linear-reg-youcef 1.0.0

`linear_reg_youcef` is a Python library for predicting values based on linear regression concept

Linear Regression

This repository contains a simple implementation of a Linear Regression model using Python and NumPy.

Overview

The LinearRegression class implements a basic linear regression model with gradient descent optimization. The model can be trained on a dataset and used to make predictions.

Installation

To use this code, you need to have Python and NumPy installed. You can install NumPy using pip:

pip install numpy
pip install linear_reg_youcef

Usage

Importing the Module

First, import the necessary module:

import numpy as np
from linear_reg_youcef import LinearRegression

Creating the Model

Create an instance of the LinearRegression class by passing the input data (x) and the target labels (y):

x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])
model = LinearRegression(x, y)

Training the Model

Train the model using the fit method. You need to specify the number of epochs and the learning rate:

epochs = 1000
learning_rate = 0.01
model.fit(epochs, learning_rate)

Making Predictions

Use the predict method to make predictions on new data:

new_data = np.array([6, 7, 8])
predictions = model.predict(new_data)
print(predictions)

Code Explanation

LinearRegression Class

• __init__(self, x, y): Initializes the model with input data x and target labels y. It also initializes the parameters m (slope) and b (intercept) to 0, and calculates the number of data points n.

• fit(self, epochs, lr): Trains the model using gradient descent. It updates the parameters m and b over a specified number of epochs with a given learning rate lr.

• predict(self, inp): Makes predictions on new input data inp using the trained model parameters.

License

MIT