Purpose
The objective of this blog is to chronicle my journey in the realm of machine learning, with a specific focus on its application in the Real Estate and Demographics sector. This project aims to leverage demographic data and real estate trends to forecast future property values across the United States.
Housing Data
To develop a predictive model for housing prices, I have gathered data from the US Census and Zillow. This data serves as the foundation for my machine learning model, which includes the following features:
- Total number of births per year: Indicates population growth trends affecting housing demand.
- Average household size: Larger households may prefer larger homes, influencing prices.
- Median mortgage loan financed per State: Reflects borrowing capacity and credit health.
- Median household income per State: Higher incomes can increase purchasing power.
- Median housing cost per month per State: Offers insight into regional affordability.
- Median real estate taxes per State: Tax rates impact the overall cost of homeownership.
- Number of occupied housing units per State: Indicates supply and demand dynamics.
- Zillow's Home Value Index per State: Provides a snapshot of current home values.
Explore the Data
Deep Learning & Neural Networks
Using TensorFlow and Keras, I apply deep learning to our housing data to make predictions. The following procedures were applied to the model:
- Data Preprocessing: Normalizing and transforming data for neural network training.
- Model Definition: Using Keras to define the architecture with layers, neurons, and activation functions.
- Model Compilation: Specifying the loss function and optimizer.
- Model Training: Feeding data and adjusting weights through backpropagation.
- Model Evaluation: Assessing performance with validation data and metrics.
- Prediction: Using the trained model to predict housing prices.
Python code
X = df.drop('Price',axis=1).values
y = df['Price'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=101)
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
model = keras.Sequential()
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(1)) #predict the price
model.compile(optimizer='adam',loss='mse')
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)
model.fit(x=X_train,y=y_train,validation_data=(X_test,y_test), epochs=360, callbacks=[early_stop])
predictions = model.predict(X_test)By leveraging TensorFlow and Keras, I trained deep learning models capable of making accurate predictions for housing prices, essential for both potential buyers and sellers in the real estate market.
Evaluate the Model
These metrics quantify how accurately the model’s predictions align with actual values. Across all states, accounting for housing market seasonality and broad data inputs, the model predicts prices ranging from $100,000 to over $1,000,000 with errors under $66,000.
Results
| Metric | Description | Value |
|---|---|---|
| RMSE | Typical prediction error made by the model. | $66,474 |
| MAE | Average distance between predictions and actual values. | $45,797 |
| Explained Variance | How well the model accounts for variation in housing prices. | 85.4% |