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{
"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."
} |