Customer_churnProject_ML_LogisticRegressionL

this project involves analyzing customer churn data to identify patterns and prevent customer churn by understanding the behaviors and characteristics that lead to it.

Overview

This project aims to understand and predict customer churn for a telecom company by analyzing customer behavior and demographics. By predicting the likelihood of churn, the company can take preemptive steps to retain valuable customers and address churn-related concerns effectively.

Dataset

The data includes customer demographics, service information, and usage metrics, providing insights into customer behavior related to churn.

Problem Statement

The goal is to predict if a customer will churn based on their usage patterns, contract, and payment methods, allowing the company to implement strategies to reduce churn.

Project Tasks

1. Data Manipulation

• Extracted specific data subsets for senior citizens, long-tenure customers, and churned accounts.
• Calculated churn counts and filtered data for custom queries.

2. Data Visualization

• Bar Plot: Internet service category distribution.
• Histogram: Tenure distribution with 30 bins.
• Scatter Plot: Tenure vs. Monthly Charges.
• Box Plot: Contract types vs. Tenure distribution.

3. Linear Regression

Built a linear regression model to predict monthly charges from tenure:

• 70:30 train-test split.
• Calculated RMSE to evaluate model accuracy.

4. Logistic Regression

Developed a logistic regression model to predict customer churn:

• Simple model: Monthly charges as the independent variable.
• Multiple logistic regression model: Tenure and Monthly Charges as independent variables.
• Achieved 79% accuracy on the test set.

5. Decision Tree

Implemented a decision tree model to predict churn with tenure as the feature:

• 80:20 train-test split, evaluated with a confusion matrix.

6. Random Forest

Built a random forest model using tenure and monthly charges to predict churn:

• 70:30 train-test split, evaluated accuracy and confusion matrix.

Results

• Logistic regression model performed best, achieving 79% accuracy in predicting customer churn.

Installation and Usage

1. Clone this repository:

   git clone https://github.com/gurramankit/customer-churn-analysis.git

2. Install the necessary libraries:

   pip install -r requirements.txt

3. Run the project:

   python main.py

Project Structure

• data/ - Contains the customer churn dataset.
• notebooks/ - Jupyter notebooks for data manipulation, EDA, and model development.
• src/ - Python scripts for data processing and model implementation.
• README.md - Overview and instructions.
• requirements.txt - List of dependencies.

Contact

For questions or suggestions, feel free to reach out at [ankithkumarankith122@gmail.com].