📊 Sales Prediction using Python

📌 Project Overview

This project predicts sales revenue based on advertising budgets using machine learning. The dataset includes advertising expenditures on TV, Radio, and Newspaper, and the goal is to build a model to predict sales based on these investments.

🗂 Dataset Overview

The dataset consists of 200 entries with the following columns:

• TV – Advertising budget for TV (in $1000s)
• Radio – Advertising budget for Radio (in $1000s)
• Newspaper – Advertising budget for Newspaper (in $1000s)
• Sales – Sales revenue generated (in $1000s) (Target variable)

⚙️ Technologies Used

• Python 🐍
• Pandas & NumPy (Data Processing)
• Matplotlib & Seaborn (Data Visualization)
• Scikit-learn (Machine Learning – Linear Regression)

🚀 Features

✅ Data Cleaning and Preprocessing
✅ Exploratory Data Analysis (EDA)
✅ Sales Prediction using Linear Regression
✅ Model Evaluation Metrics

🔥 Usage

• Load and explore the dataset.
• Perform Exploratory Data Analysis (EDA) to visualize trends in advertising and sales.
• Train a Linear Regression model to predict sales.
• Evaluate the model's performance using:
  • R² Score
  • Mean Squared Error (MSE)

📊 Visualizations

The notebook includes:
✅ Pairplots for feature relationships
✅ Correlation Heatmap to find important variables
✅ Regression Plot to visualize predictions

🤝 Contributing

Contributions are welcome! 🎉
If you’d like to contribute, please:

• Fork the repository
• Create a new branch (feature-branch)
• Submit a pull request

📜 License

This project is licensed under the MIT License.