{"id":50432698,"url":"https://github.com/ahmadrazacdx/sales-data-analysis","last_synced_at":"2026-05-31T15:01:22.569Z","repository":{"id":259838817,"uuid":"876672062","full_name":"ahmadrazacdx/Sales-Data-Analysis","owner":"ahmadrazacdx","description":"A comprehensive data analysis project focusing on sales data exploration, cleaning, and statistical analysis. This includes key insights into sales trends, customer behavior, product categories, delivery times, and seasonality effects. 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The project includes key insights into sales trends, customer behavior, product categories, delivery times, and seasonality effects. Advanced statistical tests and visualizations are employed to identify significant relationships and make data-driven decisions.\n\n## 📝 Project Overview\nIn this project, I have analyzed a comprehensive sales dataset to uncover key insights and trends. The analysis is divided into the following steps:\n1. **Data Exploration** – Initial examination of the dataset to understand its structure.\n2. **Data Cleaning** – Handling missing values, outliers, and correcting data types.\n3. **Feature Engineering** – Creating new features like delivery time and customer segments.\n4. **Exploratory Data Analysis (EDA)** – Visualizing data to identify trends and patterns.\n5. **Time Series Analysis** - Analysing Time Series Components, Trend, Seasonailty, Residue etc\n6. **Time Series Forecasting** - Forecasting Sales for next quarter\n7. **Hypothesis Testing** – Conducting statistical tests to validate hypothesis.\n\n## 🛠 Technologies Used\n- **Python**: Core programming language used for the analysis.\n- **Pandas**: Data manipulation and analysis.\n- **NumPy**: Numerical operations.\n- **Matplotlib**: Data visualization.\n- **Seaborn**: Enhanced data visualizations.\n- **Plotly**: Ingeractive data visualizations.\n- **Scipy**: Statistical hypothesis testing.\n- **Others**: Some other useful utility libraries.\n\n## 📁 Project Structure\n```bash\nSales-Data-Analysis/\n├── data/\n│   └── sales_data.csv, header.png\n├── sales_data_analysis.ipynb  \n└── README.md                  \n```\n\n## ⚙️ How to Run the Project\n1. Clone the repository:\n```\ngit clone https://github.com/ahmadrazacdx/Sales-Data-Analysis.git\n```\n2. Install the required dependencies:\n```\npip install -r requirements.txt\n```\n3. Open the Jupyter notebook and run the cells:\n```\njupyter notebook sales_data_analysis.ipynb\n```\n## 📜License\n\nThis project is licensed under a custom license. You are allowed to view, share, and use this project for non-commercial purposes, provided you give appropriate credit to the original author.\nFor the full context of the license, please visit [here](https://github.com/ahmadrazacdx/Sales-Data-Analysis?tab=License-1-ov-file).\n\n## 📧 Contact\nIf you have any questions or suggestions, feel free to contact me at: [ahmadrazacdx@gmail.com]\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmadrazacdx%2Fsales-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fahmadrazacdx%2Fsales-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmadrazacdx%2Fsales-data-analysis/lists"}