🏷️ Sales Price Prediction using Machine Learning & Streamlit

📌 Overview

This project utilizes Linear Regression to predict the Total Price of a product based on multiple input features. It includes a Streamlit-based web application for user interaction.

📊 Features

• ML Model: Linear Regression trained using scikit-learn.
• Inputs (X):
  • beverage_category
  • product_cat
  • customer_cat
  • region_cat
  • Unit_Price
  • Quantity
  • Discount
• Output (Y): Total_Price
• Frontend: Built with Streamlit for real-time prediction.

🛠️ Tech Stack

• Python (Pandas, NumPy, Scikit-Learn)
• Streamlit (Web UI)
• Jupyter Notebook (for model training)

🚀 How to Run

1. Clone the Repository

   git clone https://github.com/yourusername/your-repo-name.git
   cd your-repo-name

2. vscode terminal python -m streamlit run salary.py

Dataset - https://www.kaggle.com/api/v1/datasets/download/sebastianwillmann/beverage-sales

📊 Model Training The dataset is preprocessed and split into training and testing sets. The Linear Regression model is trained using sklearn.linear_model.LinearRegression. Model evaluation metrics include Mean Squared Error (MSE) and R² Score.

📌 Future Improvements Implement hyperparameter tuning for better accuracy. Support for multiple regression models. Deploy the model using Docker / Cloud platforms.

📝 Author Developed by Praveen R