ium_464937/02.ipynb at 844157b7574e259c648fb9759464c5727cf96120

1. Pobranie zbioru

!pip install kaggle
!pip install pandas

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!kaggle datasets download -d open-powerlifting/powerlifting-database

Downloading powerlifting-database.zip to /Users/szymonbartanowicz/studia/mag_1/inzynieria_uczenia_maszynowego/ium_464937
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!unzip -o powerlifting-database.zip

Archive:  powerlifting-database.zip
  inflating: openpowerlifting-2024-01-06-4c732975.csv  
  inflating: openpowerlifting.csv

!pip install pandas
!pip install seaborn

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import pandas as pd
data = pd.read_csv('openpowerlifting.csv')

/var/folders/82/g0638vys2hs3rk916hlpkdrr0000gn/T/ipykernel_47077/3909872695.py:2: DtypeWarning: Columns (35) have mixed types. Specify dtype option on import or set low_memory=False.
  data = pd.read_csv('openpowerlifting.csv')

2. Statystyki

data.head()
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1423354 entries, 0 to 1423353
Data columns (total 37 columns):
 #   Column           Non-Null Count    Dtype  
---  ------           --------------    -----  
 0   Name             1423354 non-null  object 
 1   Sex              1423354 non-null  object 
 2   Event            1423354 non-null  object 
 3   Equipment        1423354 non-null  object 
 4   Age              757527 non-null   float64
 5   AgeClass         786800 non-null   object 
 6   Division         1415176 non-null  object 
 7   BodyweightKg     1406622 non-null  float64
 8   WeightClassKg    1410042 non-null  object 
 9   Squat1Kg         337580 non-null   float64
 10  Squat2Kg         333349 non-null   float64
 11  Squat3Kg         323842 non-null   float64
 12  Squat4Kg         3696 non-null     float64
 13  Best3SquatKg     1031450 non-null  float64
 14  Bench1Kg         499779 non-null   float64
 15  Bench2Kg         493486 non-null   float64
 16  Bench3Kg         478485 non-null   float64
 17  Bench4Kg         9505 non-null     float64
 18  Best3BenchKg     1276181 non-null  float64
 19  Deadlift1Kg      363544 non-null   float64
 20  Deadlift2Kg      356023 non-null   float64
 21  Deadlift3Kg      339947 non-null   float64
 22  Deadlift4Kg      9246 non-null     float64
 23  Best3DeadliftKg  1081808 non-null  float64
 24  TotalKg          1313184 non-null  float64
 25  Place            1423354 non-null  object 
 26  Wilks            1304407 non-null  float64
 27  McCulloch        1304254 non-null  float64
 28  Glossbrenner     1304407 non-null  float64
 29  IPFPoints        1273286 non-null  float64
 30  Tested           1093892 non-null  object 
 31  Country          388884 non-null   object 
 32  Federation       1423354 non-null  object 
 33  Date             1423354 non-null  object 
 34  MeetCountry      1423354 non-null  object 
 35  MeetState        941545 non-null   object 
 36  MeetName         1423354 non-null  object 
dtypes: float64(22), object(15)
memory usage: 401.8+ MB

data['Sex'].value_counts()

Sex
M    1060189
F     363165
Name: count, dtype: int64

print(f"Minimum: {data['Best3SquatKg'].min()}")
print(f"Maksimum: {data['Best3SquatKg'].max()}")
print(f"Odchylenie standardowe: {data['Best3SquatKg'].std()}")
print(f"Mediana: {data['Best3SquatKg'].median()}")
data['Best3SquatKg'].value_counts()

Minimum: -477.5
Maksimum: 575.0
Odchylenie standardowe: 69.23931149707244
Mediana: 167.83

Best3SquatKg
200.00    15211
136.08    12626
190.00    12044
160.00    12043
170.00    11993
          ...  
277.30        1
143.20        1
129.60        1
131.80        1
309.58        1
Name: count, Length: 1907, dtype: int64

3. Czyszczenie zbioru

Kolumna country w 73% przypadków jest pusta, dlatego ją usuwam.

data.drop(columns=['Country'])

	Name	Sex	Event	Equipment	Age	AgeClass	Division	BodyweightKg	WeightClassKg	Squat1Kg	...	Wilks	McCulloch	Glossbrenner	IPFPoints	Tested	Federation	Date	MeetCountry	MeetState	MeetName
63986	Kylie Beutler	F	SBD	Wraps	23.0	20-23	Juniors 20-23	56.00	56	83.91	...	338.90	338.90	299.77	523.61	Yes	WPA	2011-05-21	USA	CA	World Championships
66457	Kaitlynn Naert	F	SBD	Wraps	13.0	13-15	Teen 13-15	103.69	90+	43.09	...	205.66	263.24	175.84	359.25	Yes	APA	2015-09-19	USA	MI	Wolverine Open
67030	Carol Moorhead	F	SBD	Wraps	55.0	55-59	Open	74.39	75	79.38	...	223.22	273.44	196.41	374.65	Yes	APA	2017-04-22	USA	MO	ShowMe State Raw Championships
67031	Nancy Lowther	F	SBD	Wraps	58.0	55-59	Open	87.09	90	90.72	...	260.41	336.19	227.02	449.73	Yes	APA	2017-04-22	USA	MO	ShowMe State Raw Championships
69557	Roger Shaw	M	SBD	Wraps	73.0	70-74	Masters 70-79	74.12	75	147.42	...	311.23	546.53	301.14	443.10	Yes	APA	2018-11-17	USA	MO	Midwest Raw Championships
646493	Ryan Lapadat	M	SBD	Raw	26.0	24-34	Open	81.70	82.5	142.50	...	347.04	347.04	335.10	505.60	Yes	CPO	2008-05-17	Canada	ON	Canadian Championships
646495	Denis Pronin	M	SBD	Multi-ply	20.0	20-23	Juniors 20-23	76.30	82.5	125.00	...	313.37	322.77	303.01	411.52	Yes	CPO	2008-05-17	Canada	ON	Canadian Championships
652230	Brooke Zak	F	SBD	Raw	12.5	13-15	Teen 12-13	44.54	48	40.00	...	223.45	286.01	200.16	303.20	Yes	RAW	2018-11-09	USA	NC	OBX Open
658136	Brooke Zak	F	SBD	Raw	12.0	5-12	Teen 12-13	43.27	44	37.50	...	234.91	312.43	211.11	318.59	Yes	RAW	2018-08-03	USA	NC	Southern Open
658137	Brooke Zak	F	SBD	Raw	12.0	5-12	Open	43.27	44	37.50	...	234.91	312.43	211.11	318.59	Yes	RAW	2018-08-03	USA	NC	Southern Open
658150	Frank Ferchland	M	SBD	Raw	50.5	50-54	Open	106.87	110	142.50	...	286.58	323.84	274.82	390.62	Yes	RAW	2018-08-03	USA	NC	Southern Open
658151	Frank Ferchland	M	SBD	Raw	50.5	50-54	Law/Fire/Military	106.87	110	142.50	...	286.58	323.84	274.82	390.62	Yes	RAW	2018-08-03	USA	NC	Southern Open
658152	Frank Ferchland	M	SBD	Raw	50.5	50-54	Masters 50-54	106.87	110	142.50	...	286.58	323.84	274.82	390.62	Yes	RAW	2018-08-03	USA	NC	Southern Open
919233	Michael Trentin	M	SBD	Multi-ply	18.0	18-19	MO-MP	64.45	67.5	117.50	...	298.34	316.24	289.83	394.62	Yes	CAPO	2001-08-18	Australia	WA	Nationals

14 rows × 36 columns

Wartości NaN zamieniam na 0.

data.fillna(0, inplace=True)

4. Podział zbioru na podzbiory

Używam proporcji 8:1:1 (train:dev:test)

!pip install scikit-learn

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from sklearn.model_selection import train_test_split
openpowerlifting_train, openpowerlifting_test = train_test_split(data, test_size=0.1, random_state=1)
openpowerlifting_train, openpowerlifting_dev = train_test_split(openpowerlifting_train, test_size=1/9, random_state=1)

print("Wielkość zbioru train: ", len(openpowerlifting_train))
print("Wielkość zbioru dev: ", len(openpowerlifting_dev))
print("Wielkość zbioru test: ", len(openpowerlifting_test))

Wielkość zbioru train:  1138682
Wielkość zbioru dev:  142336
Wielkość zbioru test:  142336

5. Normalizacja

from sklearn.preprocessing import StandardScaler

scaled_features = data.copy()
col_names = ['Age', 'BodyweightKg', 'Squat1Kg', 'Squat2Kg', 'Squat3Kg', 'Squat4Kg', 'Best3SquatKg', 'Bench1Kg', 'Bench2Kg', 'Bench3Kg', 'Bench4Kg', 'Best3BenchKg', 'Deadlift1Kg', 'Deadlift2Kg', 'Deadlift3Kg', 'Deadlift4Kg', 'Best3DeadliftKg', 'TotalKg']
features = scaled_features[col_names]
scaler = StandardScaler().fit(features.values)
features = scaler.transform(features.values)
scaled_features[col_names] = features
scaled_features

	Name	Sex	Event	Equipment	Age	AgeClass	Division	BodyweightKg	WeightClassKg	Squat1Kg	...	McCulloch	Glossbrenner	IPFPoints	Tested	Country	Federation	Date	MeetCountry	MeetState	MeetName
0	Abbie Murphy	F	SBD	Wraps	0.661353	24-34	F-OR	-0.944800	60	0.611664	...	324.16	286.42	511.15	0	0	GPC-AUS	2018-10-27	Australia	VIC	Melbourne Cup
1	Abbie Tuong	F	SBD	Wraps	0.661353	24-34	F-OR	-0.997210	60	0.842750	...	378.07	334.16	595.65	0	0	GPC-AUS	2018-10-27	Australia	VIC	Melbourne Cup
2	Ainslee Hooper	F	B	Raw	1.255975	40-44	F-OR	-1.122189	56	-0.312682	...	38.56	34.12	313.97	0	0	GPC-AUS	2018-10-27	Australia	VIC	Melbourne Cup
3	Amy Moldenhauer	F	SBD	Wraps	0.337014	20-23	F-OR	-0.936736	60	-1.525886	...	345.61	305.37	547.04	0	0	GPC-AUS	2018-10-27	Australia	VIC	Melbourne Cup
4	Andrea Rowan	F	SBD	Wraps	1.526258	45-49	F-OR	0.837161	110	1.073837	...	338.91	274.56	550.08	0	0	GPC-AUS	2018-10-27	Australia	VIC	Melbourne Cup
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1423349	Marian Cafalik	M	SBD	Raw	2.364134	60-64	Masters 2	-0.392472	74	1.536010	...	438.27	316.52	469.67	Yes	0	PZKFiTS	2017-04-01	Poland	0	Polish Classic Powerlifting Cup
1423350	Marian Piwowarczyk	M	SBD	Raw	2.093852	55-59	Masters 2	-0.795631	66	0.727207	...	372.60	295.66	423.03	Yes	Poland	PZKFiTS	2017-04-01	Poland	0	Polish Classic Powerlifting Cup
1423351	Andrzej Bryniarski	M	SBD	Raw	2.472248	60-64	Masters 2	0.450129	105	1.304923	...	382.36	264.22	378.84	Yes	0	PZKFiTS	2017-04-01	Poland	0	Polish Classic Powerlifting Cup
1423352	Stanisław Goroczko	M	SBD	Raw	2.526304	60-64	Masters 2	-0.098167	83	-2.219146	...	0.00	0.00	0.00	Yes	0	PZKFiTS	2017-04-01	Poland	0	Polish Classic Powerlifting Cup
1423353	Jan Sowa	M	SBD	Raw	2.904700	70-74	Masters 2	-0.049788	83	-1.641430	...	0.00	0.00	0.00	Yes	0	PZKFiTS	2017-04-01	Poland	0	Polish Classic Powerlifting Cup

1423354 rows × 37 columns

43 KiB Raw Blame History Unescape Escape

1. Pobranie zbioru

2. Statystyki

3. Czyszczenie zbioru

4. Podział zbioru na podzbiory

5. Normalizacja

43 KiB

Raw Blame History