eCommerce Transactions Data Science Assignment

This repository contains the solution to the Data Science Intern Assignment involving an eCommerce transactions dataset. The project includes exploratory data analysis (EDA), a lookalike model, and customer segmentation tasks.

** Project Structure**

The project is divided into the following tasks:

1. Exploratory Data Analysis (EDA)

• Objective: Analyze the provided dataset to extract meaningful business insights.

2. Lookalike Model

• Objective: Build a model to recommend 3 similar customers for the first 20 customers (C0001–C0020) based on their profile and transaction history.

3. Customer Segmentation / Clustering

• Objective: Segment customers using clustering techniques to group them based on profiles and transaction information.

Dataset Description

The dataset consists of three files:

1. Customers.csv
   Contains customer profiles, including:

   • CustomerID: Unique identifier for each customer.
   • CustomerName: Name of the customer.
   • Region: Customer's continent.
   • SignupDate: Date when the customer signed up.

2. Products.csv
   Contains product details, including:

   • ProductID: Unique product identifier.
   • ProductName: Name of the product.
   • Category: Product category.
   • Price: Price in USD.

3. Transactions.csv
   Contains transaction details, including:

   • TransactionID: Unique transaction identifier.
   • CustomerID: ID of the customer making the transaction.
   • ProductID: ID of the purchased product.
   • TransactionDate: Date of the transaction.
   • Quantity: Quantity purchased.
   • TotalValue: Total value of the transaction.

Getting Started

Prerequisites

• Python 3.x
• Required libraries:
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
  • seaborn

Setup

1. Clone this repository:

   git clone <repository_url>
   

2. Install the required libraries:

   pip install -r requirements.txt
   

Running the Notebooks

1. Open the notebooks (.ipynb) in Jupyter Notebook or a compatible IDE.
2. Follow the instructions and execute cells to reproduce the results.

Results

EDA

The EDA results are documented in Ishita_Paliwal_EDA.pdf, highlighting key business insights derived from the dataset.

Lookalike Model

The recommendations for the top 3 similar customers are saved in Ishita_Paliwal__Lookalike.csv.

Clustering

The clustering results, along with the DB Index value and visualizations, are detailed in Ishita_Paliwal__Clustering.pdf.