Retail Sales Forecasting & KPI Analytics

This project applies data analytics and machine learning techniques to forecast retail sales and generate KPI insights. It highlights skills in data preprocessing, exploratory analysis, feature engineering, and predictive modeling.

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Overview

The project analyzes retail datasets to uncover trends, understand seasonality, generate key business performance indicators, and build forecasting models. It demonstrates end-to-end analytics workflow from raw data to actionable insights.

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Tech Stack

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• Jupyter Notebook

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Features

• Data cleaning and preprocessing
• KPI generation (sales totals, growth, category performance, etc.)
• Exploratory data analysis with visualizations
• Feature engineering for forecasting
• Machine learning forecasting models
• Evaluation metrics and model comparison

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Project Structure

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├── data/                 # Raw and processed datasets
├── notebooks/            # Jupyter notebooks for analysis
├── models/               # Saved models (if included)
├── visuals/              # Generated plots
└── README.md             # Documentation

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Getting Started

1. Clone repository

git clone https://github.com/YSayaovong/retail-sales-forecasting-kpi-analytics.git
cd retail-sales-forecasting-kpi-analytics

2. Install dependencies

pip install -r requirements.txt

3. Open notebooks

jupyter notebook

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Potential Enhancements

• Deploy forecasting model as an API
• Add dashboard for KPI visualization
• Integrate automated data pipelines
• Add hyperparameter tuning & advanced ML models

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License

This project is open-source under the MIT License.