This project focuses on analyzing an eCommerce Transactions dataset to extract meaningful business insights, develop a customer lookalike model, and segment customers into distinct groups using clustering techniques. The goal is to enhance business decision-making and optimize marketing strategies.
Task 1: Exploratory Data Analysis (EDA) and Business Insights
Analyze the dataset to uncover trends and patterns. Derive actionable business insights.
Task 2: Lookalike Model Develop a model to recommend 3 similar customers based on input customer profiles and transaction history. Generate a similarity score for each recommendation.
Task 3: Customer Segmentation Apply clustering techniques to group customers based on transaction behavior and demographic data. Evaluate clusters using metrics like DB Index and visualize the results. Dataset The dataset consists of the following files: Customers.csv: Customer profile information. Products.csv: Product details, including price and category. Transactions.csv: Transaction history with purchase details. How to Clone and Run the Project
To clone this repository to your local machine, run the following command:
git clone https://github.com/raju096/E-Commerce-Based-Data-Analysis.git