1_Movie_Genre_Classification.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "##**Import Required Libraries**"
      ],
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      }
    },
    {
      "cell_type": "code",
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix,classification_report"
      ],
      "metadata": {
        "id": "gLsbGcEKAtMf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import itertools\n",
        "import seaborn as sns\n",
        "\n",
        "# from imblearn.over_sampling import SMOTE\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix\n",
        "from wordcloud import WordCloud, STOPWORDS\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from yellowbrick.text import FreqDistVisualizer\n",
        "from mlxtend.feature_selection import ColumnSelector"
      ],
      "metadata": {
        "id": "bnyB-guYAvNT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Load Data**"
      ],
      "metadata": {
        "id": "PwDZ1bHfcvsH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/drive/MyDrive/Movie_Genre_Dataset/train_data.txt', sep=\":::\", header=None, engine='python')"
      ],
      "metadata": {
        "id": "iizVG8ReAzmN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns=['id','Name','Genre','Plot']"
      ],
      "metadata": {
        "id": "7nTg6TvEBCgr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(5)"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                Name       Genre  \\\n",
              "0   1       Oscar et la dame rose (2009)       drama    \n",
              "1   2                       Cupid (1997)    thriller    \n",
              "2   3   Young, Wild and Wonderful (1980)       adult    \n",
              "3   4              The Secret Sin (1915)       drama    \n",
              "4   5             The Unrecovered (2007)       drama    \n",
              "\n",
              "                                                Plot  \n",
              "0   Listening in to a conversation between his do...  \n",
              "1   A brother and sister with a past incestuous r...  \n",
              "2   As the bus empties the students for their fie...  \n",
              "3   To help their unemployed father make ends mee...  \n",
              "4   The film's title refers not only to the un-re...  "
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              "      <th>id</th>\n",
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              "      <th>Genre</th>\n",
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              "      <td>Oscar et la dame rose (2009)</td>\n",
              "      <td>drama</td>\n",
              "      <td>Listening in to a conversation between his do...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Cupid (1997)</td>\n",
              "      <td>thriller</td>\n",
              "      <td>A brother and sister with a past incestuous r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Young, Wild and Wonderful (1980)</td>\n",
              "      <td>adult</td>\n",
              "      <td>As the bus empties the students for their fie...</td>\n",
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              "    <tr>\n",
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              "      <td>The Secret Sin (1915)</td>\n",
              "      <td>drama</td>\n",
              "      <td>To help their unemployed father make ends mee...</td>\n",
              "    </tr>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "##**EDA**"
      ],
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      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "counts = data['Genre'].value_counts()\n",
        "plt.bar(counts.index, counts)\n",
        "plt.xlabel('Genre')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "ySyCvARnBJ7D",
        "outputId": "10ab0f01-cd5d-4f84-9792-c171cf76d9d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYy9XHFeCIoj",
        "outputId": "346736ee-820f-4142-ef50-71b812f06c03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index([' drama ', ' documentary ', ' comedy ', ' short ', ' horror ',\n",
              "       ' thriller ', ' action ', ' western ', ' reality-tv ', ' family ',\n",
              "       ' adventure ', ' music ', ' sci-fi ', ' romance ', ' adult ',\n",
              "       ' animation ', ' crime ', ' sport ', ' talk-show ', ' fantasy ',\n",
              "       ' mystery ', ' musical ', ' biography ', ' history ', ' game-show ',\n",
              "       ' news ', ' war '],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gZ-uYmiuewli"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**As there are too many genre for classification we are taking only top 6 genres which are relatively larger than other generes**"
      ],
      "metadata": {
        "id": "BQscw9fxc9F5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "allowed_genres = [' drama ', ' documentary ', ' comedy ', ' short ', ' horror ',\n",
        "       ' thriller ']\n",
        "filtered_df = data[data['Genre'].isin(allowed_genres)]"
      ],
      "metadata": {
        "id": "UdRVbtqHBOZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Z95gF1sBDJE7",
        "outputId": "6107a45c-c720-428a-ccdc-f0508635e593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                            Name          Genre  \\\n",
              "0   1   Oscar et la dame rose (2009)          drama    \n",
              "1   2                   Cupid (1997)       thriller    \n",
              "3   4          The Secret Sin (1915)          drama    \n",
              "4   5         The Unrecovered (2007)          drama    \n",
              "5   6         Quality Control (2011)    documentary    \n",
              "\n",
              "                                                Plot  \n",
              "0   Listening in to a conversation between his do...  \n",
              "1   A brother and sister with a past incestuous r...  \n",
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              "4   The film's title refers not only to the un-re...  \n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-477eb6bc-8e3d-43a4-b0e9-a5fb181e4ddb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-477eb6bc-8e3d-43a4-b0e9-a5fb181e4ddb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-52cfb702-7c54-46e8-8b17-c695c466a773\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-52cfb702-7c54-46e8-8b17-c695c466a773')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-52cfb702-7c54-46e8-8b17-c695c466a773 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "new_counts = filtered_df['Genre'].value_counts()\n",
        "plt.bar(new_counts.index, new_counts)\n",
        "plt.xlabel('Genre')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "jYT-H5rGCw7H",
        "outputId": "4e59f301-0d60-40ea-fb12-8cd0f94d5b09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Preprocessing Data**"
      ],
      "metadata": {
        "id": "jdij2JFiezl9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.Stripping data**"
      ],
      "metadata": {
        "id": "jIzneop8fcHQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_df['Genre'].map(lambda x:str.strip(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HIuluycDELF",
        "outputId": "4109f91d-ed60-494d-96fe-2d36c4246e6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              drama\n",
              "1           thriller\n",
              "3              drama\n",
              "4              drama\n",
              "5        documentary\n",
              "            ...     \n",
              "17545    documentary\n",
              "17546          short\n",
              "17549          drama\n",
              "17550          drama\n",
              "17551         comedy\n",
              "Name: Genre, Length: 13920, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.Applying NLP**"
      ],
      "metadata": {
        "id": "TuXjN635EGZh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mX-39b0AEBmi",
        "outputId": "7f5b6d16-d123-4546-cdd5-e86abb0aba7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "en_stopwords = stopwords.words('english')\n",
        "en_stopwords+=['one', 'two', 'go','goes', 'get', 'also', 'however', 'tells',''''s''']\n",
        "en_stopwords += list(string.punctuation)\n",
        "\n",
        "# Initialize WordNet Lemmatizer\n",
        "lemmatizer = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "gdePJL6NEbh-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    text=text.lower()   #Lowercasing all text\n",
        "    text=word_tokenize(text)  #Tokenization\n",
        "    text=[w for w in text if w not in en_stopwords]  #Stopwords Filtering\n",
        "    text = [lemmatizer.lemmatize(w) for w in text]\n",
        "    text=' '.join(text)  #Reform sentence\n",
        "    return text"
      ],
      "metadata": {
        "id": "arHP8xInExXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_text=filtered_df['Plot'][3]\n",
        "sample_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "dNL5JOqxFOMA",
        "outputId": "c483878b-8890-4ded-b496-985d1cd72bf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\" To help their unemployed father make ends meet, Edith and her twin sister Grace work as seamstresses . An invalid, Grace falls prey to the temptations of Chinatown opium and becomes an addict, a condition worsened by a misguided physician who prescribes morphine to ease her pain. When their father strikes oil, the family enjoys a new prosperity and the sisters meet the eligible Jack Herron, a fellow oil prospector. To Grace's shock, Jack falls in love with Edith and in her jealousy, Grace tells Jack that Edith, not she, has a drug problem. Hinting that her sister will soon need more morphine, Grace arranges for a dinner in Chinatown with the couple. While her sister and Jack dance, Grace slips away to an opium den. Edith follows her, but ends up in the wrong den and is arrested in an ensuing drug raid. After he bails her out of jail, Edith takes an angry Jack to search for Grace and stumbles across her half-conscious body lying in the street. The truth about the sisters is revealed, and after sending Grace to a sanitarium in the country, Jack and Edith are married.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_text(sample_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "ARqR6Kp-FWyu",
        "outputId": "0a39c65a-2305-4dae-ab71-a77073f95d13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'help unemployed father make end meet edith twin sister grace work seamstress invalid grace fall prey temptation chinatown opium becomes addict condition worsened misguided physician prescribes morphine ease pain father strike oil family enjoys new prosperity sister meet eligible jack herron fellow oil prospector grace shock jack fall love edith jealousy grace jack edith drug problem hinting sister soon need morphine grace arranges dinner chinatown couple sister jack dance grace slip away opium den edith follows end wrong den arrested ensuing drug raid bail jail edith take angry jack search grace stumble across half-conscious body lying street truth sister revealed sending grace sanitarium country jack edith married'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_df['Plot'].map(lambda x:clean_text(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zp8Yp_WRFY6e",
        "outputId": "c8452c69-234f-4110-aead-f939c8fb0e23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        listening conversation doctor parent 10-year-o...\n",
              "1        brother sister past incestuous relationship cu...\n",
              "3        help unemployed father make end meet edith twi...\n",
              "4        film title refers un-recovered body ground zer...\n",
              "5        quality control consists series 16mm single ta...\n",
              "                               ...                        \n",
              "17545    arlington history four part series chronicling...\n",
              "17546    series time lapse intertwine reveal city bustl...\n",
              "17549    anniversary tragic accident four adult reunite...\n",
              "17550    josutty son joseph shoshamma belongs middle cl...\n",
              "17551                                             good-nat\n",
              "Name: Plot, Length: 13920, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Splitting Text**"
      ],
      "metadata": {
        "id": "DG7FuWBGHYOw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test , y_train, y_test = train_test_split(filtered_df['Plot'].values,filtered_df['Genre'].values,test_size=0.25,random_state=123,stratify=filtered_df['Genre'].values)"
      ],
      "metadata": {
        "id": "lBPIATIcFmnN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Vectorising Text so that it can be fed to ML algos**"
      ],
      "metadata": {
        "id": "zOsLDrzGgTja"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Here I am using two vectorisers for accuraacy**"
      ],
      "metadata": {
        "id": "Xp57RBCNhQvL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.Count Vactorizer**"
      ],
      "metadata": {
        "id": "Z2H0XZ6pLm2g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countvec = CountVectorizer(ngram_range = (1,3), max_features = 30000)\n",
        "vec_Xtr = countvec.fit_transform(X_train)\n"
      ],
      "metadata": {
        "id": "ut0OPhUOLrQV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.TF-IDF Vectorizer**"
      ],
      "metadata": {
        "id": "FQGBoa19JEDX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_vectorizer = TfidfVectorizer()\n",
        "\n",
        "tfidf_train_vectors = tfidf_vectorizer.fit_transform(X_train)\n",
        "\n",
        "tfidf_test_vectors = tfidf_vectorizer.transform(X_test)"
      ],
      "metadata": {
        "id": "0376-3vlJHRT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Training Models**"
      ],
      "metadata": {
        "id": "5et_ooxQ44As"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I will be using Dummy Grid in all of the machine learning algos folowing\n",
        "\n",
        "---\n",
        "The Dummy Grid will find best parameters to be passed to ML algo in ortder to get best scores of each Algo\n"
      ],
      "metadata": {
        "id": "04pRPrmt7BXI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Dummy Grid Classifier**"
      ],
      "metadata": {
        "id": "rUfkizUdIkUz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dummy_grid = {\n",
        "    'strategy': ['most_frequent', 'stratified', 'prior', 'uniform']\n",
        "}\n",
        "dummy = DummyClassifier( random_state = 42)\n",
        "dummy_gs = GridSearchCV(dummy, param_grid = dummy_grid, cv = 5)\n",
        "dummy_gs.fit(X_train, y_train)\n",
        "print(dummy_gs.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Weq7ru8AHqUs",
        "outputId": "45ae4249-8a34-4cff-b2a7-367b10aadd9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3153256704980843\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dummy Grid Accuracy: 0.315\n",
        "\n",
        "---\n",
        "This can be considered as our minimum accuracy limit\n"
      ],
      "metadata": {
        "id": "7bVvoyPc5Bsv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Naive Bayes- TFIDF**"
      ],
      "metadata": {
        "id": "5t43EvFbIwXA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_resultstfidf = {}\n",
        "mnb = MultinomialNB()\n",
        "\n",
        "mnb.fit(tfidf_train_vectors,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "NfsHCp_LW4ex",
        "outputId": "04186e75-a862-4080-de28-231d14048751"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = mnb.predict(tfidf_test_vectors)"
      ],
      "metadata": {
        "id": "XlSmxNZUXeG9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuray = accuracy_score(y_pred, y_test)\n",
        "f1 = f1_score(y_pred, y_test, average=\"weighted\")\n",
        "\n",
        "print(\"Accuracy:\", accuray)\n",
        "print(\"F1 Score:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMrMd1KeX5PN",
        "outputId": "5c6fd4fe-b787-4086-b5af-12a56a85ea5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.5474137931034483\n",
            "F1 Score: 0.6672478792069958\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy is increased with significant numbers!!\n",
        "\n",
        "*   Accuracy: 0.5474137931034483\n",
        "*   F1 Score: 0.6672478792069958\n"
      ],
      "metadata": {
        "id": "Wm6LjCKm6A04"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Naive Bayes- Count Vectorizer**"
      ],
      "metadata": {
        "id": "vynbs1Vd6dpJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mnb_grid = {\n",
        "    'alpha': [0, 0.2, 0.5, 0.8, 1.0, 1.0],\n",
        "    'fit_prior' : [True, False],\n",
        "    'class_prior' : [None, [.4, .3, 0.08, .07, .06, .06], [.167, .167, .167, .167, .167, .167]]\n",
        "}\n",
        "mnb = MultinomialNB()\n",
        "mnb_tfidf_gs = GridSearchCV(mnb, param_grid = mnb_grid, cv = 5)\n",
        "mnb_tfidf_gs.fit(tfidf_train_vectors, y_train)\n",
        "print(mnb_tfidf_gs.best_score_)\n",
        "\n",
        "model_resultstfidf['Multinomial Bayes']= mnb_tfidf_gs.best_score_"
      ],
      "metadata": {
        "id": "-K_7oM1nIrFe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a0de9ed-8243-4162-a83d-5ae0eb08e83a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6022030651340997\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy is nominal !\n",
        "\n",
        "*   Accuracy: 0.6022030651340997\n"
      ],
      "metadata": {
        "id": "B3Pybn4Z6nIN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Logistic Regression - TF-IDF**"
      ],
      "metadata": {
        "id": "gXoF2IqZJvRT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_grid = {\n",
        "    'penalty' : ['l1', 'l2', 'none'],\n",
        "    'C' : [1.0, 1e2, 1e4, 1e10],\n",
        "    'class_weight' : ['balanced', None],\n",
        "    'solver' : ['lbfgs', 'liblinear'],\n",
        "    'multi_class' : ['ovr', 'multinomial']\n",
        "}\n",
        "logreg = LogisticRegression(max_iter=1000,random_state = 42)\n",
        "\n",
        "logreg_tfidf_gs = GridSearchCV(logreg, param_grid = lr_grid, cv = 5)\n",
        "logreg_tfidf_gs.fit(tfidf_train_vectors, y_train)\n",
        "print(logreg_tfidf_gs.best_score_)\n",
        "model_resultstfidf['Logistic Regression'] = logreg_tfidf_gs.best_score_"
      ],
      "metadata": {
        "id": "t1ow0uheJmiE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Decision Tree- Count Vectorizer**"
      ],
      "metadata": {
        "id": "txybwclVLN_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_results_cvec = {}\n",
        "grid = {\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_depth': [1, 2, 5, 10],\n",
        "    'min_samples_split': [1, 5, 10, 20]\n",
        "}\n",
        "\n",
        "dt_clf = DecisionTreeClassifier (random_state = 42)\n",
        "gs_cvec_tree = GridSearchCV(dt_clf, param_grid = grid, cv = 5)\n",
        "gs_cvec_tree.fit(vec_Xtr, y_train)\n",
        "\n",
        "print(gs_cvec_tree.best_score_)\n",
        "model_results_cvec['Decision Tree'] = gs_cvec_tree.best_score_"
      ],
      "metadata": {
        "id": "RhzekwAMJ17P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Decision Tree- TF-IDF**"
      ],
      "metadata": {
        "id": "AXIZ4o1MYmAm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_depth': [1, 2, 5, 10],\n",
        "    'min_samples_split': [1, 5, 10, 20]\n",
        "}\n",
        "\n",
        "dt_clf = DecisionTreeClassifier (random_state = 42)\n",
        "gs_tfidf_tree = GridSearchCV(dt_clf, param_grid = grid, cv = 5)\n",
        "gs_tfidf_tree.fit(tfidf_train_vectors, y_train)\n",
        "\n",
        "print(gs_tfidf_tree.best_score_)\n",
        "model_resultstfidf['Decision Tree'] = gs_tfidf_tree.best_score_"
      ],
      "metadata": {
        "id": "cSkdJyJEYvfZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. SGD Classifier- Count Vectorizer**"
      ],
      "metadata": {
        "id": "R6Dd3qjbTdFO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier"
      ],
      "metadata": {
        "id": "OHTst6LBSXZf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {\n",
        "    'loss' : ['hinge', 'log'],\n",
        "    'penalty' : ['l2', 'l1'],\n",
        "    'alpha' : [1e-3, 1e-4, 1e-5],\n",
        "    'max_iter' : [10, 15, 20],\n",
        "    'class_weight' : ['balanced', None]\n",
        "}\n",
        "sgd = SGDClassifier(random_state=42)\n",
        "sgd_cvec_gs = GridSearchCV(sgd, param_grid = grid, cv=5)\n",
        "sgd_cvec_gs.fit(vec_Xtr, y_train)\n",
        "\n",
        "print(sgd_cvec_gs.best_score_)\n",
        "model_results_cvec['SGD'] = sgd_cvec_gs.best_score_"
      ],
      "metadata": {
        "id": "tV7BTuRUTjqi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. SGD Classifier- TF-IDF**"
      ],
      "metadata": {
        "id": "F3VLUhyoUo7f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {\n",
        "    'loss' : ['hinge', 'log'],\n",
        "    'penalty' : ['l2', 'l1'],\n",
        "    'alpha' : [1e-3, 1e-4, 1e-5],\n",
        "    'max_iter' : [20, 25, 30],\n",
        "    'class_weight' : ['balanced', None]\n",
        "}\n",
        "sgd = SGDClassifier(random_state=42)\n",
        "sgd_tfidf_gs = GridSearchCV(sgd, param_grid = grid, cv=5)\n",
        "sgd_tfidf_gs.fit(tfidf_train_vectors, y_train)\n",
        "\n",
        "print(sgd_tfidf_gs.best_score_)\n",
        "model_resultstfidf['SGD'] = sgd_tfidf_gs.best_score_"
      ],
      "metadata": {
        "id": "eBU6mFOzTozK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_results_cvec=sorted(model_results_cvec.items())\n",
        "model_resultstfidf=sorted(model_resultstfidf.items())\n"
      ],
      "metadata": {
        "id": "TnxgtxeHZxDW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_results_cvec=dict(model_results_cvec)\n",
        "model_resultstfidf=dict(model_resultstfidf)"
      ],
      "metadata": {
        "id": "MV0CboE-bJl5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Comparison of the Different Models and their Scores**"
      ],
      "metadata": {
        "id": "yLNaytC3WHF3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_resultstfidf)\n",
        "print('\\n',model_results_cvec)"
      ],
      "metadata": {
        "id": "W3ImKsaYXXC0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "tfidf_model_results = pd.DataFrame(model_resultstfidf.items(), columns=['model', 'accuracy_score'])\n",
        "count_vec_results = pd.DataFrame(model_results_cvec.items(), columns=['model', 'accuracy_score'])\n",
        "\n",
        "y1 = tfidf_model_results['accuracy_score']\n",
        "y2 = count_vec_results['accuracy_score']\n",
        "\n",
        "fig, ax = plt.subplots(figsize = (10,8))\n",
        "ind = np.arange(len(tfidf_model_results))\n",
        "\n",
        "width = 0.3\n",
        "plt.bar(ind, y1, width, color = 'orange')\n",
        "plt.bar(ind - width, y2, width,color = 'green')\n",
        "\n",
        "plt.title('Model Comparison')\n",
        "plt.xlabel('model')\n",
        "plt.ylabel('accuracy_score')\n",
        "plt.xticks(np.arange(3), ['multinomial bayes','decision tree', 'SGD'])\n",
        "plt.legend(['tfidf_model_results', 'count_vec_results'])"
      ],
      "metadata": {
        "id": "d3GHr1N6VOxU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zUtyTHtMWdwk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}