Create train_model.py

train_model.py

@@ -0,0 +1,33 @@
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.linear_model import Ridge
+from sklearn.model_selection import train_test_split
+
+DATA_URL = "https://raw.githubusercontent.com/KeeganBarbee/KeeganBarbee.github.io/main/OnlineNewsPopularity.csv"
+
+df = pd.read_csv(DATA_URL)
+df.columns = df.columns.str.strip()
+
+df['log_shares'] = np.log1p(df['shares'])
+
+feature_cols = [
+    'n_tokens_content', 'num_imgs', 'global_sentiment_polarity',
+    'global_subjectivity', 'title_sentiment_polarity',
+    'weekday_is_monday', 'weekday_is_tuesday', 'weekday_is_wednesday',
+    'weekday_is_thursday', 'weekday_is_friday', 'weekday_is_saturday',
+    'weekday_is_sunday', 'n_tokens_title', 'num_videos', 'num_hrefs'
+]
+
+X = df[feature_cols]
+y = df['log_shares']
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+model = Ridge(alpha=1.0)
+model.fit(X_train, y_train)
+
+joblib.dump(model, 'popularity_model.pkl')
+joblib.dump(feature_cols, 'model_features.pkl')