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# Python example: Loading data from CSV<br>
import pandas as pd<br>
data = pd.read_csv('dataset.csv')
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# Handling missing values<br>
data = data.fillna(data.mean())<br><br>
# Feature scaling<br>
from sklearn.preprocessing import StandardScaler<br>
scaler = StandardScaler()<br>
scaled_data = scaler.fit_transform(data)
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# Creating interaction features<br>
data['feature_interaction'] = data['feat1'] * data['feat2']<br><br>
# One-hot encoding<br>
data = pd.get_dummies(data, columns=['categorical_feature'])
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# Splitting data<br>
from sklearn.model_selection import train_test_split<br>
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)<br><br>
# Training a model<br>
from sklearn.ensemble import RandomForestClassifier<br>
model = RandomForestClassifier()<br>
model.fit(X_train, y_train)
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# Evaluation metrics<br>
from sklearn.metrics import accuracy_score, precision_score, recall_score<br>
predictions = model.predict(X_test)<br><br>
print("Accuracy:", accuracy_score(y_test, predictions))<br>
print("Precision:", precision_score(y_test, predictions))<br>
print("Recall:", recall_score(y_test, predictions))
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# Saving the model<br>
import joblib<br>
joblib.dump(model, 'model.pkl')<br><br>
# Flask API example<br>
from flask import Flask, request, jsonify<br>
app = Flask(__name__)<br>
@app.route('/predict', methods=['POST'])<br>
def predict():<br>
data = request.json<br>
prediction = model.predict([data['features']])<br>
return jsonify({'prediction': prediction.tolist()})
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