This project focuses on utilizing linear regression with gradient descent to predict the yearly amount spent by customers in an e-commerce store that offers both in-store style and clothing advice sessions. The dataset contains information about customers who engage in these sessions and subsequently make purchases either through a mobile app or website.
The dataset consists of various features including:
- Avg. Session Length: Average duration of the session.
- Time on App: Duration spent by the customer on the mobile app.
- Time on Website: Duration spent by the customer on the website.
- Length of Membership: Duration spent by the customer in the store during the session.
- Yearly Amount Spent(Target): Total amount spent by the customer annually.
- Linear Regression with Gradient Descent: Implemented linear regression from scratch utilizing gradient descent to predict the yearly amount spent by customers based on the given features.
- Normalization: Applied feature normalization to accelerate the gradient descent process and ensure efficient model training.
- Evaluation Metric: Utilized the R-squared (r2_score) metric to assess the accuracy of the model's predictions.
- Visualization: Employed various plots and visualizations to gain insights into the data distribution, relationships between features, and the performance of the linear regression model.
To utilize this project:
- Open in colab
- Explore the generated plots and visualizations to analyze the data and model performance.
- Experiment with different parameters and features to further enhance the model's accuracy.
- Python 3.x
- NumPy
- Matplotlib
- pandas
- seaborn