أول موديل: Linear Regression

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, r2_score

df = pd.read_csv("apartments.csv")
features = ["area", "rooms", "floor", "age", "metro_km"]
X_train, X_test, y_train, y_test = train_test_split(
    df[features], df["price_k"], test_size=0.2, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)            # learn the weights

preds = model.predict(X_test)          # predict prices it never saw
print(f"MAE: {mean_absolute_error(y_test, preds):.0f}K EGP")
print(f"R2:  {r2_score(y_test, preds):.3f}")

import pandas as pd
from sklearn.linear_model import LinearRegression

df = pd.read_csv("apartments.csv")
features = ["area", "rooms", "floor", "age", "metro_km"]
model = LinearRegression().fit(df[features], df["price_k"])

for name, w in zip(features, model.coef_):
    print(f"{name:9} {w:+8.1f}K per unit")

import pandas as pd
from sklearn.linear_model import LinearRegression

df = pd.read_csv("apartments.csv")
features = ["area", "rooms", "floor", "age", "metro_km"]
model = LinearRegression().fit(df[features], df["price_k"])

new_flat = pd.DataFrame([{"area": 130, "rooms": 3, "floor": 4,
                          "age": 5, "metro_km": 1.2}])
print(f"Estimated price: {model.predict(new_flat)[0]:,.0f}K EGP")