regression-linear

Simple Linear Regression

This model is designed for generic regression datasets and is not restricted to the Tetuan City Power Consumption dataset. It is a reusable regression workflow: retrain it on your own tabular data by calling train_and_predict(data=your_df, target_column="your_target") from regression_model.py.

Model type: tabular_regression
Task: regression
Framework: scikit-learn

Supported input

• Any tabular (CSV-loadable) dataset with a numeric target column.
• Numeric and categorical feature columns are auto-detected; categorical columns are one-hot encoded (handle_unknown="ignore", so unseen categories at inference time don't crash).
• Datetime-like columns are auto-detected and converted into hour/day-of-week/month/year features.

Target column requirement

• Must be numeric. Pass it explicitly: train_and_predict(data=df, target_column="...").

Files in this repository

• regression_model.py -- the reusable implementation (GenericRegressionModel, train_and_predict). Import this and call it on your own data.
• model.joblib -- a demo artifact fit on the Tetuan City power consumption dataset (development/testing only). Its learned coefficients are specific to THAT dataset's feature space and will not work on a dataset with different columns -- retrain via regression_model.py for your own data instead.
• metrics.json / metadata.json -- evaluation results from the development-dataset run and the synthetic generic-dataset validation.

Training configuration (development-dataset run)

• Train size: 41932
• Test size: 10484
• Hyperparameters: {"copy_X": true, "fit_intercept": true, "n_jobs": null, "positive": false}

Evaluation metrics (development dataset)

Generic dataset validation

Passed: True. Verified with a synthetic sklearn.datasets.make_regression dataset (different columns, feature count, and distribution) -- not uploaded here, but proving train_and_predict() is not hard-coded to any one dataset.

Limitations

• model.joblib is a demo fit on one specific dataset's feature space -- it will raise a clear error if given data with different feature columns. Use regression_model.py to retrain on your own data instead.
• Classical statistical inference is only computed where OLS assumptions apply -- see metadata.json -> statistical_summary.

Usage example

import pandas as pd
from regression_model import train_and_predict

df = pd.read_csv('your_dataset.csv')
result = train_and_predict(data=df, target_column='your_target_column')

model = result['model']
print(result['predictions'][:5])

# Predict on brand-new rows with the same feature columns:
new_predictions = model.predict(new_df)