❤️ Heart Disease Prediction using Machine Learning with Python 🐍

Description:

This project aims to provide a simple and effective way to predict heart disease based on a variety of health indicators. It includes data loading, preprocessing, model training, evaluation, and prediction functionalities. The goal is to demonstrate a basic machine learning approach to heart disease prediction. 🩺

Features:

• Data Loading: Loads heart disease data from a CSV file. 📂

• Data Exploration: Displays the first and last few rows, shape, information, statistical description, and target variable distribution of the dataset. 🔍

• Data Preprocessing: Splits the data into features (X) and target (Y). 🧹

• Train-Test Split: Divides the data into training and testing sets for model evaluation. ➗

• Model Training: Trains a Logistic Regression model using the training data. 🤖

• Model Evaluation: Evaluates the model's performance on both training and testing data using accuracy scores. 📈

• Prediction System: Creates a simple predictive system that takes input data and predicts the likelihood of heart disease. 🔮

Technologies Used:

• Python 🐍

• Pandas 🐼

• NumPy 🔢

• Scikit-learn 🔬

Getting Started:

1. Clone the repository: git clone <repo url>

2. Ensure you have the heart.csv dataset in the same directory as your script.

3. Install the necessary libraries: pip install pandas numpy scikit-learn

4. Run the Python script.

Example Usage:

The script will load the data, train the model, evaluate its performance, and then make a prediction based on sample input data. It will print the accuracy on the training and test data, as well as the prediction result (healthy or heart disease).