Ajiboyefemifaith / Auto-mobile

Analysis using various machine learning models to predict Automobile prices

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Auto-mobile

Analysis using various machine learning models to predict Automobile prices.

Context

This dataset consist of data From 1985 Ward's Automotive Yearbook. Here are the sources 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

Content

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. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe. The third factor 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/speciality, etc…), and represents the average loss per car per year. Note: Several of the attributes in the database could be used as a "class" attribute

Attribute Information:

| Attribute | Attribute Range 1.symboling: -3, -2, -1, 0, 1, 2, 3 2.normalized-losses: continuous from 65 to 256 3.make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo 4.fuel-type: diesel, gas 5.aspiration: std, turbo 6.num-of-doors: four, two 7.body-style: hardtop, wagon, sedan, hatchback, convertible 8.drive-wheels: 4wd, fwd, rwd 9.engine-location: front, rear 10.wheel-base: continuous from 86.6 120.9 11.length: continuous from 141.1 to 208.1 12.width: continuous from 60.3 to 72.3 13.height: continuous from 47.8 to 59.8 14.curb-weight: continuous from 1488 to 4066 15.engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor 16.num-of-cylinders: eight, five, four, six, three, twelve, two 17.engine-size: continuous from 61 to 326 18.fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi 19.bore: continuous from 2.54 to 3.94 20.stroke: continuous from 2.07 to 4.17 21.compression-ratio: continuous from 7 to 23 22.horsepower: continuous from 48 to 288 23.peak-rpm: continuous from 4150 to 6600 24.city-mpg: continuous from 13 to 49 25.highway-mpg: continuous from 16 to 54 26.price: continuous from 5118 to 45400.

Acknowledgements

"Automobile Data Set" from the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data.

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Analysis using various machine learning models to predict Automobile prices


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