This project aims to predict sales revenue based on advertising budgets for different channels, including TV, radio, and newspaper. Using simple linear regression model
In today's highly competitive market, understanding the impact of advertising on sales is crucial for businesses to make informed decisions and optimize their marketing strategies. This project leverages machine learning techniques, specifically linear regression, to analyze historical advertising data and predict future sales revenue.
- Data Preprocessing: Clean and preprocess the advertising sales dataset to prepare it for modeling.
- Model Building: Implement linear regression models to predict sales revenue based on advertising budgets.
- Model Evaluation: Assess model performance using metrics such as Mean Squared Error (MSE) and R-squared.
- Visualization: Visualize actual versus predicted sales to gain insights into model accuracy and performance.
To run the project locally, follow these steps:
- Clone the repository:
git clone https://github.com/typhonshambo/advertising-sales-prediction.git
- Navigate to the project directory:
cd advertising-sales-prediction
- Install the required dependencies:
pip3 install -r requirements.txt
- Run the main script:
python3 main.py
The project uses a publicly available dataset from Kaggle, containing advertising budgets for TV, radio, and newspaper ads, as well as sales revenue. The dataset can be found in the data/
directory.
Contributions are welcome! If you have any ideas for improvements or new features, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.