[Python] why my sklearn LinearRegression keep failing?

what is wrong with my code why keep failing? I keep getting this error enter image description here

the AI checker keep returning

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One or more test cases in this cell did not pass.


In [15]:
Grade cell: cell-0d30204dcfba1c7a
# Print to check results

print(f"Mean Squared Error (MSE): {mse}")
print(f"R-squared: {r_squared}")

Hidden Tests Redacted

One or more test cases in this cell did not pass.


Lab Instructions

1. Load and Explore the Data Step 1.1: Import the required Python library and load dataset.

import pandas as myPandas
import numpy as myNumpy
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler


mydataframe = myPandas.read_csv("house_prices.csv")

mydataframe.head()
mydataframe.info()
mydataframe.describe()
mydataframe.dtypes

missing_values = mydataframe.isnull().sum()

print("Missing values in each column:\\n", missing_values)

for column in mydataframe.select_dtypes(include=\['float64', 'int64'\]).columns:
median_value = mydataframe\[column\].median()
mydataframe\[column\].fillna(median_value, inplace=True)

missing_values_after = mydataframe.isnull().sum()
print("Missing values after filling:\\n", missing_values_after)

XXX = mydataframe.drop('price', axis=1) # Replace 'Price' with your actual target column name
yyy = mydataframe\['price'\]

XXX = myPandas.get_dummies(XXX, drop_first=True)

Xtrain, Xtest, ytrain, ytest = train_test_split(XXX, yyy, test_size=0.2, random_state=42)

scaler = StandardScaler()
Xtrain_scaled = scaler.fit_transform(Xtrain)
Xtest_scaled = scaler.transform(Xtest)

mydataframe = myPandas.read_csv('house_prices.csv')

features = \['sqft_living', 'bedrooms', 'bathrooms', 'condition', 'floors'\]
target = 'price'

XXX = mydataframe\[features\]
yyy = mydataframe\[target\]

print("Features (X):\\n", XXX.head())
print("\\nTarget (y):\\n", yyy.head())

X_encoded = myPandas.get_dummies(XXX, columns=\['condition'\], drop_first=True)
print(X_encoded.head())


Step 2.4: Split the Data Split the data into training and testing sets (80% train, 20% test) using train_test_split

Make sure to set the random_state parameter to 42 to ensure reproducibility and obtain the same results as the expected solution.

Xtrain, Xtest, ytrain, ytest = train_test_split(
X_encoded,
yyy,
test_size=0.2,
random_state=42
)

print(f"The X train shape: {Xtrain.shape}")
print(f"The y train shape: {ytrain.shape}")
print(f"The X test shape: {Xtest.shape}")
print(f"The y test shape: {ytest.shape}")

print(f'Training the shape: {Xtrain.shape}, Testing the shape: {Xtest.shape}')


Build and Train a Linear Regression Model Step 3.1: Import LinearRegression

from sklearn.linear_model import LinearRegression


Step 3.2: Create an Instance of the Model Create an instance of the LinearRegression model.

model = LinearRegression()


Step 3.3: Fit the Model Fit the model to the training data.

model.fit(Xtrain, ytrain)
print("Coefficients is as follow:", model.coef\_)
print("Intercept is as follow:", model.intercept\_)

1. Make Predictions and Evaluate the Model Step 4.1: Make Predictions Use the trained model to make predictions on the testing data.

ypred = model.predict(Xtest)
predictions_mydataframe = myPandas.DataFrame({'Actual': ytest, 'Predicted': ypred})
print(predictions_mydataframe.head())


Step 4.2: Evaluate the Model Calculate the Mean Squared Error (MSE) as mse and R-squared value as r_squared to evaluate the model's performance, then check your results by printing them in the following cell.

from sklearn.metrics import mean_squared_error, r2_score

mse = mean_squared_error(ytest, ypred)
r_mse = myNumpy.sqrt(mse)

r_squared = r2_score(ytest, ypred)


print(f'Mean Squared Error (MSE): {mse}')
print(f'R-squared (R²): {r_squared}')

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