Customer Churn Prediction

In the Customer Churn Prediction project, the primary objective was to develop a robust system for analyzing and predicting customer churn within a company. The project encompassed a series of functionalities aimed at extracting insights from data, applying diverse machine learning algorithms, and providing an intuitive user interface for real-time predictions. The journey began with a meticulous analysis of the data, followed by comprehensive preprocessing to ensure its suitability for predictive modeling. Various machine learning algorithms, including Logistic Regression, Decision Tree, SVM, and Random Forest, were employed to create predictive models. The exploration extended to advanced techniques such as Ensemble Learning, Stacking, and Lazy Algorithms, aiming to enhance the predictive accuracy and robustness of the system.

The integration of these models into a user-friendly web application was achieved through the Flask framework for the backend and Jinja templating for the frontend. The heart of the system lies in the design of a user interface that incorporates a form for users to input relevant data. This form serves as a bridge between the predictive models and the end-user, allowing them to determine the likelihood of a customer churning. Upon submitting data through the form, the system generates real-time predictions, providing insights into whether a customer is likely to churn. Additionally, the system offers possible solutions or strategies to mitigate churn risks, empowering businesses to proactively address customer retention challenges.

Through this Customer Churn Prediction project, the goal was not only to showcase proficiency in machine learning and web development but also to equip businesses with a proactive tool for customer retention, fostering a data-driven approach to reduce churn and enhance overall customer satisfaction.