The python code used in this project helps you to understand and estimate the accuracy of different regression algorithms on the dataset "housing.csv" which is a sub-dataset created from the original boston housing datasets. The number of features influencing the result are reduced for ease of understanding. The code produces the ouput of different algorithms including:
- Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
In the end, it produces the graph which contains error rate of each algorithm implying the most efficient algorithm.
- Numpy
- Pandas
- Matplotlib
- Seaborn
- SciKit-Learn
Once all the required libraries are installed, the program provides a CLI to work with and you are good to go.
You can use Generic_template.py to run the program on your own dataset, and get the error rates, so as to choose the best regression algorithm.
Cheers :)