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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)
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