**
- Approach A: 5 categorical variables and 7 numerical variables. This led to addition of 7*5=35 variables which gave a new dataset of 35+12=47 features.
- Approach B: 9 categorical variables and 3 numerical variables. This led to addition of 9*3=27 variables which gave a new dataset of 27+12=39 features.
- Approach C: 5 categorical variables and 4 numerical variables. This led to additionof 5*4=20 variables which gave a new dataset of 20+12=32 features
Model Building:
The final model was an ensemble of 29 models consisting of:
The optimum threshold was the one which was maximizing the f1 score for the CV predictions. 5 thresholds, 2 lower than the optimum threshold and 2 higher than the optimum threshold and the optimum threshold were chosen to give more robust predictions.
-
5 catboost models with 5 different thresholds on the raw dataset of 12 features.
-
5 catboost models with 5 different thresholds on the raw dataset of 39 features (Approach B)
-
4 xgboost models on Approach A
-
5 xgboost models on Approach B
-
5 lightgbm models on Approach C
-
5 lightgbm models on Approach A
Here is the link to the code for Rank 3
End Notes
The solutions shared above is a proof that the winners have put in great efforts and truly deserve the rewards for the same. They came up with some innovative solutions and had a well structured approach.
I hope you find these solutions useful and have learnt some key takeaways which you can implement in the upcoming hackathons! Register yourself in the upcoming hackathons at DataHack Platform.
Like this:
Like Loading…
### Aishwarya Singh
An avid reader and blogger who loves exploring the endless world of data science and artificial intelligence. Fascinated by the limitless applications of ML and AI; eager to learn and discover the depths of data science.
Don’t have an account? Sign up here
P.S. We only publish awesome content. We will never share your information with anyone.
Subscribe!
P.S. We only publish awesome content. We will never share your information with anyone.
Subscribe!