Linear Regression Model for Predicting Yearly E-Commerce Spending- This project is focused on building a linear regression model to predict yearly spending for e-commerce customers based on various features. By analyzing e-commerce data, the model can provide insights into customer behavior and spending patterns, which can help businesses strategize their marketing and customer retention efforts.

Project Overview- In this project, I built a linear regression model using a dataset of e-commerce customers.

The goal was to predict the yearly amount spent by each customer using features such as: Length of Membership Time Spent on the Website Number of Purchases Average Session Length

The model was trained and tested on this dataset to understand how these features correlate with yearly spending.

Dataset: The dataset includes the following columns: Time on Website: Average time (in minutes) a customer spends on the e-commerce website. Length of Membership: Duration (in years) of the customer's membership. Number of Purchases: Total purchases made by the customer. Yearly Amount Spent: The target variable representing the yearly expenditure by each customer. The dataset was cleaned, and exploratory data analysis was performed to visualize relationships and gain insights before building the model.