--- title: "Building your own ML problem: Datasets" description: Let's talk about the datasets that you need to build an ML problem on HackerEarth --- [Skip to content](https://help.hackerearth.com/hc/en-us/articles/360004493474-ml-problems-on-hackerearth-ml-platform#main-content) English Show submenu for translations ![HE logo.png\]](https://help.hackerearth.com/hs-fs/hubfs/HE%20logo.png?height=40&name=HE%20logo.png) Open main navigation Close main navigation - English Show submenu for translations Hello. How can we help you? - There are no suggestions because the search field is empty. 1. [Help Center](https://help.hackerearth.com/?hsLang=en) 2. [Assessments: Recruiters](https://help.hackerearth.com/assessments-recruiters?hsLang=en) 3. [Machine Learning (ML)](https://help.hackerearth.com/assessments-recruiters?hsLang=en#machine-learning-ml) # Building your own ML problem: Datasets ## Let's talk about the datasets that you need to build an ML problem on HackerEarth ### **What kinds of datasets do I need while building a Machine Learning problem on HackerEarth?** Your dataset must be divided into two parts: - Training data set - Test data set ### **What is a training data set?** A training data set is the data that candidates will use to train their models. ### **What is a test data set?** A test data set is the unseen data that candidates will use to predict an outcome. The test data must not specify the outcome. ### **How do I divide my dataset?** The following data set of 10 rows can be divided into the following: - Training data set (50% of the rows) - Test data set (remaining 50% of the rows) ### **Entire data set** | **Outlook** | **Temperature** | **Humidity** | **Wind** | **Play** | | --- | --- | --- | --- | --- | | Sunny | Hot | High | False | No | | Rainy | Mild | High | False | Yes | | Sunny | Cool | Normal | False | Yes | | Overcast | Hot | High | False | Yes | | Rainy | Mild | High | False | Yes | | Overcast | Hot | Normal | False | Yes | | Sunny | Mild | Normal | True | Yes | | Sunny | Mild | High | False | No | | Overcast | Cool | Normal | True | Yes | | Rainy | Mild | High | True | Yes | ### **Training data set** | **ID** | **Outlook** | **Temperature** | **Humidity** | **Wind** | **Play** | | --- | --- | --- | --- | --- | --- | | 1 | Sunny | Hot | High | False | No | | 2 | Rainy | Mild | High | False | Yes | | 3 | Sunny | Cool | Normal | False | Yes | | 4 | Overcast | Hot | High | False | Yes | | 5 | Rainy | Mild | High | False | Yes | ### **Test data set (test.csv)** | **ID** | **Outlook** | **Temperature** | **Humidity** | **Wind** | | --- | --- | --- | --- | --- | | 1 | Sunny | Hot | High | False | | 2 | Rainy | Mild | High | False | | 3 | Sunny | Cool | Normal | False | | 4 | Overcast | Hot | High | False | | 5 | Rainy | Mild | High | False | *Note:* We have not provided the target variable in the test data set. ### **What does the candidate do after the models are trained?** After the models have been trained, candidates are expected to do the following: 1. To predict an outcome on the test data set 2. 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