Copy_of_Comp700HonoursProject_Using_2nd_Dataset.ipynb

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    "colab": {
      "name": "Copy of Comp700HonoursProject Using 2nd Dataset.ipynb",
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      "display_name": "Python 3",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "baefb326"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "import random\n",
        "import nltk\n",
        "import re\n",
        "import string"
      ],
      "id": "baefb326",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "01e58c20",
        "outputId": "c5aacf46-add4-4a39-c528-09393261af5c"
      },
      "source": [
        "#reading csv file\n",
        "trdata = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/F3train.csv\")\n",
        "\n",
        "trdata = trdata.drop([\"Id\",\"title\",\"subject\",\"date\"],axis=1)\n",
        "\n",
        "trdata.head(10)"
      ],
      "id": "01e58c20",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>PARIS (Reuters) - A court on Friday sentenced ...</td>\n",
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              "      <td>PRAGUE (Reuters) - The Czech government will s...</td>\n",
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              "      <th>5</th>\n",
              "      <td>Could this be a little bit of KARMA coming bac...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Kansas  education system is a mess, thanks to ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>I would hope that a wise Latina woman with th...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>HOUSTON (Reuters) - U.S. President Donald Trum...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(Reuters) - Billionaire Carl Icahn will advise...</td>\n",
              "      <td>0</td>\n",
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            ],
            "text/plain": [
              "                                                text  label\n",
              "0  Paul Manafort, the chair of Donald Trump s cam...      1\n",
              "1  How very  charitable  of the Lutheran and Cath...      1\n",
              "2  In case you ve been living under a rock for yo...      1\n",
              "3  PARIS (Reuters) - A court on Friday sentenced ...      0\n",
              "4  PRAGUE (Reuters) - The Czech government will s...      0\n",
              "5  Could this be a little bit of KARMA coming bac...      1\n",
              "6  Kansas  education system is a mess, thanks to ...      1\n",
              "7   I would hope that a wise Latina woman with th...      1\n",
              "8  HOUSTON (Reuters) - U.S. President Donald Trum...      0\n",
              "9  (Reuters) - Billionaire Carl Icahn will advise...      0"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4xvsD8tzebW",
        "outputId": "1d0efa43-70a3-4b8b-c61f-0fa05de6c8b0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "n4xvsD8tzebW",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ce63488"
      },
      "source": [
        "# Data Preprocessing Process"
      ],
      "id": "3ce63488"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "3d474a4d",
        "outputId": "b5d68f58-c7e0-4e82-c9d0-23cbc7b782ba"
      },
      "source": [
        "def removeChar(text):\n",
        "    if type(text) is str:\n",
        "        text = text.lower()\n",
        "        text = re.sub('!,?','',text)\n",
        "        text = re.sub(\"'\",'',text)\n",
        "        text = re.sub('https?://\\S+|www\\.\\S+','',text)\n",
        "        text = re.sub('[%s]' % re.escape(string.punctuation),'',text)\n",
        "    return text\n",
        "\n",
        "\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "def stopping(text):\n",
        "  if type(text) is str:\n",
        "     text = text.split()\n",
        "     stops = set(stopwords.words(\"english\"))\n",
        "     text = [w for w in text if not w in stops]\n",
        "     text = \" \".join(text) \n",
        "  return text\n",
        "\n",
        "\n",
        "#Preprocessing for training data\n",
        "trdata['text'] = trdata['text'].apply(stopping)\n",
        "trdata['text'] = trdata['text'].apply(removeChar) \n",
        "trdata.head(10)  "
      ],
      "id": "3d474a4d",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>paul manafort chair donald trump campaign got ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>how charitable lutheran catholic churches brin...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>in case living rock entire life especially cam...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>paris reuters  a court friday sentenced radica...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>prague reuters  the czech government seek vote...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>could little bit karma coming back nasty comme...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>kansas education system mess thanks republican...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>i would hope wise latina woman richness experi...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>houston reuters  us president donald trump too...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>reuters  billionaire carl icahn advise donald ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  label\n",
              "0  paul manafort chair donald trump campaign got ...      1\n",
              "1  how charitable lutheran catholic churches brin...      1\n",
              "2  in case living rock entire life especially cam...      1\n",
              "3  paris reuters  a court friday sentenced radica...      0\n",
              "4  prague reuters  the czech government seek vote...      0\n",
              "5  could little bit karma coming back nasty comme...      1\n",
              "6  kansas education system mess thanks republican...      1\n",
              "7  i would hope wise latina woman richness experi...      1\n",
              "8  houston reuters  us president donald trump too...      0\n",
              "9  reuters  billionaire carl icahn advise donald ...      0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0153f77"
      },
      "source": [
        "x = trdata['text']\n",
        "y = trdata['label']\n",
        "# train test split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=6)\n",
        "\n",
        "\n",
        "# Feature Extraction Process for training data\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "Vec = TfidfVectorizer()\n",
        "xf_train = Vec.fit_transform(x_train.astype(str))\n",
        "xf_test = Vec.transform(x_test.astype(str))\n",
        "\n"
      ],
      "id": "c0153f77",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "230f48fe"
      },
      "source": [
        "# Logistic Regression "
      ],
      "id": "230f48fe"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dc4e3d4",
        "outputId": "4df97c9c-4875-4496-da3b-ad2ab863bfc2"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "Log = LogisticRegression()\n",
        "Log.fit(xf_train,y_train)\n",
        "Log.score(xf_test,y_test)"
      ],
      "id": "0dc4e3d4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9844724449535446"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "732bb630",
        "scrolled": true,
        "outputId": "d033cb19-dae5-480c-decc-2661bdb32af8"
      },
      "source": [
        "Pred = Log.predict(xf_test)\n",
        "print(classification_report(y_test,Pred))"
      ],
      "id": "732bb630",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98      3745\n",
            "           1       0.99      0.98      0.99      4112\n",
            "\n",
            "    accuracy                           0.98      7857\n",
            "   macro avg       0.98      0.98      0.98      7857\n",
            "weighted avg       0.98      0.98      0.98      7857\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIx6clDEgild",
        "outputId": "65366a98-2911-40bd-bb63-35f1aad82d3c"
      },
      "source": [
        "\n",
        "LR_MAPE = mean_absolute_error(y_test,Pred)\n",
        "print(LR_MAPE)"
      ],
      "id": "qIx6clDEgild",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.5984409373284065\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "F0Zlol18D5jw",
        "outputId": "36ee6cc5-76e5-4ad5-95d1-985487c1d082"
      },
      "source": [
        "plot_confusion_matrix(Log,xf_test,y_test)\n",
        "print(\"\\n\")\n",
        "print(confusion_matrix(y_test,Pred))\n",
        "print(\"\\n\")"
      ],
      "id": "F0Zlol18D5jw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "[[3700   45]\n",
            " [  77 4035]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24cc48ba"
      },
      "source": [
        "# Support Vector Machines "
      ],
      "id": "24cc48ba"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3413479d",
        "outputId": "c053a085-e5b1-488f-a89c-5fb098c3f5a5"
      },
      "source": [
        "from sklearn import svm\n",
        "SuVeMa = svm.SVC(kernel='linear', gamma= 'auto', C=2)\n",
        "SuVeMa.fit(xf_train,y_train)\n",
        "SuVeMa.score(xf_test,y_test)"
      ],
      "id": "3413479d",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9936362479317806"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6e08f00e",
        "outputId": "a5fc88b1-d80c-4ce2-c008-7c03f6eb7729"
      },
      "source": [
        "Pred1 = SuVeMa.predict(xf_test)\n",
        "print(classification_report(y_test,Pred1))"
      ],
      "id": "6e08f00e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99      3745\n",
            "           1       1.00      0.99      0.99      4112\n",
            "\n",
            "    accuracy                           0.99      7857\n",
            "   macro avg       0.99      0.99      0.99      7857\n",
            "weighted avg       0.99      0.99      0.99      7857\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcU9GDgUk353",
        "outputId": "b052a515-217e-4491-b7bf-485f8552de6a"
      },
      "source": [
        "SVM_MAPE = mean_absolute_error(y_test,Pred1)\n",
        "print(SVM_MAPE)"
      ],
      "id": "PcU9GDgUk353",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0835857252275252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "KNgb1CvMFdmp",
        "outputId": "8fc6c319-87c6-48af-89fb-ab4976a6dbb6"
      },
      "source": [
        "plot_confusion_matrix(SuVeMa,xf_test,y_test)\n",
        "print(\"\\n\")\n",
        "print(confusion_matrix(y_test,Pred1))\n",
        "print(\"\\n\")"
      ],
      "id": "KNgb1CvMFdmp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "[[3729   16]\n",
            " [  34 4078]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4a3916a"
      },
      "source": [
        "# K Nearest Neighbors Classifier"
      ],
      "id": "e4a3916a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bd034c76",
        "outputId": "f229962d-6d33-47c2-e8a5-8176d181ffd2"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=2)\n",
        "knn.fit(xf_train,y_train)\n",
        "knn.score(xf_test,y_test)"
      ],
      "id": "bd034c76",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7042128038691613"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "536e8106",
        "outputId": "7a28e8f9-489a-4c3d-87a3-38ff2a42095b"
      },
      "source": [
        "Pred2 = knn.predict(xf_test)\n",
        "print(classification_report(y_test,Pred2))"
      ],
      "id": "536e8106",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.41      0.57      3745\n",
            "           1       0.64      0.97      0.77      4112\n",
            "\n",
            "    accuracy                           0.70      7857\n",
            "   macro avg       0.78      0.69      0.67      7857\n",
            "weighted avg       0.78      0.70      0.68      7857\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zm6bszytlEQZ",
        "outputId": "01e4a859-1014-4a47-e7eb-6a2c4bc58331"
      },
      "source": [
        "KNN_MAPE = mean_absolute_error(y_test,Pred2)\n",
        "print(KNN_MAPE)"
      ],
      "id": "Zm6bszytlEQZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.29578719613083876\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "IZ0gwnRrFouA",
        "outputId": "15970fb4-e909-4527-80dd-7bffdb25b2f3"
      },
      "source": [
        "plot_confusion_matrix(knn,xf_test,y_test)\n",
        "print(\"\\n\")\n",
        "print(confusion_matrix(y_test,Pred2))\n",
        "print(\"\\n\")"
      ],
      "id": "IZ0gwnRrFouA",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "[[1554 2191]\n",
            " [ 133 3979]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b306e4a4"
      },
      "source": [
        "# Decision Tree Classifier"
      ],
      "id": "b306e4a4"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57025075",
        "outputId": "69d1ea49-a2f1-40f0-c5fa-d17c7f1ef801"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(xf_train,y_train)\n",
        "dt.score(xf_test,y_test)"
      ],
      "id": "57025075",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9935089728904162"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ed9bda1",
        "outputId": "6a8c4436-31a0-4620-b86a-e71b67b5d8be"
      },
      "source": [
        "Pred4 = dt.predict(xf_test)\n",
        "print(classification_report(y_test,Pred4))"
      ],
      "id": "2ed9bda1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      3745\n",
            "           1       1.00      0.99      0.99      4112\n",
            "\n",
            "    accuracy                           0.99      7857\n",
            "   macro avg       0.99      0.99      0.99      7857\n",
            "weighted avg       0.99      0.99      0.99      7857\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLHMHD8ulMsu",
        "outputId": "bbf22c15-6062-4655-9aad-0f0c2ae9575b"
      },
      "source": [
        "DTC_MAPE = mean_absolute_error(y_test,Pred4)\n",
        "print(DTC_MAPE)"
      ],
      "id": "pLHMHD8ulMsu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.006491027109583811\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "C20ynIbTGEBQ",
        "outputId": "e128b57d-e680-46d3-b227-10c28a1aa43b"
      },
      "source": [
        "plot_confusion_matrix(dt,xf_test,y_test)\n",
        "print(\"\\n\")\n",
        "print(confusion_matrix(y_test,Pred4))\n",
        "print(\"\\n\")"
      ],
      "id": "C20ynIbTGEBQ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "[[3725   20]\n",
            " [  31 4081]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df802bd4"
      },
      "source": [
        "# Gradient Boosting Classifier"
      ],
      "id": "df802bd4"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24ad22d1",
        "outputId": "5911e809-abd3-4f6a-e0f9-fe5defed44d6"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "gb = GradientBoostingClassifier(random_state=0)\n",
        "gb.fit(xf_train,y_train)\n",
        "gb.score(xf_test,y_test)\n"
      ],
      "id": "24ad22d1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9936362479317806"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ec19bc11",
        "outputId": "943de20e-2b80-4311-bbf4-ec3501240fe6"
      },
      "source": [
        "Pred5 = gb.predict(xf_test)\n",
        "print(classification_report(y_test,Pred5))"
      ],
      "id": "ec19bc11",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99      3745\n",
            "           1       1.00      0.99      0.99      4112\n",
            "\n",
            "    accuracy                           0.99      7857\n",
            "   macro avg       0.99      0.99      0.99      7857\n",
            "weighted avg       0.99      0.99      0.99      7857\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iubtcrKClTUx",
        "outputId": "b8422cee-0975-4721-8e18-b05ab1287c09"
      },
      "source": [
        "GBC_MAPE = mean_absolute_error(y_test,Pred5)\n",
        "print(GBC_MAPE)"
      ],
      "id": "iubtcrKClTUx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.006363752068219423\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "Q8lXr21bGI54",
        "outputId": "b3ce9298-5784-4e7a-fde8-1fc8784fcab2"
      },
      "source": [
        "plot_confusion_matrix(gb,xf_test,y_test)\n",
        "print(\"\\n\")\n",
        "print(confusion_matrix(y_test,Pred5))\n",
        "print(\"\\n\")"
      ],
      "id": "Q8lXr21bGI54",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "[[3733   12]\n",
            " [  38 4074]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "be42343f"
      },
      "source": [
        "# Random Forest Classifier"
      ],
      "id": "be42343f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1d134c5",
        "outputId": "7a00f381-41fb-473b-a291-69a07e34e512"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier(random_state=0)\n",
        "rf.fit(xf_train, y_train)\n",
        "rf.score(xf_test,y_test)"
      ],
      "id": "d1d134c5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9844724449535446"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07c8de38",
        "outputId": "fdf9eda1-10aa-4676-a893-854fea2bf70d"
      },
      "source": [
        "Pred6 = rf.predict(xf_test)\n",
        "print(classification_report(y_test,Pred6))"
      ],
      "id": "07c8de38",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98      3745\n",
            "           1       0.99      0.98      0.99      4112\n",
            "\n",
            "    accuracy                           0.98      7857\n",
            "   macro avg       0.98      0.98      0.98      7857\n",
            "weighted avg       0.98      0.98      0.98      7857\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PAa3X_valZgT",
        "outputId": "37bcc861-5e0d-4a4b-ce9d-efea9dbc3be9"
      },
      "source": [
        "RFC_MAPE = mean_absolute_error(y_test,Pred6)\n",
        "print(RFC_MAPE)"
      ],
      "id": "PAa3X_valZgT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.01552755504645539\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "s8IKZSyOGPVQ",
        "outputId": "f6afceb7-fcf0-46b1-c05b-584d9478eed5"
      },
      "source": [
        "plot_confusion_matrix(rf,xf_test,y_test)\n",
        "print(\"\\n\")\n",
        "print(confusion_matrix(y_test,Pred6))\n",
        "print(\"\\n\")"
      ],
      "id": "s8IKZSyOGPVQ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "[[3698   47]\n",
            " [  75 4037]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKPm1vaR88FG"
      },
      "source": [
        "# Neural Networks \n"
      ],
      "id": "AKPm1vaR88FG"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnsX9uxsbXRx"
      },
      "source": [
        "from tensorflow.keras.layers import Embedding\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing.text import one_hot\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.layers import Dense\n",
        "wordsVec = 50000\n",
        "oneHot = [one_hot(words,wordsVec)for words in trdata['text'].astype(str)]"
      ],
      "id": "ZnsX9uxsbXRx",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KaJN8uf8jy43",
        "outputId": "edf40908-440e-4213-b0e3-0cd46a912917"
      },
      "source": [
        "s_length = 20\n",
        "embedded = pad_sequences(oneHot,padding='pre',maxlen=s_length)\n",
        "print(embedded)"
      ],
      "id": "KaJN8uf8jy43",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[44981 18017 11547 ... 39078 45170 21153]\n",
            " [28092 16938 27881 ... 27881 35278  4807]\n",
            " [26839 30183 49179 ... 42440 44951 33999]\n",
            " ...\n",
            " [  396 28697 25070 ... 12249  6508 25294]\n",
            " [18896 33830 43382 ...  3817  2393 25996]\n",
            " [24483 38865 44739 ... 27184 38539 48112]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeWDTsWY_b39"
      },
      "source": [
        "embedded_Vec_features = 40\n",
        "model=Sequential()\n",
        "model.add(Embedding(wordsVec,embedded_Vec_features,input_length=s_length))\n",
        "model.add(LSTM(100))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "id": "UeWDTsWY_b39",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bsdvc-muAftb",
        "outputId": "2be5287c-c5f9-43d3-fa01-5d856a8948a8"
      },
      "source": [
        "X1 = np.array(embedded)\n",
        "Y1 = np.array(y)\n",
        "X1.shape,Y1.shape"
      ],
      "id": "bsdvc-muAftb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((31428, 20), (31428,))"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAMRQ1akCIIH",
        "outputId": "3db3d8d4-84ac-4b53-bd18-1a725fb03e46"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X1_train, X1_test, y1_train, y1_test = train_test_split(X1, Y1, test_size=0.25, random_state=6)\n",
        "History = model.fit(X1_train,y1_train,validation_data=(X1_test,y1_test),epochs=20,batch_size=32)"
      ],
      "id": "DAMRQ1akCIIH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "737/737 [==============================] - 34s 43ms/step - loss: 0.1885 - accuracy: 0.9216 - val_loss: 0.1029 - val_accuracy: 0.9635\n",
            "Epoch 2/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 0.0419 - accuracy: 0.9859 - val_loss: 0.1213 - val_accuracy: 0.9595\n",
            "Epoch 3/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 0.0128 - accuracy: 0.9962 - val_loss: 0.1435 - val_accuracy: 0.9579\n",
            "Epoch 4/20\n",
            "737/737 [==============================] - 32s 43ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.2043 - val_accuracy: 0.9542\n",
            "Epoch 5/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.2483 - val_accuracy: 0.9570\n",
            "Epoch 6/20\n",
            "737/737 [==============================] - 31s 43ms/step - loss: 7.0397e-04 - accuracy: 0.9998 - val_loss: 0.2439 - val_accuracy: 0.9569\n",
            "Epoch 7/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.2269 - val_accuracy: 0.9593\n",
            "Epoch 8/20\n",
            "737/737 [==============================] - 32s 43ms/step - loss: 0.0050 - accuracy: 0.9986 - val_loss: 0.2057 - val_accuracy: 0.9586\n",
            "Epoch 9/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 0.0020 - accuracy: 0.9993 - val_loss: 0.2566 - val_accuracy: 0.9527\n",
            "Epoch 10/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 0.0016 - accuracy: 0.9993 - val_loss: 0.2370 - val_accuracy: 0.9593\n",
            "Epoch 11/20\n",
            "737/737 [==============================] - 31s 43ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.2367 - val_accuracy: 0.9586\n",
            "Epoch 12/20\n",
            "737/737 [==============================] - 32s 43ms/step - loss: 9.1078e-04 - accuracy: 0.9998 - val_loss: 0.2645 - val_accuracy: 0.9579\n",
            "Epoch 13/20\n",
            "737/737 [==============================] - 31s 43ms/step - loss: 5.8894e-05 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9560\n",
            "Epoch 14/20\n",
            "737/737 [==============================] - 32s 43ms/step - loss: 8.6268e-06 - accuracy: 1.0000 - val_loss: 0.3130 - val_accuracy: 0.9561\n",
            "Epoch 15/20\n",
            "737/737 [==============================] - 31s 43ms/step - loss: 3.6676e-06 - accuracy: 1.0000 - val_loss: 0.3285 - val_accuracy: 0.9551\n",
            "Epoch 16/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 2.2942e-06 - accuracy: 1.0000 - val_loss: 0.3432 - val_accuracy: 0.9547\n",
            "Epoch 17/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 1.4998e-06 - accuracy: 1.0000 - val_loss: 0.3577 - val_accuracy: 0.9543\n",
            "Epoch 18/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 1.0107e-06 - accuracy: 1.0000 - val_loss: 0.3708 - val_accuracy: 0.9541\n",
            "Epoch 19/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 6.8201e-07 - accuracy: 1.0000 - val_loss: 0.3837 - val_accuracy: 0.9535\n",
            "Epoch 20/20\n",
            "737/737 [==============================] - 31s 42ms/step - loss: 4.6059e-07 - accuracy: 1.0000 - val_loss: 0.3963 - val_accuracy: 0.9532\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "grw1OBxZDeSy",
        "outputId": "ae258c1a-4fcb-4532-ec29-0607fd9929e0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(History.history['val_accuracy'])\n",
        "plt.plot(History.history['accuracy'])\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'test'], loc='upper right')"
      ],
      "id": "grw1OBxZDeSy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f1750b4b690>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCWA8XPu96I7"
      },
      "source": [
        "# ROC and AUC For SVM and logistic Regression"
      ],
      "id": "kCWA8XPu96I7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfqgQRTGWy3H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "7d72c855-4257-4ff5-c4a9-39d1afba3f3f"
      },
      "source": [
        "Pred = Log.decision_function(xf_test)\n",
        "Pred1 = SuVeMa.decision_function(xf_test)\n",
        "#Pred6 = rf.decision_function(xf_test)\n",
        "#Logistic Regression fpr,tpr\n",
        "log_fpr, log_tpr, threshold = roc_curve(y_test, Pred)\n",
        "auc_log = auc(log_fpr, log_tpr)\n",
        "\n",
        "#SVM fpr,tpr\n",
        "svm_fpr, svm_tpr, threshold = roc_curve(y_test, Pred1)\n",
        "auc_svm = auc(svm_fpr, svm_tpr)\n",
        "\n",
        "\n",
        "\n",
        "plt.figure(figsize=(5, 5), dpi=100)\n",
        "plt.plot(svm_fpr, svm_tpr, linestyle='-', label='SVM (auc = %0.3f)' % auc_svm)\n",
        "plt.plot(log_fpr, log_tpr, marker='.', label='Logistic Reg (auc = %0.3f)' % auc_log)\n",
        "\n",
        "\n",
        "\n",
        "plt.xlabel('False Positive ')\n",
        "plt.ylabel('True Positive ')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "id": "EfqgQRTGWy3H",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}