Codeclause-projects

Churn Prediction in Telecom Industry using Logistic Regression

Project Overview:

This project aims to predict customer churn in the telecom industry using the logistic regression algorithm. Churn, the rate at which customers switch to competitors, poses a significant challenge to businesses. By analyzing historical customer data and usage patterns, this project seeks to build a predictive model that identifies potential churners, enabling telecom companies to implement proactive strategies for customer retention.

Key Objectives:

• Developed a predictive model to identify customers who are likely to churn based on historical data.
• Analyzed relevant features and their impact on churn prediction.
• Evaluated the performance of the logistic regression model using appropriate metrics.
• Provided insights and recommendations for targeted retention efforts.

Project Steps:

1. Data Collection and Preprocessing:

   • Gathered customer data, including usage patterns, subscription details, and demographics.
   • Cleaned and preprocess the data, handling missing values and outliers.

2. Feature Selection and Engineering:

   • Identified key features that may influence churn based on domain knowledge.
   • Engineered new features that could enhance the model's predictive power.

3. Exploratory Data Analysis (EDA):

   • Conducted exploratory analysis to understand data distributions and correlations.
   • Visualize insights to guide feature selection and model interpretation.

4. Model Development - Logistic Regression:

   • Build a logistic regression model to predict churn.
   • Splited the data into training and testing sets for model evaluation.

5. Model Evaluation:

   • Assessed the model's performance using metrics such as accuracy, precision, recall, and F1-score.
   • Used cross-validation techniques to validate the model's robustness.

6. Interpretation and Insights:

   • Interpreted model coefficients to understand feature importance.
   • Derived actionable insights for improving customer retention strategies.

7. Deployment and Future Steps:

   • Deployed the trained model for real-time churn prediction.
   • Discussed potential enhancements, such as exploring advanced algorithms or integrating additional data sources.

Expected Outcomes:

At the project's conclusion, a logistic regression model capable of predicting customer churn will be developed. The model's insights can guide telecom companies in taking targeted actions to reduce churn rates and enhance customer satisfaction. By leveraging historical data and applying machine learning techniques, this project showcases the value of predictive analytics in addressing real-world business challenges.