sztuczna-empatia-kaczuszka/main.ipynb at 5f64cb1c0a6cce41fdb3f05824d6a5f01774936f

!pip install numpy pandas sklearn xgboost

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: numpy in c:\software\python3\lib\site-packages (1.24.2)
Requirement already satisfied: pandas in c:\software\python3\lib\site-packages (1.5.3)
Requirement already satisfied: sklearn in \\\\files\students\s478831\.appdata\python\python310\site-packages (0.0.post4)
Requirement already satisfied: xgboost in \\\\files\students\s478831\.appdata\python\python310\site-packages (1.7.5)
Requirement already satisfied: python-dateutil>=2.8.1 in c:\software\python3\lib\site-packages (from pandas) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\software\python3\lib\site-packages (from pandas) (2022.7.1)
Requirement already satisfied: scipy in c:\software\python3\lib\site-packages (from xgboost) (1.10.1)
Requirement already satisfied: six>=1.5 in c:\software\python3\lib\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)

import numpy as np
import pandas as pd
import os, sys
from sklearn.preprocessing import MinMaxScaler
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

df=pd.read_csv('./parkinsons.data')
df.head()

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335

5 rows × 24 columns

#DataFlair - Get the features and labels
features=df.loc[:,df.columns!='status'].values[:,1:]
labels=df.loc[:,'status'].values

#DataFlair - Get the count of each label (0 and 1) in labels
print(labels[labels==1].shape[0], labels[labels==0].shape[0])

147 48

#DataFlair - Scale the features to between -1 and 1
scaler=MinMaxScaler((-1,1))
x=scaler.fit_transform(features)
y=labels

#DataFlair - Split the dataset
x_train,x_test,y_train,y_test=train_test_split(x, y, test_size=0.2, random_state=7)

#DataFlair - Train the model
model=XGBClassifier()
model.fit(x_train,y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

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XGBClassifier

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

# DataFlair - Calculate the accuracy
y_pred=model.predict(x_test)
print(accuracy_score(y_test, y_pred)*100)

94.87179487179486

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