   -0.132654
highway-mpg         -0.210768
city-mpg            -0.258502
height              -0.432335
Name: normalized-losses, dtype: float64

No strong correlation of normalized-losses with other features so I'll just fill NaN with the mean

In [16]:
automobile_df['normalized-losses'].fillna(automobile_df['normalized-losses'].mean(), inplace=True)

num-of-doors

In [17]:
automobile_df[automobile_df['num-of-doors'].isnull()]
Out[17]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
27 1 148.0 dodge gas turbo NaN sedan fwd front 93.7 ... 98 mpfi 3.03 3.39 7.6 102.0 5500.0 24 30 8558.0
63 0 122.0 mazda diesel std NaN sedan fwd front 98.8 ... 122 idi 3.39 3.39 22.7 64.0 4650.0 36 42 10795.0

2 rows × 26 columns

Since sedans are four-door cars

In [18]:
automobile_df['num-of-doors'].fillna('four', inplace=True)

Now I will convert the string to integers

In [19]:
num_door_list = automobile_df['num-of-doors'].unique()
In [20]:
num_dict = {'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5, 
            'six': 6, 'seven': 7, 'eight': 8, 'nine':9, 'ten':10, 'eleven': 11,
            'twelve': 12}
num_dict
Out[20]:
{'one': 1,
 'two': 2,
 'three': 3,
 'four': 4,
 'five': 5,
 'six': 6,
 'seven': 7,
 'eight': 8,
 'nine': 9,
 'ten': 10,
 'eleven': 11,
 'twelve': 12}
In [21]:
def covert_str_to_num(num_list, df, col_name):
    for num in num_list:
        if type(num) is int or type(num) is float:
            pass
        else:
            df.loc[df[col_name] == num, col_name] = num_dict[num]
    df[col_name] = df[col_name].astype('float64')
    return df
In [22]:
automobile_df = covert_str_to_num(num_list=num_door_list, df=automobile_df, col_name='num-of-doors')
automobile_df.head(10)
Out[22]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
0 3 122.0 alfa-romero gas std 2.0 convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111.0 5000.0 21 27 13495.0
1 3 122.0 alfa-romero gas std 2.0 convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111.0 5000.0 21 27 16500.0
2 1 122.0 alfa-romero gas std 2.0 hatchback rwd front 94.5 ... 152 mpfi 2.68 3.47 9.0 154.0 5000.0 19 26 16500.0
3 2 164.0 audi gas std 4.0 sedan fwd front 99.8 ... 109 mpfi 3.19 3.40 10.0 102.0 5500.0 24 30 13950.0
4 2 164.0 audi gas std 4.0 sedan 4wd front 99.4 ... 136 mpfi 3.19 3.40 8.0 115.0 5500.0 18 22 17450.0
5 2 122.0 audi gas std 2.0 sedan fwd front 99.8 ... 136 mpfi 3.19 3.40 8.5 110.0 5500.0 19 25 15250.0
6 1 158.0 audi gas std 4.0 sedan fwd front 105.8 ... 136 mpfi 3.19 3.40 8.5 110.0 5500.0 19 25 17710.0
7 1 122.0 audi gas std 4.0 wagon fwd front 105.8 ... 136 mpfi 3.19 3.40 8.5 110.0 5500.0 19 25 18920.0
8 1 158.0 audi gas turbo 4.0 sedan fwd front 105.8 ... 131 mpfi 3.13 3.40 8.3 140.0 5500.0 17 20 23875.0
9 0 122.0 audi gas turbo 2.0 hatchback 4wd front 99.5 ... 131 mpfi 3.13 3.40 7.0 160.0 5500.0 16 22 NaN

10 rows × 26 columns

num-of-cylinders

No data missing here but I'm going to convert string to integers

In [23]:
num_cyl_list = automobile_df['num-of-cylinders'].unique()
num_cyl_list
Out[23]:
array(['four', 'six', 'five', 'three', 'twelve', 'two', 'eight'],
      dtype=object)
In [24]:
automobile_df = covert_str_to_num(num_list=num_cyl_list, df=automobile_df, col_name='num-of-cylinders')
automobile_df['num-of-cylinders']
Out[24]:
0      4.0
1      4.0
2      6.0
3      4.0
4      5.0
      ... 
200    4.0
201    4.0
202    6.0
203    6.0
204    4.0
Name: num-of-cylinders, Length: 205, dtype: float64

bore

In [25]:
automobile_df[automobile_df['bore'].isnull()]
Out[25]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
55 3 150.0 mazda gas std 2.0 hatchback rwd front 95.3 ... 70 4bbl NaN NaN 9.4 101.0 6000.0 17 23 10945.0
56 3 150.0 mazda gas std 2.0 hatchback rwd front 95.3 ... 70 4bbl NaN NaN 9.4 101.0 6000.0 17 23 11845.0
57 3 150.0 mazda gas std 2.0 hatchback rwd front 95.3 ... 70 4bbl NaN NaN 9.4 101.0 6000.0 17 23 13645.0
58 3 150.0 mazda gas std 2.0 hatchback rwd front 95.3 ... 80 mpfi NaN NaN 9.4 135.0 6000.0 16 23 15645.0

4 rows × 26 columns

In [26]:
automobile_df['bore'].describe()
Out[26]:
count    201.000000
mean       3.329751
std        0.273539
min        2.540000
25%        3.150000
50%        3.310000
75%        3.590000
max        3.940000
Name: bore, dtype: float64
In [27]:
corr_list = show_correlation('bore')
corr_list
Out[27]:
bore                 1.000000
curb-weight          0.649045
length               0.607480
engine-size          0.594090
horsepower           0.577273
width                0.559204
price                0.543436
wheel-base           0.490378
num-of-cylinders     0.243553
height               0.176195
num-of-doors         0.109945
compression-ratio    0.005203
normalized-losses   -0.029497
stroke              -0.055909
symboling           -0.134205
peak-rpm            -0.264269
highway-mpg         -0.594572
city-mpg            -0.594584
Name: bore, dtype: float64

No clear correlation here either, so mean it is

In [28]:
bore_mean = automobile_df['bore'].mean()
bore_mean
Out[28]:
3.3297512437810943
In [29]:
automobile_df['bore'].fillna(bore_mean, inplace=True)
In [30]:
# Just to double check
automobile_df[automobile_df['bore'].isnull()]
Out[30]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price

0 rows × 26 columns

stroke

In [31]:
corr_list = show_correlation('stroke')
corr_list
Out[31]:
stroke               1.000000
engine-size          0.206675
compression-ratio    0.186170
width                0.182956
curb-weight          0.168929
wheel-base           0.161477
length               0.129739
horsepower           0.090254
price                0.082310
normalized-losses    0.055363
num-of-cylinders     0.008578
num-of-doors        -0.006983
symboling           -0.008965
city-mpg            -0.042906
highway-mpg         -0.044528
bore                -0.055909
height              -0.056999
peak-rpm            -0.071493
Name: stroke, dtype: float64
In [32]:
stroke_mean = automobile_df['stroke'].mean()
stroke_mean
Out[32]:
3.255422885572139
In [33]:
automobile_df['stroke'].fillna(stroke_mean, inplace=True)

horsepower

In [34]:
automobile_df[automobile_df['horsepower'].isnull()]
Out[34]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
130 0 122.0 renault gas std 4.0 wagon fwd front 96.1 ... 132 mpfi 3.46 3.9 8.7 NaN NaN 23 31 9295.0
131 2 122.0 renault gas std 2.0 hatchback fwd front 96.1 ... 132 mpfi 3.46 3.9 8.7 NaN NaN 23 31 9895.0

2 rows × 26 columns

In [35]:
col_list = ['fuel-type', 'fuel-system', 'wheel-base', 'engine-size', 'horsepower', 'compression-ratio', 'highway-mpg']
In [36]:
g = sns.pairplot(automobile_df,
                 x_vars=['horsepower', 'peak-rpm'],
                 y_vars=col_list)
g.fig.set_figheight(20)
g.fig.set_figwidth(15)
g.fig.suptitle("horsepower and peak-rpm vs selected features", y=1.02)
Out[36]:
Text(0.5, 1.02, 'horsepower and peak-rpm vs selected features')
In [37]:
corr_list = show_correlation('horsepower')
corr_list
Out[37]:
horsepower           1.000000
engine-size          0.810773
price                0.810533
curb-weight          0.751034
num-of-cylinders     0.691633
width                0.642482
bore                 0.576398
length               0.555003
wheel-base           0.352297
normalized-losses    0.203434
peak-rpm             0.130971
stroke               0.090170
symboling            0.071622
height              -0.110711
num-of-doors        -0.128837
compression-ratio   -0.205874
highway-mpg         -0.770908
city-mpg            -0.803620
Name: horsepower, dtype: float64

Looks like, horsepower has a strong correlation with engine-size, so I will predict missing values using linear regression

In [38]:
X = automobile_df[automobile_df['horsepower'].notnull()][['engine-size']]
Y = automobile_df['horsepower'].dropna()
In [39]:
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split


def impute_lin_regression(X,Y, df, X_col_name, Y_col_name):
    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=101)
    
    lm = LinearRegression()
    lm.fit(X_train,y_train)
    predictions = lm.predict(df[[X_col_name]])
    
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=df[X_col_name], y=predictions, mode='lines', name='predicted'))
    fig.add_trace(go.Scatter(x=df[X_col_name], y=df[Y_col_name], mode='markers', name='actual'))
    fig.update_layout(xaxis_title=X_col_name, yaxis_title=Y_col_name, title="Actual Data vs Linear Regression")
    fig.show()
    
    return lm
In [40]:
lm = impute_lin_regression(X=X,Y=Y, df=automobile_df, X_col_name='engine-size', Y_col_name='horsepower')
In [41]:
# Selecting Engine Size where Horsepower is NaN
engine_size = list(automobile_df[automobile_df['horsepower'].isnull()]['engine-size'])
engine_size
Out[41]:
[132, 132]
In [42]:
index_list = automobile_df[automobile_df['horsepower'].isnull()]['engine-size'].index.values.tolist()
index_list
Out[42]:
[130, 131]
In [43]:
for i in range(0, len(engine_size)):
    predicted_hp = lm.predict([[ engine_size[i] ]])
    print(predicted_hp[0])
    automobile_df.loc[ index_list[i], ['horsepower']] = predicted_hp[0]

108.05514349408587
108.05514349408587
In [44]:
# Just to double check
automobile_df.loc[index_list]
Out[44]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
130 0 122.0 renault gas std 4.0 wagon fwd front 96.1 ... 132 mpfi 3.46 3.9 8.7 108.055143 NaN 23 31 9295.0
131 2 122.0 renault gas std 2.0 hatchback fwd front 96.1 ... 132 mpfi 3.46 3.9 8.7 108.055143 NaN 23 31 9895.0

2 rows × 26 columns

peak-rpm

In [45]:
corr_list = show_correlation('peak-rpm')
corr_list
Out[45]:
peak-rpm             1.000000
symboling            0.274573
normalized-losses    0.237748
horsepower           0.130971
highway-mpg         -0.054257
stroke              -0.068288
price               -0.101649
city-mpg            -0.113788
num-of-cylinders    -0.124434
width               -0.219957
num-of-doors        -0.241521
engine-size         -0.244618
bore                -0.255053
curb-weight         -0.266306
length              -0.287325
height              -0.322272
wheel-base          -0.361052
compression-ratio   -0.436221
Name: peak-rpm, dtype: float64

No clear correlation so I will replace NaN with mean

In [46]:
automobile_df[automobile_df['peak-rpm'].isnull()]
Out[46]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
130 0 122.0 renault gas std 4.0 wagon fwd front 96.1 ... 132 mpfi 3.46 3.9 8.7 108.055143 NaN 23 31 9295.0
131 2 122.0 renault gas std 2.0 hatchback fwd front 96.1 ... 132 mpfi 3.46 3.9 8.7 108.055143 NaN 23 31 9895.0

2 rows × 26 columns

In [47]:
peak_rpm_mean = automobile_df['peak-rpm'].mean()
peak_rpm_mean
Out[47]:
5125.369458128079
In [48]:
automobile_df['peak-rpm'].fillna(peak_rpm_mean, inplace=True)

Price

In [49]:
automobile_df[automobile_df['price'].isnull()]
Out[49]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
9 0 122.0 audi gas turbo 2.0 hatchback 4wd front 99.5 ... 131 mpfi 3.13 3.40 7.0 160.0 5500.0 16 22 NaN
44 1 122.0 isuzu gas std 2.0 sedan fwd front 94.5 ... 90 2bbl 3.03 3.11 9.6 70.0 5400.0 38 43 NaN
45 0 122.0 isuzu gas std 4.0 sedan fwd front 94.5 ... 90 2bbl 3.03 3.11 9.6 70.0 5400.0 38 43 NaN
129 1 122.0 porsche gas std 2.0 hatchback rwd front 98.4 ... 203 mpfi 3.94 3.11 10.0 288.0 5750.0 17 28 NaN

4 rows × 26 columns

In [50]:
col_list = ['normalized-losses', 'fuel-type', 'fuel-system', 'engine-size', 'horsepower', 'peak-rpm', 'highway-mpg']
In [51]:
g = sns.pairplot(automobile_df,
                 x_vars=['price'],
                 y_vars=col_list)
g.fig.set_figheight(20)
g.fig.set_figwidth(15)
g.fig.suptitle("price vs selected features", y=1.02)
Out[51]:
Text(0.5, 1.02, 'price vs selected features')
In [52]:
corr_list = show_correlation('price')
corr_list
Out[52]:
price                1.000000
engine-size          0.872335
curb-weight          0.834415
horsepower           0.809052
width                0.751265
num-of-cylinders     0.708645
length               0.690628
wheel-base           0.584642
bore                 0.543155
height               0.135486
normalized-losses    0.133999
stroke               0.082269
compression-ratio    0.071107
num-of-doors         0.042435
symboling           -0.082391
peak-rpm            -0.101616
city-mpg            -0.686571
highway-mpg         -0.704692
Name: price, dtype: float64

Price has the strongest correlation with engine-size so I will fill missing values using linear regression

In [53]:
X = automobile_df[automobile_df['price'].notnull()][['engine-size']]
Y = automobile_df['price'].dropna()
In [54]:
lm = impute_lin_regression(X=X,Y=Y, df=automobile_df, X_col_name='engine-size', Y_col_name='price')
In [55]:
engine_size_list = list(automobile_df[automobile_df['price'].isnull()]['engine-size'])
engine_size_list
Out[55]:
[131, 90, 90, 203]
In [56]:
index_list = automobile_df[automobile_df['price'].isnull()]['engine-size'].index.values.tolist()
index_list
Out[56]:
[9, 44, 45, 129]
In [57]:
for i in range(0, len(engine_size_list)):
    predicted_price = lm.predict([[ engine_size_list[i] ]])
    print(predicted_price[0])
    automobile_df.loc[index_list[i], ['price']] = predicted_price[0]

13420.20211498028
7012.595456277933
7012.595456277933
24672.58454001855
In [58]:
# Just to double check
automobile_df.loc[index_list]
Out[58]:
symboling normalized-losses make fuel-type aspiration num-of-doors body-style drive-wheels engine-location wheel-base ... engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
9 0 122.0 audi gas turbo 2.0 hatchback 4wd front 99.5 ... 131 mpfi 3.13 3.40 7.0 160.0 5500.0 16 22 13420.202115
44 1 122.0 isuzu gas std 2.0 sedan fwd front 94.5 ... 90 2bbl 3.03 3.11 9.6 70.0 5400.0 38 43 7012.595456
45 0 122.0 isuzu gas std 4.0 sedan fwd front 94.5 ... 90 2bbl 3.03 3.11 9.6 70.0 5400.0 38 43 7012.595456
129 1 122.0 porsche gas std 2.0 hatchback rwd front 98.4 ... 203 mpfi 3.94 3.11 10.0 288.0 5750.0 17 28 24672.584540

4 rows × 26 columns

Now let's look at the dataframe again to make sure there's no more NaN values

In [59]:
fig, ax = plt.subplots(figsize=(16,6))
sns.heatmap(automobile_df.isnull(),yticklabels=False,cbar=False,cmap='viridis')
Out[59]:
<matplotlib.axes._subplots.AxesSubplot at 0x1781fb50>

Data Classification

I want to use a classification algorithm to predict the number of cylinders of cars

I will use Stratified Cross Validation to determine which supervised ML method yields the best score

Let's take a closer look at our target feature

In [60]:
num_cyl_val_count = automobile_df['num-of-cylinders'].value_counts()
num_cyl_val_count
Out[60]:
4.0     159
6.0      24
5.0      11
8.0       5
2.0       4
12.0      1
3.0       1
Name: num-of-cylinders, dtype: int64

For purpose of validation splits, num-of-cylinders values 3 and 12 are ineffective since only 1 instance of them are present in the dataset. So I'll just remove them.

In [61]:
count = list(num_cyl_val_count.values)
num_of_cyl = list(num_cyl_val_count.index)

for i in range(0,len(num_of_cyl)):
    if count[i] < 3:
        automobile_df = automobile_df[automobile_df['num-of-cylinders'] != num_of_cyl[i]]
In [62]:
automobile_df['num-of-cylinders'].value_counts()
Out[62]:
4.0    159
6.0     24
5.0     11
8.0      5
2.0      4
Name: num-of-cylinders, dtype: int64
In [63]:
drop_cols = ['aspiration', 'length', 'width', 'height', 'engine-location', 'symboling', 'normalized-losses', 
             'make', 'num-of-doors', 'body-style', 'wheel-base', 'num-of-cylinders']
In [64]:
trimmed_df = automobile_df.drop(drop_cols, axis=1)
trimmed_df
Out[64]:
fuel-type drive-wheels curb-weight engine-type engine-size fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price
0 gas rwd 2548 dohc 130 mpfi 3.47 2.68 9.0 111.0 5000.0 21 27 13495.0
1 gas rwd 2548 dohc 130 mpfi 3.47 2.68 9.0 111.0 5000.0 21 27 16500.0
2 gas rwd 2823 ohcv 152 mpfi 2.68 3.47 9.0 154.0 5000.0 19 26 16500.0
3 gas fwd 2337 ohc 109 mpfi 3.19 3.40 10.0 102.0 5500.0 24 30 13950.0
4 gas 4wd 2824 ohc 136 mpfi 3.19 3.40 8.0 115.0 5500.0 18 22 17450.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
200 gas rwd 2952 ohc 141 mpfi 3.78 3.15 9.5 114.0 5400.0 23 28 16845.0
201 gas rwd 3049 ohc 141 mpfi 3.78 3.15 8.7 160.0 5300.0 19 25 19045.0
202 gas rwd 3012 ohcv 173 mpfi 3.58 2.87 8.8 134.0 5500.0 18 23 21485.0
203 diesel rwd 3217 ohc 145 idi 3.01 3.40 23.0 106.0 4800.0 26 27 22470.0
204 gas rwd 3062 ohc 141 mpfi 3.78 3.15 9.5 114.0 5400.0 19 25 22625.0

203 rows × 14 columns

Applying One-Hot-Coding on all nominal features

In [65]:
non_num_cols = ['fuel-type', 'drive-wheels', 'engine-type', 'fuel-system']
In [66]:
def make_dummy_col(df, non_num_cols):
    for col in non_num_cols:
        df_dummy = pd.get_dummies(df[col])
        df.drop(columns=[col], axis=1, inplace=True)
        df = pd.concat([df, df_dummy], axis=1)
        df_dummy = pd.DataFrame()
    return df
In [67]:
trimmed_df = make_dummy_col(df=trimmed_df, non_num_cols=non_num_cols)
In [68]:
trimmed_df.head(5)
Out[68]:
curb-weight engine-size bore stroke compression-ratio horsepower peak-rpm city-mpg highway-mpg price ... ohcv rotor 1bbl 2bbl 4bbl idi mfi mpfi spdi spfi
0 2548 130 3.47 2.68 9.0 111.0 5000.0 21 27 13495.0 ... 0 0 0 0 0 0 0 1 0 0
1 2548 130 3.47 2.68 9.0 111.0 5000.0 21 27 16500.0 ... 0 0 0 0 0 0 0 1 0 0
2 2823 152 2.68 3.47 9.0 154.0 5000.0 19 26 16500.0 ... 1 0 0 0 0 0 0 1 0 0
3 2337 109 3.19 3.40 10.0 102.0 5500.0 24 30 13950.0 ... 0 0 0 0 0 0 0 1 0 0
4 2824 136 3.19 3.40 8.0 115.0 5500.0 18 22 17450.0 ... 0 0 0 0 0 0 0 1 0 0

5 rows × 30 columns

In [69]:
from sklearn.model_selection import StratifiedKFold, KFold, cross_val_score
In [70]:
strat_kf = StratifiedKFold(n_splits = 3, shuffle = True, random_state = 2)

I am creating a generic function to apply all the classification methods and get prediction score

I will also apply GridSearchCV to fine tune these methods

In [71]:
from sklearn.model_selection import GridSearchCV

def get_prediction_score(clf, param_grid, X_train, X_test, y_train, y_test):
    grid = GridSearchCV(clf, param_grid, refit=True, verbose=2)
    grid.fit(X_train,y_train)
    return grid.score(X_test, y_test), grid.best_estimator_
In [72]:
knn_param_grid = {  'n_neighbors': list(range(1,31)), 'weights': ["uniform", "distance"]  }
SVC_param_grid = { 'C': [0.1,1, 10, 100, 1000], 'gamma': [1,0.1,0.01,0.001], 'kernel':['rbf']  }
rfc_param_grid = {  'n_estimators': list(range(1,30))  }

I am making a dictionary with some known supervised ML methods used for classification

In [73]:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier

scores_knn, scores_svc, scores_rfc = np.array([]), np.array([]), np.array([])

classifier_dict = {
    'knn': {
        'name': 'K-Nearest Neighbors',
        'clf_model': KNeighborsClassifier(),
        'param_grid': knn_param_grid,
        'scores_arr': scores_knn,
        'mean_score': 0,
        'best_params': None
    },
    
    'svc': {
        'name': 'Support Vector Machine',
        'clf_model': SVC(),
        'param_grid': SVC_param_grid,
        'scores_arr': scores_svc,
        'mean_score': 0,
        'best_params': None
    },
    
    'rfc': {
        'name': 'Random Forest Classifier',
        'clf_model': RandomForestClassifier(),
        'param_grid': rfc_param_grid,
        'scores_arr': scores_rfc,
        'mean_score': 0,
        'best_params': None
    },   
    
}
In [74]:
for train_index, test_index in strat_kf.split(trimmed_df, automobile_df['num-of-cylinders']):
    
    X_train = trimmed_df.iloc[train_index]
    X_test = trimmed_df.iloc[test_index]
    y_train = automobile_df.iloc[train_index]['num-of-cylinders']
    y_test = automobile_df.iloc[test_index]['num-of-cylinders']

    for clf in classifier_dict:
        clf_model = classifier_dict[clf]['clf_model']
        param_grid = classifier_dict[clf]['param_grid']
        scores_arr = classifier_dict[clf]['scores_arr']
        
        scores_i, best_est = get_prediction_score(clf_model, param_grid, X_train, X_test, y_train, y_test)
        
        scores_arr = np.append(scores_arr, scores_i)
        classifier_dict[clf]['best_params'] = best_est
        classifier_dict[clf]['scores_arr'] = scores_arr
    

Fitting 5 folds for each of 60 candidates, totalling 300 fits
[CV] n_neighbors=1, weights=uniform ..................................
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[CV] n_neighbors=1, weights=uniform ..................................
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[CV] n_neighbors=1, weights=uniform ..................................
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[CV] n_neighbors=1, weights=uniform ..................................
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[CV] n_neighbors=1, weights=distance .................................
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[CV] n_neighbors=1, weights=distance .................................
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[CV] n_neighbors=2, weights=uniform ..................................
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[CV] n_neighbors=2, weights=uniform ..................................
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[CV] n_neighbors=2, weights=uniform ..................................
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[CV] n_neighbors=2, weights=uniform ..................................
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[CV] n_neighbors=2, weights=distance .................................
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[CV] n_neighbors=3, weights=uniform ..................................
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[CV] n_neighbors=3, weights=uniform ..................................
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[CV] n_neighbors=3, weights=uniform ..................................
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[CV] n_neighbors=3, weights=uniform ..................................
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[CV] n_neighbors=3, weights=uniform ..................................
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[CV] n_neighbors=3, weights=distance .................................
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[CV] n_neighbors=3, weights=distance .................................
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[CV] n_neighbors=3, weights=distance .................................
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[CV] n_neighbors=3, weights=distance .................................
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[CV] n_neighbors=4, weights=uniform ..................................
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[CV] n_neighbors=4, weights=uniform ..................................
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[CV] n_neighbors=4, weights=uniform ..................................
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[CV] n_neighbors=4, weights=uniform ..................................
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[CV] n_neighbors=4, weights=uniform ..................................
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[CV] n_neighbors=4, weights=distance .................................
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[CV] n_neighbors=4, weights=distance .................................

c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 3 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
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[CV] n_neighbors=4, weights=distance .................................
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[CV] n_neighbors=5, weights=uniform ..................................
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[CV] n_neighbors=5, weights=uniform ..................................
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[CV] n_neighbors=5, weights=uniform ..................................
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[CV] n_neighbors=5, weights=distance .................................
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[CV] n_neighbors=6, weights=uniform ..................................
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[CV] n_neighbors=6, weights=uniform ..................................
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[CV] n_neighbors=6, weights=uniform ..................................
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[CV] n_neighbors=6, weights=uniform ..................................
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[CV] n_neighbors=6, weights=uniform ..................................
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[CV] n_neighbors=6, weights=distance .................................
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[CV] n_neighbors=6, weights=distance .................................
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[CV] n_neighbors=7, weights=uniform ..................................
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[CV] n_neighbors=7, weights=uniform ..................................
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[CV] n_neighbors=8, weights=uniform ..................................
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[CV] n_neighbors=8, weights=uniform ..................................
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[CV] n_neighbors=8, weights=uniform ..................................
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[CV] n_neighbors=8, weights=uniform ..................................
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[CV] n_neighbors=8, weights=uniform ..................................
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[CV] n_neighbors=9, weights=uniform ..................................
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[CV] n_neighbors=9, weights=uniform ..................................
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[CV] n_neighbors=9, weights=distance .................................
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[CV] n_neighbors=10, weights=uniform .................................
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[CV] n_neighbors=10, weights=uniform .................................
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[CV] n_neighbors=10, weights=uniform .................................
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[CV] n_neighbors=10, weights=uniform .................................
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[CV] n_neighbors=10, weights=distance ................................
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[CV] n_neighbors=10, weights=distance ................................
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[CV] n_neighbors=10, weights=distance ................................
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[CV] n_neighbors=10, weights=distance ................................
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[CV] n_neighbors=10, weights=distance ................................
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[CV] n_neighbors=11, weights=uniform .................................
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[CV] n_neighbors=11, weights=uniform .................................
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[CV] n_neighbors=11, weights=uniform .................................
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[CV] n_neighbors=11, weights=uniform .................................
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[CV] n_neighbors=11, weights=uniform .................................
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[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
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[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
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[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
Fitting 5 folds for each of 20 candidates, totalling 100 fits
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s

[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed:    1.5s finished
c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 3 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s

[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.6s finished
c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 3 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

Fitting 5 folds for each of 29 candidates, totalling 145 fits
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.1s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.1s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.1s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.1s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.1s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.1s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.1s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.1s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
Fitting 5 folds for each of 60 candidates, totalling 300 fits
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=uniform ..................................

[Parallel(n_jobs=1)]: Done 145 out of 145 | elapsed:    3.9s finished
c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 3 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
Fitting 5 folds for each of 20 candidates, totalling 100 fits
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed:    1.9s finished
c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 3 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
Fitting 5 folds for each of 29 candidates, totalling 145 fits
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................

[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.7s finished
c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 3 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
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[CV] n_estimators=4 ..................................................
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[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
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[CV] n_estimators=6 ..................................................
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[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
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[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
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[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=18 .................................................
[CV] .................................. n_estimators=18, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.1s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.1s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.1s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
Fitting 5 folds for each of 60 candidates, totalling 300 fits
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=uniform ..................................
[CV] ................... n_neighbors=1, weights=uniform, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=1, weights=distance .................................
[CV] .................. n_neighbors=1, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................

[Parallel(n_jobs=1)]: Done 145 out of 145 | elapsed:    3.6s finished
c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 2 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=uniform ..................................
[CV] ................... n_neighbors=2, weights=uniform, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=2, weights=distance .................................
[CV] .................. n_neighbors=2, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=uniform ..................................
[CV] ................... n_neighbors=3, weights=uniform, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=3, weights=distance .................................
[CV] .................. n_neighbors=3, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=uniform ..................................
[CV] ................... n_neighbors=4, weights=uniform, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=4, weights=distance .................................
[CV] .................. n_neighbors=4, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=uniform ..................................
[CV] ................... n_neighbors=5, weights=uniform, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=5, weights=distance .................................
[CV] .................. n_neighbors=5, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=uniform ..................................
[CV] ................... n_neighbors=6, weights=uniform, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=6, weights=distance .................................
[CV] .................. n_neighbors=6, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=uniform ..................................
[CV] ................... n_neighbors=7, weights=uniform, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=7, weights=distance .................................
[CV] .................. n_neighbors=7, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=uniform ..................................
[CV] ................... n_neighbors=8, weights=uniform, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=8, weights=distance .................................
[CV] .................. n_neighbors=8, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=uniform ..................................
[CV] ................... n_neighbors=9, weights=uniform, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=9, weights=distance .................................
[CV] .................. n_neighbors=9, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=uniform .................................
[CV] .................. n_neighbors=10, weights=uniform, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=10, weights=distance ................................
[CV] ................. n_neighbors=10, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=uniform .................................
[CV] .................. n_neighbors=11, weights=uniform, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=11, weights=distance ................................
[CV] ................. n_neighbors=11, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=uniform .................................
[CV] .................. n_neighbors=12, weights=uniform, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=12, weights=distance ................................
[CV] ................. n_neighbors=12, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=uniform .................................
[CV] .................. n_neighbors=13, weights=uniform, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=13, weights=distance ................................
[CV] ................. n_neighbors=13, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=uniform .................................
[CV] .................. n_neighbors=14, weights=uniform, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=14, weights=distance ................................
[CV] ................. n_neighbors=14, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=uniform .................................
[CV] .................. n_neighbors=15, weights=uniform, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=15, weights=distance ................................
[CV] ................. n_neighbors=15, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=uniform .................................
[CV] .................. n_neighbors=16, weights=uniform, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=16, weights=distance ................................
[CV] ................. n_neighbors=16, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=uniform .................................
[CV] .................. n_neighbors=17, weights=uniform, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=17, weights=distance ................................
[CV] ................. n_neighbors=17, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=uniform .................................
[CV] .................. n_neighbors=18, weights=uniform, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=18, weights=distance ................................
[CV] ................. n_neighbors=18, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=uniform .................................
[CV] .................. n_neighbors=19, weights=uniform, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=19, weights=distance ................................
[CV] ................. n_neighbors=19, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=uniform .................................
[CV] .................. n_neighbors=20, weights=uniform, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=20, weights=distance ................................
[CV] ................. n_neighbors=20, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=uniform .................................
[CV] .................. n_neighbors=21, weights=uniform, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=21, weights=distance ................................
[CV] ................. n_neighbors=21, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=uniform .................................
[CV] .................. n_neighbors=22, weights=uniform, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=22, weights=distance ................................
[CV] ................. n_neighbors=22, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=uniform .................................
[CV] .................. n_neighbors=23, weights=uniform, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=23, weights=distance ................................
[CV] ................. n_neighbors=23, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=uniform .................................
[CV] .................. n_neighbors=24, weights=uniform, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=24, weights=distance ................................
[CV] ................. n_neighbors=24, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=uniform .................................
[CV] .................. n_neighbors=25, weights=uniform, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=25, weights=distance ................................
[CV] ................. n_neighbors=25, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=uniform .................................
[CV] .................. n_neighbors=26, weights=uniform, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=26, weights=distance ................................
[CV] ................. n_neighbors=26, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=uniform .................................
[CV] .................. n_neighbors=27, weights=uniform, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=27, weights=distance ................................
[CV] ................. n_neighbors=27, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=uniform .................................
[CV] .................. n_neighbors=28, weights=uniform, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=28, weights=distance ................................
[CV] ................. n_neighbors=28, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=uniform .................................
[CV] .................. n_neighbors=29, weights=uniform, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=29, weights=distance ................................
[CV] ................. n_neighbors=29, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=uniform .................................
[CV] .................. n_neighbors=30, weights=uniform, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
[CV] n_neighbors=30, weights=distance ................................
[CV] ................. n_neighbors=30, weights=distance, total=   0.0s
Fitting 5 folds for each of 20 candidates, totalling 100 fits
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=1, kernel=rbf ......................................
[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................

[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed:    1.4s finished
c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 2 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=0.1, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=1, kernel=rbf ........................................
[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.1, kernel=rbf ......................................
[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.01, kernel=rbf .....................................
[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1, gamma=0.001, kernel=rbf ....................................
[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=1, kernel=rbf .......................................
[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.1, kernel=rbf .....................................
[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.01, kernel=rbf ....................................
[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=10, gamma=0.001, kernel=rbf ...................................
[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=1, kernel=rbf ......................................
[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.1, kernel=rbf ....................................
[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.01, kernel=rbf ...................................
[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=100, gamma=0.001, kernel=rbf ..................................
[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=1, kernel=rbf .....................................
[CV] ...................... C=1000, gamma=1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.1, kernel=rbf ...................................
[CV] .................... C=1000, gamma=0.1, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.01, kernel=rbf ..................................
[CV] ................... C=1000, gamma=0.01, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
[CV] C=1000, gamma=0.001, kernel=rbf .................................
[CV] .................. C=1000, gamma=0.001, kernel=rbf, total=   0.0s
Fitting 5 folds for each of 29 candidates, totalling 145 fits
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
[CV] n_estimators=1 ..................................................
[CV] ................................... n_estimators=1, total=   0.0s
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[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=2 ..................................................
[CV] ................................... n_estimators=2, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=3 ..................................................
[CV] ................................... n_estimators=3, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................

[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.6s finished
c:\users\shafi\desktop\data science project\venv\lib\site-packages\sklearn\model_selection\_split.py:667: UserWarning:

The least populated class in y has only 2 members, which is less than n_splits=5.

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s

[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
[CV] ................................... n_estimators=4, total=   0.0s
[CV] n_estimators=4 ..................................................
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[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=5 ..................................................
[CV] ................................... n_estimators=5, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=6 ..................................................
[CV] ................................... n_estimators=6, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=7 ..................................................
[CV] ................................... n_estimators=7, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=8 ..................................................
[CV] ................................... n_estimators=8, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=9 ..................................................
[CV] ................................... n_estimators=9, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=10 .................................................
[CV] .................................. n_estimators=10, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=11 .................................................
[CV] .................................. n_estimators=11, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
[CV] n_estimators=12 .................................................
[CV] .................................. n_estimators=12, total=   0.0s
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[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=13 .................................................
[CV] .................................. n_estimators=13, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=14 .................................................
[CV] .................................. n_estimators=14, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=15 .................................................
[CV] .................................. n_estimators=15, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=16 .................................................
[CV] .................................. n_estimators=16, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
[CV] n_estimators=17 .................................................
[CV] .................................. n_estimators=17, total=   0.0s
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[CV] .................................. n_estimators=18, total=   0.0s
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[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=19 .................................................
[CV] .................................. n_estimators=19, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=20 .................................................
[CV] .................................. n_estimators=20, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
[CV] n_estimators=21 .................................................
[CV] .................................. n_estimators=21, total=   0.0s
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[CV] .................................. n_estimators=22, total=   0.0s
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[CV] .................................. n_estimators=22, total=   0.1s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=22 .................................................
[CV] .................................. n_estimators=22, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
[CV] .................................. n_estimators=23, total=   0.0s
[CV] n_estimators=23 .................................................
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[CV] n_estimators=23 .................................................
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[CV] n_estimators=24 .................................................
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[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
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[CV] n_estimators=24 .................................................
[CV] .................................. n_estimators=24, total=   0.0s
[CV] n_estimators=24 .................................................
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[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
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[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=25 .................................................
[CV] .................................. n_estimators=25, total=   0.0s
[CV] n_estimators=26 .................................................
[CV] .................................. n_estimators=26, total=   0.0s
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[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.1s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=27 .................................................
[CV] .................................. n_estimators=27, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=28 .................................................
[CV] .................................. n_estimators=28, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.1s
[CV] n_estimators=29 .................................................
[CV] .................................. n_estimators=29, total=   0.0s

[Parallel(n_jobs=1)]: Done 145 out of 145 | elapsed:    3.8s finished
In [75]:
for clf in classifier_dict:
    classifier_dict[clf]['mean_score'] = classifier_dict[clf]['scores_arr'].mean()
In [76]:
best_clf_key = max(classifier_dict.items(), key=lambda x: x[1]['mean_score'])[0]
In [77]:
print('---------------------------------------------------SUMMARY---------------------------------------------------\n\n')

for clf in classifier_dict:
    print( f"{classifier_dict[clf]['name']}: \n" )
    print( f"Mean score: {classifier_dict[clf]['mean_score']}: \n" )
    print( f"Best parameters from grid search: \n\n{classifier_dict[clf]['best_params']}: \n\n" )
    print("---------------------------------------------------------------------------------------------------------\n\n")

---------------------------------------------------SUMMARY---------------------------------------------------


K-Nearest Neighbors: 

Mean score: 0.8325285338015803: 

Best parameters from grid search: 

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=None, n_neighbors=13, p=2,
                     weights='uniform'): 


---------------------------------------------------------------------------------------------------------


Support Vector Machine: 

Mean score: 0.783289435177056: 

Best parameters from grid search: 

SVC(C=0.1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf', max_iter=-1,
    probability=False, random_state=None, shrinking=True, tol=0.001,
    verbose=False): 


---------------------------------------------------------------------------------------------------------


Random Forest Classifier: 

Mean score: 0.9656131109160082: 

Best parameters from grid search: 

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=24,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False): 


---------------------------------------------------------------------------------------------------------
In [78]:
md("<h2> Classification algorithm with the best prediction score is:<h2> <h1>{}<h1>".format(classifier_dict[best_clf_key]['name']))
Out[78]:

Classification algorithm with the best prediction score is:

Random Forest Classifier

In [ ]: