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The candidates will have to make submissions as per the guidelines mentioned below:

We are also providing the Reference Notebook to help you begin the Auto Feature Engineering.

1.  AutoFeature Engineering Notebook

2. ZS MLDS Case Study – Armanik – Feature Engineering and Modelling


Objective 1: Auto Feature Engineering

A CSV file containing a list of all the features along with their fitness value.

Following are the Naming Conventions for the features for Auto Feature Engineering

 

 

 

 

 

 

 

 

Once the set of mandatory features is created using the training data, evaluate the fitness of these features using the following methodology. (Python Code Available in Notebook).

A python script file that can be executed on a machine with RAM of 16 GB with 4 cores to recreate all features and fitness values.

Any submission with >1% of error – sum of % errors across fitness values of all features (~40000 features), will be considered as Invalid.


Objective 2: Patient Switching Probabilities

Test Set Predictions: The test set predictions are evaluated using AUC. An excel file (.xlsx only) containing the prediction probability for all the patients in the test data.


A new submission window (FINAL SUBMISSION) will be enabled to share the following files as a zip  post 10th January:

1. Prediction classes for all the patients in the provided test data in an excel file. (Best Score.xlsx)

2. A csv file containing a list of all the features along with their fitness value. (Fitness_Score.csv)

3. A python script file that can be executed on a machine with RAM of 16 GB to recreate the features and fitness values. (Feature_Pipeline.py)

4. A well commented and reproducible source code(python script) for the best AUC which will be evaluated for time complexity. (Model.py)

5. Fully documented approach in a PPT.

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