Heart Attack Prediction

This project aims to perform a classification on the provided dataset of Heart Attack prediction. The goal is to preprocess the dataset, apply more than 3 machine learning algorithms, perform exploratory data analysis, and achieve an accuracy and F-score of more than 65%.

Libraries used:

• Numpy
• Pandas
• Sklearn
• Matplotlib

Running the Jupiter File

• The code is implemented using Jupyter Notebook.
• Make sure you have Jupyter Notebook installed and running in your system.
• Clone the repository to your local machine.
• Open the Jupyter Notebook file (Heart_Attack_Prediction) and run the cells to see the output.

Preprocessing

• Load the dataset into a pandas DataFrame
• Check for missing values and handle them appropriately (drop, fill, etc.)
• Convert categorical variables into numeric using one-hot encoding or label encoding
• Normalize/standardize the numeric variables if needed
• Split the dataset into training and testing sets

Machine Learning

• Train and test more than three different machine learning models (e.g. Logistic Regression, KNN, Random Forest, SVM, etc.)
• Evaluate the performance of each model using metrics like accuracy, f-score, precision, recall, etc.
• Implement the majority voting concept by taking predicted labels from all classifiers and assigning the final label based on the majority vote

Exploratory Data Analysis

• Plot various graphs to understand the data insights (e.g. histograms, scatter plots, box plots, etc.)
• Check for correlations between variables and the target label
• Check for class distribution

Results

• Report the final accuracy, f-score, and other performance metrics of the majority voting model
• Compare the performance of all models and provide insights on why one performed better than the others
• Discuss the insights gained from Exploratory Data Analysis