47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
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import pandas as pd
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from nltk.corpus import wordnet
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import nltk
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nltk.download('wordnet')
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# Importer le fichier CSV dans un DataFrame
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df = pd.read_csv('bank_statements.csv')
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# Correction orthographique approximative
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def correct_spelling(label):
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suggestions = wordnet.synsets(label)
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if suggestions:
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return max(suggestions, key=lambda s: s.lemma_names('eng')).lemmas()[0].name()
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else:
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return label
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# Appliquer la correction orthographique
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df['Label'] = df['Label'].apply(correct_spelling)
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# Vectoriser les labels de transaction
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vectorizer = CountVectorizer()
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labels = df['Label']
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X = vectorizer.fit_transform(labels)
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# Diviser les données en jeu d'entraînement et jeu de test
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X_train, X_test, y_train, y_test = train_test_split(X, labels, random_state=42, test_size=0.2)
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# Former le classificateur naïf Bayésien multinomial
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clf = MultinomialNB()
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clf.fit(X_train, y_train)
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# Évaluer le classificateur
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y_pred = clf.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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print(f'Classification accuracy: {accuracy:.2f}')
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# Catégoriser les dépenses
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categories = set(df['Label'])
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category_mapping = dict((cat, i) for i, cat in enumerate(categories))
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df['Category'] = df['Label'].map(category_mapping)
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df.head()
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