This project aims to build a predictive model to estimate the yearly amount spent by customers using various features available in the dataset. By understanding and predicting customer spending, businesses can make informed decisions to enhance customer experience and optimize marketing strategies.
The goal is to use historical customer data to predict future spending habits, allowing businesses to tailor their marketing efforts more effectively.
The dataset used in this project contains information about customers, including their time spent on the website, time spent on the app, average session length, and length of membership.
We perform EDA to visualize the relationships between different features and the target variable.
We build a Linear Regression model to predict the yearly amount spent by customers.
We evaluate the model's performance by making predictions on the test set and calculating error metrics.
Analyzing the residuals to check the assumptions of the Linear Regression model.
In this project, we successfully built a Linear Regression model to predict the yearly amount spent by customers based on various features. The model was evaluated using error metrics and visualizations. Residual analysis was performed to validate the model assumptions. This model can be used by businesses to better understand customer spending behavior and optimize their marketing strategies.
This project is licensed under the MIT License - see the LICENSE file for details.