Welcome to Bitcoin Price Prediction AI Challenge!

The aim of this challenge is to predict the closing price of Bitcoins for the given test dates, a time series prediction problem. The dataset consists of 3 csv files:

1. TRAIN.csv
2. TEST.csv
3. sample_submission.csv

TRAIN.csv consists of 7 attributes:

• Date
• Open
• High
• Low
• Close (Target Column)
• Adj Close
• Volume

This data should be used to train the model, no additional data is allowed to be used for the training process.

TEST.csv consists of the testing data required for prediction and consists of 4 attributes:

• Date
• Open
• High
• Low

The "Close" attribute has to be predicted for these given values in the "Date" column.

A "sample_submission".csv is also provided for ease of the participants. To know the exact submission format, please check out "Submission Guidelines".

The score is displayed in reverse order of Root Mean Square Error (RMSE). "TRAIN.csv" file contains the data required for training.

The output of the model should be "output.csv" containing the following columns in this exact order for predictions on TEST.csv:

• Date
• Close

A "sample_submission.csv" is also given for reference which already contains the correct "Date" attribute values required for submission. You may also edit the "Close" attribute values in this file for submission.

...

How to make a submission?

1. Click on "My Submission"
2. On the next page, click on "+ New Submission"
3. Upload your CSV in the next page and click on "Submit for Review"

Please note:

1. You must submit your CSV file by uploading the CSV in the "My Submissions" section of this challenge.
2. Your submission will be auto graded and you will be able to see your results instantly.
3. If there is any error in the submission, your final score will be marked as 0.

Judgement

1. Root Mean Squared Error (RMSE) will be used for evaluation

Rules

1. Submission must not include copyrighted code. If violation is found, submission will be rejected.
2. The submission should be in a proper format as described by "Submission Guidelines".
3. Late submission will not be accepted beyond provided deadline (Indian Standard Time).

Reward

1. Top three participants will get a place in Hall of Fame page.