Naive-Bayes.ipynb

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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.18.2.min']\n",
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       "            window._Plotly = Plotly;\n",
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       "        </script>\n",
       "        "
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     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import cufflinks as cf\n",
    "\n",
    "\n"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Umur</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Membeli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>Pria</td>\n",
       "      <td>20 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>15 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>Pria</td>\n",
       "      <td>18 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>25 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>Pria</td>\n",
       "      <td>17 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>22 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>Pria</td>\n",
       "      <td>19 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>28 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>16 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>Pria</td>\n",
       "      <td>21 juta</td>\n",
       "      <td>Ya</td>\n",
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      "text/plain": [
       "   ID  Umur Jenis Kelamin Pendapatan Membeli\n",
       "0   1    23          Pria    20 juta      Ya\n",
       "1   2    32        Wanita    15 juta   Tidak\n",
       "2   3    27          Pria    18 juta      Ya\n",
       "3   4    41        Wanita    25 juta      Ya\n",
       "4   5    29          Pria    17 juta   Tidak\n",
       "5   6    35        Wanita    22 juta      Ya\n",
       "6   7    26          Pria    19 juta   Tidak\n",
       "7   8    38        Wanita    28 juta      Ya\n",
       "8   9    27        Wanita    16 juta   Tidak\n",
       "9  10    30          Pria    21 juta      Ya"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Memuat dataset\n",
    "dataset = pd.read_excel(\"Data-Pembelian.xlsx\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c151f09",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Umur</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Membeli</th>\n",
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       "      <td>Pria</td>\n",
       "      <td>20 juta</td>\n",
       "      <td>Ya</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>15 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>Pria</td>\n",
       "      <td>18 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>25 juta</td>\n",
       "      <td>Ya</td>\n",
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       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>Pria</td>\n",
       "      <td>17 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>22 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>Pria</td>\n",
       "      <td>19 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>28 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>16 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>Pria</td>\n",
       "      <td>21 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Umur Jenis Kelamin Pendapatan Membeli\n",
       "0    23          Pria    20 juta      Ya\n",
       "1    32        Wanita    15 juta   Tidak\n",
       "2    27          Pria    18 juta      Ya\n",
       "3    41        Wanita    25 juta      Ya\n",
       "4    29          Pria    17 juta   Tidak\n",
       "5    35        Wanita    22 juta      Ya\n",
       "6    26          Pria    19 juta   Tidak\n",
       "7    38        Wanita    28 juta      Ya\n",
       "8    27        Wanita    16 juta   Tidak\n",
       "9    30          Pria    21 juta      Ya"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop(\"ID\", axis=1, inplace=True)\n",
    "dataset"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "8955bbe5",
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       "      <th></th>\n",
       "      <th>Umur</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Membeli</th>\n",
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       "      <td>23</td>\n",
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       "      <td>Ya</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Tidak</td>\n",
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       "      <th>2</th>\n",
       "      <td>27</td>\n",
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       "      <td>Ya</td>\n",
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       "      <td>Tidak</td>\n",
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       "      <th>5</th>\n",
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       "      <td>Ya</td>\n",
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       "      <th>6</th>\n",
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       "      <td>Tidak</td>\n",
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       "      <th>7</th>\n",
       "      <td>38</td>\n",
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       "      <td>9</td>\n",
       "      <td>Ya</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tidak</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Ya</td>\n",
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      "text/plain": [
       "   Umur  Jenis Kelamin  Pendapatan Membeli\n",
       "0    23              0           5      Ya\n",
       "1    32              1           0   Tidak\n",
       "2    27              0           3      Ya\n",
       "3    41              1           8      Ya\n",
       "4    29              0           2   Tidak\n",
       "5    35              1           7      Ya\n",
       "6    26              0           4   Tidak\n",
       "7    38              1           9      Ya\n",
       "8    27              1           1   Tidak\n",
       "9    30              0           6      Ya"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding kolom \"Jenis Kelamin\" menggunakan LabelEncoder\n",
    "le = LabelEncoder()\n",
    "dataset[\"Jenis Kelamin\"] = le.fit_transform(dataset[\"Jenis Kelamin\"])\n",
    "dataset[\"Pendapatan\"] = le.fit_transform(dataset[\"Pendapatan\"])\n",
    "dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9313aeab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Memisahkan atribut dan label\n",
    "x = dataset.iloc[:, :-1]\n",
    "y = dataset.iloc[:, -1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "488188d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Membuat objek Gaussian Naive Bayes\n",
    "gnb = GaussianNB()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "75f41e9b",
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      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melatih model dengan data training\n",
    "gnb.fit(x, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc1763f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Haris\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:439: UserWarning:\n",
      "\n",
      "X does not have valid feature names, but GaussianNB was fitted with feature names\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Memprediksi kelas pada data testing\n",
    "x_test = [[35, 1, 20]]  # Encoding untuk \"Wanita\" adalah 1\n",
    "y_pred = gnb.predict(x_test)\n"
   ]
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     "text": [
      "['Ya']\n"
     ]
    }
   ],
   "source": [
    "# Menampilkan hasil prediksi\n",
    "print(y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "137ee0b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasi: 0.9\n"
     ]
    }
   ],
   "source": [
    "# Menghitung akurasi model pada data training\n",
    "y_pred_train = gnb.predict(x)\n",
    "accuracy = accuracy_score(y, y_pred_train)\n",
    "print(\"Akurasi:\", accuracy)"
   ]
  },
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mengubah DataFrame menjadi objek plotly Figure\n",
    "dataset.iplot(kind=\"scatter\", x=\"Umur\", y=\"Pendapatan\", xTitle=\"Umur\", yTitle=\"Pendapatan\", title=\"Scatter Plot\", mode='markers')\n",
    "    "
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