mieszkania5/model_regresji_liniowej.ipynb

import pandas as pd

Wczytanie datasetów

train_dataset = pd.read_csv("./train.tsv", sep = "\t", header=None)

test_dataset = pd.read_csv("./in.tsv", sep = "\t", header=None)

Data exploration

train_dataset.head(1)

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25
0	309000.0	do zamieszkania	390 zł	spółdzielcze własnościowe	7113	https://www.otodom.pl/oferta/niezalezny-uklad-...	2	NaN	43.44	wtórny	4.0	blok	NaN	NaN	Niezależny Układ W Nowoczesnym Wydaniu	1	NaN	gazowe	plastikowe	NaN	NaN	NaN	cegła	Polecamy na sprzedaż dwupokojowe mieszkanie p...	NaN	telewizja kablowa, internet, meble, piwnica, g...

train_dataset[4].value_counts()

4
7000    42
6750    42
7100    37
7500    29
7800    20
        ..
9674     1
7565     1
8352     1
7511     1
5077     1
Name: count, Length: 1452, dtype: int64

pd.set_option("display.max_columns", None)

train_dataset.describe()

	0	4	7	10	12	13	16	19	20	24
count	2.547000e+03	2547.000000	0.0	2316.000000	0.0	0.0	0.0	1768.000000	0.0	0.0
mean	4.210372e+05	7201.618767	NaN	5.015112	NaN	NaN	NaN	2011.436086	NaN	NaN
std	2.663796e+05	1737.605837	NaN	2.797598	NaN	NaN	NaN	610.162290	NaN	NaN
min	4.500000e+04	36.000000	NaN	1.000000	NaN	NaN	NaN	50.000000	NaN	NaN
25%	2.990000e+05	6200.000000	NaN	4.000000	NaN	NaN	NaN	1976.000000	NaN	NaN
50%	3.590000e+05	7000.000000	NaN	4.000000	NaN	NaN	NaN	2011.000000	NaN	NaN
75%	4.570000e+05	7868.000000	NaN	5.000000	NaN	NaN	NaN	2019.000000	NaN	NaN
max	6.000000e+06	23005.000000	NaN	21.000000	NaN	NaN	NaN	20120.000000	NaN	NaN

train_dataset[3].value_counts()

3
pełna własność               1515
spółdzielcze własnościowe     158
spółdzielcze wł. z KW         131
udział                         15
Name: count, dtype: int64

Odfiltrowanie wartości liczbowych

filtered= train_dataset[[0,8,15,2]]

filtered.head(1)

	0	8	15	2
0	309000.0	43.44	1	390 zł

filtered[15].value_counts()

15
1           569
parter      452
2           419
4           357
3           321
5           117
6            51
7            42
8            32
10           29
> 10         24
9            21
suterena      5
poddasze      3
Name: count, dtype: int64

Zamiana wsyztskich wartości na wartosći liczbowe, odfiltrowanie wartości trudnych do zamiany oraz usunięcie brakujących wartości

filtered[15] = filtered[15].replace({"parter": 0})
string = ["> 10", "suterena", "poddasze"]
filtered = filtered[~filtered[15].isin(string)]
filtered = filtered[~filtered[15].isna()]

C:\Users\s433445\AppData\Local\temp\ipykernel_7796\2098381410.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  filtered[15] = filtered[15].replace({"parter": 0})

filtered

	0	8	15	2
0	309000.0	43.44	1	390 zł
1	314900.0	42.60	1	NaN
2	249000.0	44.30	2	300 zł
3	419000.0	88	1	490 zł
4	499000.0	77	7	850 zł
...	...	...	...	...
2541	383680.0	70.40	0	NaN
2542	507600.0	94	3	1 zł
2543	342400.0	53.50	4	NaN
2544	335000.0	55.25	4	280 zł
2545	260000.0	62	2	NaN

2410 rows × 4 columns

filtered[15].value_counts()

15
1     569
0     452
2     419
4     357
3     321
5     117
6      51
7      42
8      32
10     29
9      21
Name: count, dtype: int64

filtered[15] = filtered[15].apply(int)

filtered[8].value_counts()

8
38       59
48       40
50       35
53       28
43       26
         ..
33.79     1
42.60     1
47.82     1
53.09     1
55.25     1
Name: count, Length: 1084, dtype: int64

import numpy as np

def check_value(number):
    try:
        return float(number)
    except:
        return str(np.nan)

filtered

	0	8	15	2
0	309000.0	43.44	1	390 zł
1	314900.0	42.60	1	NaN
2	249000.0	44.30	2	300 zł
3	419000.0	88.00	1	490 zł
4	499000.0	77.00	7	850 zł
...	...	...	...	...
2541	383680.0	70.40	0	NaN
2542	507600.0	94.00	3	1 zł
2543	342400.0	53.50	4	NaN
2544	335000.0	55.25	4	280 zł
2545	260000.0	62.00	2	NaN

2410 rows × 4 columns

filtered[8] = filtered[8].apply(check_value)
filtered = filtered[~filtered[8].isna()]

filtered = filtered[[0, 15, 8]]

Model

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

X = filtered.drop(0,axis=1)
y = filtered[[0]]

scaler = StandardScaler()
trans_data = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(trans_data,y, test_size=0.33)

reg = LinearRegression()

reg.fit(X_train, y_train)
results = reg.predict(X_test)

import pickle

Zapis jako pkl

pickle.dump(reg, open("model.pkl", "wb"))

results

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from sklearn.metrics import r2_score

r2_score(y_test, results)

0.5878080999086976