# Datasets

{% hint style="info" %}
We prepared a [boilerplate primer](https://www.kaggle.com/code/danilz/datathon-1-boilerplate) to make it easier for you to jump into action! Feel free to use it as a starting point and tinker on it to get better results!
{% endhint %}

### Train Dataset

The train dataset contains financial data points for 13,104 publicly traded companies based on their quarterly and annual financial reports. This dataset is compiled using the 5 latest quarterly reports and 4 latest annual reports, and reflects financial components extracted from their corresponding balance sheet and income statements.

### Test Dataset

The test dataset contains 3227 companies with their corresponding features in the same format as the train dataset.

### The ordering is as follows:

Columns starting with Q\_($$n$$) (where $$n$$ is the number of the quarter) contain the companies' quarterly reported financial components.

Columns starting with Y\_($$n$$) (where $$n$$ is the number of the annual report) contain the companies' annually reported financial components.

Other columns represent metadata per each company.

Columns starting with: 

* `Q_0` are only present in **targets\_train.csv**: contain financial components for the latest (closest to today) quarter
* `Q_1` are a part of **X\_train.csv** and **X\_test.csv**: contain financial components of the quarter which went before `Q_0`
* `Q_4` are a part of **X\_train.csv** and **X\_test.csv**: contain financial components of the furthest reported quarter, 4 quarters before `Q_0`
* `Y_0` are a part of **X\_train.csv** and **X\_test.csv**: contain financial components from the latest annual report
* `Y_3` are a part of **X\_train.csv** and **X\_test.csv**: contain financial components from the  furthest annual report, 3 years before `Y_0`

Each quarter and each year (except for `Q_0`) contains 146 financial components, please refer to the **data\_dictionary.txt** for details.

There are 16 targets (**train\_targets.csv**) which represent the latest financial data points for each company. Participants need to train model(s) which will map the historical financial performance of the companies (**X\_train.csv**) to their latest financial indicators.

### Files

* [**X\_train.csv**](https://www.dropbox.com/scl/fi/8rajpkxgb3fwo9eketss9/X_train.csv.zip?rlkey=rimqpqqsizn3qh2sziit7izji\&st=pgro20v5\&dl=0) - training features
* [**targets\_train.csv**](https://www.dropbox.com/scl/fi/i4lr3tq4mvybnw6lmcz6w/targets_train.csv.zip?rlkey=hd0jns628vv60c7rup2v26567\&st=y2yogzt8\&dl=0) - training targets
* [**X\_test.csv**](https://www.dropbox.com/scl/fi/vz4mtb37nsvre3gm02z3g/X_test.csv.zip?rlkey=fxbsjb34vakjugsnv1g2knj9l\&st=deup9f7l\&dl=0) - testing features
* [**sample\_submission.csv**](https://www.dropbox.com/scl/fi/hzdze2fk8v8k361jsobb4/sample_submission.csv.zip?rlkey=0466hwqkzjoq4q8tsdn9778j7\&st=m1x1ylnz\&dl=0) - a sample submission file in the correct format
* [**data\_dictionary.txt**](https://www.dropbox.com/scl/fi/82ukcy3q3qb4olnlayqv7/data_dictionary.txt?rlkey=g3aes0u1qm8yis30hrbrla8tv\&st=l2zop6vw\&dl=0) - detailed data points description

Please refer to **data\_dictionary.txt** for detailed columns description.


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