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:
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Data Loading: Loads heart disease data from a CSV file. 📂
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Data Exploration: Displays the first and last few rows, shape, information, statistical description, and target variable distribution of the dataset. 🔍
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Data Preprocessing: Splits the data into features (X) and target (Y). 🧹
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Train-Test Split: Divides the data into training and testing sets for model evaluation. ➗
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Model Training: Trains a Logistic Regression model using the training data. 🤖
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Model Evaluation: Evaluates the model's performance on both training and testing data using accuracy scores. 📈
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Prediction System: Creates a simple predictive system that takes input data and predicts the likelihood of heart disease. 🔮
Technologies Used:
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Python 🐍
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Pandas 🐼
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NumPy 🔢
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Scikit-learn 🔬
Getting Started:
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Clone the repository:
git clone <repo url> -
Ensure you have the
heart.csvdataset in the same directory as your script. -
Install the necessary libraries:
pip install pandas numpy scikit-learn -
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).