Skip to content

README.md

Latest commit

 

History

History
48 lines (39 loc) · 1.78 KB

README.md

File metadata and controls

48 lines (39 loc) · 1.78 KB

📊 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.