# Column names based on the CSV preview provided earlier
columns = [
'RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure',
'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited'
]
# Template for PyTorch based on the column names and the steps discussed
# Assuming you'll start with importing necessary libraries:
code_template = """
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.model_selection import train_test_split
# 1. Data Preparation
ds = pd.read_csv('<path_to_your_file>') # Replace with the path to your CSV file
X = ds.drop("Exited", axis=1).values # Assuming "Exited" is the target column
y = ds["Exited"].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)
X_test_tensor = torch.tensor(X_test, dtype=torch.float32)
y_test_tensor = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)
batch_size = 64
train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = TensorDataset(X_test_tensor, y_test_tensor)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# 2. Model Setup
class ChurnNN(nn.Module):
def __init__(self, input_dim):
super(ChurnNN, self).__init__()
self.fc1 = nn.Linear(input_dim, 128)
self.dropout = nn.Dropout(0.5)
self.fc2 = nn.Linear(128, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.dropout(x)
x = torch.sigmoid(self.fc2(x))
return x
model = ChurnNN(input_dim=X_train.shape[1])
criterion = nn.BCELoss() # Binary Cross-Entropy Loss
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 3. Training Loop
num_epochs = 10
for epoch in range(num_epochs):
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(data)
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
# 4. Evaluation (placeholder)
# Here you can add code to evaluate the model's performance on the test set.
print("Training complete!")
"""
code_template