eCommerce Transactions Dataset Analysis

This project analyzes an eCommerce transactions dataset to derive business insights, build a lookalike model, and perform customer segmentation. The goal is to demonstrate data science capabilities in exploratory data analysis (EDA), recommendation systems, and clustering techniques.

Project Tasks

1. Exploratory Data Analysis (EDA)

• Perform EDA on the dataset to uncover trends, patterns, and actionable insights.
• Deliverables:
  • Python code (Jupyter Notebook)
  • PDF report summarizing 5 key business insights.

2. Lookalike Model

• Build a recommendation system that suggests 3 similar customers based on profile and transaction history.
• Deliverables:
  • CSV file containing lookalike recommendations for the first 20 customers.
  • Python code (Jupyter Notebook) for model development.

3. Customer Segmentation / Clustering

• Perform clustering to segment customers based on profiles and transactions.
• Deliverables:
  • Report with clustering results, including the number of clusters and evaluation metrics (e.g., DB Index).
  • Python code (Jupyter Notebook) for clustering.

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Dataset

The project uses three files:

1. Customers.csv:

   • CustomerID: Unique customer identifier.
   • CustomerName: Name of the customer.
   • Region: Customer's region.
   • SignupDate: Date of signup.

2. Products.csv:

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

3. Transactions.csv:

   • TransactionID: Unique transaction identifier.
   • CustomerID: Customer who made the transaction.
   • ProductID: Product involved in the transaction.
   • TransactionDate: Date of transaction.
   • Quantity: Quantity purchased.
   • TotalValue: Total value of the transaction (USD).

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Repository Structure

├── data/
│   ├── Customers.csv
│   ├── Products.csv
│   ├── Transactions.csv
├── notebooks/
│   ├── FirstName_LastName_EDA.ipynb
│   ├── FirstName_LastName_Lookalike.ipynb
│   ├── FirstName_LastName_Clustering.ipynb
├── reports/
│   ├── FirstName_LastName_EDA.pdf
│   ├── FirstName_LastName_Clustering.pdf
├── results/
│   ├── Lookalike.csv
├── README.md

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Getting Started

Prerequisites

• Python 3.7+
• Required libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/<username>/ecommerce-analysis.git
   cd ecommerce-analysis

2. Install required libraries:

   pip install -r requirements.txt

3. Upload the datasets to the data/ directory.

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Usage

Run EDA:

1. Navigate to the notebooks/ directory.
2. Open and execute the FirstName_LastName_EDA.ipynb notebook to perform EDA and generate insights.

Build Lookalike Model:

1. Open and execute the FirstName_LastName_Lookalike.ipynb notebook.
2. Check the output file Lookalike.csv in the results/ directory.

Perform Clustering:

1. Open and execute the FirstName_LastName_Clustering.ipynb notebook.
2. Review the clustering report in reports/.

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Results

• EDA: Uncovered trends in customer behavior, product sales, and revenue.
• Lookalike Model: Recommended 3 similar customers for each of the first 20 customers.
• Clustering: Segmented customers into distinct groups with detailed metrics.

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Author

Kanishkar V kanishvijay2005@gmail.com www.linkedin.com/in/kanishkar-v-3471782a2/

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License

This project is licensed under the MIT License. See the LICENSE file for details.