Parkinson's disease is a progressive neurological disorder that affects movement, leading to symptoms such as tremors, stiffness, and impaired balance. Early diagnosis and intervention are crucial for managing the condition effectively. Machine learning offers promising avenues for early detection and diagnosis, particularly through the analysis of voice characteristics.

The dataset consisted of voice recordings from individuals, along with indicators of Parkinson's disease status. Preprocessing steps involved data cleaning, handling missing values, and feature extraction to prepare the dataset for model training.

EDA was performed to gain insights into the distribution of data and relationships between features. Visualization techniques such as box plots and heatmaps were used to explore the dataset's characteristics.

Feature engineering techniques were applied to extract relevant features from the voice recordings. This involved selecting and transforming features to improve model performance.

Several machine learning algorithms were evaluated for their performance in predicting Parkinson's disease. Cross-validation techniques were used to assess model accuracy, and the best-performing algorithms were selected for further analysis.

The Extra Trees Classifier emerged as the top-performing model, achieving a test accuracy of 97%. This model demonstrated high accuracy for both positive and negative cases, with minimal misclassification.

The project successfully developed a predictive model for Parkinson's disease using machine learning techniques. The Extra Trees Classifier exhibited superior performance in accurately classifying individuals with and without Parkinson's disease based on voice recordings. The findings highlight the potential of machine learning in aiding early diagnosis and intervention for Parkinson's disease.

Future research could explore additional features and datasets to further enhance model performance. Additionally, collaboration with healthcare professionals could facilitate the integration of machine learning models into clinical practice for early detection and management of Parkinson's disease.