README.md

Getting Started

Prerequisites

• NumPy
• Matplotlib
• Scikit-learn

Installation

First you should install numpy, matplotlib and scikit-learn with pip, in cmd or in code cell if you use Google Colab/Jupyter Notebook

pip install numpy

pip install matplotlib

pip install scikit-learn

Then you can import this libraries

import numpy, matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

By importing the LinearRegression class from the sklearn.linear_model module, we can use it in our Python code to create linear regression models, fit them to data, make predictions, and perform other related tasks.

Usage

np.random.seed(0)

num_points = 100

np.random.seed(0) make data reproducibility, every time the code is run, the data will be the same (if you dont want't, you can delete this)

num_points = 100 number of data points

num_points = 100

x = np.random.rand(num_points) * 10 #independent variable (input values)
y = 2*X + 3 + np.random.randn(num_points) #dependent variable (output values)

We make predict with imaginary data, and so we should create them, but they must be interlinked with each other, they must based on a linear relationship, on this - 2*X + 3, for example

x = x.reshape(-1, 1)

In scikit-learn, the input features (independent variables) are expected to be in a two-dimensional array format, where each row represents a sample (data point) and each column represents a feature

model = LinearRegression()
model.fit(x, y)

Create LinearRegression() model and then perform linear regression

new_X = x + 10
new_y = model.predict(new_x)

Create a new data to test our model, and then make a prediction

Visualizations

After all we can look at our data

plt.scatter(x, y, label="data points")
plt.scatter(new_x, new_y, label="Future prediction")

plt.legend()

plt.legend() is used to add Legend (explanatory labels)

Congratulate yourself that you have create prediction model by using simple linear regression