# Description Synnax is excited to offer a meticulously assembled dataset featuring real financial indicators for 16,381 publicly traded companies across five consecutive quarters and four annual reports. We have invested significant effort in curating these statistics, believing they provide a robust foundation for a variety of financial analytics tasks, such as budget forecasting, assessing financial health and predicting loan defaults. In this competition, Synnax challenges participants to predict the financial metrics for the most recent quarter reported for each company. The dataset categorizes each quarter with 146 financial components, such as `TOTAL ASSETS`, `TOTAL LIABILITIES`, `EBITDA`, among others, with features names being encrypted for security. Each quarter is prefixed (`Q_0`, `Q_1`, …, `Q_4`), where `_0` denotes the latest quarter in the dataset. The target variables, which were extracted from the `Q_0` statistics, are found in the `targets_train.csv` file. The **Synnax Datathon 1** task is a highly creative endeavor that can be tackled as a conventional machine learning problem. Participants will train models using a variety of features to predict specific targets. Our dataset offers comprehensive financial performance statistics for each company, derived from components of income statements and balance sheets over the last five quarterly and four annual reports. Additional metadata, such as industry, sector, country, and city, provide further insight into the companies' characteristics. Contestants may also opt for a time series approach, utilizing values from previous quarters to predict future financial outcomes. ### Competition Timeframe * Start date: 8th May 2024 * End date: 19th June 2024 {% hint style="info" %} Important note: Synnax has innovated a novel method to assess the probability of default among private companies in the web3 domain, leveraging distributed machine learning efforts from numerous individual contributors. This competition serves as a precursor to the production phase, acquainting participants with the type of data they will encounter and helping to expand our community. Synnax will invite all top-performing participants to join our production pipeline, which includes opportunities for periodic profit sharing. {% endhint %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.synnax.ai/synnax/synnax-lab/data-science-competitions/synnax-datathon-1/description.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.