Real-time Sales Prediction Using Flask and Scikit-Learn

Real-time Sales Prediction Using Flask and Scikit-Learn

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Sales Prediction

Project Overview

This professional-grade Sales Prediction Web App is built to deliver real-time sales forecasts using a powerful machine learning model developed with Python, Flask, and Scikit-Learn.Designed for students, professionals, and businesses, this paid project helps improve decision-making by providing accurate sales insights. With predictive analytics, companies can avoid overstock, manage resources better, and plan ahead with confidence.

Project Details

Project Name Real-time Sales Prediction System
Language/s Used Python
Python Version (Recommended) 3.8+
Type Web Application

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Project Description

The project tackles the challenge of retail sales forecasting by building a powerful machine learning pipeline that processes historical sales data and delivers real-time predictions through a Flask-based web interface. The solution includes clean code structure, model training scripts, and integration-ready endpoints for future expansion.

This application supports:

  • Real-time predictions based on user input.
  • Categorical and numerical feature handling.
  • Outlier detection and treatment.
  • Preprocessing using One-Hot Encoding and Scalers.
  • Trained model loading via joblib or pickle.

Available Features

  • Input customer and store data for real-time sales predictions
  • Flask web application with HTML template rendering
  • Machine Learning model with saved preprocessor and trained model
  • Uses .pkl files for efficient model deployment
  • Modular Python source code (src/ folder)
  • Project artifacts including cleaned datasets and training/testing CSVs
  • Easily extendable API and front-end

Project Structure

Stores-Sales-Prediction-ML-Project/
│
├── app.py                      # Main Flask app
├── requirements.txt            # Required packages
├── Train.csv / Test.csv        # Raw data for model training/testing
├── artifacts/                  # Trained model, preprocessor, cleaned data
├── src/                        # Source code for preprocessing and modeling
├── templates/                  # HTML templates for Flask
├── static/                     # Static assets (if applicable)

Installation & Usage

Step 1: Install Required Packages

pip install -r requirements.txt

Step 2: Run the Flask App

python app.py

Step 3: Open in Browser

Visit: http://localhost:5000
Use the form to input store and product data, and receive a sales prediction instantly.

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