انشر النموذج

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import ConfusionMatrixDisplay

# مستعمرة البطاريق من Meet the Penguins، وخصائصها وتصنيفها
penguins = pd.read_csv("penguins.csv")
numbers = ["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"]
text = ["island", "sex"]
X = penguins[numbers + text]
y = penguins["species"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=0)

# من Prepare the Data: المساران
number_steps = make_pipeline(SimpleImputer(strategy="median"), StandardScaler())
text_steps = make_pipeline(SimpleImputer(strategy="constant", fill_value="unknown"), OneHotEncoder())
prep = ColumnTransformer([("numbers", number_steps, numbers), ("words", text_steps, text)])

# من Choose a Model and Tune the Winner: ‏k-NN بقيمة k = 7
model = make_pipeline(prep, KNeighborsClassifier(n_neighbors=7))

# بطريق جديد من الميدان، في جدول من صف واحد بالأعمدة نفسها
new_penguin = pd.DataFrame({
    "bill_length_mm": [float(input())],
    "bill_depth_mm": [float(input())],
    "flipper_length_mm": [float(input())],
    "body_mass_g": [float(input())],
    "island": [input()],
    "sex": [input()],
})

fig, ax = plt.subplots()
ax.set_title("Test Penguins")

# اكتب الكود الخاص بك أدناه