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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.  \n\n## 🗂 Dataset Overview  \nThe dataset consists of **200 entries** with the following columns:  \n\n- `TV` – Advertising budget for TV (in $1000s)  \n- `Radio` – Advertising budget for Radio (in $1000s)  \n- `Newspaper` – Advertising budget for Newspaper (in $1000s)  \n- `Sales` – Sales revenue generated (in $1000s) (Target variable)  \n\n## ⚙️ Technologies Used  \n- Python 🐍  \n- Pandas \u0026 NumPy (Data Processing)  \n- Matplotlib \u0026 Seaborn (Data Visualization)  \n- Scikit-learn (Machine Learning – Linear Regression)  \n\n## 🚀 Features  \n✅ Data Cleaning and Preprocessing  \n✅ Exploratory Data Analysis (EDA)  \n✅ Sales Prediction using **Linear Regression**  \n✅ Model Evaluation Metrics  \n\n## 🔥 Usage  \n- **Load and explore the dataset.**  \n- **Perform Exploratory Data Analysis (EDA)** to visualize trends in advertising and sales.  \n- **Train a Linear Regression model** to predict sales.  \n- **Evaluate the model's performance** using:  \n  - **R² Score**  \n  - **Mean Squared Error (MSE)**  \n\n## 📊 Visualizations  \nThe notebook includes:  \n✅ **Pairplots** for feature relationships  \n✅ **Correlation Heatmap** to find important variables  \n✅ **Regression Plot** to visualize predictions  \n\n## 🤝 Contributing  \nContributions are welcome! 🎉  \nIf you’d like to contribute, please:  \n- **Fork the repository**  \n- **Create a new branch (`feature-branch`)**  \n- **Submit a pull request**  \n\n## 📜 License  \nThis project is licensed under the **MIT License**.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyuvrajsaraogi%2Fsales-prediction-using-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyuvrajsaraogi%2Fsales-prediction-using-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyuvrajsaraogi%2Fsales-prediction-using-python/lists"}