https://github.com/soumyadipta2020/ecommerce_data_analysis_kaggle
https://github.com/soumyadipta2020/ecommerce_data_analysis_kaggle
data-cleaning forecasting imputation modelling python r-programming time-series-analysis time-series-forecasting
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/soumyadipta2020/ecommerce_data_analysis_kaggle
- Owner: Soumyadipta2020
- License: gpl-3.0
- Created: 2024-12-25T11:16:48.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-01-08T18:07:08.000Z (9 months ago)
- Last Synced: 2025-01-08T19:26:04.784Z (9 months ago)
- Topics: data-cleaning, forecasting, imputation, modelling, python, r-programming, time-series-analysis, time-series-forecasting
- Language: Jupyter Notebook
- Homepage:
- Size: 3.17 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Ecommerce Data Analysis - Kaggle 📊🛍️
[](http://hits.dwyl.com/Soumyadipta2020/ecommerce_data_analysis_kaggle)
This repository contains the project **"Ecommerce Data Analysis - Kaggle"**, where I performed a comprehensive analysis of ecommerce data and developed a forecasting model to predict the **net amount** and **discount** for the next 12 months. The project demonstrates my expertise in data analysis, visualization, and forecasting techniques, providing actionable insights for business decision-making.
## 🔎 Project Overview
Ecommerce platforms generate vast amounts of transactional data, which holds valuable insights about customer behavior, sales trends, and discount strategies. This project focuses on leveraging historical data to:
1. Analyze and visualize key metrics like sales, discounts, and net amount.
2. Develop a robust forecasting model for predicting future net amounts and discounts.
3. Provide insights to aid strategic planning and improve revenue generation.
## ✨ Key Features
- **Exploratory Data Analysis (EDA):**
- Sales trends over time.
- Distribution of discounts.
- Seasonal patterns in revenue.
- **Forecasting Models:**
- Time series models to predict net amount and discount for the next 12 months.
- Evaluation of forecasting accuracy.
- **Visualizations:**
- Interactive plots to illustrate key insights.
- Line plots for actual vs. forecasted values.
## 🛠️ Methodology
1. **Data Preprocessing:**
- Cleaning and handling missing values.
- Aggregating data to monthly levels for trend analysis.
2. **Exploratory Data Analysis:**
- Identifying patterns, seasonality, and outliers.
3. **Model Development:**
- Implemented time series models such as ARIMA, SARIMA, and others.
- Validated models using historical data.
4. **Forecasting:**
- Predicted net amount and discounts for the next 12 months.
- Visualized the forecasted values alongside actual data.
## 🏆 Results
- Forecasted **net amount** and **discount** with high accuracy.
- Identified key seasonal trends impacting sales and discounts.
- Provided actionable insights for optimizing discount strategies.
## 📂 Repository Structure
```bash
📁 Analysis/
|-- 📁 data/
| |-- 📁 export/
| | |-- 📑 net_amount_discount_monthly.csv # Data prepared using "Net Amount & Discount Monthly Data.R"
| | |-- 📑 net_amount_discount_vs_method_monthly.csv # Data prepared using "Net Amount & Discount Monthly by payment method Data.R"
| |-- 📑 ecommerce_data.csv # Raw data from Kaggle
| |-- 📄 Ecommerce_data.md # Raw data explanation
|-- > Net Amount & Discount Monthly Forecast.ipynb # Forecasting net amount and discount monthly
|-- > Net Amount & Discount Monthly Forecast by payment method.ipynb # Forecasting net amount and discount monthly by payment method
|-- > Net Amount & Discount Monthly Data.R # Data preparation for forecasting net amount and discount monthly
|-- > Net Amount & Discount Monthly by payment method Data.R # Data preparation for forecasting net amount and discount monthly by payment method
📄 README.md # Project overview and details
📄 LICENSE # License
```
## 🚀 Usage
To replicate the analysis or use the forecasting models:
1. Clone this repository:
```bash
git clone https://github.com/yourusername/ecommerce_data_analysis_kaggle.git
```
2. Navigate to the project directory:
```bash
cd ecommerce_data_analysis_kaggle
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Jupyter Notebooks in the `notebooks/` directory for analysis and forecasting.
## 📜 Requirements
- Python 3.8+
- R 3.4.2+
- Jupyter Notebook
- Python libraries - Pandas, NumPy, Matplotlib, Seaborn
- Python ML libraries - Scikit-learn, Statsmodels, Prophet (or other forecasting libraries)
- R libraries - dplyr, stringr, tidyr, plotly
## 💡 Contribution
Contributions are welcome! If you have ideas to enhance the app or fix issues, feel free to fork the repository, make changes, and submit a pull request.
Steps to Contribute:
1. Fork this repository.
2. Create a new branch: `git checkout -b feature-name`
3. Commit your changes: `git commit -m "Add feature-name"`
4. Push to your branch: `git push origin feature-name`
5. Open a Pull Request.