Varma2905/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)

Metric	Value
MAE	3416.8772
MSE	19319221.1505
RMSE	4395.3636
MedianAbsoluteError	2689.5062
MaxError	18730.4598
R2	0.5569
ExplainedVariance	0.5570
MAPE	20.7361
MSLE	0.0605
RMSLE	0.2459
AdjustedR2	0.5569
PearsonCorrelation	0.7463
SpearmanCorrelation	0.7468

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)