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  "fetched_at": "2026-10-20T03:20:34Z",
  "source_url": "https://www.kaggle.com/competitions/dsi-tda-02-project-2/data",
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      "content": "- `train.csv` : train data with the column `SalePrice`\n- `test.csv` : data for predicting price, without the column `SalePrice`\n- `sample_submission.csv` : an example of correctly formatted submission for this challenge\n- `sample_code.ipynb` : a sample code for creating `submission.csv`",
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      "name": "Description",
      "content": "Welcome to the **DSI-TDA-02 Project 2**!! \n\nProject 2 is a **Regression Challenge** with the aim of having you review the regression concepts you learned in class while having you struggle with real (large and messy) data. \n\nIn this challenge, you will use *Ames housing data* to create a fine regression model that predicts the price of houses in Ames. The data has a lot of features so your data science decision-making skills will be put to the test.\n\n## Goal\n- Be a thoughtful data scientist\n- Make sound decisions\n- Be an awesome teammate\n- Compete with grace and dignity\n\n## Data\n- `train.csv` : training data that includes the column `SalePrice`\n- `test.csv` : testing data for predicting price, without the column `SalePrice`\n- `sample_submission.csv` : an example of correctly formatted submission for this challenge\n- `sample_code.ipynb` : sample code for creating `submission.csv`\n\n## Metric\n\nThe metric used to evaluate your model is **Root Mean Squared Error (RMSE)**. It is automatically calculated and shown in Leaderboard after submission.\n\n$$ \\text{RMSE} = \\sqrt{\\frac{1}{n}\\sum^{n} (y-\\hat{y})^2} $$\n\nwhere *ŷ* is your prediction, *y* is the actual value\n\nYou can calculate the RMSE score like this...\n~~~\nfrom sklearn from metrics\nmetrics.mean_squared_error(y_true, y_pred, squared=False)\n~~~\n\n## Submission\n\nThe name of the file to submit must be `submission.csv`,  with a header `Order` `SalePrice`.\n\n- column `Order` has the same values as `test.csv`\n- column `SalePrice` is your predictions\n\n`submission.csv`\n~~~\nOrder,SalePrice\n2127,100\n193,100\n2407,100\n46,100\n...\n~~~\n\nIf you don't know how to format like this, refer to `sample_code.ipynb`\n\n## Presentation\n\nAfter submission, you'll prepare a presentation deck (slides). This deck must contain:\n\n- Explanation of your final model\n- What was the process of getting to that final model\n- Which features you used and why you used those features\n- How you split the data into train and test\n- How you evaluated your model performance\n- and so on and so on.",
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      "name": "Data Dictionary",
      "content": "82 columns\nFor more details, please refer to [this](https://www.kaggle.com/competitions/dsi-us-6-project-2-regression-challenge/data)\n\n- **SalePrice** (target variable, only in `train.csv`)\n- Order\n- PID\n- MS SubClass\n- MS Zoning\n- Lot Frontage\n- Lot Area\n- Street\n- Alley\n- Lot Shape\n- Land Contour\n- Utilities\n- Lot Config\n- Land Slope\n- Neighborhood\n- Condition 1\n- Condition 2\n- Bldg Type\n- House Style\n- Overall Qual\n- Overall Cond\n- Year Built\n- Year Remod/Add\n- Roof Style\n- Roof Matl\n- Exterior 1st\n- Exterior 2nd\n- Mas Vnr Type\n- Mas Vnr Area\n- Exter Qual\n- Exter Cond\n- Foundation\n- Bsmt Qual\n- Bsmt Cond\n- Bsmt Exposure\n- BsmtFin Type 1\n- BsmtFin SF 1\n- BsmtFin Type 2\n- BsmtFin SF 2\n- Bsmt Unf SF\n- Total Bsmt SF\n- Heating\n- Heating QC\n- Central Air\n- Electrical\n- 1st Flr SF\n- 2nd Flr SF\n- Low Qual Fin SF\n- Gr Liv Area\n- Bsmt Full Bath\n- Bsmt Half Bath\n- Full Bath\n- Half Bath\n- Bedroom AbvGr\n- Kitchen AbvGr\n- Kitchen Qual\n- TotRms AbvGrd\n- Functional\n- Fireplaces\n- Fireplace Qu\n- Garage Type\n- Garage Yr Blt\n- Garage Finish\n- Garage Cars\n- Garage Area\n- Garage Qual\n- Garage Cond\n- Paved Drive\n- Wood Deck SF\n- Open Porch SF\n- Enclosed Porch\n- 3Ssn Porch\n- Screen Porch\n- Pool Area\n- Pool QC\n- Fence\n- Misc Feature\n- Misc Val\n- Mo Sold\n- Yr Sold\n- Sale Type\n- Sale Condition",
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    {
      "name": "rules",
      "content": "- **Don't cheat!**\n- **Apply yourself!**\n- **Have fun!**",
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