Create app_v1.py

app_v1.py

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+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.ensemble import RandomForestClassifier
+
+# Step 1: Generate synthetic dataset
+np.random.seed(42)
+n_records = 10000
+
+data = {
+    'pe_ratio': np.random.uniform(5, 50, n_records),
+    'de_ratio': np.random.uniform(0.1, 3.0, n_records),
+    'roe': np.random.uniform(5, 40, n_records),
+    'market_cap': np.random.uniform(500, 100000, n_records),
+    'dividend_yield': np.random.uniform(0.5, 5.0, n_records),
+    'stock_rating': np.random.choice(['Buy', 'Hold', 'Sell'], n_records, p=[0.4, 0.4, 0.2])
+}
+
+df = pd.DataFrame(data)
+
+# Step 2: Prepare data
+X = df.drop('stock_rating', axis=1)
+y = df['stock_rating']
+
+# Step 3: Encode target
+le = LabelEncoder()
+y_encoded = le.fit_transform(y)
+
+# Step 4: Train/test split (stratified)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded
+)
+
+# Step 5: Feature scaling
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# Step 6: Train model
+model = RandomForestClassifier(random_state=42)
+model.fit(X_train_scaled, y_train)
+
+# Step 7️ Predict using your trained model
+y_pred = model.predict(X_test_scaled)
+
+# Step 8️ Inverse transform using correct label encoder
+y_test_labels = le.inverse_transform(y_test)
+y_pred_labels = le.inverse_transform(y_pred)
+
+# Step 9️ Print basic metrics
+print("✅ Accuracy:", accuracy_score(y_test_labels, y_pred_labels))
+print("✅ Precision:", precision_score(y_test_labels, y_pred_labels, average='weighted'))
+print("✅ Recall:", recall_score(y_test_labels, y_pred_labels, average='weighted'))
+print("✅ F1 Score:", f1_score(y_test_labels, y_pred_labels, average='weighted'))
+
+# Step 10️ Detailed breakdown per class
+print("\n📊 Classification Report:")
+print(classification_report(y_test_labels, y_pred_labels))
+
+# Step 11️ Confusion Matrix
+cm = confusion_matrix(y_test_labels, y_pred_labels, labels=le.classes_)
+
+plt.figure(figsize=(6, 5))
+sns.heatmap(cm, annot=True, fmt="d", cmap="Blues",
+            xticklabels=le.classes_,
+            yticklabels=le.classes_)
+plt.xlabel("Predicted Label")
+plt.ylabel("True Label")
+plt.title("📉 Confusion Matrix")
+plt.show()