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40 lines
1.1 KiB
40 lines
1.1 KiB
import pandas as pd
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import numpy as np
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from sklearn.calibration import LabelEncoder
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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# Load data from CSV
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df = pd.read_csv("data\\data_emple.csv")
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category = df.iloc[0:len(df),0].values
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otherData = df.iloc[0:len(df),1:6].values
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print(otherData)
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# Encode the categorical target variable
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label_encoder = LabelEncoder()
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category_encoded = label_encoder.fit_transform(category)
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model = LinearRegression()
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model.fit(otherData,category_encoded)
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new_data = np.array([224.0,228.49,346.6000000000000,1.1007253886010361,101]).reshape(1, -1)
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# Faites une prédiction avec le modèle entraîné
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prediction = model.predict(new_data)
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# Affichez la prédiction
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print("Prédiction:", prediction)
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# Supposons que label_encoder soit l'objet LabelEncoder que vous avez utilisé lors de l'entraînement
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inverse_prediction = label_encoder.inverse_transform(prediction.astype(int))
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# Affichez la prédiction sous forme de chaîne de caractères
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print("Prédiction (en chaîne de caractères):", inverse_prediction)
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