This project aims to predict the price of a house based on its area using simple linear regression. The dataset used in this project consists of 1000 houses in Monroe Township, New Jersey, and their respective areas and prices.
The data is stored in a CSV file named house-prices.csv.
This project requires Python 3 and the following Python libraries:
- NumPy
- Pandas
- Matplotlib
- scikit-learn
These libraries can be installed using pip:
pip install numpy pandas matplotlib scikit-learn
To use this project, simply clone or download the repository, navigate to the project directory, and run the analysis.ipynb Jupyter Notebook. This Notebook contains the code and explanations for loading and exploring the dataset, training a linear regression model, and visualizing the results.
The Notebook is divided into sections that correspond to different parts of the analysis, and each section includes detailed comments and explanations. You can run each section by clicking on the cell and pressing Shift+Enter.