In this project, we download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, Evaluate our model using test set, and finally use model to predict unknown value.

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.

Data Source : [Data Source] (http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)

Environment Set up :

• Importing Needed packages

• Understanding the Data

• Data Exploration

• Visualize Data

• Creating train and test dataset

###About linear regression?

Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the independed x in the dataset, and the depended y by the linear approximation.

1. Simple Regression Model
2. Non-linear regression
3. Multiple Regression Model

For each model we performed the following tasks :

• Train Data Distribution

• Modeling using sklearn package

• Plot Output

• Evaluation : Evaluate model with test data

• Plot Output