AkshitGulyan_AIML_LogisticRegression.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


# In[2]:


data=pd.read_csv(r"C:\Users\akshi\Downloads\DSDL\heart.csv")


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data.head()


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data.info()


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x=data[['Age','Sex','ChestPainType','RestingBP','Cholesterol','FastingBS','RestingECG','MaxHR','ExerciseAngina','Oldpeak','ST_Slope']]


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y=data['HeartDisease']


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x=pd.get_dummies(data,columns=(['Sex','ChestPainType','RestingECG','ExerciseAngina','ST_Slope']))


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x.head()


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from sklearn.linear_model import LogisticRegression


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from sklearn.preprocessing  import LabelEncoder


# In[10]:


le=LabelEncoder()


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data.Age=le.fit_transform(data.Age)
data.Sex=le.fit_transform(data.Sex)
data.ChestPainType=le.fit_transform(data.ChestPainType)
data.RestingBP=le.fit_transform(data.RestingBP)
data.Cholesterol=le.fit_transform(data.Cholesterol)
data.FastingBS=le.fit_transform(data.FastingBS)
data.RestingECG=le.fit_transform(data.RestingECG)
data.MaxHR=le.fit_transform(data.MaxHR)
data.ExerciseAngina=le.fit_transform(data.ExerciseAngina)
data.Oldpeak=le.fit_transform(data.Oldpeak)
data.ST_Slope=le.fit_transform(data.ST_Slope)


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data.HeartDisease=le.fit_transform(data.HeartDisease)


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data.head()


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from sklearn.model_selection import train_test_split


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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25,random_state=1)


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lr=LogisticRegression()


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lr.fit(x_train,y_train)


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y_pred = lr.predict(x_test)


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print(lr.coef_)
print(lr.intercept_)


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from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, y_pred)


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from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_pred)