ium_464863/IUM_02.ipynb

IUM_02

Wymagane zależności

# Instalacja wymaganych zależności
!pip install kaggle
!pip install pandas
!pip install scikit-learn

Import bibliotek

# Import bibliotek
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

1. Pobieranie zbioru danych

# Pobranie zbioru danych
!kaggle datasets download -d uciml/breast-cancer-wisconsin-data

# Wypakowanie archiwum
!tar -xf breast-cancer-wisconsin-data.zip

Downloading breast-cancer-wisconsin-data.zip to C:\Users\broke\PycharmProjects\ium_464863

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2. Wczytanie danych oraz wstępne przetworzenie

# Wczytanie danych, ustawienie kolumny 'id' jako indeks
df = pd.read_csv('data.csv', index_col='id')

# Usunięcie niepotrzebnych/błędnych kolumn
df = df.drop(columns=['Unnamed: 32'])

# Sprawdzenie czy istnieją wartości brakujące (NaN)
df.isnull().sum()

diagnosis                  0
radius_mean                0
texture_mean               0
perimeter_mean             0
area_mean                  0
smoothness_mean            0
compactness_mean           0
concavity_mean             0
concave points_mean        0
symmetry_mean              0
fractal_dimension_mean     0
radius_se                  0
texture_se                 0
perimeter_se               0
area_se                    0
smoothness_se              0
compactness_se             0
concavity_se               0
concave points_se          0
symmetry_se                0
fractal_dimension_se       0
radius_worst               0
texture_worst              0
perimeter_worst            0
area_worst                 0
smoothness_worst           0
compactness_worst          0
concavity_worst            0
concave points_worst       0
symmetry_worst             0
fractal_dimension_worst    0
dtype: int64

# Wyświetlenie 5 pierwszych wierszy
df.head()

	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
id
842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	...	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	...	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	0.2069	...	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	0.2597	...	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	0.1809	...	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678

5 rows × 31 columns

# Normalizacja cech do wartości z przedziału [0, 1]
scaler = MinMaxScaler()

df[df.columns[1:]] = scaler.fit_transform(df[df.columns[1:]])

3. Podział danych na zbiór treningowy, walidacyjny i testowy

# Podział zbioru na zbiór treningowy, walidacyjny i testowy w proporcji 80/10/10
df_train, df_val_test = train_test_split(df, test_size=0.2, random_state=1234)
df_val, df_test = train_test_split(df_val_test, test_size=0.5, random_state=1234)

# Wymiary zbiorów i podzbiorów
print(f"Cały zbiór: {df.shape[0]} wierszy, {df.shape[1]} kolumn")
print(f"Zbiór treningowy: {df_train.shape[0]} wierszy, {df_train.shape[1]} kolumn")
print(f"Zbiór walidacyjny: {df_val.shape[0]} wierszy, {df_val.shape[1]} kolumn")
print(f"Zbiór testowy: {df_test.shape[0]} wierszy, {df_test.shape[1]} kolumn")

Cały zbiór: 569 wierszy, 31 kolumn
Zbiór treningowy: 455 wierszy, 31 kolumn
Zbiór walidacyjny: 57 wierszy, 31 kolumn
Zbiór testowy: 57 wierszy, 31 kolumn

Statystyki dla cech numerycznych (średnia, odchylenie standardowe, min, max, kwantyle)

# Cały zbiór
df.describe()

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	...	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000
mean	0.338222	0.323965	0.332935	0.216920	0.394785	0.260601	0.208058	0.243137	0.379605	0.270379	...	0.296663	0.363998	0.283138	0.170906	0.404138	0.220212	0.217403	0.393836	0.263307	0.189596
std	0.166787	0.145453	0.167915	0.149274	0.126967	0.161992	0.186785	0.192857	0.138456	0.148702	...	0.171940	0.163813	0.167352	0.139932	0.150779	0.152649	0.166633	0.225884	0.121954	0.118466
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.223342	0.218465	0.216847	0.117413	0.304595	0.139685	0.069260	0.100944	0.282323	0.163016	...	0.180719	0.241471	0.167837	0.081130	0.300007	0.116337	0.091454	0.223127	0.185098	0.107700
50%	0.302381	0.308759	0.293345	0.172895	0.390358	0.224679	0.144189	0.166501	0.369697	0.243892	...	0.250445	0.356876	0.235320	0.123206	0.397081	0.179110	0.181070	0.343402	0.247782	0.163977
75%	0.416442	0.408860	0.416765	0.271135	0.475490	0.340531	0.306232	0.367793	0.453030	0.340354	...	0.386339	0.471748	0.373475	0.220901	0.494156	0.302520	0.305831	0.554639	0.318155	0.242949
max	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

8 rows × 30 columns

# Zbiór treningowy
df_train.describe()

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	...	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000	455.000000
mean	0.338949	0.326381	0.333644	0.217261	0.395892	0.260580	0.209922	0.243100	0.381150	0.270577	...	0.297118	0.369023	0.284164	0.171085	0.407890	0.221950	0.221686	0.394672	0.263384	0.191552
std	0.165349	0.145664	0.166246	0.147801	0.126845	0.160494	0.187617	0.192341	0.136984	0.147338	...	0.170654	0.166060	0.166564	0.138560	0.153644	0.156748	0.172039	0.226663	0.119284	0.122769
min	0.000000	0.022658	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.012527	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.225235	0.219817	0.219128	0.119321	0.304776	0.136096	0.068030	0.100497	0.283081	0.168176	...	0.182675	0.248801	0.169929	0.081781	0.303308	0.116871	0.092212	0.221753	0.188252	0.107110
50%	0.300961	0.310450	0.295833	0.170859	0.389636	0.230262	0.145150	0.168191	0.369697	0.241786	...	0.250445	0.358742	0.235370	0.123206	0.396421	0.181244	0.184505	0.347079	0.248571	0.164305
75%	0.415259	0.411397	0.414346	0.271113	0.476393	0.340991	0.310098	0.357952	0.453030	0.340354	...	0.377090	0.481343	0.369740	0.209964	0.496467	0.299318	0.309265	0.558935	0.317465	0.242785
max	1.000000	0.815015	1.000000	0.999152	0.831182	0.895712	1.000000	1.000000	1.000000	1.000000	...	0.896478	1.000000	0.890931	0.797975	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

8 rows × 30 columns

# Zbiór walidacyjny
df_val.describe()

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	...	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000
mean	0.334850	0.315985	0.331800	0.216332	0.399432	0.279956	0.216035	0.254466	0.387684	0.281045	...	0.294395	0.357428	0.281028	0.170796	0.391354	0.222054	0.209888	0.405999	0.276614	0.187256
std	0.176956	0.119165	0.181708	0.171264	0.144361	0.184302	0.208557	0.203610	0.160009	0.161084	...	0.180500	0.130661	0.179389	0.161083	0.140480	0.133225	0.152149	0.214849	0.143775	0.103423
min	0.089782	0.106865	0.089489	0.041357	0.167193	0.046500	0.003622	0.027793	0.078283	0.047810	...	0.072963	0.116205	0.074008	0.028264	0.085320	0.019239	0.006176	0.095670	0.066233	0.028073
25%	0.225709	0.226581	0.223205	0.117413	0.293220	0.165235	0.076406	0.112326	0.282323	0.169545	...	0.180719	0.267058	0.162757	0.082653	0.301327	0.126233	0.107827	0.248179	0.198502	0.107700
50%	0.282976	0.290159	0.279110	0.161909	0.396678	0.222195	0.127413	0.154026	0.381818	0.258214	...	0.225187	0.348348	0.228597	0.109836	0.416232	0.182893	0.164537	0.352234	0.234969	0.155910
75%	0.413129	0.389922	0.405017	0.260912	0.471879	0.359242	0.296626	0.371918	0.464141	0.342249	...	0.355034	0.463486	0.343593	0.197454	0.484911	0.308632	0.312380	0.517182	0.323674	0.260724
max	0.967343	0.623267	0.988943	1.000000	1.000000	1.000000	0.879569	0.839463	0.932323	0.949031	...	1.000000	0.619670	1.000000	1.000000	0.786040	0.571557	0.613498	0.902062	0.829687	0.460186

8 rows × 30 columns

# Zbiór testowy
df_test.describe()

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	...	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000	57.000000
mean	0.335794	0.312663	0.328412	0.214790	0.381297	0.241410	0.185207	0.232105	0.359197	0.258133	...	0.295299	0.330458	0.277057	0.169590	0.386967	0.204497	0.190725	0.374998	0.249384	0.176326
std	0.170785	0.167714	0.169927	0.139898	0.109590	0.150381	0.156133	0.188722	0.127273	0.148563	...	0.176543	0.173641	0.164054	0.130427	0.137458	0.138294	0.132668	0.233099	0.120215	0.096130
min	0.075252	0.000000	0.072904	0.033001	0.146249	0.021839	0.002798	0.011948	0.072222	0.021061	...	0.054891	0.000000	0.047263	0.022046	0.111074	0.015504	0.002860	0.035808	0.081411	0.001115
25%	0.192106	0.199188	0.185751	0.097222	0.308658	0.129471	0.062910	0.095179	0.271717	0.139217	...	0.146211	0.199893	0.146023	0.064368	0.285478	0.098777	0.086981	0.202131	0.159866	0.113210
50%	0.315159	0.310450	0.301223	0.180445	0.390990	0.203239	0.119845	0.154573	0.361616	0.246420	...	0.256492	0.325160	0.245530	0.128048	0.396421	0.172318	0.155511	0.292509	0.234772	0.160042
75%	0.486961	0.396348	0.481031	0.329629	0.460143	0.330102	0.261246	0.383996	0.439899	0.339090	...	0.429740	0.425640	0.410827	0.256046	0.478307	0.280205	0.275240	0.520619	0.304356	0.231011
max	0.692366	1.000000	0.695253	0.535949	0.578406	0.809214	0.658388	0.776342	0.674242	0.839090	...	0.667022	0.875533	0.627970	0.467902	0.674437	0.709327	0.563339	0.997595	0.622708	0.481175

8 rows × 30 columns

Rozkład klas w zbiorze

# Cały zbiór
df['diagnosis'].value_counts()

diagnosis
B    357
M    212
Name: count, dtype: int64

# Zbiór treningowy
df_train['diagnosis'].value_counts()

diagnosis
B    288
M    167
Name: count, dtype: int64

# Zbiór walidacyjny
df_val['diagnosis'].value_counts()

diagnosis
B    35
M    22
Name: count, dtype: int64

# Zbiór testowy
df_test['diagnosis'].value_counts()

diagnosis
B    34
M    23
Name: count, dtype: int64