Alzheimer's Disease Classification

This repository contains machine learning models for classifying Alzheimer's disease using various supervised learning techniques. The dataset is preprocessed, analyzed, and evaluated using multiple classifiers.

📌 Features

• Exploratory Data Analysis (EDA): Visualizing categorical and numerical feature distributions.
• Feature Engineering: Removing unnecessary columns, handling imbalanced data.
• Data Preprocessing: Normalization & Standardization using MinMaxScaler and StandardScaler.
• Model Selection & Training: Training multiple models and optimizing hyperparameters using GridSearchCV.
• Performance Evaluation: Confusion matrix, classification report, and accuracy comparison of models.

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🛠️ Installation & Requirements

Ensure you have Python 3.8+ installed. Install dependencies using:

pip install -r requirements.txt

Alternatively, manually install the required libraries:

 pip install pandas numpy matplotlib seaborn scikit-learn xgboost catboost

📂 File Structure

📂 Alzheimers-Disease-Classification
│-- 📄 Alzheimer's_Disease_Classification_.ipynb     # Main notebook for training models with results
│-- 📄 Dataset.py                                    # Script to download dataset
│-- 📄 README.md                                     # Project documentation
│-- 📄 alzheimer's_disease_classification.py         # Main script for model training & evaluation
│-- 📄 requirements.txt                              # Dependencies

Usage

Run the script:

python alzheimer's_disease_classification.py

Models Used

• Decision Tree
• Random Forest
• Logistic Regression
• K-Nearest Neighbors
• Support Vector Machine
• XGBoost
• CatBoost

Results

The best-performing model is displayed along with accuracy comparisons.