Used car dataset csv

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The aim of this project is to clean the Ebay car sales data and analyze the included used car listings using gas gas ec 250 programming language Python.

We will be using pandas and numpy module for our project. We will be working on a dataset of used cars from eBay Kleinanzeigen, a classifieds section of the German eBay website. Dataset file: ans phone ul40 troubleshooting. The dataset was originally scraped and uploaded to Kaggle.

The version of the dataset we are working with is a sample of 50, data points that was prepared by Dataquest including simulating a less-cleaned version of the data. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Data cleaning and exploratory analysis of Ebay car sales data. Jupyter Notebook. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Histograms representing Binned prices in Low, Medium, High. Boxplots representing effect of wheel frive with prices. Scatter plot for Prices over Engine size. Pivot table categorizing wheel drive and body style with prices. HeatMap with wheel drive in y axis and body style in x axis.

Positive Linear Relationship between engine size and price. Negetive Linear Relationship between highway-mpg and price. Weak Correlation between peak-rpm and price. Simple Linear Regression plot. Multiple Linear Regression plot. The distribution plot of Linear Regression and Multiple Regression technique shows how the model predicts the prices of automobiles based on "horsepower", "curb-weight", "engine-size" and "highway-mpg". Comparing these three models, we conclude that the MLR model is the best model to be able to predict price from our dataset.

This result makes sense, since we have 27 variables in total, and we know that more than one of those variables are potential predictors of the final car price. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price. Jupyter Notebook. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit ce51 Nov 5, Scatter plot for Prices over Engine size Pivot table categorizing wheel drive and body style with prices. Positive Linear Relationship between engine size and price Negetive Linear Relationship between highway-mpg and price Weak Correlation between peak-rpm and price Simple Linear Regression plot Multiple Linear Regression plot Conclusion The distribution plot of Linear Regression and Multiple Regression technique shows how the model predicts the prices of automobiles based on "horsepower", "curb-weight", "engine-size" and "highway-mpg" Comparing these three models, we conclude that the MLR model is the best model to be able to predict price from our dataset.

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Decision Tree Analysis with Credit Data in R | Part 1

You signed out in another tab or window.Abstract : Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods. Creator: Marko Bohanec Donors: 1. Marko Bohanec marko.

Blaz Zupan blaz. Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX, M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1 1pp. The model evaluates cars according to the following concept structure: CAR car acceptability. PRICE overall price. TECH technical characteristics. Every concept is in the original model related to its lower level descendants by a set of examples for these examples sets see [Web Link].

The Car Evaluation Database contains examples with the structural information removed, i. Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods.

Class Values: unacc, acc, good, vgood Attributes: buying: vhigh, high, med, low. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. Zupan, M. Bohanec, I. Bratko, J.This will be the review of each applicant and review the percentage of the applications that were approved but should have been rejected.

used car dataset csv

In addition, the method that will be used in reviewing credit defaults is the C5. Ross Quinlan. This algorithm is used to produce decision trees. The C5. Since we will be using the used credit dataset, you will need to download this dataset. Once the data is imported, you can run a series of commands to see sample data of the credit data.

The str command displays the internal structure of an R object. This function is an alternative to summary. When using the str function, only one line for each basic structure will be displayed. The summary function is a basic function that issued to produce the result summary of various model functions.

In order to have an idea of what data is being processed, we can use the head function to print the first 6 lines of data for the below:.

In addition, we can use the table function to print the total yes or no defaults within the credit data.

used car dataset csv

For the below, we will create a credit default plot for the above table. First, we must convert a column into a factor column by using the as. A decision tree can continuously grow because of the splitting features and how the data is divided.

Just like if you had an oversized tree in your yard, pruning would be a good idea. In this analogy, pruning is a good idea as well to reduce the size. Pruning can be pre-pruning or post-pruning. Pre-pruning is used at a certain number of decision or decision nodes. In my opinion, pre-pruning a decision tree before letting the tree grow to an optimal size could miss important patterns. The purpose of a decision tree is to learn the data in depth and pre-pruning would decrease those chances.

In my opinion, I would rather post-prune because it will allow the decision tree to maximize the depth of the decision tree. This will allow the algorithm to have all of the important data. Below shows the false positives and negatives of incorrect approval for the lender.

Below you will see a set. This function is very useful for creating simulations or random objects that can be reproduced. Predicted no, actual no — 59 Predicted yes, actual no — 8 Predicted no, actual yes — 19 Predicted yes, actual yes — Based on the decision tree, there are applicants less than 25 years-old but account about 42 percent of defaults. However, there are applicants over 25 years-old and account for 25 percent of this age category.

This breaks down the below 74 applicants defaulting under age 25 and about defaulting older than This age gap shows major underlying issues of defaulting because of the age groups. This could be for a variety of reasons such as not having financial responsibility or stable employment. As one can see below, node 2 has an employment duration of less than a year and unknown shows a total of applicants with a default percentage of 0.

On node 3, there are a total of applicants.Schlimmer Jeffrey. Schlimmer ' ' a. This data set consists of three types of entities: a the specification of an auto in terms of various characteristics, b its assigned insurance risk rating, c its normalized losses in use as compared to other cars.

The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price.

used car dataset csv

Then, if it is more risky or lessthis symbol is adjusted by moving it up or down the scale. Actuarians call this process "symboling". The third factor is the relative average loss payment per insured vehicle year. Note: Several of the attributes in the database could be used as a "class" attribute. Attribute: Attribute Range 1.

Kibler, D. Instance-based prediction of real-valued attributes. Computational Intelligence, Vol 5, Geraldine E. Rosario and Elke A. Rundensteiner and David C. Brown and Matthew O. PR Yongge Wang. Please refer to the Machine Learning Repository's citation policy.

Center for Machine Learning and Intelligent Systems.Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1 Importing Datasets 2 Cleaning the Data 3 Data frame manipulation 4 Summarizing the Data 5 Building machine learning Regression models 6 Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments.

It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets.

Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.

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Data on Cars used for Testing Fuel Economy

Loupe Copy. Data Analysis with Python. Enroll for Free. From the lesson. The Problem Understanding the Data Python Packages for Data Science Importing and Exporting Data in Python Getting Started Analyzing Data in Python Accessing Databases with Python Taught By.

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Loading in your own data - Deep Learning basics with Python, TensorFlow and Keras p.2

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Data Analysis or sometimes referred to as exploratory data analysis EDA is one of the core components of data science. It is also the part on which data scientists, data engineers and data analysts spend their majority of the time which makes it extremely important in the field of data science.

This repository demonstartes some common exploratory data analysis methods and techniques using python. For purpose of illustration the used car database dataset has been taken from kaggle since it is one of the ideal dataset for performing EDA and taking a step towards the most amazing and interesting field of data science.

Good luck with your EDA on the used car database dataset. Boxplot of prices of vehicles based on the type of vehicles after cleaning the dataset. Based on the vehicle type how the prices vary is depictable from the boxplot. Barplot of average price of the vehicles for sale based of the type of the vehicle as well as based on the gearbox of the vehicle. Barplot of average power of the vehicle based of the fueltype of the vehicle and also on the type of the vehicle.

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Understanding the Data

Jupyter Notebook Shell Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Used Cars Dataset Analysis with R

Latest commit. Latest commit c Jan 2, DataSet Overview The dataset is taken from kaggle and contains details of the used cars in germany which are on sale on ebay. The dataset is not clean and hence a lot of data cleaning is carried out. For e. Also vehicles whose registration year was greater than and less than were removed from the dataset as this data is inconsistense and would yield incorrect results.

Folders from Analysis1 - Analysis5 contain the iPython Notebookpython scripts along with the Plots for that analysis. Folder for shell scripts which automate the creation of files structures and splitting the data as mentioned above. Datapreparation folder contains the Datapreparation iPython Script for cleaning of data. CleanData folder contains the clean dataset and subsets of data as per the file structure.

RawData folder which contains the raw dataset. Output before the cleaning the data is shown below in order to highlight the importance of cleaning this dataset.

Histogram and KDE before performing data cleaning.



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Es ist schade, dass ich mich jetzt nicht aussprechen kann - es gibt keine freie Zeit. Ich werde befreit werden - unbedingt werde ich die Meinung aussprechen.

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