webC_MNB(0.9015).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "webC MNB(stopword).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shaurov05/Website-Classification/blob/master/webC_MNB(0.9015).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d2155d2a47acce9e8087db0bf0c25436f886b46d",
        "colab_type": "code",
        "id": "zIKWDITNcE0S",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import linear_model\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "%matplotlib inline \n",
        "from sklearn.utils import shuffle\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy import array\n",
        "from numpy import argmax\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_selection import chi2\n",
        "from sklearn import tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Activation\n",
        "from keras.utils import np_utils\n",
        "import re\n",
        "from keras.preprocessing import sequence\n",
        "from keras.preprocessing.text import one_hot\n",
        "from keras.preprocessing.text import text_to_word_sequence\n",
        "from sklearn.svm import LinearSVC"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BXGgPkWUABY",
        "colab_type": "code",
        "outputId": "32c056bd-cdd4-497a-8761-627a292e5904",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "09b361699e1fddf13d13c1620c40ff55fd28dab6",
        "colab_type": "code",
        "id": "Hwv5cygRwTqh",
        "outputId": "b0e3eca4-5bfb-4116-a641-6991f36dafb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "names=['URL','Category']\n",
        "df=pd.read_csv('/content/drive/My Drive/Colab Notebooks/thesis/URL Classification.csv',names=names, na_filter=False)\n",
        "df1 = df[1:2000]\n",
        "df2 = df[50000:52000]\n",
        "df3 = df[520000:522000]\n",
        "df4 =df[535300:537300]\n",
        "df5 = df[650000:652000]\n",
        "df6= df[710000:712000]\n",
        "df7=  df[764200:766200]\n",
        "df8=  df[793080:795080]\n",
        "df9=  df[839730:841730]\n",
        "df10=  df[850000:852000]\n",
        "df11=  df[955250:957250]\n",
        "df12=  df[1013000:1015000]\n",
        "df13=  df[1143000:1145000]\n",
        "df14=  df[1293000:1295000]\n",
        "df15=  df[1492000:1494000]\n",
        "#df6 = df[77000:1562978]\n",
        "dt=pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11,df12,df13,df14,df15], axis=0)\n",
        "df.drop(df.index[1:2000],inplace= True)\n",
        "df.drop(df.index[50000:52000],inplace= True)\n",
        "df.drop(df.index[520000:522000],inplace= True)\n",
        "df.drop(df.index[535300:537300],inplace= True)\n",
        "df.drop(df.index[650000:652000],inplace= True)\n",
        "df.drop(df.index[710000:712000],inplace= True)\n",
        "df.drop(df.index[764200:766200],inplace= True)\n",
        "df.drop(df.index[793080:795080],inplace= True)\n",
        "df.drop(df.index[839730:841730],inplace= True)\n",
        "df.drop(df.index[850000:852000],inplace= True)\n",
        "df.drop(df.index[955250:957250],inplace= True)\n",
        "df.drop(df.index[1013000:1015000],inplace= True)\n",
        "df.drop(df.index[1143000:1145000],inplace= True)\n",
        "df.drop(df.index[1293000:1295000],inplace= True)\n",
        "df.drop(df.index[1492000:1494000],inplace= True)\n",
        "df.tail()"
      ],
      "execution_count": 20,
      "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>URL</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1562974</th>\n",
              "      <td>http://www.maxpreps.com/</td>\n",
              "      <td>Sports</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1562975</th>\n",
              "      <td>http://www.myscore.com/</td>\n",
              "      <td>Sports</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1562976</th>\n",
              "      <td>http://sportsillustrated.cnn.com/highschool</td>\n",
              "      <td>Sports</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1562977</th>\n",
              "      <td>http://rss.cnn.com/rss/si_highschool?format=xml</td>\n",
              "      <td>Sports</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1562978</th>\n",
              "      <td>http://www.usatoday.com/sports/preps/</td>\n",
              "      <td>Sports</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                     URL Category\n",
              "1562974                         http://www.maxpreps.com/   Sports\n",
              "1562975                          http://www.myscore.com/   Sports\n",
              "1562976      http://sportsillustrated.cnn.com/highschool   Sports\n",
              "1562977  http://rss.cnn.com/rss/si_highschool?format=xml   Sports\n",
              "1562978            http://www.usatoday.com/sports/preps/   Sports"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "c31e3eabc59e1219c623e4b89df87d066ddd3082",
        "colab_type": "code",
        "id": "aHxqyiLYw-6N",
        "outputId": "8611040b-a3bf-41e7-ff1c-5add44d64514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "source": [
        "df.Category.value_counts().plot(figsize=(12,5),kind='bar',color='black');\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Total Number Of Individual Category')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Total Number Of Individual Category')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LcpskNwSeCBzRcUySJEnSgtbKUpaquiLJ84AjgXWBj1TVGR2FM9FSmQkx5smb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\nS5IkST1gYi5JkiT1gIm5JEmS1AP/H5DLL1SBpeIdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "b8211b56e497b7b82dc39f1797670a690aec23ad",
        "id": "UELRQLnQHYdS",
        "colab_type": "code",
        "outputId": "17c8af3e-4fa6-41b8-ac2e-69b2a2b41b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "source": [
        "dt.Category.value_counts().plot(figsize=(12,5),kind='bar',color='green');\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Total Number Of Individual Category')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Total Number Of Individual Category')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mwERbkiRJmgATbUmSJGkCpibRTrJTkguTXJxk/67jkSRJkkaZikQ7yarAx4An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\n1OATlI4ucq25swUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "dfa228d30b9e1f40d91fb8d8092cd53193327a59",
        "id": "clnNh5MCHYdc",
        "colab_type": "code",
        "outputId": "a051770c-0cb8-42c9-9532-25fbbd507382",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train=df['URL']\n",
        "y_train=df['Category']\n",
        "#print(X_train)\n",
        "X_train.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1532979,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "14599f7b33f92e516fcf3fe7f68497f0bcfd6df2",
        "id": "zXerbO8YHYdq",
        "colab_type": "code",
        "outputId": "c102d061-0878-40d2-94fc-b3d18fd49fb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_test=dt['URL']\n",
        "y_test=dt['Category']\n",
        "#print(X_test)\n",
        "X_test.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(29999,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "2126dbf54940e001cecc61849b06deebc30f47a8",
        "id": "yN0FxZaVHYd1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "text_clf = Pipeline([('vect',\n",
        "                     CountVectorizer(stop_words = 'english', ngram_range=(2,2))), \n",
        "                     ('tfidf', TfidfTransformer()), \n",
        "                     ('clf', MultinomialNB(fit_prior = 'False' , alpha = 0.0001 ))])\n",
        "text_clf = text_clf.fit(X_train, y_train)\n",
        "gs_clf = text_clf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sizG3OmRuCeD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from sklearn.pipeline import Pipeline\n",
        "#import nltk\n",
        "#nltk.download('stopwords')\n",
        "#gs_clf = Pipeline([('vect', CountVectorizer( ngram_range=(1,1))),\n",
        "#                      ('tfidf', TfidfTransformer()),\n",
        "#                       ('clf', MultinomialNB(fit_prior= False)),\n",
        "#  ])\n",
        "#gs_clf = gs_clf.fit(X_train , y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FoLp9yCNcc-Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from sklearn.pipeline import Pipeline\n",
        "#from sklearn.linear_model import SGDClassifier\n",
        "#gs_clf = Pipeline([\n",
        "#                    ('vect', CountVectorizer(stop_words='english')),\n",
        "#                    ('tfidf', TfidfTransformer()),\n",
        "#                    ('clf', SGDClassifier(loss='perceptron', penalty='l2',\n",
        "#                     alpha =1e-4,random_state=42, max_iter=10, shuffle = True ,tol=None)),\n",
        "#                    ])\n",
        "#gs_clf = text_clf.fit(X_train , y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "2126dbf54940e001cecc61849b06deebc30f47a8",
        "colab_type": "code",
        "id": "H5e-5FjnKLH3",
        "colab": {}
      },
      "source": [
        "#from sklearn.model_selection import GridSearchCV\n",
        "#parameters = { \n",
        "#     'vect__ngram_range': [(1, 1), (1, 2), (2,2)],\n",
        "#     'clf__alpha': (1e-2, 1e-3 , 1e-4),\n",
        "#     'clf__fit_prior' : (True, False) }\n",
        "\n",
        "#gs_clf = GridSearchCV(text_clf, parameters, cv=5, n_jobs=-1)\n",
        "#gs_clf = gs_clf.fit(X_train , y_train)\n",
        "#gs_clf.best_score_\n",
        "#gs_clf.cv_results_\n",
        "\n",
        "#for param_name in sorted(parameters.keys()):\n",
        "#     print(\"%s: %r\" % (param_name, gs_clf.best_params_[param_name]))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "892681d5eb3d8f45379fd80046676aeac800c7bf",
        "id": "NvM1Sd4oHYd8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from sklearn.model_selection import RandomizedSearchCV\n",
        "#n_iter_search = 5\n",
        "#parameters = {'vect__ngram_range': [(1, 1), (1, 2)], 'tfidf__use_idf': (True, False), 'clf__alpha': (1e-2, 1e-3)}\n",
        "#gs_clf = RandomizedSearchCV(text_clf, parameters, n_iter = n_iter_search)\n",
        "#gs_clf = gs_clf.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "424a0afe8c342a13d0c65e030be9be3b20aac950",
        "colab_type": "code",
        "id": "bpSrzz7cxFle",
        "colab": {}
      },
      "source": [
        "#X_train, X_test, y_train, y_test = train_test_split(df['URL'], df['Category'],test_size=0.3, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "16de52d34ff4a9d73658254dcbd1ba9d09116e17",
        "colab_type": "code",
        "id": "9Z-yMkIHxJnR",
        "colab": {}
      },
      "source": [
        "#y=np.array(df[names[1]])\n",
        "#print(y)\n",
        "\n",
        "#from sklearn.pipeline import Pipeline\n",
        "#from sklearn.multiclass import OneVsRestClassifier\n",
        "#from nltk.corpus import stopwords\n",
        "#stop_words = set(stopwords.words('english'))\n",
        "\n",
        "#text_clf = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())])\n",
        "#text_clf = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', OneVsRestClassifier(MultinomialNB(fit_prior=True, class_prior=None)))])\n",
        "#text_clf= Pipeline([\n",
        "                #('tfidf', TfidfVectorizer(stop_words=stop_words)),\n",
        "                #('clf', OneVsRestClassifier(MultinomialNB(\n",
        "                   # fit_prior=True, class_prior=None))),\n",
        "            #])\n",
        "#text_clf = text_clf.fit(X_train, y_train)\n",
        "#test_clf =text_clf.fit(X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "40f1dd981632c8170a6eaf580913f23caed8f181",
        "id": "oNip_quPHYeW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#text_clf.get_params().keys()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "4a9aad0926f9a98e310d9ef21086c0bdfaad1bc9",
        "colab_type": "code",
        "id": "3lcQjbu4sd-K",
        "colab": {}
      },
      "source": [
        "#from sklearn.model_selection import RandomizedSearchCV\n",
        "#n_iter_search = 5\n",
        "#parameters = {'vect__ngram_range': [(1, 1), (1, 2)], 'tfidf__use_idf': (True, False), 'clf__estimator__alpha': (1e-2, 1e-3)}\n",
        "#parameters = {'tfidf__ngram_range': [(1, 1), (1, 2)], 'tfidf__use_idf': (True, False), 'clf__estimator__alpha': (1e-2, 1e-3)}\n",
        "#gs_clf = RandomizedSearchCV(text_clf, parameters, n_iter = n_iter_search)\n",
        "#gs_clf = gs_clf.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "6d921079bdd64aba4667b55864c529edd06e0fac",
        "colab_type": "code",
        "id": "NSqLTA9-xQVe",
        "colab": {}
      },
      "source": [
        "#print(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "74268f8496fd1658a7e781249d9d579aaca5de15",
        "colab_type": "code",
        "id": "vbjsB3V5xpRM",
        "outputId": "ca25cdce-41bc-404f-8c5d-54f83a2b33f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.metrics import confusion_matrix\n",
        "#grid_mean_scores = [result.mean_validation_score for result in gs_clf.grid_scores_]\n",
        "#print(grid_mean_scores)\n",
        "y_pred=gs_clf.predict(X_test)\n",
        "precision_recall_fscore_support(y_test, y_pred, average='weighted')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9318613328936391, 0.9138637954598486, 0.9015293427558834, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "52610d1bc080a3c7e63a0583a96735d881f91098",
        "id": "Z9WlOli3HYe9",
        "colab_type": "code",
        "outputId": "51e4c22a-09ce-47ff-a2e3-5d311a41ac6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "y_pred=gs_clf.predict(X_test)\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred, digits = 4))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Adult     0.9949    0.1971    0.3290      1999\n",
            "        Arts     0.6046    0.9280    0.7321      2000\n",
            "    Business     0.7876    0.9940    0.8789      2000\n",
            "   Computers     0.9670    0.9815    0.9742      2000\n",
            "       Games     0.9757    0.9855    0.9806      2000\n",
            "      Health     0.9900    0.9945    0.9923      2000\n",
            "        Home     0.9734    0.9690    0.9712      2000\n",
            "        Kids     0.9529    0.8405    0.8932      2000\n",
            "        News     0.9936    0.9360    0.9640      2000\n",
            "  Recreation     0.9655    0.9925    0.9788      2000\n",
            "   Reference     0.9129    0.9700    0.9406      2000\n",
            "     Science     0.9649    0.9630    0.9640      2000\n",
            "    Shopping     0.9807    0.9930    0.9868      2000\n",
            "     Society     0.9258    0.9975    0.9603      2000\n",
            "      Sports     0.9882    0.9655    0.9767      2000\n",
            "\n",
            "    accuracy                         0.9139     29999\n",
            "   macro avg     0.9319    0.9138    0.9015     29999\n",
            "weighted avg     0.9319    0.9139    0.9015     29999\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "27739db4d09a08caf347fee24e1674bf54cc4951",
        "id": "kPc5UfTgHYfM",
        "colab_type": "code",
        "outputId": "35e64811-3fe3-4ec6-d3ca-61b29ceab49c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        }
      },
      "source": [
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "array = confusion_matrix(y_test, y_pred)\n",
        "cm=np.array(array)\n",
        "cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "df_cm = pd.DataFrame(cm, index = [i for i in \"0123456789ABCDE\"],\n",
        "                  columns = [i for i in \"0123456789ABCDE\"])\n",
        "plt.figure(figsize = (20,15))\n",
        "sn.heatmap(df_cm, annot=True)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1694627c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "9062c8082990e92166fbbec3bf9499ed3374fbf1",
        "colab_type": "code",
        "id": "lMdZWt5ws62k",
        "outputId": "bee203b3-1edf-4aa7-ec94-0dd7cbf5d971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Naive Bayes Train Accuracy = ',metrics.accuracy_score(y_train,gs_clf.predict(X_train)))\n",
        "print('Naive Bayes Test Accuracy = ',metrics.accuracy_score(y_test,gs_clf.predict(X_test)))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Naive Bayes Train Accuracy =  0.9717726074525482\n",
            "Naive Bayes Test Accuracy =  0.9138637954598486\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "44e5c0df369430df0006e9f04774db39bcb13d99",
        "colab_type": "code",
        "id": "EKqEmU-4xZVF",
        "outputId": "91a8a5e8-0145-42ce-8f74-73b3b9667017",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(gs_clf.predict(['http://www.businesstoday.net/']))\n",
        "print(gs_clf.predict(['http://www.gamespot.net/']))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Business']\n",
            "['Games']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "3bd7f52b1042be2ae0bc25fa3033cbaf79cf7e0f",
        "id": "MESE0LxxHYfo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}