Project 3 - Parkinson's Disease Detection

Project Overview

This project was completed as part of an internship program at Nexus Info. The internship was conducted remotely. The objective of this project is to analyze and predict Parkinson's disease using machine learning techniques. The dataset and associated files are used to build and evaluate models that can accurately diagnose the disease based on various medical parameters.

Files in the Repository

• LICENSE: The license file for the project.
• ParkinsonNames.txt: A text file containing the names of the attributes in the dataset.
• Parkinson_s Disease Prediction.ipynb: A Jupyter Notebook containing the code for data analysis, preprocessing, model building, and evaluation.
• README.md: This README file.
• parkinsons.data: The dataset used for predicting Parkinson's disease.

Table of Contents

1. Introduction
2. Dataset
3. Notebook Overview
4. Key Considerations
5. Conclusion
6. Additional Notes

Introduction

This project aims to provide a comprehensive analysis of Parkinson's disease using machine learning techniques. It involves building and evaluating models that can accurately diagnose the disease based on various medical parameters.

Dataset

The dataset parkinsons.data contains various medical parameters collected from people with and without Parkinson's disease. The ParkinsonNames.txt file provides the names and descriptions of these attributes.

Notebook Overview

The Jupyter Notebook Parkinson_s Disease Prediction.ipynb includes the following sections:

1. Data Loading: Loading the dataset and examining its structure.
2. Data Preprocessing: Cleaning and preparing the data for analysis.
3. Exploratory Data Analysis (EDA): Visualizing the data to understand patterns and relationships.
4. Model Building: Building machine learning models to predict Parkinson's disease.
5. Model Evaluation: Evaluating the performance of the models using appropriate metrics.

Installation

1. Clone the repository:

git clone https://github.com/himankgupta1/Project-3-Parkinson-Disease-Detection.git

2. Navigate to the project directory:

cd Project-3-Parkinson-Disease-Detection

3. Install the required libraries

Key Considerations

• Feature Selection: Identifying the most relevant features that contribute to the prediction of Parkinson's disease.
• Model Selection: Experimenting with various machine learning algorithms to find the best-performing model.
• Cross-validation: Ensuring the model's robustness and generalizability through cross-validation techniques.
• Hyperparameter Tuning: Optimizing the model's performance by fine-tuning hyperparameters.

Conclusion

The project successfully demonstrates the application of machine learning techniques in predicting Parkinson's disease. By leveraging the provided dataset, we can build accurate models that aid in early diagnosis and potentially improve patient outcomes.

Additional Notes

For those interested in exploring the code or conducting similar analyses, the Jupyter Notebook provides a step-by-step guide. Ongoing research and advancements in machine learning can further enhance the accuracy and reliability of such predictive models.