EMAIL_SPAM_DETECTION.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **EMAIL SPAM DETECTION WITH MACHINE LEARNING**"
      ],
      "metadata": {
        "id": "utqEiatb4hhu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![no-spam-gmail.webp](data:image/webp;base64,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)"
      ],
      "metadata": {
        "id": "glLM_VkJ4rHN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "#Download **Dataset** here [EMAIL SPAM DETECTION](https://drive.google.com/file/d/19Yz5vBQ3T174xeSOOqVzw3rWWm39stsf/view?usp=share_link)"
      ],
      "metadata": {
        "id": "RdRblOkHuMSI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Knowing about the Dataset"
      ],
      "metadata": {
        "id": "gqVmgmxgylHi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the Required Libraries**"
      ],
      "metadata": {
        "id": "R6xjm571tdTE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "eqRFPSKPaMYz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3eb45e4a-4e50-4de0-cb4a-ff12fad5461f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "# Numpy Library for Numerical Calculations\n",
        "import numpy as np\n",
        "\n",
        "# Pandas Library for Dataframe\n",
        "import pandas as pd\n",
        "\n",
        "# Matplotlib and for Plottings\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Pickle Library for Saving the Model\n",
        "import pickle\n",
        "\n",
        "# RE Library for Regular Expression\n",
        "import re\n",
        "\n",
        "# NLTK Library for Natural Language Processing\n",
        "import nltk\n",
        "nltk.download('stopwords') # Downloading the Stopwords\n",
        "\n",
        "# Stopwords for removing stopwords in the Text\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# PorterStemmer for Stemming the Words\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "\n",
        "# CountVectorizer for Bagging of Words and Vectorizing it\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# Train_Test_Split for splitting the Dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Decision Tree Classifier, Random Forest Classifier and Multinomial Naïve Bayes are Models\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "# Accuracy Score and Confusion Matrix is for Analysis of Models\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reading informations in the Dataset**"
      ],
      "metadata": {
        "id": "xu7WlNl7vbXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSacBasC8hEs",
        "outputId": "1eabf32b-1df0-4d55-f70f-8362a314af7c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spam = pd.read_csv(\"/content/drive/My Drive/Oasis Infobyte/Data Science - Internship/Email-Spam-Detection/spam.csv\", encoding='ISO-8859-1')"
      ],
      "metadata": {
        "id": "q_Exx0yo8npF"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking for null values in Data**"
      ],
      "metadata": {
        "id": "ztslY5yawK3r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spam.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ww240wGtdwBG",
        "outputId": "31a10df9-77e1-44c7-b41b-39211fe847c6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "v1               0\n",
              "v2               0\n",
              "Unnamed: 2    5522\n",
              "Unnamed: 3    5560\n",
              "Unnamed: 4    5566\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking the First Five Values in the Data**"
      ],
      "metadata": {
        "id": "0N934_m8w2aH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spam.head()"
      ],
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      "execution_count": 14,
      "outputs": [
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              "     v1                                                 v2 Unnamed: 2  \\\n",
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              "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
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              "  Unnamed: 3 Unnamed: 4  \n",
              "0        NaN        NaN  \n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "markdown",
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        "**Checking the Last Five Values in the Data**"
      ],
      "metadata": {
        "id": "11wXmVfVxERV"
      }
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    {
      "cell_type": "code",
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        "spam.tail()"
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      "execution_count": 15,
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              "        v1                                                 v2 Unnamed: 2  \\\n",
              "5567  spam  This is the 2nd time we have tried 2 contact u...        NaN   \n",
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              "\n",
              "     Unnamed: 3 Unnamed: 4  \n",
              "5567        NaN        NaN  \n",
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              "                                                     [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",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**Taking the required Columns in the Dataset**"
      ],
      "metadata": {
        "id": "4dPjFcZJfODm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spam = spam[['v1', 'v2']]\n",
        "spam.columns = ['label', 'message']\n",
        "spam.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "UOAH9GFoeS3N",
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      },
      "execution_count": 16,
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        {
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          "data": {
            "text/plain": [
              "  label                                            message\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4e85114-2906-42dc-b30d-719f71407bd6\">\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>label</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4e85114-2906-42dc-b30d-719f71407bd6')\"\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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              "  </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-a4e85114-2906-42dc-b30d-719f71407bd6 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-a4e85114-2906-42dc-b30d-719f71407bd6');\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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dimensions of the Dataset**"
      ],
      "metadata": {
        "id": "IjcRFzNmxsOO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spam.shape"
      ],
      "metadata": {
        "id": "KWpAuUoMhi71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9dc4734-ced2-4662-eb9c-6cc0e122a8ab"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking for the classes in the Data**"
      ],
      "metadata": {
        "id": "b5pbpvp8iBU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spam.groupby('label').size()"
      ],
      "metadata": {
        "id": "Czwwq-qxiB2T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b41f544-a673-475b-ed1c-58a042d7a25f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "ham     4825\n",
              "spam     747\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting"
      ],
      "metadata": {
        "id": "y_fhs72XhMaM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Plotting the Label in the Dataset**"
      ],
      "metadata": {
        "id": "vWpNSjdQhQWH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spam['label'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "id": "V7w-i2x2hGKW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "14ddadcd-20e7-4019-aa25-3a2f395f82b4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### NLP"
      ],
      "metadata": {
        "id": "ybR1UeYUfwc0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Preprocessing the Text in the Dataset**"
      ],
      "metadata": {
        "id": "s4XwZxF9f3cf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ps = PorterStemmer()\n",
        "corpus = []\n",
        "for i in range(0, len(spam)):\n",
        "    review = re.sub('[^a-zA-Z]', ' ', spam['message'][i])\n",
        "    review = review.lower()\n",
        "    review = review.split()\n",
        "    review = [ps.stem(word) for word in review if not word in stopwords.words('english')]\n",
        "    review = ' '.join(review)\n",
        "    corpus.append(review)\n",
        "\n",
        "# Printing the first 5 values in the corpus list\n",
        "corpus[1:6]"
      ],
      "metadata": {
        "id": "Pu6-6zcKfo0q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fe0e903-fe8b-4795-ba01-7fc7fcdc378c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ok lar joke wif u oni',\n",
              " 'free entri wkli comp win fa cup final tkt st may text fa receiv entri question std txt rate c appli',\n",
              " 'u dun say earli hor u c alreadi say',\n",
              " 'nah think goe usf live around though',\n",
              " 'freemsg hey darl week word back like fun still tb ok xxx std chg send rcv']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating Bag of Words Model**"
      ],
      "metadata": {
        "id": "wfIEqFUbhBad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv = CountVectorizer(max_features = 4000)\n",
        "X = cv.fit_transform(corpus).toarray()\n",
        "Y = pd.get_dummies(spam['label'])\n",
        "Y = Y.iloc[:, 1].values"
      ],
      "metadata": {
        "id": "G6iC3qhgf2VB"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Modeling"
      ],
      "metadata": {
        "id": "lixXNKv0idIG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the Dataset into Training and Testing Set**"
      ],
      "metadata": {
        "id": "OFlxID8Ui_tG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.20, random_state=42)"
      ],
      "metadata": {
        "id": "eSDRA6R5iyNP"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Building"
      ],
      "metadata": {
        "id": "y_e97KFpjO9W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating the Models**"
      ],
      "metadata": {
        "id": "EGP_PgyDjYDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 1 - Random Forest Classifier\n",
        "model1 = RandomForestClassifier()\n",
        "model1.fit(X_train, Y_train)\n",
        "\n",
        "# Model 2 - Decision Tree Classifier\n",
        "model2 = DecisionTreeClassifier()\n",
        "model2.fit(X_train, Y_train)\n",
        "\n",
        "# Model 3 - Multinomial Naïve Bayes\n",
        "model3 = MultinomialNB()\n",
        "model3.fit(X_train, Y_train)"
      ],
      "metadata": {
        "id": "s88J6PCejSHE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "d89e81b5-d630-42b8-960a-b29950ca3238"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prediction**"
      ],
      "metadata": {
        "id": "tJDHZhoKkhz4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred1 = model1.predict(X_test)\n",
        "pred2 = model2.predict(X_test)\n",
        "pred3 = model3.predict(X_test)"
      ],
      "metadata": {
        "id": "5C3Wo22nkEd4"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Testing"
      ],
      "metadata": {
        "id": "fBy8rARqkomz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Testing the Model**"
      ],
      "metadata": {
        "id": "VY3j9uW4l7AG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 1 - Random Forest Classifier\n",
        "print(\"Random Forest Classifier\")\n",
        "print(\"Confusion Matrix: \")\n",
        "print(confusion_matrix(Y_test, pred1))\n",
        "print(\"Accuracy: \", accuracy_score(Y_test, pred1))\n",
        "print(\"--------------------------------\")\n",
        "\n",
        "# Model 2 - Decision Tree Classifier\n",
        "print(\"Decision Tree Classifier\")\n",
        "print(\"Confusion Matrix: \")\n",
        "print(confusion_matrix(Y_test, pred2))\n",
        "print(\"Accuracy: \", accuracy_score(Y_test, pred2))\n",
        "print(\"--------------------------------\")\n",
        "\n",
        "# Model 3 - Multinomial Naïve Bayes\n",
        "print(\"Multinomial Naïve Bayes\")\n",
        "print(\"Confusion Matrix: \")\n",
        "print(confusion_matrix(Y_test, pred3))\n",
        "print(\"Accuracy: \", accuracy_score(Y_test, pred3))"
      ],
      "metadata": {
        "id": "C-alX1Ydk2p3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c9c93c4-91ba-4228-cf29-94d529534654"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Classifier\n",
            "Confusion Matrix: \n",
            "[[964   1]\n",
            " [ 29 121]]\n",
            "Accuracy:  0.9730941704035875\n",
            "--------------------------------\n",
            "Decision Tree Classifier\n",
            "Confusion Matrix: \n",
            "[[959   6]\n",
            " [ 22 128]]\n",
            "Accuracy:  0.9748878923766816\n",
            "--------------------------------\n",
            "Multinomial Naïve Bayes\n",
            "Confusion Matrix: \n",
            "[[955  10]\n",
            " [ 10 140]]\n",
            "Accuracy:  0.9820627802690582\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(Y_test, pred3)\n",
        "\n",
        "import seaborn as sns\n",
        "sns.heatmap(cm, annot=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0FwZadc39KCl",
        "outputId": "4a24954c-2c19-4366-fc14-428598c10088"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "report1 = classification_report(Y_test, pred1)\n",
        "print(\"Classification Report for RFC \\n\", report1)\n",
        "report2 = classification_report(Y_test, pred2)\n",
        "print(\"Classification Report for DTC \\n\", report2)\n",
        "report3 = classification_report(Y_test, pred3)\n",
        "print(\"Classification Report for MNB \\n\", report3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJ72AbvP-laX",
        "outputId": "634ee8ec-0164-49bf-c96d-55af5bebebd4"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report for RFC \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.98       965\n",
            "           1       0.99      0.81      0.89       150\n",
            "\n",
            "    accuracy                           0.97      1115\n",
            "   macro avg       0.98      0.90      0.94      1115\n",
            "weighted avg       0.97      0.97      0.97      1115\n",
            "\n",
            "Classification Report for DTC \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99       965\n",
            "           1       0.96      0.85      0.90       150\n",
            "\n",
            "    accuracy                           0.97      1115\n",
            "   macro avg       0.97      0.92      0.94      1115\n",
            "weighted avg       0.97      0.97      0.97      1115\n",
            "\n",
            "Classification Report for MNB \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       965\n",
            "           1       0.93      0.93      0.93       150\n",
            "\n",
            "    accuracy                           0.98      1115\n",
            "   macro avg       0.96      0.96      0.96      1115\n",
            "weighted avg       0.98      0.98      0.98      1115\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Best Model is Multinomial Naïve Bayes**"
      ],
      "metadata": {
        "id": "6dTSf8i__QN8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Saving Models"
      ],
      "metadata": {
        "id": "aSfn7iCxmylO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving all the Models"
      ],
      "metadata": {
        "id": "IwGL6x7fm8FC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filename = \"RFC.pkl\"\n",
        "pickle.dump(model1, open(filename, 'wb'))\n",
        "filename = \"DTC.pkl\"\n",
        "pickle.dump(model2, open(filename, 'wb'))\n",
        "filename = \"MNB.pkl\"\n",
        "pickle.dump(model3, open(filename, 'wb'))\n",
        "print(\"Saved all Models\")"
      ],
      "metadata": {
        "id": "1PQPuXLxnJas",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02e8a794-10b2-4a49-9e00-c1f406fb6f05"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved all Models\n"
          ]
        }
      ]
    }
  ]
}