Welcome to the Churn Probability Prediction repository! This project focuses on predicting customer churn using various machine learning techniques. Customer churn refers to the loss of clients or customers, and predicting it is crucial for businesses to take preemptive actions to retain customers.
The goal of this project is to build and evaluate machine learning models that can predict the probability of customer churn. By understanding the factors that contribute to churn, businesses can develop strategies to improve customer retention and increase profitability.
- Data Preprocessing: Handling missing values, feature encoding, and scaling.
- Exploratory Data Analysis (EDA): Visualizing data to uncover patterns and correlations.
- Model Building: Implementing various machine learning algorithms such as logistic regression, decision trees, random forests, and gradient boosting.
- Model Evaluation: Assessing model performance using metrics like accuracy, precision, recall, and AUC-ROC.
- Hyperparameter Tuning: Optimizing model parameters for better performance.
- Feature Importance: Identifying the most influential features contributing to churn.
data/: Contains the dataset used for the analysis.notebooks/: Jupyter notebooks with detailed steps for data preprocessing, EDA, model building, and evaluation.src/: Python scripts for data preprocessing, model training, and evaluation.results/: Output files and model performance metrics.README.md: Project documentation and instructions.
- Summary of findings from the exploratory data analysis.
- Comparison of model performance metrics.
- Insights into the most important features affecting churn.
- Experimenting with additional machine learning algorithms.
- Incorporating more advanced techniques such as deep learning.
- Exploring feature engineering to improve model performance.
- Applying the models to different datasets or industries.
For any questions or inquiries, please contact:
- Email: [email protected]
- LinkedIn: https://www.linkedin.com/in/daniq/