Telecom Churn Analysis Project

Welcome to the Telecom Churn Analysis Project! This repository contains an analysis of customer churn in the telecom industry using data science techniques.

Overview

The Telecom Churn Analysis Project aims to identify factors contributing to customer attrition and to develop predictive models for customer churn. By analyzing the dataset, we can derive insights that can help telecom companies enhance customer retention strategies.

Features

• Data Preprocessing: Clean and preprocess the dataset for analysis.
• Exploratory Data Analysis (EDA): Visualizations and statistics to understand customer behavior and churn patterns.
• Churn Prediction Models: Implementation of machine learning models to predict customer churn.
• Model Evaluation: Assessing model performance using various metrics such as accuracy, precision, and recall.

Project Structure

• Telecom_Churn_Analysis_and_EDA.ipynb: Jupyter Notebook containing the analysis, data preprocessing, and model implementation.
• churn-bigml-20.csv & churn-bigml-20.csv: The dataset used for the churn analysis.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/TejasWaghmare18/Telecom-Churn-Analysis.git
   
   

Technologies Used

• Python
• Jupyter Notebook
• Pandas (for data manipulation)
• NumPy (for numerical analysis)
• Matplotlib and Seaborn (for data visualization)
• Scikit-learn (for machine learning)