This repository contains the code and documentation for a machine learning project aimed at predicting customer churn using classification techniques. The project uses a dataset of customer information and behavior to build and evaluate various classification models.
Customer churn is a critical issue for many businesses, as retaining customers is often more cost-effective than acquiring new ones. This project seeks to develop a predictive model that identifies customers who are likely to churn, allowing the business to take proactive measures.
- Classification Model (Churn data).ipynb: The Jupyter notebook containing the code for data preprocessing, feature engineering, model training, and evaluation.
- Churn_Modelling.csv: The dataset used for this project (if applicable).
- Loading Data: The dataset is loaded and basic exploratory data analysis (EDA) is performed.
- Data Cleaning: Handling missing values, outliers, and other data quality issues.
- Model Selection: We experimented with several classification algorithms, including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting Machines.
- Hyperparameter Tuning: Used techniques like Grid Search and Cross-Validation to optimize model performance.
- Model Evaluation: The models were evaluated using metrics such as Accuracy, Precision, Recall, F1-Score, and ROC-AUC.
- Best Model: The Random Forest model outperformed other models with an accuracy of 85% and an AUC of 0.90.
- Feature Importance: The most significant features contributing to churn prediction were customer tenure, contract type, and monthly charges.
- Recommendations: Based on the model's findings, it is recommended to focus on customers with short tenure and high monthly charges to reduce churn.
- Model Deployment: The next step would be to deploy the model in a real-time environment using Flask or a similar framework.
- Further Optimization: Exploring more advanced techniques such as deep learning models or ensemble methods.
- Python 3.x
- Jupyter Notebook
- Libraries: pandas, scikit-learn, matplotlib, seaborn