{"id":95968,"date":"2025-02-04T12:02:41","date_gmt":"2025-02-04T08:32:41","guid":{"rendered":"https:\/\/nabfollower.com\/blog\/mastering-k-nearest-neighbors-k-nn-with-a-practical-python-example-1kjm\/"},"modified":"2025-02-04T12:02:41","modified_gmt":"2025-02-04T08:32:41","slug":"mastering-k-nearest-neighbors-k-nn-with-a-practical-python-example-1kjm","status":"publish","type":"post","link":"https:\/\/nabfollower.com\/blog\/mastering-k-nearest-neighbors-k-nn-with-a-practical-python-example-1kjm\/","title":{"rendered":"\u062a\u0633\u0644\u0637 \u0628\u0631 \u0647\u0645\u0633\u0627\u06cc\u06af\u0627\u0646 K-Nearest (K-NN) \u0628\u0627 \u06cc\u06a9 \u0645\u062b\u0627\u0644 \u0639\u0645\u0644\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646"},"content":{"rendered":"
\n

\u0645\u0642\u062f\u0645\u0647<\/strong>
\u062a\u0635\u0648\u0631 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0634\u0645\u0627 \u0628\u0647 \u062a\u0627\u0632\u06af\u06cc \u0628\u0647 \u06cc\u06a9 \u0634\u0647\u0631 \u062c\u062f\u06cc\u062f \u0646\u0642\u0644 \u0645\u06a9\u0627\u0646 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u062f \u0648 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u06cc\u06a9 \u0631\u0633\u062a\u0648\u0631\u0627\u0646 \u062e\u0648\u0628 \u0647\u0633\u062a\u06cc\u062f. \u0634\u0645\u0627 \u062f\u0631 \u0645\u0648\u0631\u062f \u0645\u0646\u0637\u0642\u0647 \u0686\u06cc\u0632 \u0632\u06cc\u0627\u062f\u06cc \u0646\u0645\u06cc \u062f\u0627\u0646\u06cc\u062f \u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0627\u0632 \u0633\u0647 \u0646\u0641\u0631 \u0627\u0632 \u0627\u0641\u0631\u0627\u062f \u0645\u062d\u0644\u06cc \u062a\u0648\u0635\u06cc\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u062f.
\u2022 \u062f\u0648 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0631\u0633\u062a\u0648\u0631\u0627\u0646 A.
\u2022 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0633\u062a\u0648\u0631\u0627\u0646 \u0647\u0627\u06cc B \u0631\u0627 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f
\u0627\u0632 \u0622\u0646\u062c\u0627 \u06a9\u0647 \u0627\u06a9\u062b\u0631\u06cc\u062a \u0631\u0627\u06cc \u0627\u0632 \u0631\u0633\u062a\u0648\u0631\u0627\u0646 A \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u0646\u062f \u060c \u062a\u0635\u0645\u06cc\u0645 \u0645\u06cc \u06af\u06cc\u0631\u06cc\u062f \u062f\u0631 \u0622\u0646\u062c\u0627 \u063a\u0630\u0627 \u0628\u062e\u0648\u0631\u06cc\u062f.
\u0627\u06cc\u0646 \u0641\u0631\u0622\u06cc\u0646\u062f \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u0633\u0627\u062f\u0647 \u0622\u06cc\u0646\u0647 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 K-Nearest \u0647\u0645\u0633\u0627\u06cc\u06af\u0627\u0646 (K-NN) \u062f\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f! \u062f\u0631 \u0627\u06cc\u0646 \u067e\u0633\u062a \u060c \u0645\u0627 \u0628\u0647 \u0627\u0639\u0645\u0627\u0642 K-NN \u0634\u06cc\u0631\u062c\u0647 \u0645\u06cc \u0632\u0646\u06cc\u0645 \u060c \u0645\u06a9\u0627\u0646\u06cc\u0633\u0645 \u06a9\u0627\u0631 \u0622\u0646 \u0631\u0627 \u062f\u0631\u06a9 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0622\u0646 \u0631\u0627 \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0627 \u06cc\u06a9 \u0645\u062b\u0627\u0644 \u0639\u0645\u0644\u06cc \u067e\u06cc\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.<\/p>\n

<\/p>\n

\u0647\u0645\u0633\u0627\u06cc\u06af\u0627\u0646 K-Nearest (K-NN) \u0686\u06cc\u0633\u062a\u061f<\/strong>
K-NN \u06cc\u06a9 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0646\u0638\u0627\u0631\u062a \u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0648 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0627\u0633\u062a. \u0627\u06cc\u0646 \u06cc\u06a9 \u0646\u0642\u0637\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0627\u06a9\u062b\u0631\u06cc\u062a \u0631\u0627\u06cc \u0646\u0632\u062f\u06cc\u06a9\u062a\u0631\u06cc\u0646 \u0647\u0645\u0633\u0627\u06cc\u06af\u0627\u0646 \u062e\u0648\u062f \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n

\u0686\u06af\u0648\u0646\u0647 K-nn \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f:<\/strong><\/p>\n

    \n
  1. \u062a\u0639\u062f\u0627\u062f \u0647\u0645\u0633\u0627\u06cc\u06af\u0627\u0646 (k) \u0631\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u062f.<\/li>\n
  2. \u0641\u0627\u0635\u0644\u0647 \u0628\u06cc\u0646 \u0646\u0642\u0637\u0647 \u062f\u0627\u062f\u0647 \u062c\u062f\u06cc\u062f \u0648 \u0633\u0627\u06cc\u0631 \u0645\u0648\u0627\u0631\u062f \u0645\u0648\u062c\u0648\u062f \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0645\u062d\u0627\u0633\u0628\u0647 \u06a9\u0646\u06cc\u062f.<\/li>\n
  3. \u0646\u0632\u062f\u06cc\u06a9\u062a\u0631\u06cc\u0646 \u0627\u0645\u062a\u06cc\u0627\u0632 K \u0631\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u062f.<\/li>\n
  4. \u0628\u0631\u0627\u06cc \u062a\u0639\u06cc\u06cc\u0646 \u06a9\u0644\u0627\u0633 \u0627\u0632 \u0646\u0642\u0637\u0647 \u062f\u0627\u062f\u0647 \u062c\u062f\u06cc\u062f \u060c \u0631\u0627\u06cc \u0627\u06a9\u062b\u0631\u06cc\u062a \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u062f. \u0622\u0646 \u0631\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u0627\u0641\u062a\u0646 \u0645\u0648\u0627\u0631\u062f \u0645\u0634\u0627\u0628\u0647 \u062f\u0631 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0648 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627\u06cc \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0622\u0646 \u0634\u0628\u0627\u0647\u062a \u0647\u0627 \u062f\u0631 \u0646\u0638\u0631 \u0628\u06af\u06cc\u0631\u06cc\u062f.<\/li>\n<\/ol>\n

    \u0627\u062c\u0631\u0627\u06cc K-NN \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646<\/strong><\/p>\n

    \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u060c \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u06a9\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06cc\u06a9 \u0645\u062d\u0635\u0648\u0644 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0633\u0646 \u0648 \u062d\u0642\u0648\u0642 \u062a\u062e\u0645\u06cc\u0646 \u0632\u062f\u0647 \u0634\u062f\u0647 \u0628\u0627\u0634\u062f \u060c \u06cc\u06a9 \u0645\u0631\u062d\u0644\u0647 \u0628\u0647 \u0645\u0631\u062d\u0644\u0647 \u0631\u0627 \u0637\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n

    \u0645\u0631\u062d\u0644\u0647 1: \u0648\u0627\u0631\u062f\u0627\u062a \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0644\u0627\u0632\u0645<\/strong><\/p>\n

    \n
    import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n<\/code><\/pre>\n
    \n
    \n    \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
        \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
    \u0645\u0631\u062d\u0644\u0647 2: \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0646\u0645\u0648\u0646\u0647 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u062f<\/strong><\/p>\n
    \n
    data = {\n    'Age': [22, 25, 47, 52, 46, 56, 55, 60, 62, 61, 18, 24, 33, 40, 35],\n    'EstimatedSalary': [15000, 29000, 43000, 76000, 50000, 83000, 78000, 97000, 104000, 98000, 12000, 27000, 37000, 58000, 41000],\n    'Purchased': [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0]  # 1: Purchased, 0: Not Purchased\n}\ndf = pd.DataFrame(data)\nprint(df.head())\nStep 3: Data Preprocessing\nX = df[['Age', 'EstimatedSalary']]\ny = df['Purchased']\n<\/code><\/pre>\n
    \n
    \n    \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
        \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
    # \u062a\u0642\u0633\u06cc\u0645 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0648 \u0622\u0632\u0645\u0648\u0646<\/strong><\/p>\n
    \n
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n<\/code><\/pre>\n
    \n
    \n    \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
        \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
    # \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc (\u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc)<\/strong><\/p>\n
    \n
    scaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\nStep 4: Train the k-NN Model\nk = 3\nknn = KNeighborsClassifier(n_neighbors=k)\nknn.fit(X_train, y_train)\nStep 5: Make Predictions and Evaluate the Model\ny_pred = knn.predict(X_test)\n<\/code><\/pre>\n
    \n
    \n    \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
        \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
    # \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0639\u0645\u0644\u06a9\u0631\u062f<\/strong><\/p>\n
    \n
    accuracy = accuracy_score(y_test, y_pred)\nconf_matrix = confusion_matrix(y_test, y_pred)\nreport = classification_report(y_test, y_pred)\n\nprint(f\"Accuracy: {accuracy:.2f}\")\nprint(\"Confusion Matrix:\\n\", conf_matrix)\nprint(\"Classification Report:\\n\", report)\n<\/code><\/pre>\n
    \n
    \n    \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
        \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
    \u0628\u06cc\u0646\u0634 \u0647\u0627\u06cc \u06a9\u0644\u06cc\u062f\u06cc<\/strong>
    1. \u0627\u0646\u062a\u062e\u0627\u0628 \u0645\u0642\u062f\u0627\u0631 k \u062f\u0631\u0633\u062a:<\/strong><\/p>\n
      \n
    • – K \u06a9\u0648\u0686\u06a9 (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u060c 1 \u06cc\u0627 3) \u0628\u0627\u0639\u062b \u0645\u06cc \u0634\u0648\u062f \u0645\u062f\u0644 \u0646\u0633\u0628\u062a \u0628\u0647 \u0646\u0648\u06cc\u0632 \u062d\u0633\u0627\u0633 \u0628\u0627\u0634\u062f.<\/li>\n
    • – K \u0628\u0632\u0631\u06af (\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u060c 10 \u06cc\u0627 15) \u0633\u0631 \u0648 \u0635\u062f\u0627 \u0631\u0627 \u0635\u0627\u0641 \u0645\u06cc \u06a9\u0646\u062f \u0627\u0645\u0627 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0627\u0644\u06af\u0648\u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0627\u0632 \u062f\u0633\u062a \u0628\u062f\u0647\u062f.<\/li>\n
    • \n
      \u0628\u0631\u0627\u06cc \u062a\u0639\u06cc\u06cc\u0646 \u0628\u0647\u062a\u0631\u06cc\u0646 k \u0627\u0632 \u0627\u0639\u062a\u0628\u0627\u0631 \u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0637\u0639 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f.
      2. \u0627\u0647\u0645\u06cc\u062a \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc:<\/strong><\/p>\n<\/li>\n
    • \n
      K-NN \u0628\u0647 \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0645\u0633\u0627\u0641\u062a \u0645\u062a\u06a9\u06cc \u0627\u0633\u062a \u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u062a\u0636\u0645\u06cc\u0646 \u0645\u06cc \u06a9\u0646\u062f \u06a9\u0647 \u0622\u0646\u0647\u0627 \u0628\u0647 \u0637\u0648\u0631 \u0645\u0633\u0627\u0648\u06cc \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u0646\u062f.
      3 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u06a9\u0648\u0686\u06a9:<\/strong><\/p>\n<\/li>\n
    • \n
      K-NN \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc\u06cc \u0628\u0627 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u06a9\u0645\u062a\u0631\u06cc \u0639\u0627\u0644\u06cc \u0627\u0633\u062a \u0627\u0645\u0627 \u0627\u0632 \u0646\u0638\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u06af\u0631\u0627\u0646 \u0627\u0633\u062a.<\/p>\n<\/li>\n<\/ul>\n
      \u0627\u0641\u06a9\u0627\u0631 \u0646\u0647\u0627\u06cc\u06cc<\/strong>
      K-Nearest \u0647\u0645\u0633\u0627\u06cc\u06af\u0627\u0646 (K-NN) \u06cc\u06a9 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0642\u062f\u0631\u062a\u0645\u0646\u062f \u0648 \u062f\u0631 \u0639\u06cc\u0646 \u062d\u0627\u0644 \u0633\u0627\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0631\u0627\u06cc \u0645\u0634\u06a9\u0644\u0627\u062a \u0645\u062e\u062a\u0644\u0641 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0627\u0639\u0645\u0627\u0644 \u0634\u0648\u062f. \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u06a9\u0648\u0686\u06a9\u062a\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f \u062e\u0648\u0628\u06cc \u062f\u0627\u0631\u062f \u060c \u0645\u0647\u0645 \u0627\u0633\u062a \u06a9\u0647 \u0647\u0632\u06cc\u0646\u0647 \u0647\u0627\u06cc \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0631\u0627 \u0647\u0646\u06af\u0627\u0645 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0631 \u0646\u0638\u0631 \u0628\u06af\u06cc\u0631\u06cc\u062f.
      \u0622\u06cc\u0627 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0628\u0631\u0631\u0633\u06cc \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 K-NN \u062f\u0631 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0648\u06cc\u0631 \u06cc\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f\u061f \u062f\u0631 \u0646\u0638\u0631\u0627\u062a \u0628\u0647 \u0645\u0646 \u0627\u0637\u0644\u0627\u0639 \u062f\u0647\u06cc\u062f! <\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"
      \u0645\u0642\u062f\u0645\u0647\u062a\u0635\u0648\u0631 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0634\u0645\u0627 \u0628\u0647 \u062a\u0627\u0632\u06af\u06cc \u0628\u0647 \u06cc\u06a9 \u0634\u0647\u0631 \u062c\u062f\u06cc\u062f \u0646\u0642\u0644 \u0645\u06a9\u0627\u0646 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u062f \u0648 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u06cc\u06a9 \u0631\u0633\u062a\u0648\u0631\u0627\u0646 \u062e\u0648\u0628 \u0647\u0633\u062a\u06cc\u062f. \u0634\u0645\u0627 \u062f\u0631 \u0645\u0648\u0631\u062f \u0645\u0646\u0637\u0642\u0647 \u0686\u06cc\u0632 \u0632\u06cc\u0627\u062f\u06cc \u0646\u0645\u06cc \u062f\u0627\u0646\u06cc\u062f \u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0627\u0632 \u0633\u0647 \u0646\u0641\u0631 \u0627\u0632 \u0627\u0641\u0631\u0627\u062f \u0645\u062d\u0644\u06cc \u062a\u0648\u0635\u06cc\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u062f.\u2022 \u062f\u0648 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0631\u0633\u062a\u0648\u0631\u0627\u0646 A.\u2022 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0633\u062a\u0648\u0631\u0627\u0646 \u0647\u0627\u06cc B \u0631\u0627 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f\u0627\u0632 \u0622\u0646\u062c\u0627 \u06a9\u0647 …<\/p>\n","protected":false},"author":2,"featured_media":95969,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","footnotes":""},"categories":[339],"tags":[],"class_list":["post-95968","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dev"],"_links":{"self":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/95968","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/comments?post=95968"}],"version-history":[{"count":0,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/95968\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media\/95969"}],"wp:attachment":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media?parent=95968"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/categories?post=95968"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/tags?post=95968"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}