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lead_scoring_model/lead_data.csv

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+lead_source,response_time,activity_level,region,converted
+Google,5,3,East,1
+Facebook,10,2,West,0
+Referral,3,4,North,1
+Google,7,1,South,0
+Referral,2,5,East,1

lead_scoring_model/model/model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef90f28fa9be74d51babb47666cd6f26e51b8a7232da0487c11f5bf43c6a4def
+size 1199

lead_scoring_model/train_model.py

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+import pandas as pd
+import joblib
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import classification_report
+import os
+
+# Load dataset
+data = pd.read_csv("lead_data.csv")
+
+# Encode categorical variables
+data['lead_source'] = data['lead_source'].astype('category').cat.codes
+data['region'] = data['region'].astype('category').cat.codes
+
+# Define features and label
+X = data[['lead_source', 'response_time', 'activity_level', 'region']]
+y = data['converted']
+
+# Split into training and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Train logistic regression model
+model = LogisticRegression(max_iter=200)
+model.fit(X_train, y_train)
+
+# Evaluate
+preds = model.predict(X_test)
+print("\nModel Performance:\n")
+print(classification_report(y_test, preds))
+
+# Save the model
+if not os.path.exists("model"):
+    os.mkdir("model")
+
+joblib.dump(model, "model/model.pkl")
+print("✅ Model saved as model/model.pkl")