Email Classifier/Emails_spam_or_not.ipynb

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  "metadata": {
    "colab": {
      "name": "Emails - spam or not.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xx9971offLHD"
      },
      "source": [
        "# **Classify the Emails into Spam or Not.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h9fs8CnPfKyu"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QePg24fJfGXS"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvCGxuTDlfAQ"
      },
      "source": [
        "# Reading the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "ToqX40lUlEhY",
        "outputId": "c42e66cd-cb62-413a-a641-f312e82a91ab"
      },
      "source": [
        "df = pd.read_csv('/content/spam_or_not_spam.csv')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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></th>\n",
              "      <th>email</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>date wed NUMBER aug NUMBER NUMBER NUMBER NUMB...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>martin a posted tassos papadopoulos the greek ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>man threatens explosion in moscow thursday aug...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>klez the virus that won t die already the most...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>in adding cream to spaghetti carbonara which ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3460</th>\n",
              "      <td>abc s good morning america ranks it the NUMBE...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3461</th>\n",
              "      <td>hyperlink hyperlink hyperlink let mortgage le...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3462</th>\n",
              "      <td>thank you for shopping with us gifts for all ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3463</th>\n",
              "      <td>the famous ebay marketing e course learn to s...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3464</th>\n",
              "      <td>hello this is chinese traditional 子 件 NUMBER世...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3465 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  email  label\n",
              "0      date wed NUMBER aug NUMBER NUMBER NUMBER NUMB...      0\n",
              "1     martin a posted tassos papadopoulos the greek ...      0\n",
              "2     man threatens explosion in moscow thursday aug...      0\n",
              "3     klez the virus that won t die already the most...      0\n",
              "4      in adding cream to spaghetti carbonara which ...      0\n",
              "...                                                 ...    ...\n",
              "3460   abc s good morning america ranks it the NUMBE...      1\n",
              "3461   hyperlink hyperlink hyperlink let mortgage le...      1\n",
              "3462   thank you for shopping with us gifts for all ...      1\n",
              "3463   the famous ebay marketing e course learn to s...      1\n",
              "3464   hello this is chinese traditional 子 件 NUMBER世...      1\n",
              "\n",
              "[3465 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gEV2M0blila"
      },
      "source": [
        "# Understanding the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8vwQOCwlzPh",
        "outputId": "3e255cf9-9273-49e1-b37c-f0767bd7a427"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "email    object\n",
              "label     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "InP13wOwl0bi",
        "outputId": "ae8a558e-0949-4094-85a2-fffb144ac62b"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3465, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Q_XqpGfl4dG",
        "outputId": "c76b83ec-52ea-458b-8365-beaec455da3b"
      },
      "source": [
        "df.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6930"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1P8BJLY_l6Ud",
        "outputId": "ae1a7ecf-b7df-4007-a243-adec7024ab42"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['email', 'label'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAdNaRQSl8k-",
        "outputId": "2f4e3ab1-f93b-46a2-e459-0b6094f41287"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3465 entries, 0 to 3464\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   email   3463 non-null   object\n",
            " 1   label   3465 non-null   int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 54.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "b9gsuF0mmAWT",
        "outputId": "1626a591-7d0f-4686-df38-52772576e231"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3465.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.278499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.448325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
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            ],
            "text/plain": [
              "             label\n",
              "count  3465.000000\n",
              "mean      0.278499\n",
              "std       0.448325\n",
              "min       0.000000\n",
              "25%       0.000000\n",
              "50%       0.000000\n",
              "75%       1.000000\n",
              "max       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "id": "qSQiI2ISmNmb",
        "outputId": "2bc8b46e-40b0-45ff-cad8-295e2588b867"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       label\n",
              "label    1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Slg2ekEImSge",
        "outputId": "0ce56ad4-c966-437c-96db-2e6dccc05e4d"
      },
      "source": [
        "df.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "email    2873\n",
              "label       2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_uNT-_EmVqF",
        "outputId": "c25e3caa-eff6-4d73-8810-1f58fc9f68aa"
      },
      "source": [
        "df.isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "email     True\n",
              "label    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AViWr6FXmjMC"
      },
      "source": [
        "# Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "R83cxViQmmPX",
        "outputId": "460843dd-7a8f-4b6d-b8a9-7f0122574212"
      },
      "source": [
        "import missingno as no\n",
        "no.bar(df, color='pink')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a1461bb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "JtgzgdWvmppO",
        "outputId": "abb613dd-751e-4dcf-ec6a-8c98cb9d2618"
      },
      "source": [
        "sns.heatmap(df.isnull(), yticklabels='False', cmap='YlGn_r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a0b1f9790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fge8Dvt5mluqAjwKXjdpFbwD+Zey1w8BbknwReBrw/tF9S68D3jX6fnwOePkZnrMa8rJaSWrISlaSGjJkJakhQ1aSGjJkJakhQ1aSGjJkJakhQ1aSGvp/042g0ul8VaIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMbh5VwNpbJE"
      },
      "source": [
        "df = df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "BNzDYPLvpcls",
        "outputId": "8652c9d8-78fa-4862-f16e-2a837ee6af62"
      },
      "source": [
        "df.label.value_counts().plot(kind ='pie')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a0ad06210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "OKun0CqPpvDB",
        "outputId": "e3fc2427-19bd-48d8-e5aa-f0d978c0a01e"
      },
      "source": [
        "sns.countplot(df.label, palette='gist_rainbow')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AN4BXJzmY5Npx9/R85+M+JElNHllIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJCWQJIfNcbXn+oTUpPckuSK59aZtDQMC0lSk2EhLaEkL0myN8m3k9yfZPApvSuT3J7koST/mOTF3TavS/LVJPckuSvJOWNqX1qQYSEtrZ8Ab6mq84E3ATclSTf2auDTVfWrwNPAnyY5A/gkcEVVvQ7YCdw4hr6lRa0cdwPS80yADyX5beBnzD3SfXU3dqCq/r1b/jvgz4EvA78G7OkyZQVweKQdS0MwLKSl9UfAFPC6qvrvJI8DL+rGTn62TjEXLvur6jdH16J06jwNJS2tXwSOdEHxJuCVA2PnJjkRCn8IfB14GJg6UU9yRpLXjLRjaQiGhbS0bgemk9wPXA18Z2DsYWB7koeAVcDN3c/VXgH8VZL/AO4FXj/inqUmnzorSWryyEKS1GRYSJKaDAtJUpNhIUlqMiwkSU2GhSSpybCQJDX9Dz00yN8THsi/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "0POLlgKgsIdO",
        "outputId": "b6daee30-f271-4177-e9d3-61efb33c7fa6"
      },
      "source": [
        "sns.distplot(df['label'], color='y')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fhy/SsgUiWUYFP0Pd3Y8zMtJDefnr/Y5yTGahxDy89uBFsokKfoba2+8FLCMWGJtIOLyEaLSJaLTd7ygikiYq+Bnq6LiPkpI15OVV+R1lQi+tS6O9eJFsoYKfgZGRPjo7/0x5+dl+R5lUQcF8QqGwDpcUySIq+Bno7HwY54aoqMjc+fdRo/Pw2oMXyR4q+Blob78Xs1zKyv7G7yhJic/DNxONtvkdRUTSQAU/Ax0d91Faeiq5uSV+R0nKS+vSaJpGJBuo4KcpGu2gu3vzrJieGZWfP4+cnBJ6e3f5HUVE0kAFP03x5Qlis6rgzUKEw8vp69uJczG/44iIx1Tw09TRcR+hUBFz5pzqd5QpCYdXMjLSxeBgg99RRMRjKvhpam+/l7KyvyEUKvA7ypQUF68AoK9vh89JRMRrKvhpGBjYT1/fTiorz/M7ypTl5paRnz+f3l4VvEjQqeCnobX19wBUVs7Oc4gXF6+gv/95hod7/I4ikjTnHENDjfT376Wn5ylisSG/I2U8z066HWRtbXdSWLiIcHiZ31Gmpbh4Je3tf6Sj436qq9/idxyRCUWjbbS3/5Hu7k2MjHQDcODAVzHLo6rqzcyb9wkqKs70N2SGUsFPUSw2SHv7vdTVfQAz8zvOtBQWLsYsn7a2u1TwkrGci9He/kdaW3+Lc47S0rWEw8vJzS1nzpzX0NX1CE1NN9DScguRyDs58cT/JD+/1u/YGUUFP0UdHQ8Ri/VRVTU7p2cAQqE8wuGltLff5XcUkXGNjPRx6ND36e/fTUnJyUQi7yAvr/LF+2trL6G29hJe8YqvcuDAN9i//0t0df2ZVatuo7T0ZB+TZxbNwU9RW9udhEKFGX2Cj2SEwyvo73+O/v7n/Y4icpTh4U4OHPg6/f3PUVt7GXPnbjyq3MfKySlk0aLPcfLJfwGMLVteS3v7/ekNnMFU8FPU2non5eVnkZMT9jvKjBQXrwSgrU178ZI5RkZ6OHjw20Sjrcyf/3HKyk5Paiq0tHQt69Y9TmFhPdu2vYmOjgfTkDbzqeCnoK/vOfr7n5m1R8+MlZdXQ2HhYlpbf+t3FBEgfvrLhob/JhptYt68jxIOL5/S8/Pza1mz5n4KCxexbdtF9PY+7VHS2cOzgjezH5pZk5lt92qMdGtrix8eOZvn30eZGZHIxbS338vwcKffcSTLOedobPwpAwN7qau7YtpHqOXn17B6dXwaddu2C7J+5VQv9+B/DJzv4fbTrq3tToqKllJU9Aq/o6REdfXFOBeltfUOv6NIluvouJ/u7seoqrpoxm+SFhUtYvXq2xgcbGDXrg/gnEtRytnHs4J3zj0EBObX5/BwN+3t91NV9Ua/o6TMnDmnkZ9fR0vLLX5HkSw2MHCAlpbfUFy8msrK1Px8zZlzKosXf53W1ts5ePDqlGxzNtIcfJLa2n6Pc4NUV1/sd5SUMQtRXf1WWlvvZGSk3+84koVisUGOHLmWUKiYurrLU/rZknnzPkFV1YXs2fMpuroeT9l2ZxPfC97MNprZJjPb1Nzc7HecY2puvpm8vBrKyk73O0pKVVdfTCzWR3v73X5HkSzU3PxrhoYamTv3A+TkpPbEOWbGsmU/Ij9/Ljt3vovh4a6Ubn828L3gnXPXOOfWO+fWRyIRv+OMa2RkgLa2O6iufitmOX7HSany8jPJySnTNI2kXU/PVjo7/0RFxRumfMRMsvLyKlmx4hcMDOzn+ec/5ckYmcz3gp8N2tvvZmSkh0jkbX5HSblQKJ/q6rfQ0nI7sdiw33EkS4yM9NLYeAMFBcdTXX2hp2OVlb2G+fP/kcOHf0B7+72ejpVpvDxM8hfAX4ClZnbQzK7waiyvNTf/mtzcCsrLz/I7iieqqy9meLiNzs6H/I4iWaK5+SZGRnqorb0sLX8V19dfRVHREnbtuoLh4W7Px8sUXh5F827n3FznXJ5zbr5z7jqvxvLSyEgfzc23EIlsIBTK9zuOJyor30AoVERz82/8jiJZoLd3J11df6ay8jwKCxekZcycnCKWLfshg4MvsGfPlWkZMxNoimYSra2/JRbrpabmPX5H8UxOTjFVVW+hufnXxGJRv+NIgMViAzQ2Xk9eXi2VlW9O69hlZaczf/4/cOjQd7NmKQMV/CQaG39Bfv48ysv/xu8onqqtvZRotEVr04inWlpuY3i4lbq6ywiF8tI+fn39lygsrGf37g9lxaHBKvgJRKOttLXdSU3NuwJ39MzLVVaeT25uFY2NP/M7igRUf//zdHTcT1nZmRQVneBLhpycYpYu/R/6+59j374v+pIhnVTwE2hsvB7notTVXe53FM+FQnnU1FxCa+vtWptGUi4Wi9LY+DNycyuIRPz9sGBFxeupq7uCAwe+SXf3Zl+zeE0FfwzOOQ4fvo7S0ldRUrLa7zhpUVd3eWKO9Od+R5GAaWu7g6Ghw9TWvpdQqNDvOCxe/HXy8yPs2nVFoN93UsEfQ3f3Znp7t1FX90G/o6RNaek6SkrWcujQD7J6gSZJrYGBF2hru4s5c15NcfEqv+MAkJdXwYknfpfe3ic5cOAbfsfxjAr+GA4fvoZQqJDa2nf7HSVtzIy5czfS2/sk3d3ZuXaHpJZzIzQ2/pScnBIikXf4HecokcjFVFe/nX37/pW+vt1+x/GECn4c0WgbjY3XU1t7Kbm5ZX7HSava2vcQCoU5dOj7fkeRAGhru4vBwQPU1LyHnJxiv+P8lRNP/G9ycorYvftDOBfzO07KqeDHcfjwdcRi/cyb93G/o6Rdbu4c6uouo7Hx5wwNNfodR2axwcFDtLXdQUnJOkpL1/odZ1wFBXUsXvx/6ez8E4cO/cDvOCmngn+ZWGyYhobvUFb2OkpKXul3HF/Mn/9JnBukoeF7fkeRWcq5GI2NP8WsgJqaS/yOM6G6uvdTUXEue/Z8ioGBF/yOk1Iq+Jdpbv41g4P7mT//k35H8U04vJSqqjdz6NB3s+LDIJJ67e33MDCwl5qad5GbO8fvOBMyM5Ysie+9P/30ZTg34nOi1FHBj+FcjP37v0w4vNLzFe4y3fHH/wvRaDOHD8/KJYTERwMDL9DScivFxWsoLT3F7zhJKSqq54QT/ovOzgcDdVSNCn6Mlpbb6evbwcKFn8Esu1+asrIzKCs7gxde+AojIwN+x5FZYni4h8OHryUnp5S6uvel9AxNXqurez+RyAb27v18YD4Ald0tNoZzI+zb9wWKik4gEnmn33F8Z2YsWvSvDA0d4vDha/yOI7PEc899kmi0iblzP5jyMzR5bXSqJi+vhp0738vISK/fkWZMBZ9w5MjP6O3dRn39lwmFcv2OkxEqKs6kvPxM9u//kpYvkEk1Nf2KI0euo7LyDYTDS/2OMy15eZUsX/5T+vufYffuj8z6D/yp4Imv+b5v3+cpLT2FSGSD33EyyuLF3yAabWH//n/3O4pksO7ureza9UHmzDmNqqrZ/f5VRcXZ1NdfRVPTDRw8+G2/48yICh7Yt+/fGBw8yOLF35hVc4bpUFq6jrq6yzl48Nv09T3jdxzJQENDTWzffhG5uRWsXHlzIFZeXbDgM1RXv43nn/8X2tru8TvOtGV9wff0bOPgwW9SV/fBwK/5Pl319V8mJ6eY3bs/HMhP+8n0xWKDbN/+NqLRZlavvo2Cgrl+R0oJM2PZsh8TDi9n58530d+/x+9I05LVBR+LDbFr1wfIzS1n8eL/8DtOxop/2u9bdHY+xKFD+vCTxDk3wq5dV9DV9TDLlv2I0tJ1fkdKqdzcUlatuhWAJ588l8HBBp8TTV1WF/zevZ+lp2czS5b8D3l5VX7HyWh1dZdTUfEGnn/+X+jpecrvOOIz52Ls3v0hmppuoL7+y9TUvMvvSJ4Ih0/gla/8A9FoM08+eQ5DQ81+R5qSrC34lpbbOHDgGxx33N8SibzV7zgZz8xYvvwn5OZWsGPHBoaHu/yOJD5xLsYzz3yEI0d+zKJFX2Thwk/7HclTc+a8itWrf8fAwH6eeuo8otF2vyMlLSsLvrt7Kzt3vofS0lNYvPhbfseZNfLza1mx4pf09+9hx44NxGJDfkeSNIvFouze/WEOH76WBQs+y8KFX/A7UlqUl5/BqlW30tu7k61bz2Rw8JDfkZKSdQXf27uLp546n7y8SlatupWcnCK/I80q5eVnsHTptbS3382uXR8gFhv2O5KkSTTawbZtb+HIkR+ycOHnqa+/KquOOqusPI/Vq+9gYGAPTzxxGt3dT/gdaVJZVfA9PU/x5JNnAfDKV94dmHf8023u3Mupr/8KTU0/5+mn36M9+SzQ3b2FzZvX09FxL0uXXkt9/b9lVbmPqqw8hzVrHgIcW7aczuHDP8zoD0NlTcG3tPyWLVtOB3JYs+Y+iouX+R1pVlu48EoWL/4Gzc038uSTr9fa8QEViw2xf/+/88QTpxKLDbBmzYPMnXuF37F8VVq6lnXrNjNnzqvZvfsKduzYwMDAQb9jjSvwBT8y0suzz36C7dsvpKhoCevWPUpx8Qq/YwXC8cf/M8uX/4Lu7s1s2rSGlpbb/Y4kKeKco6npRh57bAV7936O6uq3sX79VsrKXuN3tIyQn1/DSSfdzSte8TVaW+/gsceWsm/fVRm3vHZgCz5+LsgbeOyxZTQ0/Bfz53+StWsfpqBgnt/RAqW29hJOPvkR8vJq2b79IrZtu4je3qf9jiXTFItFaW6+mSeeeDU7d76TUKiQ1avvYOXKX5KfX+13vIxilsOCBZ/ilFOepqrqAvbt+wKPPbaEF174OtFoq9/xAPB0VS0zOx+4GsgBrnXOfdXL8QCGhlpoarqBhobv0N//LCUla1m+/BeUl7/W66GzVknJK1m37jEOHPgmL7zwFR5/fCVVVRcyd+6HqKw8l1CowO+IMgHnHL29T9HUdCNHjlzH0NARCgqOZ+nS66ire38glh7wUlFRPStX3khHx4Ps3fsF9uz5FPv2fYGamncTiWygvPxscnIKfcnmWcFb/H/Fd4BzgYPA42Z2u3NuZyrHcS5Gd/dm2tr+QFvb7+nqehSIUVp6KitX/obq6rdm/dru6RAK5bNw4aeZO/dDNDT8Jw0N36O19TZycuZQVfUWKirOYc6cUwiHl6owfBaLDdLbu53u7i10dT1MW9tdDA0dBkJUVV3A3Lkbqax8o1ZVnaLy8texdu2D9PRso6HhOzQ2Xs+RIz8iFApTUXEOZWWnU1q6ntLSdeTmlqUlk5f/gqcAzznn9gCY2S+Bi4CUFnwsNsTWrWcSi/VTWrqehQs/RyTy9qw9n6rf8vMj1NdfxcKFn6e9/T6am2+ipeUWmppuACAnp4RweBmFhfUUFtaTnz+XvLwKcnNHL+WEQkWEQvmEQgWYvfQ1/ovaZt3RG0cfZeFe9vVY9yfz2Bix2CCx2MCYrwPEYv0MD7cTjbYxPNxKNNrG0NARBgb2MjCwj4GBvTgXP7w1N7eCiorzqKw8n8rKN+jIshQoKVnN0qXf54QTvk1HxwO0tv6Wtra7aG196T2q/Pw6CgsXU1S0mIKCeRQUzGfevI+mPIuXBT8PODDm+4PAqakeJCenkNWrf0dx8Sry8yOp3rxMUyiUT1XV+VRVnY9z19DXt5vu7sfp7n6cvr5n6OnZSkvLbTg3k0Ms7cVLvPSP9f2oVJbrxI/NLEZeXjWFhfWUlKwlEnkHJSVrKS1dS2Fhvf7C9UhOTuGLPwMA0Wgr3d2b6O7eTH//c/T3P09Hx30MDR0hL6921hV8UsxsI7Ax8W2Pme1O09DVQEuaxpqONOT7yEyenAGvn2OCUs2AfBNKYz4HNCcujyX7pAx//T6S4fmm+vo1cPTOyJQsPNYdXhZ8A3D8mO/nJ247inPuGiDt54Qzs03OufXpHjdZyjczyjczyjczmZLPy7/NHgdONLN6M8sHLgF0oLSISJp4tgfvnBs2s78H7iJ+mOQPnXM7vBpPRESO5ukcvHPuTuBOL8eYgbRPC02R8s2M8s2M8s1MRuSzTF4oR0REpk/HR4mIBFTWFLyZVZrZ3Wb2bOJrxTiPWWNmfzGzHWb2lJl5fh4yMzvfzHab2XNmduU49xeY2a8S9z9qZou8zjTFfP9kZjsTr9e9ZnbMQ7b8yDfmcW83M2dmaT2yIZl8ZvbOxOgqPUsAAAVISURBVGu4w8x+nkn5zGyBmd1vZlsS/8YXpDHbD82sycy2H+N+M7P/TGR/ysxOTle2JPO9N5Frm5n92cxOSmc+IP7hjWy4AP8BXJm4fiXwtXEeswQ4MXH9OOAwUO5hphzgeeAVQD7wJLDiZY/5KPD9xPVLgF+l8TVLJt9ZQDhx/e8yLV/icaXAQ8AjwPpMygecCGwBKhLf12RYvmuAv0tcXwHsS2O+M4CTge3HuP8C4PfEDyA/DXg0XdmSzPeaMf+ub0x3Pudc9uzBE18m4SeJ6z8B/upErM65Z5xzzyauHwKaAC8/Hvvicg4u/pHO0eUcxhqb+ybg9Za+z+pPms85d79zri/x7SPEP++QLsm8fgBXAV8DBtKYDZLL92HgO865dgDnXFOG5XPAnMT1MiBt56pzzj0EtE3wkIuAn7q4R4ByM0vbWguT5XPO/Xn035X0/2wAWTRFA9Q65w4nrh8Baid6sJmdQnyv5nkPM423nMPL1zN+8TEuvoBIJ1DlYaZxx04YL99YVxDfo0qXSfMl/mw/3jl3RxpzjUrm9VsCLDGzh83skcQKrOmSTL4vApea2UHiR8R9PD3RkjLV/59+SvfPBpABSxWkkpndA9SNc9dnx37jnHNmdszDhxJ7AT8D3u+ci6U2ZTCZ2aXAeuB1fmcZZfFFVr4FXO5zlInkEp+mOZP4Ht5DZrbaOdfha6qXvBv4sXPum2b2auBnZrZKPxfJM7OziBd82tcsD1TBO+fOOdZ9ZtZoZnOdc4cTBT7un8JmNge4A/hs4s8+LyWznMPoYw6aWS7xP5PTdTaBpJabMLNziP8SfZ1zbjBN2WDyfKXAKuCBxKxWHXC7mV3onNuUAfkgvtf5qHMuCuw1s2eIF/7jGZLvCuB8AOfcX8yskPg6K+mcSjqWpP5/+snMXglcC7zROZf2s4Bk0xTN7cD7E9ffD9z28gckllS4hfi83k1pyJTMcg5jc28A7nOJd20yIZ+ZrQV+AFyY5vnjSfM55zqdc9XOuUXOuUXE50HTVe6T5ku4lfjeO2ZWTXzKZk8G5XsBeH0i33KgkPiqZZngduCyxNE0pwGdY6ZhfWdmC4Cbgfc5557xJUS639X160J83vpe4FngHqAycft64mebArgUiAJbx1zWeJzrAuAZ4nP9n03c9m/EiwjiP1A3As8RXwrwFWl+3SbLdw/QOOb1uj2T8r3ssQ+QxqNoknz9jPg00k5gG3BJhuVbATxM/AibrcB5acz2C+JHskWJ/6VzBfC3wN+Oee2+k8i+zYd/28nyXQu0j/nZ2JTOfM45fZJVRCSosmmKRkQkq6jgRUQCSgUvIhJQKngRkYBSwYuIBJQKXrKWmfVMcv+iY60UOMFzfmxmG2aWTCQ1VPAiIgGlgpesZ2YlibXsn0is3T12RcVcM7vBzJ42s5vMLJx4zjoze9DMNpvZXelcxVAkWSp4kfgywhc7504mvr79N8csybwU+K5zbjnQBXzUzPKA/wI2OOfWAT8E/t2H3CITCtRiYyLTZMCXzewMIEZ8ydnR5aQPOOceTly/HvgE8Afii5jdnfg9kEP8I+siGUUFLwLvJX5il3XOuaiZ7SO+BhDET3gxliP+C2GHc+7V6YsoMnWaohGJL8HclCj3s4Cx55VdkFgHHeA9wJ+A3UBk9HYzyzOzlWlNLJIEFbwI3ACsN7NtwGXArjH37QY+ZmZPAxXA91z89HYbgK+Z2egqi69Jc2aRSWk1SRGRgNIevIhIQKngRUQCSgUvIhJQKngRkYBSwYuIBJQKXkQkoFTwIiIBpYIXEQmo/w8SKEwqRUCPiwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DsCkYT1jsq6p"
      },
      "source": [
        "# Splitting the Data into Dependent and Indpendent variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEGxVeVVs7B3"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer('english')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6isboX_tDlv"
      },
      "source": [
        "x = df['email']\n",
        "X = tfidf.fit_transform(x)\n",
        "y = df['label']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIU0nFd6tcV7"
      },
      "source": [
        "# Training and Testing the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxvL2XaJtLcd"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.25, random_state=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xamDfdQbvcwT"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHYEHLBEtlxe"
      },
      "source": [
        "# Multiomial Naive Bayes Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_5YEFxGtTJz",
        "outputId": "47efb6ae-3b0d-4e72-cc4f-d605f5cd7d0a"
      },
      "source": [
        "mnb = MultinomialNB()\n",
        "mnb.fit(Xtrain, ytrain)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLXIRdkxtr5R"
      },
      "source": [
        "## Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgC0SypVtXxn"
      },
      "source": [
        "ypred_train = mnb.predict(Xtrain)\n",
        "ypred_test = mnb.predict(Xtest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4oTKpj7tw3O"
      },
      "source": [
        "## Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FgOmJGdetzyH",
        "outputId": "36694548-67ff-4ab8-d343-b9f0ed88da57"
      },
      "source": [
        "cm = confusion_matrix(ytest, ypred)\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[441, 177],\n",
              "       [185,  63]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Jw5CxTwst3p-",
        "outputId": "f4f61873-1c2f-46a7-ef9c-a2e370f4dfc6"
      },
      "source": [
        "sns.heatmap(pd.DataFrame(cm), annot=True, cmap=\"RdPu\" ,fmt='g')\n",
        "plt.title('Confusion matrix', y=5.1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_tl2EwvuKL6"
      },
      "source": [
        "## Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMwNkTv2uJLC",
        "outputId": "739a1ca1-a42f-4444-fa91-6c64a3e1db25"
      },
      "source": [
        "print(\"Accuracy of training data:\", accuracy_score(ytrain, ypred_train)*100)\n",
        "ac2 = accuracy_score(ytest, ypred_test)*100\n",
        "print(\"Accuracy of testing data:\", ac2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of training data: 94.33962264150944\n",
            "Accuracy of testing data: 92.49422632794457\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTuYTMHHxK2h"
      },
      "source": [
        "# Decission Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEef94FJ3NO7",
        "outputId": "f462c3ad-4e5a-4ed8-e647-fafebae418a5"
      },
      "source": [
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(Xtrain, ytrain)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLiVMGjonnaI"
      },
      "source": [
        "## Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6sTaGJt55Uwr"
      },
      "source": [
        "ypred_train = dt.predict(Xtrain)\n",
        "ypred_test = dt.predict(Xtest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7h1eWoOAnpxH"
      },
      "source": [
        "## Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_AvMLlpH5V8B",
        "outputId": "077362f4-54a0-48f7-9cbc-5c6563625fc4"
      },
      "source": [
        "cm = np.array(confusion_matrix(ypred_test, ytest))\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[602,   5],\n",
              "       [ 16, 243]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "DxJ7wNLL5V4r",
        "outputId": "8c7ba1ae-5c23-4aaf-da31-3d559937c57c"
      },
      "source": [
        "sns.heatmap(pd.DataFrame(cm), annot=True, cmap=\"YlOrRd\" ,fmt='g')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a03d0b2d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51CTry4hnxzf"
      },
      "source": [
        "## Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqAwUMmT5VhW",
        "outputId": "355b3fdf-53ec-4044-cb35-edd7f19d004b"
      },
      "source": [
        "print(\"Accuracy of training data:\", accuracy_score(ytrain, ypred_train)*100)\n",
        "ac1 = accuracy_score(ytest, ypred_test)*100\n",
        "print(\"Accuracy of testing data:\", ac1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of training data: 100.0\n",
            "Accuracy of testing data: 97.57505773672055\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "WuSW4zGx3frs",
        "outputId": "23155147-f8c5-4a9e-e5fe-e180b44e8a03"
      },
      "source": [
        "from sklearn import tree\n",
        "clf_tree = tree.DecisionTreeClassifier(max_depth=3)\n",
        "clf_tree.fit(Xtrain, ytrain)\n",
        "tree.plot_tree(clf_tree)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(193.15384615384616, 190.26, 'X[5129] <= 0.001\\ngini = 0.399\\nsamples = 2597\\nvalue = [1882, 715]'),\n",
              " Text(103.01538461538462, 135.9, 'X[22452] <= 0.018\\ngini = 0.267\\nsamples = 2202\\nvalue = [1852, 350]'),\n",
              " Text(51.50769230769231, 81.53999999999999, 'X[13419] <= 0.016\\ngini = 0.167\\nsamples = 1937\\nvalue = [1759, 178]'),\n",
              " Text(25.753846153846155, 27.180000000000007, 'gini = 0.131\\nsamples = 1892\\nvalue = [1759, 133]'),\n",
              " Text(77.26153846153846, 27.180000000000007, 'gini = 0.0\\nsamples = 45\\nvalue = [0, 45]'),\n",
              " Text(154.52307692307693, 81.53999999999999, 'X[21202] <= 0.002\\ngini = 0.456\\nsamples = 265\\nvalue = [93, 172]'),\n",
              " Text(128.76923076923077, 27.180000000000007, 'gini = 0.455\\nsamples = 134\\nvalue = [87, 47]'),\n",
              " Text(180.27692307692308, 27.180000000000007, 'gini = 0.087\\nsamples = 131\\nvalue = [6, 125]'),\n",
              " Text(283.2923076923077, 135.9, 'X[9795] <= 0.07\\ngini = 0.14\\nsamples = 395\\nvalue = [30, 365]'),\n",
              " Text(257.53846153846155, 81.53999999999999, 'X[6850] <= 0.026\\ngini = 0.09\\nsamples = 383\\nvalue = [18, 365]'),\n",
              " Text(231.7846153846154, 27.180000000000007, 'gini = 0.052\\nsamples = 375\\nvalue = [10, 365]'),\n",
              " Text(283.2923076923077, 27.180000000000007, 'gini = 0.0\\nsamples = 8\\nvalue = [8, 0]'),\n",
              " Text(309.04615384615386, 81.53999999999999, 'gini = 0.0\\nsamples = 12\\nvalue = [12, 0]')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}