Alzheimers_disease_Class

Developed a Machine Learning project for Alzheimer’s disease prediction using Python and Scikit-learn.

Performed data cleaning, preprocessing, and exploratory data analysis (EDA).

Handled categorical features using encoding techniques and applied feature scaling.

Built and trained multiple classification models to compare performance.

Implemented algorithms such as Random Forest, XGBoost, SVM, and Logistic Regression.

Evaluated models using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

Visualized insights and model performance using Matplotlib and Seaborn.

Improved prediction accuracy through model tuning and feature selection.

Worked with real healthcare-related data to identify important risk factors.

Focused on building an accurate and reliable predictive system for early detection support.