Skip to content
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.

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

IBM HR Attrition #166

Open
wants to merge 4 commits into
base: master
Choose a base branch
from
Open
Show file tree
Hide file tree
Changes from all commits
Commits
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
3,004 changes: 3,004 additions & 0 deletions 007/solution/IBM_HR.ipynb

Large diffs are not rendered by default.

17,729 changes: 17,729 additions & 0 deletions 007/solution/IBM_HR_Employee_Attrition_Profil.html

Large diffs are not rendered by default.

804 changes: 804 additions & 0 deletions 007/solution/catboost_info/catboost_training.json

Large diffs are not rendered by default.

Binary file not shown.
801 changes: 801 additions & 0 deletions 007/solution/catboost_info/learn_error.tsv

Large diffs are not rendered by default.

801 changes: 801 additions & 0 deletions 007/solution/catboost_info/time_left.tsv

Large diffs are not rendered by default.

41 changes: 32 additions & 9 deletions 007/solution/readme.md
Original file line number Diff line number Diff line change
@@ -1,15 +1,38 @@
## Solutions
### Inital Format:
### Model Name and File Name: (IBM_HR.ipynb)
1 svm.SVC
2 SGDClassifier
3 Perceptron
4 MultinomialNB
5 PassiveAggressiveClassifier
6 GaussianNB
7 GaussianProcessClassifier
8 KNeighborsClassifier
9 RandomForestClassifier
10 AdaBoostClassifier
11 ExtraTreesClassifier
12 GradientBoostingClassifier
13 MLPClassifier
14 QuadraticDiscriminantAnalysis
15 xgboost.XGBClassifier
16 cb.CatBoostClassifier
17 CNN

Model Name and File Name: [E.g. Random Forest Classifier, RFR_clf.nb]
### Description:
Sturge’s rule:
Number of Bins = 1+log2(N) (Number of Samples)
In this case 10 bins means 10 folds

Description: [E.g. a random forest classifier from scikit-learn]
svm.SCV 85% and remaining algorithm above 90%

Further details:
[E.g. I did a 3-fold cv-grid search to find the best number of depth hyperparameter of RFR_clf]

Model Accuracy:
- Confusion matrix: [...]
- F1 score: [...]
Grid Random Search from large values to closer to Best Parameters.

### Further details:
Fold:KFold,GroupKFold,ShuffleSplit,RepeatedStratifiedKFold,StratifiedKFold,GroupShuffleSplit,StratifiedShuffleSplit,TimeSeriesSplit

### Model Accuracy (Classification Report):
- Confusion matrix
- F1 score
- precision
- recall
- support
16,573 changes: 16,573 additions & 0 deletions 007/solution/sweetviz_report.html

Large diffs are not rendered by default.