LinkedinDevTypePredict.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LinkedinDevTypePredict.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "-ISAPZvh5h2Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "# import tensorflow.contrib.eager as tfe\n",
        "\n",
        "# tf.enable_eager_execution()\n",
        "\n",
        "# print(\"TensorFlow version: {}\".format(tf.VERSION))\n",
        "# print(\"Eager execution: {}\".format(tf.executing_eagerly()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QN27fPG65scv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bfa491b3-a158-4860-a63d-aa1f98b3746b"
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "from keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "17RSNl3Q88Di",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "90b72d4b-dd9b-4c95-96ae-d785e988d1a7"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7f6ad895-ce9b-4d94-8c90-108ff95f4e76\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-7f6ad895-ce9b-4d94-8c90-108ff95f4e76\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving linkedin_developer_nlp_analysis.xlsx to linkedin_developer_nlp_analysis (4).xlsx\n",
            "User uploaded file \"linkedin_developer_nlp_analysis.xlsx\" with length 61497 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "G0TP8IqH9LRH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "J8TB_-_L92v5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1eb086ec-eb2b-4988-d5b9-eeb87ea81fbc"
      },
      "cell_type": "code",
      "source": [
        "!pip install xlrd"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xlrd in /usr/local/lib/python3.6/dist-packages (1.1.0)\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5b6Nkzwz9Vi6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_excel(\"./linkedin_developer_nlp_analysis (4).xlsx\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HBDiXbjU9eO-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "4adc8023-40bc-4ed4-e3ea-f406363453f8"
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 8,
      "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>URL</th>\n",
              "      <th>NAME</th>\n",
              "      <th>POSITION</th>\n",
              "      <th>COMPANY NAME IN EXP</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "      <th>YEAR OF JOIN</th>\n",
              "      <th>PRESENT</th>\n",
              "      <th>TOTAL EXPER IN YEAR</th>\n",
              "      <th>LOCATION</th>\n",
              "      <th>OTHER INFO</th>\n",
              "      <th>SKILL</th>\n",
              "      <th>OTHER SKILL</th>\n",
              "      <th>TACT SKILL</th>\n",
              "      <th>DATE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>https://www.linkedin.com/in/saravanan90/</td>\n",
              "      <td>Saravanan Shanmugam</td>\n",
              "      <td>Full Stack Developer</td>\n",
              "      <td>An ardent learner getting exposed to new conce...</td>\n",
              "      <td>Tenmiles</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>4.7</td>\n",
              "      <td>Chennai, Tamil Nadu, India</td>\n",
              "      <td>Web Developer Tech Lead,Web Developer,Java Tra...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-05-28 11:04:26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>https://www.linkedin.com/in/rajeshwar-dhayalan...</td>\n",
              "      <td>Rajeshwar Dhayalan</td>\n",
              "      <td>Full Stack Developer</td>\n",
              "      <td>Looking towards the new technology and the get...</td>\n",
              "      <td>ITILITE</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>2.4</td>\n",
              "      <td>Vellore, Tamil Nadu, India</td>\n",
              "      <td>Working as Associate Software Engineer. As a S...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-05-28 11:05:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>https://www.linkedin.com/in/vigneshwara-manika...</td>\n",
              "      <td>vigneshwara manikandan</td>\n",
              "      <td>Full Stack Developer</td>\n",
              "      <td>Experienced iOS App Developer with a demonstra...</td>\n",
              "      <td>Cisco</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Chennai Area, India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-05-28 11:07:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>https://www.linkedin.com/in/madhan-chandar/</td>\n",
              "      <td>Madhan Balachandar</td>\n",
              "      <td>Full Stack Developer</td>\n",
              "      <td>I am an avid technology enthusiast. Skilled in...</td>\n",
              "      <td>Sirius Computer Solutions</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Chennai, Tamil Nadu, India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-05-28 11:07:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://www.linkedin.com/in/karthik-venkatesh-...</td>\n",
              "      <td>Karthik Venkatesh</td>\n",
              "      <td>Full-Stack Developer</td>\n",
              "      <td>Full Stack developer with the ability to desig...</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>5.3</td>\n",
              "      <td>Chennai, Tamil Nadu, India</td>\n",
              "      <td>ESTABLISHED an IMAGING LABS for developing sev...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-05-28 11:07:37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 URL                    NAME  \\\n",
              "0           https://www.linkedin.com/in/saravanan90/     Saravanan Shanmugam   \n",
              "1  https://www.linkedin.com/in/rajeshwar-dhayalan...      Rajeshwar Dhayalan   \n",
              "2  https://www.linkedin.com/in/vigneshwara-manika...  vigneshwara manikandan   \n",
              "3        https://www.linkedin.com/in/madhan-chandar/      Madhan Balachandar   \n",
              "4  https://www.linkedin.com/in/karthik-venkatesh-...       Karthik Venkatesh   \n",
              "\n",
              "                POSITION                                COMPANY NAME IN EXP  \\\n",
              "0  Full Stack Developer   An ardent learner getting exposed to new conce...   \n",
              "1  Full Stack Developer   Looking towards the new technology and the get...   \n",
              "2  Full Stack Developer   Experienced iOS App Developer with a demonstra...   \n",
              "3  Full Stack Developer   I am an avid technology enthusiast. Skilled in...   \n",
              "4   Full-Stack Developer  Full Stack developer with the ability to desig...   \n",
              "\n",
              "                  Unnamed: 4  YEAR OF JOIN  PRESENT  TOTAL EXPER IN YEAR  \\\n",
              "0                   Tenmiles          2016        1                  4.7   \n",
              "1                    ITILITE          2018        1                  2.4   \n",
              "2                      Cisco          2017        1                  2.0   \n",
              "3  Sirius Computer Solutions          2017        1                  0.1   \n",
              "4                     Amazon          2013        1                  5.3   \n",
              "\n",
              "                     LOCATION  \\\n",
              "0  Chennai, Tamil Nadu, India   \n",
              "1  Vellore, Tamil Nadu, India   \n",
              "2         Chennai Area, India   \n",
              "3  Chennai, Tamil Nadu, India   \n",
              "4  Chennai, Tamil Nadu, India   \n",
              "\n",
              "                                          OTHER INFO  SKILL  OTHER SKILL  \\\n",
              "0  Web Developer Tech Lead,Web Developer,Java Tra...    NaN          NaN   \n",
              "1  Working as Associate Software Engineer. As a S...    NaN          NaN   \n",
              "2                                                NaN    NaN          NaN   \n",
              "3                                                NaN    NaN          NaN   \n",
              "4  ESTABLISHED an IMAGING LABS for developing sev...    NaN          NaN   \n",
              "\n",
              "   TACT SKILL                 DATE  \n",
              "0         NaN  2018-05-28 11:04:26  \n",
              "1         NaN  2018-05-28 11:05:37  \n",
              "2         NaN  2018-05-28 11:07:04  \n",
              "3         NaN  2018-05-28 11:07:18  \n",
              "4         NaN  2018-05-28 11:07:37  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "9N2e7n0U99Xp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "a1091dbb-65a5-4e28-c60e-7a33c877220b"
      },
      "cell_type": "code",
      "source": [
        "df['text'] = df['OTHER INFO'] +\" \"+ df['COMPANY NAME IN EXP']\n",
        "df['text'][0]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Web Developer Tech Lead,Web Developer,Java Trainee Web Developer An ardent learner getting exposed to new concepts and sticking to fundamentals, putting them into practice to bring out innovative ideas and solutions. Currently engaged in front end development and having fun and learning in the world of web.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "7-5zgU639hWR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = df['text']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IRARE6i6b1Ad",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1092
        },
        "outputId": "8d82c7fb-ac17-4e97-8448-5dfc54a0e7bd"
      },
      "cell_type": "code",
      "source": [
        "X.fillna('.', inplace=True)\n",
        "X"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      Web Developer Tech Lead,Web Developer,Java Tra...\n",
              "1      Working as Associate Software Engineer. As a S...\n",
              "2                                                      .\n",
              "3                                                      .\n",
              "4      ESTABLISHED an IMAGING LABS for developing sev...\n",
              "5      Working as Full stack developer,Working as a W...\n",
              "6      Full stack development comprising of Ruby on R...\n",
              "7      We build awesome digital products that have th...\n",
              "8       HappyFox Helpdesk Design and Development usin...\n",
              "9                                                      .\n",
              "10     Joined the company as the first employee. Deve...\n",
              "11     ● Working as a Full Stack Developer at Tapzo s...\n",
              "12     Java/J2EE developer and Part of Technology tea...\n",
              "13     Full Stack Web Developer. Worked on maintainin...\n",
              "14     Developing digital banking application for cli...\n",
              "15     Specialized in Creating as well as integrating...\n",
              "16                                                     .\n",
              "17                                                     .\n",
              "18     nvolved in Developing Data Masking tool for Co...\n",
              "19                                                     .\n",
              "20     As an SEO executive i was responsible for -* B...\n",
              "21     • Currently working on Phased App roll-out fea...\n",
              "22      Currently working on Phased App roll-out feat...\n",
              "23     My responsibilities are to analyse the require...\n",
              "24                                                     .\n",
              "25                                                     .\n",
              "26     Responsible for developing and providing full ...\n",
              "27      Writing software solutions which help the ind...\n",
              "28     Responsible for developing the front-end of a ...\n",
              "29     Worked as a full stack developer in a product ...\n",
              "                             ...                        \n",
              "144                                                    .\n",
              "145                                                    .\n",
              "146                                                    .\n",
              "147    Developing an end-to-end web application. A sk...\n",
              "148    Working on pyhton, django, celery, html, javas...\n",
              "149                                                    .\n",
              "150    Understand requirements, create technology sta...\n",
              "151    To take care of the technical issues in the co...\n",
              "152                                                    .\n",
              "153                                                    .\n",
              "154    outsourced; Dear recruiters, unless you offer ...\n",
              "155    Backend developer (Java / Groovy / SQL) & Fron...\n",
              "156    Worked in a team, developing the company's cor...\n",
              "157    Some technologies that i use, Drupal 7 Akeneo ...\n",
              "158                                                    .\n",
              "159                                                    .\n",
              "160                                                    .\n",
              "161                                                    .\n",
              "162                                                    .\n",
              "163                                                    .\n",
              "164                                                    .\n",
              "165                                                    .\n",
              "166                                                    .\n",
              "167                                                    .\n",
              "168                                                    .\n",
              "169                                                    .\n",
              "170                                                    .\n",
              "171                                                    .\n",
              "172                                                    .\n",
              "173                                                    .\n",
              "Name: text, Length: 174, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "mrH1fRUdckNL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1092
        },
        "outputId": "7e652357-754d-45c1-9d42-dd593276ce10"
      },
      "cell_type": "code",
      "source": [
        "position = df['POSITION'].str.lower()\n",
        "position"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                  full stack developer \n",
              "1                                  full stack developer \n",
              "2                                  full stack developer \n",
              "3                                  full stack developer \n",
              "4                                   full-stack developer\n",
              "5                                  full stack developer \n",
              "6                                  full stack developer \n",
              "7                                   full stack developer\n",
              "8                      full stack developer @ hippovideo\n",
              "9                                  full stack developer \n",
              "10                              full stack developer | d\n",
              "11                                 full stack developer \n",
              "12     full stack developer | java | elasticsearch | ...\n",
              "13                       full stack developer @ gobumpr!\n",
              "14                                  full stack developer\n",
              "15                                (full stack developer)\n",
              "16                                 full stack developer \n",
              "17                                 full stack developer \n",
              "18                                 full stack developer \n",
              "19                                 full stack developer \n",
              "20     full-stack developer | angular js | laravel fr...\n",
              "21                                 full stack developer \n",
              "22                             full stack/game developer\n",
              "23                 full stack web and android developer \n",
              "24                   full stack javascript web developer\n",
              "25                                             fullstack\n",
              "26                      freelance - full stack developer\n",
              "27     full stack developer (core team) - maximl | ri...\n",
              "28                                 full stack developer \n",
              "29                                 full stack developer \n",
              "                             ...                        \n",
              "144    full stack developer | node.js | reactjs | and...\n",
              "145                              full stack js developer\n",
              "146                                full stack developer \n",
              "147                                full stack developer \n",
              "148                                full stack developer \n",
              "149                                 full stack developer\n",
              "150    full stack developer/solution architect/team lead\n",
              "151                                full stack developer \n",
              "152                              fullstack js developer \n",
              "153                            fullstack .net developer \n",
              "154                            fullstack java developer \n",
              "155                 fullstack developer (java / angular)\n",
              "156                                            fullstack\n",
              "157            fullstack software developer en dentaltix\n",
              "158                                 fullstack developer \n",
              "159                             fullstack web developer \n",
              "160                  junior fullstack developer @ agroop\n",
              "161                                 front end developer \n",
              "162                                  front end developer\n",
              "163                                  front end developer\n",
              "164                                 front end developer \n",
              "165                                 front end developer \n",
              "166                                   frontend developer\n",
              "167                                 front-end developer \n",
              "168                                         ui developer\n",
              "169                              front-end web developer\n",
              "170                                  back-end developer \n",
              "171                                  back end developer \n",
              "172                            back-end developer @ salk\n",
              "173                                  back end developer \n",
              "Name: POSITION, Length: 174, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "9ezVlrZvdbzE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3033
        },
        "outputId": "f6d6f685-2c1e-4b45-bff9-38526caacdd0"
      },
      "cell_type": "code",
      "source": [
        "pos = [None] * len(position)\n",
        "pos"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None,\n",
              " None,\n",
              " None,\n",
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              " None,\n",
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              " None,\n",
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              " None,\n",
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              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None,\n",
              " None]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "DDMUZVJm0IY9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3033
        },
        "outputId": "bd2b79a3-4f08-4367-bc4b-2fe859f0ef63"
      },
      "cell_type": "code",
      "source": [
        "for i,j in enumerate(position.values):\n",
        "  if \"full\" in j:\n",
        "    pos[i] = \"FullStack\"\n",
        "  elif \"back\" in j:\n",
        "    pos[i] = \"BackEnd\"\n",
        "  elif \"ui\" in j or \"front\" in j:\n",
        "    pos[i] = \"FrontEnd\"\n",
        "pos"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['FullStack',\n",
              " 'FullStack',\n",
              " 'FullStack',\n",
              " 'FullStack',\n",
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              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
              " 'BackEnd',\n",
              " 'BackEnd',\n",
              " 'BackEnd',\n",
              " 'BackEnd']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "6FEPEeVD-qUk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3033
        },
        "outputId": "3113f18a-6f5b-4197-c0fe-5b75e5bbe90a"
      },
      "cell_type": "code",
      "source": [
        "y = pos\n",
        "y"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['FullStack',\n",
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              " 'FullStack',\n",
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              " 'FullStack',\n",
              " 'FullStack',\n",
              " 'FullStack',\n",
              " 'FullStack',\n",
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              " 'FullStack',\n",
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              " 'FullStack',\n",
              " 'FrontEnd',\n",
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              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
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              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
              " 'FrontEnd',\n",
              " 'BackEnd',\n",
              " 'BackEnd',\n",
              " 'BackEnd',\n",
              " 'BackEnd']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "2eBKiowZMzXi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelBinarizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zRjtXUopNS5D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3033
        },
        "outputId": "5c417660-4f4c-4ac5-bffa-b83a0eb1cac7"
      },
      "cell_type": "code",
      "source": [
        "encoder = LabelBinarizer()\n",
        "transfomed_label = encoder.fit_transform(y)\n",
        "print(transfomed_label)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 1]\n",
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            " [0 1 0]\n",
            " [0 1 0]\n",
            " [0 1 0]\n",
            " [0 1 0]\n",
            " [0 1 0]\n",
            " [0 1 0]\n",
            " [1 0 0]\n",
            " [1 0 0]\n",
            " [1 0 0]\n",
            " [1 0 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oCyoPvafORrp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fd80e0f9-2fdc-4aa5-8daa-f914cbff7ff9"
      },
      "cell_type": "code",
      "source": [
        "y = transfomed_label\n",
        "encoder.classes_"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "H8bCjeDV-v9-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fuATeUdE_Dcu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "maxlen = 100\n",
        "training_samples = 125\n",
        "validation_samples = 49\n",
        "max_words = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-hioGIO-_GOk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words=max_words)\n",
        "tokenizer.fit_on_texts(X)\n",
        "sequences = tokenizer.texts_to_sequences(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xr_HbIHS_KH_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "de82b9fe-d1e2-4646-8c04-cdf4a510ec3f"
      },
      "cell_type": "code",
      "source": [
        "word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(word_index))\n",
        "data = pad_sequences(sequences, maxlen=maxlen)\n",
        "labels = np.asarray(y)\n",
        "print('Shape of data tensor:', data.shape)\n",
        "print('Shape of label tensor:', labels.shape)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1839 unique tokens.\n",
            "Shape of data tensor: (174, 100)\n",
            "Shape of label tensor: (174, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "STyj1boX_W13",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "indices = np.arange(data.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "data = data[indices]\n",
        "labels = labels[indices]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "L-NPjb1k_YSX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_train = data[:training_samples]\n",
        "y_train = labels[:training_samples]\n",
        "x_val = data[training_samples: training_samples + validation_samples]\n",
        "y_val = labels[training_samples: training_samples + validation_samples]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "L8EEdLpuO2jC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "cc37b1f7-f6a0-46c0-b413-1b6a1681cf44"
      },
      "cell_type": "code",
      "source": [
        "print('x_train.shape', x_train.shape)\n",
        "print('y_train.shape', y_train.shape)\n",
        "print('x_val.shape', x_val.shape)\n",
        "print('y_val.shape', y_val.shape)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train.shape (125, 100)\n",
            "y_train.shape (125, 3)\n",
            "x_val.shape (49, 100)\n",
            "y_val.shape (49, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5-CCvw5CHbvD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "a184abbc-19f5-4d24-fd7d-f43a872935fd"
      },
      "cell_type": "code",
      "source": [
        "train_dataset_url = \"http://nlp.stanford.edu/data/glove.6B.zip\"\n",
        "\n",
        "train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url),\n",
        "                                           origin=train_dataset_url)\n",
        "\n",
        "print(\"Local copy of the dataset file: {}\".format(train_dataset_fp))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://nlp.stanford.edu/data/glove.6B.zip\n",
            "501620736/862182613 [================>.............] - ETA: 53s"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "862183424/862182613 [==============================] - 131s 0us/step\n",
            "Local copy of the dataset file: /content/.keras/datasets/glove.6B.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ob2aaSBjIiHK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "4e084715-8889-4d9b-d9ce-c165db09f153"
      },
      "cell_type": "code",
      "source": [
        "!unzip {train_dataset_fp}"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/.keras/datasets/glove.6B.zip\n",
            "  inflating: glove.6B.50d.txt        \n",
            "  inflating: glove.6B.100d.txt       \n",
            "  inflating: glove.6B.200d.txt       \n",
            "  inflating: glove.6B.300d.txt       \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cMjoUeAqIiQw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "4307b0c8-2d22-4c2a-8a9a-98e5541137ff"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datalab\t\t   linkedin_developer_nlp_analysis (1).xlsx\r\n",
            "glove.6B.100d.txt  linkedin_developer_nlp_analysis (2).xlsx\r\n",
            "glove.6B.200d.txt  linkedin_developer_nlp_analysis (3).xlsx\r\n",
            "glove.6B.300d.txt  linkedin_developer_nlp_analysis (4).xlsx\r\n",
            "glove.6B.50d.txt   linkedin_developer_nlp_analysis.xlsx\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uBLDZSgAFYLF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4f4cdab9-eba3-4627-f5a2-3c90199fd4f7"
      },
      "cell_type": "code",
      "source": [
        "embeddings_index = {}\n",
        "f = open('./glove.6B.100d.txt')\n",
        "\n",
        "for line in f:\n",
        "  values = line.split()\n",
        "  word = values[0]\n",
        "  coefs = np.asarray(values[1:], dtype='float32')\n",
        "  embeddings_index[word] = coefs\n",
        "f.close()\n",
        "print('Found %s word vectors.' % len(embeddings_index))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 400000 word vectors.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LMspvrOLHOcf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "embedding_dim = 100\n",
        "embedding_matrix = np.zeros((max_words, embedding_dim))\n",
        "\n",
        "for word, i in word_index.items():\n",
        "  if i < max_words:\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "      embedding_matrix[i] = embedding_vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "T9YV4lloJtGJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "0dd13740-d851-4a92-a4c9-b12ed0c24973"
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, Flatten, Dense\n",
        "model = Sequential()\n",
        "model.add(Embedding(max_words, embedding_dim, input_length=maxlen))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(3, activation='sigmoid'))\n",
        "model.summary()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 100, 100)          10000     \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 10000)             0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 32)                320032    \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 3)                 99        \n",
            "=================================================================\n",
            "Total params: 330,131\n",
            "Trainable params: 330,131\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BVC0x3A_JwNJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.layers[0].set_weights([embedding_matrix])\n",
        "model.layers[0].trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hR5gpnBcLDTH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3521
        },
        "outputId": "98dd3f6e-3350-4d31-ee1c-92239b9797d8"
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='rmsprop',\n",
        "loss='categorical_crossentropy',\n",
        "metrics=['acc'])\n",
        "history = model.fit(x_train, y_train,\n",
        "epochs=100,\n",
        "batch_size=32,\n",
        "validation_data=(x_val, y_val))\n",
        "model.save_weights('pre_trained_glove_model.h5')"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 125 samples, validate on 49 samples\n",
            "Epoch 1/100\n",
            "125/125 [==============================] - 0s 1ms/step - loss: 1.1964 - acc: 0.3680 - val_loss: 1.0080 - val_acc: 0.4490\n",
            "Epoch 2/100\n",
            "125/125 [==============================] - 0s 269us/step - loss: 0.9673 - acc: 0.3040 - val_loss: 0.9522 - val_acc: 0.4490\n",
            "Epoch 3/100\n",
            "125/125 [==============================] - 0s 293us/step - loss: 0.8932 - acc: 0.4240 - val_loss: 0.9412 - val_acc: 0.4490\n",
            "Epoch 4/100\n",
            "125/125 [==============================] - 0s 245us/step - loss: 0.8600 - acc: 0.4400 - val_loss: 0.9850 - val_acc: 0.4490\n",
            "Epoch 5/100\n",
            "125/125 [==============================] - 0s 248us/step - loss: 0.8322 - acc: 0.4640 - val_loss: 0.9770 - val_acc: 0.4490\n",
            "Epoch 6/100\n",
            "125/125 [==============================] - 0s 258us/step - loss: 0.8107 - acc: 0.4720 - val_loss: 0.9498 - val_acc: 0.4490\n",
            "Epoch 7/100\n",
            "125/125 [==============================] - 0s 252us/step - loss: 0.7967 - acc: 0.5360 - val_loss: 1.1699 - val_acc: 0.4490\n",
            "Epoch 8/100\n",
            "125/125 [==============================] - 0s 242us/step - loss: 0.7714 - acc: 0.5760 - val_loss: 0.9343 - val_acc: 0.4694\n",
            "Epoch 9/100\n",
            "125/125 [==============================] - 0s 235us/step - loss: 0.7297 - acc: 0.5920 - val_loss: 0.9003 - val_acc: 0.4898\n",
            "Epoch 10/100\n",
            "125/125 [==============================] - 0s 249us/step - loss: 0.7117 - acc: 0.5920 - val_loss: 0.9126 - val_acc: 0.4898\n",
            "Epoch 11/100\n",
            "125/125 [==============================] - 0s 270us/step - loss: 0.7043 - acc: 0.5920 - val_loss: 0.9165 - val_acc: 0.4694\n",
            "Epoch 12/100\n",
            "125/125 [==============================] - 0s 251us/step - loss: 0.7008 - acc: 0.5920 - val_loss: 0.9284 - val_acc: 0.4490\n",
            "Epoch 13/100\n",
            "125/125 [==============================] - 0s 267us/step - loss: 0.6980 - acc: 0.5920 - val_loss: 0.9323 - val_acc: 0.4490\n",
            "Epoch 14/100\n",
            "125/125 [==============================] - 0s 246us/step - loss: 0.6957 - acc: 0.5920 - val_loss: 0.9333 - val_acc: 0.4490\n",
            "Epoch 15/100\n",
            "125/125 [==============================] - 0s 260us/step - loss: 0.6938 - acc: 0.5920 - val_loss: 0.9468 - val_acc: 0.4490\n",
            "Epoch 16/100\n",
            "125/125 [==============================] - 0s 253us/step - loss: 0.6923 - acc: 0.5920 - val_loss: 0.9559 - val_acc: 0.4490\n",
            "Epoch 17/100\n",
            "125/125 [==============================] - 0s 265us/step - loss: 0.6910 - acc: 0.5760 - val_loss: 0.9622 - val_acc: 0.4490\n",
            "Epoch 18/100\n",
            "125/125 [==============================] - 0s 310us/step - loss: 0.6899 - acc: 0.5920 - val_loss: 0.9663 - val_acc: 0.4490\n",
            "Epoch 19/100\n",
            "125/125 [==============================] - 0s 247us/step - loss: 0.6890 - acc: 0.5920 - val_loss: 0.9784 - val_acc: 0.4490\n",
            "Epoch 20/100\n",
            "125/125 [==============================] - 0s 239us/step - loss: 0.6882 - acc: 0.5920 - val_loss: 0.9812 - val_acc: 0.3673\n",
            "Epoch 21/100\n",
            "125/125 [==============================] - 0s 237us/step - loss: 0.6876 - acc: 0.5440 - val_loss: 0.9864 - val_acc: 0.3673\n",
            "Epoch 22/100\n",
            "125/125 [==============================] - 0s 255us/step - loss: 0.6870 - acc: 0.5680 - val_loss: 0.9933 - val_acc: 0.3673\n",
            "Epoch 23/100\n",
            " 32/125 [======>.......................] - ETA: 0s - loss: 0.7543 - acc: 0.5312"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r125/125 [==============================] - 0s 267us/step - loss: 0.6865 - acc: 0.5760 - val_loss: 0.9919 - val_acc: 0.3673\n",
            "Epoch 24/100\n",
            "125/125 [==============================] - 0s 265us/step - loss: 0.6861 - acc: 0.5680 - val_loss: 1.0044 - val_acc: 0.3673\n",
            "Epoch 25/100\n",
            "125/125 [==============================] - 0s 265us/step - loss: 0.6858 - acc: 0.6000 - val_loss: 1.0126 - val_acc: 0.3673\n",
            "Epoch 26/100\n",
            "125/125 [==============================] - 0s 224us/step - loss: 0.6856 - acc: 0.6000 - val_loss: 1.0065 - val_acc: 0.3673\n",
            "Epoch 27/100\n",
            "125/125 [==============================] - 0s 258us/step - loss: 0.6854 - acc: 0.6000 - val_loss: 1.0218 - val_acc: 0.3673\n",
            "Epoch 28/100\n",
            "125/125 [==============================] - 0s 251us/step - loss: 0.6852 - acc: 0.6000 - val_loss: 1.0226 - val_acc: 0.3673\n",
            "Epoch 29/100\n",
            "125/125 [==============================] - 0s 241us/step - loss: 0.6851 - acc: 0.6000 - val_loss: 1.0159 - val_acc: 0.3673\n",
            "Epoch 30/100\n",
            "125/125 [==============================] - 0s 251us/step - loss: 0.6849 - acc: 0.6000 - val_loss: 1.0214 - val_acc: 0.3673\n",
            "Epoch 31/100\n",
            "125/125 [==============================] - 0s 229us/step - loss: 0.6848 - acc: 0.6000 - val_loss: 1.0316 - val_acc: 0.3673\n",
            "Epoch 32/100\n",
            "125/125 [==============================] - 0s 235us/step - loss: 0.6847 - acc: 0.6000 - val_loss: 1.0247 - val_acc: 0.3673\n",
            "Epoch 33/100\n",
            "125/125 [==============================] - 0s 261us/step - loss: 0.6846 - acc: 0.6000 - val_loss: 1.0326 - val_acc: 0.3673\n",
            "Epoch 34/100\n",
            "125/125 [==============================] - 0s 247us/step - loss: 0.6845 - acc: 0.6000 - val_loss: 1.0405 - val_acc: 0.3673\n",
            "Epoch 35/100\n",
            "125/125 [==============================] - 0s 250us/step - loss: 0.6845 - acc: 0.6000 - val_loss: 1.0298 - val_acc: 0.3673\n",
            "Epoch 36/100\n",
            "125/125 [==============================] - 0s 252us/step - loss: 0.6844 - acc: 0.6000 - val_loss: 1.0366 - val_acc: 0.3673\n",
            "Epoch 37/100\n",
            "125/125 [==============================] - 0s 248us/step - loss: 0.6845 - acc: 0.6000 - val_loss: 1.0363 - val_acc: 0.3673\n",
            "Epoch 38/100\n",
            "125/125 [==============================] - 0s 251us/step - loss: 0.6844 - acc: 0.6000 - val_loss: 1.0421 - val_acc: 0.3673\n",
            "Epoch 39/100\n",
            "125/125 [==============================] - 0s 255us/step - loss: 0.6843 - acc: 0.6000 - val_loss: 1.0396 - val_acc: 0.3673\n",
            "Epoch 40/100\n",
            "125/125 [==============================] - 0s 297us/step - loss: 0.6843 - acc: 0.6000 - val_loss: 1.0391 - val_acc: 0.3673\n",
            "Epoch 41/100\n",
            "125/125 [==============================] - 0s 247us/step - loss: 0.6843 - acc: 0.6000 - val_loss: 1.0566 - val_acc: 0.3673\n",
            "Epoch 42/100\n",
            "125/125 [==============================] - 0s 253us/step - loss: 0.6843 - acc: 0.6000 - val_loss: 1.0351 - val_acc: 0.3673\n",
            "Epoch 43/100\n",
            "125/125 [==============================] - 0s 254us/step - loss: 0.6842 - acc: 0.6000 - val_loss: 1.0418 - val_acc: 0.3673\n",
            "Epoch 44/100\n",
            "125/125 [==============================] - 0s 257us/step - loss: 0.6842 - acc: 0.6000 - val_loss: 1.0530 - val_acc: 0.3673\n",
            "Epoch 45/100\n",
            " 32/125 [======>.......................] - ETA: 0s - loss: 0.6830 - acc: 0.6250"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r125/125 [==============================] - 0s 263us/step - loss: 0.6842 - acc: 0.6000 - val_loss: 1.0493 - val_acc: 0.3673\n",
            "Epoch 46/100\n",
            "125/125 [==============================] - 0s 244us/step - loss: 0.6841 - acc: 0.6000 - val_loss: 1.0404 - val_acc: 0.3673\n",
            "Epoch 47/100\n",
            "125/125 [==============================] - 0s 272us/step - loss: 0.6841 - acc: 0.6000 - val_loss: 1.0469 - val_acc: 0.3673\n",
            "Epoch 48/100\n",
            "125/125 [==============================] - 0s 241us/step - loss: 0.6841 - acc: 0.6000 - val_loss: 1.0535 - val_acc: 0.3673\n",
            "Epoch 49/100\n",
            "125/125 [==============================] - 0s 246us/step - loss: 0.6840 - acc: 0.6000 - val_loss: 1.0570 - val_acc: 0.3673\n",
            "Epoch 50/100\n",
            "125/125 [==============================] - 0s 243us/step - loss: 0.6841 - acc: 0.6000 - val_loss: 1.0680 - val_acc: 0.3673\n",
            "Epoch 51/100\n",
            "125/125 [==============================] - 0s 249us/step - loss: 0.6840 - acc: 0.6000 - val_loss: 1.0659 - val_acc: 0.3673\n",
            "Epoch 52/100\n",
            "125/125 [==============================] - 0s 276us/step - loss: 0.6841 - acc: 0.6000 - val_loss: 1.0641 - val_acc: 0.3673\n",
            "Epoch 53/100\n",
            "125/125 [==============================] - 0s 244us/step - loss: 0.6840 - acc: 0.6000 - val_loss: 1.0769 - val_acc: 0.3673\n",
            "Epoch 54/100\n",
            "125/125 [==============================] - 0s 248us/step - loss: 0.6839 - acc: 0.6000 - val_loss: 1.0659 - val_acc: 0.3673\n",
            "Epoch 55/100\n",
            "125/125 [==============================] - 0s 235us/step - loss: 0.6839 - acc: 0.6000 - val_loss: 1.0664 - val_acc: 0.3673\n",
            "Epoch 56/100\n",
            "125/125 [==============================] - 0s 229us/step - loss: 0.6839 - acc: 0.6000 - val_loss: 1.0728 - val_acc: 0.3673\n",
            "Epoch 57/100\n",
            "125/125 [==============================] - 0s 250us/step - loss: 0.6840 - acc: 0.6000 - val_loss: 1.0678 - val_acc: 0.3673\n",
            "Epoch 58/100\n",
            "125/125 [==============================] - 0s 274us/step - loss: 0.6839 - acc: 0.6000 - val_loss: 1.0698 - val_acc: 0.3673\n",
            "Epoch 59/100\n",
            "125/125 [==============================] - 0s 240us/step - loss: 0.6839 - acc: 0.6000 - val_loss: 1.0682 - val_acc: 0.3673\n",
            "Epoch 60/100\n",
            "125/125 [==============================] - 0s 233us/step - loss: 0.6838 - acc: 0.6000 - val_loss: 1.0793 - val_acc: 0.3673\n",
            "Epoch 61/100\n",
            "125/125 [==============================] - 0s 237us/step - loss: 0.6838 - acc: 0.6000 - val_loss: 1.0862 - val_acc: 0.3673\n",
            "Epoch 62/100\n",
            "125/125 [==============================] - 0s 237us/step - loss: 0.6838 - acc: 0.6000 - val_loss: 1.0687 - val_acc: 0.3673\n",
            "Epoch 63/100\n",
            "125/125 [==============================] - 0s 281us/step - loss: 0.6838 - acc: 0.6000 - val_loss: 1.0753 - val_acc: 0.3673\n",
            "Epoch 64/100\n",
            "125/125 [==============================] - 0s 277us/step - loss: 0.6838 - acc: 0.6000 - val_loss: 1.0745 - val_acc: 0.3673\n",
            "Epoch 65/100\n",
            "125/125 [==============================] - 0s 289us/step - loss: 0.6838 - acc: 0.6000 - val_loss: 1.0824 - val_acc: 0.3673\n",
            "Epoch 66/100\n",
            "125/125 [==============================] - 0s 267us/step - loss: 0.6839 - acc: 0.6000 - val_loss: 1.0818 - val_acc: 0.3673\n",
            "Epoch 67/100\n",
            " 32/125 [======>.......................] - ETA: 0s - loss: 0.6583 - acc: 0.5938"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "125/125 [==============================] - 0s 280us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0849 - val_acc: 0.3673\n",
            "Epoch 68/100\n",
            "125/125 [==============================] - 0s 267us/step - loss: 0.6838 - acc: 0.6000 - val_loss: 1.0930 - val_acc: 0.3673\n",
            "Epoch 69/100\n",
            "125/125 [==============================] - 0s 283us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0795 - val_acc: 0.3673\n",
            "Epoch 70/100\n",
            "125/125 [==============================] - 0s 277us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0839 - val_acc: 0.3673\n",
            "Epoch 71/100\n",
            "125/125 [==============================] - 0s 270us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0934 - val_acc: 0.3673\n",
            "Epoch 72/100\n",
            "125/125 [==============================] - 0s 264us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0952 - val_acc: 0.3673\n",
            "Epoch 73/100\n",
            "125/125 [==============================] - 0s 267us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0880 - val_acc: 0.3673\n",
            "Epoch 74/100\n",
            "125/125 [==============================] - 0s 277us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0965 - val_acc: 0.3673\n",
            "Epoch 75/100\n",
            "125/125 [==============================] - 0s 272us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0989 - val_acc: 0.3673\n",
            "Epoch 76/100\n",
            "125/125 [==============================] - 0s 285us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.0902 - val_acc: 0.3673\n",
            "Epoch 77/100\n",
            "125/125 [==============================] - 0s 257us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.0933 - val_acc: 0.3673\n",
            "Epoch 78/100\n",
            "125/125 [==============================] - 0s 272us/step - loss: 0.6839 - acc: 0.6000 - val_loss: 1.0933 - val_acc: 0.3673\n",
            "Epoch 79/100\n",
            "125/125 [==============================] - 0s 302us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.0990 - val_acc: 0.3673\n",
            "Epoch 80/100\n",
            "125/125 [==============================] - 0s 282us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0936 - val_acc: 0.3673\n",
            "Epoch 81/100\n",
            "125/125 [==============================] - 0s 316us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.0962 - val_acc: 0.3673\n",
            "Epoch 82/100\n",
            "125/125 [==============================] - 0s 295us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.1015 - val_acc: 0.3673\n",
            "Epoch 83/100\n",
            "125/125 [==============================] - 0s 285us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.1114 - val_acc: 0.3673\n",
            "Epoch 84/100\n",
            "125/125 [==============================] - 0s 281us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.1054 - val_acc: 0.3673\n",
            "Epoch 85/100\n",
            "125/125 [==============================] - 0s 277us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.1061 - val_acc: 0.3673\n",
            "Epoch 86/100\n",
            "125/125 [==============================] - 0s 280us/step - loss: 0.6835 - acc: 0.6000 - val_loss: 1.1046 - val_acc: 0.3673\n",
            "Epoch 87/100\n",
            "125/125 [==============================] - 0s 317us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.1019 - val_acc: 0.3673\n",
            "Epoch 88/100\n",
            "125/125 [==============================] - 0s 261us/step - loss: 0.6835 - acc: 0.6000 - val_loss: 1.1028 - val_acc: 0.3673\n",
            "Epoch 89/100\n",
            "125/125 [==============================] - 0s 267us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.1016 - val_acc: 0.3673\n",
            "Epoch 90/100\n",
            "125/125 [==============================] - 0s 265us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.0982 - val_acc: 0.3673\n",
            "Epoch 91/100\n",
            " 32/125 [======>.......................] - ETA: 0s - loss: 0.6414 - acc: 0.7188"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "125/125 [==============================] - 0s 258us/step - loss: 0.6837 - acc: 0.6000 - val_loss: 1.0976 - val_acc: 0.3673\n",
            "Epoch 92/100\n",
            "125/125 [==============================] - 0s 278us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.0973 - val_acc: 0.3673\n",
            "Epoch 93/100\n",
            "125/125 [==============================] - 0s 249us/step - loss: 0.6835 - acc: 0.6000 - val_loss: 1.0967 - val_acc: 0.3673\n",
            "Epoch 94/100\n",
            "125/125 [==============================] - 0s 279us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.0955 - val_acc: 0.3673\n",
            "Epoch 95/100\n",
            "125/125 [==============================] - 0s 316us/step - loss: 0.6836 - acc: 0.6000 - val_loss: 1.1003 - val_acc: 0.3673\n",
            "Epoch 96/100\n",
            "125/125 [==============================] - 0s 288us/step - loss: 0.6835 - acc: 0.6000 - val_loss: 1.1018 - val_acc: 0.3673\n",
            "Epoch 97/100\n",
            "125/125 [==============================] - 0s 276us/step - loss: 0.6835 - acc: 0.6000 - val_loss: 1.1061 - val_acc: 0.3673\n",
            "Epoch 98/100\n",
            "125/125 [==============================] - 0s 275us/step - loss: 0.6835 - acc: 0.6000 - val_loss: 1.1097 - val_acc: 0.3673\n",
            "Epoch 99/100\n",
            "125/125 [==============================] - 0s 278us/step - loss: 0.6835 - acc: 0.6000 - val_loss: 1.1120 - val_acc: 0.3673\n",
            "Epoch 100/100\n",
            "125/125 [==============================] - 0s 304us/step - loss: 0.6835 - acc: 0.6000 - val_loss: 1.1086 - val_acc: 0.3673\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LYQd5vwGPxES",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "testing = [\"Experienced iOS App Developer with a demonstrated history of working in the information technology and services industry. Skilled in Object-Oriented Programming (OOP), WebGL, Microsoft Excel, Java, and Microsoft Word. Strong engineering professional with a Bachelor of Engineering - BE ocused in Computer Science from Thiagarajar College of Engineering\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "S_NqTtqyLKt5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def token_hot(text):\n",
        "  \n",
        "  text = np.array(text)\n",
        "  \n",
        "  tokenizer = Tokenizer(num_words=1000)\n",
        "  tokenizer.fit_on_texts(text)\n",
        "  sequences = tokenizer.texts_to_sequences(text)\n",
        "  \n",
        "  word_index = tokenizer.word_index\n",
        "  print('Found %s unique tokens.' % len(word_index))\n",
        "  \n",
        "  data = pad_sequences(sequences, maxlen=100)\n",
        "  print('Shape of data tensor:', data.shape)\n",
        "  return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C20oxMxCRV9Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1b3e9fe0-3dda-4cfd-c6de-4b5fb5981840"
      },
      "cell_type": "code",
      "source": [
        "checknew = (token_hot(testing))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 38 unique tokens.\n",
            "Shape of data tensor: (1, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IHhv5-yBRiZi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "10c2151d-5f74-4fa9-fdd4-b5db5dcd1cc8"
      },
      "cell_type": "code",
      "source": [
        "model.predict_classes(checknew)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "metadata": {
        "id": "Y-YneOHgS4Ac",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}