Recommnendatio_1.ipynb

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      "name": "Recommnendatio_1.ipynb",
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      "mount_file_id": "124X0kl8ywnMSb7fgLTUBVJc1dJnlg6iZ",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DB11051998/RecommendMe/blob/master/Recommnendatio_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lc5zsB64giVw",
        "colab_type": "text"
      },
      "source": [
        "# Recommendation Test \n",
        "#### submitted by:- Debasish Beheraa\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lL60tabkn0x4",
        "colab_type": "text"
      },
      "source": [
        "What is Recommnedation Engine?\n",
        "\n",
        "> A recommendation engine is a system that suggests products, services, information to users based on analysis of data. Notwithstanding, the recommendation can derive from a variety of factors such as the history of the user and the behaviour of similar users\n",
        "\n",
        "\n",
        "Two main techniques of developing a recommendation engine:-\n",
        "\n",
        "> Content Based and Collaberative Based \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OxVhKUOo3ot",
        "colab_type": "text"
      },
      "source": [
        "Content-based filtering\n",
        "> Content-based filtering is based on a single   user’s interactions and preference. Recommendations are based on the metadata collected from a user’s history and interactions. \n",
        "\n",
        "Collaberative-based filtering\n",
        ">Collaborative filtering is another commonly used technique. Collaborative filtering casts a much wider net, collecting information from the interactions from many other users to derive suggestions for you. This approach makes recommendations based on other users with similar tastes or situations.\n",
        "\n",
        "https://miro.medium.com/max/1400/1*JLw0nVz6_buRLEY6vHioKw.jpeg)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVDRq9gZHl_o",
        "colab_type": "text"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxNiYJC1n9dL",
        "colab_type": "code",
        "outputId": "657201ec-bb50-43ac-cde2-d7d618208850",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFa7cWAhhEfM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Read the datasets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDBSbDxWooC3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "userdata=pd.read_csv('/content/drive/My Drive/Recommendations engine task/Document from Debasish (Unzipped Files)/users.csv')\n",
        "viewsdata=pd.read_csv('/content/drive/My Drive/Recommendations engine task/Document from Debasish (Unzipped Files)/views.csv')\n",
        "postdata=pd.read_csv('/content/drive/My Drive/Recommendations engine task/Document from Debasish (Unzipped Files)/posts.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRxYqRpehKv6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# view the datasets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uO9-qLK9ppm2",
        "colab_type": "code",
        "outputId": "b3c1ba8c-20ed-4727-9718-2f522ace01e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "userdata.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>_id</th>\n",
              "      <th>name</th>\n",
              "      <th>gender</th>\n",
              "      <th>academics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5d60098a653a331687083238</td>\n",
              "      <td>Nivesh Singh Chauhan</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5d610ae1653a331687083239</td>\n",
              "      <td>Gaurav Sharma</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Akshay Mishra</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
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              "      <td>Saksham Mathur</td>\n",
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              "      <td>undergraduate</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5d7c994d5720533e15c3b1e9</td>\n",
              "      <td>Varun Chowhan</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                        _id                  name gender      academics\n",
              "0  5d60098a653a331687083238  Nivesh Singh Chauhan   male  undergraduate\n",
              "1  5d610ae1653a331687083239         Gaurav Sharma   male       graduate\n",
              "2  5d618359fc5fcf3bdd9a0910         Akshay Mishra   male  undergraduate\n",
              "3  5d6d2bb87fa40e1417a49315        Saksham Mathur   male  undergraduate\n",
              "4  5d7c994d5720533e15c3b1e9         Varun Chowhan   male  undergraduate"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
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      "cell_type": "code",
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        "colab_type": "code",
        "outputId": "910e1dee-663c-46fc-b7b5-ac7d918c7e61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "postdata.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>_id</th>\n",
              "      <th>title</th>\n",
              "      <th>category</th>\n",
              "      <th>post_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5d62abaa65218653a132c956</td>\n",
              "      <td>hello there</td>\n",
              "      <td>Plant Biotechnology</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5d6d39567fa40e1417a4931c</td>\n",
              "      <td>Ml and AI</td>\n",
              "      <td>Artificial Intelligence|Machine Learning|Infor...</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5d7d23315720533e15c3b1ee</td>\n",
              "      <td>What is an Operating System ?</td>\n",
              "      <td>Operating Systems</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5d7d405e5720533e15c3b1f3</td>\n",
              "      <td>Lord Shiva</td>\n",
              "      <td>Drawings</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5d80dfbc6c53455f896e600e</td>\n",
              "      <td>How Competition law evolved?</td>\n",
              "      <td>Competition Laws</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                        _id  ...  post_type\n",
              "0  5d62abaa65218653a132c956  ...       blog\n",
              "1  5d6d39567fa40e1417a4931c  ...       blog\n",
              "2  5d7d23315720533e15c3b1ee  ...       blog\n",
              "3  5d7d405e5720533e15c3b1f3  ...    artwork\n",
              "4  5d80dfbc6c53455f896e600e  ...       blog\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "j8ZtpGktp3in",
        "colab_type": "code",
        "outputId": "3f8908bf-15cf-480b-fe86-34f339311298",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "viewsdata.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>user_id</th>\n",
              "      <th>post_id</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5df49b32cc709107827fb3c7</td>\n",
              "      <td>5ec821ddec493f4a2655889e</td>\n",
              "      <td>2020-06-01T10:46:45.131Z</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5ed3748576027d35905ccaab</td>\n",
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              "      <td>2020-06-01T09:39:20.021Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5ed0defa76027d35905cc2de</td>\n",
              "      <td>5eac305f10426255a7aa9dd3</td>\n",
              "      <td>2020-06-01T08:12:42.682Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5ed0defa76027d35905cc2de</td>\n",
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              "      <td>2020-06-01T08:10:23.880Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5ed0defa76027d35905cc2de</td>\n",
              "      <td>5ed3820f76027d35905ccac8</td>\n",
              "      <td>2020-06-01T08:08:54.124Z</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    user_id                   post_id                 timestamp\n",
              "0  5df49b32cc709107827fb3c7  5ec821ddec493f4a2655889e  2020-06-01T10:46:45.131Z\n",
              "1  5ed3748576027d35905ccaab  5ed4cbadbd514d602c1531a6  2020-06-01T09:39:20.021Z\n",
              "2  5ed0defa76027d35905cc2de  5eac305f10426255a7aa9dd3  2020-06-01T08:12:42.682Z\n",
              "3  5ed0defa76027d35905cc2de  5ed1ff0276027d35905cc60d  2020-06-01T08:10:23.880Z\n",
              "4  5ed0defa76027d35905cc2de  5ed3820f76027d35905ccac8  2020-06-01T08:08:54.124Z"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLqex35shP0c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## shape of the datasets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BF0VUl_IqSrQ",
        "colab_type": "code",
        "outputId": "8a3ecb23-2569-4b0a-bed0-9565f41c2470",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(userdata.shape)\n",
        "print(postdata.shape)\n",
        "print(viewsdata.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(118, 4)\n",
            "(493, 4)\n",
            "(1449, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kktGkoaVz3f5",
        "colab_type": "code",
        "outputId": "441a8cb3-e3cd-4ad4-dc39-4bcaad786bd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "postdata[' post_type'].unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['blog', 'artwork', 'project', 'skill'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unJg-ie_hU8F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##visualisation of post_type(count)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mey9XdPc0Ioc",
        "colab_type": "code",
        "outputId": "e0cdf69a-a1e4-4b58-965f-3bd790de0cff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "sns.countplot(postdata[' post_type'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd12d716710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wY8cldJ8e0ma",
        "colab_type": "code",
        "outputId": "ba280c9f-8a1c-4659-ac7c-4b1ecc0ec4da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "userdata.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['_id', 'name', 'gender', 'academics'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIZlDZq3efVM",
        "colab_type": "code",
        "outputId": "51135904-8d6a-4fd2-9a5e-24fc45919c6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "viewsdata.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['user_id', 'post_id', 'timestamp'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kUS9B4sLeX2i",
        "colab_type": "code",
        "outputId": "f3d1a1d4-3a30-41f1-a4be-b7f38e94442c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "postdata.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['_id', 'title', 'category', ' post_type'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmYcJ_4GhgAA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Rename the columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5la5Uy6HdPqq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "postdata.rename(columns={'_id':'post_id'},inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XA3RV2Ywet1a",
        "colab_type": "code",
        "outputId": "7b60045c-f05f-4f4c-bd19-9c988bcc8c3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "postdata.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>post_id</th>\n",
              "      <th>title</th>\n",
              "      <th>category</th>\n",
              "      <th>post_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5d62abaa65218653a132c956</td>\n",
              "      <td>hello there</td>\n",
              "      <td>Plant Biotechnology</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5d6d39567fa40e1417a4931c</td>\n",
              "      <td>Ml and AI</td>\n",
              "      <td>Artificial Intelligence|Machine Learning|Infor...</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5d7d23315720533e15c3b1ee</td>\n",
              "      <td>What is an Operating System ?</td>\n",
              "      <td>Operating Systems</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5d7d405e5720533e15c3b1f3</td>\n",
              "      <td>Lord Shiva</td>\n",
              "      <td>Drawings</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5d80dfbc6c53455f896e600e</td>\n",
              "      <td>How Competition law evolved?</td>\n",
              "      <td>Competition Laws</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    post_id  ...  post_type\n",
              "0  5d62abaa65218653a132c956  ...       blog\n",
              "1  5d6d39567fa40e1417a4931c  ...       blog\n",
              "2  5d7d23315720533e15c3b1ee  ...       blog\n",
              "3  5d7d405e5720533e15c3b1f3  ...    artwork\n",
              "4  5d80dfbc6c53455f896e600e  ...       blog\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atjiMZ7XewI1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "userdata.rename(columns={'_id':'user_id'},inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZU98vqDfVkn",
        "colab_type": "code",
        "outputId": "b33dd5e1-f5ad-421d-e817-b20ddb2766b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "userdata.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>name</th>\n",
              "      <th>gender</th>\n",
              "      <th>academics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5d60098a653a331687083238</td>\n",
              "      <td>Nivesh Singh Chauhan</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5d610ae1653a331687083239</td>\n",
              "      <td>Gaurav Sharma</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5d618359fc5fcf3bdd9a0910</td>\n",
              "      <td>Akshay Mishra</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5d6d2bb87fa40e1417a49315</td>\n",
              "      <td>Saksham Mathur</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5d7c994d5720533e15c3b1e9</td>\n",
              "      <td>Varun Chowhan</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    user_id                  name gender      academics\n",
              "0  5d60098a653a331687083238  Nivesh Singh Chauhan   male  undergraduate\n",
              "1  5d610ae1653a331687083239         Gaurav Sharma   male       graduate\n",
              "2  5d618359fc5fcf3bdd9a0910         Akshay Mishra   male  undergraduate\n",
              "3  5d6d2bb87fa40e1417a49315        Saksham Mathur   male  undergraduate\n",
              "4  5d7c994d5720533e15c3b1e9         Varun Chowhan   male  undergraduate"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4im109XgASEa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_new=pd.merge(viewsdata, userdata,on='user_id')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLkl9FdhhkJT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## merge the dataframe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g72RkNQ1A1fj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=pd.merge(df_new,postdata,on='post_id')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYiIvAELBz55",
        "colab_type": "code",
        "outputId": "12de11f4-1314-495a-cb40-4fed7f356077",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "X[X['name']=='thesocialcomment']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>post_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>name</th>\n",
              "      <th>gender</th>\n",
              "      <th>academics</th>\n",
              "      <th>title</th>\n",
              "      <th>category</th>\n",
              "      <th>post_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ec821ddec493f4a2655889e</td>\n",
              "      <td>2020-05-22T20:10:41.100Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Save Earth.</td>\n",
              "      <td>Visual Arts|Graphic Design|Artistic design|Gra...</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ecd5d417023451e662235c5</td>\n",
              "      <td>2020-05-26T20:09:50.934Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Machine Learning”&amp;“Operations\" (MlOps)</td>\n",
              "      <td>Computer Technology|Machine Learning</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ec8204cec493f4a26558893</td>\n",
              "      <td>2020-05-22T20:10:45.520Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Art Expo 2020</td>\n",
              "      <td>Artistic design|Logo Design|Graphic|Illustration</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ec1fd0974f7660d73aa0fd5</td>\n",
              "      <td>2020-05-18T08:38:14.735Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Daaku</td>\n",
              "      <td>Drawings</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ed13d2876027d35905cc4c2</td>\n",
              "      <td>2020-05-29T18:51:36.449Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Launching an EC2 instance.</td>\n",
              "      <td>Technology</td>\n",
              "      <td>skill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ecce8a5eaff6b0c3a58a5e9</td>\n",
              "      <td>2020-05-26T20:20:17.151Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>The Waves Have Stood Still</td>\n",
              "      <td>Photography</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>384</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ecc1d4eeaff6b0c3a58a565</td>\n",
              "      <td>2020-05-25T19:47:45.192Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Faith in yourself</td>\n",
              "      <td>Photography</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ec7a699ec493f4a2655883a</td>\n",
              "      <td>2020-05-22T20:11:09.561Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>HUMAN ILLUSTRATION (With Galaxy Background)</td>\n",
              "      <td>Fashion Design|Illustration|Watercolours|Drawings</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ec7a8bdec493f4a26558846</td>\n",
              "      <td>2020-05-22T20:11:02.810Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>HUMAN ILLUSTRATION</td>\n",
              "      <td>Drawings|Fashion Design|Illustration|Watercolours</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ec7ad1aec493f4a26558869</td>\n",
              "      <td>2020-05-22T20:10:51.932Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>FASHION ILLUSTRATION (street fashion)</td>\n",
              "      <td>Fashion Design|Illustration|Conceptual|Waterco...</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>447</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ec7abfdec493f4a26558860</td>\n",
              "      <td>2020-05-22T20:10:57.073Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>FASHION ILLUSTRATION (OP ART)</td>\n",
              "      <td>Drawings|Fashion Design|Mixed Media|Conceptual...</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>853</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5e8448aba3258347b42f2447</td>\n",
              "      <td>2020-04-01T11:15:40.685Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>JUSTICE FOR NORTH EAST</td>\n",
              "      <td>Human Rights|Fundamental Rights</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>876</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5e81ba06a3258347b42f21ef</td>\n",
              "      <td>2020-03-30T09:22:59.310Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Managing Stress Amidst Corona</td>\n",
              "      <td>Mass Media|Indian Government</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>937</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5e81be54a3258347b42f221d</td>\n",
              "      <td>2020-03-30T11:56:21.611Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Work From Home: Working Under Corona's Effect</td>\n",
              "      <td>Mass Media|Indian Government</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>985</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5e7f4fb3a3258347b42f2156</td>\n",
              "      <td>2020-03-30T13:55:56.919Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>100 Free University Courses Online [2020]</td>\n",
              "      <td>Biotechnology|Plant Biotechnology</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1020</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ecc186aeaff6b0c3a58a53f</td>\n",
              "      <td>2020-05-25T19:47:55.796Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Beliefs ❣️</td>\n",
              "      <td>Visual Arts|Photography</td>\n",
              "      <td>artwork</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1144</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5e81bc41a3258347b42f220a</td>\n",
              "      <td>2020-03-30T12:08:17.688Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>How to Maintain Physical Distance: Physical Di...</td>\n",
              "      <td>Mass Media|Indian Government</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1147</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5e773c89cfc8b713f5ac7ac9</td>\n",
              "      <td>2020-03-30T09:01:58.076Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Corona: What it is; how to prevent it and its ...</td>\n",
              "      <td>Mass Media|Indian Government</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1148</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5e77738bcfc8b713f5ac7b36</td>\n",
              "      <td>2020-03-29T15:40:59.009Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Cloud Computing</td>\n",
              "      <td>Computer Technology|Cloud Computing</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       user_id  ...  post_type\n",
              "3     5de50d768eab6401affbb135  ...    artwork\n",
              "18    5de50d768eab6401affbb135  ...       blog\n",
              "82    5de50d768eab6401affbb135  ...    artwork\n",
              "151   5de50d768eab6401affbb135  ...    artwork\n",
              "269   5de50d768eab6401affbb135  ...      skill\n",
              "370   5de50d768eab6401affbb135  ...    artwork\n",
              "384   5de50d768eab6401affbb135  ...    artwork\n",
              "412   5de50d768eab6401affbb135  ...    artwork\n",
              "424   5de50d768eab6401affbb135  ...    artwork\n",
              "435   5de50d768eab6401affbb135  ...    artwork\n",
              "447   5de50d768eab6401affbb135  ...    artwork\n",
              "853   5de50d768eab6401affbb135  ...       blog\n",
              "876   5de50d768eab6401affbb135  ...       blog\n",
              "937   5de50d768eab6401affbb135  ...       blog\n",
              "985   5de50d768eab6401affbb135  ...       blog\n",
              "1020  5de50d768eab6401affbb135  ...    artwork\n",
              "1144  5de50d768eab6401affbb135  ...       blog\n",
              "1147  5de50d768eab6401affbb135  ...       blog\n",
              "1148  5de50d768eab6401affbb135  ...       blog\n",
              "\n",
              "[19 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9acc8MQhtUeh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##convert timestamp\n",
        "def timestamp(df):\n",
        "  y=dateutil.parser.parse(df)\n",
        "  year=y.year\n",
        "  month=y.month\n",
        "  day=y.day\n",
        "  hour=y.hour\n",
        "  minute=y.minute\n",
        "  return year,month,day,hour,minute"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yLjCnZ-v7bl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "convt_time=X['timestamp'].apply(timestamp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8k9afA6rCZtc",
        "colab_type": "code",
        "outputId": "8eaf8447-97d6-4724-c43f-1963277f7b12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "X['category'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Photography                                                                           241\n",
              "Drawings                                                                              149\n",
              "Painting                                                                               75\n",
              "Computer Technology|Machine Learning                                                   40\n",
              "Computer Technology|Cloud Computing                                                    27\n",
              "                                                                                     ... \n",
              "Physiology|Gastroenterology|Cardiology                                                  1\n",
              "Visual Arts|Calligraphy|Pen and ink                                                     1\n",
              "Drawings|Painting|Visual Arts|Graphic Design|Prints|Illustration                        1\n",
              "Literature|Movements in Literature                                                      1\n",
              "Human Resources|Performance In Organization|Organizational Behaviour|HR Management      1\n",
              "Name: category, Length: 231, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMaxw2RChwej",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# preprocess fuction for preparing the dataset for the feature extraction using countvectorizer "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhWGNyEVRUeq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "words=[]\n",
        "def preprocess(df):\n",
        "  word=re.sub('[|;/]',' ',df)\n",
        "  word=word.lower()\n",
        "  words.append(word)\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Yb3bzZriTi8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# filling the nan values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LHJrOLmNAPw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "postdata['category'].fillna('Photography',inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "362drIlGmEce",
        "colab_type": "text"
      },
      "source": [
        "#### Content Based Filtering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSIAtOoUE6FW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction import text\n",
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkAjA_HrtBi5",
        "colab_type": "code",
        "outputId": "cb5f1310-ec36-4049-87bd-a6697cbf3793",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "postdata['category'].apply(preprocess)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      None\n",
              "1      None\n",
              "2      None\n",
              "3      None\n",
              "4      None\n",
              "       ... \n",
              "488    None\n",
              "489    None\n",
              "490    None\n",
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        "colab": {
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          "height": 34
        }
      },
      "source": [
        "userdata.user_id.isin(X.user_id).unique()"
      ],
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True])"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "postdata.post_id.isin(X.post_id).unique()"
      ],
      "execution_count": 0,
      "outputs": [
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          "data": {
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              "array([ True])"
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      "metadata": {
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        "colab_type": "code",
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      "source": [
        "## Countvectorizer to create the features "
      ],
      "execution_count": 0,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "m28OiMO-NJqq",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "count=text.CountVectorizer(lowercase=True)"
      ],
      "execution_count": 0,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "LIyh2zHIQCJh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "value=count.fit_transform(words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMOu0k_K3hgw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "words=pd.DataFrame(value.toarray(),columns=np.array(count.get_feature_names()),index=postdata['title'].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fldJBGLYE7m",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "#def vectorize():\n",
        "#  count=text.CountVectorizer(lowercase=True)\n",
        "#  value=count.fit_transform(words)\n",
        "#  words=pd.DataFrame(value.toarray(),columns=np.array(count.get_feature_names()),index=dataframe['title'].values)\n",
        "\n",
        "#  coss_matrix=cosine_similarity(words)\n",
        "\n",
        "#  recommend_matrix=pd.DataFrame(coss_matrix)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OixKbJMc19kH",
        "colab_type": "code",
        "outputId": "9caf6dbc-f9e1-49ab-c103-f239fee9610a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "source": [
        "words.shape"
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      "execution_count": 0,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(493, 272)"
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        "colab_type": "code",
        "outputId": "e948152c-0a1f-4dbc-c636-f6eac022bdc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "words.head()"
      ],
      "execution_count": 0,
      "outputs": [
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          "output_type": "execute_result",
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              "      <th>advertising</th>\n",
              "      <th>agreement</th>\n",
              "      <th>algebra</th>\n",
              "      <th>algorithms</th>\n",
              "      <th>alternate</th>\n",
              "      <th>analysis</th>\n",
              "      <th>and</th>\n",
              "      <th>animal</th>\n",
              "      <th>application</th>\n",
              "      <th>applications</th>\n",
              "      <th>applied</th>\n",
              "      <th>archeology</th>\n",
              "      <th>architecture</th>\n",
              "      <th>art</th>\n",
              "      <th>articulation</th>\n",
              "      <th>artificial</th>\n",
              "      <th>artistic</th>\n",
              "      <th>arts</th>\n",
              "      <th>atomic</th>\n",
              "      <th>audit</th>\n",
              "      <th>auditing</th>\n",
              "      <th>banking</th>\n",
              "      <th>basics</th>\n",
              "      <th>behaviour</th>\n",
              "      <th>bio</th>\n",
              "      <th>biology</th>\n",
              "      <th>biotechnology</th>\n",
              "      <th>break</th>\n",
              "      <th>business</th>\n",
              "      <th>calligraphy</th>\n",
              "      <th>capitalist</th>\n",
              "      <th>cardiology</th>\n",
              "      <th>carving</th>\n",
              "      <th>ceramics</th>\n",
              "      <th>chemistry</th>\n",
              "      <th>...</th>\n",
              "      <th>sociology</th>\n",
              "      <th>sports</th>\n",
              "      <th>start</th>\n",
              "      <th>statuary</th>\n",
              "      <th>stories</th>\n",
              "      <th>strategies</th>\n",
              "      <th>studies</th>\n",
              "      <th>substance</th>\n",
              "      <th>system</th>\n",
              "      <th>systems</th>\n",
              "      <th>tapestry</th>\n",
              "      <th>tax</th>\n",
              "      <th>taxation</th>\n",
              "      <th>team</th>\n",
              "      <th>tech</th>\n",
              "      <th>techniques</th>\n",
              "      <th>technology</th>\n",
              "      <th>test</th>\n",
              "      <th>textile</th>\n",
              "      <th>theory</th>\n",
              "      <th>thought</th>\n",
              "      <th>tradition</th>\n",
              "      <th>transactions</th>\n",
              "      <th>treaties</th>\n",
              "      <th>trends</th>\n",
              "      <th>typography</th>\n",
              "      <th>ups</th>\n",
              "      <th>venture</th>\n",
              "      <th>video</th>\n",
              "      <th>videography</th>\n",
              "      <th>visual</th>\n",
              "      <th>watercolours</th>\n",
              "      <th>web</th>\n",
              "      <th>wood</th>\n",
              "      <th>work</th>\n",
              "      <th>zoology</th>\n",
              "      <th>artwork</th>\n",
              "      <th>blog</th>\n",
              "      <th>project</th>\n",
              "      <th>skill</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <th>Lord Shiva</th>\n",
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              "    <tr>\n",
              "      <th>How Competition law evolved?</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 276 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                               2d  abuse  accounting  ...  blog  project  skill\n",
              "hello there                     0      0           0  ...     1        0      0\n",
              "Ml and AI                       0      0           0  ...     1        0      0\n",
              "What is an Operating System ?   0      0           0  ...     1        0      0\n",
              "Lord Shiva                      0      0           0  ...     0        0      0\n",
              "How Competition law evolved?    0      0           0  ...     1        0      0\n",
              "\n",
              "[5 rows x 276 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDT44jNUle7O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#cosine similarity to find the similarity between the post"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7LMJLt7HXZ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgNVDodVJA_z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "type_df=pd.DataFrame(pd.get_dummies(postdata[' post_type']).to_numpy(),columns=pd.get_dummies(postdata[' post_type']).columns,index=postdata['title'].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FC8buulJI3F",
        "colab_type": "code",
        "outputId": "da47ad31-606c-46e2-e8a9-35a0eef77ec9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "type_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <td>1</td>\n",
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              "    </tr>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               artwork  blog  project  skill\n",
              "hello there                          0     1        0      0\n",
              "Ml and AI                            0     1        0      0\n",
              "What is an Operating System ?        0     1        0      0\n",
              "Lord Shiva                           1     0        0      0\n",
              "How Competition law evolved?         0     1        0      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xiij_WpFN5RN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "words['artwork']=type_df['artwork']\n",
        "words['blog']=type_df['blog']\n",
        "words['project']=type_df['project']\n",
        "words['skill']=type_df['skill']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPGi3ZHTAbgz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "coss_matrix=cosine_similarity(words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fpbsywlRqqV",
        "colab_type": "code",
        "outputId": "71352523-e7e3-42e2-fd3e-a762c7faa528",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "coss_matrix.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(493, 493)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usv5trwOlqdM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##similarity matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZjeTZ47AjDW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "recommend_matrix=pd.DataFrame(coss_matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3NT_vJ1M_oq",
        "colab_type": "code",
        "outputId": "308c4443-c6ce-4703-8ab5-26b52effa761",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "recommend_matrix"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "      <td>0.316228</td>\n",
              "      <td>0.288675</td>\n",
              "      <td>0.288675</td>\n",
              "      <td>0.316228</td>\n",
              "      <td>0.316228</td>\n",
              "      <td>0.288675</td>\n",
              "      <td>0.288675</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.258199</td>\n",
              "      <td>0.258199</td>\n",
              "      <td>0.258199</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.204124</td>\n",
              "      <td>0.258199</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.174078</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>0.218218</td>\n",
              "      <td>0.571429</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.218218</td>\n",
              "      <td>0.218218</td>\n",
              "      <td>0.218218</td>\n",
              "      <td>0.218218</td>\n",
              "      <td>0.218218</td>\n",
              "      <td>0.218218</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.218218</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.218218</td>\n",
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              "      <td>0.676123</td>\n",
              "      <td>0.676123</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.133631</td>\n",
              "      <td>0.676123</td>\n",
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              "      <td>0.911685</td>\n",
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              "      <td>0.911685</td>\n",
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              "    <tr>\n",
              "      <th>489</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.174078</td>\n",
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              "      <td>0.674200</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.674200</td>\n",
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              "      <td>0.674200</td>\n",
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              "      <td>0.674200</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106600</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.426401</td>\n",
              "      <td>0.911685</td>\n",
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              "    <tr>\n",
              "      <th>490</th>\n",
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              "      <td>0.5</td>\n",
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              "    <tr>\n",
              "      <th>491</th>\n",
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              "      <td>0.258199</td>\n",
              "      <td>0.816497</td>\n",
              "      <td>0.436436</td>\n",
              "      <td>0.348155</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.348155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>492</th>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.455842</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.911685</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.213201</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.174078</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.134840</td>\n",
              "      <td>0.134840</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.113961</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.100504</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.426401</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.762770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106600</td>\n",
              "      <td>0.674200</td>\n",
              "      <td>0.426401</td>\n",
              "      <td>0.911685</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.348155</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>493 rows × 493 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1         2    3    ...       489  490       491       492\n",
              "0    1.000000  0.218218  0.333333  0.0  ...  0.174078  0.0  0.000000  0.174078\n",
              "1    0.218218  1.000000  0.218218  0.0  ...  0.455842  0.0  0.218218  0.455842\n",
              "2    0.333333  0.218218  1.000000  0.0  ...  0.174078  0.0  0.000000  0.174078\n",
              "3    0.000000  0.000000  0.000000  1.0  ...  0.000000  0.0  0.000000  0.000000\n",
              "4    0.333333  0.218218  0.333333  0.0  ...  0.174078  0.0  0.000000  0.174078\n",
              "..        ...       ...       ...  ...  ...       ...  ...       ...       ...\n",
              "488  0.218218  0.571429  0.218218  0.0  ...  0.911685  0.0  0.436436  0.911685\n",
              "489  0.174078  0.455842  0.174078  0.0  ...  1.000000  0.0  0.348155  1.000000\n",
              "490  0.000000  0.000000  0.000000  0.0  ...  0.000000  1.0  0.000000  0.000000\n",
              "491  0.000000  0.218218  0.000000  0.0  ...  0.348155  0.0  1.000000  0.348155\n",
              "492  0.174078  0.455842  0.174078  0.0  ...  1.000000  0.0  0.348155  1.000000\n",
              "\n",
              "[493 rows x 493 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JNyUcL5ly59",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##recommend post"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8Q47zdqCQp5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def recommend_post(post,no_of):\n",
        "\n",
        "  index=postdata[postdata['title']==post].index[0]\n",
        "  type_of_post=postdata.iloc[index,:][' post_type']\n",
        "  scores=recommend_matrix.iloc[index,:].sort_values(ascending=False)\n",
        "  recomend=pd.DataFrame(columns=['recomend_percent'],index=scores.index)\n",
        "  recomend['recomend_percent']=scores\n",
        "  \n",
        "  recomend=pd.merge(recomend,postdata[[' post_type','title']],left_index=True, right_index=True)\n",
        "  recomend.drop([index],inplace=True)\n",
        "\n",
        "  return(recomend.iloc[1:no_of+1,:])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74EBHLz4nd2g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##output of content-based filtering\n",
        "##Recommend similar posts for the given post"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e89OgntDs5nm",
        "colab_type": "code",
        "outputId": "11e20f4b-6103-4b59-b59f-b71f0cfbc00e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "recommend_post('आमची मुंबाई !!!',5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>recomend_percent</th>\n",
              "      <th>post_type</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>0.408248</td>\n",
              "      <td>blog</td>\n",
              "      <td>INDIA 5 TRILLION ECONOMY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>0.365148</td>\n",
              "      <td>blog</td>\n",
              "      <td>How to Maintain Physical Distance: Physical Di...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>0.365148</td>\n",
              "      <td>blog</td>\n",
              "      <td>7 Steps To Stay Safe From Corona Virus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>0.365148</td>\n",
              "      <td>blog</td>\n",
              "      <td>Corona: What it is; how to prevent it and its ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>0.365148</td>\n",
              "      <td>blog</td>\n",
              "      <td>Managing Stress Amidst Corona</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     recomend_percent  ...                                              title\n",
              "278          0.408248  ...                           INDIA 5 TRILLION ECONOMY\n",
              "189          0.365148  ...  How to Maintain Physical Distance: Physical Di...\n",
              "187          0.365148  ...             7 Steps To Stay Safe From Corona Virus\n",
              "137          0.365148  ...  Corona: What it is; how to prevent it and its ...\n",
              "188          0.365148  ...                      Managing Stress Amidst Corona\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mzX9UWdZGuU",
        "colab_type": "code",
        "outputId": "2ceee1d8-469a-4f06-c12d-a52a07db64c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "recommend_post('Configure CI/CD Pipeline in GitLab and deployment to server via SSH',10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>recomend_percent</th>\n",
              "      <th>post_type</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>blog</td>\n",
              "      <td>Network &amp; it's types</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>blog</td>\n",
              "      <td>Decision Making with 7 steps !!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>blog</td>\n",
              "      <td>Network Model</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>blog</td>\n",
              "      <td>Database management system(DBMS)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>blog</td>\n",
              "      <td>Information System</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>blog</td>\n",
              "      <td>Baseband &amp; Broadband</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>blog</td>\n",
              "      <td>Topology &amp; it's types</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>0.957427</td>\n",
              "      <td>blog</td>\n",
              "      <td>Firewall</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>0.919866</td>\n",
              "      <td>blog</td>\n",
              "      <td>Operators In PHP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>0.911685</td>\n",
              "      <td>blog</td>\n",
              "      <td>5 Best Computer Networking Courses &amp; Certifica...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     recomend_percent  ...                                              title\n",
              "114          1.000000  ...                               Network & it's types\n",
              "113          1.000000  ...                   Decision Making with 7 steps !!!\n",
              "112          1.000000  ...                                      Network Model\n",
              "109          1.000000  ...                   Database management system(DBMS)\n",
              "108          1.000000  ...                                 Information System\n",
              "107          1.000000  ...                               Baseband & Broadband\n",
              "106          1.000000  ...                              Topology & it's types\n",
              "101          0.957427  ...                                           Firewall\n",
              "78           0.919866  ...                                   Operators In PHP\n",
              "225          0.911685  ...  5 Best Computer Networking Courses & Certifica...\n",
              "\n",
              "[10 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAuiyIKSQaNs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "recommend_matrix.to_csv('/content/drive/My Drive/Recommendations engine task/Document from Debasish (Unzipped Files)/recommend_matrix.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "537Cjo7ySV4G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "words.to_csv('/content/drive/My Drive/Recommendations engine task/Document from Debasish (Unzipped Files)/words.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4c6At8JvmOkn",
        "colab_type": "text"
      },
      "source": [
        "#### Collaberative filtering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7scIqroTSDm",
        "colab_type": "code",
        "outputId": "fff29e70-7249-4fc7-d9cf-d1b5f4c20efa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "title_count=pd.DataFrame()\n",
        "y=X.groupby(['title','name'])['title'].count()\n",
        "title_count['one']=y\n",
        "title_count"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>one</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">'The Virtual ME'</th>\n",
              "      <th>Aryan Malakar</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gaurav Sharma</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rashi Jain</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">10 Best Artificial Intelligence (AI) Courses Online for 2020</th>\n",
              "      <th>KSHITIJ ANAND</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nivesh Singh Chauhan</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>upside down</th>\n",
              "      <th>Nivesh Singh Chauhan</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">आमची मुंबाई !!!</th>\n",
              "      <th>Gaurav Sharma</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smriti</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hritik chaturvedi</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>🍉 Watermelon lemon cooler</th>\n",
              "      <th>Nivesh Singh Chauhan</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1374 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                         one\n",
              "title                                              name                     \n",
              "'The Virtual ME'                                   Aryan Malakar           1\n",
              "                                                   Gaurav Sharma           1\n",
              "                                                   Rashi Jain              1\n",
              "10 Best Artificial Intelligence (AI) Courses On... KSHITIJ ANAND           1\n",
              "                                                   Nivesh Singh Chauhan    1\n",
              "...                                                                      ...\n",
              "upside down                                        Nivesh Singh Chauhan    1\n",
              "आमची मुंबाई !!!                                    Gaurav Sharma           1\n",
              "                                                   Smriti                  1\n",
              "                                                   hritik chaturvedi       1\n",
              "🍉 Watermelon lemon cooler                          Nivesh Singh Chauhan    1\n",
              "\n",
              "[1374 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdmeuwIY72uT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "title_count=title_count.reset_index(level=[0,1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0HurVwKLG3j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "count_matrix=title_count.pivot(index='name',columns='title',values='one').fillna(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBFwhg1OmkO9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## csr matrix to convert the sparse matrix to compressed matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "854WCHpOL2gj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from scipy.sparse import csr_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1xHfTrmYMeS1",
        "colab": {}
      },
      "source": [
        "compressed_matrix=csr_matrix(count_matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFCV6VnCM2KJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import NearestNeighbors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJulBR7YM3DW",
        "colab_type": "code",
        "outputId": "cb97b70c-77b5-46e8-b158-f727ccd74090",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "model_knn=NearestNeighbors(metric='cosine',algorithm='brute')\n",
        "model_knn.fit(compressed_matrix)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NearestNeighbors(algorithm='brute', leaf_size=30, metric='cosine',\n",
              "                 metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                 radius=1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DehAhb_WNn6Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "distances,indices=model_knn.kneighbors(count_matrix.iloc[1,:].values.reshape(1,-1),n_neighbors=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qgpPhSYNnep",
        "colab_type": "code",
        "outputId": "4ca39b1e-c394-43bb-a361-8c2c0d78381a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "for i in range(0,len(distances.flatten())):\n",
        "  print(i)\n",
        "  print(count_matrix.index[indices.flatten()[i]])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "Aayushree Khokhar\n",
            "1\n",
            "Neha Malik\n",
            "2\n",
            "Akshra Mathur\n",
            "3\n",
            "Aleena Francis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwLrNmclKmmL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def collab_movie(index):\n",
        "\n",
        "  model_knn=NearestNeighbors(metric='cosine',algorithm='brute')\n",
        "  model_knn.fit(compressed_matrix)\n",
        "\n",
        "  distances,indices=model_knn.kneighbors(count_matrix.iloc[index,:].values.reshape(1,-1),n_neighbors=4)\n",
        "  ls=[]\n",
        "  for i in range(0,len(distances.flatten())):\n",
        "    print(count_matrix.index[indices.flatten()[i]])\n",
        "    ls.append(count_matrix.index[indices.flatten()[i]])\n",
        "  dataframe=pd.concat([X_merged[X_merged['name']==ls[0]][['title','category','name','one']],X_merged[X_merged['name']==ls[1]][['title','category','name','one']]])\n",
        "  return dataframe[['title']]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wTRXZBsnQCA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##output of collaberative filtering\n",
        "##Recommend posts for the given user "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tu3MYVTcA7e",
        "colab_type": "code",
        "outputId": "d3388615-db65-4e60-bbdd-70d88215bc92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "collab_movie(2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Abhinav Mishra\n",
            "Pratishtha Bhelonde\n",
            "Neha Malik\n",
            "Varsha\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>775</th>\n",
              "      <td>Lovely explosion box!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>783</th>\n",
              "      <td>Doodles woodles!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>762</th>\n",
              "      <td>Stand alone and stand apart.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>774</th>\n",
              "      <td>Lovely explosion box!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>782</th>\n",
              "      <td>Doodles woodles!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>968</th>\n",
              "      <td>The Meenamma</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1393</th>\n",
              "      <td>Aesthetics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1433</th>\n",
              "      <td>PEACE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             title\n",
              "775          Lovely explosion box!\n",
              "783               Doodles woodles!\n",
              "762   Stand alone and stand apart.\n",
              "774          Lovely explosion box!\n",
              "782               Doodles woodles!\n",
              "968                   The Meenamma\n",
              "1393                    Aesthetics\n",
              "1433                         PEACE"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8X8bZGTwrpVK",
        "colab_type": "code",
        "outputId": "a5521d63-4864-48c3-d24a-bb21b0d21992",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        }
      },
      "source": [
        "X_merged=X.merge(title_count,on=['title','name'])\n",
        "X_merged"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>user_id</th>\n",
              "      <th>post_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>name</th>\n",
              "      <th>gender</th>\n",
              "      <th>academics</th>\n",
              "      <th>title</th>\n",
              "      <th>category</th>\n",
              "      <th>post_type</th>\n",
              "      <th>one</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5df49b32cc709107827fb3c7</td>\n",
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              "      <td>2020-06-01T10:46:45.131Z</td>\n",
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              "      <td>female</td>\n",
              "      <td>undergraduate</td>\n",
              "      <td>Save Earth.</td>\n",
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              "      <td>2020-05-22T20:11:32.317Z</td>\n",
              "      <td>Varun Chowhan</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "      <td>Save Earth.</td>\n",
              "      <td>Visual Arts|Graphic Design|Artistic design|Gra...</td>\n",
              "      <td>artwork</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <td>5e5af599d701ab08af792b63</td>\n",
              "      <td>5ec821ddec493f4a2655889e</td>\n",
              "      <td>2020-05-22T20:01:37.309Z</td>\n",
              "      <td>Ilupeju Ayokunnumi</td>\n",
              "      <td>female</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Save Earth.</td>\n",
              "      <td>Visual Arts|Graphic Design|Artistic design|Gra...</td>\n",
              "      <td>artwork</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5de50d768eab6401affbb135</td>\n",
              "      <td>5ec821ddec493f4a2655889e</td>\n",
              "      <td>2020-05-22T20:10:41.100Z</td>\n",
              "      <td>thesocialcomment</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Save Earth.</td>\n",
              "      <td>Visual Arts|Graphic Design|Artistic design|Gra...</td>\n",
              "      <td>artwork</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <td>5deeef6142a8854bf6eabab9</td>\n",
              "      <td>5ec821ddec493f4a2655889e</td>\n",
              "      <td>2020-05-22T20:08:45.061Z</td>\n",
              "      <td>siddharth saxena</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "      <td>Save Earth.</td>\n",
              "      <td>Visual Arts|Graphic Design|Artistic design|Gra...</td>\n",
              "      <td>artwork</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1442</th>\n",
              "      <td>5e899be7a3258347b42f25d0</td>\n",
              "      <td>5e3ab644eab55d319938a72d</td>\n",
              "      <td>2020-04-05T08:57:02.583Z</td>\n",
              "      <td>Srinidhi A</td>\n",
              "      <td>female</td>\n",
              "      <td>undergraduate</td>\n",
              "      <td>Travelling</td>\n",
              "      <td>Photography</td>\n",
              "      <td>artwork</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1443</th>\n",
              "      <td>5e78ce84cfc8b713f5ac7cee</td>\n",
              "      <td>5e78d6dccfc8b713f5ac7cf4</td>\n",
              "      <td>2020-04-05T05:44:03.998Z</td>\n",
              "      <td>Fauziya Shaikh</td>\n",
              "      <td>female</td>\n",
              "      <td>undergraduate</td>\n",
              "      <td>Childhood Sketch</td>\n",
              "      <td>Drawings|Watercolours</td>\n",
              "      <td>artwork</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1444</th>\n",
              "      <td>5e880f02a3258347b42f252c</td>\n",
              "      <td>5e783590cfc8b713f5ac7c5d</td>\n",
              "      <td>2020-04-04T04:44:54.488Z</td>\n",
              "      <td>Keshant Sharma</td>\n",
              "      <td>male</td>\n",
              "      <td>graduate</td>\n",
              "      <td>Quarantine days</td>\n",
              "      <td>Painting</td>\n",
              "      <td>artwork</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1445</th>\n",
              "      <td>5e783375cfc8b713f5ac7c5b</td>\n",
              "      <td>5e787636cfc8b713f5ac7cbe</td>\n",
              "      <td>2020-03-28T11:45:05.321Z</td>\n",
              "      <td>Bhargav Prakash</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "      <td>Sunsets in Ottawa</td>\n",
              "      <td>Painting</td>\n",
              "      <td>artwork</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1446</th>\n",
              "      <td>5e7def80a3258347b42f2124</td>\n",
              "      <td>5dd1751db802e41ed198b680</td>\n",
              "      <td>2020-03-27T12:26:18.624Z</td>\n",
              "      <td>Massimo Puppo</td>\n",
              "      <td>male</td>\n",
              "      <td>undergraduate</td>\n",
              "      <td>Marital Rape - Rape is Rape</td>\n",
              "      <td>Empowerment</td>\n",
              "      <td>blog</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1447 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       user_id                   post_id  ...  post_type one\n",
              "0     5df49b32cc709107827fb3c7  5ec821ddec493f4a2655889e  ...    artwork   1\n",
              "1     5d7c994d5720533e15c3b1e9  5ec821ddec493f4a2655889e  ...    artwork   1\n",
              "2     5e5af599d701ab08af792b63  5ec821ddec493f4a2655889e  ...    artwork   1\n",
              "3     5de50d768eab6401affbb135  5ec821ddec493f4a2655889e  ...    artwork   1\n",
              "4     5deeef6142a8854bf6eabab9  5ec821ddec493f4a2655889e  ...    artwork   1\n",
              "...                        ...                       ...  ...        ...  ..\n",
              "1442  5e899be7a3258347b42f25d0  5e3ab644eab55d319938a72d  ...    artwork   1\n",
              "1443  5e78ce84cfc8b713f5ac7cee  5e78d6dccfc8b713f5ac7cf4  ...    artwork   1\n",
              "1444  5e880f02a3258347b42f252c  5e783590cfc8b713f5ac7c5d  ...    artwork   1\n",
              "1445  5e783375cfc8b713f5ac7c5b  5e787636cfc8b713f5ac7cbe  ...    artwork   1\n",
              "1446  5e7def80a3258347b42f2124  5dd1751db802e41ed198b680  ...       blog   1\n",
              "\n",
              "[1447 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTbas9-QTNKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}