ium_464962/ium_01.ipynb at main

Instalacja i import bibliotek

!pip install kaggle
!pip install pandas

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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

Pobranie zbioru danych

!kaggle datasets download -d syedanwarafridi/vehicle-sales-data

vehicle-sales-data.zip: Skipping, found more recently modified local copy (use --force to force download)

#conda install git pip

#!pip install unzip

!unzip -o vehicle-sales-data.zip

Archive:  vehicle-sales-data.zip
  inflating: car_prices.csv

Opis i czyszczenie danych danych

df = pd.read_csv('car_prices.csv')
df.head()

	year	make	model	trim	body	transmission	vin	state	condition	odometer	color	interior	seller	mmr	sellingprice	saledate
0	2015	Kia	Sorento	LX	SUV	automatic	5xyktca69fg566472	ca	5.0	16639.0	white	black	kia motors america inc	20500.0	21500.0	Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
1	2015	Kia	Sorento	LX	SUV	automatic	5xyktca69fg561319	ca	5.0	9393.0	white	beige	kia motors america inc	20800.0	21500.0	Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
2	2014	BMW	3 Series	328i SULEV	Sedan	automatic	wba3c1c51ek116351	ca	45.0	1331.0	gray	black	financial services remarketing (lease)	31900.0	30000.0	Thu Jan 15 2015 04:30:00 GMT-0800 (PST)
3	2015	Volvo	S60	T5	Sedan	automatic	yv1612tb4f1310987	ca	41.0	14282.0	white	black	volvo na rep/world omni	27500.0	27750.0	Thu Jan 29 2015 04:30:00 GMT-0800 (PST)
4	2014	BMW	6 Series Gran Coupe	650i	Sedan	automatic	wba6b2c57ed129731	ca	43.0	2641.0	gray	black	financial services remarketing (lease)	66000.0	67000.0	Thu Dec 18 2014 12:30:00 GMT-0800 (PST)

df.shape

(558837, 16)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 558837 entries, 0 to 558836
Data columns (total 16 columns):
 #   Column        Non-Null Count   Dtype  
---  ------        --------------   -----  
 0   year          558837 non-null  int64  
 1   make          548536 non-null  object 
 2   model         548438 non-null  object 
 3   trim          548186 non-null  object 
 4   body          545642 non-null  object 
 5   transmission  493485 non-null  object 
 6   vin           558833 non-null  object 
 7   state         558837 non-null  object 
 8   condition     547017 non-null  float64
 9   odometer      558743 non-null  float64
 10  color         558088 non-null  object 
 11  interior      558088 non-null  object 
 12  seller        558837 non-null  object 
 13  mmr           558799 non-null  float64
 14  sellingprice  558825 non-null  float64
 15  saledate      558825 non-null  object 
dtypes: float64(4), int64(1), object(11)
memory usage: 68.2+ MB

df.describe(include='all')

	year	make	model	trim	body	transmission	vin	state	condition	odometer	color	interior	seller	mmr	sellingprice	saledate
count	558837.000000	548536	548438	548186	545642	493485	558833	558837	547017.000000	558743.000000	558088	558088	558837	558799.000000	558825.000000	558825
unique	NaN	96	973	1963	87	4	550297	64	NaN	NaN	46	17	14263	NaN	NaN	3766
top	NaN	Ford	Altima	Base	Sedan	automatic	automatic	fl	NaN	NaN	black	black	nissan-infiniti lt	NaN	NaN	Tue Feb 10 2015 01:30:00 GMT-0800 (PST)
freq	NaN	93554	19349	55817	199437	475915	22	82945	NaN	NaN	110970	244329	19693	NaN	NaN	5334
mean	2010.038927	NaN	NaN	NaN	NaN	NaN	NaN	NaN	30.672365	68320.017767	NaN	NaN	NaN	13769.377495	13611.358810	NaN
std	3.966864	NaN	NaN	NaN	NaN	NaN	NaN	NaN	13.402832	53398.542821	NaN	NaN	NaN	9679.967174	9749.501628	NaN
min	1982.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.000000	1.000000	NaN	NaN	NaN	25.000000	1.000000	NaN
25%	2007.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	23.000000	28371.000000	NaN	NaN	NaN	7100.000000	6900.000000	NaN
50%	2012.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	35.000000	52254.000000	NaN	NaN	NaN	12250.000000	12100.000000	NaN
75%	2013.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	42.000000	99109.000000	NaN	NaN	NaN	18300.000000	18200.000000	NaN
max	2015.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	49.000000	999999.000000	NaN	NaN	NaN	182000.000000	230000.000000	NaN

df = df.dropna()
df.shape

(472325, 16)

df.isna().sum()

year            0
make            0
model           0
trim            0
body            0
transmission    0
vin             0
state           0
condition       0
odometer        0
color           0
interior        0
seller          0
mmr             0
sellingprice    0
saledate        0
dtype: int64

df['body'] = df['body'].replace({'sedan': 'Sedan'})
df['body'] = df['body'].replace({'Suv': 'SUV'})
df['body'] = df['body'].replace({'suv': 'SUV'})

numeric_columns = df.select_dtypes(include=['int', 'float']).columns
scaler = MinMaxScaler(feature_range=(0, 1))

df_scaled = df.copy()
df_scaled[numeric_columns] = scaler.fit_transform(df[numeric_columns])

df_scaled.head()

	year	make	model	trim	body	transmission	vin	state	condition	odometer	color	interior	seller	mmr	sellingprice	saledate
0	1.00	Kia	Sorento	LX	SUV	automatic	5xyktca69fg566472	ca	0.083333	0.016638	white	black	kia motors america inc	0.112515	0.093474	Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
1	1.00	Kia	Sorento	LX	SUV	automatic	5xyktca69fg561319	ca	0.083333	0.009392	white	beige	kia motors america inc	0.114164	0.093474	Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
2	0.96	BMW	3 Series	328i SULEV	Sedan	automatic	wba3c1c51ek116351	ca	0.916667	0.001330	gray	black	financial services remarketing (lease)	0.175161	0.130431	Thu Jan 15 2015 04:30:00 GMT-0800 (PST)
3	1.00	Volvo	S60	T5	Sedan	automatic	yv1612tb4f1310987	ca	0.833333	0.014281	white	black	volvo na rep/world omni	0.150982	0.120648	Thu Jan 29 2015 04:30:00 GMT-0800 (PST)
4	0.96	BMW	6 Series Gran Coupe	650i	Sedan	automatic	wba6b2c57ed129731	ca	0.875000	0.002640	gray	black	financial services remarketing (lease)	0.362550	0.291301	Thu Dec 18 2014 12:30:00 GMT-0800 (PST)

Podział danych na podzbiory

car_train, car_dev_test = train_test_split(df, random_state = 0, train_size = 0.8)
car_dev, car_test = train_test_split(car_dev_test, random_state = 0, train_size = 0.5)

print(car_train.shape)
print(car_dev.shape)
print(car_test.shape)

(377860, 16)
(47232, 16)
(47233, 16)

Statystyki zbioru

df['make'].value_counts().head(10)

Ford         81013
Chevrolet    54150
Nissan       44043
Toyota       35313
Dodge        27181
Honda        24781
Hyundai      18659
BMW          17509
Kia          15828
Chrysler     15133
Name: make, dtype: int64

df['body'].value_counts().head(10)

Sedan          211298
SUV            120968
Hatchback       19351
Minivan         18305
Coupe           13121
Wagon           12023
Crew Cab        11508
Convertible      7725
SuperCrew        6195
G Sedan          5644
Name: body, dtype: int64

df['transmission'].value_counts()

automatic    455963
manual        16362
Name: transmission, dtype: int64

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Instalacja i import bibliotek

Pobranie zbioru danych

Opis i czyszczenie danych danych

Podział danych na podzbiory

Statystyki zbioru

36 KiB

Raw Permalink Blame History