nikhiljsk / Generic_regression_algo

A Python template to evaluate different regression models on a dataset. Includes metrics to cross-compare models on the data. Assumes the data to be numeric.

Evalutaing Regression models on a dataset

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.

Libraries Required

• Numpy
• Pandas
• Matplotlib
• Seaborn
• SciKit-Learn

Running the program

Once all the required libraries are installed, the program provides a CLI to work with and you are good to go.

Running the program on your own dataset

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 :)