---
tags:
- sklearn
- linear-regression
- example
---
# Linear Regression Model for Ashpgsem

This is a simple linear regression model trained on dummy data.

## Model Description

This model is a `sklearn.linear_model.LinearRegression` instance. It was trained to predict a target variable `y_train` based on two features, `feature1` and `feature2`.

## Training Data

The model was trained on the following dummy data:

**Features (X_train):**

|   feature1 |   feature2 |
|-----------:|-----------:|
|          1 |          5 |
|          2 |          4 |
|          3 |          3 |
|          4 |          2 |
|          5 |          1 |


**Target (y_train):**

|   0 |
|----:|
|   2 |
|   4 |
|   5 |
|   4 |
|   5 |


## Training Procedure

The model was trained using the default parameters of `sklearn.linear_model.LinearRegression`.

## Usage

This model can be loaded using `skops.io`:


import skops.io as sio
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="Ashpgsem/rdmai_v2", filename="linear_regression_model.skops")
model = sio.load(model_path)

# Example prediction
import pandas as pd
new_data = pd.DataFrame({'feature1': [6, 7], 'feature2': [0, -1]})
predictions = model.predict(new_data)
print(predictions)


## Limitations

This model is trained on very limited dummy data and should not be used for any real-world applications. It serves purely as an example for demonstrating model saving and sharing on Hugging Face Hub.