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shahidazam2020-oss/A-Comparative-Machine-Learning-Framework-for-Heart-Attack-Risk-Prediction-Using-Data-Preprocessing

This project presents a comparative machine learning framework for heart attack risk prediction using Python. It includes data preprocessing, feature engineering, SMOTE for class balancing, cross validation, and performance evaluation of multiple ML models.

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Jupyter Notebook
132
2
1 contributors
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How fast shahidazam2020-oss/A-Comparative-Machine-Learning-Framework-for-Heart-Attack-Risk-Prediction-Using-Data-Preprocessing is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Sep 26 – Oct 24, 2026

Stars +17 in 29 days

0510
9

Forks +2 in 29 days

01
1

Merged PRs 0 in 29 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Not in the Trendshift top 25 in this period

Sep 26Sep 30Oct 4Oct 8Oct 12Oct 16Oct 20Oct 24
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRs
Sat, Sep 26, 2026+0+10
Sun, Sep 27, 2026+0+00
Mon, Sep 28, 2026+0+00
Tue, Sep 29, 2026+0+00
Wed, Sep 30, 2026+0+00
Thu, Oct 1, 2026+0+00
Fri, Oct 2, 2026+0+00
Sat, Oct 3, 2026+0+00
Sun, Oct 4, 2026+0+00
Mon, Oct 5, 2026+0+10
Tue, Oct 6, 2026+0+00
Wed, Oct 7, 2026+0+00
Thu, Oct 8, 2026+1+00
Fri, Oct 9, 2026+5+00
Sat, Oct 10, 2026+0+00
Sun, Oct 11, 2026+0+00
Mon, Oct 12, 2026+0+00
Tue, Oct 13, 2026+0+00
Wed, Oct 14, 2026+0+00
Thu, Oct 15, 2026+1+00
Fri, Oct 16, 2026+0+00
Sat, Oct 17, 2026+0+00
Sun, Oct 18, 2026+0+00
Mon, Oct 19, 2026+1+00
Tue, Oct 20, 2026+0+00
Wed, Oct 21, 2026+0+00
Thu, Oct 22, 2026+9+00
Fri, Oct 23, 2026+0+00
Sat, Oct 24, 2026+0+00
Total+17+20
  • 1#1 on the list
  • 7Rank that day
  • Not trending

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A machine learning project that compares five classification algorithms for predicting the presence of heart disease from clinical patient data.

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