luksaiterugile / data-analysis

Parkinson's diagnosis, kidney disease detection, email classification, and crime rate prediction. Each project utilizes Weka classifiers or algorithms.

Data Analysis Projects

This repository contains four data analysis projects completed during my university course, all utilizing the Weka application. Each project focuses on different aspects of data analysis and machine learning. Below is a brief overview of each project:

1. Mutual-Information-Parkinsons

• Objective: Investigate the impact of specified biomedical voice parameters on the diagnosis of Parkinson's disease.
• Description: This project involves the comparison of estimates of information between attributes and the Parkinson's disease indicator (column 18).
• Methods Used: Weka classifiers and analysis.

2. Kidney-Disease-Decision-Trees

• Objective: Construct decision trees for diagnosing chronic kidney disease based on the attributes.
• Description: In this project, decision tree models are built using Weka classifiers such as J48, RandomTree, and REPTree.
• Methods Used: Weka decision tree classifiers.

3. Email-Classifier-Spam-Filter

• Objective: Build an email classifier ('spam' filter).
• Description: This project focuses on constructing an email classification model using decision trees and classification rules.
• Methods Used: Weka algorithms including J48, RandomTree, REPTree, ZeroR, OneR, and JRip.

4. Crime-Rate-Prediction

• Objective: Perform numerical prediction of crime rates based on specified attributes.
• Description: This project involves building a numerical prediction model using linear regression.
• Methods Used: Weka functions such as SimpleLinearRegression and LinearRegression.

Each project directory contains the necessary code, datasets, and documentation related to the respective analysis.