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Project 1

KKothapally Ravinder6 Oct 2022
import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
import seaborn as sns
In [3]:
df = pd.read_csv('heart.csv')
In [4]:
df
Out[4]:
age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp caa thall output
0 63 1 3 145 233 1 0 150 0 2.3 0 0 1 1
1 37 1 2 130 250 0 1 187 0 3.5 0 0 2 1
2 41 0 1 130 204 0 0 172 0 1.4 2 0 2 1
3 56 1 1 120 236 0 1 178 0 0.8 2 0 2 1
4 57 0 0 120 354 0 1 163 1 0.6 2 0 2 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
298 57 0 0 140 241 0 1 123 1 0.2 1 0 3 0
299 45 1 3 110 264 0 1 132 0 1.2 1 0 3 0
300 68 1 0 144 193 1 1 141 0 3.4 1 2 3 0
301 57 1 0 130 131 0 1 115 1 1.2 1 1 3 0
302 57 0 1 130 236 0 0 174 0 0.0 1 1 2 0

303 rows × 14 columns

In [5]:
df.isnull().sum()
Out[5]:
age         0
sex         0
cp          0
trtbps      0
chol        0
fbs         0
restecg     0
thalachh    0
exng        0
oldpeak     0
slp         0
caa         0
thall       0
output      0
dtype: int64
In [6]:
 
df.dtypes
Out[6]:
age           int64
sex           int64
cp            int64
trtbps        int64
chol          int64
fbs           int64
restecg       int64
thalachh      int64
exng          int64
oldpeak     float64
slp           int64
caa           int64
thall         int64
output        int64
dtype: object
In [7]:
x = df.drop(['output'], axis = 1)
y = df['output']
In [10]:
 
df.shape
Out[10]:
(303, 14)
In [8]:
x
Out[8]:
age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp caa thall
0 63 1 3 145 233 1 0 150 0 2.3 0 0 1
1 37 1 2 130 250 0 1 187 0 3.5 0 0 2
2 41 0 1 130 204 0 0 172 0 1.4 2 0 2
3 56 1 1 120 236 0 1 178 0 0.8 2 0 2
4 57 0 0 120 354 0 1 163 1 0.6 2 0 2
... ... ... ... ... ... ... ... ... ... ... ... ... ...
298 57 0 0 140 241 0 1 123 1 0.2 1 0 3
299 45 1 3 110 264 0 1 132 0 1.2 1 0 3
300 68 1 0 144 193 1 1 141 0 3.4 1 2 3
301 57 1 0 130 131 0 1 115 1 1.2 1 1 3
302 57 0 1 130 236 0 0 174 0 0.0 1 1 2

303 rows × 13 columns

In [11]:
y
Out[11]:
0      1
1      1
2      1
3      1
4      1
      ..
298    0
299    0
300    0
301    0
302    0
Name: output, Length: 303, dtype: int64
In [14]:
 
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3,random_state = 1)
In [15]:
 
x_train.shape,y_train.shape
Out[15]:
((212, 13), (212,))
In [16]:
 
x_test.shape,y_test.shape
Out[16]:
((91, 13), (91,))
In [17]:
 
x_train.head()
Out[17]:
age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp caa thall
34 51 1 3 125 213 0 0 125 1 1.4 2 1 2
185 44 1 0 112 290 0 0 153 0 0.0 2 1 2
187 54 1 0 124 266 0 0 109 1 2.2 1 1 3
147 60 0 3 150 240 0 1 171 0 0.9 2 0 2
202 58 1 0 150 270 0 0 111 1 0.8 2 0 3
In [18]:
 
# NUMERICAL COLUMNS
x_train_num = x_train.select_dtypes(include=['int64','float64'])
x_train_num
​
x_train_num.head()
Out[18]:
age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp caa thall
34 51 1 3 125 213 0 0 125 1 1.4 2 1 2
185 44 1 0 112 290 0 0 153 0 0.0 2 1 2
187 54 1 0 124 266 0 0 109 1 2.2 1 1 3
147 60 0 3 150 240 0 1 171 0 0.9 2 0 2
202 58 1 0 150 270 0 0 111 1 0.8 2 0 3
In [19]:
 
# scaling the numerical features 
from sklearn.preprocessing import StandardScaler
​
scaler = StandardScaler()
​
# column names are (annoying) lost after scaling 
# i.e., the datframe is converted into a numpy ndarray)
​
x_train_num_rescaled = pd.DataFrame(scaler.fit_transform(x_train_num),
                                   columns = x_train_num.columns,
                                   index = x_train_num.index)
​
x_train_num_rescaled.head()
Out[19]:
age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp caa thall
34 -0.334915 0.635589 1.992234 -0.332611 -0.655815 -0.413849 -1.030043 -1.074173 1.439605 0.356309 0.924204 0.295586 -0.535052
185 -1.097207 0.635589 -0.947582 -1.071867 1.000106 -0.413849 -1.030043 0.121905 -0.694635 -0.931786 0.924204 0.295586 -0.535052
187 -0.008219 0.635589 -0.947582 -0.389477 0.483975 -0.413849 -1.030043 -1.757646 1.439605 1.092364 -0.722277 0.295586 1.133051
147 0.645174 -1.573344 1.992234 1.089034 -0.075168 -0.413849 0.902427 0.890812 -0.694635 -0.103725 0.924204 -0.699084 -0.535052
202 0.427376 0.635589 -0.947582 1.089034 0.569996 -0.413849 -1.030043 -1.672212 1.439605 -0.195731 0.924204 -0.699084 1.133051
In [20]:
 
#CATEGORICAL COLUMNS
x_test_num = x_test.select_dtypes(include=['int64','float64'])
x_test_num
​
x_test_num.head()
Out[20]:
age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp caa thall
204 62 0 0 160 164 0 0 145 0 6.2 0 3 3
159 56 1 1 130 221 0 0 163 0 0.0 2 0 3
219 48 1 0 130 256 1 0 150 1 0.0 2 2 3
174 60 1 0 130 206 0 0 132 1 2.4 1 2 3
184 50 1 0 150 243 0 0 128 0 2.6 1 0 3
In [21]:
 
x_test_num_rescaled = pd.DataFrame(scaler.transform(x_test_num),
                                  columns=x_test_num.columns,
                                   index = x_test_num.index)
​
x_test_num_rescaled.head()
Out[21]:
age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp caa thall
204 0.862972 -1.573344 -0.947582 1.657692 -1.709583 -0.413849 -1.030043 -0.219832 -0.694635 4.772635 -2.368758 2.284925 1.133051
159 0.209579 0.635589 0.032356 -0.048282 -0.483772 -0.413849 -1.030043 0.549075 -0.694635 -0.931786 0.924204 -0.699084 1.133051
219 -0.661612 0.635589 -0.947582 -0.048282 0.268920 2.416342 -1.030043 -0.006246 1.439605 -0.931786 0.924204 1.290255 1.133051
174 0.645174 0.635589 -0.947582 -0.048282 -0.806354 -0.413849 -1.030043 -0.775153 1.439605 1.276377 -0.722277 1.290255 1.133051
184 -0.443814 0.635589 -0.947582 1.089034 -0.010651 -0.413849 -1.030043 -0.946022 -0.694635 1.460391 -0.722277 -0.699084 1.133051
In [23]:
from sklearn.linear_model import LogisticRegression
regressor = LogisticRegression()
regressor.fit(x_train_num_rescaled,y_train)
Out[23]:
LogisticRegression()
In [25]:
 
y_test_pred = regressor.predict(x_test_num_rescaled)
In [26]:
 
temp_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_test_pred})
​
temp_df.head()
Out[26]:
Actual Predicted
204 0 0
159 1 1
219 0 0
174 0 0
184 0 0
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