🧠 Sales Data Analysis & Forecasting

This project applies machine learning to analyze historical sales data. It aims to forecast future sales, classify demand trends, detect anomalies, cluster similar items, and uncover seasonal patterns using Python.

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📌 Project Goals

Goal	Machine Learning Type	Example
Forecast future sales	Time Series Forecasting	Predict Jan 2024 sales
Classify demand trend	Classification	Will demand increase or decrease?
Detect anomalies	Anomaly Detection	Flag sudden spikes or drops
Cluster similar items	Clustering	Group items with similar sales patterns
Analyze seasonality/trends	Time Series Decomposition (Unsupervised)	Detect monthly or seasonal cycles

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🗂 Project Structure

.
├── prophet_forecasts/  # Collection of Prophet Model Predictions
├── rf_forecast_plots
├── requirements.txt    # Dependencies
├── .gitignore          # Git ignore file
└── README.md           # Project documentation

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🛠️ Setup Instructions

1. Clone the Repository

git clone https://github.com/fglend/UPMO-ML.git
cd UPMO-ML

2. Create and Activate Virtual Environment

python3.9 -m venv venv
source venv/bin/activate        # On Windows: venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Launch Jupyter

jupyter notebook

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📦 Dependencies

See requirements.txt for exact versions.

• pandas
• numpy
• matplotlib, seaborn
• scikit-learn
• prophet
• statsmodels
• tslearn
• pyod
• jupyter

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🧪 Example Use Cases

• Visualizing sales trends across months or years
• Forecasting item-level sales for the next quarter
• Detecting stock anomalies due to unusual sales behavior
• Grouping SKUs by seasonal demand patterns
• Identifying products with consistent growth or decline

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🐍 Python Version

• Python 3.9.6

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🤝 Contributing

Feel free to fork this repo and submit pull requests. If you spot issues or have ideas, open a GitHub issue!

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📄 License

This project is open source and available under the MIT License.