Datasets:
ArXiv:
License:
| { | |
| "name": "11_House_Price_Prediction_LinearRegression_BostonHousing_ML", | |
| "query": "Hi. Set up a house price prediction project using a Linear Regression model on the Boston Housing dataset. Load the dataset using `from datasets import load_dataset` and `ds = load_dataset(\"~/mrseba/boston_house_price\")` in `src/data_loader.py`. Ensure feature scaling and data standardization are performed in `src/data_loader.py`. Implement the Linear Regression model in `src/model.py`. Use cross-validation to evaluate the model in `src/train.py`. Print the Mean Squared Error (MSE), Mean Absolute Error (MAE), and $R^2$ score, and save them under `results/metrics/metrics.txt`. Visualize the comparison between predicted and actual values and save the result as `results/figures/`prediction_vs_actual.png`. The visualizations should clearly demonstrate the model's accuracy (which, if done right, should be good).", | |
| "tags": [ | |
| "Financial Analysis", | |
| "Regression", | |
| "Supervised Learning" | |
| ], | |
| "requirements": [ | |
| { | |
| "requirement_id": 0, | |
| "prerequisites": [], | |
| "criteria": "The \"Boston Housing\" dataset is utilized using `from datasets import load_dataset` and `ds = load_dataset(\"mrseba/boston_house_price\")` in `src/data_loader.py`.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 1, | |
| "prerequisites": [ | |
| 0 | |
| ], | |
| "criteria": "Feature scaling and data standardization are performed in `src/data_loader.py`.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [], | |
| "criteria": "The \"Linear Regression\" model is implemented in `src/model.py`.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 3, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "criteria": "\"Cross-validation\" is used to evaluate the model in `src/train.py`.", | |
| "category": "Performance Metrics", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 4, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "\"Mean Squared Error (MSE),\" \"Mean Absolute Error (MAE),\" and \"R^2 score\" are printed, and saved as `results/metrics/metrics.txt`.", | |
| "category": "Performance Metrics", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "The comparison of predicted vs. actual values is visualized and saved as `results/figures/prediction_vs_actual.png`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| } | |
| ], | |
| "preferences": [ | |
| { | |
| "preference_id": 0, | |
| "criteria": "The dataset should load smoothly using the provided `load_dataset` code, and other methods should be tried if issues arise.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 1, | |
| "criteria": "The visualizations should clearly demonstrate the model's accuracy by highlighting the differences between predicted and actual values.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": false, | |
| "hint": "`ds = load_dataset(\"~/mrseba/boston_house_price\")` in the query is wrong, and it should be `ds = load_dataset(\"mrseba/boston_house_price\")`. We leave it here to check the self-debugging skill of the agents." | |
| } |