Project 1
Data Cleaning on Automobile 1985 dataset and perform descriptive analytics:-
No. of data points: 205
Number of Attributes: 26 total
-- 15 continuous
-- 1 integer
-- 10 nominal
Attribute Information:
| Attribute | Attribute Range
-- symboling: -3, -2, -1, 0, 1, 2, 3
-- normalized-losses: continuous from 65 to 256
-- 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
-- fuel-type: diesel, gas
-- aspiration: std, turbo
-- num-of-doors: four, two
-- body-style: hardtop, wagon, sedan, hatchback, convertible
-- drive-wheels: 4wd, fwd, rwd
-- engine-location: front, rear
-- wheel-base: continuous from 86.6 120.9
-- length: continuous from 141.1 to 208.1
-- width: continuous from 60.3 to 72.3
-- height: continuous from 47.8 to 59.8
-- curb-weight: continuous from 1488 to 4066
-- engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor
-- num-of-cylinders: eight, five, four, six, three, twelve, two
-- engine-size: continuous from 61 to 326
-- fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi
-- bore: continuous from 2.54 to 3.94
-- stroke: continuous from 2.07 to 4.17
-- compression-ratio: continuous from 7 to 23
-- horsepower: continuous from 48 to 288
-- peak-rpm: continuous from 4150 to 6600
-- city-mpg: continuous from 13 to 49
-- highway-mpg: continuous from 16 to 54
-- price: continuous from 5118 to 45400.
Importing Libraries:
Reading the data:
Information about the data:
Observation:
Looking into categorical and numerical features separately:
Observation:
1. Loss:
there are entrie with '?' in it
Distribution:
Outliers handling:
2. Bore:
there are entries with '?' in it.
Handling outliers:
Handling outliers:
3. Stroke:
there are entries with '?' in it
Handling Outliers:
4. hp:
there are entries with '?' in it
Handling outliers:
5. Peak RPM:
there are entries with '?' in it
Handling Outliers:
6. Price:
Now checking the categorical and numerical features:
Analysing Numerical features:
Observation:
Performing feature engineering on the observed features:
Feature Engineering on remaining features:
dropping the features with less correlation with price and mpg:
Observation:
Analysing Categorical Features:
Observation:
Observation:
