Working Python code for various linear regression models
This repository contains an in-depth analysis of the California Housing dataset using the Regression Analysis technique, data visualization, and assumptions.
The Jupyter Notebook includes the following analyses:
- Data Exploration
- Linear Regression Modeling
- Residual Analysis
- Outlier Detection and Handling
- Feature Visualization
To run the notebook, you will need to install the following packages:
You can install them using pip:
pip install scikit-learn statsmodels matplotlib seaborn numpy scipyA key aspect of this project is the use of advanced visualization techniques to understand the data's structure and relationships. Scatter plots, histograms, residual plots, and more are utilized.
We employ Linear Regression, a robust statistical modeling technique, to predict median housing prices based on various features such as median income, housing median age, average rooms, etc.
We welcome contributions to this project. To contribute:
- Fork the project.
- Create your feature branch (
git checkout -b feature/AmazingFeature). - Commit your changes (
git commit -m 'Add some AmazingFeature'). - Push to the branch (
git push origin feature/AmazingFeature). - Open a Pull Request.
For any questions or inquiries, please contact support@pyfi.com - Subject: Github Repo Q, Linear-Trends-and-Linear-Regression.
For a full article walkthrough please visit > https://pyfi.com/blogs/articles/linear-trend-and-regression < and learn more about PyFi's award winning Python for Finance courses which have been trusted by the top financial institutions in the United States and Canada multiple years running here >> https://www.pyfi.com <<
