对 UCI 汽车数据集进行了探索性数据分析

# UCI_Automobile_Dataset_Exploratory_dataAnalysis In this project, I have done exploratory data analysis on the UCI Automobile dataset available at https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data This dataset consists of data From the 1985 Ward's Automotive Yearbook. Here are the sources 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037 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) This data set consists of three types of entities: I - The specification of an auto in terms of various characteristics II - Tts assigned an insurance risk rating. This corresponds to the degree to which the auto is riskier than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is riskier (or less), this symbol is adjusted by moving it up (or down) the scale. Actuaries call this process "symboling". III - Its normalized losses in use as compared to other cars. This is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc…), and represents the average loss per car per year. The analysis is divided into two parts: Data Wrangling Pre-processing data in python Dealing with missing values Data formatting Data normalization Binning Exploratory Data Analysis Descriptive statistics Groupby Analysis of variance Correlation Correlation stats Acknowledgment Dataset: UCI Machine Learning Repository Data link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data