29 lines
1.0 KiB
Python
29 lines
1.0 KiB
Python
import pandas as pd
|
|
import tensorflow as tf
|
|
from sklearn.preprocessing import StandardScaler, OneHotEncoder
|
|
from sklearn.compose import ColumnTransformer
|
|
from sklearn.pipeline import Pipeline
|
|
from sklearn.model_selection import train_test_split
|
|
|
|
loaded_model = tf.keras.models.load_model('powerlifting_model.h5')
|
|
|
|
data = pd.read_csv('openpowerlifting.csv')
|
|
data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna() # Usunięcie wierszy z brakującymi danymi
|
|
features = data[['Sex', 'Age', 'BodyweightKg']]
|
|
target = data['TotalKg']
|
|
|
|
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
|
|
|
|
preprocessor = ColumnTransformer(
|
|
transformers=[
|
|
('num', StandardScaler(), ['Age', 'BodyweightKg']),
|
|
('cat', OneHotEncoder(), ['Sex'])
|
|
]
|
|
)
|
|
X_test_transformed = preprocessor.fit_transform(X_test)
|
|
|
|
predictions = loaded_model.predict(X_test_transformed)
|
|
|
|
predictions_df = pd.DataFrame(predictions, columns=['predicted_TotalKg'])
|
|
predictions_df.to_csv('powerlifting_test_predictions.csv', index=False)
|