Retail Sales Forecasting System
Retail businesses need good sales predictions to take smart decisions. This Retail Sales Analysis and Forecast project is made using Python and Streamlit. It has all main features like data analysis, model training, and easy tools to make predictions.
🧾 Project Overview
| Project Name | Language Used |
|---|---|
| Retail Sales Analysis and Forecast | Python |
| Type | Web Application |
| Database Used | PostgreSQL |
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Key Technologies and Skills
- Python
- Scikit-learn
- XGBoost
- Streamlit
- Pandas, NumPy
- Matplotlib, Seaborn, Plotly
- PostgreSQL
Available Features
- Interactive Streamlit Dashboard for forecasting, comparison, and trend analysis.
- Weekly Sales Prediction using two advanced ML models (with and without Markdown data).
- Exploratory Data Analysis (EDA) through dynamic charts and heatmaps.
- Data Preprocessing including handling of missing values, encoding, and feature creation.
- Model Evaluation & Metrics such as R², MAE, MSE, RMSE.
- Top Stores and Departments Viewer filtered by date and department.
- Feature-wise Impact Study on weekly sales (e.g., CPI, Temperature, Holiday, Unemployment).
- SQL-Integrated Backend using PostgreSQL for structured query access.
- Personalized Forecast Tool using input parameters to generate custom predictions.
- Model Persistence with Pickle for fast load and reuse of trained models.
Installation
To set up the environment, install the dependencies using pip:
pip install -r requirements.txtHow to Use
Follow these steps to run the application
Install required packages:
pip install -r requirements.txt
Launch the Streamlit app:
streamlit run app.py
Visit the web app:
Open your browser and go to http://localhost:8501.
Forecasting Models
Two models are trained using Random Forest and Extra Trees Regressor:
- Model 1: Includes Markdown columns (97.4% accuracy)
- Model 2: Excludes Markdown columns (97.7% accuracy)
The Random Forest Regressor is ultimately selected due to its balance of performance and generalizability.
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