IBM_StackOverflow_Challenge_final.ipynb

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        "!python --version"
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            "Python 3.10.12\n"
          ]
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      "cell_type": "markdown",
      "source": [
        "## Loading dataset"
      ],
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      }
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        "id": "vULeRHxUa5_b"
      },
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      "source": [
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = '/content/'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d stackoverflow/stacksample"
      ],
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        },
        "id": "rVADiHPAj8jt",
        "outputId": "72da4be0-610d-49a4-949f-b3e7d9f92efb"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /content/kaggle.json'\n",
            "Downloading stacksample.zip to /content\n",
            "100% 1.11G/1.11G [00:41<00:00, 31.8MB/s]\n",
            "100% 1.11G/1.11G [00:41<00:00, 28.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile('/content/stacksample.zip', 'r') as zip_ref:\n",
        "    zip_ref.extractall('/content/')"
      ],
      "metadata": {
        "id": "413Kqgf8j_Nz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "z7sTxF1jkKLg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Qdf = pd.read_csv('/content/Questions.csv', encoding = \"ISO-8859-1\")\n",
        "Tdf = pd.read_csv('/content/Tags.csv', encoding = \"ISO-8859-1\")"
      ],
      "metadata": {
        "id": "PsfmXZFqkfGa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Qdf1 = Qdf.copy()\n",
        "Tdf1 = Tdf.copy()"
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      "metadata": {
        "id": "P6tXy1C_lSiF"
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      "execution_count": null,
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        "Qdf1.head()"
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              "   Id             Tag\n",
              "0  80            flex\n",
              "1  80  actionscript-3\n",
              "2  80             air\n",
              "3  90             svn\n",
              "4  90     tortoisesvn"
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              "</svg>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\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-bd2a8dd4-cb06-4d05-8474-2562845b40de 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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Qdf1.shape , Tdf1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9yHEuBgWl1OK",
        "outputId": "5a4f26d8-5298-488d-eb1b-c1cef0f4928d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1264216, 7), (3750994, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "Qdf1.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ta-pnuBl9Ki",
        "outputId": "5e803f97-6100-4e6e-9c8f-f54cb6fe2542"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'OwnerUserId', 'CreationDate', 'ClosedDate', 'Score', 'Title',\n",
              "       'Body'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "Qdf1 = Qdf1.drop(['OwnerUserId', 'CreationDate', 'ClosedDate'],axis=1)"
      ],
      "metadata": {
        "id": "uDtxul4DmEYK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Tdf1['Tag'] = Tdf1['Tag'].astype(str)\n",
        "grouped_tags = Tdf1.groupby(\"Id\")['Tag'].apply(lambda tags: ' '.join(tags))\n",
        "grouped_tags.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9P7hedu6mWVT",
        "outputId": "37e37001-01c8-4115-d928-0db8de6159a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id\n",
              "80                            flex actionscript-3 air\n",
              "90       svn tortoisesvn branch branching-and-merging\n",
              "120                               sql asp.net sitemap\n",
              "180    algorithm language-agnostic colors color-space\n",
              "260           c# .net scripting compiler-construction\n",
              "Name: Tag, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_tags.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "A1S-8nOzmjWS",
        "outputId": "afbf12dc-e6e2-4420-c42b-6209c8c064a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Id                                             Tag\n",
              "0              80                         flex actionscript-3 air\n",
              "1              90    svn tortoisesvn branch branching-and-merging\n",
              "2             120                             sql asp.net sitemap\n",
              "3             180  algorithm language-agnostic colors color-space\n",
              "4             260         c# .net scripting compiler-construction\n",
              "...           ...                                             ...\n",
              "1264211  40143210                                   php .htaccess\n",
              "1264212  40143300                                 google-bigquery\n",
              "1264213  40143340                          android android-studio\n",
              "1264214  40143360                               javascript vue.js\n",
              "1264215  40143380                                 npm mocha babel\n",
              "\n",
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              "        const element = document.querySelector('#df-ef9ace0d-9319-4a0d-b7b8-91a6068caa15');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\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",
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              "    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-b84f0f52-687e-42e0-81f6-d61aaff46625 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 13
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_tags_final = pd.DataFrame({'Id':grouped_tags.index, 'Tags':grouped_tags.values})\n",
        "grouped_tags_final.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7ssoRgtnmnlm",
        "outputId": "f547e930-29a8-464d-ed3f-d3341ec9ac89"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Id                                            Tags\n",
              "0   80                         flex actionscript-3 air\n",
              "1   90    svn tortoisesvn branch branching-and-merging\n",
              "2  120                             sql asp.net sitemap\n",
              "3  180  algorithm language-agnostic colors color-space\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e9344864-aaf8-49c4-ab37-8de18952a463');\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-b911c6f7-71b6-46d1-acb2-7bb3bcc39938\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b911c6f7-71b6-46d1-acb2-7bb3bcc39938')\"\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-b911c6f7-71b6-46d1-acb2-7bb3bcc39938 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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "ohW9ixatvNYY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine all tags into a single string\n",
        "all_tags = ' '.join(grouped_tags_final['Tags'])"
      ],
      "metadata": {
        "id": "t_-taPE_vPls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA"
      ],
      "metadata": {
        "id": "hVZLbC9E_DtK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(all_tags)\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "JlGSMfNhvfQK",
        "outputId": "2769ccef-8213-4a06-a987-da259153fb1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_tags = grouped_tags_final['Tags'].str.split(', ').explode()\n",
        "tag_counts = all_tags.value_counts()"
      ],
      "metadata": {
        "id": "SAkxz3nhzjZU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "tag_counts[:20].plot(kind='bar', color='black')\n",
        "plt.title('Top 20 Tag Frequencies')\n",
        "plt.xlabel('Tag')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "9MiobpHQ0LIM",
        "outputId": "134cc6c7-f0f9-4d72-92b4-d8480741862b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "JQ2JqA1Z_GsK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = Qdf1.merge(grouped_tags_final, on='Id')\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xOD7t9v1m08q",
        "outputId": "e4cfafc3-860f-4050-b59b-9099743e1971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Id  Score                                              Title  \\\n",
              "0   80     26  SQLStatement.execute() - multiple queries in o...   \n",
              "1   90    144  Good branching and merging tutorials for Torto...   \n",
              "2  120     21                                  ASP.NET Site Maps   \n",
              "3  180     53                 Function for creating color wheels   \n",
              "4  260     49  Adding scripting functionality to .NET applica...   \n",
              "\n",
              "                                                Body  \\\n",
              "0  <p>I've written a database generation script i...   \n",
              "1  <p>Are there any really good tutorials explain...   \n",
              "2  <p>Has anyone got experience creating <strong>...   \n",
              "3  <p>This is something I've pseudo-solved many t...   \n",
              "4  <p>I have a little game written in C#. It uses...   \n",
              "\n",
              "                                             Tags  \n",
              "0                         flex actionscript-3 air  \n",
              "1    svn tortoisesvn branch branching-and-merging  \n",
              "2                             sql asp.net sitemap  \n",
              "3  algorithm language-agnostic colors color-space  \n",
              "4         c# .net scripting compiler-construction  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb1cac11-b399-4ab6-8741-20067f140324\" class=\"colab-df-container\">\n",
              "    <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>Score</th>\n",
              "      <th>Title</th>\n",
              "      <th>Body</th>\n",
              "      <th>Tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>80</td>\n",
              "      <td>26</td>\n",
              "      <td>SQLStatement.execute() - multiple queries in o...</td>\n",
              "      <td>&lt;p&gt;I've written a database generation script i...</td>\n",
              "      <td>flex actionscript-3 air</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>90</td>\n",
              "      <td>144</td>\n",
              "      <td>Good branching and merging tutorials for Torto...</td>\n",
              "      <td>&lt;p&gt;Are there any really good tutorials explain...</td>\n",
              "      <td>svn tortoisesvn branch branching-and-merging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>21</td>\n",
              "      <td>ASP.NET Site Maps</td>\n",
              "      <td>&lt;p&gt;Has anyone got experience creating &lt;strong&gt;...</td>\n",
              "      <td>sql asp.net sitemap</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>180</td>\n",
              "      <td>53</td>\n",
              "      <td>Function for creating color wheels</td>\n",
              "      <td>&lt;p&gt;This is something I've pseudo-solved many t...</td>\n",
              "      <td>algorithm language-agnostic colors color-space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>260</td>\n",
              "      <td>49</td>\n",
              "      <td>Adding scripting functionality to .NET applica...</td>\n",
              "      <td>&lt;p&gt;I have a little game written in C#. It uses...</td>\n",
              "      <td>c# .net scripting compiler-construction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb1cac11-b399-4ab6-8741-20067f140324')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    background-color: #434B5C;\n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = df[df['Score']>8]\n",
        "new_df.info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VscHb2ogm6s2",
        "outputId": "3efa8382-ddc1-46d1-c61c-3297604147fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of                Id  Score                                              Title  \\\n",
              "0              80     26  SQLStatement.execute() - multiple queries in o...   \n",
              "1              90    144  Good branching and merging tutorials for Torto...   \n",
              "2             120     21                                  ASP.NET Site Maps   \n",
              "3             180     53                 Function for creating color wheels   \n",
              "4             260     49  Adding scripting functionality to .NET applica...   \n",
              "...           ...    ...                                                ...   \n",
              "1257356  39981800     11  How to achieve test isolation with Symfony for...   \n",
              "1260386  40053550      9  The compiler suggests I add a 'static lifetime...   \n",
              "1261242  40073530     16  Why doesn't ${@:-1} return the last element of...   \n",
              "1262332  40098470     19  If multiple classes have a static variable in ...   \n",
              "1262668  40106430     14   Using lambda in default initializer gcc vs clang   \n",
              "\n",
              "                                                      Body  \\\n",
              "0        <p>I've written a database generation script i...   \n",
              "1        <p>Are there any really good tutorials explain...   \n",
              "2        <p>Has anyone got experience creating <strong>...   \n",
              "3        <p>This is something I've pseudo-solved many t...   \n",
              "4        <p>I have a little game written in C#. It uses...   \n",
              "...                                                    ...   \n",
              "1257356  <p><strong><em>Note:</strong> This is Symfony ...   \n",
              "1260386  <p>Sorry for the vague title, but I'm fairly n...   \n",
              "1261242  <p><em>I thought to post up a Q&amp;A on this ...   \n",
              "1262332  <p>I have the following example code:</p>\\n\\n<...   \n",
              "1262668  <pre><code>#include &lt;cassert&gt;\\n\\nint mai...   \n",
              "\n",
              "                                                      Tags  \n",
              "0                                  flex actionscript-3 air  \n",
              "1             svn tortoisesvn branch branching-and-merging  \n",
              "2                                      sql asp.net sitemap  \n",
              "3           algorithm language-agnostic colors color-space  \n",
              "4                  c# .net scripting compiler-construction  \n",
              "...                                                    ...  \n",
              "1257356            php unit-testing symfony2 symfony-forms  \n",
              "1260386                                      rust lifetime  \n",
              "1261242                                               bash  \n",
              "1262332                          c++ static static-members  \n",
              "1262668  c++ lambda c++14 language-lawyer member-initia...  \n",
              "\n",
              "[42395 rows x 5 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPW3djDknqBT",
        "outputId": "6d04ee11-6e67-4166-ea93-71e362b9092e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id       0\n",
              "Score    0\n",
              "Title    0\n",
              "Body     0\n",
              "Tags     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQvBMRcfn2CE",
        "outputId": "19acec60-a888-411b-84db-10a79597739b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = new_df.drop(['Id', 'Score'],axis=1)"
      ],
      "metadata": {
        "id": "PBRlpWuen5jv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Tags'] = new_df['Tags'].apply(lambda x: x.split())"
      ],
      "metadata": {
        "id": "vjTV1slun8DQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_tags = [item for sublist in new_df['Tags'].values for item in sublist]\n",
        "len(all_tags)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jG2GdqHCoQ9d",
        "outputId": "e58295d4-89c8-413e-ab7b-0b14dedf6216"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "129631"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_tags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgdkWe0DoSxm",
        "outputId": "109fb063-b7c3-4790-a813-be0a22a75c43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'nant',\n",
              " 'c#',\n",
              " 'sql-server',\n",
              " 'tsql',\n",
              " 'ado.net',\n",
              " 'sql-server-2000',\n",
              " 'data-structures',\n",
              " 'haskell',\n",
              " 'types',\n",
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              " 'algebraic-data-types',\n",
              " 'unit-testing',\n",
              " 'c',\n",
              " 'process',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'python',\n",
              " 'zip',\n",
              " 'opengl',\n",
              " 'glew',\n",
              " '.net-3.5',\n",
              " 'intellisense',\n",
              " 'knowledge-management',\n",
              " 'parsing',\n",
              " 'monads',\n",
              " 'mysql',\n",
              " 'powershell',\n",
              " 'configuration-files',\n",
              " 'version-control',\n",
              " 'ankhsvn',\n",
              " 'ruby-on-rails',\n",
              " 'security',\n",
              " 'deployment',\n",
              " '.net',\n",
              " 'open-source',\n",
              " 'mono',\n",
              " 'open-source',\n",
              " 'licensing',\n",
              " 'javascript',\n",
              " 'objective-c',\n",
              " 'xcode',\n",
              " 'osx',\n",
              " 'refactoring',\n",
              " '.net',\n",
              " 'flex',\n",
              " 'flash',\n",
              " 'silverlight',\n",
              " 'c#',\n",
              " 'sql',\n",
              " 'linq',\n",
              " 'django',\n",
              " 'shared-hosting',\n",
              " 'hostmonster',\n",
              " '.net',\n",
              " 'asp.net',\n",
              " 'url',\n",
              " 'uri',\n",
              " 'authority',\n",
              " 'postgresql',\n",
              " 'indexing',\n",
              " 'gin',\n",
              " 'gist-index',\n",
              " 'caching',\n",
              " '.net-2.0',\n",
              " 'memcached',\n",
              " 'enterprise-library',\n",
              " 'c',\n",
              " 'kr-c',\n",
              " 'c89',\n",
              " 'sql',\n",
              " 'sql-server',\n",
              " 'tsql',\n",
              " 'datetime',\n",
              " 'user-defined-functions',\n",
              " 'security',\n",
              " 'session',\n",
              " 'cookies',\n",
              " 'c#',\n",
              " 'coding-style',\n",
              " 'windows-vista',\n",
              " 'database',\n",
              " 'database-design',\n",
              " 'postgresql',\n",
              " 'algorithm',\n",
              " 'language-agnostic',\n",
              " 'ruby',\n",
              " 'serialization',\n",
              " 'lambda',\n",
              " 'proc-object',\n",
              " 'c#',\n",
              " 'sql',\n",
              " 'sql-server',\n",
              " 'sql-server-2005',\n",
              " 'language-agnostic',\n",
              " 'oop',\n",
              " 'ruby',\n",
              " 'shoes',\n",
              " 'irc',\n",
              " 'model-view-controller',\n",
              " 'web-frameworks',\n",
              " 'sql-server',\n",
              " 'tsql',\n",
              " 'pivot',\n",
              " 'visual-studio',\n",
              " 'build-automation',\n",
              " 'visual-studio',\n",
              " 'vim',\n",
              " 'ide',\n",
              " 'editor',\n",
              " 'svn',\n",
              " 'vb6',\n",
              " 'css',\n",
              " 'abstraction',\n",
              " 'java',\n",
              " 'unix',\n",
              " 'windows-services',\n",
              " 'daemon',\n",
              " 'c++',\n",
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              " 'asp.net',\n",
              " 'c#',\n",
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              " 'jquery',\n",
              " 'ajax',\n",
              " 'json',\n",
              " 'character-encoding',\n",
              " 'c#',\n",
              " 'timespan',\n",
              " '.net',\n",
              " 'xpath',\n",
              " 'excel',\n",
              " 'excel-vba',\n",
              " 'javascript',\n",
              " 'arrays',\n",
              " 'comparison',\n",
              " 'c++',\n",
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              " 'java',\n",
              " 'enumeration',\n",
              " 'iterable',\n",
              " 'c#',\n",
              " 'file',\n",
              " 'recursion',\n",
              " 'c#',\n",
              " 'trace',\n",
              " 'c++',\n",
              " 'qt',\n",
              " 'mfc',\n",
              " 'regex',\n",
              " 'doi',\n",
              " 'c++',\n",
              " 'casting',\n",
              " 'rtti',\n",
              " 'dynamic-cast',\n",
              " 'c++',\n",
              " 'performance',\n",
              " 'file-organization',\n",
              " 'android',\n",
              " 'authentication',\n",
              " 'proxy',\n",
              " 'open-source',\n",
              " 'architecture',\n",
              " 'rest',\n",
              " 'soap',\n",
              " '.net',\n",
              " 'wpf',\n",
              " 'version-control',\n",
              " 'tracking',\n",
              " 'c++',\n",
              " 'sockets',\n",
              " 'udp',\n",
              " 'c#',\n",
              " '2d',\n",
              " 'css',\n",
              " 'asp.net-mvc',\n",
              " 'mysql',\n",
              " 'replication',\n",
              " 'mysql-error-1062',\n",
              " 'binlog',\n",
              " 'database',\n",
              " 'unit-testing',\n",
              " 'linq',\n",
              " 'oracle',\n",
              " 'ruby-on-rails',\n",
              " 'ruby',\n",
              " 'python',\n",
              " 'multithreading',\n",
              " 'sql',\n",
              " 'indexing',\n",
              " 'wcf',\n",
              " 'web-services',\n",
              " 'java',\n",
              " 'logging',\n",
              " 'log4j',\n",
              " 'java.util.logging',\n",
              " 'xslt',\n",
              " 'regex',\n",
              " 'language-agnostic',\n",
              " 'web-services',\n",
              " 'testing',\n",
              " 'sql',\n",
              " 'puzzle',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'email',\n",
              " 'smtp',\n",
              " 'gmail',\n",
              " 'java',\n",
              " 'nan',\n",
              " 'assert',\n",
              " 'javascript',\n",
              " 'model-view-controller',\n",
              " 'architecture',\n",
              " 'client',\n",
              " 'ria',\n",
              " 'asp.net-mvc',\n",
              " 'unit-testing',\n",
              " 'mocking',\n",
              " 'moq',\n",
              " 'rhino-mocks',\n",
              " 'java',\n",
              " 'process',\n",
              " 'javascript',\n",
              " 'html',\n",
              " 'css',\n",
              " 'open-source',\n",
              " 'wolfram-mathematica',\n",
              " 'symbolic-math',\n",
              " 'computer-algebra-systems',\n",
              " 'xcode',\n",
              " 'osx',\n",
              " 'templates',\n",
              " 'xcode4',\n",
              " 'xcode4.5',\n",
              " 'java',\n",
              " 'networking',\n",
              " 'ethernet',\n",
              " 'python',\n",
              " 'multithreading',\n",
              " 'php',\n",
              " 'html',\n",
              " 'screen-scraping',\n",
              " 'asp.net',\n",
              " 'css',\n",
              " 'visual-studio-2008',\n",
              " 'user-controls',\n",
              " 'ruby',\n",
              " 'hash',\n",
              " 'sha1',\n",
              " 'multithreading',\n",
              " 'concurrency',\n",
              " 'terminology',\n",
              " 'race-condition',\n",
              " 'python',\n",
              " 'tdd',\n",
              " 'pair-programming',\n",
              " 'javascript',\n",
              " 'jquery',\n",
              " 'perl',\n",
              " 'file',\n",
              " 'locking',\n",
              " 'verilog',\n",
              " 'jquery',\n",
              " 'ajax',\n",
              " 'http',\n",
              " 'forms',\n",
              " 'iframe',\n",
              " 'python',\n",
              " 'static-analysis',\n",
              " 'pylint',\n",
              " 'pyflakes',\n",
              " 'api',\n",
              " 'networking',\n",
              " 'messaging',\n",
              " 'distributed',\n",
              " 'cluster-computing',\n",
              " 'algorithm',\n",
              " 'graphics',\n",
              " 'audio',\n",
              " 'photoshop',\n",
              " 'signal-processing',\n",
              " 'svn',\n",
              " 'c',\n",
              " 'cross-platform',\n",
              " '32-bit',\n",
              " 'sorting',\n",
              " 'comparison',\n",
              " 'f#',\n",
              " 'ocaml',\n",
              " 'c#',\n",
              " 'arrays',\n",
              " 'sql',\n",
              " 'count',\n",
              " 'version-control',\n",
              " 'ms-word',\n",
              " 'windows',\n",
              " 'installation',\n",
              " 'x86',\n",
              " 'sse',\n",
              " 'assembly',\n",
              " 'java',\n",
              " 'unit-testing',\n",
              " 'tdd',\n",
              " 'graphics',\n",
              " 'asp.net',\n",
              " 'file',\n",
              " 'download',\n",
              " 'wpf',\n",
              " 'user-interface',\n",
              " 'asp.net',\n",
              " 'maps',\n",
              " 'bash',\n",
              " '.net',\n",
              " 'winapi',\n",
              " 'disk',\n",
              " 'research',\n",
              " 'c#',\n",
              " 'asp.net',\n",
              " 'visual-studio',\n",
              " 'oop',\n",
              " 'class-design',\n",
              " 'database',\n",
              " 'data-structures',\n",
              " 'indexing',\n",
              " 'schema',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'io',\n",
              " 'javascript',\n",
              " 'python',\n",
              " 'c#',\n",
              " 'overloading',\n",
              " 'java',\n",
              " 'methods',\n",
              " 'parameter-passing',\n",
              " 'pass-by-reference',\n",
              " 'pass-by-value',\n",
              " 'c#',\n",
              " 'asp.net',\n",
              " 'user-controls',\n",
              " 'c#',\n",
              " 'automatic-properties',\n",
              " 'codeigniter',\n",
              " 'zend-framework',\n",
              " 'smarty',\n",
              " 'windows',\n",
              " 'emacs',\n",
              " 'c++',\n",
              " 'c',\n",
              " 'variadic-functions',\n",
              " 'licensing',\n",
              " 'c++',\n",
              " 'namespaces',\n",
              " 'fuzzy-search',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'unit-testing',\n",
              " 'windows-services',\n",
              " '.net',\n",
              " 'winforms',\n",
              " 'user-interface',\n",
              " 'windows-xp',\n",
              " 'sql',\n",
              " '.net',\n",
              " 'wcf',\n",
              " 'c++',\n",
              " 'python',\n",
              " 'date',\n",
              " 'osx',\n",
              " 'configuration',\n",
              " 'mercurial',\n",
              " 'perforce',\n",
              " 'p4merge',\n",
              " 'c#',\n",
              " 'java',\n",
              " 'regex',\n",
              " 'regex-negation',\n",
              " 'operating-system',\n",
              " 'kernel',\n",
              " 'osdev',\n",
              " 'python',\n",
              " 'django',\n",
              " 'url',\n",
              " 'django-urls',\n",
              " '.net',\n",
              " 'session',\n",
              " 'nhibernate',\n",
              " 'flush',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'python',\n",
              " 'mime',\n",
              " 'sql',\n",
              " 'postgresql',\n",
              " 'group-by',\n",
              " 'string-concatenation',\n",
              " 'concat',\n",
              " 'mp3',\n",
              " 'ffmpeg',\n",
              " 'sql-server',\n",
              " 'database',\n",
              " 'c#',\n",
              " 'performance',\n",
              " 'loops',\n",
              " 'visual-studio',\n",
              " 'clickonce',\n",
              " 'versioning',\n",
              " '.net',\n",
              " 'cil',\n",
              " 'c#',\n",
              " 'windows',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'string',\n",
              " '.net-3.5',\n",
              " 'nullable',\n",
              " 'asp.net',\n",
              " 'url',\n",
              " 'rest',\n",
              " 'friendly-url',\n",
              " 'version-control',\n",
              " 'perl',\n",
              " 'model-view-controller',\n",
              " 'visual-studio',\n",
              " 'projects-and-solutions',\n",
              " 'c#',\n",
              " 'winforms',\n",
              " 'svn',\n",
              " 'tortoisesvn',\n",
              " 'iphone',\n",
              " 'xib',\n",
              " 'key-value-coding',\n",
              " 'sql',\n",
              " 'database',\n",
              " 'oracle',\n",
              " 'ora-00942',\n",
              " 'database',\n",
              " 'unit-testing',\n",
              " 'ms-access',\n",
              " 'vba',\n",
              " 'c++',\n",
              " 'templates',\n",
              " 'compiler-errors',\n",
              " 'asp.net',\n",
              " 'scalability',\n",
              " 'viewstate',\n",
              " 'ruby-on-rails',\n",
              " 'ruby',\n",
              " 'social-networking',\n",
              " 'asp.net',\n",
              " '.net',\n",
              " 'deployment',\n",
              " 'msbuild',\n",
              " 'php',\n",
              " 'oop',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'winforms',\n",
              " 'focus',\n",
              " 'bash',\n",
              " 'iterator',\n",
              " 'svn',\n",
              " 'apache',\n",
              " 'mod-rewrite',\n",
              " 'objective-c',\n",
              " 'cocoa',\n",
              " 'c++',\n",
              " 'mfc',\n",
              " 'bounds-checker',\n",
              " 'purify',\n",
              " 'java',\n",
              " 'build-process',\n",
              " 'build-automation',\n",
              " 'ant',\n",
              " 'css',\n",
              " 'html',\n",
              " 'positioning',\n",
              " 'unit-testing',\n",
              " 'project-management',\n",
              " 'tdd',\n",
              " 'python',\n",
              " 'datetime',\n",
              " 'time',\n",
              " 'usability',\n",
              " 'ui-guidelines',\n",
              " 'demoscene',\n",
              " 'visual-studio',\n",
              " 'java',\n",
              " 'windows',\n",
              " 'java',\n",
              " 'applet',\n",
              " 'sql',\n",
              " 'database',\n",
              " 'oracle',\n",
              " 'sequence',\n",
              " 'reset',\n",
              " 'python',\n",
              " 'path',\n",
              " 'relative-path',\n",
              " 'absolute-path',\n",
              " '.net',\n",
              " 'unit-testing',\n",
              " 'project-management',\n",
              " 'specifications',\n",
              " 'specs',\n",
              " '.net',\n",
              " 'c',\n",
              " 'operators',\n",
              " 'comma-operator',\n",
              " 'api',\n",
              " 'google-reader',\n",
              " 'windows',\n",
              " 'git',\n",
              " 'c#',\n",
              " 'algorithm',\n",
              " 'string',\n",
              " 'fuzzy-search',\n",
              " 'scala',\n",
              " 'list',\n",
              " 'security',\n",
              " 'encryption',\n",
              " 'settings',\n",
              " 'php',\n",
              " 'cakephp',\n",
              " 'permissions',\n",
              " 'acl',\n",
              " 'security-roles',\n",
              " '.net',\n",
              " 'web-services',\n",
              " 'iis',\n",
              " 'wcf-data-services',\n",
              " 'postgresql',\n",
              " 'image',\n",
              " 'msbuild',\n",
              " 'mono',\n",
              " 'python',\n",
              " 'sorting',\n",
              " 'dictionary',\n",
              " 'list',\n",
              " 'algorithm',\n",
              " 'language-agnostic',\n",
              " 'puzzle',\n",
              " 'c++',\n",
              " 'c',\n",
              " 'variables',\n",
              " 'php',\n",
              " 'mysql',\n",
              " 'performance',\n",
              " 'profiling',\n",
              " 'svn',\n",
              " 'merge',\n",
              " 'visualization',\n",
              " 'c',\n",
              " 'multithreading',\n",
              " 'python',\n",
              " 'floating-point',\n",
              " 'oop',\n",
              " 'definition',\n",
              " 'solid-principles',\n",
              " 'design-principles',\n",
              " 'lsp',\n",
              " 'c#',\n",
              " '.net-3.5',\n",
              " 'collections',\n",
              " '.net',\n",
              " 'fluent-interface',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'windows',\n",
              " 'svn',\n",
              " 'version-control',\n",
              " 'sourcegear-vault',\n",
              " 'version-control-migration',\n",
              " 'language-agnostic',\n",
              " 'loops',\n",
              " 'goto',\n",
              " 'continue',\n",
              " 'c#',\n",
              " 'c++',\n",
              " 'visual-studio',\n",
              " 'debugging',\n",
              " '.net',\n",
              " 'sql-server',\n",
              " 'tsql',\n",
              " 'sqlclr',\n",
              " '.net',\n",
              " 'vb.net',\n",
              " 'exception-handling',\n",
              " 'windows-services',\n",
              " '.net',\n",
              " 'wpf',\n",
              " 'testing',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'exception',\n",
              " 'c#',\n",
              " 'mime',\n",
              " 'mime-types',\n",
              " 'c++',\n",
              " 'c',\n",
              " 'pdf',\n",
              " 'open-source',\n",
              " 'google-analytics',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'png',\n",
              " 'svg',\n",
              " 'sql',\n",
              " 'sql-server',\n",
              " 'tsql',\n",
              " 'asp.net',\n",
              " 'unicode',\n",
              " 'localization',\n",
              " 'globalization',\n",
              " 'captcha',\n",
              " 'c#',\n",
              " 'multithreading',\n",
              " 'design-patterns',\n",
              " 'locking',\n",
              " 'c++',\n",
              " 'string',\n",
              " 'gcc',\n",
              " 'sql',\n",
              " 'database',\n",
              " 'stored-procedures',\n",
              " 'c++',\n",
              " 'inheritance',\n",
              " 'oop',\n",
              " 'member-function-pointers',\n",
              " 'javascript',\n",
              " 'internet-explorer',\n",
              " 'java',\n",
              " 'regex',\n",
              " 'escaping',\n",
              " 'c++',\n",
              " 'oop',\n",
              " 'information-hiding',\n",
              " 'pimpl-idiom',\n",
              " 'php',\n",
              " 'javascript',\n",
              " 'download',\n",
              " 'python',\n",
              " 'webserver',\n",
              " 'jquery',\n",
              " 'internet-explorer-6',\n",
              " 'png',\n",
              " 'c#',\n",
              " 'performance',\n",
              " 'memory',\n",
              " 'profiler',\n",
              " 'c++',\n",
              " 'inline-functions',\n",
              " 'osx',\n",
              " 'emacs',\n",
              " 'lisp',\n",
              " 'aquamacs',\n",
              " 'windows',\n",
              " 'command-line',\n",
              " 'console',\n",
              " 'terminal',\n",
              " 'command-line-interface',\n",
              " 'c#',\n",
              " '.net',\n",
              " 'console',\n",
              " 'stdout',\n",
              " 'java',\n",
              " 'unit-testing',\n",
              " 'mocking',\n",
              " 'static-block',\n",
              " 'css',\n",
              " 'layout',\n",
              " 'svn',\n",
              " 'eclipse',\n",
              " 'subclipse',\n",
              " 'subversive',\n",
              " 'unit-testing',\n",
              " 'language-agnostic',\n",
              " 'tdd',\n",
              " 'integration-testing',\n",
              " 'testing-strategies',\n",
              " 'c#',\n",
              " 'properties',\n",
              " 'database',\n",
              " 'oop',\n",
              " 'rdbms',\n",
              " 'relational',\n",
              " 'oodbms',\n",
              " 'linq',\n",
              " 'sum',\n",
              " 'entity-framework',\n",
              " 'ado.net',\n",
              " 'regex',\n",
              " 'svn',\n",
              " 'tortoisesvn',\n",
              " 'java',\n",
              " 'xml',\n",
              " 'database',\n",
              " 'database-design',\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_set = set(all_tags)\n",
        "unique_tags = list(my_set)\n",
        "len(unique_tags)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7oF_4exJomTJ",
        "outputId": "7c11911a-b763-490a-ce77-83a48beadd10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11282"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk"
      ],
      "metadata": {
        "id": "6299LV_FovMs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flat_list = [item for sublist in new_df['Tags'].values for item in sublist]\n",
        "\n",
        "keywords = nltk.FreqDist(flat_list)\n",
        "\n",
        "keywords = nltk.FreqDist(keywords)\n",
        "\n",
        "frequencies_words = keywords.most_common(100)\n",
        "tags_features = [word[0] for word in frequencies_words]\n",
        "tags_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6sWFnzLoqWl",
        "outputId": "0d779b99-c3a4-4178-ee9c-0b9fe3c3fa28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['java',\n",
              " 'c#',\n",
              " 'javascript',\n",
              " 'android',\n",
              " 'python',\n",
              " 'c++',\n",
              " '.net',\n",
              " 'php',\n",
              " 'jquery',\n",
              " 'ios',\n",
              " 'html',\n",
              " 'css',\n",
              " 'iphone',\n",
              " 'objective-c',\n",
              " 'c',\n",
              " 'ruby-on-rails',\n",
              " 'sql',\n",
              " 'ruby',\n",
              " 'mysql',\n",
              " 'asp.net',\n",
              " 'git',\n",
              " 'r',\n",
              " 'asp.net-mvc',\n",
              " 'linux',\n",
              " 'angularjs',\n",
              " 'string',\n",
              " 'node.js',\n",
              " 'sql-server',\n",
              " 'performance',\n",
              " 'django',\n",
              " 'xcode',\n",
              " 'eclipse',\n",
              " 'algorithm',\n",
              " 'arrays',\n",
              " 'wpf',\n",
              " 'windows',\n",
              " 'haskell',\n",
              " 'c++11',\n",
              " 'scala',\n",
              " 'visual-studio',\n",
              " 'json',\n",
              " 'multithreading',\n",
              " 'bash',\n",
              " 'regex',\n",
              " 'swift',\n",
              " 'osx',\n",
              " 'html5',\n",
              " 'database',\n",
              " 'unit-testing',\n",
              " 'xml',\n",
              " 'spring',\n",
              " 'cocoa-touch',\n",
              " 'ajax',\n",
              " 'ruby-on-rails-3',\n",
              " 'visual-studio-2010',\n",
              " 'debugging',\n",
              " 'vim',\n",
              " 'linq',\n",
              " 'entity-framework',\n",
              " 'shell',\n",
              " 'twitter-bootstrap',\n",
              " 'postgresql',\n",
              " 'mongodb',\n",
              " 'winforms',\n",
              " 'list',\n",
              " 'cocoa',\n",
              " 'generics',\n",
              " 'google-chrome',\n",
              " 'css3',\n",
              " 'maven',\n",
              " 'oop',\n",
              " 'asp.net-mvc-3',\n",
              " 'perl',\n",
              " 'http',\n",
              " 'gcc',\n",
              " 'delphi',\n",
              " 'templates',\n",
              " 'svn',\n",
              " 'tsql',\n",
              " 'image',\n",
              " 'github',\n",
              " 'rest',\n",
              " 'security',\n",
              " 'exception',\n",
              " 'facebook',\n",
              " 'math',\n",
              " 'datetime',\n",
              " 'oracle',\n",
              " 'hibernate',\n",
              " 'numpy',\n",
              " 'class',\n",
              " 'file',\n",
              " 'function',\n",
              " 'android-layout',\n",
              " 'version-control',\n",
              " 'unix',\n",
              " 'types',\n",
              " 'emacs',\n",
              " 'date',\n",
              " 'ipad']"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('tags_feature.pkl','wb') as f:\n",
        "  pickle.dump(tags_features,f)"
      ],
      "metadata": {
        "id": "HTPCEj1a7mPE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def most_common(tags):\n",
        "    tags_filtered = []\n",
        "    for i in range(0, len(tags)):\n",
        "        if tags[i] in tags_features:\n",
        "            tags_filtered.append(tags[i])\n",
        "    return tags_filtered"
      ],
      "metadata": {
        "id": "WkdBbyrDoyVN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Tags'] = new_df['Tags'].apply(lambda x: most_common(x))\n",
        "new_df['Tags'] = new_df['Tags'].apply(lambda x: x if len(x)>0 else None)\n",
        "new_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-GMfYxuo26V",
        "outputId": "4c9968c5-4436-4a86-c583-2d85d038a4af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42395, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.dropna(subset=['Tags'], inplace=True)\n",
        "print(new_df.shape)\n",
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "HaKH4A9Oo-Ar",
        "outputId": "57ba309f-3296-4c16-b77c-00fcf75bd1a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(37075, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "1  Good branching and merging tutorials for Torto...   \n",
              "2                                  ASP.NET Site Maps   \n",
              "3                 Function for creating color wheels   \n",
              "4  Adding scripting functionality to .NET applica...   \n",
              "5          Should I use nested classes in this case?   \n",
              "\n",
              "                                                Body               Tags  \n",
              "1  <p>Are there any really good tutorials explain...              [svn]  \n",
              "2  <p>Has anyone got experience creating <strong>...     [sql, asp.net]  \n",
              "3  <p>This is something I've pseudo-solved many t...        [algorithm]  \n",
              "4  <p>I have a little game written in C#. It uses...         [c#, .net]  \n",
              "5  <p>I am working on a collection of classes use...  [c++, oop, class]  "
            ],
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              "      <th>1</th>\n",
              "      <td>Good branching and merging tutorials for Torto...</td>\n",
              "      <td>&lt;p&gt;Are there any really good tutorials explain...</td>\n",
              "      <td>[svn]</td>\n",
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              "      <th>2</th>\n",
              "      <td>ASP.NET Site Maps</td>\n",
              "      <td>&lt;p&gt;Has anyone got experience creating &lt;strong&gt;...</td>\n",
              "      <td>[sql, asp.net]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Function for creating color wheels</td>\n",
              "      <td>&lt;p&gt;This is something I've pseudo-solved many t...</td>\n",
              "      <td>[algorithm]</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adding scripting functionality to .NET applica...</td>\n",
              "      <td>&lt;p&gt;I have a little game written in C#. It uses...</td>\n",
              "      <td>[c#, .net]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Should I use nested classes in this case?</td>\n",
              "      <td>&lt;p&gt;I am working on a collection of classes use...</td>\n",
              "      <td>[c++, oop, class]</td>\n",
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              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-f45699f2-67e8-4598-9fe7-c646b64d4aa5 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-f45699f2-67e8-4598-9fe7-c646b64d4aa5');\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",
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              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            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-ae0387c4-a65a-40d4-a407-c81808e170fc 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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup"
      ],
      "metadata": {
        "id": "foCgkDTYpBVH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Body'] = new_df['Body'].apply(lambda x: BeautifulSoup(x).get_text())\n",
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bzjD3Cqzpirz",
        "outputId": "e4bfa7b8-da41-4f93-f1c6-1d8c696bd25d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "1  Good branching and merging tutorials for Torto...   \n",
              "2                                  ASP.NET Site Maps   \n",
              "3                 Function for creating color wheels   \n",
              "4  Adding scripting functionality to .NET applica...   \n",
              "5          Should I use nested classes in this case?   \n",
              "\n",
              "                                                Body               Tags  \n",
              "1  Are there any really good tutorials explaining...              [svn]  \n",
              "2  Has anyone got experience creating SQL-based A...     [sql, asp.net]  \n",
              "3  This is something I've pseudo-solved many time...        [algorithm]  \n",
              "4  I have a little game written in C#. It uses a ...         [c#, .net]  \n",
              "5  I am working on a collection of classes used f...  [c++, oop, class]  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3861afbf-4184-491a-82f3-35939d1ae87f\" class=\"colab-df-container\">\n",
              "    <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",
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              "\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",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Good branching and merging tutorials for Torto...</td>\n",
              "      <td>Are there any really good tutorials explaining...</td>\n",
              "      <td>[svn]</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ASP.NET Site Maps</td>\n",
              "      <td>Has anyone got experience creating SQL-based A...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Function for creating color wheels</td>\n",
              "      <td>This is something I've pseudo-solved many time...</td>\n",
              "      <td>[algorithm]</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>[c#, .net]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Should I use nested classes in this case?</td>\n",
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              "      <td>[c++, oop, class]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3861afbf-4184-491a-82f3-35939d1ae87f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-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-3861afbf-4184-491a-82f3-35939d1ae87f 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-3861afbf-4184-491a-82f3-35939d1ae87f');\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-6c0cec07-05f3-4f49-b130-dcff8d3346a0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c0cec07-05f3-4f49-b130-dcff8d3346a0')\"\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",
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              "  }\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-6c0cec07-05f3-4f49-b130-dcff8d3346a0 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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import ToktokTokenizer"
      ],
      "metadata": {
        "id": "6R36D2BdpryV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "token=ToktokTokenizer()"
      ],
      "metadata": {
        "id": "PbdmAYc3plH4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import string"
      ],
      "metadata": {
        "id": "9iDFS9BPp7kM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_punctuation(text):\n",
        "    words=token.tokenize(text)\n",
        "    filtered=[]\n",
        "    for w in words:\n",
        "        if w not in tags_features:\n",
        "            new_w=w.translate(str.maketrans('', '', string.punctuation))\n",
        "            filtered.append(new_w)\n",
        "        else:\n",
        "            filtered.append(w)\n",
        "    s = \" \"\n",
        "    s=s.join(filtered)\n",
        "    return s\n"
      ],
      "metadata": {
        "id": "ojYWkQ_ZppHg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text=\"I have issue with pandas and c# immitation. / \"\n",
        "words=token.tokenize(text)\n",
        "filtered=[]\n",
        "for w in words:\n",
        "    if w not in tags_features:\n",
        "        new_w=w.translate(str.maketrans('', '', string.punctuation))\n",
        "        filtered.append(new_w)\n",
        "    else:\n",
        "        filtered.append(w)\n",
        "print(filtered)\n",
        "s = \" \"\n",
        "s=s.join(filtered)\n",
        "print(s)\n",
        "print(\"Working well......................\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SC0kL8c7p2vc",
        "outputId": "daa472e2-4186-457c-e72c-d284d60aa91e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['I', 'have', 'issue', 'with', 'pandas', 'and', 'c#', 'immitation', '']\n",
            "I have issue with pandas and c# immitation \n",
            "Working well......................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re"
      ],
      "metadata": {
        "id": "JdRC-T7SqAh5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    text = re.sub(r\"what's\", \"what is \", text)\n",
        "    text = re.sub(r\"\\'s\", \" \", text)\n",
        "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
        "    text = re.sub(r\"can't\", \"can not \", text)\n",
        "    text = re.sub(r\"n't\", \" not \", text)\n",
        "    text = re.sub(r\"i'm\", \"i am \", text)\n",
        "    text = re.sub(r\"\\'re\", \" are \", text)\n",
        "    text = re.sub(r\"\\'d\", \" would \", text)\n",
        "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
        "    text = re.sub(r\"\\'scuse\", \" excuse \", text)\n",
        "    text = re.sub(r\"\\'\\n\", \" \", text)\n",
        "    text = re.sub(r\"\\'\\xa0\", \" \", text)\n",
        "    text = re.sub('\\s+', ' ', text)\n",
        "    text = text.strip(' ')\n",
        "    return text"
      ],
      "metadata": {
        "id": "J5Te117SqdBt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Body'] = new_df['Body'].apply(lambda x: clean_text(x))"
      ],
      "metadata": {
        "id": "oyiKFwuUqeRR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Body']= new_df['Body'].apply(lambda x: re.sub('<[^<]+?>','',x))\n",
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jr4-voz-qf1w",
        "outputId": "685359fa-9c7e-439b-f227-ea057a8184c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "1  Good branching and merging tutorials for Torto...   \n",
              "2                                  ASP.NET Site Maps   \n",
              "3                 Function for creating color wheels   \n",
              "4  Adding scripting functionality to .NET applica...   \n",
              "5          Should I use nested classes in this case?   \n",
              "\n",
              "                                                Body               Tags  \n",
              "1  are there any really good tutorials explaining...              [svn]  \n",
              "2  has anyone got experience creating sql-based a...     [sql, asp.net]  \n",
              "3  this is something i have pseudo-solved many ti...        [algorithm]  \n",
              "4  i have a little game written in c#. it uses a ...         [c#, .net]  \n",
              "5  i am working on a collection of classes used f...  [c++, oop, class]  "
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              "      <td>[svn]</td>\n",
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              "    <tr>\n",
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              "      <td>ASP.NET Site Maps</td>\n",
              "      <td>has anyone got experience creating sql-based a...</td>\n",
              "      <td>[sql, asp.net]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Function for creating color wheels</td>\n",
              "      <td>this is something i have pseudo-solved many ti...</td>\n",
              "      <td>[algorithm]</td>\n",
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              "      <th>4</th>\n",
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              "      <td>[c#, .net]</td>\n",
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              "      <td>[c++, oop, class]</td>\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c61972a3-9731-45cc-b063-348f82d65aae 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-c61972a3-9731-45cc-b063-348f82d65aae');\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",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-02d9798b-bce6-4e66-b946-b401079dcf88')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "</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-02d9798b-bce6-4e66-b946-b401079dcf88 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": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Body'] = new_df['Body'].apply(lambda x: remove_punctuation(x))"
      ],
      "metadata": {
        "id": "RtJAJOMnqihn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Body'][10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "IcJVpMO2qlNT",
        "outputId": "7de068a8-12d4-433a-fff6-ce70817ad167"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'what is the simplest way to connect and query a database for a set of records in c# '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords"
      ],
      "metadata": {
        "id": "ObwKGiofqqYw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoFWj_U3rfyN",
        "outputId": "97f0745c-6dde-4053-e9ed-c6f5c3bdbd9a"
      },
      "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lemma = WordNetLemmatizer()\n",
        "stopword = set(stopwords.words('english'))"
      ],
      "metadata": {
        "id": "NUQw2PX9rDbz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def lemitizeWords(text):\n",
        "    words=token.tokenize(text)\n",
        "    listLemma=[]\n",
        "    for w in words:\n",
        "        x=lemma.lemmatize(w, pos=\"v\")\n",
        "        listLemma.append(x)\n",
        "    return ' '.join(map(str, listLemma))\n",
        "\n",
        "def stopWordsRemove(text):\n",
        "\n",
        "    stop_words = set(stopwords.words(\"english\"))\n",
        "\n",
        "    words=token.tokenize(text)\n",
        "\n",
        "    filtered = [w for w in words if not w in stop_words]\n",
        "\n",
        "    return ' '.join(map(str, filtered))"
      ],
      "metadata": {
        "id": "bTxuW_X5rcNP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjGj2OuUroJ3",
        "outputId": "0162fbeb-fecc-4c45-bf3c-5466c9e8ca38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Body'] = new_df['Body'].apply(lambda x: lemitizeWords(x))\n",
        "new_df['Body'] = new_df['Body'].apply(lambda x: stopWordsRemove(x))"
      ],
      "metadata": {
        "id": "utvyT0Y-rjh3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Title'] = new_df['Title'].apply(lambda x: str(x))\n",
        "new_df['Title'] = new_df['Title'].apply(lambda x: clean_text(x))\n",
        "new_df['Title'] = new_df['Title'].apply(lambda x: remove_punctuation(x))\n",
        "new_df['Title'] = new_df['Title'].apply(lambda x: lemitizeWords(x))\n",
        "new_df['Title'] = new_df['Title'].apply(lambda x: stopWordsRemove(x))"
      ],
      "metadata": {
        "id": "WcVH22ldrlwJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ol85y3e9uKxO",
        "outputId": "9df154b4-439c-4ab9-848d-bb99c38c25c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        Title  \\\n",
              "1     good branch merge tutorials tortoisesvn   \n",
              "2                            asp.net site map   \n",
              "3                 function create color wheel   \n",
              "4  add script functionality .net applications   \n",
              "5                         use nest class case   \n",
              "\n",
              "                                                Body               Tags  \n",
              "1  really good tutorials explain branch merge apa...              [svn]  \n",
              "2  anyone get experience create sqlbased asp.net ...     [sql, asp.net]  \n",
              "3  something pseudosolved many time never quite f...        [algorithm]  \n",
              "4  little game write c use database backend trade...         [c#, .net]  \n",
              "5  work collection class use video playback recor...  [c++, oop, class]  "
            ],
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              "      <th>Title</th>\n",
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              "      <th>Tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>good branch merge tutorials tortoisesvn</td>\n",
              "      <td>really good tutorials explain branch merge apa...</td>\n",
              "      <td>[svn]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>asp.net site map</td>\n",
              "      <td>anyone get experience create sqlbased asp.net ...</td>\n",
              "      <td>[sql, asp.net]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>function create color wheel</td>\n",
              "      <td>something pseudosolved many time never quite f...</td>\n",
              "      <td>[algorithm]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>add script functionality .net applications</td>\n",
              "      <td>little game write c use database backend trade...</td>\n",
              "      <td>[c#, .net]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>use nest class case</td>\n",
              "      <td>work collection class use video playback recor...</td>\n",
              "      <td>[c++, oop, class]</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a2b3fbb9-c523-44f6-8136-e8e702c91e5a');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
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              "</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",
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              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "metadata": {
        "id": "FT4-RpkAuMtO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Vectorization"
      ],
      "metadata": {
        "id": "Rd_FPG5Dgt9S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Title']= new_df['Title'].astype(str)\n",
        "vectorizer = TfidfVectorizer(max_features=5000)\n",
        "X1 = vectorizer.fit_transform(new_df['Title'].str.lower())"
      ],
      "metadata": {
        "id": "4fKpKsNDuX3L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['Body']= new_df['Body'].astype(str)\n",
        "X2 = vectorizer.fit_transform(new_df['Body'].str.lower())"
      ],
      "metadata": {
        "id": "o2uFXQRWudna"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MultiLabelBinarizer"
      ],
      "metadata": {
        "id": "6eVq1YXGujLw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = new_df['Tags']\n",
        "multilabel_binarizer = MultiLabelBinarizer()\n",
        "new_y = multilabel_binarizer.fit_transform(y)"
      ],
      "metadata": {
        "id": "jKkkUYI3ufUV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('label.pkl','wb') as f:\n",
        "  pickle.dump(multilabel_binarizer,f)"
      ],
      "metadata": {
        "id": "y16yVUWfxU8k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MEQMP9OUuiD1",
        "outputId": "59433392-c9d6-4222-e666-43ceb76371e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 1, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.sparse import hstack"
      ],
      "metadata": {
        "id": "-ni8VwACu14f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=hstack([X1,X2])"
      ],
      "metadata": {
        "id": "axYUwq_SusOQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "id": "I-GmYmwO0ZoH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71a28c78-309b-4377-dcc4-1ae652b7a867"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<37075x10000 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 1584037 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('TokToktokenizer.pkl','wb') as f:\n",
        "  pickle.dump(token,f)"
      ],
      "metadata": {
        "id": "gPsmYII_3KxF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('tfidfVector.pkl','wb') as f:\n",
        "  pickle.dump(vectorizer,f)"
      ],
      "metadata": {
        "id": "xszeXnRK19Vo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('WordLemma.pkl','wb') as f:\n",
        "  pickle.dump(lemma,f)"
      ],
      "metadata": {
        "id": "uC3uaAcw2h07"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import  train_test_split"
      ],
      "metadata": {
        "id": "C0Um_s3svG08"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, new_y, test_size = 0.3, random_state = 42)"
      ],
      "metadata": {
        "id": "Z4tZ_h3yuu5j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejZ-u_hFvGCx",
        "outputId": "24589d44-a3e3-4bed-a929-6385626b1e2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25952, 10000) (11123, 10000) (25952, 100) (11123, 100)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import hamming_loss"
      ],
      "metadata": {
        "id": "uXrOH0yVwlHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_score(y_pred, clf):\n",
        "    print(\"Classifier: \", clf.__class__.__name__)\n",
        "    jacard = np.minimum(y_test,y_pred).sum(axis=1) / np.maximum(y_test,y_pred).sum(axis=1)\n",
        "    score=jacard.mean()*100\n",
        "    print(\"Jacard score: {}\".format(score))\n",
        "    print(\"Hamming loss: {}\".format(hamming_loss(y_pred, y_test)*100))\n",
        "    F1_Score=metrics.classification_report(y_test, y_pred).split()[-2]\n",
        "    print('F1 Score of ',clf.__class__.__name__,\":\", F1_Score)\n",
        "    print(\"-----------------------------------------------------------\")\n",
        "    return F1_Score"
      ],
      "metadata": {
        "id": "DFHEbTq-vMsf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "8s5SHBj4vaCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier, LogisticRegression, PassiveAggressiveClassifier, Perceptron\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.multiclass import OneVsRestClassifier"
      ],
      "metadata": {
        "id": "Fagf16YNveGQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training and Testing Model"
      ],
      "metadata": {
        "id": "uLNM4STT_U_s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "sgd = SGDClassifier()\n",
        "lr = LogisticRegression()\n",
        "# rf = RandomForestClassifier()\n",
        "svc = LinearSVC()\n",
        "perceptron = Perceptron()\n",
        "pac = PassiveAggressiveClassifier()\n",
        "Scores={}\n",
        "for classifier in [sgd, lr, svc, perceptron, pac]:\n",
        "    clf = OneVsRestClassifier(classifier)\n",
        "    clf.fit(X_train, y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    score=print_score(y_pred, classifier)\n",
        "    Scores[classifier]=score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62AqMX8jvbax",
        "outputId": "f6e8dc22-d7c1-4ab4-b58b-a75a369ce83c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifier:  SGDClassifier\n",
            "Jacard score: 49.12418711978184\n",
            "Hamming loss: 0.9476759866942371\n",
            "F1 Score of  SGDClassifier : 0.54\n",
            "-----------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifier:  LogisticRegression\n",
            "Jacard score: 42.51176241421678\n",
            "Hamming loss: 1.041086037939405\n",
            "F1 Score of  LogisticRegression : 0.47\n",
            "-----------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifier:  LinearSVC\n",
            "Jacard score: 53.04788875902785\n",
            "Hamming loss: 0.9718601096826396\n",
            "F1 Score of  LinearSVC : 0.59\n",
            "-----------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifier:  Perceptron\n",
            "Jacard score: 49.21875735819816\n",
            "Hamming loss: 1.2774431358446463\n",
            "F1 Score of  Perceptron : 0.56\n",
            "-----------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifier:  PassiveAggressiveClassifier\n",
            "Jacard score: 50.85126057975109\n",
            "Hamming loss: 1.1780095298031108\n",
            "F1 Score of  PassiveAggressiveClassifier : 0.58\n",
            "-----------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "0vjUGt6vyVxk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('clf.pkl', 'wb') as f:\n",
        "    pickle.dump(clf, f)"
      ],
      "metadata": {
        "id": "9rDzcaJwQGIp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Prediction"
      ],
      "metadata": {
        "id": "6ZXg7G5ZJTla"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "predData = {\n",
        "    \"title\": [\"serialization/deserialization using Java in python\"],\n",
        "    \"body\": [\"trying python to serialize Python and deserialize from class object to Python using Java package. After looking into some documentation succeeded in Java the json class object but, when json out with complex class am not able to deserialize json  objects\"]\n",
        "}\n",
        "\n",
        "pData = pd.DataFrame(predData)\n",
        "pData.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "baqaPcDbJSBR",
        "outputId": "2890f9aa-3f1c-455a-e050-6e2229aab8fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  \\\n",
              "0  serialization/deserialization using Java in py...   \n",
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              "    async function quickchart(key) {\n",
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          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pData['body'] = pData['body'].apply(lambda x: BeautifulSoup(x).get_text())\n",
        "pData['body'] = pData['body'].apply(lambda x: clean_text(x))\n",
        "pData['body']= pData['body'].apply(lambda x: re.sub('<[^<]+?>','',x))\n",
        "pData['body'] = pData['body'].apply(lambda x: remove_punctuation(x))\n",
        "pData['body'] = pData['body'].apply(lambda x: lemitizeWords(x))\n",
        "pData['body'] = pData['body'].apply(lambda x: stopWordsRemove(x))"
      ],
      "metadata": {
        "id": "3VrkEXisJ69B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pData['title'] = pData['title'].apply(lambda x: str(x))\n",
        "pData['title'] = pData['title'].apply(lambda x: clean_text(x))\n",
        "pData['title'] = pData['title'].apply(lambda x: remove_punctuation(x))\n",
        "pData['title'] = pData['title'].apply(lambda x: lemitizeWords(x))\n",
        "pData['title'] = pData['title'].apply(lambda x: stopWordsRemove(x))\n",
        "pData['title']= pData['title'].astype(str)"
      ],
      "metadata": {
        "id": "v51Mwe3XMJZG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pData['title']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yR2KjguLMx_g",
        "outputId": "fc30a50d-058b-4559-b518-3b5c3ae624db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    serializationdeserialization use java python\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pData['body']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWcqIWHRM4Oe",
        "outputId": "0b8ca3d9-652a-43b9-81c5-3b7777e90a84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    try python serialize python deserialize class ...\n",
              "Name: body, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/tfidfVector.pkl', 'rb') as f:\n",
        "    vecto = pickle.load(f)"
      ],
      "metadata": {
        "id": "bgkWmIiouq8_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X1_pred = vecto.transform(pData['title'].str.lower())\n",
        "X2_pred = vecto.transform(pData['body'].str.lower())"
      ],
      "metadata": {
        "id": "RKzIFVJoM7EW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pData['body']= pData['body'].astype(str)"
      ],
      "metadata": {
        "id": "EYHzfndgNGhb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X2 = vectorizer.fit_transform(pData['body'].str.lower())"
      ],
      "metadata": {
        "id": "LEuNFh5wNMLR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X1_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_O-fsaqNRC4",
        "outputId": "fbe4890a-c3e8-4b33-dc9a-c9f011a7156f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 5000)"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X2_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPLD3E2LNRyh",
        "outputId": "aa7b0bd0-3534-4903-a13a-254e25190a41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 5000)"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_pred = hstack([X1_pred, X2_pred])"
      ],
      "metadata": {
        "id": "GLWOkt1INUtj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/clf.pkl', 'rb') as model_file:\n",
        "    loaded_model = pickle.load(model_file)"
      ],
      "metadata": {
        "id": "flyhFoKgNd9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = loaded_model.predict(X_pred)"
      ],
      "metadata": {
        "id": "U-M8b7ukN6fo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76tWpPDVONdP",
        "outputId": "cfead8aa-49a5-4db3-c7ae-e0c4753adc62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_labels = multilabel_binarizer.inverse_transform(prediction)\n",
        "print(predicted_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T8wM0K3JboIl",
        "outputId": "15d1e3d9-69f1-4591-e0ff-284d0125ceab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('java', 'json', 'python')]\n"
          ]
        }
      ]
    }
  ]
}