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Data Science Dojo <br/>
Copyright (c) 2016 - 2019
---
**Level** Intermediate <br/>
**Recommended Use:** Regression Models<br/>
**Domain:** Automobiles<br/>
## Auto MPG Data Set
### Can you predict the fuel-efficieny of a car?
---
![](tim-mossholder-680992-unsplash.jpg)
---
This *intermediate* level data set has 398 rows and 9 columns and provides mileage, horsepower, model year, and other technical specifications for cars. This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **regression modelling techniques**. Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
---
### Data Dictionary
**Column Position**|**Attribute Name**|**Description** |**Examples** |**Attribute Type** |**Nulls Ratio**
|-------------------|------------------|------------------------------------------------------------|---------------------------|---------------------|----------------|
| #1 | mpg | fuel efficiency measured in miles per gallon (mpg) | 9.0, 13.0, 41.5 | quantitative | 0% |
| #2 | cylinders | number of cylinders in the engine | 3, 4, 8 | qualitative | 0% |
| #3 | displacement | engine displacement (in cubic inches) | 68.0, 112.0, 455.0 | quantitative | 0% |
| #4 | horsepower | engine horsepower | 46.0, 70.0, 230.0 | quantitative | 2% |
| #5 | weight | vehicle weight (in pounds) | 1613, 3615, 5140 | quantitative | 0% |
| #6 | acceleration | time to accelerate from O to 60 mph (in seconds) | 8.00, 15.50, 24.80 | quantitative | 0% |
| #7 | model year | model year | 73, 79, 82 | qualitative | 0% |
| #8 | origin | origin of car (1: American, 2: European, 3: Japanese) | 1, 2, 3 | qualitative | 0% |
| #9 | car name | car name | audi fox, subaru | qualitative | 0% |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Auto MPG Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/auto+mpg). The UCI page mentions [StatLib (Carnegie Mellon University)](http://lib.stat.cmu.edu/datasets/) as the original source of the data set.
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Auto MPG Data Set/auto-mpg.data deleted 100644 → 0
18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu"
15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320"
18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite"
16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst"
17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino"
15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500"
14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala"
14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii"
14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina"
15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl"
15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se"
14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340"
15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo"
14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)"
24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii"
22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster"
18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet"
21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick"
27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510"
26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan"
25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504"
24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls"
25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e"
26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002"
21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin"
10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250"
10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20"
11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200"
9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d"
27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510"
28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300"
25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona"
25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto"
19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin"
16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom"
17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu"
19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500"
18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador"
14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala"
14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham"
14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500"
14.0 8 318.0 150.0 4096. 13.0 71 1 "plymouth fury iii"
12.0 8 383.0 180.0 4955. 11.5 71 1 "dodge monaco (sw)"
13.0 8 400.0 170.0 4746. 12.0 71 1 "ford country squire (sw)"
13.0 8 400.0 175.0 5140. 12.0 71 1 "pontiac safari (sw)"
18.0 6 258.0 110.0 2962. 13.5 71 1 "amc hornet sportabout (sw)"
22.0 4 140.0 72.00 2408. 19.0 71 1 "chevrolet vega (sw)"
19.0 6 250.0 100.0 3282. 15.0 71 1 "pontiac firebird"
18.0 6 250.0 88.00 3139. 14.5 71 1 "ford mustang"
23.0 4 122.0 86.00 2220. 14.0 71 1 "mercury capri 2000"
28.0 4 116.0 90.00 2123. 14.0 71 2 "opel 1900"
30.0 4 79.00 70.00 2074. 19.5 71 2 "peugeot 304"
30.0 4 88.00 76.00 2065. 14.5 71 2 "fiat 124b"
31.0 4 71.00 65.00 1773. 19.0 71 3 "toyota corolla 1200"
35.0 4 72.00 69.00 1613. 18.0 71 3 "datsun 1200"
27.0 4 97.00 60.00 1834. 19.0 71 2 "volkswagen model 111"
26.0 4 91.00 70.00 1955. 20.5 71 1 "plymouth cricket"
24.0 4 113.0 95.00 2278. 15.5 72 3 "toyota corona hardtop"
25.0 4 97.50 80.00 2126. 17.0 72 1 "dodge colt hardtop"
23.0 4 97.00 54.00 2254. 23.5 72 2 "volkswagen type 3"
20.0 4 140.0 90.00 2408. 19.5 72 1 "chevrolet vega"
21.0 4 122.0 86.00 2226. 16.5 72 1 "ford pinto runabout"
13.0 8 350.0 165.0 4274. 12.0 72 1 "chevrolet impala"
14.0 8 400.0 175.0 4385. 12.0 72 1 "pontiac catalina"
15.0 8 318.0 150.0 4135. 13.5 72 1 "plymouth fury iii"
14.0 8 351.0 153.0 4129. 13.0 72 1 "ford galaxie 500"
17.0 8 304.0 150.0 3672. 11.5 72 1 "amc ambassador sst"
11.0 8 429.0 208.0 4633. 11.0 72 1 "mercury marquis"
13.0 8 350.0 155.0 4502. 13.5 72 1 "buick lesabre custom"
12.0 8 350.0 160.0 4456. 13.5 72 1 "oldsmobile delta 88 royale"
13.0 8 400.0 190.0 4422. 12.5 72 1 "chrysler newport royal"
19.0 3 70.00 97.00 2330. 13.5 72 3 "mazda rx2 coupe"
15.0 8 304.0 150.0 3892. 12.5 72 1 "amc matador (sw)"
13.0 8 307.0 130.0 4098. 14.0 72 1 "chevrolet chevelle concours (sw)"
13.0 8 302.0 140.0 4294. 16.0 72 1 "ford gran torino (sw)"
14.0 8 318.0 150.0 4077. 14.0 72 1 "plymouth satellite custom (sw)"
18.0 4 121.0 112.0 2933. 14.5 72 2 "volvo 145e (sw)"
22.0 4 121.0 76.00 2511. 18.0 72 2 "volkswagen 411 (sw)"
21.0 4 120.0 87.00 2979. 19.5 72 2 "peugeot 504 (sw)"
26.0 4 96.00 69.00 2189. 18.0 72 2 "renault 12 (sw)"
22.0 4 122.0 86.00 2395. 16.0 72 1 "ford pinto (sw)"
28.0 4 97.00 92.00 2288. 17.0 72 3 "datsun 510 (sw)"
23.0 4 120.0 97.00 2506. 14.5 72 3 "toyouta corona mark ii (sw)"
28.0 4 98.00 80.00 2164. 15.0 72 1 "dodge colt (sw)"
27.0 4 97.00 88.00 2100. 16.5 72 3 "toyota corolla 1600 (sw)"
13.0 8 350.0 175.0 4100. 13.0 73 1 "buick century 350"
14.0 8 304.0 150.0 3672. 11.5 73 1 "amc matador"
13.0 8 350.0 145.0 3988. 13.0 73 1 "chevrolet malibu"
14.0 8 302.0 137.0 4042. 14.5 73 1 "ford gran torino"
15.0 8 318.0 150.0 3777. 12.5 73 1 "dodge coronet custom"
12.0 8 429.0 198.0 4952. 11.5 73 1 "mercury marquis brougham"
13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic"
13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd"
14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan"
13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham"
12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom"
13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham"
18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant"
16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom"
18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet"
18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick"
23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster"
26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle"
11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala"
12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country"
13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb"
12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser"
18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin"
20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina"
21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega"
22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610"
18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3"
19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto"
21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6"
26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe"
15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s"
16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix"
29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128"
24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta"
20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls"
19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea"
15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom"
24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le"
20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii"
11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega"
20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster"
21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick"
19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet"
15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova"
31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210"
26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto"
32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200"
25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega"
16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic"
16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador"
18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring"
16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino"
13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)"
14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)"
14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)"
14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)"
29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox"
26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher"
26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta"
31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona"
32.0 4 83.00 61.00 2003. 19.0 74 3 "datsun 710"
28.0 4 90.00 75.00 2125. 14.5 74 1 "dodge colt"
24.0 4 90.00 75.00 2108. 15.5 74 2 "fiat 128"
26.0 4 116.0 75.00 2246. 14.0 74 2 "fiat 124 tc"
24.0 4 120.0 97.00 2489. 15.0 74 3 "honda civic"
26.0 4 108.0 93.00 2391. 15.5 74 3 "subaru"
31.0 4 79.00 67.00 2000. 16.0 74 2 "fiat x1.9"
19.0 6 225.0 95.00 3264. 16.0 75 1 "plymouth valiant custom"
18.0 6 250.0 105.0 3459. 16.0 75 1 "chevrolet nova"
15.0 6 250.0 72.00 3432. 21.0 75 1 "mercury monarch"
15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick"
16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina"
15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air"
16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury"
14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd"
17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century"
16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu"
15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador"
18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury"
21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk"
20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2"
13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii"
29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla"
23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto"
20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin"
23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro"
24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona"
25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher"
24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710"
18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto"
29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit"
19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer"
23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls"
23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504"
22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl"
25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le"
33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc"
28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131"
25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900"
25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii"
26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt"
27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl"
17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic"
16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham"
15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador"
14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino"
22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant"
22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova"
24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick"
22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet"
29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette"
24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody"
29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit"
33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic"
20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se"
18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia"
18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj"
17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l"
29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit"
32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210"
28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla"
26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto"
20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245"
13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8"
19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504"
19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii"
16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s"
16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville"
13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10"
13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108"
13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100"
31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc"
30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe"
36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl"
25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs"
33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback"
17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic"
17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme"
15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham"
15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham"
17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours"
20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark"
19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom"
18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada"
16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj"
15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau"
15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba"
16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird"
29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom"
24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe"
26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback"
25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2"
30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette"
33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m"
30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl"
30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher"
22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810"
21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i"
21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4"
43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel"
36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta"
32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe"
39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx"
36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc"
19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham"
19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat"
20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia"
19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj"
20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu"
20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)"
25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)"
20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare"
19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord"
20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special"
20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr"
18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen"
18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l"
19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau"
17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)"
18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura"
17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe"
30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette"
27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona"
27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510"
30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni"
21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback"
23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo"
23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx"
23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx"
20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000"
17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl"
21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle"
16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl"
31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco"
29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx"
21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6"
19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6"
22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4"
20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6"
20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6"
17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic"
17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau"
16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis"
18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis"
16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)"
15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)"
19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)"
18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)"
31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom"
34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe"
35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom"
27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl"
25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d"
23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado"
27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504"
23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham"
34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon"
34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3"
31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210"
37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom"
28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited"
28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation"
26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham"
33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix"
41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit"
38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel"
32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette"
37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310"
28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation"
26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont"
24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord"
19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen"
34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000"
29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback"
31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626"
37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback"
32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla"
46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc"
27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt"
40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210"
44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)"
43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)"
36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)"
30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d"
44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl"
40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe"
33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl"
29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit"
32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx"
23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs"
35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe"
23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra"
32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord"
27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant"
26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark"
25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)"
23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation"
30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant"
39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet"
39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ"
35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300"
32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru"
37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg"
37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel"
34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4"
34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4"
34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w"
29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h"
33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta"
34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i"
33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude"
32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla"
32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx"
31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626"
28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel"
30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel"
25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida"
24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima"
22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century"
26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls"
20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl"
17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon"
28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier"
27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon"
34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door"
31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback"
29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se"
27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix"
24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura"
23.0 4 151.0 ? 3035. 20.5 82 1 "amc concord dl"
36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l"
37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l"
31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom"
38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser"
36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l"
36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe"
36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord"
34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla"
38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic"
32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)"
38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx"
25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited"
38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)"
26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion"
22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l"
32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt"
36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2"
27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro"
27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl"
44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup"
32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage"
28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger"
31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10"
Big Mart Sales DataSet/README.md deleted 100644 → 0
## Big Mart Sales Data
### Introduction
The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.
Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.
### Data Dictionary
Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios
--- | --- | --- | --- | --- | ---
1 | Item_Identifier | It is a unique product ID assigned to every distinct item. It consists of an alphanumeric string of length 5 | Alphanumeric | FDN15 | 0
2 | Item_Weight | This field includes the wieght of the product | Numeric (float) | 17.5 | 17.16531738
3 | Item_Fat_Content | This attribute is categorical and describes whether the product is low fat or not. There are 2 categories of this attribute: ['Low Fat', 'Regular']. However, it is important to note that 'Low Fat' has also been written as 'low fat' and 'LF' in dataset, whereas, 'Regular' has been referred as 'reg' as well | Alpha | Low Fat | 0
4 | Item_Visibility | This field mentions the percentage of total display area of all products in a store allocated to the particular product | Numeric (float) | 0.01676 | 0
5 | Item_Type | This is a categorical attribute and describes the food category to which the item belongs. There are 16 different categories listed as follows: ['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables', 'Household', 'Baking Goods', 'Snack Foods', 'Frozen Foods', 'Breakfast', 'Health and Hygiene', 'Hard Drinks', 'Canned', 'Breads', 'Starchy Foods', 'Others', 'Seafood'] | Alpha | Meat | 0
6 | Item_MRP | This is the Maximum Retail Price (list price) of the product | Numeric (float) | 141.618 | 0
7 | Outlet_Identifier | It is a unique store ID assigned. It consists of an alphanumeric string of length 6 | Alphanumeric | OUT049 | 0
8 | Outlet_Establishment_Year | This attribute mentions the year in which store was established | Numeric (Integer) | 1998 | 0
9 | Outlet_Size | The attribute tells the size of the store in terms of ground area covered. It is a categorical value and described in 3 categories: ['High', 'Medium', 'Small'] | Alpha | Medium | 28.27642849
10 | Outlet_Location_Type | This field has categorical data and tells about the size of the city in which the store is located through 3 categories: ['Tier 1', 'Tier 2', 'Tier 3'] | Alpha | Tier 3 | 0
11 | Outlet_Type | This field contains categorical value and tells whether the outlet is just a grocery store or some sort of supermarket. Following are the 4 categories in which the data is divided: ['Supermarket Type1', 'Supermarket Type2', 'Grocery Store','Supermarket Type3'] | Alpha | Supermarket Type2 | 0
12 | Item_Outlet_Sales | This is the outcome variable to be predicted. It contains the sales of the product in the particulat store | Numeric (float) | 2097.27 | 0
### Source:
https://datahack.analyticsvidhya.com/contest/practice-problem-big-mart-sales-iii/
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Blood Transfusion Service Center Data Set/README.md deleted 100644 → 0
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
---
**Level** Advanced <br/>
**Recommended Use:** Classification Models<br/>
**Domain:** Business<br/>
## Blood Transfusion Service Center Data Set
### Predict if a donor will give blood in March 2007
---
![](hush-naidoo-1170844-unsplash.jpg)
---
This *advanced* level data set has 748 instances and 5 attributes.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **classification modelling techniques**.
Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques.
Following is the information about the Data Set provided in the source:
To demonstrate the RFMTC marketing model (a modified version of RFM), this study adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes their blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. To
build a FRMTC model, we selected 748 donors at random from the donor database. These 748 donor data, each one included R (Recency - months since last donation), F (Frequency - total number of donation), M (Monetary - total blood
donated in c.c.), T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood)
The Following data dictionary gives more details on this data set:
---
### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|------------------- |---------------------------------------------- |------------------------------------------------------------------------------------------------------ |-------------- |----------------- |--------------- |
| 1 | Recency (months) | Number of months since the particular donor's most recent donation | Quantitative | 5, 0, 2 | 0 |
| 2 | Frequency (times) | Total number of donations that the donor has made | Quantitative | 50, 10, 9 | 0 |
| 3 | Monetary (c.c. blood) | Total amound of blood that the donor has donated (cubic centimeters) | Quantitative | 4000, 2750, 500 | 0 |
| 4 | Time (months) | Number of months since the donor's first donation | Quantitative | 16, 58, 69 | 0 |
| 5 | whether he/she donated blood in March 2007 | This is a binary variable which represents whether the donor donated blood in March 2007: (1, 0) | Quantitative | 1, 0 | 0 |
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Blood Transfusion Service Center Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center). The UCI page mentions Blood Transfusion Service Center, Hsin-Chu City, Taiwan as the original source of the data set.
\ No newline at end of file
Blood Transfusion Service Center Data Set/transfusion.data.csv deleted 100644 → 0
Recency (months),Frequency (times),Monetary (c.c. blood),Time (months),"whether he/she donated blood in March 2007"
2 ,50,12500,98 ,1
0 ,13,3250,28 ,1
1 ,16,4000,35 ,1
2 ,20,5000,45 ,1
1 ,24,6000,77 ,0
4 ,4,1000,4 ,0
2 ,7,1750,14 ,1
1 ,12,3000,35 ,0
2 ,9,2250,22 ,1
5 ,46,11500,98 ,1
4 ,23,5750,58 ,0
0 ,3,750,4 ,0
2 ,10,2500,28 ,1
1 ,13,3250,47 ,0
2 ,6,1500,15 ,1
2 ,5,1250,11 ,1
2 ,14,3500,48 ,1
2 ,15,3750,49 ,1
2 ,6,1500,15 ,1
2 ,3,750,4 ,1
2 ,3,750,4 ,1
4 ,11,2750,28 ,0
2 ,6,1500,16 ,1
2 ,6,1500,16 ,1
9 ,9,2250,16 ,0
4 ,14,3500,40 ,0
4 ,6,1500,14 ,0
4 ,12,3000,34 ,1
4 ,5,1250,11 ,1
4 ,8,2000,21 ,0
1 ,14,3500,58 ,0
4 ,10,2500,28 ,1
4 ,10,2500,28 ,1
4 ,9,2250,26 ,1
2 ,16,4000,64 ,0
2 ,8,2000,28 ,1
2 ,12,3000,47 ,1
4 ,6,1500,16 ,1
2 ,14,3500,57 ,1
4 ,7,1750,22 ,1
2 ,13,3250,53 ,1
2 ,5,1250,16 ,0
2 ,5,1250,16 ,1
2 ,5,1250,16 ,0
4 ,20,5000,69 ,1
4 ,9,2250,28 ,1
2 ,9,2250,36 ,0
2 ,2,500,2 ,0
2 ,2,500,2 ,0
2 ,2,500,2 ,0
2 ,11,2750,46 ,0
2 ,11,2750,46 ,1
2 ,6,1500,22 ,0
2 ,12,3000,52 ,0
4 ,5,1250,14 ,1
4 ,19,4750,69 ,1
4 ,8,2000,26 ,1
2 ,7,1750,28 ,1
2 ,16,4000,81 ,0
3 ,6,1500,21 ,0
2 ,7,1750,29 ,0
2 ,8,2000,35 ,1
2 ,10,2500,49 ,0
4 ,5,1250,16 ,1
2 ,3,750,9 ,1
3 ,16,4000,74 ,0
2 ,4,1000,14 ,1
0 ,2,500,4 ,0
4 ,7,1750,25 ,0
1 ,9,2250,51 ,0
2 ,4,1000,16 ,0
2 ,4,1000,16 ,0
4 ,17,4250,71 ,1
2 ,2,500,4 ,0
2 ,2,500,4 ,1
2 ,2,500,4 ,1
2 ,4,1000,16 ,1
2 ,2,500,4 ,0
2 ,2,500,4 ,0
2 ,2,500,4 ,0
4 ,6,1500,23 ,1
2 ,4,1000,16 ,0
2 ,4,1000,16 ,0
2 ,4,1000,16 ,0
2 ,6,1500,28 ,1
2 ,6,1500,28 ,0
4 ,2,500,4 ,0
4 ,2,500,4 ,0
4 ,2,500,4 ,0
2 ,7,1750,35 ,1
4 ,2,500,4 ,1
4 ,2,500,4 ,0
4 ,2,500,4 ,0
4 ,2,500,4 ,0
12 ,11,2750,23 ,0
4 ,7,1750,28 ,0
3 ,17,4250,86 ,0
4 ,9,2250,38 ,1
4 ,4,1000,14 ,1
5 ,7,1750,26 ,1
4 ,8,2000,34 ,1
2 ,13,3250,76 ,1
4 ,9,2250,40 ,0
2 ,5,1250,26 ,0
2 ,5,1250,26 ,0
6 ,17,4250,70 ,0
0 ,8,2000,59 ,0
3 ,5,1250,26 ,0
2 ,3,750,14 ,0
2 ,10,2500,64 ,0
4 ,5,1250,23 ,1
4 ,9,2250,46 ,0
4 ,5,1250,23 ,0
4 ,8,2000,40 ,1
2 ,12,3000,82 ,0
11 ,24,6000,64 ,0
2 ,7,1750,46 ,1
4 ,11,2750,61 ,0
1 ,7,1750,57 ,0
2 ,11,2750,79 ,1
2 ,3,750,16 ,1
4 ,5,1250,26 ,1
2 ,6,1500,41 ,1
2 ,5,1250,33 ,1
2 ,4,1000,26 ,0
2 ,5,1250,34 ,0
4 ,8,2000,46 ,1
2 ,4,1000,26 ,0
4 ,8,2000,48 ,1
2 ,2,500,10 ,1
4 ,5,1250,28 ,0
2 ,12,3000,95 ,0
2 ,2,500,10 ,0
4 ,6,1500,35 ,0
2 ,11,2750,88 ,0
2 ,3,750,19 ,0
2 ,5,1250,37 ,0
2 ,12,3000,98 ,0
9 ,5,1250,19 ,0
2 ,2,500,11 ,0
2 ,9,2250,74 ,0
5 ,14,3500,86 ,0
4 ,3,750,16 ,0
4 ,3,750,16 ,0
4 ,2,500,9 ,1
4 ,3,750,16 ,1
6 ,3,750,14 ,0
2 ,2,500,11 ,0
2 ,2,500,11 ,1
2 ,2,500,11 ,0
2 ,7,1750,58 ,1
4 ,6,1500,39 ,0
4 ,11,2750,78 ,0
2 ,1,250,2 ,1
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,1
2 ,1,250,2 ,1
2 ,1,250,2 ,1
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
2 ,1,250,2 ,0
11 ,10,2500,35 ,0
11 ,4,1000,16 ,1
4 ,5,1250,33 ,1
4 ,6,1500,41 ,1
2 ,3,750,22 ,0
4 ,4,1000,26 ,1
10 ,4,1000,16 ,0
2 ,4,1000,35 ,0
4 ,12,3000,88 ,0
13 ,8,2000,26 ,0
11 ,9,2250,33 ,0
4 ,5,1250,34 ,0
4 ,4,1000,26 ,0
8 ,15,3750,77 ,0
4 ,5,1250,35 ,1
4 ,7,1750,52 ,0
4 ,7,1750,52 ,0
2 ,4,1000,35 ,0
11 ,11,2750,42 ,0
2 ,2,500,14 ,0
2 ,5,1250,47 ,1
9 ,8,2000,38 ,1
4 ,6,1500,47 ,0
11 ,7,1750,29 ,0
9 ,9,2250,45 ,0
4 ,6,1500,52 ,0
4 ,7,1750,58 ,0
6 ,2,500,11 ,1
4 ,7,1750,58 ,0
11 ,9,2250,38 ,0
11 ,6,1500,26 ,0
2 ,2,500,16 ,0
2 ,7,1750,76 ,0
11 ,6,1500,27 ,0
11 ,3,750,14 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,1
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,1
4 ,1,250,4 ,1
4 ,1,250,4 ,0
4 ,1,250,4 ,1
4 ,1,250,4 ,1
4 ,1,250,4 ,0
4 ,3,750,24 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,0
4 ,1,250,4 ,1
4 ,1,250,4 ,0
10 ,8,2000,39 ,0
14 ,7,1750,26 ,0
8 ,10,2500,63 ,0
11 ,3,750,15 ,0
4 ,2,500,14 ,0
2 ,4,1000,43 ,0
8 ,9,2250,58 ,0
8 ,8,2000,52 ,1
11 ,22,5500,98 ,0
4 ,3,750,25 ,1
11 ,17,4250,79 ,1
9 ,2,500,11 ,0
4 ,5,1250,46 ,0
11 ,12,3000,58 ,0
7 ,12,3000,86 ,0
11 ,2,500,11 ,0
11 ,2,500,11 ,0
11 ,2,500,11 ,0
2 ,6,1500,75 ,0
11 ,8,2000,41 ,1
11 ,3,750,16 ,1
12 ,13,3250,59 ,0
2 ,3,750,35 ,0
16 ,8,2000,28 ,0
11 ,7,1750,37 ,0
4 ,3,750,28 ,0
12 ,12,3000,58 ,0
4 ,4,1000,41 ,0
11 ,14,3500,73 ,1
2 ,2,500,23 ,0
2 ,3,750,38 ,1
4 ,5,1250,58 ,0
4 ,4,1000,43 ,1
3 ,2,500,23 ,0
11 ,8,2000,46 ,0
4 ,7,1750,82 ,0
13 ,4,1000,21 ,0
16 ,11,2750,40 ,0
16 ,7,1750,28 ,0
7 ,2,500,16 ,0
4 ,5,1250,58 ,0
4 ,5,1250,58 ,0
4 ,4,1000,46 ,0
14 ,13,3250,57 ,0
4 ,3,750,34 ,0
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39 ,1,250,39 ,0
72 ,1,250,72 ,0
\ No newline at end of file
Census Income Data Set/README.md deleted 100644 → 0
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
---
**<span style="color:#E57932">Level:</span>** Intermediate <br/>
**<span style="color:#E57932">Recommended Use:</span>** Classification Models<br/>
**<span style="color:#E57932">Domain:</span>** Social<br/>
## Census Income Data Set
### Predict whether income exceeds $50K/year:
![](rawpixel-557125-unsplash.jpg)
This *intermediate* level data set was extracted from the census bureau database. There are 48842 instances of data set, mix of continuous and discrete (train=32561, test=16281).
The data set has 15 attribute which include age, sex, education level and other relevant details of a person. The data set will help to improve your skills in **Exploratory Data Analysis**, **Data Wrangling**, **Data Visualization** and **Classification Models**.
Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
### Data Dictionary:
| **Column Position** | **Attribute Name** | **Definition** | **Data Type** | **Example** | **% Null Ratios** |
|------------------- |---------------- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |-------------- |----------------------------------------- |--------------- |
| 1 | age | Age (years) | Quantitative | 38, 42, 71 | 0 |
| 2 | workclass | Workclass 8 different categories: (Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked) | Qualitative | "Private", Local-gov", "Never-worked" | 6 |
| 3 | fnlwgt | Final Weight* | Quantitative | 83311, 338409 | 0 |
| 4 | education | Education: (Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool) | Qualitative | "Bachelors", "9th", "Preschool" | 0 |
| 5 | education-num | Years of education | Quantitative | 13, 9, 7 | 0 |
| 6 | marital-status | Marital Status: (Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse) | Qualitative | "Divorced", Separated", "Widowed" | 0 |
| 7 | occupation | Occupation: (Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces) | Qualitative | "Tech-support", "Armed Forces", "Sales" | 6 |
| 8 | relationship | Relationship:(Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried) | Qualitative | "Wife", "Unmarried", "Own-child" | 0 |
| 9 | race | Race: (White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black) | Qualitative | "White", "Asian-Pac-Islander", "Other" | 0 |
| 10 | sex | Sex: (Male, Female) | Qualitative | Male, Female | 0 |
| 11 | capital-gain | Amount of capital gained | Quantitative | 14084, 0, 5178 | 0 |
| 12 | capital-loss | Amount of capital lost | Quantitative | 0, 2042, 1902 | 0 |
| 13 | hours-per-week | Number of hours worked per week | Quantitative | 40, 50, 70 | 0 |
| 14 | native-country | Native country: (United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands) | Qualitative | "China", "Italy", "Vietnam" | 2 |
| 15 | income | Either the income is greater than $50,000 or lesser than and equal to $50,000: (>50K, <=50K) | Qualitative | ">50K", "<=50K" | 0 |
*Description of fnlwgt (final weight):
The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls.
These are:
1. A single cell estimate of the population 16+ for each state.
2. Controls for Hispanic Origin by age and sex.
3. Controls by Race, age and sex.
We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used.
The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population.
People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state.
### Acknowledgement:
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Census Income Data Set (UC Irvine)](http://mlr.cs.umass.edu/ml/datasets/Census+Income). The UCI page mentions [US Census Bureau](http://www.census.gov/ftp/pub/DES/www/welcome.html) as the original source of the data set.
Census Income Data Set/adult.data.csv deleted 100644 → 0
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Census Income Data Set/adult.test.csv deleted 100644 → 0
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