Import bibliotek

import sklearn
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from google.colab import files
import pandas as pd

Pobranie danych

#Zainstalujmy potrzebne biblioteki
!pip install --user kaggle #API Kaggle, do pobrania zbioru
!pip install --user pandas

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files.upload()
! mkdir ~/.kaggle
! cp kaggle.json ~/.kaggle/
! chmod 600 ~/.kaggle/kaggle.json

Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.

Saving kaggle.json to kaggle (4).json
mkdir: cannot create directory ‘/root/.kaggle’: File exists

!kaggle datasets download -d muhammadbinimran/housing-price-prediction-data

housing-price-prediction-data.zip: Skipping, found more recently modified local copy (use --force to force download)

!unzip -o housing-price-prediction-data.zip

Archive:  housing-price-prediction-data.zip
  inflating: housing_price_dataset.csv

Wczytanie zbioru

!pip install --user pandas

Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)
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Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)
Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)

housing_price_dataset = pd.read_csv('housing_price_dataset.csv')
housing_price_dataset

	SquareFeet	Bedrooms	Bathrooms	Neighborhood	YearBuilt	Price
0	2126	4	1	Rural	1969	215355.283618
1	2459	3	2	Rural	1980	195014.221626
2	1860	2	1	Suburb	1970	306891.012076
3	2294	2	1	Urban	1996	206786.787153
4	2130	5	2	Suburb	2001	272436.239065
...	...	...	...	...	...	...
49995	1282	5	3	Rural	1975	100080.865895
49996	2854	2	2	Suburb	1988	374507.656727
49997	2979	5	3	Suburb	1962	384110.555590
49998	2596	5	2	Rural	1984	380512.685957
49999	1572	5	3	Rural	2011	221618.583218

50000 rows × 6 columns

Podział zbioru

hp_train_test, hp_dev = sklearn.model_selection.train_test_split(housing_price_dataset, test_size=0.1)
hp_train, hp_test = sklearn.model_selection.train_test_split(hp_train_test, test_size=1000)

Normalizacja danych

housing_price_dataset["Neighborhood"].unique()

array(['Rural', 'Suburb', 'Urban'], dtype=object)

hp_train = pd.get_dummies(hp_train, columns=['Neighborhood'])
hp_dev = pd.get_dummies(hp_dev, columns=['Neighborhood'])
hp_test = pd.get_dummies(hp_test, columns=['Neighborhood'])

hp_train

	SquareFeet	Bedrooms	Bathrooms	YearBuilt	Price	Neighborhood_Rural	Neighborhood_Suburb	Neighborhood_Urban
7616	2027	3	3	2013	237960.032012	0	0	1
47787	1292	5	1	2021	86121.435887	0	1	0
35285	1964	2	3	1970	208054.904277	0	0	1
8718	2581	4	2	1990	230475.439055	1	0	0
36680	2020	5	2	2011	278860.337033	0	0	1
...	...	...	...	...	...	...	...	...
22830	1245	5	1	1975	167679.728402	1	0	0
43699	2065	4	2	2021	257521.317661	0	1	0
21160	1967	3	1	1951	262332.423882	0	1	0
30915	2867	2	3	1990	311233.596471	0	0	1
19117	1631	3	1	1967	200594.974438	1	0	0

44000 rows × 8 columns

hp_dev

	SquareFeet	Bedrooms	Bathrooms	YearBuilt	Price	Neighborhood_Rural	Neighborhood_Suburb	Neighborhood_Urban
46301	2845	4	3	1954	354875.353057	0	0	1
10023	2362	4	3	2010	292371.871755	1	0	0
37044	1058	3	2	2007	155277.040755	0	1	0
17462	2891	5	1	2005	239120.147027	1	0	0
13804	2244	5	2	1966	254005.280471	1	0	0
...	...	...	...	...	...	...	...	...
35925	1684	4	1	1950	212224.505489	1	0	0
21799	1021	5	3	1995	139005.940982	1	0	0
4318	2741	4	2	1962	339074.548520	1	0	0
31492	2053	3	3	2014	239382.414641	0	0	1
26727	2963	3	1	2004	321585.613385	0	1	0

5000 rows × 8 columns

hp_test

	SquareFeet	Bedrooms	Bathrooms	YearBuilt	Price	Neighborhood_Rural	Neighborhood_Suburb	Neighborhood_Urban
49356	1174	5	3	1996	143866.306649	0	1	0
18656	1776	3	1	1964	125553.381347	0	0	1
27368	2524	2	2	2010	327261.077660	1	0	0
27243	1633	2	1	1953	241231.423110	1	0	0
24653	2811	4	2	1982	315724.479288	1	0	0
...	...	...	...	...	...	...	...	...
20015	2106	2	2	2014	216406.701646	0	0	1
40921	1704	3	3	1986	153770.810572	1	0	0
30027	1150	5	3	1973	138938.157678	0	0	1
16008	2822	2	2	1982	296193.916437	0	1	0
23919	1348	2	2	1983	133497.577808	1	0	0

1000 rows × 8 columns

Statystyki

Wielkość podzbiorów

housing_price_dataset.describe()

	SquareFeet	Bedrooms	Bathrooms	YearBuilt	Price
count	50000.000000	50000.000000	50000.000000	50000.000000	50000.000000
mean	2006.374680	3.498700	1.995420	1985.404420	224827.325151
std	575.513241	1.116326	0.815851	20.719377	76141.842966
min	1000.000000	2.000000	1.000000	1950.000000	-36588.165397
25%	1513.000000	3.000000	1.000000	1967.000000	169955.860225
50%	2007.000000	3.000000	2.000000	1985.000000	225052.141166
75%	2506.000000	4.000000	3.000000	2003.000000	279373.630052
max	2999.000000	5.000000	3.000000	2021.000000	492195.259972

hp_train.shape

(44000, 8)

hp_dev.shape

(5000, 8)

hp_test.shape

(1000, 8)

Statystyki kolumn

hp_train.describe()

	SquareFeet	Bedrooms	Bathrooms	YearBuilt	Price	Neighborhood_Rural	Neighborhood_Suburb	Neighborhood_Urban
count	44000.000000	44000.000000	44000.000000	44000.000000	44000.000000	44000.000000	44000.000000	44000.000000
mean	2006.261182	3.499636	1.997864	1985.416750	224928.983383	0.332841	0.333636	0.333523
std	575.306280	1.117315	0.815760	20.700559	76107.652516	0.471235	0.471517	0.471477
min	1000.000000	2.000000	1.000000	1950.000000	-36588.165397	0.000000	0.000000	0.000000
25%	1513.000000	3.000000	1.000000	1967.000000	170088.571867	0.000000	0.000000	0.000000
50%	2007.000000	3.000000	2.000000	1985.000000	225246.904135	0.000000	0.000000	0.000000
75%	2505.000000	5.000000	3.000000	2003.000000	279365.119289	1.000000	1.000000	1.000000
max	2999.000000	5.000000	3.000000	2021.000000	492195.259972	1.000000	1.000000	1.000000

hp_dev.describe()

	SquareFeet	Bedrooms	Bathrooms	YearBuilt	Price	Neighborhood_Rural	Neighborhood_Suburb	Neighborhood_Urban
count	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000
mean	2008.190800	3.487200	1.972600	1985.485400	224290.794530	0.337800	0.341600	0.320600
std	576.206366	1.104753	0.816077	20.960049	76778.005658	0.473007	0.474294	0.466754
min	1000.000000	2.000000	1.000000	1950.000000	-18159.685676	0.000000	0.000000	0.000000
25%	1510.750000	3.000000	1.000000	1967.000000	169103.151768	0.000000	0.000000	0.000000
50%	2007.000000	3.000000	2.000000	1985.000000	223614.924625	0.000000	0.000000	0.000000
75%	2503.000000	4.000000	3.000000	2004.000000	279651.548644	1.000000	1.000000	1.000000
max	2999.000000	5.000000	3.000000	2021.000000	467492.827823	1.000000	1.000000	1.000000

hp_test.describe()

	SquareFeet	Bedrooms	Bathrooms	YearBuilt	Price	Neighborhood_Rural	Neighborhood_Suburb	Neighborhood_Urban
count	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000
mean	2002.288000	3.515000	2.002000	1984.457000	223037.016061	0.342000	0.333000	0.325000
std	581.670136	1.130953	0.817719	20.330949	74475.155327	0.474617	0.471522	0.468609
min	1000.000000	2.000000	1.000000	1950.000000	-7550.504574	0.000000	0.000000	0.000000
25%	1507.250000	2.000000	1.000000	1967.000000	168905.529102	0.000000	0.000000	0.000000
50%	2021.500000	4.000000	2.000000	1983.000000	220416.485632	0.000000	0.000000	0.000000
75%	2524.000000	5.000000	3.000000	2002.000000	279628.697596	1.000000	1.000000	1.000000
max	2999.000000	5.000000	3.000000	2021.000000	437047.713441	1.000000	1.000000	1.000000

def print_sum(df_name, df):
  columns = ['Neighborhood_Rural', 'Neighborhood_Suburb', 'Neighborhood_Urban']
  print(df_name)
  for col in columns:
    print(col, df[col].sum())
  print()

print_sum("hp_train", hp_train)
print_sum("hp_dev", hp_dev)
print_sum("hp_test", hp_test)

hp_train
Neighborhood_Rural 14645
Neighborhood_Suburb 14680
Neighborhood_Urban 14675

hp_dev
Neighborhood_Rural 1689
Neighborhood_Suburb 1708
Neighborhood_Urban 1603

hp_test
Neighborhood_Rural 342
Neighborhood_Suburb 333
Neighborhood_Urban 325

184 KiB Raw Blame History Unescape Escape