Luxury car listing model

models/luxury-detector (1).py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, accuracy_score
+from imblearn.over_sampling import SMOTE
+from imblearn.pipeline import make_pipeline
+
+# Load the dataset
+data = pd.read_csv('model_data_with_anomalies.csv') # Replace with your file path
+
+# Define a list of luxury car brands
+luxury_brands = ['Mercedes-Benz', 'BMW', 'Audi', 'Lexus', 'Porsche', 'Tesla', 
+ 'Jaguar', 'Land Rover', 'Maserati', 'Ferrari', 'Lamborghini', 
+ 'Bentley', 'Rolls-Royce']
+
+# Function to check if the title contains any luxury brand
+def is_luxury(title):
+ for brand in luxury_brands:
+ if brand.lower() in title.lower():
+ return 1
+ return 0
+
+# Apply the function to the 'title' column
+data['is_luxury'] = data['title'].apply(is_luxury)
+
+# Splitting the dataset
+X_train, X_test, y_train, y_test = train_test_split(data['title'], data['is_luxury'], test_size=0.3, random_state=42)
+
+# Creating a pipeline with TF-IDF Vectorizer, SMOTE, and RandomForestClassifier
+pipeline = make_pipeline(TfidfVectorizer(), SMOTE(random_state=42), RandomForestClassifier(random_state=42))
+
+# Training the model
+pipeline.fit(X_train, y_train)
+
+# Predictions
+y_pred = pipeline.predict(X_test)
+
+# Evaluating the model
+accuracy = accuracy_score(y_test, y_pred)
+report = classification_report(y_test, y_pred)
+
+print(f"Accuracy: {accuracy}")
+print("Classification Report:")
+print(report)