Real-time Sales Prediction Using Flask and Scikit-Learn

Sales Prediction

Project Overview

A simple yet professional-grade Sales Prediction Web Application built using Python, Flask, and Scikit-Learn. The main goal of this project is to predict future sales for products or businesses by analyzing past sales data and applying machine learning algorithms.

In today’s fast-moving business world, predicting sales is one of the most important tasks for organizations. Accurate forecasting helps companies avoid overstock or understock situations, optimize supply chain operations, and improve financial planning. This project demonstrates how predictive analytics can provide valuable insights for making smarter business decisions.

This is a paid project, but it is also an excellent choice for students and beginners in data science who want to practice real-world concepts like data preprocessing, regression models, Flask integration, and deployment of ML models.

Project Overview

The Sales Prediction Web App is a lightweight yet powerful web application where users can input historical sales or related data and instantly receive predictions for future sales. The app runs on Flask (Python web framework) and uses a Scikit-Learn machine learning model in the backend for generating forecasts.

This project is designed for:

• Students – who want to learn machine learning and web app integration.

• Researchers – who are exploring predictive analytics.

• Businesses – that need a simple solution for sales forecasting and planning.

Project Details

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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

The Sales Prediction Web App comes with a variety of powerful and practical features that make it useful for both learning and real-world applications. Below is a detailed breakdown:

1. Input Customer and Store Data for Real-Time Sales Predictions

   • Users can easily enter details related to customers, store information, or sales history.

   • Based on this input, the system immediately generates real-time sales forecasts, helping users understand future demand trends.

2. Flask Web Application with HTML Template Rendering

   • The entire project is built as a Flask web app, ensuring a smooth, interactive, and user-friendly experience.

   • Templates are rendered using HTML, CSS, and Bootstrap, providing a clean and responsive design.

3. Machine Learning Model with Preprocessor and Trained Model

   • The backend integrates a Scikit-Learn trained ML model along with a saved preprocessor for data transformation.

   • This ensures that raw user inputs are processed correctly before predictions are made.

4. Efficient Model Deployment Using .pkl Files

   • Both the preprocessor and trained ML model are stored in .pkl (pickle) format.

   • This allows for efficient deployment, as the model can be loaded quickly without retraining.

5. Modular Python Source Code (src/ Folder)

   • The project follows a modular structure with a dedicated src/ folder containing all the Python scripts.

   • This makes the codebase clean, maintainable, and easy to extend for new features.

6. Project Artifacts for Transparency and Learning

   • Includes cleaned datasets used during training and testing.

   • CSV files for both training and testing phases are provided, allowing students to explore the data pipeline and understand preprocessing steps.

7. Easily Extendable API and Front-End

   • The backend can be extended to provide a REST API for integration with other platforms or mobile apps.

   • The front-end can also be customized or scaled to include additional business metrics, dashboards, or visualizations.

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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