About Project

Heart Disease Prediction using Machine Learning

This project is a comprehensive machine learning pipeline designed to predict the presence of heart disease in individuals based on various health attributes. It demonstrates key steps in data preprocessing, exploratory data analysis (EDA), model building, and evaluation.

Key Features:

- Data Cleaning and Preprocessing: Includes handling missing values, encoding categorical variables, and scaling numerical features.
- Exploratory Data Analysis (EDA): Visualizations and statistical summaries to understand data patterns and relationships.
- Model Training and Evaluation: Implements and compares several machine learning models, including:
  - Logistic Regression
  - Decision Tree
  - Random Forest
  - Support Vector Machine (SVM)
  - Gradient Boosting
- Performance Metrics: Evaluates models using accuracy, precision, recall, F1-score, and ROC-AUC curves.

Project Workflow:

1. Import Libraries: Load required Python libraries like pandas, NumPy, Matplotlib, and scikit-learn.
2. Load Dataset: Use the dataset containing health-related features for heart disease prediction.
3. Data Preprocessing:
   - Handle missing values.
   - Encode categorical variables.
   - Scale features for model compatibility.
4. EDA:
   - Analyze distributions, correlations, and trends.
   - Visualize key relationships.
5. Feature Engineering:
   - Create or transform features to enhance predictive performance.
6. Model Building:
   - Train multiple models.
   - Perform hyperparameter tuning.
7. Model Evaluation:
   - Compare performance metrics across models.
   - Identify the best-performing model.
8. Save the Model:
   - Store the trained model for deployment.

Technologies Used:

- Languages: Python
- Libraries: pandas, NumPy, Matplotlib, seaborn, scikit-learn
- Tools: Google Colab 

How to Use:

1. Clone this repository:
   git clone https://github.com/username/repo-name.git
2. Navigate to the project directory:
   cd repo-name
3. Install dependencies:
   pip install -r requirements.txt
4. Run the Jupyter Notebook:
   jupyter notebook Classification_Project.ipynb
5. Follow the steps in the notebook to preprocess data, train models, and evaluate results.

Results:

- Best-performing model: LogisticRegression
- Accuracy: 88%

Future Scope:

- Integrate the model into a web application for real-time predictions.
- Explore additional datasets for broader applicability.
- Experiment with deep learning models for improved accuracy.