Advanced House Price Prediction Tutorial Using Pytorch Deep Learning on Live Kaggle Data – Tutorial 5

In this tutorial, we will be using PyTorch to build a deep learning model for advance house price prediction. Kaggle is a popular platform for data science competitions, and the House Prices: Advanced Regression Techniques competition is a well-known challenge in the field of machine learning. In this competition, participants are tasked with predicting the final price of houses based on various features such as the size of the house, number of bedrooms, location, and so on.

For this tutorial, we will be using the PyTorch library to build our deep learning model. PyTorch is an open-source machine learning framework that provides a flexible and dynamic computational graph, making it ideal for building and training deep learning models.

Before we begin, make sure you have PyTorch installed on your machine. You can install PyTorch using pip by running the following command:

pip install torch

You will also need to install other required libraries such as pandas, numpy, and scikit-learn. You can install these libraries using pip as well:

pip install pandas numpy scikit-learn

Now, let’s get started with building our deep learning model for advance house price prediction using PyTorch.

Step 1: Importing the necessary libraries
First, we need to import the necessary libraries. We will be using pandas for data manipulation, PyTorch for building our deep learning model, and numpy for numerical computations.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split

Step 2: Loading the dataset
Next, we will load the dataset that contains information about houses and their respective prices. You can download the dataset from Kaggle’s House Prices: Advanced Regression Techniques competition page.

data = pd.read_csv('train.csv')

Step 3: Preprocessing the data
Before we can build our deep learning model, we need to preprocess the data. This includes handling missing values, encoding categorical variables, and scaling the numerical features.

# Handling missing values
data.fillna(0, inplace=True)

# Encoding categorical variables
data = pd.get_dummies(data)

# Scaling numerical features
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)

Step 4: Splitting the data into training and testing sets
Next, we will split the data into training and testing sets. We will use 80% of the data for training and 20% for testing.

X = data_scaled.drop('SalePrice', axis=1)
y = data_scaled['SalePrice']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 5: Creating a custom dataset class
In PyTorch, we need to create a custom dataset class to load the data into our model. This class should inherit from the Dataset class and override the len and getitem methods.

class HouseDataset(Dataset):
    def __init__(self, X, y):
        self.X = torch.tensor(X, dtype=torch.float32)
        self.y = torch.tensor(y.values, dtype=torch.float32).view(-1, 1)

    def __len__(self):
        return len(self.X)

    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

Step 6: Creating the neural network model
Now, we will create our neural network model using PyTorch. For this tutorial, we will use a simple fully connected neural network with three hidden layers.

class HousePricePredictor(nn.Module):
    def __init__(self):
        super(HousePricePredictor, self).__init__()
        self.fc1 = nn.Linear(288, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

Step 7: Training the model
Now, we will train our model using the training data. We will define a loss function, an optimizer, and train the model for a specific number of epochs.

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = HousePricePredictor().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

train_dataset = HouseDataset(X_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

num_epochs = 100
for epoch in range(num_epochs):
    model.train()
    for i, (inputs, labels) in enumerate(train_loader):
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}')

Step 8: Evaluating the model
Finally, we will evaluate the performance of our model on the testing data by calculating the mean squared error and r-squared score.

model.eval()
with torch.no_grad():
    test_dataset = HouseDataset(X_test, y_test)
    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

    total_loss = 0
    for inputs, labels in test_loader:
        inputs, labels = inputs.to(device), labels.to(device)

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        total_loss += loss.item()

    mse = total_loss / len(test_loader)
    print(f'Mean Squared Error: {mse}')

Congratulations! You have successfully built a deep learning model for advance house price prediction using PyTorch. You can further improve the performance of the model by tuning hyperparameters, adding more layers, or using different optimization techniques.

In this tutorial, we covered the following steps:

1. Importing the necessary libraries
2. Loading the dataset
3. Preprocessing the data
4. Splitting the data into training and testing sets
5. Creating a custom dataset class
6. Creating the neural network model
7. Training the model
8. Evaluating the model

I hope you found this tutorial helpful. Thank you for reading!