This repository contains an implementation of multiple linear regression using Python.

Overview

Multiple linear regression is an extension of simple linear regression, where the relationship between a dependent variable and two or more independent variables is modeled. It assumes a linear relationship between the input variables (features) and the output variable (target), allowing for more complex modeling scenarios.

In this repository, we demonstrate how to perform multiple linear regression using Python. We utilize libraries such as NumPy, pandas, and scikit-learn to implement and visualize the regression model. Additionally, we provide a simple example along with explanations to help you understand how to apply multiple linear regression to your own datasets.

Requirements

To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:

• NumPy
• pandas
• scikit-learn
• matplotlib

You can install these libraries using pip:

pip install numpy pandas scikit-learn matplotlib

Work steps

Programming Quiz: Multiple Linear Regression In this quiz, you'll use the California housing dataset(opens in a new tab). The dataset consists of 8 features of 20,640 houses and the median home value in $100,000's. You'll fit a model on the 8 features to predict the value of the houses.

You'll need to complete each of the following steps:

1. Build a linear regression model

Create a regression model using scikit-learn's LinearRegression(opens in a new tab) and assign it to model. Fit the model to the data. 2. Predict using the model

Predict the value of sample_house.

Usage

1. Clone this repository to your local machine:

git clone https://github.com/BaraSedih11/multiple-linear-Regression.git

2. Navigate to the repository directory:

cd multiple-linear-Regression

3. Open and run the Jupyter Notebook multiple_linear_Regression.ipynb using Jupyter Notebook or JupyterLab.

4. Follow along with the code and comments in the notebook to understand how multiple linear regression is implemented using Python.

Acknowledgements

• scikit-learn: The scikit-learn library for machine learning in Python.
• NumPy: The NumPy library for numerical computing in Python.
• pandas: The pandas library for data manipulation and analysis in Python.
• matplotlib: The matplotlib library for data visualization in Python.