The Machine Learning Challenge

Cash prizes worth INR 1,00,000

Rules

Evaluation Criteria

1. The solution shall be evaluated on the architecture design of the entire application.

2. The solution shall be evaluated on the data pre-processing, exploration and inferences drawn.

3. The solution shall be evaluated based on the optimizations applied and the model accuracy on an unseen dataset.

4. The solution shall be evaluated based on the final deployment and testing with the bulk dataset as well as with a single datapoint.

5. The solution shall be evaluated based on the code readability, formatting and proper commenting whenever and wherever required.

6. The solution shall be evaluated based on the interactiveness and ease of use of the user interface and its aesthetics.


Submissions

The solutions need to be submitted on or before the last date for submission. The solution submission should include a working application satisfying all the requirements along with full documentation and the video recording for the project as mentioned in the solution requirements section. The links for all code, documentations and videos can be provided below separately for each of the problem statements.


Please submit the code, documentation and video link one by one for each of the projects.


Challenges

Individual problem statements can be accessed by opening the problem tab. For getting the detailed problem statement, attribute information and data, please click on the ‘Download All Files’ button to download all the related files.

  • ML CHALLENGE 1

    To build an application which predicts the possibility of backorder for a given product based on the given attributes.

    Back order prediction problem statment.docxProblem Description

    Training_Dataset_v2.csvData

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    To build an application which predicts whether a bank is going to get bankrupt using the given set of features.

    Bankruptcy detection_problem statement.docxProblem Description

    1year.csvData

    2year.csvData

    3year.csvData

    4year.csvData

    5year.csvData

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    To build an application which predicts restaurant ratings based on given attributes.

    Restaurant_Rating.docxProblem Description

    TA_restaurants_curated.csvData

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  • ML CHALLENGE 2

    To build an application to classify the patients to be healthy or suffering from cardiovascular disease based on the given attributes.

    CardiovascularDiseaseDetection.docxProblem Description

    cardio_train.csvData

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    To build an application which predicts the winner based on the given attributes and the hero chosen by the team.

    dota_2_problem_statment.txtProblem Description

    dota2Train.csvData

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    To build an application which predicts the monthly rental of a house based on the given attributes.

    housePricing.docxProblem Description

    housing_train.csvData

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  • ML CHALLENGE 3

    To build an application which predicts if the client will subscribe (yes/no) to a term deposit (variable y).

    Banking_problem_statement.docxProblem Description

    bank-additional-full.csvData

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    To build an application which predicts the estimated time and date for the final closure of a opened ticket based on the given attributes.

    Incident_response_problem_staement.txtProblem Description

    incident_event_log_train.csvData

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    To build an application which predicts if a given user will click on a given offer based on the given attributes.

    kasandr_problem_statement.txtProblem Description

    train_de.csvData

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Overview
Description

This machine learning challenge has been created with the intent to engage the audience to participate. This also has the potential to bring out the best technical skill sets via the means of competitive learning and personal research. We, at iNeuron, believe that while classroom training is essential, self-learning and competitive practices help in a holistic and a long-lasting learning experience.

Thus, we are proud to have launched these challenges, which will help our candidates worldwide achieve their dreams. This challenge is to help the community learn and nurture together by creating a common platform for the exchange of ideas and solutions, and motivating the individuals and teams with appropriate rewards.

Solution Requirements

1. The problems are divided into 3 bundles each containing 3 problems. The candidate is to select one bundle out of these three and solve all three problems inside it. The solution can be deployed to any one of these cloud environments- Amazon AWS/ Microsoft Azure/ Google Cloud Platform as stated below:

  • ML Challenge 1 - Google Cloud Platform
  • ML Challenge 2 - Amazon AWS
  • ML Challenge 3 - Microsoft Azure.

2. A descriptive analysis of the given dataset and inferences drawn from the data along with Exploratory Data Analysis.

3. Thorough design documentation containing the solution design architecture right from the data ingestion(intake) till deployment.

4. A dedicated User Interface for testing the API for the bulk load as well as a single test instance.

5. Create and update detailed logs for all the training and prediction steps as well as detailed application logging with a robust exception handling framework.

6. A detailed Low-Level Design Document describing every step, method and module in detail, the final deployment process, sequence diagrams and failure point analysis.

7. A well-scripted retraining and application maintenance approach to make the application better, robust, and comparatively less prone to failures with time.

8. A descriptive video of project development and deployment process encompassing all the functionalities achieved through the project.

Timeline

All deadlines are at 11:59 PM IST on the corresponding day unless otherwise notified. The competition organizers reserve the right to update the contest timeline if they deem it necessary.

Prizes

  • 1st Place - ₹50,000

  • 2nd Place - ₹30,000
  • 3rd Place - ₹20,000
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