Mileage-Prediction
Predict Mileage based on Technical Specs of Automobile
Project Description:
The following project aims to predict mileage per gallon(mps) using various technical specifications (features) as input to the regression algorithms.
Database Description:
The data is technical spec of cars. This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute. The original dataset is available in the file "auto-mpg.data-original".
"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)
Number of Instances: 398
Number of Attributes: 9 including the class attribute
Attribute Information:
mpg: continuous cylinders: multi-valued discrete displacement: continuous horsepower: continuous weight: continuous acceleration: continuous model year: multi-valued discrete origin: multi-valued discrete car name: string (unique for each instance) Missing Attribute Values: horsepower has 6 missing values
Libraries Involved:
- pandas
- Numpy
- Seaborn
- Matplotlib
Steps Involved:
- Importing the libraries
- Loading the dataset
- Data Preprocessing
- Study Correlation
- Univariate Analysis
- Bivariate Analysis
- train and test data split
- Building the model
Machine Learning Steps Involved
- Linear Regression
- Polynomial Regression