Scikit-Learn 1.8.0

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split

# 1. تحميل البيانات واختيار الميزات
iowa_file_path = '../input/train.csv'
home_data = pd.read_csv(iowa_file_path)
y = home_data.SalePrice
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = home_data[features]

# 2. تقسيم البيانات (Training & Validation)
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

# 3. تعريف النموذج والتدريب
rf_model = RandomForestRegressor(random_state=1)
rf_model.fit(train_X, train_y)

# 4. التقييم (Validation MAE)
predictions = rf_model.predict(val_X)
mae = mean_absolute_error(predictions, val_y)
print(f"Validation MAE: {mae:,.0f}")

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split

# 1. تحميل البيانات واختيار الميزات
iowa_file_path = '../input/train.csv'
home_data = pd.read_csv(iowa_file_path)
y = home_data.SalePrice
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = home_data[features]

# 2. تقسيم البيانات (Training & Validation)
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

# 3. تعريف النموذج والتدريب
rf_model = RandomForestRegressor(random_state=1)
rf_model.fit(train_X, train_y)

# 4. التقييم (Validation MAE)
predictions = rf_model.predict(val_X)
mae = mean_absolute_error(predictions, val_y)
print(f"Validation MAE: {mae:,.0f}")