Metadata-Version: 2.4
Name: sara-linear-regression-practice-2026
Version: 0.1.0
Summary: A simple Linear Regression model in Python
Author: Sara
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Dynamic: author
Dynamic: description
Dynamic: description-content-type
Dynamic: license-file
Dynamic: requires-python
Dynamic: summary


# rocket

## Description
This project implements a simple Linear Regression model in Python.
It calculates the slope and intercept from training data and uses
them to make predictions.

## Features
- Train a linear regression model.
- Calculate the slope and intercept.
- Predict values using the trained model.
- Validate the input data.

## Installation
Install the package locally using pip:

```bash
pip install .
```

## Usage
```python
from linear_regression import LinearRegression

# Create training data
x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 4, 5]

# Create and train the model
model = LinearRegression()
model.fit(x, y)

# Make predictions
predictions = model.predict([6, 7])

print("Slope:", model.slope)
print("Intercept:", model.intercept)
print("Predictions:", predictions)
```

## Requirements
- Python 3


