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.
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.
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.
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.csvDataDownload All files
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
5year.csvDataDownload All files
To build an application to classify the patients to be healthy or suffering from cardiovascular disease based on the given attributes.
cardio_train.csvDataDownload All files
To build an application which predicts the winner based on the given attributes and the hero chosen by the team.
dota2Train.csvDataDownload All files
To build an application which predicts if the client will subscribe (yes/no) to a term deposit (variable y).
bank-additional-full.csvDataDownload All files
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_event_log_train.csvDataDownload All files