Example: House Price Prediction

This example demonstrates how AIDE ML can tackle a classic regression task: predicting house prices based on a set of features.

Task Description

The goal is to predict the sales price for each house in Ames, Iowa, using 79 explanatory variables describing various aspects of residential homes.

• Goal: It is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable.
• Evaluation: Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.)
• Data: The dataset includes train.csv, test.csv, and a data_description.txt file.

Running the Example

This task is included in the repository. You can run it with the following command:

aide data_dir="aide/example_tasks/house_prices" \
     goal="Predict the sales price for each house" \
     eval="RMSE between the logarithm of the predicted and observed values."

Example Solution

Below is an example of a complete, high-quality script that AIDE ML can generate for this task. This particular solution uses a Lasso regression model and demonstrates feature engineering, data preprocessing, and model training within a single, executable script.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_squared_error
import numpy as np

# Load the data
train = pd.read_csv("./input/train.csv")
test = pd.read_csv("./input/test.csv")

# Identify key features for interaction terms based on domain knowledge
key_features = ["OverallQual", "GrLivArea", "TotalBsmtSF", "GarageCars"]

# Create interaction terms for both train and test datasets
for i in range(len(key_features)):
    for j in range(i + 1, len(key_features)):
        name = key_features[i] + "_X_" + key_features[j]
        train[name] = train[key_features[i]] * train[key_features[j]]
        test[name] = test[key_features[i]] * test[key_features[j]]

# Separate features and target variable
X = train.drop(["SalePrice", "Id"], axis=1)
y = np.log(train["SalePrice"])  # Log transformation
test_ids = test["Id"]
test = test.drop(["Id"], axis=1)

# Preprocessing for numerical data
numerical_transformer = Pipeline(
    steps=[("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]
)

# Preprocessing for categorical data
categorical_transformer = Pipeline(
    steps=[
        ("imputer", SimpleImputer(strategy="most_frequent")),
        ("onehot", OneHotEncoder(handle_unknown="ignore")),
    ]
)

# Bundle preprocessing for numerical and categorical data
preprocessor = ColumnTransformer(
    transformers=[
        ("num", numerical_transformer, X.select_dtypes(exclude=["object"]).columns),
        ("cat", categorical_transformer, X.select_dtypes(include=["object"]).columns),
    ]
)

# Define model
model = Pipeline(
    steps=[("preprocessor", preprocessor), ("regressor", Lasso(alpha=0.001))]
)

# Split data into training and validation sets
X_train, X_valid, y_train, y_valid = train_test_split(
    X, y, train_size=0.8, test_size=0.2, random_state=0
)

# Train the model
model.fit(X_train, y_train)

# Predict on validation set
preds_valid = model.predict(X_valid)

# Evaluate the model
score = mean_squared_error(y_valid, preds_valid, squared=False)
print(f"Validation RMSE: {score}")

# Predict on test data
test_preds = model.predict(test)

# Save test predictions to file
output = pd.DataFrame(
    {"Id": test_ids, "SalePrice": np.exp(test_preds)}
)  # Re-transform to original scale
output.to_csv("./working/submission.csv", index=False)