Diabetes prediction using machine learning¶

importing necessary packages¶

In [1]:
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression

reading the dataset and getting getting started with analysis¶

In [2]:
dataset=pd.read_csv("diabetes.csv")
In [3]:
dataset.head()
Out[3]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
In [4]:
dataset["Age"].max()
Out[4]:
81
In [5]:
dataset.shape
Out[5]:
(768, 9)
In [6]:
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB
In [7]:
dataset.describe()
Out[7]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000
mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 0.471876 33.240885 0.348958
std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 0.331329 11.760232 0.476951
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.078000 21.000000 0.000000
25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 0.243750 24.000000 0.000000
50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 0.372500 29.000000 0.000000
75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250 41.000000 1.000000
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000
In [8]:
dataset.isnull().sum()
Out[8]:
Pregnancies                 0
Glucose                     0
BloodPressure               0
SkinThickness               0
Insulin                     0
BMI                         0
DiabetesPedigreeFunction    0
Age                         0
Outcome                     0
dtype: int64

Observation:¶

1) No object type values¶
2) No null values¶
3) there is high standard deviation in some columns¶
4) Almost every feature contains false values¶

Converting Age into categorical variable¶

In [9]:
dataset['Age'].median()
Out[9]:
29.0
In [10]:
dataset.loc[dataset['Age']>=29, 'Age_mod']=0
dataset.loc[dataset['Age']< 29, 'Age_mod']=1
In [11]:
dataset.head()
Out[11]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0
1 1 85 66 29 0 26.6 0.351 31 0 0.0
2 8 183 64 0 0 23.3 0.672 32 1 0.0
3 1 89 66 23 94 28.1 0.167 21 0 1.0
4 0 137 40 35 168 43.1 2.288 33 1 0.0
In [12]:
dataset['Age_mod'].value_counts()
Out[12]:
Age_mod
0.0    401
1.0    367
Name: count, dtype: int64

Correlation¶

In [13]:
dataset.corr()
Out[13]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod
Pregnancies 1.000000 0.129459 0.141282 -0.081672 -0.073535 0.017683 -0.033523 0.544341 0.221898 -0.577589
Glucose 0.129459 1.000000 0.152590 0.057328 0.331357 0.221071 0.137337 0.263514 0.466581 -0.246323
BloodPressure 0.141282 0.152590 1.000000 0.207371 0.088933 0.281805 0.041265 0.239528 0.065068 -0.197117
SkinThickness -0.081672 0.057328 0.207371 1.000000 0.436783 0.392573 0.183928 -0.113970 0.074752 0.089299
Insulin -0.073535 0.331357 0.088933 0.436783 1.000000 0.197859 0.185071 -0.042163 0.130548 0.035277
BMI 0.017683 0.221071 0.281805 0.392573 0.197859 1.000000 0.140647 0.036242 0.292695 -0.093753
DiabetesPedigreeFunction -0.033523 0.137337 0.041265 0.183928 0.185071 0.140647 1.000000 0.033561 0.173844 -0.050555
Age 0.544341 0.263514 0.239528 -0.113970 -0.042163 0.036242 0.033561 1.000000 0.238356 -0.756872
Outcome 0.221898 0.466581 0.065068 0.074752 0.130548 0.292695 0.173844 0.238356 1.000000 -0.312100
Age_mod -0.577589 -0.246323 -0.197117 0.089299 0.035277 -0.093753 -0.050555 -0.756872 -0.312100 1.000000
In [14]:
sns.pairplot(dataset)

C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
Out[14]:
<seaborn.axisgrid.PairGrid at 0x21f2d37dcd0>
No description has been provided for this image

Observation:¶

There is very negligible correlation between the features¶

EDA¶

In [15]:
dataset.head()
Out[15]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0
1 1 85 66 29 0 26.6 0.351 31 0 0.0
2 8 183 64 0 0 23.3 0.672 32 1 0.0
3 1 89 66 23 94 28.1 0.167 21 0 1.0
4 0 137 40 35 168 43.1 2.288 33 1 0.0

Creating a function to get the barplots¶

In [16]:
dataset.columns
Out[16]:
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome', 'Age_mod'],
      dtype='object')

Defined a function called graph that outputs the bar graph for Outcome vs desired column from the dataset¶

In [17]:
def graph(feature2, color):

    print(plt.bar(dataset['Outcome'], dataset[feature2], color= color))
    print(plt.xlabel('Outcome'))
    print(plt.ylabel(feature2))

Pregnancies vs Outcome¶

In [18]:
graph('Pregnancies', 'purple')

<BarContainer object of 768 artists>
Text(0.5, 0, 'Outcome')
Text(0, 0.5, 'Pregnancies')
No description has been provided for this image

Observation:¶

The people who have diabetes have approximately 18 kids and those who don't they have a maximum of 13 kids¶

Glucose vs Outcome¶

In [19]:
graph('Glucose', 'grey')

<BarContainer object of 768 artists>
Text(0.5, 0, 'Outcome')
Text(0, 0.5, 'Glucose')
No description has been provided for this image

Observation:¶

The people who have diabetes have slightly more glucose levels¶
In [20]:
a=dataset.columns

BloodPressure vs Outcome¶

In [21]:
graph('BloodPressure', 'orange')

<BarContainer object of 768 artists>
Text(0.5, 0, 'Outcome')
Text(0, 0.5, 'BloodPressure')
No description has been provided for this image

Observation:¶

The people who have diabetes have a slight lower blood pressure than those who don't have diabetes¶
In [22]:
a
Out[22]:
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome', 'Age_mod'],
      dtype='object')

SkinThickness vs Outcome¶

In [23]:
graph('SkinThickness', 'brown')

<BarContainer object of 768 artists>
Text(0.5, 0, 'Outcome')
Text(0, 0.5, 'SkinThickness')
No description has been provided for this image

Observation:¶

The people who have diabetes have more ski8n thickness (nearly double) than that of a person who has no diabetes¶
In [24]:
a
Out[24]:
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome', 'Age_mod'],
      dtype='object')

Insulin vs Outcome¶

In [25]:
graph('Insulin', 'green')

<BarContainer object of 768 artists>
Text(0.5, 0, 'Outcome')
Text(0, 0.5, 'Insulin')
No description has been provided for this image

Observation:¶

The people who have diabetes have insulin levels greater than 740 and those who don't have diabetes, they have maximum insulin level of 740¶
In [26]:
a
Out[26]:
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome', 'Age_mod'],
      dtype='object')

BMI vs Outcome¶

In [27]:
graph('BMI', 'magenta');

<BarContainer object of 768 artists>
Text(0.5, 0, 'Outcome')
Text(0, 0.5, 'BMI')
No description has been provided for this image

Observation:¶

The people who have diabetes, their BMI levels reach the maximum of 680 and those who don't have diabetes, their BMI levels reach a mximumof 570.¶

DiabetesPedigreeFunction vs Outcome¶

In [28]:
graph('DiabetesPedigreeFunction', 'yellow');

<BarContainer object of 768 artists>
Text(0.5, 0, 'Outcome')
Text(0, 0.5, 'DiabetesPedigreeFunction')
No description has been provided for this image

Observation:¶

The people who have diabetes, their Diabetes Pedigree Function levels reach the maximum of 2.4 and those who don't have diabetes, their DiabetesPedigreeFunction levels reach a mximumof 2.3.¶

Age vs Outcome¶

In [29]:
graph('Age', 'magenta');

<BarContainer object of 768 artists>
Text(0.5, 0, 'Outcome')
Text(0, 0.5, 'Age')
No description has been provided for this image

Observation:¶

The people who have diabetes, they have a maximum Age of 80 and those who don't have diabetes, their Age is approximately 65.¶

Outliers¶

Pregnancies¶

In [30]:
sns.distplot(dataset['Pregnancies'])

C:\Users\kiran\AppData\Local\Temp\ipykernel_25516\951228210.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['Pregnancies'])
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
Out[30]:
<Axes: xlabel='Pregnancies', ylabel='Density'>
No description has been provided for this image
In [31]:
plt.boxplot(dataset['Pregnancies']);
No description has been provided for this image
In [32]:
IQR= dataset['Pregnancies'].quantile(0.75)-dataset['Pregnancies'].quantile(0.25)
IQR
Out[32]:
5.0
In [33]:
upper_preg=dataset['Pregnancies'].quantile(0.75)+(IQR*3)
upper_preg
Out[33]:
21.0
In [34]:
dataset['Pregnancies'].nunique()
Out[34]:
17
In [35]:
dataset['Pregnancies'].unique()
Out[35]:
array([ 6,  1,  8,  0,  5,  3, 10,  2,  4,  7,  9, 11, 13, 15, 17, 12, 14],
      dtype=int64)
In [36]:
dataset['Pregnancies'].value_counts()
Out[36]:
Pregnancies
1     135
0     111
2     103
3      75
4      68
5      57
6      50
7      45
8      38
9      28
10     24
11     11
13     10
12      9
14      2
15      1
17      1
Name: count, dtype: int64
In [37]:
50+45+38+28+24+11+10+9+2+2
Out[37]:
219
In [38]:
11+10+9+2+2
Out[38]:
34
In [39]:
768-219
Out[39]:
549
In [40]:
dataset.shape
Out[40]:
(768, 10)
In [41]:
(219*100)/768
Out[41]:
28.515625
In [42]:
(34*100)/768
Out[42]:
4.427083333333333
In [43]:
dataset['Pregnancies_mod']=dataset['Pregnancies']
In [44]:
dataset.loc[dataset['Pregnancies']> 10, 'Pregnancies_mod']=5
In [45]:
dataset['Pregnancies'].max()
Out[45]:
17
In [46]:
dataset['Pregnancies_mod'].max()
Out[46]:
10

We capped every value > 10 to 10¶

In [47]:
dataset.head()
Out[47]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod Pregnancies_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0 6
1 1 85 66 29 0 26.6 0.351 31 0 0.0 1
2 8 183 64 0 0 23.3 0.672 32 1 0.0 8
3 1 89 66 23 94 28.1 0.167 21 0 1.0 1
4 0 137 40 35 168 43.1 2.288 33 1 0.0 0

Glucose¶

In [48]:
sns.distplot(dataset['Glucose']);

C:\Users\kiran\AppData\Local\Temp\ipykernel_25516\3413697017.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['Glucose']);
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [49]:
plt.boxplot(dataset['Glucose']);
No description has been provided for this image
In [50]:
IQR= dataset['Glucose'].quantile(0.75)-dataset['Glucose'].quantile(0.25)
IQR
Out[50]:
41.25
In [51]:
lower_glucose=dataset['Glucose'].quantile(0.25)-(IQR*3)
lower_glucose
Out[51]:
-24.75
In [52]:
dataset['Glucose'].min()
Out[52]:
0
In [53]:
dataset['Glucose'].max()
Out[53]:
199
In [54]:
dataset['Glucose_mod']=dataset['Glucose']
In [55]:
dataset.loc[dataset['Glucose']<20, 'Glucose_mod']=20
In [56]:
dataset.head(10)
Out[56]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod Pregnancies_mod Glucose_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0 6 148
1 1 85 66 29 0 26.6 0.351 31 0 0.0 1 85
2 8 183 64 0 0 23.3 0.672 32 1 0.0 8 183
3 1 89 66 23 94 28.1 0.167 21 0 1.0 1 89
4 0 137 40 35 168 43.1 2.288 33 1 0.0 0 137
5 5 116 74 0 0 25.6 0.201 30 0 0.0 5 116
6 3 78 50 32 88 31.0 0.248 26 1 1.0 3 78
7 10 115 0 0 0 35.3 0.134 29 0 0.0 10 115
8 2 197 70 45 543 30.5 0.158 53 1 0.0 2 197
9 8 125 96 0 0 0.0 0.232 54 1 0.0 8 125
In [57]:
dataset['Glucose_mod'].min()
Out[57]:
20

We capped the glucose level < 20 to 20¶

Blood Pressure¶

In [58]:
sns.distplot(dataset['BloodPressure'])

C:\Users\kiran\AppData\Local\Temp\ipykernel_25516\2509938823.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['BloodPressure'])
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
Out[58]:
<Axes: xlabel='BloodPressure', ylabel='Density'>
No description has been provided for this image
In [59]:
plt.boxplot(dataset['BloodPressure']);
No description has been provided for this image
In [60]:
dataset['BloodPressure'].nunique()
Out[60]:
47
In [61]:
dataset['BloodPressure'].min()
Out[61]:
0
In [62]:
dataset['BloodPressure'].max()
Out[62]:
122
In [63]:
IQR= dataset['BloodPressure'].quantile(0.75)-dataset['BloodPressure'].quantile(0.25)
IQR
Out[63]:
18.0
In [64]:
lower_bp=dataset['BloodPressure'].quantile(0.25)-(IQR*3)
lower_bp
Out[64]:
8.0
In [65]:
upper_bp=dataset['BloodPressure'].quantile(0.75)+(IQR*3)
upper_bp
Out[65]:
134.0
In [66]:
dataset['BloodPressure_mod']=dataset['BloodPressure']
In [67]:
dataset.loc[dataset['BloodPressure']<40, 'BloodPressure_mod']=40
In [68]:
dataset.head()
Out[68]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod Pregnancies_mod Glucose_mod BloodPressure_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0 6 148 72
1 1 85 66 29 0 26.6 0.351 31 0 0.0 1 85 66
2 8 183 64 0 0 23.3 0.672 32 1 0.0 8 183 64
3 1 89 66 23 94 28.1 0.167 21 0 1.0 1 89 66
4 0 137 40 35 168 43.1 2.288 33 1 0.0 0 137 40
In [69]:
import matplotlib.pyplot as plt
import seaborn as sns

We capped the bp <40 to 40¶

Skin Thickness¶

In [70]:
sns.distplot(dataset['SkinThickness']);

C:\Users\kiran\AppData\Local\Temp\ipykernel_25516\1550414908.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['SkinThickness']);
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [71]:
plt.boxplot(dataset['SkinThickness']);
No description has been provided for this image
In [72]:
dataset['SkinThickness'].max()
Out[72]:
99
In [73]:
dataset['SkinThickness'].min()
Out[73]:
0
In [74]:
dataset['SkinThickness_mod']=dataset['SkinThickness']
In [75]:
dataset.loc[dataset['SkinThickness']<2, 'SkinThickness_mod']=2
In [76]:
IQ1R= dataset['SkinThickness'].quantile(0.75)-dataset['SkinThickness'].quantile(0.25)
IQ1R
Out[76]:
32.0
In [77]:
upperst=dataset['SkinThickness'].quantile(0.75)+(IQ1R*3)
upperst
Out[77]:
128.0
In [78]:
dataset.head()
Out[78]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod Pregnancies_mod Glucose_mod BloodPressure_mod SkinThickness_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0 6 148 72 35
1 1 85 66 29 0 26.6 0.351 31 0 0.0 1 85 66 29
2 8 183 64 0 0 23.3 0.672 32 1 0.0 8 183 64 2
3 1 89 66 23 94 28.1 0.167 21 0 1.0 1 89 66 23
4 0 137 40 35 168 43.1 2.288 33 1 0.0 0 137 40 35

Insulin¶

In [79]:
sns.distplot(dataset['Insulin']);

C:\Users\kiran\AppData\Local\Temp\ipykernel_25516\681389340.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['Insulin']);
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [80]:
plt.boxplot(dataset['Insulin']);
No description has been provided for this image
In [81]:
dataset['Insulin'].min()
Out[81]:
0
In [82]:
dataset['Insulin'].max()
Out[82]:
846
In [83]:
dataset['Insulin_mod']=dataset['Insulin']
In [84]:
dataset.loc[dataset['Insulin']<23, 'Insulin_mod']=23
In [85]:
IQ1R= dataset['Insulin'].quantile(0.75)-dataset['Insulin'].quantile(0.25)
IQ1R
Out[85]:
127.25
In [86]:
upper_IQR=dataset['Insulin'].quantile(0.75)+(IQ1R*3)
upper_IQR
Out[86]:
509.0
In [87]:
dataset.loc[dataset['Insulin']>509, 'Insulin_mod']=509
In [88]:
dataset['Insulin_mod'].min()
Out[88]:
23
In [89]:
dataset['Insulin_mod'].max()
Out[89]:
509
In [90]:
dataset['Insulin_mod'].max()
Out[90]:
509
In [91]:
dataset.head(10)
Out[91]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod Pregnancies_mod Glucose_mod BloodPressure_mod SkinThickness_mod Insulin_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0 6 148 72 35 23
1 1 85 66 29 0 26.6 0.351 31 0 0.0 1 85 66 29 23
2 8 183 64 0 0 23.3 0.672 32 1 0.0 8 183 64 2 23
3 1 89 66 23 94 28.1 0.167 21 0 1.0 1 89 66 23 94
4 0 137 40 35 168 43.1 2.288 33 1 0.0 0 137 40 35 168
5 5 116 74 0 0 25.6 0.201 30 0 0.0 5 116 74 2 23
6 3 78 50 32 88 31.0 0.248 26 1 1.0 3 78 50 32 88
7 10 115 0 0 0 35.3 0.134 29 0 0.0 10 115 40 2 23
8 2 197 70 45 543 30.5 0.158 53 1 0.0 2 197 70 45 509
9 8 125 96 0 0 0.0 0.232 54 1 0.0 8 125 96 2 23

BMI¶

In [92]:
sns.distplot(dataset['BMI']);

C:\Users\kiran\AppData\Local\Temp\ipykernel_25516\3186949395.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['BMI']);
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Variance is the spread of data wrt to the mean of that column¶

In [93]:
dataset['BMI'].describe()
Out[93]:
count    768.000000
mean      31.992578
std        7.884160
min        0.000000
25%       27.300000
50%       32.000000
75%       36.600000
max       67.100000
Name: BMI, dtype: float64
In [94]:
plt.boxplot(dataset['BMI']);
No description has been provided for this image
In [95]:
IQ1R= dataset['BMI'].quantile(0.75)-dataset['BMI'].quantile(0.25)
IQ1R
Out[95]:
9.3
In [96]:
upper_BMI=dataset['BMI'].quantile(0.75)+(IQ1R*3)
upper_BMI
Out[96]:
64.5
In [97]:
dataset['BMI_mod']=dataset['BMI']
In [98]:
dataset.loc[dataset['BMI']<15, 'BMI_mod']=15
dataset.loc[dataset['BMI']>64.5, 'BMI_mod']=64.5
In [99]:
print("BMI:")
print(dataset['BMI'].describe())
print('------------------------------------------------------')
print("BMI_mod:")
print(dataset['BMI_mod'].describe())

BMI:
count    768.000000
mean      31.992578
std        7.884160
min        0.000000
25%       27.300000
50%       32.000000
75%       36.600000
max       67.100000
Name: BMI, dtype: float64
------------------------------------------------------
BMI_mod:
count    768.000000
mean      32.204036
std        7.165761
min       15.000000
25%       27.300000
50%       32.000000
75%       36.600000
max       64.500000
Name: BMI_mod, dtype: float64
In [100]:
dataset.head()
Out[100]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod Pregnancies_mod Glucose_mod BloodPressure_mod SkinThickness_mod Insulin_mod BMI_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0 6 148 72 35 23 33.6
1 1 85 66 29 0 26.6 0.351 31 0 0.0 1 85 66 29 23 26.6
2 8 183 64 0 0 23.3 0.672 32 1 0.0 8 183 64 2 23 23.3
3 1 89 66 23 94 28.1 0.167 21 0 1.0 1 89 66 23 94 28.1
4 0 137 40 35 168 43.1 2.288 33 1 0.0 0 137 40 35 168 43.1

DiabetesPedigreeFunction¶

In [101]:
sns.distplot(dataset['DiabetesPedigreeFunction']);

C:\Users\kiran\AppData\Local\Temp\ipykernel_25516\471758895.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['DiabetesPedigreeFunction']);
C:\Users\kiran\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [102]:
plt.boxplot(dataset['DiabetesPedigreeFunction']);
No description has been provided for this image
In [103]:
dataset['DiabetesPedigreeFunction'].describe()
Out[103]:
count    768.000000
mean       0.471876
std        0.331329
min        0.078000
25%        0.243750
50%        0.372500
75%        0.626250
max        2.420000
Name: DiabetesPedigreeFunction, dtype: float64
In [104]:
dataset['DiabetesPedigreeFunction'].nunique()
Out[104]:
517
In [105]:
dataset['DiabetesPedigreeFunction'].unique()
Out[105]:
array([0.627, 0.351, 0.672, 0.167, 2.288, 0.201, 0.248, 0.134, 0.158,
       0.232, 0.191, 0.537, 1.441, 0.398, 0.587, 0.484, 0.551, 0.254,
       0.183, 0.529, 0.704, 0.388, 0.451, 0.263, 0.205, 0.257, 0.487,
       0.245, 0.337, 0.546, 0.851, 0.267, 0.188, 0.512, 0.966, 0.42 ,
       0.665, 0.503, 1.39 , 0.271, 0.696, 0.235, 0.721, 0.294, 1.893,
       0.564, 0.586, 0.344, 0.305, 0.491, 0.526, 0.342, 0.467, 0.718,
       0.962, 1.781, 0.173, 0.304, 0.27 , 0.699, 0.258, 0.203, 0.855,
       0.845, 0.334, 0.189, 0.867, 0.411, 0.583, 0.231, 0.396, 0.14 ,
       0.391, 0.37 , 0.307, 0.102, 0.767, 0.237, 0.227, 0.698, 0.178,
       0.324, 0.153, 0.165, 0.443, 0.261, 0.277, 0.761, 0.255, 0.13 ,
       0.323, 0.356, 0.325, 1.222, 0.179, 0.262, 0.283, 0.93 , 0.801,
       0.207, 0.287, 0.336, 0.247, 0.199, 0.543, 0.192, 0.588, 0.539,
       0.22 , 0.654, 0.223, 0.759, 0.26 , 0.404, 0.186, 0.278, 0.496,
       0.452, 0.403, 0.741, 0.361, 1.114, 0.457, 0.647, 0.088, 0.597,
       0.532, 0.703, 0.159, 0.268, 0.286, 0.318, 0.272, 0.572, 0.096,
       1.4  , 0.218, 0.085, 0.399, 0.432, 1.189, 0.687, 0.137, 0.637,
       0.833, 0.229, 0.817, 0.204, 0.368, 0.743, 0.722, 0.256, 0.709,
       0.471, 0.495, 0.18 , 0.542, 0.773, 0.678, 0.719, 0.382, 0.319,
       0.19 , 0.956, 0.084, 0.725, 0.299, 0.244, 0.745, 0.615, 1.321,
       0.64 , 0.142, 0.374, 0.383, 0.578, 0.136, 0.395, 0.187, 0.905,
       0.15 , 0.874, 0.236, 0.787, 0.407, 0.605, 0.151, 0.289, 0.355,
       0.29 , 0.375, 0.164, 0.431, 0.742, 0.514, 0.464, 1.224, 1.072,
       0.805, 0.209, 0.666, 0.101, 0.198, 0.652, 2.329, 0.089, 0.645,
       0.238, 0.394, 0.293, 0.479, 0.686, 0.831, 0.582, 0.446, 0.402,
       1.318, 0.329, 1.213, 0.427, 0.282, 0.143, 0.38 , 0.284, 0.249,
       0.926, 0.557, 0.092, 0.655, 1.353, 0.612, 0.2  , 0.226, 0.997,
       0.933, 1.101, 0.078, 0.24 , 1.136, 0.128, 0.422, 0.251, 0.677,
       0.296, 0.454, 0.744, 0.881, 0.28 , 0.259, 0.619, 0.808, 0.34 ,
       0.434, 0.757, 0.613, 0.692, 0.52 , 0.412, 0.84 , 0.839, 0.156,
       0.215, 0.326, 1.391, 0.875, 0.313, 0.433, 0.626, 1.127, 0.315,
       0.345, 0.129, 0.527, 0.197, 0.731, 0.148, 0.123, 0.127, 0.122,
       1.476, 0.166, 0.932, 0.343, 0.893, 0.331, 0.472, 0.673, 0.389,
       0.485, 0.349, 0.279, 0.346, 0.252, 0.243, 0.58 , 0.559, 0.302,
       0.569, 0.378, 0.385, 0.499, 0.306, 0.234, 2.137, 1.731, 0.545,
       0.225, 0.816, 0.528, 0.509, 1.021, 0.821, 0.947, 1.268, 0.221,
       0.66 , 0.239, 0.949, 0.444, 0.463, 0.803, 1.6  , 0.944, 0.196,
       0.241, 0.161, 0.135, 0.376, 1.191, 0.702, 0.674, 1.076, 0.534,
       1.095, 0.554, 0.624, 0.219, 0.507, 0.561, 0.421, 0.516, 0.264,
       0.328, 0.233, 0.108, 1.138, 0.147, 0.727, 0.435, 0.497, 0.23 ,
       0.955, 2.42 , 0.658, 0.33 , 0.51 , 0.285, 0.415, 0.381, 0.832,
       0.498, 0.212, 0.364, 1.001, 0.46 , 0.733, 0.416, 0.705, 1.022,
       0.269, 0.6  , 0.571, 0.607, 0.17 , 0.21 , 0.126, 0.711, 0.466,
       0.162, 0.419, 0.63 , 0.365, 0.536, 1.159, 0.629, 0.292, 0.145,
       1.144, 0.174, 0.547, 0.163, 0.738, 0.314, 0.968, 0.409, 0.297,
       0.525, 0.154, 0.771, 0.107, 0.493, 0.717, 0.917, 0.501, 1.251,
       0.735, 0.804, 0.661, 0.549, 0.825, 0.423, 1.034, 0.16 , 0.341,
       0.68 , 0.591, 0.3  , 0.121, 0.502, 0.401, 0.601, 0.748, 0.338,
       0.43 , 0.892, 0.813, 0.693, 0.575, 0.371, 0.206, 0.417, 1.154,
       0.925, 0.175, 1.699, 0.682, 0.194, 0.4  , 0.1  , 1.258, 0.482,
       0.138, 0.593, 0.878, 0.157, 1.282, 0.141, 0.246, 1.698, 1.461,
       0.347, 0.362, 0.393, 0.144, 0.732, 0.115, 0.465, 0.649, 0.871,
       0.149, 0.695, 0.303, 0.61 , 0.73 , 0.447, 0.455, 0.133, 0.155,
       1.162, 1.292, 0.182, 1.394, 0.217, 0.631, 0.88 , 0.614, 0.332,
       0.366, 0.181, 0.828, 0.335, 0.856, 0.886, 0.439, 0.253, 0.598,
       0.904, 0.483, 0.565, 0.118, 0.177, 0.176, 0.295, 0.441, 0.352,
       0.826, 0.97 , 0.595, 0.317, 0.265, 0.646, 0.426, 0.56 , 0.515,
       0.453, 0.785, 0.734, 1.174, 0.488, 0.358, 1.096, 0.408, 1.182,
       0.222, 1.057, 0.766, 0.171])
In [106]:
IQ1R= dataset['DiabetesPedigreeFunction'].quantile(0.75)-dataset['DiabetesPedigreeFunction'].quantile(0.25)
IQ1R
Out[106]:
0.38249999999999995
In [107]:
upper_DPF=dataset['DiabetesPedigreeFunction'].quantile(0.75)+(IQ1R*3)
upper_DPF
Out[107]:
1.77375
In [108]:
dataset['DiabetesPedigreeFunction_mod']=dataset['DiabetesPedigreeFunction']
In [109]:
dataset.loc[dataset['DiabetesPedigreeFunction']>1.77375, 'DiabetesPedigreeFunction_mod']=1.77375
In [110]:
dataset.head()
Out[110]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod Pregnancies_mod Glucose_mod BloodPressure_mod SkinThickness_mod Insulin_mod BMI_mod DiabetesPedigreeFunction_mod
0 6 148 72 35 0 33.6 0.627 50 1 0.0 6 148 72 35 23 33.6 0.62700
1 1 85 66 29 0 26.6 0.351 31 0 0.0 1 85 66 29 23 26.6 0.35100
2 8 183 64 0 0 23.3 0.672 32 1 0.0 8 183 64 2 23 23.3 0.67200
3 1 89 66 23 94 28.1 0.167 21 0 1.0 1 89 66 23 94 28.1 0.16700
4 0 137 40 35 168 43.1 2.288 33 1 0.0 0 137 40 35 168 43.1 1.77375
In [111]:
dataset.shape
Out[111]:
(768, 17)
In [112]:
dataset.describe()
Out[112]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome Age_mod Pregnancies_mod Glucose_mod BloodPressure_mod SkinThickness_mod Insulin_mod BMI_mod DiabetesPedigreeFunction_mod
count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000
mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 0.471876 33.240885 0.348958 0.477865 3.520833 121.024740 70.977865 21.127604 89.735677 32.204036 0.469005
std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 0.331329 11.760232 0.476951 0.499835 2.836176 31.516899 13.725500 15.198534 101.188603 7.165761 0.317492
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.078000 21.000000 0.000000 0.000000 0.000000 20.000000 40.000000 2.000000 23.000000 15.000000 0.078000
25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 0.243750 24.000000 0.000000 0.000000 1.000000 99.000000 62.000000 2.000000 23.000000 27.300000 0.243750
50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 0.372500 29.000000 0.000000 0.000000 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 0.372500
75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250 41.000000 1.000000 1.000000 5.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000 1.000000 10.000000 199.000000 122.000000 99.000000 509.000000 64.500000 1.773750
In [113]:
dataset.columns
Out[113]:
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome', 'Age_mod',
       'Pregnancies_mod', 'Glucose_mod', 'BloodPressure_mod',
       'SkinThickness_mod', 'Insulin_mod', 'BMI_mod',
       'DiabetesPedigreeFunction_mod'],
      dtype='object')
In [114]:
col=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age']
for i in col:
    dataset.drop([i], axis=1, inplace =
                 True)
In [115]:
dataset.head()
Out[115]:
Outcome Age_mod Pregnancies_mod Glucose_mod BloodPressure_mod SkinThickness_mod Insulin_mod BMI_mod DiabetesPedigreeFunction_mod
0 1 0.0 6 148 72 35 23 33.6 0.62700
1 0 0.0 1 85 66 29 23 26.6 0.35100
2 1 0.0 8 183 64 2 23 23.3 0.67200
3 0 1.0 1 89 66 23 94 28.1 0.16700
4 1 0.0 0 137 40 35 168 43.1 1.77375
In [116]:
col=dataset.columns
In [117]:
for i in col:
    print(i,'--->', dataset[i].min())
    
    print(i,'--->', dataset[i].max())
    print("---------------------------------------------")

Outcome ---> 0
Outcome ---> 1
---------------------------------------------
Age_mod ---> 0.0
Age_mod ---> 1.0
---------------------------------------------
Pregnancies_mod ---> 0
Pregnancies_mod ---> 10
---------------------------------------------
Glucose_mod ---> 20
Glucose_mod ---> 199
---------------------------------------------
BloodPressure_mod ---> 40
BloodPressure_mod ---> 122
---------------------------------------------
SkinThickness_mod ---> 2
SkinThickness_mod ---> 99
---------------------------------------------
Insulin_mod ---> 23
Insulin_mod ---> 509
---------------------------------------------
BMI_mod ---> 15.0
BMI_mod ---> 64.5
---------------------------------------------
DiabetesPedigreeFunction_mod ---> 0.078
DiabetesPedigreeFunction_mod ---> 1.77375
---------------------------------------------

Model Building¶

In [118]:
X=dataset.drop('Outcome', axis=1)
Y=dataset['Outcome']
In [119]:
from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test =train_test_split(X,Y,test_size=0.2,random_state=0)

Logistic Regression¶

In [121]:
lr=LogisticRegression()
In [122]:
%%time
lr.fit(X_train,Y_train)

CPU times: total: 31.2 ms
Wall time: 102 ms

C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
Out[122]:
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()
In [123]:
pred_lr= lr.predict(X_test)
In [124]:
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
In [125]:
accuracy_lr=accuracy_score(Y_test, pred_lr)
print("Accuracy: %.2f%%" % (accuracy_lr * 100.0))
print("-----------------------------------------------")
print(classification_report(Y_test, pred_lr))
print("-----------------------------------------------")
print(confusion_matrix(Y_test, pred_lr))

Accuracy: 79.87%
-----------------------------------------------
              precision    recall  f1-score   support

           0       0.83      0.89      0.86       107
           1       0.70      0.60      0.64        47

    accuracy                           0.80       154
   macro avg       0.77      0.74      0.75       154
weighted avg       0.79      0.80      0.79       154

-----------------------------------------------
[[95 12]
 [19 28]]
In [126]:
from sklearn.metrics import roc_auc_score

# auc scores
auc_score1 = roc_auc_score(Y_test, pred_lr)


print(auc_score1*100)

74.17975740703918

Hyperparameter Tuning¶

In [127]:
param_grid= [{'penalty': ['l1', 'l2', 'elasticnet', 'none'],
             'C': np.logspace(-4,4,20),
             'solver': ['lbfgs', 'newton-cg', 'liblinear', 'sag', 'saga'],
             'max_iter': [100,500,1000,1500,2000,2500,5000]}]
In [128]:
from sklearn.model_selection import GridSearchCV
In [129]:
clf= GridSearchCV(lr, param_grid= param_grid, cv=3, verbose=True, n_jobs=-1)
In [131]:
%%time
best_clf= clf.fit(X_train, Y_train)

Fitting 3 folds for each of 2800 candidates, totalling 8400 fits
CPU times: total: 8.03 s
Wall time: 1min 25s

C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:378: FitFailedWarning: 
3780 fits failed out of a total of 8400.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1162, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 54, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1162, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 54, in _check_solver
    raise ValueError(
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1162, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 54, in _check_solver
    raise ValueError(
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1162, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 54, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1162, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 54, in _check_solver
    raise ValueError(
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1162, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 64, in _check_solver
    raise ValueError(
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1162, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 54, in _check_solver
    raise ValueError(
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1291, in fit
    fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer=prefer)(
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 63, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\parallel.py", line 1048, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\parallel.py", line 864, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\parallel.py", line 782, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\parallel.py", line 263, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 123, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 521, in _logistic_regression_path
    alpha = (1.0 / C) * (1 - l1_ratio)
                         ~~^~~~~~~~~~
TypeError: unsupported operand type(s) for -: 'int' and 'NoneType'

--------------------------------------------------------------------------------
420 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1162, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 71, in _check_solver
    raise ValueError("penalty='none' is not supported for the liblinear solver")
ValueError: penalty='none' is not supported for the liblinear solver

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan        nan 0.64006855 ...        nan 0.73283118 0.74261916]
  warnings.warn(
In [132]:
best_clf.best_estimator_
Out[132]:
LogisticRegression(C=0.23357214690901212, solver='newton-cg')
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LogisticRegression(C=0.23357214690901212, solver='newton-cg')
In [133]:
best_clf.best_params_
Out[133]:
{'C': 0.23357214690901212,
 'max_iter': 100,
 'penalty': 'l2',
 'solver': 'newton-cg'}
In [134]:
lr2=LogisticRegression(C= 4.281332398719396,penalty='l1',max_iter=100,solver= 'liblinear' )
In [135]:
lr2.fit(X_train,Y_train)
Out[135]:
LogisticRegression(C=4.281332398719396, penalty='l1', solver='liblinear')
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LogisticRegression(C=4.281332398719396, penalty='l1', solver='liblinear')
In [136]:
pred_lr2= lr2.predict(X_test)
In [137]:
accuracy_lr2=accuracy_score(Y_test, pred_lr2)
print("Accuracy: %.2f%%" % (accuracy_lr2 * 100.0))
print("-----------------------------------------------")
print(classification_report(Y_test, pred_lr2))
print("-----------------------------------------------")
print(confusion_matrix(Y_test, pred_lr2))

Accuracy: 79.87%
-----------------------------------------------
              precision    recall  f1-score   support

           0       0.83      0.89      0.86       107
           1       0.70      0.60      0.64        47

    accuracy                           0.80       154
   macro avg       0.77      0.74      0.75       154
weighted avg       0.79      0.80      0.79       154

-----------------------------------------------
[[95 12]
 [19 28]]
In [138]:
auc_score = roc_auc_score(Y_test, pred_lr2)


print(auc_score)

0.7417975740703917

SVM¶

In [139]:
from sklearn import svm
In [140]:
sVm = svm.SVC()
In [141]:
%%time
sVm.fit(X_train,Y_train)

CPU times: total: 15.6 ms
Wall time: 22 ms
Out[141]:
SVC()
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SVC()
In [142]:
pred_svm= sVm.predict(X_test)
In [143]:
accuracy_svm=accuracy_score(Y_test, pred_svm)
print("Accuracy: %.2f%%" % (accuracy_svm * 100.0))
print("-----------------------------------------------")
print(classification_report(Y_test, pred_svm))
print("-----------------------------------------------")
print(confusion_matrix(Y_test, pred_svm))

Accuracy: 79.22%
-----------------------------------------------
              precision    recall  f1-score   support

           0       0.81      0.92      0.86       107
           1       0.73      0.51      0.60        47

    accuracy                           0.79       154
   macro avg       0.77      0.71      0.73       154
weighted avg       0.78      0.79      0.78       154

-----------------------------------------------
[[98  9]
 [23 24]]
In [144]:
 param_grid1=[{'C': [1.0,2.0,0,5.0,6.0,7.0,10.0],
             'kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'],
             'gamma':['scale', 'auto']
             }]
In [145]:
from sklearn.model_selection import RandomizedSearchCV
In [147]:
svm1 = svm.SVC(kernel= 'Linear')
In [148]:
clf_svm_random= RandomizedSearchCV(estimator=svm1,param_distributions=param_grid1,cv=3, verbose=100, n_jobs=-1)
In [150]:
%%time
best_clf_svm= clf_svm_random.fit(X_train, Y_train)

Fitting 3 folds for each of 10 candidates, totalling 30 fits

---------------------------------------------------------------------------
_RemoteTraceback                          Traceback (most recent call last)
_RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\externals\loky\process_executor.py", line 436, in _process_worker
    r = call_item()
        ^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\externals\loky\process_executor.py", line 288, in __call__
    return self.fn(*self.args, **self.kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 595, in __call__
    return self.func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\parallel.py", line 263, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 123, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 678, in _fit_and_score
    X_train, y_train = _safe_split(estimator, X, y, train)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\kiran\anaconda3\Lib\site-packages\sklearn\utils\metaestimators.py", line 227, in _safe_split
    raise ValueError("X should be a square kernel matrix")
ValueError: X should be a square kernel matrix
"""

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
File <timed exec>:1

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:874, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    868     results = self._format_results(
    869         all_candidate_params, n_splits, all_out, all_more_results
    870     )
    872     return results
--> 874 self._run_search(evaluate_candidates)
    876 # multimetric is determined here because in the case of a callable
    877 # self.scoring the return type is only known after calling
    878 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1768, in RandomizedSearchCV._run_search(self, evaluate_candidates)
   1766 def _run_search(self, evaluate_candidates):
   1767     """Search n_iter candidates from param_distributions"""
-> 1768     evaluate_candidates(
   1769         ParameterSampler(
   1770             self.param_distributions, self.n_iter, random_state=self.random_state
   1771         )
   1772     )

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:821, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    813 if self.verbose > 0:
    814     print(
    815         "Fitting {0} folds for each of {1} candidates,"
    816         " totalling {2} fits".format(
    817             n_splits, n_candidates, n_candidates * n_splits
    818         )
    819     )
--> 821 out = parallel(
    822     delayed(_fit_and_score)(
    823         clone(base_estimator),
    824         X,
    825         y,
    826         train=train,
    827         test=test,
    828         parameters=parameters,
    829         split_progress=(split_idx, n_splits),
    830         candidate_progress=(cand_idx, n_candidates),
    831         **fit_and_score_kwargs,
    832     )
    833     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    834         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    835     )
    836 )
    838 if len(out) < 1:
    839     raise ValueError(
    840         "No fits were performed. "
    841         "Was the CV iterator empty? "
    842         "Were there no candidates?"
    843     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:63, in Parallel.__call__(self, iterable)
     58 config = get_config()
     59 iterable_with_config = (
     60     (_with_config(delayed_func, config), args, kwargs)
     61     for delayed_func, args, kwargs in iterable
     62 )
---> 63 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1061, in Parallel.__call__(self, iterable)
   1058     self._iterating = False
   1060 with self._backend.retrieval_context():
-> 1061     self.retrieve()
   1062 # Make sure that we get a last message telling us we are done
   1063 elapsed_time = time.time() - self._start_time

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:938, in Parallel.retrieve(self)
    936 try:
    937     if getattr(self._backend, 'supports_timeout', False):
--> 938         self._output.extend(job.get(timeout=self.timeout))
    939     else:
    940         self._output.extend(job.get())

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:542, in LokyBackend.wrap_future_result(future, timeout)
    539 """Wrapper for Future.result to implement the same behaviour as
    540 AsyncResults.get from multiprocessing."""
    541 try:
--> 542     return future.result(timeout=timeout)
    543 except CfTimeoutError as e:
    544     raise TimeoutError from e

File ~\anaconda3\Lib\concurrent\futures\_base.py:449, in Future.result(self, timeout)
    447     raise CancelledError()
    448 elif self._state == FINISHED:
--> 449     return self.__get_result()
    451 self._condition.wait(timeout)
    453 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:

File ~\anaconda3\Lib\concurrent\futures\_base.py:401, in Future.__get_result(self)
    399 if self._exception:
    400     try:
--> 401         raise self._exception
    402     finally:
    403         # Break a reference cycle with the exception in self._exception
    404         self = None

ValueError: X should be a square kernel matrix
In [151]:
X_test.head()
Out[151]:
Age_mod Pregnancies_mod Glucose_mod BloodPressure_mod SkinThickness_mod Insulin_mod BMI_mod DiabetesPedigreeFunction_mod
661 1.0 1 199 76 43 23 42.9 1.394
122 1.0 2 107 74 30 100 33.6 0.404
113 1.0 4 76 62 2 23 34.0 0.391
14 0.0 5 166 72 19 175 25.8 0.587
529 0.0 0 111 65 2 23 24.6 0.660

Decision Trees¶

In [152]:
from sklearn import tree
In [153]:
dt = tree.DecisionTreeClassifier()
#clf = clf.fit(X, Y)
In [154]:
param_grid_dt= [{'criterion': ["gini", "entropy"],
             'splitter': ["best", "random"],
             'max_depth': [5,10,20,25,30,50],
             'max_features': [2,4,6,8,10,"auto", "sqrt", "log2"]}]
In [155]:
clf_svm_random= RandomizedSearchCV(estimator=dt,param_distributions=param_grid_dt,cv=5, verbose=100, n_jobs=-1)
In [156]:
%%time
clf_svm_random.fit(X_train, Y_train)

Fitting 5 folds for each of 10 candidates, totalling 50 fits
CPU times: total: 203 ms
Wall time: 5.82 s
Out[156]:
RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,
                   param_distributions=[{'criterion': ['gini', 'entropy'],
                                         'max_depth': [5, 10, 20, 25, 30, 50],
                                         'max_features': [2, 4, 6, 8, 10,
                                                          'auto', 'sqrt',
                                                          'log2'],
                                         'splitter': ['best', 'random']}],
                   verbose=100)
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RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,
                   param_distributions=[{'criterion': ['gini', 'entropy'],
                                         'max_depth': [5, 10, 20, 25, 30, 50],
                                         'max_features': [2, 4, 6, 8, 10,
                                                          'auto', 'sqrt',
                                                          'log2'],
                                         'splitter': ['best', 'random']}],
                   verbose=100)
DecisionTreeClassifier()
DecisionTreeClassifier()
In [157]:
clf_svm_random.best_estimator_
Out[157]:
DecisionTreeClassifier(max_depth=5, max_features=10, splitter='random')
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DecisionTreeClassifier(max_depth=5, max_features=10, splitter='random')
In [158]:
clf_svm_random.best_params_
Out[158]:
{'splitter': 'random', 'max_features': 10, 'max_depth': 5, 'criterion': 'gini'}

Decision Tree¶

In [159]:
%%time
dt1 = tree.DecisionTreeClassifier(criterion='entropy',splitter='best',max_depth=10,max_features='log2')
dtfit = dt1.fit(X_train, Y_train)

CPU times: total: 0 ns
Wall time: 4 ms
In [160]:
pred_dt= dtfit.predict(X_test)
In [161]:
accuracy_dt=accuracy_score(Y_test, pred_dt)
print("Accuracy: %.2f%%" % (accuracy_dt * 100.0))
print("-----------------------------------------------")
print(classification_report(Y_test, pred_dt))
print("-----------------------------------------------")
print(confusion_matrix(Y_test, pred_dt))

Accuracy: 72.73%
-----------------------------------------------
              precision    recall  f1-score   support

           0       0.80      0.81      0.81       107
           1       0.56      0.53      0.54        47

    accuracy                           0.73       154
   macro avg       0.68      0.67      0.67       154
weighted avg       0.72      0.73      0.73       154

-----------------------------------------------
[[87 20]
 [22 25]]
In [ ]: