https://github.com/youseftareq33/java_ai_3_machine-learning

building a predictive model using the Linear Regression algorithm
https://github.com/youseftareq33/java_ai_3_machine-learning

java linear-regression machine-learning training-data weka

Last synced: 9 days ago
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building a predictive model using the Linear Regression algorithm

• Host: GitHub
• URL: https://github.com/youseftareq33/java_ai_3_machine-learning
• Owner: youseftareq33
• Created: 2024-07-17T16:17:54.000Z (6 months ago)
• Default Branch: master
• Last Pushed: 2024-07-17T16:29:48.000Z (6 months ago)
• Last Synced: 2024-11-06T06:26:09.455Z (about 2 months ago)
• Topics: java, linear-regression, machine-learning, training-data, weka
• Language: Java
• Homepage:
• Size: 14.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning Linear Regression

## Description:

This project involves building a predictive model using the Linear Regression algorithm.

You will work in pairs to submit the code and a detailed report of your work.

The goal is to predict a person's weight based on their height using the provided dataset.

## DataSet:

Link: https://www.dropbox.com/scl/fi/lhau97ll9bwvewqa0rm2q/Height_Weight.csv?rlkey=b92qo151troherwy7t9h3gnil&e=1&dl=0

- The dataset includes instances of height (in inches) and weight (in pounds).

## Tasks

### Data Preprocessing:

- Convert heights from inches to centimeters.

- Convert weights from pounds to kilograms.

- Print main statistics (mean, median, standard deviation, min, and max) of the features in a table.

### Model Construction:

- Split the data into 70% training and 30% test sets.

- Generate models using subsets of the dataset and evaluate their performance:

   M1: 100 random instances

   M2: 1,000 random instances

   M3: 5,000 random instances

   M4: Entire dataset

### Model Evaluation:

- Use appropriate regression metrics to test the performance of each model.

- Compare and analyze the performance of the models.