Machine_Learning

This simple program computes a multiple linear regression based on Gradient Descent as an optimization method.

Sample_dataset

This code could be used for a vast majority of datasets, one of which is provided in the result section. This dataset could be found on the Kaggle website (https://www.kaggle.com/datasets/akshaydattatraykhare/data-for-admission-in-the-university)

A multiple linear regression is derived based on 70 percent of participants as the Training Set, and the remaining 30 percent is used to verify the model as the Test Set.

How to use

1. Download a Python file ("Code.py")

2. Insert the Training Set (CSV format) in the " importData " function as input.

3. Determine a file path for the total cost in the " reportTotalCost " function.

4. Determine a file path for the final coefficients in multiple linear regression in the " reportTheta " function.

5. Determine a file path for the Test Set to predict its result and also a file path for reporting the result in the " findAnswer " function.

6. Run the program

Importing dataset

the dataset file must be specified in the CSV data format.

A wide range of real datasets (in CSV format) is available at data science websites such as Kaggle (www.kaggle.com).

Acknowledgments

The entire program is written by Ashkan Fouladi (fooladiashkang@gmail.com).