Logistic Regression in ML and AI with Examples and Projects

 Logistic Regression in ML and AI with Examples and Projects

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The Complete Code

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split

train = pd.read_csv("titanic_train.csv")

def impute_age(cols):

    Age = cols[0]

    Pclass = cols[1]

    if pd.isnull(Age):

        if Pclass == 1:

            return 37

        elif Pclass == 2:

            return 29

        else:

            return 24

    else:

        return Age

train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)

train.drop('Cabin',axis=1,inplace=True)

train.dropna(inplace=True)

Sex = pd.get_dummies(train['Sex'],drop_first=True)

Embark = pd.get_dummies(train['Embarked'],drop_first=True)

train = pd.concat([train,Sex,Embark],axis=1)

train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)

train.drop('PassengerId',axis=1,inplace=True)

X_train, X_test, y_train, y_test = train_test_split(train.drop('Survived',axis=1),

                                                    train['Survived'], test_size=0.30,

                                                    random_state=101)

from sklearn.linear_model import LogisticRegression

logmodel = LogisticRegression()

logmodel.fit(X_train,y_train)

predictions = logmodel.predict(X_test)

from sklearn.metrics import classification_report

print(classification_report(y_test,predictions))

from sklearn.metrics import confusion_matrix

confusion_matrix(y_test,predictions)