JantahackIndependenceDay.ipynb

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      "name": "JantahackIndependenceDay.ipynb",
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        "<a href=\"https://colab.research.google.com/github/ria3999/Machine_Learning-Portfolio/blob/master/JantahackIndependenceDay.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "tnNwl3p0O8Jd"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dt9QZgPyPBCs"
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      "source": [
        "train=pd.read_csv('/content/train.csv')\n",
        "test=pd.read_csv('/content/test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Reconstructing Subject-Specific Effect Maps</td>\n",
              "      <td>Predictive models allow subject-specific inf...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Rotation Invariance Neural Network</td>\n",
              "      <td>Rotation invariance and translation invarian...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Spherical polyharmonics and Poisson kernels fo...</td>\n",
              "      <td>We introduce and develop the notion of spher...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>A finite element approximation for the stochas...</td>\n",
              "      <td>The stochastic Landau--Lifshitz--Gilbert (LL...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Comparative study of Discrete Wavelet Transfor...</td>\n",
              "      <td>Fourier-transform infra-red (FTIR) spectra o...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "   ID  ... Quantitative Finance\n",
              "0   1  ...                    0\n",
              "1   2  ...                    0\n",
              "2   3  ...                    0\n",
              "3   4  ...                    0\n",
              "4   5  ...                    0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
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            "tags": []
          },
          "execution_count": 135
        }
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          "base_uri": "https://localhost:8080/",
          "height": 202
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      },
      "source": [
        "test.head()"
      ],
      "execution_count": null,
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          "output_type": "execute_result",
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              "      <td>20973</td>\n",
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              "      <th>1</th>\n",
              "      <td>20974</td>\n",
              "      <td>Laboratory mid-IR spectra of equilibrated and ...</td>\n",
              "      <td>Meteorites contain minerals from Solar Syste...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20975</td>\n",
              "      <td>Case For Static AMSDU Aggregation in WLANs</td>\n",
              "      <td>Frame aggregation is a mechanism by which mu...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20976</td>\n",
              "      <td>The $Gaia$-ESO Survey: the inner disk intermed...</td>\n",
              "      <td>Milky Way open clusters are very diverse in ...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20977</td>\n",
              "      <td>Witness-Functions versus Interpretation-Functi...</td>\n",
              "      <td>Proving that a cryptographic protocol is cor...</td>\n",
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              "      ID  ...                                           ABSTRACT\n",
              "0  20973  ...    We present novel understandings of the Gamma...\n",
              "1  20974  ...    Meteorites contain minerals from Solar Syste...\n",
              "2  20975  ...    Frame aggregation is a mechanism by which mu...\n",
              "3  20976  ...    Milky Way open clusters are very diverse in ...\n",
              "4  20977  ...    Proving that a cryptographic protocol is cor...\n",
              "\n",
              "[5 rows x 3 columns]"
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          "base_uri": "https://localhost:8080/"
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      },
      "source": [
        "print('Train shape:',train.shape)\n",
        "print('Test shape:',test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape: (20972, 9)\n",
            "Test shape: (8989, 3)\n"
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        "train.isnull().sum()"
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              "ID                      0\n",
              "TITLE                   0\n",
              "ABSTRACT                0\n",
              "Computer Science        0\n",
              "Physics                 0\n",
              "Mathematics             0\n",
              "Statistics              0\n",
              "Quantitative Biology    0\n",
              "Quantitative Finance    0\n",
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              "ID          0\n",
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      "source": [
        "l = ['Computer Science', 'Physics', 'Mathematics', 'Statistics', 'Quantitative Biology', 'Quantitative Finance']\n",
        "\n",
        "for col in l:\n",
        "  print(col,':\\n',train[col].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Computer Science :\n",
            " 0    12378\n",
            "1     8594\n",
            "Name: Computer Science, dtype: int64\n",
            "Physics :\n",
            " 0    14959\n",
            "1     6013\n",
            "Name: Physics, dtype: int64\n",
            "Mathematics :\n",
            " 0    15354\n",
            "1     5618\n",
            "Name: Mathematics, dtype: int64\n",
            "Statistics :\n",
            " 0    15766\n",
            "1     5206\n",
            "Name: Statistics, dtype: int64\n",
            "Quantitative Biology :\n",
            " 0    20385\n",
            "1      587\n",
            "Name: Quantitative Biology, dtype: int64\n",
            "Quantitative Finance :\n",
            " 0    20723\n",
            "1      249\n",
            "Name: Quantitative Finance, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
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    {
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      "metadata": {
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        "train['combined'] = train['TITLE']+' '+train['ABSTRACT']\n",
        "test['combined'] = test['TITLE']+' '+test['ABSTRACT']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WV7YTVfnQSlO"
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      "source": [
        "X = train.loc[:,['combined']]\n",
        "y = train.loc[:,l]"
      ],
      "execution_count": null,
      "outputs": []
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              "      <td>Rotation Invariance Neural Network   Rotation ...</td>\n",
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              "      <th>2</th>\n",
              "      <td>Spherical polyharmonics and Poisson kernels fo...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A finite element approximation for the stochas...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Comparative study of Discrete Wavelet Transfor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>20967</th>\n",
              "      <td>Contemporary machine learning: a guide for pra...</td>\n",
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              "    <tr>\n",
              "      <th>20968</th>\n",
              "      <td>Uniform diamond coatings on WC-Co hard alloy c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20969</th>\n",
              "      <td>Analysing Soccer Games with Clustering and Con...</td>\n",
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              "    <tr>\n",
              "      <th>20970</th>\n",
              "      <td>On the Efficient Simulation of the Left-Tail o...</td>\n",
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              "    <tr>\n",
              "      <th>20971</th>\n",
              "      <td>Why optional stopping is a problem for Bayesia...</td>\n",
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              "                                                combined\n",
              "0      Reconstructing Subject-Specific Effect Maps   ...\n",
              "1      Rotation Invariance Neural Network   Rotation ...\n",
              "2      Spherical polyharmonics and Poisson kernels fo...\n",
              "3      A finite element approximation for the stochas...\n",
              "4      Comparative study of Discrete Wavelet Transfor...\n",
              "...                                                  ...\n",
              "20967  Contemporary machine learning: a guide for pra...\n",
              "20968  Uniform diamond coatings on WC-Co hard alloy c...\n",
              "20969  Analysing Soccer Games with Clustering and Con...\n",
              "20970  On the Efficient Simulation of the Left-Tail o...\n",
              "20971  Why optional stopping is a problem for Bayesia...\n",
              "\n",
              "[20972 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
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              "      <th>20971</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20972 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Computer Science  Physics  ...  Quantitative Biology  Quantitative Finance\n",
              "0                     1        0  ...                     0                     0\n",
              "1                     1        0  ...                     0                     0\n",
              "2                     0        0  ...                     0                     0\n",
              "3                     0        0  ...                     0                     0\n",
              "4                     1        0  ...                     0                     0\n",
              "...                 ...      ...  ...                   ...                   ...\n",
              "20967                 1        1  ...                     0                     0\n",
              "20968                 0        1  ...                     0                     0\n",
              "20969                 1        0  ...                     0                     0\n",
              "20970                 0        0  ...                     0                     0\n",
              "20971                 0        0  ...                     0                     0\n",
              "\n",
              "[20972 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyjokgSbR7S1",
        "outputId": "50fe54b1-cdf5-464a-d2db-95cab4130d75",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'TITLE', 'ABSTRACT', 'Computer Science', 'Physics', 'Mathematics',\n",
              "       'Statistics', 'Quantitative Biology', 'Quantitative Finance',\n",
              "       'combined'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-j2GqVz-SR_x",
        "outputId": "8696b4a8-13da-4a54-f1df-54e650a10623",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'TITLE', 'ABSTRACT', 'combined'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMfxomNNSUKi",
        "outputId": "8bdb1c22-4bb3-4a16-d537-b828dbadff8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "X.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['combined'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfXetMIHSYT4",
        "outputId": "c198d560-74db-46e3-de54-1f6e07cf43e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "\n",
        "y.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Computer Science', 'Physics', 'Mathematics', 'Statistics',\n",
              "       'Quantitative Biology', 'Quantitative Finance'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DlYMJcNQUX3"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=555, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwFSROKGQWXr",
        "outputId": "5f89507d-298f-42c4-e3b8-060098940287",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(X_train.shape, X_test.shape)\n",
        "print(y_train.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(18874, 1) (2098, 1)\n",
            "(18874, 6) (2098, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcIsECi1Qa5V"
      },
      "source": [
        "y_test.reset_index(drop=True,inplace=True)\n",
        "X_test.reset_index(drop=True,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ifqnvp-Qier"
      },
      "source": [
        "y1 = np.array(y_train)\n",
        "y2 = np.array(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SguifXPzQk75"
      },
      "source": [
        "X_train.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
        "X_test.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
        "test.replace('[^a-zA-Z]',' ', regex=True, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCKgsgFYQqen"
      },
      "source": [
        "for index in X_train.columns:\n",
        "  X_train[index] = X_train[index].str.lower()\n",
        "for index in X_test.columns:\n",
        "  X_test[index] = X_test[index].str.lower()\n",
        "\n",
        "for index in test.columns:\n",
        "  test[index] = test[index].astype(str).str.lower()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGszg_ICsVpm",
        "outputId": "4a8f234c-ebad-4274-fc15-af6894dfefba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'TITLE', 'ABSTRACT', 'combined'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpcA4U0BsZtS"
      },
      "source": [
        "#test.drop(['ID', 'TITLE', 'ABSTRACT'],axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTV8rlessjiZ",
        "outputId": "7965de5d-0a65-47a9-920a-6068e79033ce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'TITLE', 'ABSTRACT', 'combined'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwQM5iMGQtgd"
      },
      "source": [
        "X_train['combined'] = X_train['combined'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
        "X_test['combined'] = X_test['combined'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
        "test['combined']=test['combined'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWOUU2v1Qy0Z"
      },
      "source": [
        "X_train = X_train.replace('\\s+', ' ', regex=True)\n",
        "X_test = X_test.replace('\\s+', ' ', regex=True)\n",
        "test = test.replace('\\s+', ' ', regex=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BK0hP5KnQ2r3",
        "outputId": "1b0f2164-0041-4f75-d55f-b5877ac53b41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "from nltk import sent_tokenize, word_tokenize\n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3qtlEBjVQ5K9"
      },
      "source": [
        "stop_words = set(stopwords.words('english')) \n",
        "len(stop_words)\n",
        "X_train['combined'] = X_train['combined'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))\n",
        "X_test['combined'] = X_test['combined'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkU92Y_lsxVQ"
      },
      "source": [
        "test['combined'] = test['combined'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3bDH_nUQ9z4"
      },
      "source": [
        "train_lines = []\n",
        "for row in range(0,X_train.shape[0]):\n",
        "  train_lines.append(' '.join(str(x) for x in X_train.iloc[row,:]))\n",
        "\n",
        "test_lines = []\n",
        "for row in range(0,X_test.shape[0]):\n",
        "  test_lines.append(' '.join(str(x) for x in X_test.iloc[row,:]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mH1DXf0Ys4mT"
      },
      "source": [
        "test_Set=[]\n",
        "for row in range(0,test.shape[0]):\n",
        "  test_Set.append(' '.join(str(x) for x in test.iloc[row,:]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XMLyRdYTBzy"
      },
      "source": [
        "X.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
        "\n",
        "for index in X.columns:\n",
        "  X[index] = X[index].str.lower()\n",
        "\n",
        "X['combined'] = X['combined'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
        "\n",
        "#X['combined'] = X['combined'].apply(lambda x: decontracted(x))\n",
        "\n",
        "X = X.replace('\\s+', ' ', regex=True)\n",
        "\n",
        "X['combined'] = X['combined'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))\n",
        "\n",
        "#X['combined'] = X['combined'].apply(get_adjectives)\n",
        "\n",
        "X_lines = []\n",
        "for row in range(0,X.shape[0]):\n",
        "  X_lines.append(' '.join(str(x) for x in X.iloc[row,:]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pjNga62RA77"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "countvector = CountVectorizer(ngram_range=(1,2))\n",
        "X_train_cv = countvector.fit_transform(train_lines)\n",
        "X_test_cv = countvector.transform(test_lines)\n",
        "test_cv=countvector.transform(test_Set)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6zSTA1ZiRVBQ"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\n",
        "\n",
        "tfidfvector = TfidfTransformer()\n",
        "X_train_tf = tfidfvector.fit_transform(X_train_cv)\n",
        "X_test_tf = tfidfvector.fit_transform(X_test_cv)\n",
        "test_tf=tfidfvector.fit_transform(test_cv)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ONBFceuRW6J"
      },
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.multioutput import MultiOutputClassifier\n",
        "\n",
        "model = LinearSVC(class_weight='balanced',C=0.5)\n",
        "models = MultiOutputClassifier(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVSWkazgRZqC"
      },
      "source": [
        "X_cv = countvector.transform(X_lines)\n",
        "\n",
        "X_tf = tfidfvector.fit_transform(X_cv)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZaUcQ7s_thtl"
      },
      "source": [
        "X_cv1=countvector.transform(test_Set)\n",
        "X_tf1=tfidfvector.fit_transform(X_cv1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzgk0K3rRba-",
        "outputId": "8a7a1551-865d-4dd4-a11e-18574a81cb7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "models.fit(X_tf, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiOutputClassifier(estimator=LinearSVC(C=0.5, class_weight='balanced',\n",
              "                                          dual=True, fit_intercept=True,\n",
              "                                          intercept_scaling=1,\n",
              "                                          loss='squared_hinge', max_iter=1000,\n",
              "                                          multi_class='ovr', penalty='l2',\n",
              "                                          random_state=None, tol=0.0001,\n",
              "                                          verbose=0),\n",
              "                      n_jobs=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qpx_OOZuEJG",
        "outputId": "ea920379-3776-4995-8d16-a7bd1a8831ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "preds = models.predict(X_tf1)\n",
        "preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 1, 0, 0],\n",
              "       [0, 1, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, 1, 1, 0],\n",
              "       [0, 0, 0, 1, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ka5YTwe4RyME",
        "outputId": "646ec562-0ae3-4ada-bc1a-d4177fc14f20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "\n",
        "preds1 = models.predict(X_test_tf)\n",
        "preds1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0, 0, 0, 0],\n",
              "       [0, 1, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 1, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, 1, 0, 0],\n",
              "       [1, 0, 0, 1, 0, 0],\n",
              "       [0, 0, 0, 1, 1, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHm-P8rLSCOl",
        "outputId": "3b5b3e93-cc77-425f-af22-6ae6f1814c3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "#print(confusion_matrix(y2,preds))\n",
        "print(classification_report(y2,preds1))\n",
        "print(accuracy_score(y2,preds1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      1.00      0.96       833\n",
            "           1       0.97      1.00      0.98       623\n",
            "           2       0.95      0.99      0.97       573\n",
            "           3       0.86      1.00      0.92       525\n",
            "           4       0.78      1.00      0.88        51\n",
            "           5       0.92      1.00      0.96        23\n",
            "\n",
            "   micro avg       0.92      1.00      0.96      2628\n",
            "   macro avg       0.90      1.00      0.95      2628\n",
            "weighted avg       0.92      1.00      0.96      2628\n",
            " samples avg       0.95      1.00      0.97      2628\n",
            "\n",
            "0.8913250714966635\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KH_UuejNSFhe",
        "outputId": "894e5da4-32d4-47d1-c819-4f276ee2801c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 1, 0, 0],\n",
              "       [0, 1, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, 1, 1, 0],\n",
              "       [0, 0, 0, 1, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6QRosJYu2Ml",
        "outputId": "983a159b-25a9-441a-a141-acdf2a0f3e70",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'TITLE', 'ABSTRACT', 'combined'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-QcdTbbuXh6"
      },
      "source": [
        "submit = pd.DataFrame({'ID': test.ID, 'Computer Science': preds[:,0],'Physics':preds[:,1],'Mathematics':preds[:,2],'Statistics':preds[:,3],'Quantitative Biology':preds[:,4],'Quantitative Finance':preds[:,5]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBUc8LvEuo4Z",
        "outputId": "78b925f3-a75e-4288-8144-f40f1886a416",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "submit.head()"
      ],
      "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>ID</th>\n",
              "      <th>Computer Science</th>\n",
              "      <th>Physics</th>\n",
              "      <th>Mathematics</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>Quantitative Biology</th>\n",
              "      <th>Quantitative Finance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20973</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20975</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20976</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20977</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ID  Computer Science  ...  Quantitative Biology  Quantitative Finance\n",
              "0  20973                 0  ...                     0                     0\n",
              "1  20974                 0  ...                     0                     0\n",
              "2  20975                 1  ...                     0                     0\n",
              "3  20976                 0  ...                     0                     0\n",
              "4  20977                 1  ...                     0                     0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SO6i5SalwxpG"
      },
      "source": [
        "submit.to_csv('submission_janta.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdMCk2JmwRB5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}