Skip to content

zxcvvbnmas #289

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 10 commits into
base: dependabot/pip/DataScience/CelebrityFaceRecognition/model/opencv-python-4.2.0.32
Choose a base branch
from
Open

zxcvvbnmas #289

Changes from 1 commit
Commits
Show all changes
10 commits
Select commit Hold shift + click to select a range
6f604ba
bagging
Oct 20, 2021
6e41dfd
bagging
Oct 20, 2021
70b9f64
regex
Nov 28, 2021
800a074
regex
Nov 28, 2021
5a3f649
Update data_analyst_learning_path_2020.md
dhavalsays Jun 20, 2022
24f477a
Update data_science_roadmap_2020.md
dhavalsays Jun 20, 2022
e98e82c
Update data_analyst_learning_path_2020.md
dhavalsays Nov 21, 2022
1590d37
test
dhavalsays Jan 4, 2023
184ca22
test
dhavalsays Jan 4, 2023
801ee0e
Update data_science_roadmap_2020.md
dhavalsays Jan 9, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
bagging
  • Loading branch information
codebasics committed Oct 20, 2021
commit 6e41dfd32d8d21535c436e2f62e3bfa32ee2c0a0
16 changes: 16 additions & 0 deletions ML/19_Bagging/bagging_exercise.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,16 @@
Download heart disease dataset heart.csv in [Exercise](https://github.com/codebasics/py/tree/master/ML/19_Bagging/Exercise) folder and do following, (credits of dataset: https://www.kaggle.com/fedesoriano/heart-failure-prediction)

1. Load heart disease dataset in pandas dataframe
1. Remove outliers using Z score. Usual guideline is to remove anything that has Z score > 3 formula or Z score < -3
1. Convert text columns to numbers using label encoding and one hot encoding
1. Apply scaling
1. Build a classification model using support vector machine. Use standalone model as well as Bagging model and check if you see any difference in the performance.
1. Now use decision tree classifier. Use standalone model as well as Bagging and check if you notice any difference in performance
1. Comparing performance of svm and decision tree classifier figure out where it makes most sense to use bagging and why. Use internet to figure out in what conditions bagging works the best.


[Solution Link](https://github.com/codebasics/py/blob/master/ML/19_Bagging/Exercise/bagging_heart_disease_prediction.ipynb)