E-Commerce Data Analysis Project

Project Overview

This project focuses on analyzing e-commerce data to gain insights into customer behavior and predict future trends using linear regression. The main goal is to build a predictive model that helps understand customer spending patterns on mobile apps and websites.

Key Learning Outcomes:

• Performing Exploratory Data Analysis (EDA) on e-commerce data.
• Building a Linear Regression model to forecast customer behavior.
• Visualizing data to extract meaningful insights.
• Gaining hands-on experience with regression analysis and data visualization techniques.

Why This Project?

• Improve skills in Linear Regression analysis.
• Learn the art of EDA (Exploratory Data Analysis).
• Enhance my ability to visualize data and interpret it from charts/graphs.
• Practice reading and extracting insights from data visualizations.

Dataset

https://www.kaggle.com/datasets/kolawale/focusing-on-mobile-app-or-website/data

My Plan to Execute This Project

• Exploratory Data Analysis (EDA):

  • Data Understanding
  • Data Cleaning and Preparation
  • Univariate Analysis (Understanding individual features)
  • Bivariate Analysis (Feature relationships)
  • Correlation Analysis

• Linear Regression Analysis:

  • Split data into training and testing sets.
  • Normalize the dataset.
  • Build and train the linear regression model.
  • Evaluate the model’s performance.
  • Visualize the results.

How to Run the Notebook

1. Clone the repository:

git clone https://github.com/marvinraj/e-commerce-data-analysis-linear-regression.git

2. Install dependencies:

npm install pandas numpy seaborn scikit-learn matplotlib

3. Simply open the notebook in Visual Studio Code.

Useful Resources

These are resources I used to complete this project by fully understanding any related concepts.

Locate the file plan.txt and scroll to section labeled interesting reading materials.