Upload api.py

api.py

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+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+class PlacementModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(PlacementModel, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# Load and preprocess data
+df = pd.read_csv("Placement (2).csv")
+df = df.drop(columns=["sl_no","stream","ssc_p","ssc_b","hsc_p","hsc_b","etest_p"])
+df['internship'] = df['internship'].map({'Yes':1,'No':0})
+df['status'] = df['status'].map({'Placed':1,'Not Placed':0})
+
+X_fullstk = df.drop(['status','management','leadership','communication','sales'], axis=1)
+y = df['status']
+
+X_train_fullstk, X_test_fullstk, y_train, y_test = train_test_split(X_fullstk, y, test_size=0.20, random_state=42)
+
+# Define model hyperparameters
+input_size = X_fullstk.shape[1]
+hidden_size = 128
+output_size = 2
+learning_rate = 0.01
+epochs = 100
+
+# Initialize model
+model = PlacementModel(input_size, hidden_size, output_size)
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train model
+for epoch in range(epochs):
+    inputs = torch.tensor(X_train_fullstk.values, dtype=torch.float32)
+    labels = torch.tensor(y_train.values, dtype=torch.long)
+
+    optimizer.zero_grad()
+
+    outputs = model(inputs)
+    loss = criterion(outputs, labels)
+    loss.backward()
+    optimizer.step()
+
+    if epoch % 10 == 0:
+        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')
+
+# Evaluate model
+with torch.no_grad():
+    inputs = torch.tensor(X_test_fullstk.values, dtype=torch.float32)
+    labels = torch.tensor(y_test.values, dtype=torch.long)
+
+    outputs = model(inputs)
+    _, predicted = torch.max(outputs.data, 1)
+    accuracy = accuracy_score(labels, predicted)
+
+    print(f'Test Accuracy: {accuracy:.4f}')
+
+# Save model
+torch.save(model.state_dict(), 'placement_model.pth')