new

Author: machphy

Random_Forest_project/data/Churn_Modelling.csv renamed to Random_Forest_project/src/Churn_Modelling.csv

@@ -1,13 +1,28 @@
+import sys
+import os
 import pandas as pd
-from datapreproc import preprocess_data
-from model_training import train_model
-from modelevelution import evaluate_model
+from src.model_evaluation import evaluate_model
+from src.data_preprocessing import preprocess_data
+from src.model_training import train_model
 
+# Add the parent directory of 'src' to the Python path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
-data = pd.read_csv('Churn_Modelling.csv')
+# Specify the path to the CSV file
+csv_file_path = os.path.join(os.path.dirname(__file__), '..', 'Churn_Modelling.csv')
 
+# Load the data
+try:
+    data = pd.read_csv(csv_file_path)
+except FileNotFoundError:
+    print(f"The file {csv_file_path} was not found.")
+    exit()
+
+# Preprocess the data
 X_train, X_test, y_train, y_test = preprocess_data(data)
 
+# Train the model
 model = train_model(X_train, y_train)
 
-evaluate_model(model, X_test, y_test)
+# Evaluate the model
+evaluate_model(model, X_test, y_test)

Random_Forest_project/src/__pycache__/data_preprocessing.cpython-312.pyc

@@ -0,0 +1,24 @@
+# model_evaluation.py
+
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, roc_curve, roc_auc_score
+
+def evaluate_model(model, X_test, y_test):
+    y_pred = model.predict(X_test)
+    accuracy = accuracy_score(y_test, y_pred)
+    precision = precision_score(y_test, y_pred)
+    recall = recall_score(y_test, y_pred)
+    f1 = f1_score(y_test, y_pred)
+    print("Accuracy:", accuracy)
+    print("Precision:", precision)
+    print("Recall:", recall)
+    print("F1-score:", f1)
+
+    # Confusion matrix
+    cm = confusion_matrix(y_test, y_pred)
+    print("Confusion Matrix:\n", cm)
+
+    # ROC and AUC
+    y_pred_proba = model.predict_proba(X_test)
+    fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba[:, 1])
+    auc = roc_auc_score(y_test, y_pred_proba[:, 1])
+    print("AUC:", auc)

Random_Forest_project/src/__pycache__/kuchbhi.cpython-312.pyc

@@ -0,0 +1,7 @@
+
+from sklearn.ensemble import RandomForestClassifier
+
+def train_model(X_train, y_train):
+    rf_model = RandomForestClassifier(random_state=42)
+    rf_model.fit(X_train, y_train)
+    return rf_model