status gizi by bb-u.ipynb

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      "text/plain": [
       "        Umur (bulan)  Berat Badan (kg) Jenis Kelamin           Status Gizi\n",
       "0                  0          0.715441     perempuan  severely underweight\n",
       "1                  0          5.610281     perempuan            overweight\n",
       "2                  0          0.907997     perempuan  severely underweight\n",
       "3                  0          5.509305     perempuan            overweight\n",
       "4                  0          3.565381     perempuan                normal\n",
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       "\n",
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      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# membaca dataset dari file CSV\n",
    "df = pd.read_csv('dataset_berat_balita.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e3d45be",
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       "      <th>Jenis Kelamin</th>\n",
       "      <th>Status Gizi</th>\n",
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      ],
      "text/plain": [
       "        Umur (bulan)  Berat Badan (kg)  Jenis Kelamin  Status Gizi\n",
       "0                  0          0.715441              1            0\n",
       "1                  0          5.610281              1            3\n",
       "2                  0          0.907997              1            0\n",
       "3                  0          5.509305              1            3\n",
       "4                  0          3.565381              1            2\n",
       "...              ...               ...            ...          ...\n",
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      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mengkodekan jenis kelamin\n",
    "df['Jenis Kelamin'] = df['Jenis Kelamin'].map({'laki-laki': 0, 'perempuan': 1})\n",
    "\n",
    "# mengkodekan status gizi\n",
    "df['Status Gizi'] = df['Status Gizi'].map({'severely underweight': 0, 'underweight': 1, 'normal': 2, 'overweight': 3})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15ddbe2f",
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   "outputs": [
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      ],
      "text/plain": [
       "        Umur (bulan)  Berat Badan (kg)  Jenis Kelamin\n",
       "0                  0          0.715441              1\n",
       "1                  0          5.610281              1\n",
       "2                  0          0.907997              1\n",
       "3                  0          5.509305              1\n",
       "4                  0          3.565381              1\n",
       "...              ...               ...            ...\n",
       "122117            60         17.450974              0\n",
       "122118            60         20.970287              0\n",
       "122119            60         24.600348              0\n",
       "122120            60         19.047613              0\n",
       "122121            60         24.324064              0\n",
       "\n",
       "[122122 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# memisahkan fitur dan target\n",
    "X = df.iloc[:, 1:6]\n",
    "X = df.drop('Status Gizi', axis=1)\n",
    "y = df['Status Gizi']\n",
    "\n",
    "# membagi dataset menjadi data latih dan data uji\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61f6effa",
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   "outputs": [
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      ],
      "text/plain": [
       "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# membuat objek KNN dengan k=3\n",
    "knn = KNeighborsClassifier(algorithm='kd_tree', n_neighbors=1)\n",
    "\n",
    "# melatih model KNN dengan data latih\n",
    "knn.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "136c852b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasi model KNN: 0.9981440034936405\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# membuat prediksi dengan data uji\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "# menghitung akurasi model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Akurasi model KNN:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fcff326c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2742aa3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      4802\n",
      "           1       0.99      0.99      0.99      1304\n",
      "           2       1.00      1.00      1.00      5179\n",
      "           3       1.00      1.00      1.00      7034\n",
      "\n",
      "    accuracy                           1.00     18319\n",
      "   macro avg       1.00      1.00      1.00     18319\n",
      "weighted avg       1.00      1.00      1.00     18319\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test.values.reshape(-1, 1), y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f91f2f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf091ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross val:  [0.99836245 0.99781659 0.99863537 0.99808952 0.997816  ]\n",
      "Accuracy :  0.9981439855894004\n"
     ]
    }
   ],
   "source": [
    "print('Cross val: ', cross_val_score(knn, y_test.values.reshape(-1, 1), y_pred, cv = 5))\n",
    "print('Accuracy : ', np.mean(cross_val_score(knn, y_test.values.reshape(-1, 1), y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbe08b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score = [2]\n"
     ]
    }
   ],
   "source": [
    "test = pd.DataFrame({'Umur (bulan)': [3], 'Berat Badan (kg)': [6], 'Jenis Kelamin': [0]})\n",
    "\n",
    "pred = knn.predict(test)\n",
    "print(\"Predicted Score = {}\".format(np.array(pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f2fc8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn_berat_status_gizi.model']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export the model\n",
    "import joblib\n",
    "\n",
    "joblib.dump(knn, 'knn_berat_status_gizi.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f1a8b52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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