Club Induction Task - Machine Learning Project(wine_dataest)

Overview

This repository contains the code and analysis for the ML4E club induction task focusing on applying machine learning algorithms such as linear regression, logistic regression, and random forest to a given dataset.

Project Structure

The project is organized as follows:

-wine.csv:Given dataset

• wine_linearregression.ipynb: Linear regression analysis.
• wine_logisticregression.ipynb: Logistic regression analysis.
• wine_randomforest.ipynb: Random Forest analysis.

Clone this repository:

git clone https://github.com/Praneeth2312/wine_dataset_induction_task.git

Results:

Linear_Regression:

R-squared Score: 0.26758604762487337 Mean Squared Error: 0.5597727581304021

Logistic_Regression:

Classification Report:
             precision    recall  f1-score   support

          3       0.00      0.00      0.00         2
          4       0.00      0.00      0.00        29
          5       0.57      0.51      0.53       293
          6       0.50      0.76      0.60       435
          7       0.54      0.16      0.25       184
          8       0.00      0.00      0.00        37

   accuracy                           0.52       980
  macro avg       0.27      0.24      0.23       980
weighted avg       0.49      0.52      0.48       980

Random_Forest:

Accuracy_percentage: 70.3061224489796
Classification Report:
               precision    recall  f1-score   support

           3       0.00      0.00      0.00         5
           4       0.75      0.36      0.49        25
           5       0.71      0.69      0.70       291
           6       0.66      0.80      0.73       432
           7       0.78      0.62      0.70       192
           8       0.94      0.43      0.59        35

    accuracy                           0.70       980
   macro avg       0.64      0.48      0.53       980
weighted avg       0.71      0.70      0.70       980

Contributor:

Akshay Praneeth Email me