This project focuses on optimizing a linear regression model using gradient descent to predict TV sales. The goal is to create a model that can effectively forecast TV sales based on multiple variables.
- Clone the repository:
git clone https://github.com/kaybrian/Optimization-with-multiple-variables.git - Navigate to the project directory:
cd Optimization-with-multiple-variables - Create a virtual environment (optional but recommended):
python -m venv env - Activate the virtual environment:
- On Windows:
env\Scripts\activate - On Unix or Linux:
source env/bin/activate
- On Windows:
- Install the required dependencies:
pip install -r requirements.txt
- Open the provided Jupyter Notebook (
Summative Assignment.ipynb) in your preferred Python development environment. - Follow the instructions in the notebook to complete the code snippets for the five exercises.
- The notebook includes unit tests that you need to pass to ensure your code is working correctly.
- After completing the linear regression model, you will be required to create decision trees and random forests models.
- Compare the Root Mean Squared Errors (RMSE) of the models and rank them accordingly.
Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.
This project is Not Open.
- Backend for the fast api application House Prediction backend
- Mobile application for the fast api application House Prediction app
- Live api for the backend House Prediction live backend
- Video recording for the application mobile application