diff --git a/22-06-10-Naive_Bayes/Codes.ipynb b/22-06-10-Naive_Bayes/Codes.ipynb
new file mode 100644
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--- /dev/null
+++ b/22-06-10-Naive_Bayes/Codes.ipynb
@@ -0,0 +1,2316 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Naive Bayes.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "authorship_tag": "ABX9TyMuPZYgLeZZEYtTydvuY0vm",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Diwakar-Gupta/Data-Science-APRIL/blob/main/22-06-10-Naive_Bayes/Codes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Notes**\n",
+        "\n",
+        "[open in browser]('https://onedrive.live.com/view.aspx?resid=B7999797B4FEAE64%211201&id=documents&wd=target%28Naive%20Bayes.one%7C5A03ED1E-CF67-4C12-AD8B-C16112311CD6%2FIntro%7C26B8385D-A330-42F6-9B6F-C565F43A2472%2F%29')\n",
+        "\n",
+        "[open in OneNote]('onenote:https://d.docs.live.net/b7999797b4feae64/Documents/DS%20December/Naive%20Bayes.one#Intro&section-id={5A03ED1E-CF67-4C12-AD8B-C16112311CD6}&page-id={26B8385D-A330-42F6-9B6F-C565F43A2472}&end')"
+      ],
+      "metadata": {
+        "id": "tkHggcg_bKPf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "bqorb4nTy4XE"
+      },
+      "outputs": [],
+      "source": [
+        "import plotly.express as px\n",
+        "import pandas as pd\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Scratch\n",
+        "\n",
+        "i used this to explane implementation **play_tennis**"
+      ],
+      "metadata": {
+        "id": "Tj6lBRxtTDBD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv('https://gist.githubusercontent.com/DiogoRibeiro7/c6590d0cf119e87c39e31c21a9c0f3a8/raw/4a8e3da267a0c1f0d650901d8295a5153bde8b21/PlayTennis.csv')\n",
+        "df"
+      ],
+      "metadata": {
+        "id": "ti-n0DM8sgYd",
+        "outputId": "5030f2f9-6091-4406-fd66-827f57d10250",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 488
+        }
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Outlook Temperature Humidity    Wind Play Tennis\n",
+              "0      Sunny         Hot     High    Weak          No\n",
+              "1      Sunny         Hot     High  Strong          No\n",
+              "2   Overcast         Hot     High    Weak         Yes\n",
+              "3       Rain        Mild     High    Weak         Yes\n",
+              "4       Rain        Cool   Normal    Weak         Yes\n",
+              "5       Rain        Cool   Normal  Strong          No\n",
+              "6   Overcast        Cool   Normal  Strong         Yes\n",
+              "7      Sunny        Mild     High    Weak          No\n",
+              "8      Sunny        Cool   Normal    Weak         Yes\n",
+              "9       Rain        Mild   Normal    Weak         Yes\n",
+              "10     Sunny        Mild   Normal  Strong         Yes\n",
+              "11  Overcast        Mild     High  Strong         Yes\n",
+              "12  Overcast         Hot   Normal    Weak         Yes\n",
+              "13      Rain        Mild     High  Strong          No"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f8755733-731a-4994-94a9-c71f013a4e23\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe tbody tr th {\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Outlook</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Humidity</th>\n",
+              "      <th>Wind</th>\n",
+              "      <th>Play Tennis</th>\n",
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+              "      <th>0</th>\n",
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+              "      <td>Hot</td>\n",
+              "      <td>High</td>\n",
+              "      <td>Weak</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Sunny</td>\n",
+              "      <td>Hot</td>\n",
+              "      <td>High</td>\n",
+              "      <td>Strong</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Overcast</td>\n",
+              "      <td>Hot</td>\n",
+              "      <td>High</td>\n",
+              "      <td>Weak</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Rain</td>\n",
+              "      <td>Mild</td>\n",
+              "      <td>High</td>\n",
+              "      <td>Weak</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Rain</td>\n",
+              "      <td>Cool</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>Weak</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Rain</td>\n",
+              "      <td>Cool</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>Strong</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Overcast</td>\n",
+              "      <td>Cool</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>Strong</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Sunny</td>\n",
+              "      <td>Mild</td>\n",
+              "      <td>High</td>\n",
+              "      <td>Weak</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Sunny</td>\n",
+              "      <td>Cool</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>Weak</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Rain</td>\n",
+              "      <td>Mild</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>Weak</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Sunny</td>\n",
+              "      <td>Mild</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>Strong</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Overcast</td>\n",
+              "      <td>Mild</td>\n",
+              "      <td>High</td>\n",
+              "      <td>Strong</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Overcast</td>\n",
+              "      <td>Hot</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>Weak</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Rain</td>\n",
+              "      <td>Mild</td>\n",
+              "      <td>High</td>\n",
+              "      <td>Strong</td>\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "  </style>\n",
+              "\n",
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+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-f8755733-731a-4994-94a9-c71f013a4e23');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
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+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.crosstab(df['Outlook'], df['Play Tennis'], normalize='columns')"
+      ],
+      "metadata": {
+        "id": "o20tVEUhslOa",
+        "outputId": "36bc0063-99d6-4885-b767-73f1cecdef03",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        }
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Play Tennis   No       Yes\n",
+              "Outlook                   \n",
+              "Overcast     0.0  0.444444\n",
+              "Rain         0.4  0.333333\n",
+              "Sunny        0.6  0.222222"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-583895f6-022e-4443-bde3-73810ca2965b\">\n",
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+              "\n",
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+              "\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>Play Tennis</th>\n",
+              "      <th>No</th>\n",
+              "      <th>Yes</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Outlook</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Overcast</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.444444</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rain</th>\n",
+              "      <td>0.4</td>\n",
+              "      <td>0.333333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Sunny</th>\n",
+              "      <td>0.6</td>\n",
+              "      <td>0.222222</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-583895f6-022e-4443-bde3-73810ca2965b')\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
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+              "\n",
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+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-583895f6-022e-4443-bde3-73810ca2965b button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-583895f6-022e-4443-bde3-73810ca2965b');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "Kg84gTlSslLj"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "iris = px.data.iris()"
+      ],
+      "metadata": {
+        "id": "d6nEo9SGzWUy"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# X = iris.drop(['species_id', 'species'], axis = 1)\n",
+        "# Y = iris['species_id']\n",
+        "\n",
+        "from sklearn.preprocessing import OrdinalEncoder\n",
+        "X = df.drop(['Play Tennis'], axis = 1)\n",
+        "Y = df['Play Tennis']"
+      ],
+      "metadata": {
+        "id": "CKh0-frNzll3"
+      },
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "class NaiveBayes():\n",
+        "    def __init__(self):\n",
+        "        pass\n",
+        "    \n",
+        "    def fit(self, X, Y):\n",
+        "        self.label_type = Y.dtype\n",
+        "        self.prob = {}\n",
+        "        self.label_prob = Y.value_counts(normalize=True)\n",
+        "        self.features = X.columns\n",
+        "\n",
+        "        for col in X.columns:\n",
+        "            self.prob[col] = pd.crosstab(X[col], Y, normalize = 'columns')\n",
+        "        \n",
+        "        return self.prob\n",
+        "    \n",
+        "    def pred(self, x):\n",
+        "        \n",
+        "        maxprob = 0\n",
+        "        maxlab = ''\n",
+        "        for lab in self.label_prob.index:\n",
+        "            currprob = self.label_prob[lab]\n",
+        "            currlab = lab\n",
+        "            \n",
+        "            for fet in self.features:\n",
+        "                p = self.prob[fet][lab][x[fet]]\n",
+        "                currprob = currprob*p\n",
+        "                \n",
+        "            if currprob > maxprob:\n",
+        "                maxprob = currprob\n",
+        "                maxlab = currlab\n",
+        "            \n",
+        "        return maxlab\n",
+        "\n",
+        "    def predict(self, X):\n",
+        "        y_pred = np.full(X.shape[0], 0,dtype = self.label_type)\n",
+        "\n",
+        "        for i, (_, x) in enumerate(X.iterrows()):\n",
+        "            lab = self.pred(x)\n",
+        "            y_pred[i] = lab\n",
+        "        \n",
+        "        return y_pred\n",
+        "    \n",
+        "    def score(self, X, Y):\n",
+        "        return accuracy_score(self.predict(X), Y)"
+      ],
+      "metadata": {
+        "id": "-6klPoA31hkI"
+      },
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = NaiveBayes()\n",
+        "prob = model.fit(X, Y)"
+      ],
+      "metadata": {
+        "id": "ucIUTwH42gZC"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.predict(X)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Zj8LwA9TMD4n",
+        "outputId": "4441d02f-ef4c-4573-a6dd-79a8ae80d315"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['No', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'Yes',\n",
+              "       'Yes', 'Yes', 'Yes', 'No'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.score(X, Y)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "l1q-nnUIJMiK",
+        "outputId": "03f80920-8f45-4184-90eb-2e5a92513672"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9285714285714286"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.naive_bayes import MultinomialNB\n",
+        "\n",
+        "modelsk = MultinomialNB()\n",
+        "\n",
+        "X = OrdinalEncoder().fit_transform(X)\n",
+        "\n",
+        "modelsk.fit(X, Y)\n",
+        "# accuracy_score(modelsk.predict(X), Y)\n",
+        "modelsk.score(X, Y)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SPMrudjK2mIW",
+        "outputId": "8d34222c-6087-4f97-f449-68cc6cd2dcf4"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8571428571428571"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Project"
+      ],
+      "metadata": {
+        "id": "alxgN5iQTAOX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = df = pd.read_csv(\"https://raw.githubusercontent.com/DependerKumarSoni/Naive-Bayes/main/adult.csv\")\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "4t3OZXtcTBKr",
+        "outputId": "c035feff-1507-454a-baae-b7fb4da2aade"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   39          State-gov   77516   Bachelors   13        Never-married  \\\n",
+              "0  50   Self-emp-not-inc   83311   Bachelors   13   Married-civ-spouse   \n",
+              "1  38            Private  215646     HS-grad    9             Divorced   \n",
+              "2  53            Private  234721        11th    7   Married-civ-spouse   \n",
+              "3  28            Private  338409   Bachelors   13   Married-civ-spouse   \n",
+              "4  37            Private  284582     Masters   14   Married-civ-spouse   \n",
+              "\n",
+              "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
+              "0     Exec-managerial         Husband   White     Male      0   0   13   \n",
+              "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
+              "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
+              "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
+              "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
+              "\n",
+              "    United-States   <=50K  \n",
+              "0   United-States   <=50K  \n",
+              "1   United-States   <=50K  \n",
+              "2   United-States   <=50K  \n",
+              "3            Cuba   <=50K  \n",
+              "4   United-States   <=50K  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5ca06f35-3dd3-4aec-a192-f80d79f51136\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>39</th>\n",
+              "      <th>State-gov</th>\n",
+              "      <th>77516</th>\n",
+              "      <th>Bachelors</th>\n",
+              "      <th>13</th>\n",
+              "      <th>Never-married</th>\n",
+              "      <th>Adm-clerical</th>\n",
+              "      <th>Not-in-family</th>\n",
+              "      <th>White</th>\n",
+              "      <th>Male</th>\n",
+              "      <th>2174</th>\n",
+              "      <th>0</th>\n",
+              "      <th>40</th>\n",
+              "      <th>United-States</th>\n",
+              "      <th>&lt;=50K</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>50</td>\n",
+              "      <td>Self-emp-not-inc</td>\n",
+              "      <td>83311</td>\n",
+              "      <td>Bachelors</td>\n",
+              "      <td>13</td>\n",
+              "      <td>Married-civ-spouse</td>\n",
+              "      <td>Exec-managerial</td>\n",
+              "      <td>Husband</td>\n",
+              "      <td>White</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13</td>\n",
+              "      <td>United-States</td>\n",
+              "      <td>&lt;=50K</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>38</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>215646</td>\n",
+              "      <td>HS-grad</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Divorced</td>\n",
+              "      <td>Handlers-cleaners</td>\n",
+              "      <td>Not-in-family</td>\n",
+              "      <td>White</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>40</td>\n",
+              "      <td>United-States</td>\n",
+              "      <td>&lt;=50K</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>53</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>234721</td>\n",
+              "      <td>11th</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Married-civ-spouse</td>\n",
+              "      <td>Handlers-cleaners</td>\n",
+              "      <td>Husband</td>\n",
+              "      <td>Black</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>40</td>\n",
+              "      <td>United-States</td>\n",
+              "      <td>&lt;=50K</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>28</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>338409</td>\n",
+              "      <td>Bachelors</td>\n",
+              "      <td>13</td>\n",
+              "      <td>Married-civ-spouse</td>\n",
+              "      <td>Prof-specialty</td>\n",
+              "      <td>Wife</td>\n",
+              "      <td>Black</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>40</td>\n",
+              "      <td>Cuba</td>\n",
+              "      <td>&lt;=50K</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>37</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>284582</td>\n",
+              "      <td>Masters</td>\n",
+              "      <td>14</td>\n",
+              "      <td>Married-civ-spouse</td>\n",
+              "      <td>Exec-managerial</td>\n",
+              "      <td>Wife</td>\n",
+              "      <td>White</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>40</td>\n",
+              "      <td>United-States</td>\n",
+              "      <td>&lt;=50K</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ca06f35-3dd3-4aec-a192-f80d79f51136')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-5ca06f35-3dd3-4aec-a192-f80d79f51136 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-5ca06f35-3dd3-4aec-a192-f80d79f51136');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data Cleaning"
+      ],
+      "metadata": {
+        "id": "Wq691TTZT_sY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "col_names = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship',             'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']\n",
+        "df.columns = col_names\n",
+        "df.sample(2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "id": "276K_qWYTyWe",
+        "outputId": "5f44e832-385f-4de0-cedd-65a24196212d"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       age workclass  fnlwgt education  education_num  marital_status  \\\n",
+              "4260    26   Private  333541   HS-grad              9   Never-married   \n",
+              "30961   35   Private  301862   HS-grad              9   Never-married   \n",
+              "\n",
+              "           occupation    relationship    race    sex  capital_gain  \\\n",
+              "4260    Other-service   Not-in-family   White   Male             0   \n",
+              "30961    Craft-repair       Unmarried   White   Male             0   \n",
+              "\n",
+              "       capital_loss  hours_per_week  native_country  income  \n",
+              "4260              0              24   United-States   <=50K  \n",
+              "30961             0              50   United-States   <=50K  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bd0c876b-5a5b-4291-a3c5-fe68549a6a33\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
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+              "\n",
+              "    .dataframe thead th {\n",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>workclass</th>\n",
+              "      <th>fnlwgt</th>\n",
+              "      <th>education</th>\n",
+              "      <th>education_num</th>\n",
+              "      <th>marital_status</th>\n",
+              "      <th>occupation</th>\n",
+              "      <th>relationship</th>\n",
+              "      <th>race</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>capital_gain</th>\n",
+              "      <th>capital_loss</th>\n",
+              "      <th>hours_per_week</th>\n",
+              "      <th>native_country</th>\n",
+              "      <th>income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4260</th>\n",
+              "      <td>26</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>333541</td>\n",
+              "      <td>HS-grad</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Never-married</td>\n",
+              "      <td>Other-service</td>\n",
+              "      <td>Not-in-family</td>\n",
+              "      <td>White</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>24</td>\n",
+              "      <td>United-States</td>\n",
+              "      <td>&lt;=50K</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30961</th>\n",
+              "      <td>35</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>301862</td>\n",
+              "      <td>HS-grad</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Never-married</td>\n",
+              "      <td>Craft-repair</td>\n",
+              "      <td>Unmarried</td>\n",
+              "      <td>White</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>50</td>\n",
+              "      <td>United-States</td>\n",
+              "      <td>&lt;=50K</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd0c876b-5a5b-4291-a3c5-fe68549a6a33')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-bd0c876b-5a5b-4291-a3c5-fe68549a6a33 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-bd0c876b-5a5b-4291-a3c5-fe68549a6a33');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Objective**\n",
+        "The goal of this machine learning project is to predict whether a person makes over 50K a year or not given their demographic variation. This is a classification problem.\n",
+        "\n",
+        "\n",
+        "**1. Categorical Attributes**\n",
+        "\n",
+        "*   **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
+        "Individual work category\n",
+        "*   **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
+        "Individual's highest education degree\n",
+        "*   **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
+        "Individual marital status\n",
+        "*   **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
+        "Individual's occupation\n",
+        "*   **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
+        "Individual's relation in a family\n",
+        "*   **race**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
+        "Race of Individual\n",
+        "*   **sex**: Female, Male.\n",
+        "*   **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\n",
+        "Individual's native country\n",
+        "\n",
+        "\n",
+        "**2. Continuous Attributes**\n",
+        "\n",
+        "*   **age**: continuous.\n",
+        "Age of an individual\n",
+        "*   **fnlwgt**: final weight, continuous.\n",
+        "The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau.\n",
+        "*   **capital-gain**: continuous.\n",
+        "*   **capital-loss**: continuous.\n",
+        "*   **hours-per-week**: continuous.\n",
+        "Individual's working hour per week"
+      ],
+      "metadata": {
+        "id": "YxoEyfG-UZ3Q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "attrib, counts = np.unique(data['workclass'], return_counts = True)\n",
+        "most_freq_attrib = attrib[np.argmax(counts, axis = 0)]\n",
+        "data['workclass'][data['workclass'] == ' ?'] = most_freq_attrib"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kdWMMYohUB4m",
+        "outputId": "135c50ab-ea5d-4157-ae41-ef11ddbeec98"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['occupation'].replace({' ?': np.nan}, inplace = True)\n",
+        "data['native_country'].replace({' ?': np.nan}, inplace = True)"
+      ],
+      "metadata": {
+        "id": "B-wD2y7fUi-q"
+      },
+      "execution_count": 36,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.impute import SimpleImputer\n",
+        "\n",
+        "si = SimpleImputer(strategy = 'most_frequent')\n",
+        "data[['occupation', 'native_country']] = si.fit_transform(data[['occupation', 'native_country']])"
+      ],
+      "metadata": {
+        "id": "jPOZFeaaUou2"
+      },
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['income'] = np.where(data['income'] == ' <=50K', 0, 1)"
+      ],
+      "metadata": {
+        "id": "w-BrC12KUuSz"
+      },
+      "execution_count": 38,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## EDA"
+      ],
+      "metadata": {
+        "id": "qTlskxwTU5Hk"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Feature Engineering"
+      ],
+      "metadata": {
+        "id": "IuG6784AU8sx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "num_col = ['age', 'fnlwgt', 'capital_gain', 'capital_loss', 'hours_per_week']"
+      ],
+      "metadata": {
+        "id": "Ly--b-01Uzxy"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = data['income']"
+      ],
+      "metadata": {
+        "id": "TPlyHYjkVBZy"
+      },
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "num_data = data[num_col]\n",
+        "cat_data = data.drop(num_col, axis = 1).drop(['income'], axis = 1)"
+      ],
+      "metadata": {
+        "id": "4mz1e_gvVCzl"
+      },
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Select and Train"
+      ],
+      "metadata": {
+        "id": "BpBROLCKVPZU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Numerical"
+      ],
+      "metadata": {
+        "id": "u0bE1N-ZVTfn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "for col in num_col:\n",
+        "    sns.distplot(num_data[col])\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "0l8POSxKVOGQ",
+        "outputId": "71e31fee-ed79-4ac5-c5de-1a2f7acb02d4"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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XYHkFhEg2BYTkrb2tnRQkjFRB/3/mPUN+697UIgdSQEje2tPSSdkAZzBB1oB9qkGIHEABIXmrobVrwP4HgIJkgqqSQvVBiPSigJC81dDamVNAQM+AfWpiEsmmgJC8FQTEwE1MEPRD6DRXkQMpICRvNbQMtgahgBDJpoCQvNSdcRrbunKvQZSlVIMQ6UUBIXmpqa2LjENZP3eTyzauLKWhNkR6UUBIXmrI8SrqHmNKC2nv0oB9ItkUEJKX3gyI3JqYxpVquA2R3hQQkpfeHGYj1xqEhtsQ6S3WgDCzi8xstZmtMbMbIuYXmdlPw/lLzWxmOH2mmbWZ2Qvh45Y4yyn5p25fBwAVxYU5LT8uHLBPw22IvCm3+vchMLMkcDNwAbAFWGZmS9x9VdZiHwca3H2umV0OfA34UDhvrbufElf5JL/t2teOGZTnMNQG6J4QIlHirEEsBNa4+zp37wTuBi7ptcwlwJ3h8/uAd5mZxVgmOUrs2tfBuNIUyURuf077R3Rt7oizWCKjSpwBUQNsznq9JZwWuYy7p4FGYHw4b5aZPW9mT5jZ2TGWU/LQrqYOqiuKcl5+bGmKwqSxc58CQqRHbE1Mh2k7MN3dd5vZ6cD9ZnaCuzdlL2Rm1wDXAEyfPn0Yiikj1a597UysLM55+UTCmFhRzM7G9hhLJTK6xFmD2ApMy3o9NZwWuYyZFQBVwG5373D33QDuvgJYCxzb+w3c/TZ3r3X32urq6hh2QUarXU0dTBxEDQLgmKpitisgRPaLMyCWAfPMbJaZpYDLgSW9llkCXBU+vwz4nbu7mVWHndyY2WxgHrAuxrJKHslknPrmDiZVDj4gdjYpIER6xBYQYZ/CdcBDwKvAPe6+0sxuNLOLw8VuB8ab2RrgC0DPqbDnAC+Z2QsEndd/6e574iqr5Jc9rZ2kM87EitybmACOqQxqEO4eU8lERpdY+yDc/QHggV7Tvpz1vB34QMR6PwN+FmfZJH/tago6midWFA3qHg+Tq4pp6+qmqT1NVUlu10/k6idLNx007cpF6jeTkU1XUkve2bUvaCaaOMgmpklhp7aamUQCCgjJO2/WIAbZxFQVLK+OapGAAkLyTk8NYjDXQUDQBwHoVFeRkAJC8s6ufR1UFhdQXJjbQH09epqYdqiJSQRQQEge2tXUsf/DfjBSBQnGl6XUxCQSUkBI3gmuoh5c81IPXQsh8iYFhOSdbXvbOaay5JDWPaaymB2qQYgACgjJMy0daXY0tTO7uuyQ1p9UVcz2xrYhLpXI6KSAkLyyvr4FgNkTDi0gZk8oo6G1i90a9ltEASH5ZW1dMwCzq8sPaf3jj6kE4LUd+4asTCKjlQJC8sq6uhbMYMb40kNaf/7kCgBe3d40wJIi+U8BIXllXX0LU8eWDPoaiB7jy4uoriji1e2qQYiM1BsGiRySdXXNzJ5waM1LPY4/poLXdrxZg4gaaA802J7kP9UgJG+4O+vrWw75DKYe8ydX8sbOZtLdmSEqmcjopICQvLGjqZ3Wzu5D7qDuMX9yBZ3dGdaFZ0SJHK3UxCR5Y11d8IE+5xBPce3RcybTq9ubOHZSxWFt65cvbGX1jn0UFSQHPXigyHBTQEjeeGlLIwDzDvNDfU51OWWpJE+8Xsclp9Qc0jbcna8/tJpvP752/7QP1k7jlGljDqtsIkeSAkJGnb46jR9bvYsFkysP+5t6qiDBpafWcN+KLXz5fQsOaRu/fGEb3358LVcsnM7175rLB295hl+9tI1jJ5VTmtK/nYwO6oOQvNDe1c2KjQ2ce1z1kGzvw4tm0JHOcN+KLYNet7UzzU2/eY2TplbxL5eeyOSqEi49tYb2rm4eWrljSMo3kJ8s3XTQQ2SwFBCSF97Y1Ux3xjn3uIlDsr0FUyo5bfoYfvDMRpo70oNa99Yn1rGjqZ0vv28BiYQBMLmqhEWzx7NiYwNNbbnfJ1tkOCkgJC+8vnMfFcUFnDZ96Nr4P3f+sexoaue/H32Dp9bUU79v4PGZtu1t49bfr+VPT55C7cxxB8x72+zxZByWbdgzZGUUiZMaQyWy+WE0XQTW1tnNq9ubOO+4iRQkh+47zznHVvPLa8/iL76/jF+/vJ1fv7ydBZMruejEY5hQHt3PcdNvXsMdbnjP8QfNG19exLGTylm2Yc+Q1XRE4qSAkCNuqAPpwZU7aOvs5tPnzjmcYkWaP7mSz7xzHg0tnTy3qYEn19TzrcfXcOXCGQct++Qb9Sx5cRvXv3MuNWOi70exaNZ4fvjsRo31JKOCmphkVHttexPLNuzhrLkTOLGmKrb3GVuW4l3zJ3H9u+ZRVVLI959ef0DQ7Wxq53M/fZ65E8v5y36C6rhjKhhTUsjS9btjK6vIUFENQkYld+eJ1+t4ZNVOJlcVc/78SUfkfceWpvjUOXO4e9kmvvSLl/nDG3XUzhzHj57dSEtHN3d98rR+T2NNmHHGrHE8smona+uamXOYV32LxEk1CBl1Orq6+ckfN/Hwqp28ZWoVnzpnDqmCI/enXFyY5CNnzuS68+byzLrd/POvVlFUkOC2j56e00V6tTPGkjTjx8/q1FMZ2VSDkFGlsbWL259az7a9bbz3xGM4a+4EzOyIlyOZML747uO4/l3z2Li7hbkTy3MuR0VxIQumVHLvis189vygyWoo1Td3UN/c0WdHukiuFBByAHc/4h+4GXcyGd9/zUBf9rR0svi7S9ne2M6HF81g/uTKI1TCvqUKEoc0tMc7jq3m5cca+d5T6/nc+ccOSVl27WvnSz9/mcdW19GdcaZUFXPxyVOYPv7wxqaSo5cCQgDYUN/Cw6t2sr2xjdJUku2NbXzqHXMoL8rtT2SwZyZl3FmxoYE/rKlnT0sH/+/h1Zw/fxKfOHs2xx1z8Afurn3tLP7uUjbubuUjZ8447EH0htuUMSVcuGAStz+5nqvfNouq0jdrEYdy/4nNe1pZfPtSdjV18KlzZrNxdytPr63njqc38LG3zmTmYQ5gKEcnBcRRzt15ck09D76yncqSQk6ZNoY9LZ3c/NgaHnh5O7cszq1dfTCaO9L8eOlGNu5uZdrYEs6eV824shS/eWUH9z23hT8/dSqfv2AeU8cGtw19dt1urr/refa1p/nex85gw+7WyO2OtuEkPnf+sTz8X3/g/z7wKje9/y2HXHPbta+dK77zLPva0/z4k4s4bfpYfrJ0E2+pqeK7T67nzmc2xHIKsOQ/BUSeyuUbfXNHmr+97yUeCC8Au+z0qftv1TlrQhmfues5Lrn5Kb72/pP405OnHLQ9d2fV9iZe2LyXJ9+oZ2JFMbMmlPXbYbxmVzO3PLGWprYuLjt9KqdOG4OZceWi6TS0dPLtJ9by/ac3sOTFrcyfXEl7Vzev72xm5vhSvnf1GZwwpYoNu0dGEBzu9RwLplRy7XlzuPmxtcybVM7H3z5r0CHR2NrF1d9bxu7mTu6+5kxOzhottrKkkL84ayY3P76WHz27iY+/fRYVxUPb3yH5TQFxlHp95z7+8kcr2FDfwkUnHMPZ8w7s7H3rnPH8+vqzufbHz/GZu57n1y9t57p3zmVOdTkbdrfwu9d2cf/zW3ljV/MB2y0qSHDm7PGcNXfCQe/51Jp6Pv2jFXQ7fPLs2UwbV3rA/LFlKb703vlcfdZMvv/0BlZubaKiuIDLz5jOB2qn5uWH219fcBxv7Gzmq79+lf99aTsnT63ila2N7G7upCOdIZkwasaUMHdiOe1d3Qfca3tXUzsfveOPrK1r5raP1h4QDj3GlKa4YuE07nhyPV+450VuXXz6gH09Ij0UEHloV1M7L27Zy+7mDgoSCUpTScqLC3hx817qmzt4ZNVO7luxhTGlKX78iTNZ38ed0yZVFnPXNWdyy+Nr+dbja3mw10iktTPG8tVLT+S84yfy4Ms72NLQyvKNDfz+9TqeXlvPloZWzplXTbc7D76yg188v5W5E8u59JQaxpWl+iz/5KoS/u4984f0dzJSJRLG/1x5Gvcs38ydT2/gf1/cRsZhXFmKcWUpOtMZXtyylz9u2MOSF7dx4QmTOKmmim2N7dy1dBPd7tzxsTM4e17fo9jOnlDOe98ymV+9tJ3//t0aPnv+vCO4hzKaKSDywM6mdp5dt5tn1+1h6brdfd4q8wfPbAQglUxw5aLpXPfOuUysKO4zIAAKkwk+8655fGjhNJ5Zu5uNu1uZMb6U2pnjDhhOoiSVZN6kCuZNqmDXvnaeWF3H3X/cfMB7/tW5c7j2vLn88oVtQ7j3Q2s4+jFSBQkWnzmDxWfOiCxDd8ZZV99MS0ea37yyg58/txUzeM+Jx/C584/NqcP+rbPHkypI8O+/fZ2CpPFX584ZltODZXRRQIwAjW1drK1rZs2uZtbuaubx1XU0tAZNDJ3pDOlMhoJEgqqSQkpSSYoKEpSkkhQkjM172tjR1A5ARVEBC2eN44qF09nb2sWkyiK63Wnt7Ka5Pc1pM8YyrqyQYydVDLq5ZmJFMZecUsNPlm6ipaObJ1bX9bvsB2qncddpNby0pZGiggRzJ5ZT1s8ZUYdy5s7hGE0d2smEMW9iBVcums5XL30LLR1pUgWJfn+fvZkZN/35SWQyzjceWs3zm/byufPnsWByJXcv23zQ8oP5vd/59AbaurrJZJzSVMH+PqjRNOCjRIs1IMzsIuA/gSTwXXe/qdf8IuAHwOnAbuBD7r4hnPd3wMeBbuB6d38ozrIOJXenpbOb+n0d7G7poKGli8a2Lva2ddHY2sneti4aWrvY0tDKxt2t7Gnp3L9uqiDBuNIUE8qLKC5MkipIUJAw0hkn3Z2hsztDuttpbk+TzjhvmzueBZMrOXP2eOZPriQZti/3fAAWAEUFScaWprhgwZEZjqJHcWGShbPGDbyg5CxVkCBV0Hfz3EDrfvODpzBvUgW3PL6W9/33TsqLgg/0pBnJRPAoSBpPr62nZmwJNWOCx5QxJUyqLKalI82G3S2s3NYUPhpZX9eCZ71PVUkhNWNK2NPSwQk1VZw4pUr34x6lYgsIM0sCNwMXAFuAZWa2xN1XZS32caDB3eea2eXA14APmdkC4HLgBGAK8FszO9bdu+Mqb3/cneaONI1tXfsfe1o6qd/XQX1z5/4rV7Oft3dlIrdlBB+cpakkY0oLmVtdzriZKaoriphYUcTYshSJQVT9B/Mt7Uh/ax5N39KHw2B/P0MxCm4iYVx73lw+vGg6D6/cycptjby4pZF0JrhYsTvjdHZneHlrIw+v3Elnd/TfMUDNmBIWTKlk5vgyKooLSGC0dKbZ2dTO1r1t/NvDr+9fdlJlEdPHlTKhvIjx5SnKiwpJJS0MvASFyTd/FoU/M+40taVpau+iobWTuvD/bX1dMw64gxmUFCYpLkxy0tQqqkoKqSwppCp8lBQmKUgGwVeYDL5sFSbffJ+iwgRFBcEXsaLwy1gcTW+ZjNOVCVoE2jq7aenspqUjTWv4s6UzTWtHd/Czs5uChFFWVEB5UQFlRQWUFSX3P+/5WVqYjP2EgzhrEAuBNe6+DsDM7gYuAbID4hLgH8Pn9wH/Y8HRuQS42907gPVmtibc3jNDXcg9LZ382beeIt3tZNxJh/8kPY90JkNXd/A8isH+g1ZeXMCE8iJmji8LXmcd3JLCJKWpAooKE4MKgIHE9SE8Uj7cR0o5RqpD/f2MKU3xwTOmAdP6DJ5Mxqlv7mDL3ja27W1jV1MHFcUF1IwpYf7kSsaGJxr0VYb2rm62NbaxbW872/e2Ud/cyYbdrTS3p+nszvT5PxUllUwwoTz4IlVcmMQMDCPjTltXN3taOtm2t43Gti7Sg9ju4TKDpBmJhFGQsKAmljQSZqS7g8+Oru5MLGUyg7JU8PlyyrQx3PqR2iF/jzgDogbIbtzcAizqaxl3T5tZIzA+nP5sr3Vrer+BmV0DXBO+bDaz1UNT9H5NAOqPwPsMp3zfx9j278NxbPTQHLSPgynbCNqP/d44eFK+/51Cjvv4R+C2jx7yexx8c5PQqO6kdvfbgNuO5Hua2XJ3H/qoHkHyfR/zff9A+5gvhnsf4xwjeSswLev11HBa5DJmVgBUEXRW57KuiIjEKM6AWAbMM7NZZpYi6HRe0muZJcBV4fPLgN+5u4fTLzezIjObBcwjqA96VZgAAAceSURBVEWJiMgRElsTU9incB3wEMFprne4+0ozuxFY7u5LgNuBH4ad0HsIQoRwuXsIOrTTwLXDdQZThCPapDVM8n0f833/QPuYL4Z1Hy34wi4iInIg3XJUREQiKSBERCSSAiJHZnaRma02szVmdsNwl2comNk0M3vMzFaZ2Uoz+2w4fZyZPWJmb4Q/xw53WQ+XmSXN7Hkz+1X4epaZLQ2P50/DEylGLTMbY2b3mdlrZvaqmb01n46jmX0+/Bt9xczuMrPi0X4MzewOM9tlZq9kTYs8Zhb4r3BfXzKz045EGRUQOcgaNuQ9wALginA4kNEuDfy1uy8AzgSuDffrBuBRd58HPBq+Hu0+C7ya9fprwL+7+1yggWDYl9HsP4EH3f144GSCfc2L42hmNcD1QK27n0hw0kvP0Dyj+Rh+H7io17S+jtl7CM7mnEdwcfC3j0QBFRC52T9siLt3Aj3Dhoxq7r7d3Z8Ln+8j+FCpIdi3O8PF7gQuHZ4SDg0zmwr8CfDd8LUB7yQY3gVG+T6aWRVwDsFZgbh7p7vvJb+OYwFQEl4vVQpsZ5QfQ3f/PcHZm9n6OmaXAD/wwLPAGDObHHcZFRC5iRo25KChP0YzM5sJnAosBSa5+/Zw1g7gyA4DO/T+A/g/QM/Ic+OBve6eDl+P9uM5C6gDvhc2o33XzMrIk+Po7luBfwM2EQRDI7CC/DqGPfo6ZsPyGaSAEMysHPgZ8Dl3b8qeF164OGrPhTaz9wG73H3FcJclRgXAacC33f1UoIVezUmj+TiG7fCXEAThFKCMg5tm8s5IOGYKiNzk7dAfZlZIEA4/dvefh5N39lRfw5+7hqt8Q+As4GIz20DQNPhOgvb6MWFzBYz+47kF2OLuS8PX9xEERr4cx/OB9e5e5+5dwM8Jjms+HcMefR2zYfkMUkDkJpdhQ0adsC3+duBVd/9m1qzsIVCuAn55pMs2VNz979x9qrvPJDhuv3P3DwOPEQzvAqN/H3cAm83suHDSuwhGIciX47gJONPMSsO/2Z79y5tjmKWvY7YE+Gh4NtOZQGNWU1RsdCV1jszsvQRt2T3DhvzLMBfpsJnZ24E/AC/zZvv8lwj6Ie4BpgMbgQ+6e+/OtFHHzM4Fvuju7zOz2QQ1inHA88Di8P4jo5KZnULQCZ8C1gFXE3wBzIvjaGb/BHyI4My754FPELTBj9pjaGZ3AecSDOm9E/gKcD8RxywMxv8haFprBa529+Wxl1EBISIiUdTEJCIikRQQIiISSQEhIiKRFBAiIhJJASEiIpEUECIiEkkBIXnBzGZmD5ss0fR7ksFQQIj0IWsYh7x6L5FcKSAknyTN7DvhjWUeNrMSMzvFzJ4Nb7Lyi6wbsDxuZrXh8wnhWE2Y2cfMbImZ/Q541Mwmm9nvzeyF8GY1Z/f15mbWbGb/Hr7/o2ZWHU6fY2YPmtkKM/uDmR0fTv++md1iZkuBr/exzZfDmwGZme02s4+G039gZhdYcCOkb5jZsnAfP5W17t9kTf+niG3PDkd/PeMQf9+S5xQQkk/mATe7+wnAXuD9wA+Av3X3kwiGFPlKDts5DbjM3d8BXAk85O6nENyI54V+1isDlofv/0TWe90GfMbdTwe+CHwra52pwNvc/Qt9bPMpgoHpTiAYQqMnoN4KPE1wk5xGdz8DOAP4ZDhm2IXh72MhcApwupmd07PRcNymnwEfc/dlA/1C5Oikaq3kk/Xu3vMBvgKYA4xx9yfCaXcC9+awnUeyxixaBtwRjnp7f9b2o2SAn4bPfwT8PBxK/W3AvcFwOgAUZa1zr7t397PNPxDcDGgjwV3ErgnvsNbg7i1hEJxkZj2D1lURBMOF4eP5cHp5OH0TUE0wCNyfu/uqft5bjnKqQUg+yR6orRsY08+yad78+y/uNa+l50l4169zCIZW/n5PE0+OPHyPve5+StZjftR79eH3BLWGs4HHCW4MdBlBcAAYQe2kZ9uz3P3hcPq/Zk2f6+63h+s0EgTF2wexL3IUUkBIPmsEGrL6DT5C0PQDsAE4PXx+GX0wsxnATnf/DsFoqf3dLD6Rta0rgSfDGzCtN7MPhNszMzs51x1w980Eo33Oc/d1wJMEzVS/Dxd5CPh0WMPBzI4N7yb3EPAXYQ0GM6sxs4nhOp3AnxEMH31lrmWRo4+amCTfXQXcYmalvDkMNgS3sLzHzK4Bft3P+ucCf2NmXUAz0F8NogVYaGb/QHCjlw+F0z8MfDucXkgwRPWLg9iHpQTDzENQc/hXgqCAILRmAs+FQ0LXAZe6+8NmNh94JmzaagYWE9SsCJun3gc8YmbN7j7q728iQ0/DfYsMkfCDtny4yyEyVNTEJCIikdTEJDJI4XULRb0mf+Rwag9mdjXw2V6Tn3L3aw91myKHS01MIiISSU1MIiISSQEhIiKRFBAiIhJJASEiIpH+P9OJbcVk0iIhAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = num_data.drop(['capital_gain', 'capital_loss'], axis = 1)\n",
+        "X['capital_change'] = data['capital_gain']-data['capital_loss']"
+      ],
+      "metadata": {
+        "id": "xhzpnZ7WVbeM"
+      },
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import RobustScaler, MinMaxScaler\n",
+        "\n",
+        "a = RobustScaler().fit_transform(X)\n",
+        "X = pd.DataFrame(a, columns = X.columns)\n",
+        "\n",
+        "xtrain, xtest, ytrain, ytest = train_test_split(X, y, random_state=20)"
+      ],
+      "metadata": {
+        "id": "nziUKkh-VwUm"
+      },
+      "execution_count": 44,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "\n",
+        "gnb = GaussianNB()\n",
+        "gnb.fit(xtrain, ytrain)\n",
+        "gnb.score(xtest, ytest)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QnFAULkAV-sZ",
+        "outputId": "ac38acc2-cf96-485e-8fa6-2160337d8e04"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.7896805896805896"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Categorical "
+      ],
+      "metadata": {
+        "id": "yiPgoRtKWOFw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Z = cat_data"
+      ],
+      "metadata": {
+        "id": "hlcb3OT_WOaT"
+      },
+      "execution_count": 46,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder\n",
+        "# Label Encoder Can be used here\n",
+        "\n",
+        "od = OrdinalEncoder()\n",
+        "Z[Z.columns] = od.fit_transform(Z)"
+      ],
+      "metadata": {
+        "id": "OQZYtDBAWQen"
+      },
+      "execution_count": 47,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Z.sample(7)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "id": "8FOCsjO2Wyua",
+        "outputId": "f0f4c1f3-483e-416a-81eb-9deeb6861d09"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       workclass  education  education_num  marital_status  occupation  \\\n",
+              "30801        3.0        7.0           11.0             2.0        11.0   \n",
+              "16043        3.0       11.0            8.0             0.0        11.0   \n",
+              "9015         3.0        9.0           12.0             2.0        11.0   \n",
+              "22087        3.0       15.0            9.0             2.0         3.0   \n",
+              "22054        3.0       15.0            9.0             2.0         0.0   \n",
+              "28671        3.0       15.0            9.0             2.0         2.0   \n",
+              "16541        3.0       12.0           13.0             2.0         9.0   \n",
+              "\n",
+              "       relationship  race  sex  native_country  \n",
+              "30801           0.0   3.0  1.0            23.0  \n",
+              "16043           2.0   4.0  0.0            38.0  \n",
+              "9015            0.0   2.0  1.0            38.0  \n",
+              "22087           3.0   4.0  1.0            38.0  \n",
+              "22054           0.0   1.0  1.0            25.0  \n",
+              "28671           0.0   4.0  1.0            38.0  \n",
+              "16541           0.0   4.0  1.0            38.0  "
+            ],
+            "text/html": [
+              "\n",
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+              "      <th></th>\n",
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+              "      <th>education_num</th>\n",
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+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e33234ab-529c-4131-89c7-a966e1e22806')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-e33234ab-529c-4131-89c7-a966e1e22806 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-e33234ab-529c-4131-89c7-a966e1e22806');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "xtrain, xtest, ytrain, ytest = train_test_split(Z, y, random_state=20)"
+      ],
+      "metadata": {
+        "id": "YVN65qCBW53H"
+      },
+      "execution_count": 49,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.naive_bayes import MultinomialNB\n",
+        "\n",
+        "mnb = MultinomialNB()\n",
+        "mnb.fit(xtrain, ytrain)\n",
+        "print('Test', mnb.score(xtest, ytest))\n",
+        "print('Train', mnb.score(xtrain, ytrain))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F11VxUB6W6ZK",
+        "outputId": "977f1538-f394-4825-e885-1b4afd334885"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test 0.7932432432432432\n",
+            "Train 0.8002866502866502\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### MIX type features"
+      ],
+      "metadata": {
+        "id": "WddLwBfXXDhu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mixed-naive-bayes"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_r8yYxr0XmGz",
+        "outputId": "95c8b30d-61ee-40a4-b454-4924b701b99d"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting mixed-naive-bayes\n",
+            "  Downloading mixed_naive_bayes-0.0.1-py3-none-any.whl (8.9 kB)\n",
+            "Requirement already satisfied: numpy>=1.16.1 in /usr/local/lib/python3.7/dist-packages (from mixed-naive-bayes) (1.21.6)\n",
+            "Requirement already satisfied: scikit-learn>=0.20.2 in /usr/local/lib/python3.7/dist-packages (from mixed-naive-bayes) (1.0.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.2->mixed-naive-bayes) (3.1.0)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.2->mixed-naive-bayes) (1.1.0)\n",
+            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.2->mixed-naive-bayes) (1.4.1)\n",
+            "Installing collected packages: mixed-naive-bayes\n",
+            "Successfully installed mixed-naive-bayes-0.0.1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = num_data\n",
+        "X[X.columns] = RobustScaler().fit_transform(X)\n",
+        "\n",
+        "Y = cat_data\n",
+        "od = OrdinalEncoder()\n",
+        "Y[Y.columns] = od.fit_transform(Y)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HsWURJ4nXrak",
+        "outputId": "b8f76962-8e19-466e-8d9d-b7ef907d3183"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  self[col] = igetitem(value, i)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = X.join(Y)\n",
+        "X.sample(7)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 333
+        },
+        "id": "sW8oU_bMXxuX",
+        "outputId": "383fecf9-b1f0-41ba-e423-f78f3aed689d"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        age    fnlwgt  capital_gain  capital_loss  hours_per_week  workclass  \\\n",
+              "4330   1.55  0.526836           0.0           0.0            -4.8        5.0   \n",
+              "28464 -0.25  1.116907           0.0           0.0            -0.4        3.0   \n",
+              "27872  0.25  1.179747        2885.0           0.0             1.0        3.0   \n",
+              "310   -0.15 -0.297476        7298.0           0.0            -1.0        3.0   \n",
+              "24317  0.05 -0.480008           0.0           0.0             0.0        3.0   \n",
+              "24178 -0.65 -0.434061           0.0           0.0            -4.0        1.0   \n",
+              "4327  -0.35  0.145836           0.0           0.0            -2.6        3.0   \n",
+              "\n",
+              "       education  education_num  marital_status  occupation  relationship  \\\n",
+              "4330         5.0            3.0             2.0         3.0           0.0   \n",
+              "28464       11.0            8.0             4.0         2.0           3.0   \n",
+              "27872       11.0            8.0             2.0        13.0           0.0   \n",
+              "310         12.0           13.0             2.0         3.0           0.0   \n",
+              "24317       11.0            8.0             2.0         9.0           0.0   \n",
+              "24178        9.0           12.0             4.0         9.0           3.0   \n",
+              "4327        11.0            8.0             4.0         5.0           3.0   \n",
+              "\n",
+              "       race  sex  native_country  \n",
+              "4330    4.0  1.0            38.0  \n",
+              "28464   2.0  1.0            38.0  \n",
+              "27872   4.0  1.0            38.0  \n",
+              "310     1.0  1.0            35.0  \n",
+              "24317   4.0  1.0            38.0  \n",
+              "24178   4.0  0.0            38.0  \n",
+              "4327    4.0  1.0            38.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6c7016b9-0492-4812-998e-bddc7f98ce7c\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>age</th>\n",
+              "      <th>fnlwgt</th>\n",
+              "      <th>capital_gain</th>\n",
+              "      <th>capital_loss</th>\n",
+              "      <th>hours_per_week</th>\n",
+              "      <th>workclass</th>\n",
+              "      <th>education</th>\n",
+              "      <th>education_num</th>\n",
+              "      <th>marital_status</th>\n",
+              "      <th>occupation</th>\n",
+              "      <th>relationship</th>\n",
+              "      <th>race</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>native_country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4330</th>\n",
+              "      <td>1.55</td>\n",
+              "      <td>0.526836</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-4.8</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>38.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28464</th>\n",
+              "      <td>-0.25</td>\n",
+              "      <td>1.116907</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>38.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27872</th>\n",
+              "      <td>0.25</td>\n",
+              "      <td>1.179747</td>\n",
+              "      <td>2885.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>38.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>310</th>\n",
+              "      <td>-0.15</td>\n",
+              "      <td>-0.297476</td>\n",
+              "      <td>7298.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>35.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24317</th>\n",
+              "      <td>0.05</td>\n",
+              "      <td>-0.480008</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>38.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24178</th>\n",
+              "      <td>-0.65</td>\n",
+              "      <td>-0.434061</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-4.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>38.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4327</th>\n",
+              "      <td>-0.35</td>\n",
+              "      <td>0.145836</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-2.6</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>38.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c7016b9-0492-4812-998e-bddc7f98ce7c')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-6c7016b9-0492-4812-998e-bddc7f98ce7c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-6c7016b9-0492-4812-998e-bddc7f98ce7c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "xtrain, xtest, ytrain, ytest = train_test_split(X, y, random_state=20)"
+      ],
+      "metadata": {
+        "id": "KYMpJ-7GX0Fe"
+      },
+      "execution_count": 54,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from mixed_naive_bayes import MixedNB\n",
+        "\n",
+        "model = MixedNB(categorical_features=np.arange(5, 14))\n",
+        "model.fit(xtrain, ytrain)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a3hpx7bdX2zG",
+        "outputId": "0907d88f-1337-4271-8233-4b9178f80e23"
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[ 8 16 16  7 14  6  5  2 41]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "MixedNB(alpha=0.5, var_smoothing=1e-09)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.score(xtest, ytest)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s-g1suFPYE2D",
+        "outputId": "57a308b6-3d10-4e21-9279-f7d5b2d323f0"
+      },
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8242014742014742"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = MixedNB(alpha=1, categorical_features=np.arange(5, 14))\n",
+        "model.fit(xtrain, ytrain)\n",
+        "model.score(xtest, ytest)"
+      ],
+      "metadata": {
+        "id": "8vQ85-Ln09hN",
+        "outputId": "fa6c0d8e-8347-464f-bde6-d890ede2e1d1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[ 8 16 16  7 14  6  5  2 41]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8242014742014742"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 62
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "tVoWYMgQ1NI9"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file