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29 changes: 29 additions & 0 deletions Machine Learning/AI-Powered Expense Categorizer
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import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

# Sample training data
data = {
"description": [
"McDonald's burger",
"Uber ride home",
"Amazon purchase",
"Netflix subscription",
"Grocery store"
],
"category": ["Food", "Transport", "Shopping", "Entertainment", "Groceries"]
}

df = pd.DataFrame(data)
X = CountVectorizer().fit_transform(df["description"])
y = df["category"]

model = MultinomialNB().fit(X, y)

# Predict category
def predict_category(text):
vector = CountVectorizer(vocabulary=X.vocabulary_).fit_transform([text])
return model.predict(vector)[0]

print(predict_category("Pizza Hut order")) # → Food
print(predict_category("Bought a movie ticket")) # → Entertainment