Telecom-Customer-Churn-Project

Welcome to my Telecom Churn Analysis Project!

📊 Exploring Customer Churn in the Telecom Industry:

Gender Analysis: Does gender influence customer retention, and if so, what patterns emerge?
Customer Switching: What percentage of customers switch to other providers?
Contract Types: How do different contract types impact customer retention?
Payment Methods: What is the relationship between payment methods and customer churn?
Partner Presence: How does the presence or absence of a partner affect churn?
Internet Service Types: Which type of internet service leads to more churn?

Explanatory Data Analysis (EDA): Explore the dataset and identify key insights.
Data Visualization: Visualize the data to better understand trends and patterns.
Machine Learning: Utilize decision tree classifier and random forest classifier to predict future customer churn.

Separate the dataset into two DataFrames:
- feats: Containing the explanatory variables.
- target: Containing the target variable Churn.
Train a model on one subset of the data that learns patterns from the independent features.
Making Predictions: Leverage the trained model to make predictions on new, unseen data.

Follow along as I delve into these questions and explore the steps leading up to predictive churn analysis.