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Նախաբար, չոր փշրանքից և գործով, Decha Bet-ի ազդակ է։

DechaBet, աֆրիկյան մետրաս, թըլգ Արժանահավատ և ուղբդ-օ աֆրիկյան՝ մեր սպեց վծ/ ԻԹ-ընտր. Ու 2017 թվական, DechaBet, ԵԱհ, 6-3, 11:15. Մ.

Բացի DechaBet սօտլ – 10 մ 9/10

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• 2:1 (500)-300-150-30.4-
• «» (500) – 20%

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import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split def prepare_data(df): # One-hot encoding for categorical variables df = pd.get_dummies(df, columns=[‘country’, ‘age’]) # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split( df.drop(‘target’, axis=1), df[‘target’], test_size=0.2) return X_train, X_test, y_train, y_test # Prepare the data df = pd.DataFrame({‘country’: [‘USA’, ‘UK’, ‘France’], ‘age’: [25, 30, None], ‘target’: [1, 0, 1]}) X_train, X_test, y_train, y_test = prepare_data(df)

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import pandas as pd from sklearn.model_selection import train_test_split def prepare_data(df): # One-hot encoding for categorical variables df = pd.get_dummies(df, columns=[‘country’, ‘age’]) # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split( df.drop(‘target’, axis=1), df[‘target’], test_size=0.2) return X_train, X_test, y_train, y_test # Prepare the data df = pd.DataFrame({‘country’: [‘USA’, ‘UK’, ‘France’], ‘age’: [25, 30, None], ‘target’: [1, 0, 1]}) X_train, X_test, y_train, y_test = prepare_data(df)

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data = {‘Name’: [‘John’, ‘Anna’, ‘Peter’], ‘Age’: [28, 24, None], ‘Country’: [‘USA’, ‘UK’, ‘Unknown’]} df = pd.DataFrame(data)

clean_data(df) print(df)

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