https://github.com/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.
https://github.com/nikhiljsk/generic_regression_algo
dataset generic machine-learning prediction python3 regression scikit-learn seaborn supervised-learning
Last synced: 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/nikhiljsk/generic_regression_algo
- Owner: nikhiljsk
- Created: 2018-09-29T19:20:24.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2019-07-04T07:24:46.000Z (almost 7 years ago)
- Last Synced: 2025-03-16T21:59:02.187Z (over 1 year ago)
- Topics: dataset, generic, machine-learning, prediction, python3, regression, scikit-learn, seaborn, supervised-learning
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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 :)