Retail Sales Forecasting System Using Machine Learning

Retail Sales Forecasting System Using Machine Learning

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.txt

How 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.

Retail-Sales-Forecasting-System-1024x533 Retail Sales Forecasting System Using Machine Learning

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