eCommerce Transactions Dataset 📊

Welcome to the eCommerce Transactions Dataset repository! This project is all about analyzing customer behavior, product performance, and sales trends in a simulated eCommerce environment. If you're interested in data analytics, machine learning, or customer segmentation, this repository is for you. 🚀

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🛠 What’s in this Project?

1. Datasets:

   • Customers.csv: Information about customers, like region, signup date, etc.
   • Products.csv: Details about the products, including categories.
   • Transactions.csv: Transaction history, including purchase details like quantity, price, and dates.

2. Scripts:

   • Data Cleaning and Preprocessing: Handle missing data, standardize columns, and prepare datasets for analysis.
   • Customer Segmentation: Use clustering techniques to group customers by their spending habits.
   • Lookalike Recommendations: Find customers with similar profiles to target for personalized marketing.
   • Sales Trend Analysis: Visualize monthly trends in sales and identify top-performing categories.

3. Visualizations:

   • Customer distribution by region 🗺️
   • Product performance by category 📦
   • Sales trends over time 📈
   • Customer segmentation in 2D space using PCA 🎨

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🚀 Key Features

• Customer Segmentation:

  • Groups customers into meaningful clusters based on spending patterns, frequency, and transaction behaviors.

• Lookalike Recommendations:

  • Identifies similar customers using machine learning, enabling better targeting and retention strategies.

• Sales Analysis:

  • Tracks monthly sales performance to uncover trends and seasonal patterns.

• Clustering Validation:

  • Uses metrics like Davies-Bouldin Index to find the optimal number of clusters.

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📂 File Descriptions

• Customers.csv: Contains:

  • CustomerID, Region, SignupDate, and more.

• Products.csv: Includes:

  • ProductID, Category, and other product details.

• Transactions.csv: Tracks:

  • TransactionID, CustomerID, ProductID, Quantity, Price, and TransactionDate.

• Scripts:

  • data_analysis.py: For data cleaning, trend analysis, and customer profiling.
  • segmentation.py: For clustering and customer segmentation.
  • recommendations.py: For generating lookalike recommendations.

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🖥️ How to Use

1. Clone the Repository:

   git clone https://github.com/HimKasera/eCommerce-Transactions-Dataset.git
   cd eCommerce-Transactions-Dataset
   

2. Set Up the Environment: Install the required Python libraries:

   pip install -r requirements.txt
   

3. Run the Scripts:

   • For data analysis:

     python data_analysis.py
     

   • For customer segmentation:

     python segmentation.py
     

4. Explore the Results:

   • Check visualizations for insights.
   • Use the generated CSV files for further analysis.

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📊 Example Insights

• Top Performing Categories: Electronics and Fashion.
• Sales Peaks: Increased during festive seasons.
• Key Customer Segments: High-value customers with frequent transactions.

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🤝 Contributing

Have ideas to improve this project? Feel free to fork the repository, make your changes, and submit a pull request. Contributions are always welcome! 🌟

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📧 Contact

If you have any questions or feedback, reach out to me at himanshukasera2238@gmail.com.

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🌟 Star this Repository

If you found this project helpful or interesting, don’t forget to give it a ⭐ on GitHub! Thank you! 😊