https://github.com/anas436/multiple-linear-regression-with-python

https://github.com/anas436/multiple-linear-regression-with-python

jupyterlab linear-models matplotlib-pyplot numpy pandas pylab python3 scikit-learn sklearn

Last synced: 28 days ago
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• Host: GitHub
• URL: https://github.com/anas436/multiple-linear-regression-with-python
• Owner: Anas436
• Created: 2022-08-12T16:24:21.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-08-12T16:27:54.000Z (over 3 years ago)
• Last Synced: 2025-02-01T15:30:40.463Z (12 months ago)
• Topics: jupyterlab, linear-models, matplotlib-pyplot, numpy, pandas, pylab, python3, scikit-learn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 50.8 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Multiple-Linear-Regression-with-Python

## Objectives

After completing this lab you will be able to:

*   Use scikit-learn to implement Multiple Linear Regression

*   Create a model, train it, test it and use the model

Table of contents




    


        
Understanding the Data

        
Reading the Data in

        
Multiple Regression Model

        
Prediction

        
Practice

    








Understanding the Data


### `FuelConsumption.csv`:

We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)

*   **MODELYEAR** e.g. 2014

*   **MAKE** e.g. Acura

*   **MODEL** e.g. ILX

*   **VEHICLE CLASS** e.g. SUV

*   **ENGINE SIZE** e.g. 4.7

*   **CYLINDERS** e.g 6

*   **TRANSMISSION** e.g. A6

*   **FUELTYPE** e.g. z

*   **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9

*   **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9

*   **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2

*   **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0