4.4 Classification - Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d76be0fd-d9ce-4edf-baa4-f94c46a7d949",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Naive Bayes Classifier "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4101a94c-bf97-4648-aab9-a249aeca0584",
   "metadata": {},
   "source": [
    "- Naive Bayes merupakan algoritma yang mengandalkan probabilitas atau kemungkinan peluang terjadi dalam menyelesaikan kasusunya.\n",
    "- sebagaimana sebuah dadu yang dilemparkan, maka setiap masing-masing angka memiliki peluang 1/6."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e2f196-1790-43a6-a482-7eb7e1224b3a",
   "metadata": {},
   "source": [
    "## a. Import Library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb383e6b-6452-480d-8a8c-640b6612ddb8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1729afc-030d-4447-b87f-9fad08e37390",
   "metadata": {},
   "source": [
    "## b. Membaca file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c724256-1091-44ca-8a68-bd502cb8c979",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JURUSAN</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>ASAL_SEKOLAH</th>\n",
       "      <th>RERATA_SKS</th>\n",
       "      <th>ASISTEN</th>\n",
       "      <th>LAMA_STUDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    JURUSAN  GENDER  ASAL_SEKOLAH  RERATA_SKS  ASISTEN  LAMA_STUDI\n",
       "0         1       1             1           1        1           1\n",
       "1         1       1             1           1        1           1\n",
       "2         1       1             1           1        1           1\n",
       "3         2       2             1           1        1           1\n",
       "4         2       2             2           1        1           1\n",
       "5         2       1             2           1        1           1\n",
       "6         2       1             1           1        2           2\n",
       "7         1       1             1           2        1           1\n",
       "8         1       2             1           2        1           1\n",
       "9         2       1             1           1        2           2\n",
       "10        3       1             1           1        1           1\n",
       "11        2       1             2           2        1           1\n",
       "12        2       2             1           2        1           2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('dataset/studi_num.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7581a399-e9a5-452a-af6f-e94f45215504",
   "metadata": {},
   "source": [
    "## c. Penentuan Variable target dan prediktor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38e36443-52fc-4c82-b260-660d0e0a5038",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, 5].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02de2831-fabc-402c-b063-8e39eca446be",
   "metadata": {},
   "source": [
    "## d. Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "312cc9f2-e82d-4bd0-a071-e05125c4447b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8657c801-2194-4eac-8601-2989a579de3a",
   "metadata": {},
   "source": [
    "## e. Pemodelan Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95d106ec-2a1f-4103-a367-d5bca821c595",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9122104d-4fa5-43a3-ac94-914a4c8491bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GaussianNB()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc5382f-200c-483d-bb53-a298d4a0837a",
   "metadata": {},
   "source": [
    "## f. Membuat Nilai Prediksi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1457dcd3-2606-41de-94ba-3600c17c8ed5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7829cd0-745d-4997-9a0f-1460e1bf66a7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca843b6-d9ec-4457-93f3-4606bb9a3d76",
   "metadata": {},
   "source": [
    "## g. Uji Performa Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "838ba04f-974f-4d64-ac04-55435ae483ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.75\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e9d468-d9a4-4ba2-a9a8-ff8719e0a7c2",
   "metadata": {},
   "source": [
    "## Kelebihan dan Kekurangan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc88295-d40f-4b93-934e-cb1b13bbac3e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Kelebihan\n",
    "\n",
    "- mudah dipahami\n",
    "- dapat digunakan untuk data kuantitatif dan kualitatif\n",
    "- tidak memerlukan data yang banyak\n",
    "- perhitungan cepat dan efisien\n",
    "- dapat digunakan untuk klasifikasi biner ataupun multiclass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76461b6f-588a-42a4-9802-d5535a3f5ae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Kekurangan\n",
    "\n",
    "- keakuratan tidak dapat diukur menggunakan sat"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vulcan_da",
   "language": "python",
   "name": "vulcan_da"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}