README.md

Orange Telecom Churned Or Not

Imported all the required library

import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LinearRegression

Loading and Viewing the data

data=pd.read_csv('Orange_Telecom_Churn_Data.csv')
data.head()

Data Visualisation

Ploting the churned people

Ploting Area code Wise Churning

Ploting Effect of intial plan

Ploting Effect of voice plan

Ploting Effect of number of service calls

Using Different Model's

Creating Training and Testing Data set

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42)

Training the model using Logistic Regression

model=LogisticRegression()
model.fit(x_train,y_train)

Making the prediction

new_prediction=model.predict(testing_data)

Getting the accuracy score

from sklearn.metrics import accuracy_score


acc_logreg = round(accuracy_score(prediction, y_test) * 100, 2)
print(acc_logreg)

got 86.29 using Logistic regression

Training the model using KNN

model=KNeighborsClassifier(n_neighbors=10)
model.fit(x_train,y_train)

Making the prediction

new_prediction=model.predict(testing_data)

Getting the accuracy score

from sklearn.metrics import accuracy_score


acc_logreg = round(accuracy_score(prediction, y_test) * 100, 2)
print(acc_logreg)

got 88.86 using KNN

Training the model using Decision Tree Classifier

model=DecisionTreeClassifier()
model.fit(x_train,y_train)

Making the prediction

new_prediction=model.predict(testing_data)

Getting the accuracy score

from sklearn.metrics import accuracy_score


acc_logreg = round(accuracy_score(prediction, y_test) * 100, 2)
print(acc_logreg)

got 92.35 using Decision Tree Classifier