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      "content": "## Overview\nThe data for this competition has been split into two groups:\n\n- training set (train.csv)\n- test set (test.csv)\n\nThe training set should be used to build the machine learning model. For the training set, we provide the rent for each accommodation along with 35 other features.\n\nThe test set should be used to see how the model performs on unseen data. Thus, the rent for each accommodation is is not provided. The purpose of the model is to predict these values.\n\nTo illustrate the format of a submission file, we have provided **sample_submission.csv**\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Data Dictionary\n|  Variable | Definition |\n| ---  | --- |\n| title | Name of the accommodation |\n| city | City |\n| postalCode | Postal Code |\n| latitude | Latitude in degrees |\n| longitude | Longitude in degrees |\n| areaSqm | Size in square meters |\n| firstSeenAt | Registration time of the proprietor (YYYY-MM-DD HH-MM-SS) GMT |\n| lastSeenAt | Latest appearance of the proprietor (YYYY-MM-DD HH-MM-SS) GMT |\n| isRoomActive | Current Availability |\n| rawAvailability | Time period of availability (DD-MM-YYYY) |\n| postedago | How long ago the property was posed |\n| descriptionNonTranslated | Original description |\n| descriptionTranslated\t | Translated Description   |\n| rentDetail\t |  Justification of rent  |\n| propertyType\t |  Type of accommodation  |\n| furnish\t | Presence of furniture |\n| energyLabel\t |  Energy Efficiency  |\n| gender\t | Proprietor's gender |\n| internet\t |  Availability of Internet  |\n| roommates\t |  # of roommates  |\n| shower\t |  Ownership of shower  |\n| toilet\t | Ownership of toilet |\n| kitchen\t | Ownership of kitchen |\n| living\t |  Ownership of living room  |\n| pets\t | Pets allowed  |\n| smokingInside\t |  Smoking allowed  |\n| matchAge\t |  Desired tenant age (Range in years)  |\n| matchGender\t |  Desired tenant gender  |\n| matchCapacity\t | # of people that can live in the accommodation  |\n| matchLanguages\t |  Desired language  |\n| matchStatus\t |  Desired Status |\n| coverImageUrl\t | Url of the covering image of the accommodation |\n| rent\t |  Rent  (goal feature) |",
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      "name": "Description",
      "content": "## Competition Description\nWelcome to the Machine Learning Month 2022. In this competition, you are tasked to use machine learning to determine the rent of properties in the Netherlands. \n\n## Challenge\nThe Dutch housing crisis is one of the biggest problems that residents face.\n\nDue to multiple factors, such as population growth and a shortage of construction workers, the availability of housing has decreased significantly. This decline has pushed rent to sky-high prices, which leaves many wondering whether they are being taken advantage of.\n\nIn order to answer this question, you are tasked to predict the rent of a house given its data (i.e. location, size, facilities etc.).\n\n## Acknowledgments\nThe [Netherlands Rent Properties dataset](https://www.kaggle.com/datasets/juangesino/netherlands-rent-properties) that is used in the competition was compiled by Juan Gesino.\n\n---\n\n**Attention:** Winners will be determined not only by leaderboard, but also by their solution. Teams included in the top will have to provide a complete reproducible code with a solution in google colab or jupyter notebook. In the absence of a code (or if the code is not reproducible), the team is not considered for a winning place.\n\nYou can use any machine learning model, in particular, you can use any pre-trained models in the process of building a solution. If you are using a pre-trained model, be sure to include its source. Additional datasets not provided in the task cannot be used.",
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      "name": "Evaluation",
      "content": "## Goal\nIt is your job to predict the sales rent price for each accommodation. For each `id` in the test set, you must predict the value of the `rent` variable. \n\n## Metric\nSubmissions are evaluated on the [Mean Absolute Error (MAE)](https://en.wikipedia.org/wiki/Mean_absolute_error) metric between predicted value and the observed price. \n\n![formula](https://latex.codecogs.com/svg.latex?%5Ctext%7BMAE%7D%20%3D%20%5Cfrac%7B%5Csum_%7Bi%3D1%7D%5E%7Bn%7D%7B%7Cy_i%20-%20%5Chat%7By_i%7D%7C%7D%7D%7Bn%7D)\n\n## Submission File Format\n\nThe file should contain a header and have the following format:\n\n```\nid, rent\n1, 550\n2, 550\n4, 550\n5, 550\nect.\n```\n\nYou can download an example submission file (sample_submission.csv) on the [data page](https://www.kaggle.com/competitions/fcg-2022-netherlands-accommodation-prices/data).",
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    {
      "name": "rules",
      "content": "Hello and welcome to the first machine learning competition by FCG. To participate in the competition you have to follow the rules:\n\n- Do not use external datasets\n- Do not copy each other's work\n- Have fun 🐙 \n- There can be up to 3 people in a team\n\nPlease read the overview for more information",
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      "name": "New To Kaggle?",
      "content": "## Overview of Kaggle Competitions\n\n1. **Register for the competition**\nRead the description, accept the rules, and join the competition.\n\n2. **Build a machine learning model**\nDownload the [dataset][data] and develop the model locally or using an online hosting service such as [Google Colab][colab]. You can start with our [baseline solution][base] or open it straight in [Google Colab][base_colab].\n\n3. **Make a submission**\nUpload your predictions to Kaggle via _Submit Predictions_ button.\n\n4. **Inspect leaderboard standing**\nObserve how your model fares against other competitors.\n\n5. **Refine your model**\nThe competition does not end upon the first submission. Change your code and research other methods. Come to the [lectures](https://www.kaggle.com/competitions/fcg-2022-netherlands-accommodation-prices/overview/timeline) and gain insight to further optimize your model.\n\n[data]: https://www.kaggle.com/competitions/fcg-2022-netherlands-accommodation-prices/data\n[colab]: https://colab.research.google.com/\n[base]: https://fully-connected-graph.github.io/GMLM-2022/baseline_solution.ipynb\n[base_colab]: https://colab.research.google.com/github/Fully-Connected-Graph/GMLC-2022/blob/main/baseline_solution.ipynb\n",
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    {
      "name": "Timeline",
      "content": "## Lecture timeline\n- `Nov 17, 2022 @ 15:00-17:00` – Introduction to Machine Learning with Sklearn\n- `Nov 24, 2022 @ 17:00-19:00` – Introduction to NLP: How to extract features from text?\n- `Dec _2, 2022 @ 13:00-15:00` – Introduction to Neural Networks\n\n## Competition timeline\n- `Nov 14, 2022 @ 00:00` – Competition start.\n- `Dec _7, 2022 @ 23:59` – Final submission deadline.\n- `Dec _9, 2022 @ 13:00-15:00` – Social; Solution presentation; Award ceremony.\n\nDeadline is at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.",
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      "name": "Lecture Materials",
      "content": "Download the baseline solution [here][1] or open it straight in [Google Colab][2].\n\n| Lecture | Notebook | Solutions | Extra | Recordings |\n| ------- | -------- | --------- |------- | --------- |\n| Intro to ML with Scikit-Learn | [![Notebook][0]][3] | [![Solutions][0]][4] | [Slides][5] |  [Recording][12] |\n| Intro to Natural Language Processing  | [![Notebook][0]][6]|  | 👉[Feedback][7] 👈| |\n| Intro to Neural Networks | [![Notebook][0]][9] | [![Solution][0]][10] | [![Notebook][0]][13] [Slides][11] | | |\n\n_P.S. The recordings will be uploaded within 1-2 days after the lecture_\n\n[0]: https://colab.research.google.com/assets/colab-badge.svg\n[1]: https://fully-connected-graph.github.io/GMLM-2022/baseline_solution.ipynb\n[2]: https://colab.research.google.com/github/Fully-Connected-Graph/GMLC-2022/blob/main/baseline_solution.ipynb\n\n[3]: https://colab.research.google.com/github/Fully-Connected-Graph/GMLM-2022/blob/main/lecture1/notebook.ipynb\n[4]: https://colab.research.google.com/github/Fully-Connected-Graph/GMLM-2022/blob/main/lecture1/solution.ipynb\n[5]: https://fully-connected-graph.github.io/GMLM-2022/lecture1/slides.pdf\n\n[6]: https://colab.research.google.com/github/Fully-Connected-Graph/GMLM-2022/blob/main/lecture2/notebook.ipynb\n[7]:\nhttps://docs.google.com/forms/d/e/1FAIpQLScAiqJor46V0HdJ2S8snm2BTdc1QDmMjR04i9Tvya3jCFkk5g/viewform\n\n[9]: https://colab.research.google.com/github/Fully-Connected-Graph/GMLM-2022/blob/main/lecture3/notebook.ipynb\n[10]: https://colab.research.google.com/github/Fully-Connected-Graph/GMLM-2022/blob/main/lecture3/solution.ipynb\n[11]: https://fully-connected-graph.github.io/GMLM-2022/lecture3/slides.pdf\n[13]: https://colab.research.google.com/github/Fully-Connected-Graph/GMLM-2022/blob/main/lecture3/lecture3_titanic.ipynb\n\n[12]: https://video.rug.nl/media/0_fizeluih\n ",
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