Sentiment Analysis on IMDB Dataset.ipynb

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  "metadata": {
    "colab": {
      "name": "Sentiment Analysis on Movie Review Data_ACASmai.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "HYJeNhiN-Be1",
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        "wEVF0Hvo5_W3",
        "60SUuGc-GOG6",
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        "ASgXD8M3pSJp",
        "ynjDzIKYDntl",
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        "p6xj_omvmqlV",
        "gCfRgD1GqsVs",
        "NT0KMqyYBfqQ",
        "D3zDuiNlGOAi",
        "jrcWgAQQCSJW",
        "rWclTKPwg5-7",
        "HZ5JU85PMM2q",
        "gSWV476LjREu"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chirag-Shilwant/Sentiment-Analysis-on-IMDB-Dataset/blob/main/Sentiment%20Analysis%20on%20IMDB%20Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8qJsl751kUb"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYJeNhiN-Be1"
      },
      "source": [
        "# **Loading the Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hfwyU4P2O2T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc9463fc-a7ca-4c5f-b6ec-c48aecb5ff60"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YNotLbL1x_5"
      },
      "source": [
        "dataset_imbd = pd.read_csv('/content/drive/MyDrive/IMDB Dataset.csv', na_values='?') \n",
        "dataset_imbd = dataset_imbd.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcVdqo6n10Et",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "67d133de-3626-4b0c-925f-abfd066093d6"
      },
      "source": [
        "dataset_imbd.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50000</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>49582</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Loved today's show!!! It was a variety and not...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>5</td>\n",
              "      <td>25000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   review sentiment\n",
              "count                                               50000     50000\n",
              "unique                                              49582         2\n",
              "top     Loved today's show!!! It was a variety and not...  negative\n",
              "freq                                                    5     25000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEgk6Eh512ti",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db5216d5-dcc2-4193-a896-f114b60f55a4"
      },
      "source": [
        "dataset_imbd['sentiment'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "negative    25000\n",
              "positive    25000\n",
              "Name: sentiment, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHUG7Tfm141G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "cdf90b80-f0c2-48be-818e-27f5aa336b69"
      },
      "source": [
        "print(dataset_imbd.shape)\n",
        "dataset_imbd.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 2)\n"
          ],
          "name": "stdout"
        },
        {
          "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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>One of the other reviewers has mentioned that ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Probably my all-time favorite movie, a story o...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>I sure would like to see a resurrection of a u...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>This show was an amazing, fresh &amp; innovative i...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Encouraged by the positive comments about this...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>If you like original gut wrenching laughter yo...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review sentiment\n",
              "0  One of the other reviewers has mentioned that ...  positive\n",
              "1  A wonderful little production. <br /><br />The...  positive\n",
              "2  I thought this was a wonderful way to spend ti...  positive\n",
              "3  Basically there's a family where a little boy ...  negative\n",
              "4  Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
              "5  Probably my all-time favorite movie, a story o...  positive\n",
              "6  I sure would like to see a resurrection of a u...  positive\n",
              "7  This show was an amazing, fresh & innovative i...  negative\n",
              "8  Encouraged by the positive comments about this...  negative\n",
              "9  If you like original gut wrenching laughter yo...  positive"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QO6-AnlP16Tb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a500c85-f22f-4d0a-d7d6-b4d348d11290"
      },
      "source": [
        "#train dataset\n",
        "X = dataset_imbd.review\n",
        "y = dataset_imbd.sentiment\n",
        "print(X.shape, y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000,) (50000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9PoAnrfCFk0",
        "outputId": "4d661d6a-0bf2-4036-fe51-937edd8822d6"
      },
      "source": [
        "dataset_imbd.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "review       0\n",
              "sentiment    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__kVCmag7t6S"
      },
      "source": [
        "**Sample Review**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OS36HLMB283A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "outputId": "71188283-479f-46e4-847a-1683e1a88ba2"
      },
      "source": [
        "X[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"One of the other reviewers has mentioned that after watching just 1 Oz episode you'll be hooked. They are right, as this is exactly what happened with me.<br /><br />The first thing that struck me about Oz was its brutality and unflinching scenes of violence, which set in right from the word GO. Trust me, this is not a show for the faint hearted or timid. This show pulls no punches with regards to drugs, sex or violence. Its is hardcore, in the classic use of the word.<br /><br />It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary. It focuses mainly on Emerald City, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. Em City is home to many..Aryans, Muslims, gangstas, Latinos, Christians, Italians, Irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.<br /><br />I would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. Forget pretty pictures painted for mainstream audiences, forget charm, forget romance...OZ doesn't mess around. The first episode I ever saw struck me as so nasty it was surreal, I couldn't say I was ready for it, but as I watched more, I developed a taste for Oz, and got accustomed to the high levels of graphic violence. Not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) Watching Oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side.\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qg1cVPN-6kNA"
      },
      "source": [
        "import re\n",
        "import os\n",
        "import sys\n",
        "import json\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import spacy\n",
        "from spacy.lang.en.stop_words import STOP_WORDS as stopwords\n",
        "from bs4 import BeautifulSoup\n",
        "import unicodedata\n",
        "import en_core_web_sm\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "def removeEmails(x):\n",
        "\treturn re.sub(r'([a-z0-9+._-]+@[a-z0-9+._-]+\\.[a-z0-9+_-]+)',\"\", x)\n",
        "\n",
        "def removeUrls(x):\n",
        "\treturn re.sub(r'(http|https|ftp|ssh)://([\\w_-]+(?:(?:\\.[\\w_-]+)+))([\\w.,@?^=%&:/~+#-]*[\\w@?^=%&/~+#-])?', '' , x)\n",
        "\n",
        "def removeRt(x):\n",
        "\treturn re.sub(r'\\brt\\b', '', x).strip()\n",
        "\n",
        "def removeSpecialChars(x):\n",
        "\tx = re.sub(r'[^\\w ]+', \"\", x)\n",
        "\tx = ' '.join(x.split())\n",
        "\treturn x\n",
        "\n",
        "def removeAccentedChars(x):\n",
        "\tx = unicodedata.normalize('NFKD', x).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
        "\treturn x\n",
        "\n",
        "def removeStopwords(x):\n",
        "\treturn ' '.join([t for t in x.split() if t not in stopwords])\t\n",
        "\n",
        "def removeDupsChar(x):\n",
        "\tx = re.sub(\"(.)\\\\1{2,}\", \"\\\\1\", x)\n",
        "\treturn x\n",
        "\n",
        "def removeHTMLtags(x):\n",
        "  return BeautifulSoup(x, 'lxml').get_text().strip()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tr-6-81Dvmlp"
      },
      "source": [
        "def dataClean(x):\n",
        "    x = str(x).lower().replace('\\\\', '').replace('_', ' ')\n",
        "    x = removeHTMLtags(x)\n",
        "    x = removeStopwords(x)\n",
        "    x = removeEmails(x)\n",
        "    x = removeUrls(x)\n",
        "    x = removeRt(x)\n",
        "    x = removeDupsChar(x)\n",
        "    x = removeAccentedChars(x)\n",
        "    x = removeSpecialChars(x)\n",
        "    x = re.sub(\"(.)\\\\1{2,}\", \"\\\\1\", x)\n",
        "    return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sr-SQ_6cw5Hi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0237b00-450b-412b-a46c-e4759157daad"
      },
      "source": [
        "print(dataClean(X[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "reviewers mentioned watching 1 oz episode youll hooked right exactly happened methe thing struck oz brutality unflinching scenes violence set right word go trust me faint hearted timid pulls punches regards drugs sex violence hardcore classic use wordit called oz nickname given oswald maximum security state penitentary focuses mainly emerald city experimental section prison cells glass fronts face inwards privacy high agenda em city home manyaryans muslims gangstas latinos christians italians irish moreso scuffles death stares dodgy dealings shady agreements far awayi main appeal fact goes shows wouldnt dare forget pretty pictures painted mainstream audiences forget charm forget romanceoz doesnt mess around episode saw struck nasty surreal couldnt ready it watched more developed taste oz got accustomed high levels graphic violence violence injustice crooked guards wholl sold nickel inmates wholl kill order away it mannered middle class inmates turned prison bitches lack street skills prison experience watching oz comfortable uncomfortable viewingthats touch darker side\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3IBGefDmyGjp"
      },
      "source": [
        "for i in range(len(X)):\n",
        "  X[i] = dataClean(X[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDgUJGFCz_9W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "316386d5-9b31-42fb-96bb-91e78c40fc8a"
      },
      "source": [
        "for sentiment in X[0:5]:\n",
        "  print(sentiment)\n",
        "  print(\"\\n\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "reviewers mentioned watching 1 oz episode youll hooked right exactly happened methe thing struck oz brutality unflinching scenes violence set right word go trust me faint hearted timid pulls punches regards drugs sex violence hardcore classic use wordit called oz nickname given oswald maximum security state penitentary focuses mainly emerald city experimental section prison cells glass fronts face inwards privacy high agenda em city home manyaryans muslims gangstas latinos christians italians irish moreso scuffles death stares dodgy dealings shady agreements far awayi main appeal fact goes shows wouldnt dare forget pretty pictures painted mainstream audiences forget charm forget romanceoz doesnt mess around episode saw struck nasty surreal couldnt ready it watched more developed taste oz got accustomed high levels graphic violence violence injustice crooked guards wholl sold nickel inmates wholl kill order away it mannered middle class inmates turned prison bitches lack street skills prison experience watching oz comfortable uncomfortable viewingthats touch darker side\n",
            "\n",
            "\n",
            "\n",
            "wonderful little production filming technique unassuming oldtimebbc fashion gives comforting discomforting sense realism entire piece actors extremely chosen michael sheen has got polari voices pat too truly seamless editing guided references williams diary entries worth watching terrificly written performed piece masterful production great masters comedy life realism comes home little things fantasy guard which use traditional dream techniques remains solid disappears plays knowledge senses particularly scenes concerning orton halliwell sets particularly flat halliwells murals decorating surface terribly done\n",
            "\n",
            "\n",
            "\n",
            "thought wonderful way spend time hot summer weekend sitting air conditioned theater watching lighthearted comedy plot simplistic dialogue witty characters likable even bread suspected serial killer disappointed realize match point 2 risk addiction thought proof woody allen fully control style grown lovethis id laughed woodys comedies years dare decade ive impressed scarlet johanson managed tone sexy image jumped right average spirited young womanthis crown jewel career wittier devil wears prada interesting superman great comedy friends\n",
            "\n",
            "\n",
            "\n",
            "basically theres family little boy jake thinks theres zombie closet parents fighting timethis movie slower soap opera suddenly jake decides rambo kill zombieok youre going film decide thriller drama drama movie watchable parents divorcing arguing like real life jake closet totally ruins film expected boogeyman similar movie instead watched drama meaningless thriller spots3 10 playing parents descent dialogs shots jake ignore them\n",
            "\n",
            "\n",
            "\n",
            "petter matteis love time money visually stunning film watch mr mattei offers vivid portrait human relations movie telling money power success people different situations encounter variation arthur schnitzlers play theme director transfers action present time new york different characters meet connect connected way person know previous point contact stylishly film sophisticated luxurious look taken people live world live habitatthe thing gets souls picture different stages loneliness inhabits big city exactly best place human relations find sincere fulfillment discerns case people encounterthe acting good mr matteis direction steve buscemi rosario dawson carol kane michael imperioli adrian grenier rest talented cast characters come alivewe wish mr mattei good luck await anxiously work\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnmVyvoOoAp7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e861ed4-0e69-42e8-f240-644141fe873f"
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        reviewers mentioned watching 1 oz episode youl...\n",
              "1        wonderful little production filming technique ...\n",
              "2        thought wonderful way spend time hot summer we...\n",
              "3        basically theres family little boy jake thinks...\n",
              "4        petter matteis love time money visually stunni...\n",
              "                               ...                        \n",
              "49995    thought movie right good job wasnt creative or...\n",
              "49996    bad plot bad dialogue bad acting idiotic direc...\n",
              "49997    catholic taught parochial elementary schools n...\n",
              "49998    im going disagree previous comment maltin one ...\n",
              "49999    expects star trek movies high art fans expect ...\n",
              "Name: review, Length: 50000, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_GNjQEPoCnY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d79b3f4b-44ee-470b-d38c-cb708209d8c4"
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        positive\n",
              "1        positive\n",
              "2        positive\n",
              "3        negative\n",
              "4        positive\n",
              "           ...   \n",
              "49995    positive\n",
              "49996    negative\n",
              "49997    negative\n",
              "49998    negative\n",
              "49999    negative\n",
              "Name: sentiment, Length: 50000, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzALYZoG18Ay"
      },
      "source": [
        "#split the dataset \n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,y,test_size = 0.3,random_state = 45)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNqzvVRH19vS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79082f6f-e389-4da9-84ed-c79a518db91a"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(Y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(35000,)\n",
            "(35000,)\n",
            "(15000,)\n",
            "(15000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBSB__ygju_n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23471f92-c0d6-41e0-b18d-3af165dc7a34"
      },
      "source": [
        "X_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9855     national lampoons dorm daze easily worst movie...\n",
              "24367    carrot tops chairman board att commercials liv...\n",
              "43552    like kinski great actor ive seen movie ive hea...\n",
              "49483    got copy dvd disc set perfect found overall ex...\n",
              "41722    movie rich action gore story line strong suppo...\n",
              "                               ...                        \n",
              "32469    definitely minority opinion one the hurt locke...\n",
              "16317    friend gave boxed set 12 amazing scifihorror m...\n",
              "12895    question immediately arises extremely idiosync...\n",
              "6012     like jane austin novels love pride prejudice s...\n",
              "6558     movie disaster disaster film great action scen...\n",
              "Name: review, Length: 35000, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8mHqojueCqpE"
      },
      "source": [
        "def get_sentiments(d, _d):\n",
        "    positive = (d==\"positive\").sum()\n",
        "    negative = (d==\"negative\").sum()\n",
        "    print('Sentiments in {}:\\n Positive: {}\\n Negative: {}'.format(_d, positive, negative))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Voqp-0NYCrfb",
        "outputId": "2ec46e64-46d4-4525-cfde-de9659fae2b2"
      },
      "source": [
        "get_sentiments(Y_train, 'Train data')\n",
        "get_sentiments(Y_test, 'Test data')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sentiments in Train data:\n",
            " Positive: 17494\n",
            " Negative: 17506\n",
            "Sentiments in Test data:\n",
            " Positive: 7506\n",
            " Negative: 7494\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHlTD3Ry7SIz"
      },
      "source": [
        "# **Visualize Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mfa9Jtcf7XBa"
      },
      "source": [
        "from wordcloud import WordCloud,STOPWORDS"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66xfBLOo8CBh"
      },
      "source": [
        "**WORD CLOUD OF POSITIVE WORDS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "8Aq6UQbI75ld",
        "outputId": "33071f64-9eb8-4fd5-8850-9c1d993ab275"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "positive_text=X[1]\n",
        "WC=WordCloud(width=2000,height=1000,max_words=500,min_font_size=5)\n",
        "positive_words=WC.generate(positive_text)\n",
        "plt.imshow(positive_words,interpolation='bilinear')\n",
        "plt.show"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWf7Egeq8xdm"
      },
      "source": [
        "**WORD CLOUD OF NEGATIVE WORDS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "4gyRx3798w3w",
        "outputId": "6c61cb29-b85d-4c5c-c90f-fc6d5951ae73"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "negative_text=X[49996]\n",
        "WC=WordCloud(width=2000,height=1000,max_words=500,min_font_size=5)\n",
        "negative_words=WC.generate(negative_text)\n",
        "plt.imshow(negative_words,interpolation='bilinear')\n",
        "plt.show"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muAY0VLv6Y2L"
      },
      "source": [
        "# **Using Bag of Words**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lqaouSA3IA3"
      },
      "source": [
        "#bag of words\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vectorizer = CountVectorizer()\n",
        "\n",
        "X_train = vectorizer.fit_transform(X_train)\n",
        "X_test = vectorizer.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PiZ1RxwfjNH6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40a9af7c-178e-4284-a0e6-1b619654af97"
      },
      "source": [
        "featureName = vectorizer.get_feature_names()\n",
        "print(\"Number of features - \", len(featureName))\n",
        "print(\"Last 30 features are\\n\", featureName[-30:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of features -  174914\n",
            "Last 30 features are\n",
            " ['zuluagain', 'zulunation', 'zungia', 'zuni', 'zuniga', 'zunigas', 'zuotian', 'zurer', 'zurers', 'zurich', 'zutaut', 'zuthe', 'zuwarriors', 'zuzuzz', 'zvezda', 'zvonimir', 'zvyagvatsev', 'zwart', 'zwartboek', 'zwarts', 'zweite', 'zwick', 'zwicks', 'zwrite', 'zx81', 'zy', 'zyada', 'zylbersteinand', 'zyuranger', 'zz']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEuJF9iisyD2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b02ac2d-dc6c-4144-beef-14cd5ade4ee6"
      },
      "source": [
        "vectorizer.vocabulary_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'national': 104673,\n",
              " 'lampoons': 86962,\n",
              " 'dorm': 45177,\n",
              " 'daze': 38960,\n",
              " 'easily': 47622,\n",
              " 'worst': 172257,\n",
              " 'movie': 102042,\n",
              " 'seen': 135904,\n",
              " 'ive': 81383,\n",
              " 'kazaam': 84193,\n",
              " 'reading': 125638,\n",
              " 'thinks': 154837,\n",
              " 'good': 65558,\n",
              " 'way': 168021,\n",
              " 'shape': 138138,\n",
              " 'form': 59990,\n",
              " 'idea': 76034,\n",
              " 'is': 80282,\n",
              " 'watch': 167659,\n",
              " 'stupidits': 148355,\n",
              " 'hard': 69596,\n",
              " 'wrong': 172731,\n",
              " 'off': 109125,\n",
              " 'plot': 118398,\n",
              " 'place': 117729,\n",
              " 'cant': 25059,\n",
              " 'follow': 59355,\n",
              " 'multiple': 103304,\n",
              " 'misunderstandings': 99912,\n",
              " 'all': 7184,\n",
              " 'acting': 4375,\n",
              " 'awful': 13562,\n",
              " 'least': 88211,\n",
              " 'thing': 154630,\n",
              " 'poorly': 119403,\n",
              " 'made': 93048,\n",
              " 'worked': 171824,\n",
              " 'kid': 84723,\n",
              " 'againit': 5951,\n",
              " 'sum': 149332,\n",
              " 'paragraph': 113891,\n",
              " 'two': 160689,\n",
              " 'bad': 14165,\n",
              " 'want': 167116,\n",
              " 'turn': 160251,\n",
              " 'television': 152734,\n",
              " 'usa': 163892,\n",
              " 'better': 17756,\n",
              " 'movies': 102471,\n",
              " 'this': 154930,\n",
              " 'friend': 61121,\n",
              " 'carrot': 25863,\n",
              " 'tops': 157757,\n",
              " 'chairman': 27258,\n",
              " 'board': 19765,\n",
              " 'att': 12552,\n",
              " 'commercials': 32321,\n",
              " 'living': 90666,\n",
              " 'proof': 122319,\n",
              " 'flyblown': 59163,\n",
              " 'fecal': 55994,\n",
              " 'matter': 95799,\n",
              " 'available': 13187,\n",
              " 'color': 31708,\n",
              " 'orange': 111087,\n",
              " 'freely': 60896,\n",
              " 'however': 74881,\n",
              " 'hbo': 70393,\n",
              " 'charges': 27929,\n",
              " 'garbage': 62807,\n",
              " 'bleh': 19186,\n",
              " 'saving': 133390,\n",
              " 'grace': 66290,\n",
              " 'cotb': 35109,\n",
              " 'surely': 150005,\n",
              " 'fills': 56922,\n",
              " 'suicide': 149214,\n",
              " 'hotlines': 74611,\n",
              " 'country': 35316,\n",
              " 'providing': 122647,\n",
              " 'employment': 49504,\n",
              " 'thousands': 155404,\n",
              " 'telephone': 152698,\n",
              " 'therapists': 154315,\n",
              " 'lost': 91587,\n",
              " 'jobs': 82643,\n",
              " 'recovered': 126489,\n",
              " 'memory': 97327,\n",
              " 'treatment': 159057,\n",
              " 'discredited': 43345,\n",
              " 'sadly': 132255,\n",
              " 'probably': 121652,\n",
              " 'contributes': 34129,\n",
              " 'sponsor': 144957,\n",
              " 'atts': 12763,\n",
              " 'line': 90052,\n",
              " 'increased': 77621,\n",
              " 'phone': 116796,\n",
              " 'traffic': 158412,\n",
              " 'devastated': 41452,\n",
              " 'viewers': 165612,\n",
              " 'visualize': 166160,\n",
              " 'hordes': 74138,\n",
              " 'traumatized': 158915,\n",
              " 'tv': 160386,\n",
              " 'hand': 69049,\n",
              " 'fully': 61675,\n",
              " 'loaded': 90761,\n",
              " '45': 2322,\n",
              " 'auto': 13116,\n",
              " 'dialing': 41760,\n",
              " 'lastminute': 87397,\n",
              " 'desperation': 41157,\n",
              " 'walking': 166913,\n",
              " 'plank': 117954,\n",
              " 'life': 89412,\n",
              " 'escape': 51362,\n",
              " 'specter': 144335,\n",
              " 'cotbs': 35110,\n",
              " 'orangehaired': 111089,\n",
              " 'monster': 100826,\n",
              " 'echoing': 47814,\n",
              " 'synapses': 151020,\n",
              " 'like': 89761,\n",
              " 'agony': 6263,\n",
              " 'searing': 135406,\n",
              " 'irreversible': 80246,\n",
              " 'meningeal': 97454,\n",
              " 'swelling': 150707,\n",
              " 'kinski': 85289,\n",
              " 'great': 66744,\n",
              " 'actor': 4671,\n",
              " 'heard': 70613,\n",
              " 'autobiographic': 13119,\n",
              " 'aspects': 12001,\n",
              " 'moviethe': 102681,\n",
              " 'film': 56925,\n",
              " 'symbols': 150964,\n",
              " 'piano': 116978,\n",
              " 'sinking': 140563,\n",
              " 'river': 130057,\n",
              " 'strange': 147572,\n",
              " 'shadowpictures': 137891,\n",
              " 'walls': 166990,\n",
              " 'narrator': 104559,\n",
              " 'says': 133538,\n",
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              " 'family': 54800,\n",
              " 'comedysthe': 32025,\n",
              " 'cat': 26348,\n",
              " 'outer': 111967,\n",
              " 'space': 143922,\n",
              " 'unidentified': 162490,\n",
              " 'flying': 59172,\n",
              " 'oddball': 108988,\n",
              " 'instance': 78888,\n",
              " 'utterly': 164162,\n",
              " 'devoid': 41613,\n",
              " 'creative': 36130,\n",
              " 'beating': 16087,\n",
              " 'cliches': 30491,\n",
              " 'death': 39186,\n",
              " 'vaguely': 164279,\n",
              " 'mindless': 98952,\n",
              " 'sort': 143582,\n",
              " 'wont': 171579,\n",
              " 'win': 170592,\n",
              " 'awards': 13408,\n",
              " 'fx': 62212,\n",
              " 'inoffensive': 78618,\n",
              " 'ness': 105491,\n",
              " 'bland': 19029,\n",
              " 'predicatability': 120671,\n",
              " 'vague': 164277,\n",
              " 'entertainment': 50666,\n",
              " 'had': 68360,\n",
              " 'robot': 130295,\n",
              " 'dog': 44495,\n",
              " 'security': 135725,\n",
              " 'system': 151132,\n",
              " 'holes': 73430,\n",
              " 'colander': 31433,\n",
              " 'incredibly': 77652,\n",
              " 'repetative': 127986,\n",
              " 'disco': 43267,\n",
              " 'soundtrack': 143769,\n",
              " 'stick': 146619,\n",
              " 'head': 70416,\n",
              " 'beware': 17954,\n",
              " 'saw': 133417,\n",
              " 'hamlet': 68971,\n",
              " 'ago': 6220,\n",
              " 'since': 140387,\n",
              " 'remains': 127543,\n",
              " 'truest': 159790,\n",
              " 'depiction': 40690,\n",
              " 'derek': 40838,\n",
              " 'jacobi': 81561,\n",
              " 'shakespeare': 137956,\n",
              " 'simply': 140341,\n",
              " 'gibberish': 64206,\n",
              " 'tried': 159338,\n",
              " 'elizabethan': 48938,\n",
              " 'jacobis': 81562,\n",
              " 'times': 156377,\n",
              " 'knowledge': 85720,\n",
              " 'literature': 90460,\n",
              " 'promptly': 122291,\n",
              " 'checked': 28259,\n",
              " 'play': 118087,\n",
              " 'library': 89314,\n",
              " 'read': 125613,\n",
              " 'poured': 120247,\n",
              " 'accompanying': 4040,\n",
              " 'recording': 126460,\n",
              " 'rendition': 127790,\n",
              " 'attracted': 12731,\n",
              " 'deeper': 39724,\n",
              " 'yet': 173652,\n",
              " 'avail': 13185,\n",
              " 'rating': 125285,\n",
              " 'why': 170121,\n",
              " 'then': 154166,\n",
              " 'released': 127359,\n",
              " 'id': 76026,\n",
              " 'invest': 79882,\n",
              " 'in': 77264,\n",
              " 'appears': 10560,\n",
              " 'critics': 36639,\n",
              " 'woody': 171665,\n",
              " 'allen': 7286,\n",
              " 'drama': 45746,\n",
              " 'unpalatable': 162925,\n",
              " 'reason': 125984,\n",
              " 'unbearably': 161492,\n",
              " 'wooden': 171601,\n",
              " 'pretentious': 121221,\n",
              " 'imitations': 76745,\n",
              " 'bergman': 17426,\n",
              " 'ourselves': 111844,\n",
              " 'supportive': 149900,\n",
              " 'allens': 7299,\n",
              " 'pretensions': 121212,\n",
              " 'whining': 169782,\n",
              " 'accusations': 4133,\n",
              " 'contrary': 34107,\n",
              " 'notwithstanding': 107859,\n",
              " 'dont': 44962,\n",
              " 'generally': 63444,\n",
              " 'applauded': 10596,\n",
              " 'originality': 111338,\n",
              " 'imitating': 76742,\n",
              " 'contemporaneous': 33903,\n",
              " 'brian': 21990,\n",
              " 'depalma': 40626,\n",
              " 'excoriated': 52843,\n",
              " 'ripping': 129919,\n",
              " 'hitchcock': 73070,\n",
              " 'suspensehorror': 150351,\n",
              " 'robin': 130269,\n",
              " 'woods': 171633,\n",
              " 'cultural': 37287,\n",
              " 'snobbery': 142348,\n",
              " 'agree': 6297,\n",
              " 'of': 109094,\n",
              " 'extravagant': 53859,\n",
              " 'props': 122445,\n",
              " 'car': 25299,\n",
              " 'chases': 28073,\n",
              " 'clever': 30445,\n",
              " 'oneliners': 110314,\n",
              " 'dealing': 39129,\n",
              " 'peoplewilliam': 115633,\n",
              " 'macy': 93007,\n",
              " 'unlikely': 162756,\n",
              " 'hitman': 73144,\n",
              " 'works': 171928,\n",
              " 'father': 55579,\n",
              " 'donald': 44824,\n",
              " 'sutherland': 150418,\n",
              " 'dutiful': 47144,\n",
              " 'son': 143247,\n",
              " 'domineering': 44781,\n",
              " 'wants': 167145,\n",
              " 'business': 23716,\n",
              " 'let': 88988,\n",
              " 'him': 72515,\n",
              " 'loves': 91958,\n",
              " 'played': 118116,\n",
              " 'beautifully': 16168,\n",
              " 'david': 38710,\n",
              " 'dorfman': 45149,\n",
              " 'the': 153731,\n",
              " 'ring': 129820,\n",
              " 'starts': 146044,\n",
              " 'love': 91796,\n",
              " 'neve': 105588,\n",
              " 'campbell': 24758,\n",
              " 'girl': 64469,\n",
              " 'meets': 96944,\n",
              " 'waiting': 166807,\n",
              " 'psychiatrists': 122901,\n",
              " 'officeits': 109265,\n",
              " 'juxtaposition': 83601,\n",
              " 'follows': 59404,\n",
              " 'reluctant': 127524,\n",
              " 'killer': 84978,\n",
              " 'balances': 14601,\n",
              " 'boy': 21231,\n",
              " 'youd': 173891,\n",
              " 'expect': 53213,\n",
              " 'william': 170482,\n",
              " 'theres': 154429,\n",
              " 'tooexcellent': 157491,\n",
              " 'job': 82590,\n",
              " 'around': 11395,\n",
              " 'nice': 105854,\n",
              " 'small': 141884,\n",
              " 'drunken': 46469,\n",
              " 'millionaire': 98791,\n",
              " 'playboy': 118104,\n",
              " 'arthur': 11603,\n",
              " 'bach': 13897,\n",
              " 'dudley': 46673,\n",
              " 'moore': 101108,\n",
              " 'grown': 67415,\n",
              " 'childhood': 28731,\n",
              " 'turned': 160259,\n",
              " 'disappointments': 43133,\n",
              " 'followed': 59364,\n",
              " 'including': 77496,\n",
              " 'pale': 113522,\n",
              " 'sequel': 136995,\n",
              " 'doesnt': 44449,\n",
              " 'diminish': 42513,\n",
              " 'charm': 27990,\n",
              " 'appeal': 10506,\n",
              " 'picture': 117066,\n",
              " 'cleverly': 30457,\n",
              " 'written': 172708,\n",
              " 'directed': 42704,\n",
              " 'moores': 101117,\n",
              " 'drunk': 46462,\n",
              " 'forced': 59660,\n",
              " 'parts': 114375,\n",
              " 'wobbly': 171273,\n",
              " 'performs': 115970,\n",
              " 'relish': 127501,\n",
              " 'difficult': 42312,\n",
              " 'resist': 128374,\n",
              " 'gives': 64701,\n",
              " 'oscarwinner': 111525,\n",
              " 'john': 82756,\n",
              " 'gielgud': 64236,\n",
              " 'sly': 141865,\n",
              " 'dryly': 46510,\n",
              " 'amusing': 8607,\n",
              " 'arthurs': 11607,\n",
              " 'valet': 164355,\n",
              " 'hobson': 73283,\n",
              " 'delicious': 40130,\n",
              " 'miraculous': 99377,\n",
              " 'rapport': 125141,\n",
              " 'liza': 90700,\n",
              " 'minnelli': 99214,\n",
              " 'as': 11766,\n",
              " 'bluecollar': 19618,\n",
              " 'loveinterest': 91872,\n",
              " 'sassy': 133150,\n",
              " 'lowkey': 92118,\n",
              " 'brash': 21611,\n",
              " 'deft': 39957,\n",
              " 'lively': 90584,\n",
              " 'shook': 138961,\n",
              " ...}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOL8seK153YS"
      },
      "source": [
        "## **1. Multinomial Naive Baise**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZJo8m9_s9BA",
        "outputId": "26e726b3-8035-4f29-8436-b7ab853bfe0a"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "clf = MultinomialNB()\n",
        "clf.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOZb9Bfps89h",
        "outputId": "c4f5bbbb-85b2-4bf3-ecd4-dee9d43f76e7"
      },
      "source": [
        "print(\"Accuracy Score on Train\", clf.score(X_train, Y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Train 0.9250571428571429\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BlYob97as85D",
        "outputId": "9610668a-2740-4861-f7de-ac7998e30f53"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "predictNB = clf.predict(X_test)\n",
        "print(\"Accuracy Score on Test\", accuracy_score(Y_test, predictNB))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Test 0.8550666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uI0Dy4gSYbmw",
        "outputId": "29cb143b-f94c-4aef-b6a9-57686035ea84"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "predictNB = clf.predict(X_test)\n",
        "print(\"Accuracy Score on Test\", accuracy_score(Y_test, predictNB))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Test 84.6516\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCzlWbv-xUjz",
        "outputId": "75d3b989-f37c-422e-9975-ea28f59698d6"
      },
      "source": [
        "matrix = confusion_matrix(Y_test, predictNB)\n",
        "print(matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6495  999]\n",
            " [1175 6331]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAQPKJoOxa7Z",
        "outputId": "45c405bb-9076-4650-9d3d-dd7b8207bb7e"
      },
      "source": [
        "print(classification_report(Y_test, predictNB))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.85      0.87      0.86      7494\n",
            "    positive       0.86      0.84      0.85      7506\n",
            "\n",
            "    accuracy                           0.86     15000\n",
            "   macro avg       0.86      0.86      0.86     15000\n",
            "weighted avg       0.86      0.86      0.86     15000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "mPlfaulSxav8",
        "outputId": "71db3da0-086f-4174-f41d-11dcc1ff7f88"
      },
      "source": [
        "print(sns.heatmap(matrix/np.sum(matrix), annot=True,fmt='.2%'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.62x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEVF0Hvo5_W3"
      },
      "source": [
        "## **2. Gaussian Naive Bayes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEHg4JQ9Tfrd",
        "outputId": "2f808388-1488-46c5-bf23-b2078f1b9e85"
      },
      "source": [
        "print(X_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(35000, 174914)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxOXv26a6HVn"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "clf = GaussianNB()\n",
        "X_train = X_train.toarray()\n",
        "for i in range (0,3600):\n",
        "  clf.partial_fit(X_train[i*10:(i+1)*10],Y_train[i*10:(i+1)*10])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xmxWr91aYS_y",
        "outputId": "582178a2-9de5-49b6-dff5-39c8ea4e37b5"
      },
      "source": [
        "print(\"Accuracy Score on Train\", clf.score(X_train, Y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Train 91.58065714285715\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tWGM92oYHgg",
        "outputId": "29cb143b-f94c-4aef-b6a9-57686035ea84"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "predictGB = clf.predict(X_test)\n",
        "print(\"Accuracy Score on Test\", accuracy_score(Y_test, predictGB))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Test 84.6516\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kGsxslTYlmh",
        "outputId": "75d3b989-f37c-422e-9975-ea28f59698d6"
      },
      "source": [
        "matrix = confusion_matrix(Y_test, predictGB)\n",
        "print(matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6495  999]\n",
            " [1175 6331]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "DyKFiMYEXl6I",
        "outputId": "f898f6be-4ad5-4336-8c9e-f222c71bfe8a"
      },
      "source": [
        "print(sns.heatmap(matrix/np.sum(matrix), annot=True,fmt='.2%'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.62x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60SUuGc-GOG6"
      },
      "source": [
        "## **3. Support Vector Machine**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNybUhQnGSJx"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "from sklearn.metrics import accuracy_score\n",
        "svmpolynomial = SVC(gamma = 0.1, kernel = 'poly', random_state = 45)\n",
        "svmpolynomial.fit(X_train,Y_train)\n",
        "labels_predict = svmpolynomial.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvm6P4WDGuqN",
        "outputId": "0d0f6f83-0a5e-4897-c47e-0d8f814c07a6"
      },
      "source": [
        "print(accuracy_score(Y_test,labels_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8336\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uk81aYAtGxfm"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFbmtLQ1G4ms",
        "outputId": "98616baf-81ec-44c4-b6f2-bac72f913603"
      },
      "source": [
        "print(\"Confusion Matrix\")\n",
        "matrix = confusion_matrix(Y_test,labels_predict)\n",
        "print(confusion_matrix(Y_test,labels_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[5891 1603]\n",
            " [ 893 6613]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKXlS-7AG5OK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b501afdb-d50c-43f0-80b1-e26fbbab6e61"
      },
      "source": [
        "print(\"CLassification Report\")\n",
        "print(classification_report(Y_test, labels_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classifiction Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.87      0.79      0.83      7494\n",
            "    positive       0.80      0.88      0.84      7506\n",
            "\n",
            "    accuracy                           0.83     15000\n",
            "   macro avg       0.84      0.83      0.83     15000\n",
            "weighted avg       0.84      0.83      0.83     15000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IDkdM4nHNf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "855d5c2e-8659-46b3-8b2e-76620f151f52"
      },
      "source": [
        "print(sns.heatmap(matrix/np.sum(matrix), annot=True,fmt='.2%'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.62x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NBtvD6unIvg"
      },
      "source": [
        "## **4. Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxMksoAQnhCz"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier.fit(X_train, Y_train)\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgjStUIXoxob",
        "outputId": "cd68b2c4-4a00-4d59-9a4a-fa46edc2b5e2"
      },
      "source": [
        "print(accuracy_score(Y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.734\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVzMUF2Dn6Im",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4ec508f-2097-48a2-8005-ea09556725fd"
      },
      "source": [
        "print(\"Confusion Matrix\")\n",
        "matrix = confusion_matrix(Y_test,y_pred)\n",
        "print(confusion_matrix(Y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[5891 1603]\n",
            " [ 893 6613]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KaETt3NCoe9-",
        "outputId": "0be9a570-9cbb-48f8-b15c-1f4bde8fcece"
      },
      "source": [
        "print(\"CLassification Report\")\n",
        "print(classification_report(Y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CLassification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.73      0.74      0.74      7494\n",
            "    positive       0.74      0.73      0.73      7506\n",
            "\n",
            "    accuracy                           0.73     15000\n",
            "   macro avg       0.73      0.73      0.73     15000\n",
            "weighted avg       0.73      0.73      0.73     15000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "i5EV4Dk0pINW",
        "outputId": "0af17f2b-77bc-486e-b927-11c1c8d204d3"
      },
      "source": [
        "print(sns.heatmap(matrix/np.sum(matrix), annot=True,fmt='.2%'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.62x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASgXD8M3pSJp"
      },
      "source": [
        "## **5. Maximum Entropy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EORf5bIfs4G7"
      },
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vb-4kVYEs490",
        "outputId": "7df8c193-5aa2-49b1-93e3-c51c574e299f"
      },
      "source": [
        "maxent = linear_model.LogisticRegression(penalty = 'l2', C=1.0, max_iter=800)\n",
        "maxent.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=800,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Epei91Gks4RW",
        "outputId": "ed4f253f-90ea-4db7-ead7-23d38a78d027"
      },
      "source": [
        "y_pred = maxent.predict(X_test)\n",
        "print(\"Accuracy Score on Test\", accuracy_score(Y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Test 0.8846\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cStBQERvs4N6",
        "outputId": "36c0d449-448b-4fd8-afae-8ac61d936ba6"
      },
      "source": [
        "print(\"Confusion Matrix\")\n",
        "matrix = confusion_matrix(Y_test,y_pred)\n",
        "print(confusion_matrix(Y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[6578  916]\n",
            " [ 815 6691]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKwE4dQXtePO",
        "outputId": "80ba4adc-1d24-47e5-943d-bb6936f4071d"
      },
      "source": [
        "print(\"CLassification Report\")\n",
        "print(classification_report(Y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CLassification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.89      0.88      0.88      7494\n",
            "    positive       0.88      0.89      0.89      7506\n",
            "\n",
            "    accuracy                           0.88     15000\n",
            "   macro avg       0.88      0.88      0.88     15000\n",
            "weighted avg       0.88      0.88      0.88     15000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "JjezkijZteGu",
        "outputId": "0043d0bf-f1a0-48ad-ae68-2e689125e21f"
      },
      "source": [
        "print(sns.heatmap(matrix/np.sum(matrix), annot=True,fmt='.2%'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.62x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ynjDzIKYDntl"
      },
      "source": [
        "## **Hyperparameter Tuning on 2 best model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vSwpH572Xl9m"
      },
      "source": [
        "### **Naive Bayes Optimization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9hZWGKZeEJY"
      },
      "source": [
        "alpha_ = [float(x) for x in np.linspace(start = 1, stop = 5, num = 10)]\n",
        "random_grid = {'alpha': alpha_,\n",
        "               'fit_prior': ['true', 'false']\n",
        "              }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4us88bKFXsko",
        "outputId": "d188ef84-b7ba-4919-c20b-1c0151f84265"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "nb = MultinomialNB()\n",
        "rf_randomcv=RandomizedSearchCV(estimator=nb,param_distributions=random_grid,n_iter=100,cv=3,verbose=2,\n",
        "                               random_state=100,n_jobs=-1)\n",
        "### fit the randomized model\n",
        "rf_randomcv.fit(X_train,Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:281: UserWarning: The total space of parameters 20 is smaller than n_iter=100. Running 20 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  % (grid_size, self.n_iter, grid_size), UserWarning)\n",
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:    5.0s\n",
            "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:    7.8s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3, error_score=nan,\n",
              "                   estimator=MultinomialNB(alpha=1.0, class_prior=None,\n",
              "                                           fit_prior=True),\n",
              "                   iid='deprecated', n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={'alpha': [1.0, 1.4444444444444444,\n",
              "                                                  1.8888888888888888,\n",
              "                                                  2.333333333333333,\n",
              "                                                  2.7777777777777777,\n",
              "                                                  3.2222222222222223,\n",
              "                                                  3.6666666666666665,\n",
              "                                                  4.111111111111111,\n",
              "                                                  4.555555555555555, 5.0],\n",
              "                                        'fit_prior': ['true', 'false']},\n",
              "                   pre_dispatch='2*n_jobs', random_state=100, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gz3_oD65gN1H",
        "outputId": "f9cda284-03bd-4f5d-b5d6-29c39864c01e"
      },
      "source": [
        "rf_randomcv.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'alpha': 2.7777777777777777, 'fit_prior': 'true'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybYaYqbQfJRk"
      },
      "source": [
        "clf = rf_randomcv.best_estimator_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AmJQ9x_XseL",
        "outputId": "259a9e14-7570-4536-a870-911c20b08f06"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "predictNB = clf.predict(X_test)\n",
        "print(\"Accuracy Score on Test\", accuracy_score(Y_test, predictNB))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Test 0.8542666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UuOAmgV4XsM4",
        "outputId": "d57c0621-9969-4f7e-934a-86425b7a2127"
      },
      "source": [
        "matrix = confusion_matrix(Y_test, predictNB)\n",
        "print(matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6473 1021]\n",
            " [1165 6341]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6xj_omvmqlV"
      },
      "source": [
        "### **ME opt**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ul3SuR8Nm-uG"
      },
      "source": [
        "tol_ = [float(x) for x in np.linspace(start = 0.00001, stop = 0.0005, num = 10)]\n",
        "verbose_ = [int(x) for x in np.linspace(start = 1, stop = 10, num = 1)]\n",
        "random_state_ = [int(x) for x in np.linspace(start = 1, stop = 10, num = 1)]\n",
        "C_ = [float(x) for x in np.linspace(start = 0.01, stop = 0.1, num = 10)]\n",
        "max_iter_ = [int(x) for x in np.linspace(start = 100, stop = 800, num = 8)]\n",
        "intercept_scaling_ = [float(x) for x in np.linspace(start = 1, stop = 5, num = 10)]\n",
        "l1_ratio_ = [float(x) for x in np.linspace(start = 0, stop = 1, num = 10)]\n",
        "\n",
        "random_grid1 = {'penalty':['l1', 'l2', 'none'],\n",
        "                'tol':tol_,\n",
        "                'C': C_,\n",
        "                'max_iter': max_iter_,\n",
        "                'intercept_scaling': intercept_scaling_,\n",
        "                'class_weight':'balanced',\n",
        "                'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
        "                'random_state':random_state_,\n",
        "                'multi_class': ['auto', 'ovr', 'multinomial'],\n",
        "                'verbose':verbose_,\n",
        "                'l1_ratio': l1_ratio_\n",
        "              }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LFLAqOwmqIS",
        "outputId": "6300b2b2-aebf-4c73-94e6-f74cce32a78e"
      },
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "me = linear_model.LogisticRegression()\n",
        "rf_randomcv=RandomizedSearchCV(estimator=me,param_distributions=random_grid1,n_iter=100,cv=2,verbose=2,\n",
        "                               random_state=100,n_jobs=-1)\n",
        "### fit the randomized model\n",
        "rf_randomcv.fit(X_train,Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 2 folds for each of 100 candidates, totalling 200 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  1.1min\n",
            "[Parallel(n_jobs=-1)]: Done 158 tasks      | elapsed: 10.1min\n",
            "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed: 13.7min finished\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1501: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
            "  \"(penalty={})\".format(self.penalty))\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "max_iter reached after 18 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   17.5s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=2, error_score=nan,\n",
              "                   estimator=LogisticRegression(C=1.0, class_weight=None,\n",
              "                                                dual=False, fit_intercept=True,\n",
              "                                                intercept_scaling=1,\n",
              "                                                l1_ratio=None, max_iter=100,\n",
              "                                                multi_class='auto', n_jobs=None,\n",
              "                                                penalty='l2', random_state=None,\n",
              "                                                solver='lbfgs', tol=0.0001,\n",
              "                                                verbose=0, warm_start=False),\n",
              "                   iid='deprecated', n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={'C': [...\n",
              "                                        'solver': ['newton-cg', 'lbfgs',\n",
              "                                                   'liblinear', 'sag', 'saga'],\n",
              "                                        'tol': [1e-05, 6.444444444444444e-05,\n",
              "                                                0.00011888888888888889,\n",
              "                                                0.00017333333333333334,\n",
              "                                                0.00022777777777777778,\n",
              "                                                0.00028222222222222223,\n",
              "                                                0.0003366666666666667,\n",
              "                                                0.0003911111111111112,\n",
              "                                                0.0004455555555555556, 0.0005],\n",
              "                                        'verbose': [1]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=100, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDDI1ed5qhc1",
        "outputId": "a85c3985-135b-4cb4-8ce9-31b3aadf154c"
      },
      "source": [
        "rf_randomcv.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.06000000000000001,\n",
              " 'class_weight': 'n',\n",
              " 'intercept_scaling': 4.111111111111111,\n",
              " 'l1_ratio': 0.6666666666666666,\n",
              " 'max_iter': 200,\n",
              " 'multi_class': 'ovr',\n",
              " 'penalty': 'l2',\n",
              " 'random_state': 1,\n",
              " 'solver': 'sag',\n",
              " 'tol': 0.00011888888888888889,\n",
              " 'verbose': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2zg7Yn0qlBK"
      },
      "source": [
        "clf = rf_randomcv.best_estimator_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tlppTQK5qk5p",
        "outputId": "083d6ebc-a6b2-4124-cdd9-529582b72e25"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "predictNB = clf.predict(X_test)\n",
        "print(\"Accuracy Score on Test\", accuracy_score(Y_test, predictNB))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Test 0.8888\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ih6rqRIPqtu0",
        "outputId": "ae41eeff-f12f-413e-8543-2aa8e5d50e02"
      },
      "source": [
        "matrix = confusion_matrix(Y_test, predictNB)\n",
        "print(matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6593  901]\n",
            " [ 767 6739]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCfRgD1GqsVs"
      },
      "source": [
        "# **Using Word2Vec**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHvNCq3cuWhy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65309187-476b-4831-e59e-f27c9ef604ea"
      },
      "source": [
        "!pip install spacy\n",
        "!python -m spacy download en_core_web_lg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (2.2.4)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.23.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.8.2)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy) (56.0.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.19.5)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.1.3)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (4.41.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.0.5)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.4.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (7.4.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.0.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.24.3)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy) (3.10.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy) (3.7.4.3)\n",
            "Collecting en_core_web_lg==2.2.5\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.2.5/en_core_web_lg-2.2.5.tar.gz (827.9MB)\n",
            "\u001b[K     |████████████████████████████████| 827.9MB 1.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_lg==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.19.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (56.0.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.0.5)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.8.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.10.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.7.4.3)\n",
            "Building wheels for collected packages: en-core-web-lg\n",
            "  Building wheel for en-core-web-lg (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for en-core-web-lg: filename=en_core_web_lg-2.2.5-cp37-none-any.whl size=829180944 sha256=398ac96d25a2824be10a5ff19ca43effc0de2f43d6a954f2c28625fd87d29c6e\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-imrswpu1/wheels/2a/c1/a6/fc7a877b1efca9bc6a089d6f506f16d3868408f9ff89f8dbfc\n",
            "Successfully built en-core-web-lg\n",
            "Installing collected packages: en-core-web-lg\n",
            "Successfully installed en-core-web-lg-2.2.5\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_lg')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LY7M9Yuan1XO",
        "outputId": "bdfa5e44-4ebc-475f-8daa-45a2baf27567"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "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": {
        "id": "nWmETiThn1Tq"
      },
      "source": [
        "dataset_imbd = pd.read_csv('/content/drive/MyDrive/IMDB Dataset.csv', na_values='?') \n",
        "dataset_imbd = dataset_imbd.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "ij6keYSyn1QB",
        "outputId": "e34aaef2-ba5c-4884-8c12-8ea0cd93aed3"
      },
      "source": [
        "dataset_imbd.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50000</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>49582</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Loved today's show!!! It was a variety and not...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>5</td>\n",
              "      <td>25000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   review sentiment\n",
              "count                                               50000     50000\n",
              "unique                                              49582         2\n",
              "top     Loved today's show!!! It was a variety and not...  negative\n",
              "freq                                                    5     25000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "c8CiQJn_oB3h",
        "outputId": "2224d1c1-66dc-4268-cebc-4e0078003e7e"
      },
      "source": [
        "print(dataset_imbd.shape)\n",
        "dataset_imbd.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 2)\n"
          ],
          "name": "stdout"
        },
        {
          "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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>One of the other reviewers has mentioned that ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Probably my all-time favorite movie, a story o...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>I sure would like to see a resurrection of a u...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>This show was an amazing, fresh &amp; innovative i...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Encouraged by the positive comments about this...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>If you like original gut wrenching laughter yo...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review sentiment\n",
              "0  One of the other reviewers has mentioned that ...  positive\n",
              "1  A wonderful little production. <br /><br />The...  positive\n",
              "2  I thought this was a wonderful way to spend ti...  positive\n",
              "3  Basically there's a family where a little boy ...  negative\n",
              "4  Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
              "5  Probably my all-time favorite movie, a story o...  positive\n",
              "6  I sure would like to see a resurrection of a u...  positive\n",
              "7  This show was an amazing, fresh & innovative i...  negative\n",
              "8  Encouraged by the positive comments about this...  negative\n",
              "9  If you like original gut wrenching laughter yo...  positive"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_zPuUmHjoBU1"
      },
      "source": [
        "dataset_imbd['review'] = dataset_imbd['review'].apply(lambda x: dataClean(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "g55piWm4MeBF",
        "outputId": "23e27ff4-9797-45e2-8898-412735bd7cf6"
      },
      "source": [
        "dataset_imbd.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>reviewers mentioned watching 1 oz episode youl...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>wonderful little production filming technique ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>thought wonderful way spend time hot summer we...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>basically theres family little boy jake thinks...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>petter matteis love time money visually stunni...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>probably alltime favorite movie story selfless...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>sure like resurrection dated seahunt series te...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>amazing fresh innovative idea 70s aired 7 8 ye...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>encouraged positive comments film looking forw...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>like original gut wrenching laughter like movi...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review sentiment\n",
              "0  reviewers mentioned watching 1 oz episode youl...  positive\n",
              "1  wonderful little production filming technique ...  positive\n",
              "2  thought wonderful way spend time hot summer we...  positive\n",
              "3  basically theres family little boy jake thinks...  negative\n",
              "4  petter matteis love time money visually stunni...  positive\n",
              "5  probably alltime favorite movie story selfless...  positive\n",
              "6  sure like resurrection dated seahunt series te...  positive\n",
              "7  amazing fresh innovative idea 70s aired 7 8 ye...  negative\n",
              "8  encouraged positive comments film looking forw...  negative\n",
              "9  like original gut wrenching laughter like movi...  positive"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ae-3kwftrPE7"
      },
      "source": [
        "import spacy\n",
        "import en_core_web_lg\n",
        "nlp = en_core_web_lg.load()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhaDWCy3rPBj"
      },
      "source": [
        "def get_vec(x):\n",
        "  doc = nlp(x)\n",
        "  vec = doc.vector\n",
        "  return vec"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbMymquarO-Y"
      },
      "source": [
        "dataset_imbd['vec'] = dataset_imbd['review'].apply(lambda x: get_vec(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21XCqSBCqrc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "c750785c-fa37-45af-aa42-412d5f81aea3"
      },
      "source": [
        "dataset_imbd.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>vec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>reviewers mentioned watching 1 oz episode youl...</td>\n",
              "      <td>positive</td>\n",
              "      <td>[-0.08648403, 0.06491846, -0.06025309, -0.0454...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>wonderful little production filming technique ...</td>\n",
              "      <td>positive</td>\n",
              "      <td>[-0.03153765, 0.08566651, -0.041610494, 0.0076...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>thought wonderful way spend time hot summer we...</td>\n",
              "      <td>positive</td>\n",
              "      <td>[-0.016099233, 0.10197374, -0.028735312, -0.10...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>basically theres family little boy jake thinks...</td>\n",
              "      <td>negative</td>\n",
              "      <td>[-0.040155314, -0.019167345, -0.09651553, -0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>petter matteis love time money visually stunni...</td>\n",
              "      <td>positive</td>\n",
              "      <td>[0.0045765135, 0.09241039, -0.108158626, -0.10...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>probably alltime favorite movie story selfless...</td>\n",
              "      <td>positive</td>\n",
              "      <td>[-0.08721831, 0.14089604, -0.052242003, -0.046...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>sure like resurrection dated seahunt series te...</td>\n",
              "      <td>positive</td>\n",
              "      <td>[-0.030027747, 0.10627971, -0.07668429, -0.087...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>amazing fresh innovative idea 70s aired 7 8 ye...</td>\n",
              "      <td>negative</td>\n",
              "      <td>[-0.06726362, 0.14709032, -0.036618788, -0.089...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>encouraged positive comments film looking forw...</td>\n",
              "      <td>negative</td>\n",
              "      <td>[-0.08907281, 0.06900939, -0.060642082, -0.102...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>like original gut wrenching laughter like movi...</td>\n",
              "      <td>positive</td>\n",
              "      <td>[-0.009328372, 0.10151131, -0.1637328, -0.0873...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review  ...                                                vec\n",
              "0  reviewers mentioned watching 1 oz episode youl...  ...  [-0.08648403, 0.06491846, -0.06025309, -0.0454...\n",
              "1  wonderful little production filming technique ...  ...  [-0.03153765, 0.08566651, -0.041610494, 0.0076...\n",
              "2  thought wonderful way spend time hot summer we...  ...  [-0.016099233, 0.10197374, -0.028735312, -0.10...\n",
              "3  basically theres family little boy jake thinks...  ...  [-0.040155314, -0.019167345, -0.09651553, -0.0...\n",
              "4  petter matteis love time money visually stunni...  ...  [0.0045765135, 0.09241039, -0.108158626, -0.10...\n",
              "5  probably alltime favorite movie story selfless...  ...  [-0.08721831, 0.14089604, -0.052242003, -0.046...\n",
              "6  sure like resurrection dated seahunt series te...  ...  [-0.030027747, 0.10627971, -0.07668429, -0.087...\n",
              "7  amazing fresh innovative idea 70s aired 7 8 ye...  ...  [-0.06726362, 0.14709032, -0.036618788, -0.089...\n",
              "8  encouraged positive comments film looking forw...  ...  [-0.08907281, 0.06900939, -0.060642082, -0.102...\n",
              "9  like original gut wrenching laughter like movi...  ...  [-0.009328372, 0.10151131, -0.1637328, -0.0873...\n",
              "\n",
              "[10 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ni88K663qKY_",
        "outputId": "6a6c09a5-3008-46cc-fff9-9ad5326d432f"
      },
      "source": [
        "X = dataset_imbd['vec'].to_numpy()\n",
        "X = X.reshape(-1, 1)\n",
        "X = np.concatenate(np.concatenate(X, axis = 0), axis = 0).reshape(-1, 300)\n",
        "Y = dataset_imbd['sentiment']\n",
        "print(X.shape, Y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 300) (50000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zp-ZcbdoqKI1"
      },
      "source": [
        "#split the dataset \n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.3,random_state = 45, stratify = Y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exC2JRN5934W",
        "outputId": "431614f2-38e2-4a1c-bf9c-aa221c3bebe6"
      },
      "source": [
        "X_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.00503179,  0.05165735, -0.00405053, ..., -0.01077633,\n",
              "         0.04364449,  0.07400434],\n",
              "       [-0.01619452,  0.04124891, -0.10208272, ..., -0.10940965,\n",
              "         0.00814665,  0.0257794 ],\n",
              "       [-0.03833186,  0.08911838, -0.0936385 , ...,  0.02506642,\n",
              "         0.04823593,  0.05245833],\n",
              "       ...,\n",
              "       [-0.05682486, -0.00705045, -0.04737088, ..., -0.17241804,\n",
              "         0.05473878,  0.1137126 ],\n",
              "       [ 0.01256995,  0.08299407,  0.03736777, ..., -0.02662358,\n",
              "        -0.07519144, -0.00937006],\n",
              "       [-0.02495763,  0.1223387 , -0.0619137 , ..., -0.00100804,\n",
              "         0.01360072,  0.05953594]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzPxOX-x7NKI",
        "outputId": "ca07b8df-0bdf-4622-f7a0-3456ce44fedb"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(Y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(35000, 300)\n",
            "(35000,)\n",
            "(15000, 300)\n",
            "(15000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rEafWaiu7QQE",
        "outputId": "93159384-c598-4167-be5c-35bd4cd56c51"
      },
      "source": [
        "get_sentiments(Y_train, 'Train data')\n",
        "get_sentiments(Y_test, 'Test data')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sentiments in Train data:\n",
            " Positive: 17500\n",
            " Negative: 17500\n",
            "Sentiments in Test data:\n",
            " Positive: 7500\n",
            " Negative: 7500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NT0KMqyYBfqQ"
      },
      "source": [
        "##**1.Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XYfm8EjBg3L"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier.fit(X_train, Y_train)\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXyywg-8BrtS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18d1c76c-5789-4aa8-8e5a-7033734e223c"
      },
      "source": [
        "print(accuracy_score(Y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6904\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsXuR71dBzQp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d33704b3-ed97-422c-8a7e-e4ab97bb2d73"
      },
      "source": [
        "print(\"Confusion Matrix\")\n",
        "matrix = confusion_matrix(Y_test,y_pred)\n",
        "print(confusion_matrix(Y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[5166 2334]\n",
            " [2310 5190]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3RiEQVgCGQl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49195f63-d19e-4b5e-fb23-dac0988993b1"
      },
      "source": [
        "print(\"CLassification Report\")\n",
        "print(classification_report(Y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CLassification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.69      0.69      0.69      7500\n",
            "    positive       0.69      0.69      0.69      7500\n",
            "\n",
            "    accuracy                           0.69     15000\n",
            "   macro avg       0.69      0.69      0.69     15000\n",
            "weighted avg       0.69      0.69      0.69     15000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-G8WVuAUCLtA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "96aecfca-0f32-4fb4-c5ce-d5e29720ff40"
      },
      "source": [
        "print(sns.heatmap(matrix/np.sum(matrix), annot=True,fmt='.2%'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.62x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3zDuiNlGOAi"
      },
      "source": [
        "## **2.SVM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgjRE3A9GTv9"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "from sklearn.metrics import accuracy_score\n",
        "svmpolynomial = SVC(gamma = 0.1, kernel = 'poly', random_state = 45)\n",
        "svmpolynomial.fit(X_train,Y_train)\n",
        "labels_predict = svmpolynomial.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kX21F2GyGgNy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de435b56-ac41-43e1-9c98-67f41de78682"
      },
      "source": [
        "print(accuracy_score(Y_test,labels_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.844602\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RI41TF5sGlPp"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alMPWijHGquv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00897274-94dd-4ffa-d8f8-605dc55d070c"
      },
      "source": [
        "print(\"Confusion Matrix\")\n",
        "matrix = confusion_matrix(Y_test,labels_predict)\n",
        "print(confusion_matrix(Y_test,labels_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[6314 1186]\n",
            " [1017 6483]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNOZIKaTGu9y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c3543f7-f683-4f49-c46b-fb361ff10e03"
      },
      "source": [
        "print(\"CLassification Report\")\n",
        "print(classification_report(Y_test, labels_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CLassification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.86      0.84      0.85      7500\n",
            "    positive       0.85      0.86      0.85      7500\n",
            "\n",
            "    accuracy                           0.85     15000\n",
            "   macro avg       0.85      0.85      0.85     15000\n",
            "weighted avg       0.85      0.85      0.85     15000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8709EsdHGycd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "fcb7a1ce-416d-4198-868f-2383601eec9c"
      },
      "source": [
        "print(sns.heatmap(matrix/np.sum(matrix), annot=True,fmt='.2%'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.62x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrcWgAQQCSJW"
      },
      "source": [
        "## **3. Maximum Entorpy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9mf-hs18yDe"
      },
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lQgEgVU19hbo",
        "outputId": "25cddadc-8f31-4e1e-976d-809303e3010e"
      },
      "source": [
        "maxent = linear_model.LogisticRegression(penalty = 'l2', C=1.0, max_iter=800)\n",
        "maxent.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=800,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJUxz8H59hYP",
        "outputId": "7b3162fd-7768-420a-b121-db2cfbe4a6a9"
      },
      "source": [
        "y_pred = maxent.predict(X_test)\n",
        "print(\"Accuracy Score on Test\", accuracy_score(Y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score on Test 0.8509333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrObjJYq8xn6",
        "outputId": "66c1e058-f729-4797-8d98-7c32583d0b59"
      },
      "source": [
        "print(\"Confusion Matrix\")\n",
        "matrix = confusion_matrix(Y_test,y_pred)\n",
        "print(confusion_matrix(Y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "[[6362 1138]\n",
            " [1098 6402]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkvjAiQN9vlJ",
        "outputId": "f4eab0b7-080e-48a9-ffee-45ae8d0bc5aa"
      },
      "source": [
        "print(\"CLassification Report\")\n",
        "print(classification_report(Y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CLassification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.85      0.85      0.85      7500\n",
            "    positive       0.85      0.85      0.85      7500\n",
            "\n",
            "    accuracy                           0.85     15000\n",
            "   macro avg       0.85      0.85      0.85     15000\n",
            "weighted avg       0.85      0.85      0.85     15000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "2z-e9FMv9wKh",
        "outputId": "8f5c2a67-f71c-4156-d1c8-badb859eaf8f"
      },
      "source": [
        "print(sns.heatmap(matrix/np.sum(matrix), annot=True,fmt='.2%'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AxesSubplot(0.125,0.125;0.62x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rWclTKPwg5-7"
      },
      "source": [
        "# **Convolutional Neural Network**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3nYN7aRrC7D"
      },
      "source": [
        "import numpy\n",
        "from keras.datasets import imdb\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM, Convolution1D, Flatten, Dropout\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.preprocessing import sequence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iLgJj8ENrC2q"
      },
      "source": [
        "# Using keras to load the dataset with the top_words\n",
        "top_words = 10000\n",
        "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RXRiHPQrCzo"
      },
      "source": [
        "# Pad the sequence to the same length\n",
        "max_review_length = 1600\n",
        "X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)\n",
        "X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlviWlbwrCwa"
      },
      "source": [
        "# Using embedding from Keras\n",
        "embedding_vecor_length = 300\n",
        "model = Sequential()\n",
        "model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))\n",
        "\n",
        "# Convolutional model (3x conv, flatten, 2x dense)\n",
        "model.add(Convolution1D(64, 3, padding='same'))\n",
        "model.add(Convolution1D(32, 3, padding='same'))\n",
        "model.add(Convolution1D(16, 3, padding='same'))\n",
        "model.add(Flatten())\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(180,activation='sigmoid'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bk9yRnZzrTa3"
      },
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vF3vy66irVPA",
        "outputId": "b317ef31-2335-42f7-e4d3-34679154bb3b"
      },
      "source": [
        "model.fit(X_train, y_train, epochs=3, batch_size=64)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "391/391 [==============================] - 371s 945ms/step - loss: 0.5729 - accuracy: 0.7408\n",
            "Epoch 2/3\n",
            "391/391 [==============================] - 369s 944ms/step - loss: 0.1568 - accuracy: 0.9416\n",
            "Epoch 3/3\n",
            "391/391 [==============================] - 369s 945ms/step - loss: 0.0458 - accuracy: 0.9861\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f6e5549ced0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bPUwKBeg5NN",
        "outputId": "313befa9-1689-4720-c09a-a9b1697cb364"
      },
      "source": [
        "scores = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 85.84%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZ5JU85PMM2q"
      },
      "source": [
        "# **ACCURACY SUMMARY USING BAG OF WORDS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "VLNahJlCMR7b",
        "outputId": "0861200a-fe4b-42ae-db56-cc8e650af64f"
      },
      "source": [
        "import pandas as pd\n",
        "data = {'MODEL':['MNB','GNB', 'DT', 'SVM', 'ME'  , 'CNN'],\n",
        "        'ACCURACY':[85.42,84.6, 73.40, 83.36, 88.46,86.19],\n",
        "        'Precision':[86,85.8,73,84,88,85],\n",
        "        'Recall':[86,85.8,73,84,88,85],\n",
        "        'F-Score':[86,85.6,73,84,88,85]}\n",
        "df = pd.DataFrame(data)\n",
        "df        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MODEL</th>\n",
              "      <th>ACCURACY</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F-Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MNB</td>\n",
              "      <td>85.42</td>\n",
              "      <td>86.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>86.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GNB</td>\n",
              "      <td>84.60</td>\n",
              "      <td>85.8</td>\n",
              "      <td>85.8</td>\n",
              "      <td>85.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DT</td>\n",
              "      <td>73.40</td>\n",
              "      <td>73.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>73.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVM</td>\n",
              "      <td>83.36</td>\n",
              "      <td>84.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>84.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ME</td>\n",
              "      <td>88.46</td>\n",
              "      <td>88.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CNN</td>\n",
              "      <td>86.19</td>\n",
              "      <td>85.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>85.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  MODEL  ACCURACY  Precision  Recall  F-Score\n",
              "0   MNB     85.42       86.0    86.0     86.0\n",
              "1   GNB     84.60       85.8    85.8     85.6\n",
              "2    DT     73.40       73.0    73.0     73.0\n",
              "3   SVM     83.36       84.0    84.0     84.0\n",
              "4    ME     88.46       88.0    88.0     88.0\n",
              "5   CNN     86.19       85.0    85.0     85.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "D4CFKkbWN2CM",
        "outputId": "5300d795-561a-44ac-d1f4-03369e356a8c"
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt \n",
        "name = df['MODEL']\n",
        "price = df['ACCURACY']\n",
        "fig = plt.figure(figsize =(10, 7))\n",
        "plt.bar(name[0:6], price[0:6])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "-REPaUxqOu8Y",
        "outputId": "aee0cc31-cb4b-42f1-967f-a851ef6daa8f"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "x = name\n",
        "y = price \n",
        "plt.plot(x, y) \n",
        "plt.xlabel('Model') \n",
        "plt.ylabel('Accuracies')\n",
        "plt.title('Accuracy Summary')\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "V1IFCatFSg-7",
        "outputId": "479924c8-7c9c-4a85-8b1a-f858e36bb33a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(projection='3d')\n",
        "\n",
        "colors = ['c','r', 'g', 'b', 'm']\n",
        "yticks = [4,3, 2, 1, 0]\n",
        "for c, k in zip(colors, yticks):\n",
        "    # Generate the random data for the y=k 'layer'.\n",
        "    xs = name\n",
        "    ys = price\n",
        "    cs = [c] * len(xs)\n",
        "    cs[0] = 'c'\n",
        "    ax.bar(xs, ys, zs=k, zdir='y', color=cs, alpha=1.0)\n",
        "\n",
        "ax.set_xlabel('Model')\n",
        "ax.set_ylabel('Number of model')\n",
        "ax.set_zlabel('Accuracy')\n",
        "ax.set_yticks(yticks)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gSWV476LjREu"
      },
      "source": [
        "# **Accuracy Summary using WORD2VEC**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "7Ls3MxarjWwB",
        "outputId": "d1df3262-0a4c-43f3-da8d-463fc5fab5b5"
      },
      "source": [
        "import pandas as pd\n",
        "data = {'MODEL':[ 'DT', 'SVM', 'ME'  , 'CNN'],\n",
        "        'ACCURACY':[69.04,84.6, 85.46,86.19],\n",
        "        'Precision':[69.04,84.6, 85.46,85],\n",
        "        'Recall':[69.04,84.6, 85.46,85],\n",
        "        'F-Score':[69.04,84.6, 85.46,85]}\n",
        "df = pd.DataFrame(data)\n",
        "df        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MODEL</th>\n",
              "      <th>ACCURACY</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F-Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DT</td>\n",
              "      <td>69.04</td>\n",
              "      <td>69.04</td>\n",
              "      <td>69.04</td>\n",
              "      <td>69.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SVM</td>\n",
              "      <td>84.60</td>\n",
              "      <td>84.60</td>\n",
              "      <td>84.60</td>\n",
              "      <td>84.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ME</td>\n",
              "      <td>85.46</td>\n",
              "      <td>85.46</td>\n",
              "      <td>85.46</td>\n",
              "      <td>85.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CNN</td>\n",
              "      <td>86.19</td>\n",
              "      <td>85.00</td>\n",
              "      <td>85.00</td>\n",
              "      <td>85.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  MODEL  ACCURACY  Precision  Recall  F-Score\n",
              "0    DT     69.04      69.04   69.04    69.04\n",
              "1   SVM     84.60      84.60   84.60    84.60\n",
              "2    ME     85.46      85.46   85.46    85.46\n",
              "3   CNN     86.19      85.00   85.00    85.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "dssAn71PjkK5",
        "outputId": "99fb3462-aebb-484d-b235-ca038bc740b4"
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt \n",
        "name = df['MODEL']\n",
        "price = df['ACCURACY IN PERCENTAGE']\n",
        "fig = plt.figure(figsize =(10, 7))\n",
        "plt.bar(name[0:5], price[0:5])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3LD0EHz7jpRG",
        "outputId": "1ac3d82a-6536-41d4-9d9c-fee1cda2565c"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "x = name\n",
        "y = price \n",
        "plt.plot(x, y) \n",
        "plt.xlabel('Model') \n",
        "plt.ylabel('Accuracies')\n",
        "plt.title('Accuracy Summary')\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "SY6QAcsfjs8v",
        "outputId": "f55b12ee-abb3-40a1-fd4c-ed186cba0c8f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(projection='3d')\n",
        "\n",
        "colors = ['r', 'g', 'b', 'm']\n",
        "yticks = [3, 2, 1, 0]\n",
        "for c, k in zip(colors, yticks):\n",
        "    # Generate the random data for the y=k 'layer'.\n",
        "    xs = name\n",
        "    ys = price\n",
        "    cs = [c] * len(xs)\n",
        "    cs[0] = 'c'\n",
        "    ax.bar(xs, ys, zs=k, zdir='y', color=cs, alpha=1.0)\n",
        "\n",
        "ax.set_xlabel('Model')\n",
        "ax.set_ylabel('Number of model')\n",
        "ax.set_zlabel('Accuracy')\n",
        "ax.set_yticks(yticks)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}