Welcome to the wacky world of Heart Stoke Detection! This Python project is all about predicting strokes and having a heart-stoking good time with machine learning. 🎢💻
In this eccentric project, we dived headfirst into the realm of machine learning to detect strokes. We explored a dataset with attributes like age, gender, hypertension, heart disease, and more to unravel the secrets of stroke prediction. Prepare yourself for a thrilling journey through the twists and turns of data analysis and model training! 🎢🔍
Let's take a moment to appreciate the eccentric attributes we encountered on this wild ride. Here's a table to showcase the quirkiest details:
| Attribute | Description |
|---|---|
| id | Unique identifier |
| gender | "Male", "Female", or "Other" |
| age | Age of the patient |
| hypertension | 0 if the patient doesn't have hypertension, 1 otherwise |
| heart_disease | 0 if the patient doesn't have any heart diseases, 1 otherwise |
| ever_married | "No" or "Yes" |
| work_type | "Children", "Govt_job", "Never_worked", "Private", or "Self-employed" |
| Residence_type | "Rural" or "Urban" |
| avg_glucose_level | Average glucose level in blood |
| bmi | Body mass index |
| smoking_status | "Formerly smoked", "Never smoked", "Smokes", or "Unknown" |
| stroke | 1 if the patient had a stroke, 0 otherwise (Class Label) |
These attributes definitely kept us on our toes! 😄
To tackle this heart-pounding challenge, we unleashed the power of three incredible algorithms. Hold onto your hats as we introduce them:
-
Logistic Regression 📈 - This algorithm slithered its way into our hearts with an accuracy of 95.62%! It knows how to curve those predictions just right.
-
Decision Tree 🌳 - With its branching logic, the Decision Tree algorithm wowed us with an accuracy of 91.75%! It definitely knows how to make a calculated choice.
-
Random Forest 🌲 - The forest was alive with the sound of accurate predictions! The Random Forest algorithm secured an accuracy of 95.41%. It's like a magical forest full of accurate decisions.
After an adrenaline-fueled journey through data analysis and model training, we're thrilled to share the final accuracy results of our algorithms:
- Logistic Regression: 95.62% accuracy! 🎉📈
- Decision Tree: 91.75% accuracy! 🌳✨
- Random Forest: 95.41% accuracy! 🌲🔮
These results had our hearts racing with excitement! The algorithms did an impressive job of predicting strokes and left us in awe of their capabilities. 💓💥
We believe that the wackier, the better! If you have any wild ideas to enhance this project or want to share your stroke-detection adventures, feel free to join the fun. Open an issue or submit
a pull request, and let's collaborate on this heart-pumping project together! 👩💻👨💻
This repository is licensed under the MIT License. You're free to use the code, laugh at the jokes, and even dance to the beat of machine learning. Just remember to embrace the madness and keep the fun spirit alive! 🕺💃
Thank you for joining us on this whimsical journey through Heart Stoke Detection Using ML. Let's continue spreading smiles and laughter with the power of technology! 😄✨