🧠 Linear Regression on E-Commerce Dataset

This project applies Linear Regression to an e-commerce dataset containing 500 customer records and 8 features sourced from Kaggle. The goal was to explore how various customer behavior metrics — such as time spent on the website, time on the app, and length of membership — influence their yearly spending.

🔍 Key Steps

• Exploratory Data Analysis (EDA): Visualized relationships using joint plots and pair plots to detect correlations between key variables.

• Correlation Analysis: Found that Length of Membership showed the strongest positive correlation (r = 0.8091) with Yearly Amount Spent.

• Model Development: Built a regression model using Scikit-learn, training on 70% of the data and testing on 30%.

• Model Evaluation:

  • Mean Absolute Error (MAE): 8.43
  • Mean Squared Error (MSE): 103.92
  • Root Mean Squared Error (RMSE): 10.19

A scatter plot of predicted vs. actual values indicated strong model performance and minimal error.

🧩 Tech Stack

• Python
• Pandas, NumPy, Stats
• Matplotlib, Seaborn, Scipy, Math
• Scikit-learn

📊 Insights

• Customer membership duration is the strongest predictor of annual spending.
• Time on App contributes more significantly than Time on Website to customer purchases.

---

🏷️ Tags

#DataScience #MachineLearning #LinearRegression #EDA #Python #ScikitLearn #DataAnalytics #Kaggle #RegressionAnalysis