I am very proud to share one of the projects we worked on, which is the project to analyze Parkinson's disease data using AI and Machine Learning.

Project Highlights:

1️⃣Class Imbalance: Handling data imbalance using SMOTE.

2️⃣Visualization: Analyze data using Correlation Heatmap and Statistical Distribution of Features.

3️⃣Design an advanced model:

- Using a Neural Network with three layers and Dropout Layers to improve performance and prevent Overfitting.

- Adam Optimizer with a low learning rate to improve results.

4️⃣Evaluation and performance analysis: Confusion matrix, classification report, and plotting the evolution of accuracy and loss.

5️⃣Importance of features:Using Random Forest to identify the most influential factors.

The most important outputs:

-Model accuracy: reached {98.31%} on test data!

-Technical specifications:Python, libraries such as Pandas, Scikit-learn, Keras, and Seaborn.