Auto mpg linear regression python Sep 4, 2022 · Using simple linear regression, we will create a linear function with “highway-mpg” as the predictor variable and the “price” as the response variable. a) Use th lm() function to perform a simple linear regression with mpg as the response and horsepower as the predictor. The project explores data preprocessing, feature selection, and model building with a focus on learning core ML concepts. ) acceleration: Time to The Auto MPG dataset is a well-known dataset from the UCI Machine Learning Repository. Auto MPG Predictor – Linear Regression CLI Tool A Python CLI application that predicts a car's MPG (Miles Per Gallon) using multivariate linear regression. Auto MPG dataset We will take the example dataset of measuring fuel efficiency of cars. So I will use weight as the X to fit the linear regression model Use the lm () function to perform a multiple linear regression with mpg as the response and all other variables except name as the predictors. This dataset is a classic in machine learning, containing information The Auto-MPG dataset for regression analysis. We can see more details on Scikit-Learn ML Pipelines here. Contrast this with a classification problem, where we aim to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture). The monotonicity_indicator corrsponding to these features are set to -1, since the relationship is a monotonically decreasing one with the observation between the 'mpg' and other attributes indicate the relationship is not linear. This implies that,as any one of those variables increases,the mpg decreases. py Predict on testset python . , from the Auto MPG dataset. 6 environment called auto-mpg. Format: A data frame with 392 observations on the following 9 variables. The dependant variable MPG is monotonically decreasing with respect to features Weigh, Displacement, and Horsepower. Ridge Regression: Implemented to regularize the model, addressing multicollinearity and improving model May 7, 2024 · Today, we are going to explore a fascinating concept in statistics called Bayesian linear regression. Implementation of Types of Linear Regression We will discuss three types of linear regression: Simple linear regression: This involves predicting a dependent variable based on a single independent variable. Scikit-Learn pipelines can have multiple stages which will be run in specific order. This program demonstrates the working of Linear Regression using the Boston Housing Dataset and Polynomial Regression using the Auto MPG Dataset. APPLIED: The Auto Dataset (Multiple Linear Regression) (a) Scatterplot Matrix (b) Correlation Matrix (c) Predicting mpg with all variables (except name) (d) Diagnostic Plots (e) Interaction Terms (f) Variable Transformations (a) Scatterplot Matrix (b) Correlation Matrix (c) Predicting mpg with all variables (except name) (d) Diagnostic Plots **This question involves the use of simple linear regression on the `Auto` data set. Note that linear models such as linear regression would not be able to capture such a non-linear trend. Use the summary() function to print the results. ics. The target (y) is defined as the miles per gallon (mpg) for 392 automobiles (6 rows containing "NaN"s have been removed. We will use the Auto MPG dataset which contains features like engine displacement, horsepower, weight and other car specifications to predict miles per gallon (MPG Uses Python requests to obtain data from the Auto. api as smfimport seaborn as snsimport matplotlib. Use the summary () function to print the results. Clean data, and extract brand from car names . Solution Explained in Simple Steps: Transforming Data and Comparing ML Regression Models Performance (by using R) We will prepare data for machine learning and compare the performance of different machine learning regression models on the Auto-MPG data set. Auto-mpg with one feature A 2D scatter plot of mpg versus displ shows that the mpg-consumption decreases nonlinearly with displacement. Jul 23, 2025 · Linear regression is a machine learning technique used for predicting continuous outcome variable based on one or more input variables. The arguments to ModelSpec() can be quite general, but in this case a list of column names suffice. ) and their mileage. mpg dataset hosted on https://archive. (a) Use the lm() function to perform a simple linear regression with mpg as the response and horsepower as the predictor. 9. Scripts must be run in order each depends on output of the last. Explore and run machine learning code with Kaggle Notebooks | Using data from Auto-mpg dataset Jul 23, 2025 · Predicting fuel efficiency is a important task in automotive design and environmental sustainability. The scikit-learn module in python is basically used to perform 3. Contribute to am19s300/python-linear-regression-model development by creating an account on GitHub. X_train_poly = poly_features. Question p121 This question involves the use of simple linear regression on the Auto data set. mpg: miles per gallon cylinders: Number of cylinders between 4 and 8 displacement: Engine displacement (cu. Four different models can be created: simple linear regression, multiple linear regression, higher exponents model, and interaction terms model. It examines the effectiveness of algorithms such as Decision Tree Regressors, Random Forests, Support Vector Regressors, and neural network-based models like LSTM and GRU. For example: SOLUTION: First, I have installed the package ISLR to call the Auto dataset,followed by first few rows of the dataset is presented. The file contains information about various cars made between 1970 and 1982 . Utilizing packages in Python (pandas, matplotlib. ** I (UCI Machine Learning Repository: Auto MPG Data Set) (Please use python and share codes) Evaluate a linear regression for auto-mpg data using 10-Fold CV. The model was able to predict the fuel efficiency of cars with reasonable This project analyzes the Auto MPG dataset using Linear and Ridge Regression. Build an input pipeline Jul 6, 1993 · The original dataset is available in the file "auto-mpg. We will be performing Multiple linear Regression on the Auto Data sets in which we will consider Mpg as a Dependent variable and all other Quantitative variables present in the Auto Data as independent variables. Linear regression analysis of the Boston Housing Dataset using Python and scikit-learn. The content is designed for educational purposes, suitable for workshops, tutorials, and as a reference for those interested in practical machine Jul 21, 2023 · Armed with a comprehensive understanding of the dataset, I proceeded to build a powerful Linear Regression model using scikit-learn. Table of Contents You can skip to a specific section of this Python machine learning tutorial using the table Three multiple linear regression models were developed to understand the relationship between the miles per gallon (mpg) of cars and their predictor variables (cylinders, weight, and horsepower). I have also created a simple Flask web application for model deployment. def create_polynomial_regression_model (degree): "Creates a polynomial regression model for the given degree" poly_features = PolynomialFeatures (degree=degree) # transforms the existing features to higher degree features. Print the results and comment on the output. We will leverage the popular Auto MPG dataset for the demonstration. Jan 20, 2021 · Tensors are nothing but multidimensional array or a list. " Oct 20, 2023 · Implementing Linear Regression from Scratch with Python Starting our adventure in machine learning, we all begin by mastering the basics of linear regression — the foundational technique for … Jul 25, 2023 · By building a predictive model based on the Auto MPG dataset, we can estimate a vehicle's fuel efficiency accurately. To get hands-on linear regression we will take an original dataset Feb 10, 2025 · Develop a program to demonstrate the working of Linear Regression and Polynomial Regression. "The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes. ) Specify whether there is a header or not Name different columns Handle missing values (ex. It helps you identify relationships, test hypotheses, and make predictions with a clear view of how different factors influence outcomes. Oct 20, 2023 · Implementing Linear Regression from Scratch with Python Starting our adventure in machine learning, we all begin by mastering the basics of linear regression — the foundational technique for … Jul 25, 2023 · By building a predictive model based on the Auto MPG dataset, we can estimate a vehicle's fuel efficiency accurately. Develop a multiple linear regression model that predicts car price based on engine size, year, mileage, and mpg. Autoregression Model An autoregression model is a linear regression model that uses lagged variables as input variables. The goal is to predict the miles per gallon (mpg) of vehicles based on various features such as engine size, weight, and model year. Use the lm () function to perform a simple linear regression with mpg as the response and horsepower as the predictor. (c) Use the lm() function to perform a multiple linear regression with mpg as the response and all other variables except name as the predictors. /prep_data. sh Plot pairwise feature comparison gnuplot . /linear_regression/ Train linear regression python . csv Auto MPG - Análisis de Regresión Lineal Este proyecto usa Python para realizar un análisis de regresión lineal simple y múltiple en el conjunto de datos Auto MPG de UC Irvine Machine Learning Repository. /predict_mpg. 1 Simple Linear Regression import pandas as pdimport numpy as npimport statsmodels. Learn how to predict car fuel efficiency (MPG) using Multiple Linear Regression in Python with the popular Auto MPG dataset from UCI. The Auto MPG Dataset is a regression dataset [1] with 7 features: Cylinders Displacement Horsepower Weight Acceleration Model Year Origin. pyplot as plt Rafael de Souza Toledo September 24, 2016 Applied Exercises Q8. We consider a fit here with the two variables lstat Nov 10, 2023 · Linear regression is a fundamental machine learning algorithm that helps in understanding the relationship between independent and dependent variables. In this video, we create a regression model for predicting MPG for the vehicles for the autompg dataset from kaggle. It uses the Auto MPG dataset which contains fuel efficiency data for late-1970s and early 1980s automobiles. Performed Multi Linear Regression on Auto-mpg dataset found on Kaggle to predict, MPG – Miles Per Gallon of automobiles with the given features. uci. We’ll explore a relationship between a different feature, horsepower, and mpg. This tutorial will teach you how to create, train, and test your first linear regression machine learning model in Python using the scikit-learn library. After reading the data, we will convert it to numpy for all numerical processing including running machine learning algorithms. Greg Foletta 8) Auto data set a) Use the lm () function to perform a simple linear regression with mpg as the response and horsepower as the predictor. Oct 3, 2023 · Multiple Linear Regression Model analysis Hypothesis: The car features (Cylinders, Displacement, Horsepower, Weight, Acceleration) affects its fuel efficiency (MPG). data and understand the accuracy of a Linear Regression Model by obtaining the Root Mean Square Error. In this blog, we have compiled a In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. csv. fit_transform (X_train) # fit the transformed features to Linear Regression Applied Exercise 1 ¶ This question involves the use of simple linear regression on the Auto data set. The outputs include Sep 28, 2019 · We can see that there is a relationship between the mpg variable and the other variables and this satisfies the first assumption of Linear regression. However the plots also indicate the linearity quite a bit of information/pattern. The study aims to enhance fuel efficiency prediction by analyzing factors like engine Linear regression with one variable Begin with a single-variable linear regression to predict 'MPG' from 'Horsepower'. " Auto-MPG Data Analysis & MPG Prediction: Explored car attributes' impact on fuel efficiency using auto-mpg dataset. com for the software and data and links to related videos First step in regression analysis: obtain and prepare the data For this exercise: the source data is the famous “auto‐mpg” data set In this post we will see how we can use Scikit-Learn pipelines to transform the data and train ML models. Linear and Ridge regression analysis on the Auto MPG dataset using scikit-learn. It is widely used in various fields for predicting numerical outcomes based on one or more input features. edu/ml/machine-learning-databases/auto-mpg/auto-mpg. In this article we will build a fuel efficiency prediction model using TensorFlow one of the most popular deep learning libraries. The Boston Housing Dataset is used to predict house prices based on features like crime rate and number of rooms It is A Mchine Learning College Project Which Was Done By Using Python (For Data Processing) Jupyter Notebook,VS Code (For Development) - Auto-Miles-Per-Gallon/ (auto_mpg)_linear_regression. pyplot, seaborn, skikit-learn) to present descriptive statistics, various plots and a pairplot, a predictive linear regression model. I implemented this regression model in Python programming language using Scikit-learn,numpy,seaborn,matplotlib and model accuracy is based on cross validation result. About Use the Auto MPG dataset from UCI Machine Learning Repository to predict the fuel efficiency of a vehicle using a basic regression with TensorFlow. Sep 28, 2019 · We can see that there is a relationship between the mpg variable and the other variables and this satisfies the first assumption of Linear regression. By accurately predicting the MPG, we can provide valuable insights for manufacturers, consumers, and policymakers, enabling them to make informed This project demonstrates the implementation of Linear Regression to predict the Miles Per Gallon (MPG) of vehicles using the Auto MPG dataset. The project covers data preprocessing, model training, evaluation, and visualization, providing a comprehensive May 11, 2020 · Once we regressed the log of mpg on the predictors, the residuals are no longer displaying the heteroskedasticity we saw before, while there is still no apparent non-linear shape and they are still approximately normal. /pairwise_comparison. data data file and save it to the local directory where you would run the code on this page. Multiple Linear Regression: Extended the model to include more features to capture complex interactions. This data also ships with the scikit-learn library. For example:. inches) horsepower: Engine horsepower weight: Vehicle weight (lbs. keras. " Data Tasks Read in file Different types of separators (',',' ', '\t', '\s', etc. This question involves the use of simple linear regression on the Auto data set. In this article, we will demonstrate an example for both the Simple Linear Regression and the Multiple Linear Regression using scikit-learn. Built regression model to predict MPG based on horsepower, acceleration, model year, origin, & weight. Explore and run machine learning code with Kaggle Notebooks | Using data from Auto-mpg dataset The document outlines multiple programs demonstrating various machine learning techniques including Linear Regression, Polynomial Regression, Decision Trees, Naive Bayes Classifier, and K-Means Clustering. Oct 28, 2022 · Convert the dataframes to NumPy arrays and we can visualize the relationship between cars’ weight and mpg. Leveraging Python’s data science stack, the analysis aims to explain how vehicle specifications affect MPG and visualize the trends and Análisis de regresión linear simple y múltiple para predecir el consumo de combustible de vehículos - alvarezayelen11/Python_LinearRegression_Auto_MPG Auto-mpg regression case with the QLattice alvarezayelen11 / Python_LinearRegression_Auto_MPG Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Projects Security Insights 1. (a) Produce a scatterplot matrix which includes all of the variables in the data set. Load the dataset 'Auto. In other words, cars become more fuel efficient every year by almost 1 mpg / year. Training a model with tf. Firstly, our analysis showed that the two predictor variables - horsepower, weight - are Polynomial Regression on Boston Housing Dataset In this notebook we do a comparative study of Linear Regression and Polynomial Regression accuracy on the Boston Housing Dataset This data was originally a part of UCI Machine Learning Repository and has been removed now. X = df[["highway-mpg"]] About This project predicts the fuel efficiency (MPG - Miles Per Gallon) of cars using machine learning techniques in Python. Linear Regression with TensorFlow # In a regression problem, the aim is to predict the output of a continuous value, like a price or a probability. Comment on the output. Is there a please ues the python to solve it This question involves the use of simple linear regression on the Auto data set. Later, we will look at polynomial regression and linear regression with more than one feature using Auto MPG dataset. The aim behind a regression problem is to predict the output of a continuous or discrete variable, such as a price, probability, whether it would rain or not and so on. It explores performance trade-offs between these models and compares them with Lasso conceptually. The data is shuffled 10 times with different seeds and split into 70% training and 30% testing. We could calculate the linear regression model manually using the LinearRegession class in scikit-learn and manually specify the lag input variables to use. 1. Mileage per gallon performances of various cars This creates a new Python 3. It assumes a linear relationship between the input variables and the target variable which make it simple and easy for beginners. The Mileage per Gallon (MPG) Prediction project aims to develop a predictive model using linear regression to estimate the fuel efficiency of vehicles. 8(a) Use the lm() function to perform a simple linear regression with mpg as the response and horsepower as the predictor. Let's see how you can train a decision tree with scikit-learn to solve this regression problem. Conducted data cleaning, visualization, & statistical analysis. We applied multiple regression models to predict fuel efficiency: Linear Regression: Started with a simple model focusing on 'weight' as it showed a strong inverse correlation with MPG. Includes model evaluation, feature handling, and a comparison with Lasso regression. Trained on the Auto MPG dataset, the model uses features like displacement, horsepower, weight, acceleration, cylinders, and model year. /train_mpg. Regression analysis is a fundamental technique in machine learning used to model relationships between variables and make predictions. py To fit regression models using the Auto MPG Dataset, you can use R, Python, or Matlab. These stages can be either Transformers or Estimators. May 23, 2017 · In this post, we’ll be exploring Linear Regression using scikit-learn in python. Several assumptions of classsical linear regression seemed to be violated including the assumption Heteroscedasticity Split Data In [74]: Regression task for Auto-MPG dataset using different regression models and evaluating their performance along with feature selection - arjun-majumdar/auto_mpg_dataset The video discusses in TensorFlow: Regression to predict vehicle fuel efficiency using Auto MPG dataset. MPG is a crucial metric for evaluating the performance and environmental impact of automobiles. Parameters are optimized via gradient descent for accurate and explainable predictions. For example Is there a relationship between the predictor and the response? How strong is the relationship Oct 15, 2020 · In statistics, linear regression is a linear approach to modeling the relationship between a scalar response and one or more explanatory variables. You can change the name of the environment but it is recommended to have the same name for both the project and the virtual environment. - msolanky/au Load the dataset 'Auto. Jul 11, 2024 · CO2Emission Prediction Model Tutorial — Multiple Linear Regression with Python We will create a CO2Emission Prediction Model that will predict the carbon dioxide emissions of a car based on its … Here we use the bootstrap approach in order to assess the variability of the estimates for $\beta_0$ and $\beta_1$, the intercept and slope terms for the linear regression model that uses horsepower to predict mpg in the Auto data set. There are two steps in your single-variable linear regression model: Oct 8, 2022 · Perform Simple Linear Regression on Auto Dataset We have to perform simple linear regression On Auto data sets with mpg as the dependent variable and horsepower as an independent variable. Use the summarize () function to print the results. Dec 14, 2023 · Fuel consumption cycle of a city is explored by creating visualizations and building linear regression model to understand relationship between MPG and other factors like horsepower, weight Question: Vehicle MPGs Using Linear Regression Load the data from the file auto-mpg. In this project, I have compared 11 forms of regressions on Auto-MPG data set. Sequential model, which represents a sequence of steps. There is a strong negative correlation between the displacement,horsepower,weight,and cylinders. (a) Use the sm OLS () function to perform a simple linear regression with mpg as the response and horsepower as the predictor. 7507727, suggests that for every one year, mpg increases by the coefficient. Introduction to Linear Regression in Machine Learning Linear Regression is a machine learning algorithm which uses a dependent variable to predict future Multiple Linear Regression # In order to fit a multiple linear regression model using least squares, we again use the ModelSpec() transform to construct the required model matrix and response. csv'. more Nov 12, 2025 · Data and tech trends come and go, but linear regression has remained one of the most reliable tools in a data analyst’s toolbox. The Boston Housing Dataset is used to predict house prices based on features like crime rate and number of rooms Oct 5, 2018 · Linear Regression on Boston Housing Dataset In my previous blog, I covered the basics of linear regression and gradient descent. Mean squared This question involves the use of multiple linear regression on the Auto data set. Your mission is to figure out how the weight of a car affects its fuel efficiency, which is measured in miles per gallon (mpg). It contains information about cars and is commonly used for regression tasks, particularly for predicting the miles per gallon (MPG) of cars based on other features. Imagine you’re a detective, and you have a set of clues about how different cars consume fuel. In the last lesson of this course, you learned about the history and theory behind a linear regression machine learning algorithm. An exercise on Linear and Polynomial Regression. “These are all the files I have accessed during the course I completed at GreatLearning Academy. csv, Car_prices_train. To practice and learn about linear regression, it is essential to have access to good quality datasets. It contains fuel efficiency of 1970s and 1980s automobiles. Contrast this with a classification problem, where the aim is to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture). data-original". A polynomial regression model is required to capture non-linear relationships between the features and MPG. Datasets to be used: Car_features_train. Contribute to pedroafleite/auto-mpg development by creating an account on GitHub. Comment on any problems you see with the fit. formula. 回帰問題では、価格や確率といった連続的な値の出力を予測することが目的となります。これは、分類問題の目的が、（たとえば、写真にリンゴが写っているかオレンジが写っているかといった）離散的なラベルを予測することであるのとは対照的です。 このノートブックでは、古典的な Auto The regression coefficient for year, 0. **This question involves the use of simple linear regression on the `Auto` data set. keras typically starts by defining the model architecture. Jul 23, 2025 · Linear regression is a statistical method of modeling relationships between a dependent variable with a given set of independent variables. Explore and run machine learning code with Kaggle Notebooks | Using data from Auto-mpg dataset This project explores the fundamentals of machine learning by building regression models to predict automobile MPG using the UCI Auto MPG dataset. Linear Regression in Python: 8. Jul 6, 1993 · The original dataset is available in the file "auto-mpg. The dataset we use is called the ‘Auto MPG’ dataset. This tutorial uses the classic Auto Nov 9, 2019 · In this article,I’m going to walk you through how to perform a multiple linear regression in python using the scikit-learn module. The model development process includes thorough data cleaning, data visualization, and data scaling techniques to ensure accurate and reliable predictions. Explore and run machine learning code with Kaggle Notebooks | Using data from Auto-mpg dataset Nov 20, 2020 · In machine learning, linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. plot All python scripts are in . org This notebook performs a complete linear regression analysis on the Auto MPG dataset. Introduction In this notebook, I build Regression model to study the relationship between miles per gallon of a car with different continous and discrete attributes. Whether you’re working in finance, marketing, manufacturing, or real estate, few methods Problem: The task is to predict the fuel efficiency of cars (measured as Miles Per Gallon, MPG) using various features like horsepower, weight, etc. Each program utilizes specific datasets such as the Boston Housing, Auto MPG, Breast Cancer, and Olivetti Face datasets to train models and evaluate their performance. The Jan 16, 2023 · In this tutorial, we will define linear regression, identify the tools we need to use to implement it, and explore how to create an actual prediction model in Python including the code details. ?, NA, etc. Use Boston Housing Dataset for Linear Regression and Auto MPG Dataset (for vehicle fuel efficiency prediction) for Polynomial Regression. Check out my recently launched course on Linear regression analysis of auto-mpg data set Data preparation Visit regressit. The dataset contains features such as weight, horsepower, cylinders, and model year, which are used to build and evaluate the model. We begin by loading the packages. This notebook uses the classic Auto MPG Dataset and builds a model to predict the Explore and run machine learning code with Kaggle Notebooks | Using data from Auto-mpg dataset Auto-mpg regression case with the QLattice Sep 26, 2019 · Building a multiple linear regression model and fine tuning the model Findings: The multivariate linear regression analysis of the Auto data set revealed some interesting findings about the relationship between different car characteristics and their fuel efficiency, as measured by miles per gallon (MPG). I have used a machine learning pipeline to build a linear regression model to predict the mileage of an automobile given its various characteristics. Analysis-and-Prediction-of-Auto-MPG Linear regression model using auto-MPG dataset to see fuel efficiency of car tp predict mpg using Python. Jan 9, 2023 · In this tutorial, we'll define linear regression, identify the tools to implement it, and explore how to create a prediction model. ” - supremkc05/DataScienceWithPython_ Jun 11, 2025 · This project explores the relationships between a car’s weight, manufacturing origin, and its fuel efficiency (MPG) using multivariate linear regression. May 23, 2023 · A simple machine learning project to predict car fuel efficiency (MPG) using Python. Use a tf. We also discuss underfitting and overfitting and feature encoding. In this article, I will introduce you to linear regression with the Python programming language. ipynb at main · kolli555/Auto-Miles-Per-Gallon Mileage per gallon performances of various cars This creates a new Python 3. The data for this demo comes from a See full list on tensorflow. Let's dive into the process of utilizing Tensorflow in Python to make accurate fuel efficiency predictions. This video About This repository contains a comprehensive linear regression model for predicting the fuel efficiency (miles per gallon - MPG) of automobiles using the Auto-MPG dataset. The average weight and the average mpg can also be explored using the following codes. This model would be the backbone of the MPG predictor. Auto MPG dataset To predict fuel efficiency accurately, we need a reliable dataset. You can see that weight and displacement look to have a linear corrleation with MPG but weight looks more linear than displacement. The dataset: Description: Gas mileage, horsepower, and other information for 392 vehicles. Normalization is required before training. Jul 11, 2024 · In this project, we built a Car Mileage Predictor Model using the “Auto MPG” dataset and Linear Regression. Aug 15, 2024 · This quickstart tutorial demonstrates how you can use the TensorFlow Core low-level APIs to build and train a multiple linear regression model that predicts fuel efficiency. You will follow the typical stages of a machine learning process: Load the dataset. Regression is a type of supervised machine learning algorithm used to predict a continuous numerical outcome variable based on one or more predictor variables. ) remove these examples? set these values to an arbitrary value like 0 or NA replace missing values with the mean Select columns for the regression tasks Select columns I want to use as predictors Select which Jan 1, 2024 · This study explores the application of various machine learning models to predict vehicle fuel consumption using the Auto MPG dataset. It utilizes the Auto MPG dataset and applies Linear Regression to model the relationship between various car features (like horsepower, weight, displacement, etc. We will use the physical attributes of a car to predict its miles per gallon (mpg). Download the auto-mpg. Use the plot() function to produce diagnostic plots of the linear regression fit. The original dataset is available in the file "auto-mpg. zhehq axupvslx fuxqeo btfiz zpg gffkco ruhgwpfc kgba zwgpw nxmlw qybqi jzjwfit ywz jgxm cgr