diff --git a/script5_4.py b/script5_4.py
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+++ b/script5_4.py
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+import pandas as pd
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Dataset
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
+import pickle
+
+# Define the neural network model
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.fc1 = nn.Linear(1, 64)
+        self.fc2 = nn.Linear(64, 1)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+# Define a custom dataset
+class CustomDataset(Dataset):
+    def __init__(self, X, y):
+        self.X = torch.FloatTensor(X.values.reshape(-1, 1))
+        self.y = torch.FloatTensor(y.values.reshape(-1, 1))
+
+    def __len__(self):
+        return len(self.X)
+
+    def __getitem__(self, idx):
+        return self.X[idx], self.y[idx]
+
+# Load the dataset
+df = pd.read_csv('data.csv')
+
+# Select the relevant columns (e.g., 'Rating' and 'Writer')
+data = df[['Rating', 'Writer']]
+
+# Drop rows with missing values
+data = data.dropna()
+
+# Convert the 'Writer' column to numeric using label encoding
+encoder = LabelEncoder()
+data['Writer'] = encoder.fit_transform(data['Writer'])
+
+# Split the data into training and testing sets
+X = data['Writer']
+y = data['Rating']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create the model instance
+model = Model()
+
+# Load the saved model
+model.load_state_dict(torch.load('model.pth'))
+
+# Create dataloaders for evaluation
+test_dataset = CustomDataset(X_test, y_test)
+test_dataloader = DataLoader(test_dataset, batch_size=64)
+
+# Evaluate the model
+model.eval()
+predictions = []
+targets = []
+for inputs, targets_batch in test_dataloader:
+    outputs = model(inputs)
+    predictions.extend(outputs.tolist())
+    targets.extend(targets_batch.tolist())
+
+# Calculate evaluation metrics
+predictions = torch.FloatTensor(predictions).squeeze()
+targets = torch.FloatTensor(targets).squeeze()
+
+accuracy = accuracy_score(targets, torch.round(predictions))
+precision = precision_score(targets, torch.round(predictions), average='micro')
+recall = recall_score(targets, torch.round(predictions), average='micro')
+f1 = f1_score(targets, torch.round(predictions), average='micro')
+rmse = mean_squared_error(targets, predictions, squared=False)
+
+print("Accuracy:", accuracy)
+print("Micro-average Precision:", precision)
+print("Micro-average Recall:", recall)
+print("F1 Score:", f1)
+print("RMSE:", rmse)