Logistic Regression (heart disease) - Python

🚀 Project Showcase: Heart Disease Prediction Using Python 🐍❤
I recently completed an exciting data science project where I analyzed a heart disease dataset and built a predictive model using Python. Here's a brief overview of the steps I followed:
Data Loading and Exploration:
Imported necessary libraries like Pandas, NumPy, Matplotlib, Seaborn, Plotly, and Scikit-learn.
Loaded the dataset and checked for missing values, data types, and unique values.
Generated descriptive statistics to understand the data better.
Data Visualization:
Created histograms with KDE plots for each variable to understand their distributions.
Visualized the correlation matrix to identify relationships between features.
Built bar charts to visualize heart disease occurrences by sex and exercise-induced angina.
Logistic Regression Model:
Used Statsmodels for logistic regression to identify significant variables.
Split the data into training and testing sets.
Trained a logistic regression model using Scikit-learn.
Evaluated the model using accuracy score, classification report, and confusion matrix.
Results:
Achieved an accuracy of 81.79% in predicting heart disease.
Visualized key findings and shared insights from the model.
This project was a great opportunity to apply data science techniques and gain deeper insights into heart disease risk factors. Check out the code snippets and visualizations below!
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