Sales Prediction using Machine Learning

📌 Overview

This project predicts product sales based on advertising budgets using Machine Learning.
The dataset contains marketing spend on TV, Radio, and Newspaper and the corresponding Sales.

The goal is to identify which advertising channel impacts sales the most and build a predictive model.

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📂 Project Structure

├── sales_prediction_project.py       # Main ML script
├── Sales_Prediction_Project.pptx     # Project Presentation
├── advertising.csv (optional)        # Dataset
└── README.md                         # Project Documentation

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🔧 Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib / Seaborn
• Scikit-learn
• Linear Regression

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📊 Exploratory Data Analysis (EDA)

• TV has the strongest impact on Sales
• Radio has moderate contribution
• Newspaper has very low impact
• Correlation heatmap confirms strong linear relationship with TV

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🤖 Model Used

Linear Regression

• Simple
• Fast
• Interpretable
• Works well for numeric prediction

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📈 Model Performance (Typical Results)

Metric	Value
R² Score	~0.90
RMSE	~1.25
MAE	~1.02

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🧠 Predictions

Example prediction from model:

predict_sales(tv=150, radio=20, newspaper=15)

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🚀 How to Run the Project

python sales_prediction_project.py

Make sure the dataset advertising.csv is in the same folder.

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🎯 Future Improvements

• Add Random Forest & XGBoost
• Hyperparameter tuning
• Add more marketing features
• Deploy model using Flask or Streamlit

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👨‍💻 Author

Shreyash Hedaoo
Machine Learning & Data Science Learner

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