Project 1

I have successfully completed the project on Heart Disease Prediction using various Machine Learning Models. I tried to incorporate all Features with Target Column to identify the most important marker to predict a Heart attack.

I used graphs where necessary and incorporated Skewness and Kurtosis taking each feature with target and graphs for the same.

The various steps in the project:

Table of Contents Import Packages

EDA

Preparing ML models

Models evaluation

Ensembling

Conclusion

I used various Machine Learning Algorithms

Logistic Regression

Naive Bayes

Random Forest Classifier

Extreme Gradient Boost

K-Nearest Neighbour

Decision Tree

Support Vector Machine

In Feature Engineering we analyse features and its importance representing through a barplot.

Finally we arrived at the best Model depending on which Model gave least False positives

Extreme Gradient Boost is the best Model for Heart Disease Prediction

I am attaching all files where necessary and using RISE extension I built a presentation in pdf.

For EDA I used numba

import pandas as pd

from ydata_profiling import ProfileReport

# Generate the profile report

try:

profile = ProfileReport(data, title="Heart Dataset Profiling Report", explorative=False, minimal=True)

profile.to_file("heart_dataset_basic_report.html")

print("Profile report generated successfully.")

except Exception as e:

print(f"An error occurred: {e}")