Telecom-Customer-Churn-Prediction

This is a supervised machine learning project using telecom customer data to predict customers that would churn based on customer Age Group, Relationship Status, Subscribed Services, Charges, and Financial Responsibilities, etc.

Project Guide

In this project i made use of Pandas, Numpy, train_test_split, RandomForestClassifier, SVM, LogisticRegression, cross_val_score, and StandardScaler

This project was carried out using the following steps:

• Importing the necessary libraries
• Reading CSV into a dataframe
• Cleaning data by
  • Dropping irrelevant columns
  • Dropping rows with empty columns
  • Applying appropriate column format (astype(float))
  • Checking for outliers using Z-score
• Use histogram to represent customer churn based on tenure and monthly charges
• One hot encoding
• Grouping dependent and independent variables and scaling the independent variables appropriately
• Use GridSearchCV to determine the best performing model
• Train the best performing model to make predictions
• Make predictions