Parkinson 🧠 Disease ⚕️ Detection 🔍

This project aims to make use of Machine Learning techniques to detect instances of Parkinson's Disease. The project performs the following tasks:
1️⃣ Data Collection
2️⃣ Data Preprocessing
3️⃣ Exploratory Data Analysis
4️⃣ Dataset Balancing & Scaling
5️⃣ Machine Learning Models Training & Evaluation

Dataset Details

Dataset Used : Parkinsons Disease Dataset
Dataset Source : UCI Machine Learning Repository
Dataset Hosting URL : https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/parkinsons.data

Machine Learning Models Trained & Evaluated

The following Machine Learning models were trained and evaluated:
1️⃣ Decision Tree Classifier
2️⃣ Random Forest Classifier
3️⃣ Logistic Regression
4️⃣ Support Vector Machine Classifier
5️⃣ Naive Bayes Classifier
6️⃣ K Nearest Neighbor Classifier
7️⃣ XGBoost Classifier

Best Performing Machine Learning Model

Random Forest Classifier was found to be the best performing Classifier with:

• Accuracy: 0.996102
• F1 Score : 0.961538
• R2 Score : 0.862471