COVID19_Challenge.ipynb

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  "metadata": {
    "colab": {
      "name": "COVID19 Challenge.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyN3D5rcurfP2qtGJkHOM+ZD",
      "include_colab_link": true
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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/vanessaaleung/hiring-amid-covid/blob/master/COVID19_Challenge.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAX11VrWxGWP",
        "colab_type": "text"
      },
      "source": [
        "# COVID-19 Challenge:  Who's still hiring?\n",
        "\n",
        "**Group Member: Vannesa Leung/Minke Wang/Yuping Qi/Stella Zhang**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caKSqJMg8tBP",
        "colab_type": "text"
      },
      "source": [
        "## Install & Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rG5fCrcuugi",
        "colab_type": "code",
        "outputId": "f0463273-9a66-4f6c-c298-d9d9a956eb62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "# !pip install -q kaggle\n",
        "# ! apt-get install default-jre\n",
        "# !java -version\n",
        "! pip install h2o\n",
        "# data preprocessing packages\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "#machine learning package\n",
        "import h2o\n",
        "#file handling\n",
        "import os\n",
        "from google.colab import files"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting h2o\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/96/b2/b6f5e8c7fd1efe0f6d2640b34164b10b0e28231b89cc06542a6b3a3903a0/h2o-3.30.0.1.tar.gz (129.5MB)\n",
            "\u001b[K     |████████████████████████████████| 129.5MB 88kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from h2o) (2.21.0)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from h2o) (0.8.7)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from h2o) (0.16.0)\n",
            "Collecting colorama>=0.3.8\n",
            "  Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (2020.4.5.1)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (1.24.3)\n",
            "Building wheels for collected packages: h2o\n",
            "  Building wheel for h2o (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for h2o: filename=h2o-3.30.0.1-py2.py3-none-any.whl size=129557718 sha256=c11af33ac6580a7fdcae7f4d076271019e5ae642a406f9db1aad75d0f927fd47\n",
            "  Stored in directory: /root/.cache/pip/wheels/db/42/63/d118d7218432054da842f5d10bd4f99e978bde0931164e6c7a\n",
            "Successfully built h2o\n",
            "Installing collected packages: colorama, h2o\n",
            "Successfully installed colorama-0.4.3 h2o-3.30.0.1\n"
          ],
          "name": "stdout"
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "4Ua73y8u-dYo",
        "colab_type": "text"
      },
      "source": [
        "## Import Dataset from Multiple Sources"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpK8Fq1xu0lX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import the candor dataset of company hiring status\n",
        "data=pd.read_csv(\"https://raw.githubusercontent.com/vanessaaleung/rawdata/master/updated_apr_10.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEgh8ucO0Ds7",
        "colab_type": "code",
        "outputId": "97b2ce78-5f5e-4769-c2bc-6bd03fd5f5e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "# import crunchbase company dataset of funding status\n",
        "os.environ['KAGGLE_USERNAME'] = \"minkewang\" # username\n",
        "os.environ['KAGGLE_KEY'] = \"4ac64942fb1cdf679a628708e3cae405\" # key\n",
        "!kaggle datasets download -d arindam235/startup-investments-crunchbase # api copied from kaggle\n",
        "cbdata = pd.read_csv('startup-investments-crunchbase.zip', compression='zip', header=0, sep=',', quotechar='\"',thousands=r',',encoding= 'unicode_escape')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading startup-investments-crunchbase.zip to /content\n",
            "  0% 0.00/2.67M [00:00<?, ?B/s]\n",
            "100% 2.67M/2.67M [00:00<00:00, 90.3MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "115oW_OjyWIA",
        "colab_type": "text"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0uWkgKn11_-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# clear unncessary strings within the dataset\n",
        "cbdata['name']=cbdata['name'].str.strip()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEdnwAxe19Yb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# merge data from multiple sources\n",
        "merge=pd.merge(cbdata,data,how='inner',left_on=\"name\",right_on='Company name')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htGH6mxi5FPw",
        "colab_type": "code",
        "outputId": "bf0267eb-39cf-4c88-9f5d-065c7a20c2fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "merge.sample(5)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>Number of Investments</th>\n",
              "      <th>Estimated Revenue Range</th>\n",
              "      <th>IPO Status</th>\n",
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              "      <td>operating</td>\n",
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              "      <td>2011-07-22</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>BMC Software</td>\n",
              "      <td>\\n</td>\n",
              "      <td>Hiring</td>\n",
              "      <td>2020-04-10T15:29:22.000Z</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>/organization/npr</td>\n",
              "      <td>NPR</td>\n",
              "      <td>http://www.npr.org</td>\n",
              "      <td>|Music|Nonprofits|</td>\n",
              "      <td>Music</td>\n",
              "      <td>1,70,00,000</td>\n",
              "      <td>operating</td>\n",
              "      <td>USA</td>\n",
              "      <td>DC</td>\n",
              "      <td>Washington, D.C.</td>\n",
              "      <td>Washington</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1970-01-01</td>\n",
              "      <td>1970-01</td>\n",
              "      <td>1970-Q1</td>\n",
              "      <td>1970.0</td>\n",
              "      <td>2013-12-15</td>\n",
              "      <td>2013-12-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NPR</td>\n",
              "      <td>Summer 2020 Internships cancelled\\n</td>\n",
              "      <td>Hiring</td>\n",
              "      <td>2020-04-07T14:22:52.000Z</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      permalink          name  ... Total Funding Amount Hub Tags\n",
              "722      /organization/trendyol      Trendyol  ...                  NaN      NaN\n",
              "200       /organization/dataart       DataArt  ...                  NaN      NaN\n",
              "679      /organization/tailwind      Tailwind  ...                  NaN      NaN\n",
              "91   /organization/bmc-software  BMC Software  ...                  NaN      NaN\n",
              "485           /organization/npr           NPR  ...                  NaN      NaN\n",
              "\n",
              "[5 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKxlloXXXGRb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# convert string data to numerical \n",
        "merge[' funding_total_usd ']=merge[' funding_total_usd '].str.replace(',', '').str.replace('-', '0').str.replace(' ', '').astype(float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-aXAojv4Wtp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# convert hiring status, hiring=1, hiring freeze/layoffs=0; y variable\n",
        "merge['HiringStatus']=(merge['Status'].isin(['Hiring'])).astype(int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1xalBtE51M2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##calculating new metric: company age\n",
        "merge['company_age']=2020-merge['founded_year']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOZU7pMz5BCX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# drop unnecessary columns\n",
        "drop_col=['permalink','homepage_url', 'category_list','country_code', 'state_code', 'region',\n",
        "       'city','founded_at', 'founded_month',\n",
        "       'founded_quarter', 'founded_year', 'first_funding_at',\n",
        "       'last_funding_at','equity_crowdfunding','Company name', 'Notes', 'Status', 'Last modified', 'Where to apply',\n",
        "       'Industry', 'Location', 'Founded Date', 'Founders', 'Funding Status',\n",
        "       'Last Funding Type', 'Number of Employees', 'Revenue Range', 'Hug Tags',\n",
        "       'Ipo Status', 'Company Type', 'Number of Acquisitions', 'Total Funding',\n",
        "       'Headquarters Location', 'Headquarters Regions',\n",
        "       'Number of Investments', 'Estimated Revenue Range', 'IPO Status',\n",
        "       'Total Funding Amount', 'Hub Tags']\n",
        "merge=merge.drop(columns=drop_col)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXN5Kf_M7PBj",
        "colab_type": "code",
        "outputId": "5283b597-a8b4-406a-ffcc-3a0e1264e987",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "merge.head()"
      ],
      "execution_count": 78,
      "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>name</th>\n",
              "      <th>market</th>\n",
              "      <th>funding_total_usd</th>\n",
              "      <th>status</th>\n",
              "      <th>funding_rounds</th>\n",
              "      <th>seed</th>\n",
              "      <th>venture</th>\n",
              "      <th>undisclosed</th>\n",
              "      <th>convertible_note</th>\n",
              "      <th>debt_financing</th>\n",
              "      <th>angel</th>\n",
              "      <th>grant</th>\n",
              "      <th>private_equity</th>\n",
              "      <th>post_ipo_equity</th>\n",
              "      <th>post_ipo_debt</th>\n",
              "      <th>secondary_market</th>\n",
              "      <th>product_crowdfunding</th>\n",
              "      <th>round_A</th>\n",
              "      <th>round_B</th>\n",
              "      <th>round_C</th>\n",
              "      <th>round_D</th>\n",
              "      <th>round_E</th>\n",
              "      <th>round_F</th>\n",
              "      <th>round_G</th>\n",
              "      <th>round_H</th>\n",
              "      <th>HiringStatus</th>\n",
              "      <th>company_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1010data</td>\n",
              "      <td>Software</td>\n",
              "      <td>35000000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15Five</td>\n",
              "      <td>Software</td>\n",
              "      <td>3400000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2200000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1200000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1stdibs</td>\n",
              "      <td>E-Commerce</td>\n",
              "      <td>117000000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57000000.0</td>\n",
              "      <td>60000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23andMe</td>\n",
              "      <td>Biotechnology</td>\n",
              "      <td>111949900.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>110549900.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1400000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9000000.0</td>\n",
              "      <td>12600000.0</td>\n",
              "      <td>31000000.0</td>\n",
              "      <td>57949900.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2U</td>\n",
              "      <td>Software</td>\n",
              "      <td>95875000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2275000.0</td>\n",
              "      <td>93600000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10000000.0</td>\n",
              "      <td>20000000.0</td>\n",
              "      <td>32500000.0</td>\n",
              "      <td>31100000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       name          market   ...  HiringStatus company_age\n",
              "0  1010data        Software   ...             1        20.0\n",
              "1    15Five        Software   ...             0         9.0\n",
              "2   1stdibs      E-Commerce   ...             0        19.0\n",
              "3   23andMe   Biotechnology   ...             0        14.0\n",
              "4        2U        Software   ...             0        12.0\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-Bj441s61Gf",
        "colab_type": "code",
        "outputId": "e6566630-3fc6-4e4b-fe89-154f7999dd84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "merge.HiringStatus.value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    459\n",
              "1    375\n",
              "Name: HiringStatus, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsLlNmtoKCtc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # connect to drive for output\n",
        "# from google.colab import (drive, files)\n",
        "# drive.mount('/content/gdrive', force_remount=True)\n",
        "# path = \"/content/gdrive\"\n",
        "# os.chdir(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EE7DAQuuJ496",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#merge.to_csv('My Drive/Colab Notebooks/COVID19.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KydfSrnaLOgP",
        "colab_type": "code",
        "outputId": "4badb7a3-834a-4ca2-d74d-a8878a2acd04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114
        }
      },
      "source": [
        "#check companies with recent large scale layoffs\n",
        "merge[merge['name']=='Yelp']"
      ],
      "execution_count": 15,
      "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>name</th>\n",
              "      <th>market</th>\n",
              "      <th>funding_total_usd</th>\n",
              "      <th>status</th>\n",
              "      <th>funding_rounds</th>\n",
              "      <th>seed</th>\n",
              "      <th>venture</th>\n",
              "      <th>undisclosed</th>\n",
              "      <th>convertible_note</th>\n",
              "      <th>debt_financing</th>\n",
              "      <th>angel</th>\n",
              "      <th>grant</th>\n",
              "      <th>private_equity</th>\n",
              "      <th>post_ipo_equity</th>\n",
              "      <th>post_ipo_debt</th>\n",
              "      <th>secondary_market</th>\n",
              "      <th>product_crowdfunding</th>\n",
              "      <th>round_A</th>\n",
              "      <th>round_B</th>\n",
              "      <th>round_C</th>\n",
              "      <th>round_D</th>\n",
              "      <th>round_E</th>\n",
              "      <th>round_F</th>\n",
              "      <th>round_G</th>\n",
              "      <th>round_H</th>\n",
              "      <th>HiringStatus</th>\n",
              "      <th>company_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>800</th>\n",
              "      <td>Yelp</td>\n",
              "      <td>Customer Service</td>\n",
              "      <td>56000000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000000.0</td>\n",
              "      <td>5000000.0</td>\n",
              "      <td>10000000.0</td>\n",
              "      <td>15000000.0</td>\n",
              "      <td>25000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     name             market   ...  HiringStatus company_age\n",
              "800  Yelp   Customer Service   ...             0        16.0\n",
              "\n",
              "[1 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLNPeKb76yE3",
        "colab_type": "code",
        "outputId": "0f7a062f-47a7-4f6b-820f-5e3effc4db1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114
        }
      },
      "source": [
        "merge[merge['name']=='Thumbtack']"
      ],
      "execution_count": 16,
      "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>name</th>\n",
              "      <th>market</th>\n",
              "      <th>funding_total_usd</th>\n",
              "      <th>status</th>\n",
              "      <th>funding_rounds</th>\n",
              "      <th>seed</th>\n",
              "      <th>venture</th>\n",
              "      <th>undisclosed</th>\n",
              "      <th>convertible_note</th>\n",
              "      <th>debt_financing</th>\n",
              "      <th>angel</th>\n",
              "      <th>grant</th>\n",
              "      <th>private_equity</th>\n",
              "      <th>post_ipo_equity</th>\n",
              "      <th>post_ipo_debt</th>\n",
              "      <th>secondary_market</th>\n",
              "      <th>product_crowdfunding</th>\n",
              "      <th>round_A</th>\n",
              "      <th>round_B</th>\n",
              "      <th>round_C</th>\n",
              "      <th>round_D</th>\n",
              "      <th>round_E</th>\n",
              "      <th>round_F</th>\n",
              "      <th>round_G</th>\n",
              "      <th>round_H</th>\n",
              "      <th>HiringStatus</th>\n",
              "      <th>company_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>709</th>\n",
              "      <td>Thumbtack</td>\n",
              "      <td>Curated Web</td>\n",
              "      <td>148200000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1200000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4500000.0</td>\n",
              "      <td>12500000.0</td>\n",
              "      <td>30000000.0</td>\n",
              "      <td>100000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          name        market   ...  HiringStatus company_age\n",
              "709  Thumbtack   Curated Web   ...             0        12.0\n",
              "\n",
              "[1 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbXXYcKYXxpd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#merge=merge.drop(columns=['name','status'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X6ZGPhIt0rza",
        "colab_type": "text"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYxkChdS1Erl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=merge.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0IXh7L6zVnq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "round_avg = pd.DataFrame(df.groupby(by=[\"HiringStatus\"])[[\"round_A\", \"round_B\", \"round_C\", \"round_D\"]].mean().reset_index())\n",
        "round_avg = pd.melt(round_avg, id_vars=['HiringStatus'], value_vars=[\"round_A\", \"round_B\", \"round_C\", \"round_D\"], value_name=\"avg funding\", var_name=\"round\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyhqzsRa0xD4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "3ca6051e-af02-48a2-d0cd-374c54a73b16"
      },
      "source": [
        "## hiring status and company funding rounds\n",
        "sns.catplot(x=\"round\", \n",
        "            y=\"avg funding\", \n",
        "            hue=\"HiringStatus\", \n",
        "            kind=\"bar\", \n",
        "            data=round_avg,\n",
        "            );\n",
        "plt.title('Average funding and rounds by hiring status')"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average funding and rounds by hiring status')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 429.875x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTpbV1gS3Dro",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "6c0b92d2-dd49-4b92-a793-adce7c00cb8e"
      },
      "source": [
        "top_indsutries=pd.DataFrame(df[' market '].value_counts()>10)[:15].index\n",
        "df[df[' market '].isin(top_indsutries)]"
      ],
      "execution_count": 44,
      "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>name</th>\n",
              "      <th>market</th>\n",
              "      <th>funding_total_usd</th>\n",
              "      <th>status</th>\n",
              "      <th>funding_rounds</th>\n",
              "      <th>seed</th>\n",
              "      <th>venture</th>\n",
              "      <th>undisclosed</th>\n",
              "      <th>convertible_note</th>\n",
              "      <th>debt_financing</th>\n",
              "      <th>angel</th>\n",
              "      <th>grant</th>\n",
              "      <th>private_equity</th>\n",
              "      <th>post_ipo_equity</th>\n",
              "      <th>post_ipo_debt</th>\n",
              "      <th>secondary_market</th>\n",
              "      <th>product_crowdfunding</th>\n",
              "      <th>round_A</th>\n",
              "      <th>round_B</th>\n",
              "      <th>round_C</th>\n",
              "      <th>round_D</th>\n",
              "      <th>round_E</th>\n",
              "      <th>round_F</th>\n",
              "      <th>round_G</th>\n",
              "      <th>round_H</th>\n",
              "      <th>HiringStatus</th>\n",
              "      <th>company_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1010data</td>\n",
              "      <td>Software</td>\n",
              "      <td>35000000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15Five</td>\n",
              "      <td>Software</td>\n",
              "      <td>3400000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2200000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1200000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1stdibs</td>\n",
              "      <td>E-Commerce</td>\n",
              "      <td>117000000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57000000.0</td>\n",
              "      <td>60000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23andMe</td>\n",
              "      <td>Biotechnology</td>\n",
              "      <td>111949900.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>110549900.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1400000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9000000.0</td>\n",
              "      <td>12600000.0</td>\n",
              "      <td>31000000.0</td>\n",
              "      <td>57949900.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2U</td>\n",
              "      <td>Software</td>\n",
              "      <td>95875000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2275000.0</td>\n",
              "      <td>93600000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10000000.0</td>\n",
              "      <td>20000000.0</td>\n",
              "      <td>32500000.0</td>\n",
              "      <td>31100000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</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",
              "      <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>823</th>\n",
              "      <td>Zola</td>\n",
              "      <td>E-Commerce</td>\n",
              "      <td>5849999.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5849999.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5849999.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>824</th>\n",
              "      <td>Zones</td>\n",
              "      <td>E-Commerce</td>\n",
              "      <td>700000.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>700000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>828</th>\n",
              "      <td>Zopa</td>\n",
              "      <td>Finance</td>\n",
              "      <td>56628771.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33900000.0</td>\n",
              "      <td>22728771.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000000.0</td>\n",
              "      <td>20000000.0</td>\n",
              "      <td>12900000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>832</th>\n",
              "      <td>Zymergen</td>\n",
              "      <td>Biotechnology</td>\n",
              "      <td>0.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>833</th>\n",
              "      <td>Zynga</td>\n",
              "      <td>Technology</td>\n",
              "      <td>866550786.0</td>\n",
              "      <td>operating</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>866550786.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15026000.0</td>\n",
              "      <td>355187000.0</td>\n",
              "      <td>490000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>381 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         name          market   ...  HiringStatus company_age\n",
              "0    1010data        Software   ...             1        20.0\n",
              "1      15Five        Software   ...             0         9.0\n",
              "2     1stdibs      E-Commerce   ...             0        19.0\n",
              "3     23andMe   Biotechnology   ...             0        14.0\n",
              "4          2U        Software   ...             0        12.0\n",
              "..        ...              ...  ...           ...         ...\n",
              "823      Zola      E-Commerce   ...             0         7.0\n",
              "824     Zones      E-Commerce   ...             0        32.0\n",
              "828      Zopa         Finance   ...             1        15.0\n",
              "832  Zymergen   Biotechnology   ...             1         7.0\n",
              "833     Zynga      Technology   ...             1        13.0\n",
              "\n",
              "[381 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-V_CjoXz2-UE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "129c40c6-f6e0-4c5b-9e30-dbfa60a769da"
      },
      "source": [
        "# explore the industry pattern towards hiring status\n",
        "table=pd.crosstab(df[df[' market '].isin(top_indsutries)][' market '],df[df[' market '].isin(top_indsutries)].HiringStatus)\n",
        "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
        "plt.title('Stacked Bar Chart of Hiring Stauts by company industry')\n",
        "plt.xlabel('Company indsury')\n",
        "plt.ylabel('Proportion of Hiring Company')\n",
        "plt.legend(title='Hiring Status',bbox_to_anchor=(1.1, 1))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f135b52f198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "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": "I_dN85Gm7Tup",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "6c3cabe2-2d01-47de-c887-262f813b845f"
      },
      "source": [
        "plt.hist(merge['company_age'],bins=20)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([389., 210.,  81.,  30.,   9.,  12.,   6.,   3.,   4.,   4.,   0.,\n",
              "          2.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   2.]),\n",
              " array([  6.  ,  11.55,  17.1 ,  22.65,  28.2 ,  33.75,  39.3 ,  44.85,\n",
              "         50.4 ,  55.95,  61.5 ,  67.05,  72.6 ,  78.15,  83.7 ,  89.25,\n",
              "         94.8 , 100.35, 105.9 , 111.45, 117.  ]),\n",
              " <a list of 20 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "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": "sdATVet107p_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "e3b4d75b-2ce6-4914-f89c-ae0a6e593cf4"
      },
      "source": [
        "# explore the funding rounds \n",
        "bins = [0, 5, 10, 15]\n",
        "funding_rounds_count = pd.DataFrame(df.groupby(['HiringStatus', pd.cut(df.funding_rounds, bins)]).size()).reset_index().rename(columns={0: \"count\"})\n",
        "funding_rounds_count[\"percentage\"] = funding_rounds_count[\"count\"] \n",
        "for idx, row in funding_rounds_count.iterrows():\n",
        "  hiring_status = row[\"HiringStatus\"]\n",
        "  sums = funding_rounds_count[funding_rounds_count[\"HiringStatus\"] == hiring_status][\"count\"].sum()\n",
        "  funding_rounds_count.loc[idx, \"percentage\"]  = row[\"count\"] / sums\n",
        "sns.distplot(df[df[\"HiringStatus\"] == 0][\"funding_rounds\"], kde_kws={\"label\": \"Hiring Freeze\"})\n",
        "sns.distplot(df[df[\"HiringStatus\"] == 1][\"funding_rounds\"], kde_kws={\"label\": \"Hiring\"})\n",
        "plt.title('Funding rounds and hiring status')"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'funding rounds and hiring status')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "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": "UnCwIv1V8CJ0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "0c4c9e64-51fb-4e24-d99f-3d47fa21ec9b"
      },
      "source": [
        "#company establish years vs hiring status\n",
        "bins = [0, 5, 10, 15]\n",
        "age = pd.DataFrame(df.groupby(['HiringStatus', pd.cut(df['company_age'], bins)]).size()).reset_index().rename(columns={0: \"count\"})\n",
        "age[\"percentage\"] = age[\"count\"] \n",
        "for idx, row in age.iterrows():\n",
        "  hiring_status = row[\"HiringStatus\"]\n",
        "  sums = age[age[\"HiringStatus\"] == hiring_status][\"count\"].sum()\n",
        "  age.loc[idx, \"percentage\"]  = row[\"count\"] / sums\n",
        "sns.distplot(df[df[\"HiringStatus\"] == 0][\"company_age\"], kde_kws={\"label\": \"Hiring Freeze\"})\n",
        "sns.distplot(df[df[\"HiringStatus\"] == 1][\"company_age\"], kde_kws={\"label\": \"Hiring\"})\n",
        "plt.title('Company age and hiring status')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Company age and hiring status')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "MJ-CBanO-iA6",
        "colab_type": "text"
      },
      "source": [
        "## H2O AutoML\n",
        "H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQEAKowm8eGO",
        "colab_type": "code",
        "outputId": "f58a6a3a-55f0-40d7-ad36-8e69e6c41122",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "source": [
        "##initialize environment\n",
        "h2o.init()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
            "Attempting to start a local H2O server...\n",
            "  Java Version: openjdk version \"11.0.6\" 2020-01-14; OpenJDK Runtime Environment (build 11.0.6+10-post-Ubuntu-1ubuntu118.04.1); OpenJDK 64-Bit Server VM (build 11.0.6+10-post-Ubuntu-1ubuntu118.04.1, mixed mode, sharing)\n",
            "  Starting server from /usr/local/lib/python3.6/dist-packages/h2o/backend/bin/h2o.jar\n",
            "  Ice root: /tmp/tmpzp6cmgaj\n",
            "  JVM stdout: /tmp/tmpzp6cmgaj/h2o_unknownUser_started_from_python.out\n",
            "  JVM stderr: /tmp/tmpzp6cmgaj/h2o_unknownUser_started_from_python.err\n",
            "  Server is running at http://127.0.0.1:54321\n",
            "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>02 secs</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.30.0.1</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>11 days </td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_dhmk5w</td></tr>\n",
              "<tr><td>H2O_cluster_total_nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O_cluster_free_memory:</td>\n",
              "<td>3.180 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>accepting new members, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://127.0.0.1:54321</td></tr>\n",
              "<tr><td>H2O_connection_proxy:</td>\n",
              "<td>{\"http\": null, \"https\": null}</td></tr>\n",
              "<tr><td>H2O_internal_security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>H2O_API_Extensions:</td>\n",
              "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.6.9 final</td></tr></table></div>"
            ],
            "text/plain": [
              "--------------------------  ------------------------------------------------------------------\n",
              "H2O_cluster_uptime:         02 secs\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.30.0.1\n",
              "H2O_cluster_version_age:    11 days\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_dhmk5w\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    3.180 Gb\n",
              "H2O_cluster_total_cores:    2\n",
              "H2O_cluster_allowed_cores:  2\n",
              "H2O_cluster_status:         accepting new members, healthy\n",
              "H2O_connection_url:         http://127.0.0.1:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
              "H2O_internal_security:      False\n",
              "H2O_API_Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
              "Python_version:             3.6.9 final\n",
              "--------------------------  ------------------------------------------------------------------"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vaYTYpxWj7k0",
        "colab_type": "code",
        "outputId": "730a94b2-594b-4857-aceb-c1c855a60f3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# convert data to H2O data frame\n",
        "merge_h2o=h2o.H2OFrame(merge)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TE5az6RWkFh6",
        "colab_type": "code",
        "outputId": "acee0b17-5f4d-430a-8aa3-120c1fac9052",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "merge_h2o.types"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{' funding_total_usd ': 'int',\n",
              " ' market ': 'enum',\n",
              " 'HiringStatus': 'int',\n",
              " 'angel': 'int',\n",
              " 'company_age': 'int',\n",
              " 'convertible_note': 'int',\n",
              " 'debt_financing': 'int',\n",
              " 'funding_rounds': 'int',\n",
              " 'grant': 'int',\n",
              " 'name': 'string',\n",
              " 'post_ipo_debt': 'int',\n",
              " 'post_ipo_equity': 'int',\n",
              " 'private_equity': 'int',\n",
              " 'product_crowdfunding': 'int',\n",
              " 'round_A': 'int',\n",
              " 'round_B': 'int',\n",
              " 'round_C': 'int',\n",
              " 'round_D': 'int',\n",
              " 'round_E': 'int',\n",
              " 'round_F': 'int',\n",
              " 'round_G': 'int',\n",
              " 'round_H': 'int',\n",
              " 'secondary_market': 'int',\n",
              " 'seed': 'int',\n",
              " 'status': 'enum',\n",
              " 'undisclosed': 'int',\n",
              " 'venture': 'int'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsSuC1adkXYz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#split train test dataset using h2o train split\n",
        "train,test,valid = merge_h2o.split_frame(ratios=[.7, .15])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhEk-GkV7mQd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define X and y variables \n",
        "y = 'HiringStatus'\n",
        "x = train.columns\n",
        "x.remove(y)\n",
        "train[y] = train[y].asfactor()\n",
        "test[y] = test[y].asfactor()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLxzOo5a8mHg",
        "colab_type": "code",
        "outputId": "a988929e-1d81-4932-9291-15be4b80c853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#auto machine learning package to predict results\n",
        "from h2o.automl import H2OAutoML\n",
        "aml = H2OAutoML(max_models=10, \n",
        "                balance_classes = True,\n",
        "                max_runtime_secs=300, \n",
        "                seed = 1234,\n",
        "                )\n",
        "aml.train(x = x, y = y, training_frame = train)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aYX34sl8rqa",
        "colab_type": "code",
        "outputId": "b5e9b07a-0b91-4314-acd0-18a99e49234c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        }
      },
      "source": [
        "aml.leaderboard"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>GBM_4_AutoML_20200415_083036                       </td><td style=\"text-align: right;\">0.543452</td><td style=\"text-align: right;\"> 0.842174</td><td style=\"text-align: right;\">0.495204</td><td style=\"text-align: right;\">              0.494654</td><td style=\"text-align: right;\">0.541316</td><td style=\"text-align: right;\">0.293023</td></tr>\n",
              "<tr><td>GBM_2_AutoML_20200415_083036                       </td><td style=\"text-align: right;\">0.531138</td><td style=\"text-align: right;\"> 0.879555</td><td style=\"text-align: right;\">0.494613</td><td style=\"text-align: right;\">              0.495926</td><td style=\"text-align: right;\">0.55139 </td><td style=\"text-align: right;\">0.304031</td></tr>\n",
              "<tr><td>GBM_5_AutoML_20200415_083036                       </td><td style=\"text-align: right;\">0.527234</td><td style=\"text-align: right;\"> 0.814816</td><td style=\"text-align: right;\">0.475408</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.536464</td><td style=\"text-align: right;\">0.287794</td></tr>\n",
              "<tr><td>XGBoost_3_AutoML_20200415_083036                   </td><td style=\"text-align: right;\">0.521696</td><td style=\"text-align: right;\"> 0.793185</td><td style=\"text-align: right;\">0.46881 </td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.534734</td><td style=\"text-align: right;\">0.28594 </td></tr>\n",
              "<tr><td>GBM_3_AutoML_20200415_083036                       </td><td style=\"text-align: right;\">0.515755</td><td style=\"text-align: right;\"> 0.941277</td><td style=\"text-align: right;\">0.479228</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.569341</td><td style=\"text-align: right;\">0.32415 </td></tr>\n",
              "<tr><td>DRF_1_AutoML_20200415_083036                       </td><td style=\"text-align: right;\">0.515176</td><td style=\"text-align: right;\"> 0.820841</td><td style=\"text-align: right;\">0.482865</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.541751</td><td style=\"text-align: right;\">0.293494</td></tr>\n",
              "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20200415_083036</td><td style=\"text-align: right;\">0.51137 </td><td style=\"text-align: right;\"> 0.691973</td><td style=\"text-align: right;\">0.472515</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.499393</td><td style=\"text-align: right;\">0.249393</td></tr>\n",
              "<tr><td>GBM_1_AutoML_20200415_083036                       </td><td style=\"text-align: right;\">0.507274</td><td style=\"text-align: right;\"> 0.913711</td><td style=\"text-align: right;\">0.468914</td><td style=\"text-align: right;\">              0.493381</td><td style=\"text-align: right;\">0.566748</td><td style=\"text-align: right;\">0.321203</td></tr>\n",
              "<tr><td>StackedEnsemble_AllModels_AutoML_20200415_083036   </td><td style=\"text-align: right;\">0.503697</td><td style=\"text-align: right;\"> 0.692856</td><td style=\"text-align: right;\">0.464693</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.499834</td><td style=\"text-align: right;\">0.249834</td></tr>\n",
              "<tr><td>GLM_1_AutoML_20200415_083036                       </td><td style=\"text-align: right;\">0.502266</td><td style=\"text-align: right;\"> 0.690052</td><td style=\"text-align: right;\">0.449594</td><td style=\"text-align: right;\">              0.496942</td><td style=\"text-align: right;\">0.498474</td><td style=\"text-align: right;\">0.248476</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XYLg4UV9ASO",
        "colab_type": "code",
        "outputId": "05374b7c-2fcd-4bac-b502-29174fd69206",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "aml.leader"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Details\n",
            "=============\n",
            "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
            "Model Key:  GBM_4_AutoML_20200415_083036\n",
            "\n",
            "\n",
            "Model Summary: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>number_of_trees</th>\n",
              "      <th>number_of_internal_trees</th>\n",
              "      <th>model_size_in_bytes</th>\n",
              "      <th>min_depth</th>\n",
              "      <th>max_depth</th>\n",
              "      <th>mean_depth</th>\n",
              "      <th>min_leaves</th>\n",
              "      <th>max_leaves</th>\n",
              "      <th>mean_leaves</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>27453.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.9</td>\n",
              "      <td>19.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>34.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     number_of_trees  ...  max_leaves  mean_leaves\n",
              "0               30.0  ...        42.0         34.3\n",
              "\n",
              "[1 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "ModelMetricsBinomial: gbm\n",
            "** Reported on train data. **\n",
            "\n",
            "MSE: 0.07792947766752131\n",
            "RMSE: 0.2791585170965079\n",
            "LogLoss: 0.2858035390720864\n",
            "Mean Per-Class Error: 0.05483736447039189\n",
            "AUC: 0.9851959966638867\n",
            "AUCPR: 0.9844986275978437\n",
            "Gini: 0.9703919933277734\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.4559483812604928: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>Error</th>\n",
              "      <th>Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>301.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0795</td>\n",
              "      <td>(26.0/327.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0.0303</td>\n",
              "      <td>(10.0/330.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>311.0</td>\n",
              "      <td>346.0</td>\n",
              "      <td>0.0548</td>\n",
              "      <td>(36.0/657.0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              0      1   Error           Rate\n",
              "0      0  301.0   26.0  0.0795   (26.0/327.0)\n",
              "1      1   10.0  320.0  0.0303   (10.0/330.0)\n",
              "2  Total  311.0  346.0  0.0548   (36.0/657.0)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>metric</th>\n",
              "      <th>threshold</th>\n",
              "      <th>value</th>\n",
              "      <th>idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>max f1</td>\n",
              "      <td>0.455948</td>\n",
              "      <td>0.946746</td>\n",
              "      <td>209.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>max f2</td>\n",
              "      <td>0.450854</td>\n",
              "      <td>0.961654</td>\n",
              "      <td>212.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>max f0point5</td>\n",
              "      <td>0.513085</td>\n",
              "      <td>0.946276</td>\n",
              "      <td>193.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>max accuracy</td>\n",
              "      <td>0.484137</td>\n",
              "      <td>0.945205</td>\n",
              "      <td>202.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max precision</td>\n",
              "      <td>0.952755</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>max recall</td>\n",
              "      <td>0.334423</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>253.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>max specificity</td>\n",
              "      <td>0.952755</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>max absolute_mcc</td>\n",
              "      <td>0.455948</td>\n",
              "      <td>0.891443</td>\n",
              "      <td>209.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>max min_per_class_accuracy</td>\n",
              "      <td>0.497719</td>\n",
              "      <td>0.941896</td>\n",
              "      <td>196.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>max mean_per_class_accuracy</td>\n",
              "      <td>0.484137</td>\n",
              "      <td>0.945163</td>\n",
              "      <td>202.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>max tns</td>\n",
              "      <td>0.952755</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>max fns</td>\n",
              "      <td>0.952755</td>\n",
              "      <td>329.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>max fps</td>\n",
              "      <td>0.025285</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>max tps</td>\n",
              "      <td>0.334423</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>253.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>max tnr</td>\n",
              "      <td>0.952755</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>max fnr</td>\n",
              "      <td>0.952755</td>\n",
              "      <td>0.996970</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>max fpr</td>\n",
              "      <td>0.025285</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>max tpr</td>\n",
              "      <td>0.334423</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>253.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         metric  threshold       value    idx\n",
              "0                        max f1   0.455948    0.946746  209.0\n",
              "1                        max f2   0.450854    0.961654  212.0\n",
              "2                  max f0point5   0.513085    0.946276  193.0\n",
              "3                  max accuracy   0.484137    0.945205  202.0\n",
              "4                 max precision   0.952755    1.000000    0.0\n",
              "5                    max recall   0.334423    1.000000  253.0\n",
              "6               max specificity   0.952755    1.000000    0.0\n",
              "7              max absolute_mcc   0.455948    0.891443  209.0\n",
              "8    max min_per_class_accuracy   0.497719    0.941896  196.0\n",
              "9   max mean_per_class_accuracy   0.484137    0.945163  202.0\n",
              "10                      max tns   0.952755  327.000000    0.0\n",
              "11                      max fns   0.952755  329.000000    0.0\n",
              "12                      max fps   0.025285  327.000000  399.0\n",
              "13                      max tps   0.334423  330.000000  253.0\n",
              "14                      max tnr   0.952755    1.000000    0.0\n",
              "15                      max fnr   0.952755    0.996970    0.0\n",
              "16                      max fpr   0.025285    1.000000  399.0\n",
              "17                      max tpr   0.334423    1.000000  253.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Gains/Lift Table: Avg response rate: 50.23 %, avg score: 47.90 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>group</th>\n",
              "      <th>cumulative_data_fraction</th>\n",
              "      <th>lower_threshold</th>\n",
              "      <th>lift</th>\n",
              "      <th>cumulative_lift</th>\n",
              "      <th>response_rate</th>\n",
              "      <th>score</th>\n",
              "      <th>cumulative_response_rate</th>\n",
              "      <th>cumulative_score</th>\n",
              "      <th>capture_rate</th>\n",
              "      <th>cumulative_capture_rate</th>\n",
              "      <th>gain</th>\n",
              "      <th>cumulative_gain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>0.010654</td>\n",
              "      <td>0.944993</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.947232</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.947232</td>\n",
              "      <td>0.021212</td>\n",
              "      <td>0.021212</td>\n",
              "      <td>99.090909</td>\n",
              "      <td>99.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>2</td>\n",
              "      <td>0.021309</td>\n",
              "      <td>0.942208</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.943576</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.945404</td>\n",
              "      <td>0.021212</td>\n",
              "      <td>0.042424</td>\n",
              "      <td>99.090909</td>\n",
              "      <td>99.090909</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>3</td>\n",
              "      <td>0.030441</td>\n",
              "      <td>0.939458</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.939947</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.943767</td>\n",
              "      <td>0.018182</td>\n",
              "      <td>0.060606</td>\n",
              "      <td>99.090909</td>\n",
              "      <td>99.090909</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>4</td>\n",
              "      <td>0.041096</td>\n",
              "      <td>0.936283</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.938164</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.942314</td>\n",
              "      <td>0.021212</td>\n",
              "      <td>0.081818</td>\n",
              "      <td>99.090909</td>\n",
              "      <td>99.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>5</td>\n",
              "      <td>0.050228</td>\n",
              "      <td>0.933980</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.935123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.941007</td>\n",
              "      <td>0.018182</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>99.090909</td>\n",
              "      <td>99.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td></td>\n",
              "      <td>6</td>\n",
              "      <td>0.100457</td>\n",
              "      <td>0.921866</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.928130</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.934568</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>99.090909</td>\n",
              "      <td>99.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td></td>\n",
              "      <td>7</td>\n",
              "      <td>0.152207</td>\n",
              "      <td>0.875365</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.924175</td>\n",
              "      <td>0.103030</td>\n",
              "      <td>0.303030</td>\n",
              "      <td>99.090909</td>\n",
              "      <td>99.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td></td>\n",
              "      <td>8</td>\n",
              "      <td>0.200913</td>\n",
              "      <td>0.815749</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.847412</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.905566</td>\n",
              "      <td>0.096970</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>99.090909</td>\n",
              "      <td>99.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td></td>\n",
              "      <td>9</td>\n",
              "      <td>0.299848</td>\n",
              "      <td>0.714447</td>\n",
              "      <td>1.929650</td>\n",
              "      <td>1.970697</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.759843</td>\n",
              "      <td>0.989848</td>\n",
              "      <td>0.857485</td>\n",
              "      <td>0.190909</td>\n",
              "      <td>0.590909</td>\n",
              "      <td>92.965035</td>\n",
              "      <td>97.069682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td></td>\n",
              "      <td>10</td>\n",
              "      <td>0.400304</td>\n",
              "      <td>0.613820</td>\n",
              "      <td>1.900413</td>\n",
              "      <td>1.953059</td>\n",
              "      <td>0.954545</td>\n",
              "      <td>0.662628</td>\n",
              "      <td>0.980989</td>\n",
              "      <td>0.808585</td>\n",
              "      <td>0.190909</td>\n",
              "      <td>0.781818</td>\n",
              "      <td>90.041322</td>\n",
              "      <td>95.305911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td></td>\n",
              "      <td>11</td>\n",
              "      <td>0.500761</td>\n",
              "      <td>0.498659</td>\n",
              "      <td>1.568595</td>\n",
              "      <td>1.875933</td>\n",
              "      <td>0.787879</td>\n",
              "      <td>0.559068</td>\n",
              "      <td>0.942249</td>\n",
              "      <td>0.758530</td>\n",
              "      <td>0.157576</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>56.859504</td>\n",
              "      <td>87.593258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "      <td>0.599696</td>\n",
              "      <td>0.344977</td>\n",
              "      <td>0.520699</td>\n",
              "      <td>1.652353</td>\n",
              "      <td>0.261538</td>\n",
              "      <td>0.418881</td>\n",
              "      <td>0.829949</td>\n",
              "      <td>0.702497</td>\n",
              "      <td>0.051515</td>\n",
              "      <td>0.990909</td>\n",
              "      <td>-47.930070</td>\n",
              "      <td>65.235348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td></td>\n",
              "      <td>13</td>\n",
              "      <td>0.700152</td>\n",
              "      <td>0.230335</td>\n",
              "      <td>0.090496</td>\n",
              "      <td>1.428261</td>\n",
              "      <td>0.045455</td>\n",
              "      <td>0.280758</td>\n",
              "      <td>0.717391</td>\n",
              "      <td>0.641986</td>\n",
              "      <td>0.009091</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-90.950413</td>\n",
              "      <td>42.826087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td></td>\n",
              "      <td>14</td>\n",
              "      <td>0.799087</td>\n",
              "      <td>0.118154</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.251429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.170069</td>\n",
              "      <td>0.628571</td>\n",
              "      <td>0.583558</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>25.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td></td>\n",
              "      <td>15</td>\n",
              "      <td>0.899543</td>\n",
              "      <td>0.056794</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.111675</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078661</td>\n",
              "      <td>0.558376</td>\n",
              "      <td>0.527174</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>11.167513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td></td>\n",
              "      <td>16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.025285</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.047601</td>\n",
              "      <td>0.502283</td>\n",
              "      <td>0.478998</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      group  ...        gain  cumulative_gain\n",
              "0         1  ...   99.090909        99.090909\n",
              "1         2  ...   99.090909        99.090909\n",
              "2         3  ...   99.090909        99.090909\n",
              "3         4  ...   99.090909        99.090909\n",
              "4         5  ...   99.090909        99.090909\n",
              "5         6  ...   99.090909        99.090909\n",
              "6         7  ...   99.090909        99.090909\n",
              "7         8  ...   99.090909        99.090909\n",
              "8         9  ...   92.965035        97.069682\n",
              "9        10  ...   90.041322        95.305911\n",
              "10       11  ...   56.859504        87.593258\n",
              "11       12  ...  -47.930070        65.235348\n",
              "12       13  ...  -90.950413        42.826087\n",
              "13       14  ... -100.000000        25.142857\n",
              "14       15  ... -100.000000        11.167513\n",
              "15       16  ... -100.000000         0.000000\n",
              "\n",
              "[16 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "ModelMetricsBinomial: gbm\n",
            "** Reported on cross-validation data. **\n",
            "\n",
            "MSE: 0.29302304448417876\n",
            "RMSE: 0.5413160301378288\n",
            "LogLoss: 0.8421741159347108\n",
            "Mean Per-Class Error: 0.4560452162516382\n",
            "AUC: 0.5434523809523809\n",
            "AUCPR: 0.49520374724833727\n",
            "Gini: 0.08690476190476182\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.042358505038721087: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>Error</th>\n",
              "      <th>Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0.9786</td>\n",
              "      <td>(320.0/327.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>0.0107</td>\n",
              "      <td>(3.0/280.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>10.0</td>\n",
              "      <td>597.0</td>\n",
              "      <td>0.5321</td>\n",
              "      <td>(323.0/607.0)</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "             0      1   Error            Rate\n",
              "0      0   7.0  320.0  0.9786   (320.0/327.0)\n",
              "1      1   3.0  277.0  0.0107     (3.0/280.0)\n",
              "2  Total  10.0  597.0  0.5321   (323.0/607.0)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>metric</th>\n",
              "      <th>threshold</th>\n",
              "      <th>value</th>\n",
              "      <th>idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>max f1</td>\n",
              "      <td>0.042359</td>\n",
              "      <td>0.631699</td>\n",
              "      <td>390.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>max f2</td>\n",
              "      <td>0.020824</td>\n",
              "      <td>0.810654</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>max f0point5</td>\n",
              "      <td>0.155116</td>\n",
              "      <td>0.528261</td>\n",
              "      <td>338.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>max accuracy</td>\n",
              "      <td>0.892048</td>\n",
              "      <td>0.551895</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max precision</td>\n",
              "      <td>0.957026</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>max recall</td>\n",
              "      <td>0.020824</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>max specificity</td>\n",
              "      <td>0.957026</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>max absolute_mcc</td>\n",
              "      <td>0.321955</td>\n",
              "      <td>0.094917</td>\n",
              "      <td>277.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>max min_per_class_accuracy</td>\n",
              "      <td>0.461337</td>\n",
              "      <td>0.525994</td>\n",
              "      <td>203.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>max mean_per_class_accuracy</td>\n",
              "      <td>0.321955</td>\n",
              "      <td>0.543955</td>\n",
              "      <td>277.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>max tns</td>\n",
              "      <td>0.957026</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>max fns</td>\n",
              "      <td>0.957026</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>max fps</td>\n",
              "      <td>0.024972</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>398.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>max tps</td>\n",
              "      <td>0.020824</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>max tnr</td>\n",
              "      <td>0.957026</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>max fnr</td>\n",
              "      <td>0.957026</td>\n",
              "      <td>0.996429</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>max fpr</td>\n",
              "      <td>0.024972</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>398.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>max tpr</td>\n",
              "      <td>0.020824</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         metric  threshold       value    idx\n",
              "0                        max f1   0.042359    0.631699  390.0\n",
              "1                        max f2   0.020824    0.810654  399.0\n",
              "2                  max f0point5   0.155116    0.528261  338.0\n",
              "3                  max accuracy   0.892048    0.551895   31.0\n",
              "4                 max precision   0.957026    1.000000    0.0\n",
              "5                    max recall   0.020824    1.000000  399.0\n",
              "6               max specificity   0.957026    1.000000    0.0\n",
              "7              max absolute_mcc   0.321955    0.094917  277.0\n",
              "8    max min_per_class_accuracy   0.461337    0.525994  203.0\n",
              "9   max mean_per_class_accuracy   0.321955    0.543955  277.0\n",
              "10                      max tns   0.957026  327.000000    0.0\n",
              "11                      max fns   0.957026  279.000000    0.0\n",
              "12                      max fps   0.024972  327.000000  398.0\n",
              "13                      max tps   0.020824  280.000000  399.0\n",
              "14                      max tnr   0.957026    1.000000    0.0\n",
              "15                      max fnr   0.957026    0.996429    0.0\n",
              "16                      max fpr   0.024972    1.000000  398.0\n",
              "17                      max tpr   0.020824    1.000000  399.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Gains/Lift Table: Avg response rate: 46.13 %, avg score: 44.81 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>group</th>\n",
              "      <th>cumulative_data_fraction</th>\n",
              "      <th>lower_threshold</th>\n",
              "      <th>lift</th>\n",
              "      <th>cumulative_lift</th>\n",
              "      <th>response_rate</th>\n",
              "      <th>score</th>\n",
              "      <th>cumulative_response_rate</th>\n",
              "      <th>cumulative_score</th>\n",
              "      <th>capture_rate</th>\n",
              "      <th>cumulative_capture_rate</th>\n",
              "      <th>gain</th>\n",
              "      <th>cumulative_gain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>0.011532</td>\n",
              "      <td>0.946519</td>\n",
              "      <td>1.238776</td>\n",
              "      <td>1.238776</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.952055</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.952055</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>23.877551</td>\n",
              "      <td>23.877551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>2</td>\n",
              "      <td>0.021417</td>\n",
              "      <td>0.935583</td>\n",
              "      <td>0.722619</td>\n",
              "      <td>1.000549</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.941452</td>\n",
              "      <td>0.461538</td>\n",
              "      <td>0.947161</td>\n",
              "      <td>0.007143</td>\n",
              "      <td>0.021429</td>\n",
              "      <td>-27.738095</td>\n",
              "      <td>0.054945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>3</td>\n",
              "      <td>0.031301</td>\n",
              "      <td>0.927535</td>\n",
              "      <td>0.722619</td>\n",
              "      <td>0.912782</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.930938</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.942038</td>\n",
              "      <td>0.007143</td>\n",
              "      <td>0.028571</td>\n",
              "      <td>-27.738095</td>\n",
              "      <td>-8.721805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>4</td>\n",
              "      <td>0.041186</td>\n",
              "      <td>0.924009</td>\n",
              "      <td>1.806548</td>\n",
              "      <td>1.127286</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.925813</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.938144</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.046429</td>\n",
              "      <td>80.654762</td>\n",
              "      <td>12.728571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>5</td>\n",
              "      <td>0.051071</td>\n",
              "      <td>0.914780</td>\n",
              "      <td>1.445238</td>\n",
              "      <td>1.188825</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.919317</td>\n",
              "      <td>0.548387</td>\n",
              "      <td>0.934500</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>0.060714</td>\n",
              "      <td>44.523810</td>\n",
              "      <td>18.882488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td></td>\n",
              "      <td>6</td>\n",
              "      <td>0.100494</td>\n",
              "      <td>0.821247</td>\n",
              "      <td>1.083929</td>\n",
              "      <td>1.137237</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.874594</td>\n",
              "      <td>0.524590</td>\n",
              "      <td>0.905038</td>\n",
              "      <td>0.053571</td>\n",
              "      <td>0.114286</td>\n",
              "      <td>8.392857</td>\n",
              "      <td>13.723653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td></td>\n",
              "      <td>7</td>\n",
              "      <td>0.149918</td>\n",
              "      <td>0.726500</td>\n",
              "      <td>1.011667</td>\n",
              "      <td>1.095840</td>\n",
              "      <td>0.466667</td>\n",
              "      <td>0.765238</td>\n",
              "      <td>0.505495</td>\n",
              "      <td>0.858950</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.164286</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>9.583987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td></td>\n",
              "      <td>8</td>\n",
              "      <td>0.200988</td>\n",
              "      <td>0.639610</td>\n",
              "      <td>0.979032</td>\n",
              "      <td>1.066159</td>\n",
              "      <td>0.451613</td>\n",
              "      <td>0.674613</td>\n",
              "      <td>0.491803</td>\n",
              "      <td>0.812110</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>-2.096774</td>\n",
              "      <td>6.615925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td></td>\n",
              "      <td>9</td>\n",
              "      <td>0.299835</td>\n",
              "      <td>0.574515</td>\n",
              "      <td>1.120060</td>\n",
              "      <td>1.083929</td>\n",
              "      <td>0.516667</td>\n",
              "      <td>0.606274</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.744252</td>\n",
              "      <td>0.110714</td>\n",
              "      <td>0.325000</td>\n",
              "      <td>12.005952</td>\n",
              "      <td>8.392857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td></td>\n",
              "      <td>10</td>\n",
              "      <td>0.400329</td>\n",
              "      <td>0.520387</td>\n",
              "      <td>1.101698</td>\n",
              "      <td>1.088389</td>\n",
              "      <td>0.508197</td>\n",
              "      <td>0.548167</td>\n",
              "      <td>0.502058</td>\n",
              "      <td>0.695029</td>\n",
              "      <td>0.110714</td>\n",
              "      <td>0.435714</td>\n",
              "      <td>10.169789</td>\n",
              "      <td>8.838918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td></td>\n",
              "      <td>11</td>\n",
              "      <td>0.500824</td>\n",
              "      <td>0.458550</td>\n",
              "      <td>0.959543</td>\n",
              "      <td>1.062535</td>\n",
              "      <td>0.442623</td>\n",
              "      <td>0.490821</td>\n",
              "      <td>0.490132</td>\n",
              "      <td>0.654053</td>\n",
              "      <td>0.096429</td>\n",
              "      <td>0.532143</td>\n",
              "      <td>-4.045667</td>\n",
              "      <td>6.253524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "      <td>0.599671</td>\n",
              "      <td>0.387303</td>\n",
              "      <td>0.939405</td>\n",
              "      <td>1.042239</td>\n",
              "      <td>0.433333</td>\n",
              "      <td>0.420747</td>\n",
              "      <td>0.480769</td>\n",
              "      <td>0.615596</td>\n",
              "      <td>0.092857</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>-6.059524</td>\n",
              "      <td>4.223901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td></td>\n",
              "      <td>13</td>\n",
              "      <td>0.700165</td>\n",
              "      <td>0.312535</td>\n",
              "      <td>1.208314</td>\n",
              "      <td>1.066076</td>\n",
              "      <td>0.557377</td>\n",
              "      <td>0.349708</td>\n",
              "      <td>0.491765</td>\n",
              "      <td>0.577433</td>\n",
              "      <td>0.121429</td>\n",
              "      <td>0.746429</td>\n",
              "      <td>20.831382</td>\n",
              "      <td>6.607563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td></td>\n",
              "      <td>14</td>\n",
              "      <td>0.799012</td>\n",
              "      <td>0.187869</td>\n",
              "      <td>0.722619</td>\n",
              "      <td>1.023586</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.259939</td>\n",
              "      <td>0.472165</td>\n",
              "      <td>0.538156</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.817857</td>\n",
              "      <td>-27.738095</td>\n",
              "      <td>2.358616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td></td>\n",
              "      <td>15</td>\n",
              "      <td>0.899506</td>\n",
              "      <td>0.077282</td>\n",
              "      <td>1.030621</td>\n",
              "      <td>1.024372</td>\n",
              "      <td>0.475410</td>\n",
              "      <td>0.124091</td>\n",
              "      <td>0.472527</td>\n",
              "      <td>0.491896</td>\n",
              "      <td>0.103571</td>\n",
              "      <td>0.921429</td>\n",
              "      <td>3.062061</td>\n",
              "      <td>2.437206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td></td>\n",
              "      <td>16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.020824</td>\n",
              "      <td>0.781850</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.360656</td>\n",
              "      <td>0.055655</td>\n",
              "      <td>0.461285</td>\n",
              "      <td>0.448056</td>\n",
              "      <td>0.078571</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-21.814988</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      group  ...       gain  cumulative_gain\n",
              "0         1  ...  23.877551        23.877551\n",
              "1         2  ... -27.738095         0.054945\n",
              "2         3  ... -27.738095        -8.721805\n",
              "3         4  ...  80.654762        12.728571\n",
              "4         5  ...  44.523810        18.882488\n",
              "5         6  ...   8.392857        13.723653\n",
              "6         7  ...   1.166667         9.583987\n",
              "7         8  ...  -2.096774         6.615925\n",
              "8         9  ...  12.005952         8.392857\n",
              "9        10  ...  10.169789         8.838918\n",
              "10       11  ...  -4.045667         6.253524\n",
              "11       12  ...  -6.059524         4.223901\n",
              "12       13  ...  20.831382         6.607563\n",
              "13       14  ... -27.738095         2.358616\n",
              "14       15  ...   3.062061         2.437206\n",
              "15       16  ... -21.814988         0.000000\n",
              "\n",
              "[16 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Cross-Validation Metrics Summary: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>sd</th>\n",
              "      <th>cv_1_valid</th>\n",
              "      <th>cv_2_valid</th>\n",
              "      <th>cv_3_valid</th>\n",
              "      <th>cv_4_valid</th>\n",
              "      <th>cv_5_valid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.49581358</td>\n",
              "      <td>0.031209296</td>\n",
              "      <td>0.5409836</td>\n",
              "      <td>0.4918033</td>\n",
              "      <td>0.45454547</td>\n",
              "      <td>0.5041322</td>\n",
              "      <td>0.4876033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>auc</td>\n",
              "      <td>0.5474711</td>\n",
              "      <td>0.06946262</td>\n",
              "      <td>0.6047078</td>\n",
              "      <td>0.44642374</td>\n",
              "      <td>0.54732394</td>\n",
              "      <td>0.52021855</td>\n",
              "      <td>0.6186813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>aucpr</td>\n",
              "      <td>0.5190139</td>\n",
              "      <td>0.07236685</td>\n",
              "      <td>0.5711449</td>\n",
              "      <td>0.44788682</td>\n",
              "      <td>0.464421</td>\n",
              "      <td>0.49464008</td>\n",
              "      <td>0.6169765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>err</td>\n",
              "      <td>0.50418645</td>\n",
              "      <td>0.031209296</td>\n",
              "      <td>0.45901638</td>\n",
              "      <td>0.5081967</td>\n",
              "      <td>0.54545456</td>\n",
              "      <td>0.49586776</td>\n",
              "      <td>0.5123967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>err_count</td>\n",
              "      <td>61.2</td>\n",
              "      <td>3.6331804</td>\n",
              "      <td>56.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>f0point5</td>\n",
              "      <td>0.5322422</td>\n",
              "      <td>0.028972657</td>\n",
              "      <td>0.5544355</td>\n",
              "      <td>0.5319149</td>\n",
              "      <td>0.48638132</td>\n",
              "      <td>0.559633</td>\n",
              "      <td>0.52884614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>f1</td>\n",
              "      <td>0.64289194</td>\n",
              "      <td>0.02647919</td>\n",
              "      <td>0.6626506</td>\n",
              "      <td>0.6395349</td>\n",
              "      <td>0.60240966</td>\n",
              "      <td>0.6703297</td>\n",
              "      <td>0.6395349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>f2</td>\n",
              "      <td>0.81213635</td>\n",
              "      <td>0.017584646</td>\n",
              "      <td>0.8233533</td>\n",
              "      <td>0.8017493</td>\n",
              "      <td>0.79113925</td>\n",
              "      <td>0.8356164</td>\n",
              "      <td>0.8088235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>lift_top_group</td>\n",
              "      <td>1.5379273</td>\n",
              "      <td>0.95314735</td>\n",
              "      <td>2.1785715</td>\n",
              "      <td>2.1403508</td>\n",
              "      <td>1.21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.1607144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>logloss</td>\n",
              "      <td>0.84212995</td>\n",
              "      <td>0.11672736</td>\n",
              "      <td>0.7289072</td>\n",
              "      <td>0.98217493</td>\n",
              "      <td>0.81241506</td>\n",
              "      <td>0.94648874</td>\n",
              "      <td>0.74066377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>max_per_class_error</td>\n",
              "      <td>0.92488986</td>\n",
              "      <td>0.059652288</td>\n",
              "      <td>0.8333333</td>\n",
              "      <td>0.9230769</td>\n",
              "      <td>0.92957747</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.93846154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>mcc</td>\n",
              "      <td>0.1556029</td>\n",
              "      <td>0.07197089</td>\n",
              "      <td>0.24900596</td>\n",
              "      <td>0.08975107</td>\n",
              "      <td>0.17422555</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.109429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>mean_per_class_accuracy</td>\n",
              "      <td>0.5304749</td>\n",
              "      <td>0.027599258</td>\n",
              "      <td>0.5744048</td>\n",
              "      <td>0.52091765</td>\n",
              "      <td>0.53521127</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.52184063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>mean_per_class_error</td>\n",
              "      <td>0.46952513</td>\n",
              "      <td>0.027599258</td>\n",
              "      <td>0.42559522</td>\n",
              "      <td>0.47908232</td>\n",
              "      <td>0.46478873</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.47815934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>mse</td>\n",
              "      <td>0.29299417</td>\n",
              "      <td>0.038374133</td>\n",
              "      <td>0.25981155</td>\n",
              "      <td>0.3437087</td>\n",
              "      <td>0.2874867</td>\n",
              "      <td>0.31949827</td>\n",
              "      <td>0.2544656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>pr_auc</td>\n",
              "      <td>0.5190139</td>\n",
              "      <td>0.07236685</td>\n",
              "      <td>0.5711449</td>\n",
              "      <td>0.44788682</td>\n",
              "      <td>0.464421</td>\n",
              "      <td>0.49464008</td>\n",
              "      <td>0.6169765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>precision</td>\n",
              "      <td>0.4775131</td>\n",
              "      <td>0.029096613</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.47826087</td>\n",
              "      <td>0.43103448</td>\n",
              "      <td>0.5041322</td>\n",
              "      <td>0.47413793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>r2</td>\n",
              "      <td>-0.1828626</td>\n",
              "      <td>0.1518937</td>\n",
              "      <td>-0.046275776</td>\n",
              "      <td>-0.38077193</td>\n",
              "      <td>-0.18565996</td>\n",
              "      <td>-0.27808034</td>\n",
              "      <td>-0.023524974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>recall</td>\n",
              "      <td>0.9858396</td>\n",
              "      <td>0.014716643</td>\n",
              "      <td>0.98214287</td>\n",
              "      <td>0.9649123</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.98214287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>rmse</td>\n",
              "      <td>0.54036987</td>\n",
              "      <td>0.035259154</td>\n",
              "      <td>0.50971717</td>\n",
              "      <td>0.58626676</td>\n",
              "      <td>0.5361779</td>\n",
              "      <td>0.56524175</td>\n",
              "      <td>0.50444585</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   mean  ...   cv_4_valid    cv_5_valid\n",
              "0                  accuracy  0.49581358  ...    0.5041322     0.4876033\n",
              "1                       auc   0.5474711  ...   0.52021855     0.6186813\n",
              "2                     aucpr   0.5190139  ...   0.49464008     0.6169765\n",
              "3                       err  0.50418645  ...   0.49586776     0.5123967\n",
              "4                 err_count        61.2  ...         60.0          62.0\n",
              "5                  f0point5   0.5322422  ...     0.559633    0.52884614\n",
              "6                        f1  0.64289194  ...    0.6703297     0.6395349\n",
              "7                        f2  0.81213635  ...    0.8356164     0.8088235\n",
              "8            lift_top_group   1.5379273  ...          0.0     2.1607144\n",
              "9                   logloss  0.84212995  ...   0.94648874    0.74066377\n",
              "10      max_per_class_error  0.92488986  ...          1.0    0.93846154\n",
              "11                      mcc   0.1556029  ...          NaN      0.109429\n",
              "12  mean_per_class_accuracy   0.5304749  ...          0.5    0.52184063\n",
              "13     mean_per_class_error  0.46952513  ...          0.5    0.47815934\n",
              "14                      mse  0.29299417  ...   0.31949827     0.2544656\n",
              "15                   pr_auc   0.5190139  ...   0.49464008     0.6169765\n",
              "16                precision   0.4775131  ...    0.5041322    0.47413793\n",
              "17                       r2  -0.1828626  ...  -0.27808034  -0.023524974\n",
              "18                   recall   0.9858396  ...          1.0    0.98214287\n",
              "19                     rmse  0.54036987  ...   0.56524175    0.50444585\n",
              "\n",
              "[20 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "See the whole table with table.as_data_frame()\n",
            "\n",
            "Scoring History: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>duration</th>\n",
              "      <th>number_of_trees</th>\n",
              "      <th>training_rmse</th>\n",
              "      <th>training_logloss</th>\n",
              "      <th>training_auc</th>\n",
              "      <th>training_pr_auc</th>\n",
              "      <th>training_lift</th>\n",
              "      <th>training_classification_error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-15 08:31:00</td>\n",
              "      <td>2.074 sec</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.501673</td>\n",
              "      <td>0.696508</td>\n",
              "      <td>0.500000</td>\n",
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              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-15 08:31:00</td>\n",
              "      <td>2.134 sec</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.430673</td>\n",
              "      <td>0.560511</td>\n",
              "      <td>0.942577</td>\n",
              "      <td>0.942750</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>0.124810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-15 08:31:00</td>\n",
              "      <td>2.218 sec</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.379133</td>\n",
              "      <td>0.465810</td>\n",
              "      <td>0.956130</td>\n",
              "      <td>0.956146</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>0.115677</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-15 08:31:01</td>\n",
              "      <td>2.301 sec</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.345670</td>\n",
              "      <td>0.405293</td>\n",
              "      <td>0.968024</td>\n",
              "      <td>0.967988</td>\n",
              "      <td>1.990909</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-15 08:31:01</td>\n",
              "      <td>2.378 sec</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.318815</td>\n",
              "      <td>0.355893</td>\n",
              "      <td>0.976494</td>\n",
              "      <td>0.976575</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>0.076104</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-15 08:31:01</td>\n",
              "      <td>2.446 sec</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.298271</td>\n",
              "      <td>0.318261</td>\n",
              "      <td>0.981772</td>\n",
              "      <td>0.981632</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>0.065449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td></td>\n",
              "      <td>2020-04-15 08:31:01</td>\n",
              "      <td>2.509 sec</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.279159</td>\n",
              "      <td>0.285804</td>\n",
              "      <td>0.985196</td>\n",
              "      <td>0.984499</td>\n",
              "      <td>1.990909</td>\n",
              "      <td>0.054795</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp  ... training_lift  training_classification_error\n",
              "0    2020-04-15 08:31:00  ...      1.000000                       0.497717\n",
              "1    2020-04-15 08:31:00  ...      1.990909                       0.124810\n",
              "2    2020-04-15 08:31:00  ...      1.990909                       0.115677\n",
              "3    2020-04-15 08:31:01  ...      1.990909                       0.100457\n",
              "4    2020-04-15 08:31:01  ...      1.990909                       0.076104\n",
              "5    2020-04-15 08:31:01  ...      1.990909                       0.065449\n",
              "6    2020-04-15 08:31:01  ...      1.990909                       0.054795\n",
              "\n",
              "[7 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Variable Importances: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        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>variable</th>\n",
              "      <th>relative_importance</th>\n",
              "      <th>scaled_importance</th>\n",
              "      <th>percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>market</td>\n",
              "      <td>408.679657</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.662026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>company_age</td>\n",
              "      <td>33.508488</td>\n",
              "      <td>0.081992</td>\n",
              "      <td>0.054281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>venture</td>\n",
              "      <td>27.888950</td>\n",
              "      <td>0.068242</td>\n",
              "      <td>0.045178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>seed</td>\n",
              "      <td>22.603373</td>\n",
              "      <td>0.055308</td>\n",
              "      <td>0.036616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>round_A</td>\n",
              "      <td>22.201441</td>\n",
              "      <td>0.054325</td>\n",
              "      <td>0.035964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>funding_total_usd</td>\n",
              "      <td>19.975935</td>\n",
              "      <td>0.048879</td>\n",
              "      <td>0.032359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>round_C</td>\n",
              "      <td>17.563290</td>\n",
              "      <td>0.042976</td>\n",
              "      <td>0.028451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>angel</td>\n",
              "      <td>15.647558</td>\n",
              "      <td>0.038288</td>\n",
              "      <td>0.025348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>round_B</td>\n",
              "      <td>14.981423</td>\n",
              "      <td>0.036658</td>\n",
              "      <td>0.024269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>funding_rounds</td>\n",
              "      <td>13.190541</td>\n",
              "      <td>0.032276</td>\n",
              "      <td>0.021368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>debt_financing</td>\n",
              "      <td>7.652103</td>\n",
              "      <td>0.018724</td>\n",
              "      <td>0.012396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>status</td>\n",
              "      <td>6.190577</td>\n",
              "      <td>0.015148</td>\n",
              "      <td>0.010028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>round_D</td>\n",
              "      <td>3.301721</td>\n",
              "      <td>0.008079</td>\n",
              "      <td>0.005349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>grant</td>\n",
              "      <td>3.015090</td>\n",
              "      <td>0.007378</td>\n",
              "      <td>0.004884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>private_equity</td>\n",
              "      <td>0.718804</td>\n",
              "      <td>0.001759</td>\n",
              "      <td>0.001164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>round_E</td>\n",
              "      <td>0.197691</td>\n",
              "      <td>0.000484</td>\n",
              "      <td>0.000320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>undisclosed</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>convertible_note</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>post_ipo_equity</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>post_ipo_debt</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               variable  relative_importance  scaled_importance  percentage\n",
              "0               market            408.679657           1.000000    0.662026\n",
              "1           company_age            33.508488           0.081992    0.054281\n",
              "2               venture            27.888950           0.068242    0.045178\n",
              "3                  seed            22.603373           0.055308    0.036616\n",
              "4               round_A            22.201441           0.054325    0.035964\n",
              "5    funding_total_usd             19.975935           0.048879    0.032359\n",
              "6               round_C            17.563290           0.042976    0.028451\n",
              "7                 angel            15.647558           0.038288    0.025348\n",
              "8               round_B            14.981423           0.036658    0.024269\n",
              "9        funding_rounds            13.190541           0.032276    0.021368\n",
              "10       debt_financing             7.652103           0.018724    0.012396\n",
              "11               status             6.190577           0.015148    0.010028\n",
              "12              round_D             3.301721           0.008079    0.005349\n",
              "13                grant             3.015090           0.007378    0.004884\n",
              "14       private_equity             0.718804           0.001759    0.001164\n",
              "15              round_E             0.197691           0.000484    0.000320\n",
              "16          undisclosed             0.000000           0.000000    0.000000\n",
              "17     convertible_note             0.000000           0.000000    0.000000\n",
              "18      post_ipo_equity             0.000000           0.000000    0.000000\n",
              "19        post_ipo_debt             0.000000           0.000000    0.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "See the whole table with table.as_data_frame()\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgz23tIz-sWo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}