Vehicle_Loan_Prediction.ipynb

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  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
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        "name": "ipython",
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      "name": "python",
      "mimetype": "text/x-python"
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    "colab": {
      "name": "Vehicle_Loan_Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "or7_Fzb6RLQz",
        "h1EFjimtQ-hy",
        "cP5tIH60RVJe"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "or7_Fzb6RLQz"
      },
      "source": [
        "# <hr/>**VEHICLE LOAN PREDICTION** <hr/>\n",
        "\n",
        "**By**<br>\n",
        "Aditi Chatterjee<br>\n",
        "VTU 14148, 18UECS0024<br>\n",
        "III year/ VI semester<br>\n",
        "B. Tech, CSE<br>\n",
        "Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology<br>\n",
        "<br>\n",
        "<br>\n",
        "**As part of the course**<br>\n",
        "1157CS994 - Machine Learning and Reinforcement Learning in Finance<br>\n",
        "Industry/Higher Learning category<br>\n",
        "1 credit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1EFjimtQ-hy"
      },
      "source": [
        "##**Using Intel Optimized Library**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XREyR6DqSINX"
      },
      "source": [
        "> This project has been created using **Google Colaboratory**, hence a mini anaconda environment has been created first, and subsequently Intel optimization has been performed.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfQVhaKGOxE-",
        "outputId": "92a57aff-1770-4a80-b97f-265e412efc47"
      },
      "source": [
        "!which python # should return /usr/local/bin/python"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/bin/python\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_GTP2JLNpY2",
        "outputId": "14eaea4b-7a7c-48be-99f8-ce3887d58320"
      },
      "source": [
        "!python --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 3.6.13 :: Anaconda, Inc.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKnC1dWTO3qw",
        "outputId": "ae06f9ba-1867-4d41-c2e5-0caddda9c138"
      },
      "source": [
        "!echo $PYTHONPATH"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/env/python\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GX9pxSBJO3lK",
        "outputId": "c7ae3e0d-1c1f-4f60-eb6c-419a53a52373"
      },
      "source": [
        "%%bash\n",
        "MINICONDA_INSTALLER_SCRIPT=Miniconda3-4.5.4-Linux-x86_64.sh\n",
        "MINICONDA_PREFIX=/usr/local\n",
        "wget https://repo.continuum.io/miniconda/$MINICONDA_INSTALLER_SCRIPT\n",
        "chmod +x $MINICONDA_INSTALLER_SCRIPT\n",
        "./$MINICONDA_INSTALLER_SCRIPT -b -f -p $MINICONDA_PREFIX"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PREFIX=/usr/local\n",
            "installing: python-3.6.5-hc3d631a_2 ...\n",
            "installing: ca-certificates-2018.03.07-0 ...\n",
            "installing: conda-env-2.6.0-h36134e3_1 ...\n",
            "installing: libgcc-ng-7.2.0-hdf63c60_3 ...\n",
            "installing: libstdcxx-ng-7.2.0-hdf63c60_3 ...\n",
            "installing: libffi-3.2.1-hd88cf55_4 ...\n",
            "installing: ncurses-6.1-hf484d3e_0 ...\n",
            "installing: openssl-1.0.2o-h20670df_0 ...\n",
            "installing: tk-8.6.7-hc745277_3 ...\n",
            "installing: xz-5.2.4-h14c3975_4 ...\n",
            "installing: yaml-0.1.7-had09818_2 ...\n",
            "installing: zlib-1.2.11-ha838bed_2 ...\n",
            "installing: libedit-3.1.20170329-h6b74fdf_2 ...\n",
            "installing: readline-7.0-ha6073c6_4 ...\n",
            "installing: sqlite-3.23.1-he433501_0 ...\n",
            "installing: asn1crypto-0.24.0-py36_0 ...\n",
            "installing: certifi-2018.4.16-py36_0 ...\n",
            "installing: chardet-3.0.4-py36h0f667ec_1 ...\n",
            "installing: idna-2.6-py36h82fb2a8_1 ...\n",
            "installing: pycosat-0.6.3-py36h0a5515d_0 ...\n",
            "installing: pycparser-2.18-py36hf9f622e_1 ...\n",
            "installing: pysocks-1.6.8-py36_0 ...\n",
            "installing: ruamel_yaml-0.15.37-py36h14c3975_2 ...\n",
            "installing: six-1.11.0-py36h372c433_1 ...\n",
            "installing: cffi-1.11.5-py36h9745a5d_0 ...\n",
            "installing: setuptools-39.2.0-py36_0 ...\n",
            "installing: cryptography-2.2.2-py36h14c3975_0 ...\n",
            "installing: wheel-0.31.1-py36_0 ...\n",
            "installing: pip-10.0.1-py36_0 ...\n",
            "installing: pyopenssl-18.0.0-py36_0 ...\n",
            "installing: urllib3-1.22-py36hbe7ace6_0 ...\n",
            "installing: requests-2.18.4-py36he2e5f8d_1 ...\n",
            "installing: conda-4.5.4-py36_0 ...\n",
            "unlinking: ca-certificates-2021.5.25-h06a4308_1\n",
            "unlinking: certifi-2021.5.30-py36h06a4308_0\n",
            "unlinking: cffi-1.14.5-py36h261ae71_0\n",
            "unlinking: chardet-4.0.0-py36h06a4308_1003\n",
            "unlinking: conda-4.10.3-py36h06a4308_0\n",
            "unlinking: cryptography-3.4.7-py36hd23ed53_0\n",
            "unlinking: idna-2.10-pyhd3eb1b0_0\n",
            "unlinking: libffi-3.3-he6710b0_2\n",
            "unlinking: libgcc-ng-9.3.0-h5101ec6_17\n",
            "unlinking: libstdcxx-ng-9.3.0-hd4cf53a_17\n",
            "unlinking: ncurses-6.2-he6710b0_1\n",
            "unlinking: openssl-1.1.1k-h27cfd23_0\n",
            "unlinking: pip-21.1.3-py36h06a4308_0\n",
            "unlinking: pycosat-0.6.3-py36h27cfd23_0\n",
            "unlinking: pycparser-2.20-py_2\n",
            "unlinking: pyopenssl-20.0.1-pyhd3eb1b0_1\n",
            "unlinking: pysocks-1.7.1-py36h06a4308_0\n",
            "unlinking: python-3.6.13-h12debd9_1\n",
            "unlinking: readline-8.1-h27cfd23_0\n",
            "unlinking: requests-2.25.1-pyhd3eb1b0_0\n",
            "unlinking: ruamel_yaml-0.15.100-py36h27cfd23_0\n",
            "unlinking: setuptools-52.0.0-py36h06a4308_0\n",
            "unlinking: six-1.16.0-pyhd3eb1b0_0\n",
            "unlinking: sqlite-3.36.0-hc218d9a_0\n",
            "unlinking: tk-8.6.10-hbc83047_0\n",
            "unlinking: urllib3-1.26.6-pyhd3eb1b0_1\n",
            "unlinking: wheel-0.36.2-pyhd3eb1b0_0\n",
            "unlinking: xz-5.2.5-h7b6447c_0\n",
            "unlinking: yaml-0.2.5-h7b6447c_0\n",
            "unlinking: zlib-1.2.11-h7b6447c_3\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Miniconda3: /usr/local\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "--2021-07-02 18:23:42--  https://repo.continuum.io/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n",
            "Resolving repo.continuum.io (repo.continuum.io)... 104.18.200.79, 104.18.201.79, 2606:4700::6812:c84f, ...\n",
            "Connecting to repo.continuum.io (repo.continuum.io)|104.18.200.79|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh [following]\n",
            "--2021-07-02 18:23:42--  https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8303, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 58468498 (56M) [application/x-sh]\n",
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            "  6100K .......... .......... .......... .......... .......... 10%  268M 1s\n",
            "  6150K .......... .......... .......... .......... .......... 10%  305M 1s\n",
            "  6200K .......... .......... .......... .......... .......... 10%  346M 1s\n",
            "  6250K .......... .......... .......... .......... .......... 11% 8.92M 1s\n",
            "  6300K .......... .......... .......... .......... .......... 11% 72.7M 1s\n",
            "  6350K .......... .......... .......... .......... .......... 11% 70.0M 1s\n",
            "  6400K .......... .......... .......... .......... .......... 11% 76.0M 1s\n",
            "  6450K .......... .......... .......... .......... .......... 11% 7.16M 1s\n",
            "  6500K .......... .......... .......... .......... .......... 11% 61.6M 1s\n",
            "  6550K .......... .......... .......... .......... .......... 11%  296M 1s\n",
            "  6600K .......... .......... .......... .......... .......... 11%  275M 1s\n",
            "  6650K .......... .......... .......... .......... .......... 11%  219M 1s\n",
            "  6700K .......... .......... .......... .......... .......... 11% 9.41M 1s\n",
            "  6750K .......... .......... .......... .......... .......... 11% 80.5M 1s\n",
            "  6800K .......... .......... .......... .......... .......... 11%  299M 1s\n",
            "  6850K .......... .......... .......... .......... .......... 12%  315M 1s\n",
            "  6900K .......... .......... .......... .......... .......... 12%  316M 1s\n",
            "  6950K .......... .......... .......... .......... .......... 12%  302M 1s\n",
            "  7000K .......... .......... .......... .......... .......... 12%  338M 1s\n",
            "  7050K .......... .......... .......... .......... .......... 12%  331M 1s\n",
            "  7100K .......... .......... .......... .......... .......... 12% 10.8M 1s\n",
            "  7150K .......... .......... .......... .......... .......... 12% 74.9M 1s\n",
            "  7200K .......... .......... .......... .......... .......... 12% 73.2M 1s\n",
            "  7250K .......... .......... .......... .......... .......... 12%  194M 1s\n",
            "  7300K .......... .......... .......... .......... .......... 12% 21.5M 1s\n",
            "  7350K .......... .......... .......... .......... .......... 12% 44.0M 1s\n",
            "  7400K .......... .......... .......... .......... .......... 13%  128M 1s\n",
            "  7450K .......... .......... .......... .......... .......... 13%  338M 1s\n",
            "  7500K .......... .......... .......... .......... .......... 13%  281M 1s\n",
            "  7550K .......... .......... .......... .......... .......... 13% 8.28M 1s\n",
            "  7600K .......... .......... .......... .......... .......... 13% 69.1M 1s\n",
            "  7650K .......... .......... .......... .......... .......... 13% 64.1M 1s\n",
            "  7700K .......... .......... .......... .......... .......... 13% 83.9M 1s\n",
            "  7750K .......... .......... .......... .......... .......... 13% 26.9M 1s\n",
            "  7800K .......... .......... .......... .......... .......... 13%  264M 1s\n",
            "  7850K .......... .......... .......... .......... .......... 13%  260M 1s\n",
            "  7900K .......... .......... .......... .......... .......... 13%  337M 1s\n",
            "  7950K .......... .......... .......... .......... .......... 14% 4.78M 1s\n",
            "  8000K .......... .......... .......... .......... .......... 14% 23.2M 1s\n",
            "  8050K .......... .......... .......... .......... .......... 14% 70.0M 1s\n",
            "  8100K .......... .......... .......... .......... .......... 14% 80.4M 1s\n",
            "  8150K .......... .......... .......... .......... .......... 14% 62.1M 1s\n",
            "  8200K .......... .......... .......... .......... .......... 14%  185M 1s\n",
            "  8250K .......... .......... .......... .......... .......... 14%  307M 1s\n",
            "  8300K .......... .......... .......... .......... .......... 14%  312M 1s\n",
            "  8350K .......... .......... .......... .......... .......... 14%  267M 1s\n",
            "  8400K .......... .......... .......... .......... .......... 14%  343M 1s\n",
            "  8450K .......... .......... .......... .......... .......... 14%  326M 1s\n",
            "  8500K .......... .......... .......... .......... .......... 14%  271M 1s\n",
            "  8550K .......... .......... .......... .......... .......... 15%  280M 1s\n",
            "  8600K .......... .......... .......... .......... .......... 15%  330M 1s\n",
            "  8650K .......... .......... .......... .......... .......... 15%  319M 1s\n",
            "  8700K .......... .......... .......... .......... .......... 15%  330M 1s\n",
            "  8750K .......... .......... .......... .......... .......... 15% 44.4M 1s\n",
            "  8800K .......... .......... .......... .......... .......... 15% 50.4M 1s\n",
            "  8850K .......... .......... .......... .......... .......... 15% 64.9M 1s\n",
            "  8900K .......... .......... .......... .......... .......... 15% 71.0M 1s\n",
            "  8950K .......... .......... .......... .......... .......... 15% 66.0M 1s\n",
            "  9000K .......... .......... .......... .......... .......... 15% 2.15M 1s\n",
            "  9050K .......... .......... .......... .......... .......... 15% 63.8M 1s\n",
            "  9100K .......... .......... .......... .......... .......... 16% 72.5M 1s\n",
            "  9150K .......... .......... .......... .......... .......... 16% 62.1M 1s\n",
            "  9200K .......... .......... .......... .......... .......... 16% 20.2M 1s\n",
            "  9250K .......... .......... .......... .......... .......... 16% 48.1M 1s\n",
            "  9300K .......... .......... .......... .......... .......... 16% 75.2M 1s\n",
            "  9350K .......... .......... .......... .......... .......... 16% 78.9M 1s\n",
            "  9400K .......... .......... .......... .......... .......... 16%  324M 1s\n",
            "  9450K .......... .......... .......... .......... .......... 16% 7.58M 1s\n",
            "  9500K .......... .......... .......... .......... .......... 16% 46.7M 1s\n",
            "  9550K .......... .......... .......... .......... .......... 16%  103M 1s\n",
            "  9600K .......... .......... .......... .......... .......... 16%  321M 1s\n",
            "  9650K .......... .......... .......... .......... .......... 16% 19.7M 1s\n",
            "  9700K .......... .......... .......... .......... .......... 17% 11.3M 1s\n",
            "  9750K .......... .......... .......... .......... .......... 17% 45.9M 1s\n",
            "  9800K .......... .......... .......... .......... .......... 17%  297M 1s\n",
            "  9850K .......... .......... .......... .......... .......... 17%  278M 1s\n",
            "  9900K .......... .......... .......... .......... .......... 17%  325M 1s\n",
            "  9950K .......... .......... .......... .......... .......... 17% 12.4M 1s\n",
            " 10000K .......... .......... .......... .......... .......... 17% 74.3M 1s\n",
            " 10050K .......... .......... .......... .......... .......... 17% 78.2M 1s\n",
            " 10100K .......... .......... .......... .......... .......... 17% 89.6M 1s\n",
            " 10150K .......... .......... .......... .......... .......... 17%  233M 1s\n",
            " 10200K .......... .......... .......... .......... .......... 17% 20.5M 1s\n",
            " 10250K .......... .......... .......... .......... .......... 18% 53.1M 1s\n",
            " 10300K .......... .......... .......... .......... .......... 18% 60.4M 1s\n",
            " 10350K .......... .......... .......... .......... .......... 18%  238M 1s\n",
            " 10400K .......... .......... .......... .......... .......... 18%  332M 1s\n",
            " 10450K .......... .......... .......... .......... .......... 18%  342M 1s\n",
            " 10500K .......... .......... .......... .......... .......... 18%  316M 1s\n",
            " 10550K .......... .......... .......... .......... .......... 18%  225M 1s\n",
            " 10600K .......... .......... .......... .......... .......... 18%  316M 1s\n",
            " 10650K .......... .......... .......... .......... .......... 18%  334M 1s\n",
            " 10700K .......... .......... .......... .......... .......... 18%  327M 1s\n",
            " 10750K .......... .......... .......... .......... .......... 18%  278M 1s\n",
            " 10800K .......... .......... .......... .......... .......... 19%  320M 1s\n",
            " 10850K .......... .......... .......... .......... .......... 19%  264M 1s\n",
            " 10900K .......... .......... .......... .......... .......... 19% 25.5M 1s\n",
            " 10950K .......... .......... .......... .......... .......... 19% 63.9M 1s\n",
            " 11000K .......... .......... .......... .......... .......... 19% 72.5M 1s\n",
            " 11050K .......... .......... .......... .......... .......... 19% 72.0M 1s\n",
            " 11100K .......... .......... .......... .......... .......... 19% 15.4M 1s\n",
            " 11150K .......... .......... .......... .......... .......... 19% 63.5M 1s\n",
            " 11200K .......... .......... .......... .......... .......... 19% 72.8M 1s\n",
            " 11250K .......... .......... .......... .......... .......... 19% 42.4M 1s\n",
            " 11300K .......... .......... .......... .......... .......... 19% 75.9M 1s\n",
            " 11350K .......... .......... .......... .......... .......... 19% 63.4M 1s\n",
            " 11400K .......... .......... .......... .......... .......... 20%  106M 1s\n",
            " 11450K .......... .......... .......... .......... .......... 20%  336M 1s\n",
            " 11500K .......... .......... .......... .......... .......... 20%  271M 1s\n",
            " 11550K .......... .......... .......... .......... .......... 20%  265M 1s\n",
            " 11600K .......... .......... .......... .......... .......... 20%  322M 1s\n",
            " 11650K .......... .......... .......... .......... .......... 20% 34.3M 1s\n",
            " 11700K .......... .......... .......... .......... .......... 20% 70.4M 1s\n",
            " 11750K .......... .......... .......... .......... .......... 20% 66.3M 1s\n",
            " 11800K .......... .......... .......... .......... .......... 20% 74.3M 1s\n",
            " 11850K .......... .......... .......... .......... .......... 20% 70.2M 1s\n",
            " 11900K .......... .......... .......... .......... .......... 20% 10.1M 1s\n",
            " 11950K .......... .......... .......... .......... .......... 21%  252M 1s\n",
            " 12000K .......... .......... .......... .......... .......... 21%  322M 1s\n",
            " 12050K .......... .......... .......... .......... .......... 21% 14.6M 1s\n",
            " 12100K .......... .......... .......... .......... .......... 21% 72.6M 1s\n",
            " 12150K .......... .......... .......... .......... .......... 21% 64.9M 1s\n",
            " 12200K .......... .......... .......... .......... .......... 21%  206M 1s\n",
            " 12250K .......... .......... .......... .......... .......... 21% 10.8M 1s\n",
            " 12300K .......... .......... .......... .......... .......... 21% 59.0M 1s\n",
            " 12350K .......... .......... .......... .......... .......... 21% 38.8M 1s\n",
            " 12400K .......... .......... .......... .......... .......... 21% 34.1M 1s\n",
            " 12450K .......... .......... .......... .......... .......... 21%  208M 1s\n",
            " 12500K .......... .......... .......... .......... .......... 21%  217M 1s\n",
            " 12550K .......... .......... .......... .......... .......... 22%  213M 1s\n",
            " 12600K .......... .......... .......... .......... .......... 22%  207M 1s\n",
            " 12650K .......... .......... .......... .......... .......... 22%  199M 1s\n",
            " 12700K .......... .......... .......... .......... .......... 22%  176M 1s\n",
            " 12750K .......... .......... .......... .......... .......... 22%  179M 1s\n",
            " 12800K .......... .......... .......... .......... .......... 22%  220M 1s\n",
            " 12850K .......... .......... .......... .......... .......... 22%  203M 1s\n",
            " 12900K .......... .......... .......... .......... .......... 22%  320M 1s\n",
            " 12950K .......... .......... .......... .......... .......... 22% 21.0M 1s\n",
            " 13000K .......... .......... .......... .......... .......... 22%  231M 1s\n",
            " 13050K .......... .......... .......... .......... .......... 22%  340M 1s\n",
            " 13100K .......... .......... .......... .......... .......... 23%  247M 1s\n",
            " 13150K .......... .......... .......... .......... .......... 23% 14.7M 1s\n",
            " 13200K .......... .......... .......... .......... .......... 23%  164M 1s\n",
            " 13250K .......... .......... .......... .......... .......... 23%  194M 1s\n",
            " 13300K .......... .......... .......... .......... .......... 23%  180M 1s\n",
            " 13350K .......... .......... .......... .......... .......... 23%  182M 1s\n",
            " 13400K .......... .......... .......... .......... .......... 23% 35.4M 1s\n",
            " 13450K .......... .......... .......... .......... .......... 23%  182M 1s\n",
            " 13500K .......... .......... .......... .......... .......... 23%  197M 1s\n",
            " 13550K .......... .......... .......... .......... .......... 23%  109M 1s\n",
            " 13600K .......... .......... .......... .......... .......... 23% 7.31M 1s\n",
            " 13650K .......... .......... .......... .......... .......... 23%  108M 1s\n",
            " 13700K .......... .......... .......... .......... .......... 24%  145M 1s\n",
            " 13750K .......... .......... .......... .......... .......... 24%  161M 1s\n",
            " 13800K .......... .......... .......... .......... .......... 24%  192M 1s\n",
            " 13850K .......... .......... .......... .......... .......... 24% 12.2M 1s\n",
            " 13900K .......... .......... .......... .......... .......... 24%  183M 1s\n",
            " 13950K .......... .......... .......... .......... .......... 24% 87.5M 1s\n",
            " 14000K .......... .......... .......... .......... .......... 24%  157M 1s\n",
            " 14050K .......... .......... .......... .......... .......... 24% 12.4M 1s\n",
            " 14100K .......... .......... .......... .......... .......... 24%  164M 1s\n",
            " 14150K .......... .......... .......... .......... .......... 24%  101M 1s\n",
            " 14200K .......... .......... .......... .......... .......... 24%  180M 1s\n",
            " 14250K .......... .......... .......... .......... .......... 25%  148M 1s\n",
            " 14300K .......... .......... .......... .......... .......... 25% 10.2M 1s\n",
            " 14350K .......... .......... .......... .......... .......... 25%  138M 1s\n",
            " 14400K .......... .......... .......... .......... .......... 25%  197M 1s\n",
            " 14450K .......... .......... .......... .......... .......... 25%  199M 1s\n",
            " 14500K .......... .......... .......... .......... .......... 25% 10.3M 1s\n",
            " 14550K .......... .......... .......... .......... .......... 25%  173M 1s\n",
            " 14600K .......... .......... .......... .......... .......... 25%  195M 1s\n",
            " 14650K .......... .......... .......... .......... .......... 25%  205M 1s\n",
            " 14700K .......... .......... .......... .......... .......... 25%  205M 1s\n",
            " 14750K .......... .......... .......... .......... .......... 25% 10.0M 1s\n",
            " 14800K .......... .......... .......... .......... .......... 26%  192M 1s\n",
            " 14850K .......... .......... .......... .......... .......... 26%  191M 1s\n",
            " 14900K .......... .......... .......... .......... .......... 26%  207M 1s\n",
            " 14950K .......... .......... .......... .......... .......... 26% 14.4M 1s\n",
            " 15000K .......... .......... .......... .......... .......... 26%  174M 1s\n",
            " 15050K .......... .......... .......... .......... .......... 26%  116M 1s\n",
            " 15100K .......... .......... .......... .......... .......... 26%  189M 1s\n",
            " 15150K .......... .......... .......... .......... .......... 26% 19.8M 1s\n",
            " 15200K .......... .......... .......... .......... .......... 26% 77.3M 1s\n",
            " 15250K .......... .......... .......... .......... .......... 26%  166M 1s\n",
            " 15300K .......... .......... .......... .......... .......... 26%  240M 1s\n",
            " 15350K .......... .......... .......... .......... .......... 26%  176M 1s\n",
            " 15400K .......... .......... .......... .......... .......... 27% 15.7M 1s\n",
            " 15450K .......... .......... .......... .......... .......... 27% 97.8M 1s\n",
            " 15500K .......... .......... .......... .......... .......... 27%  198M 1s\n",
            " 15550K .......... .......... .......... .......... .......... 27%  171M 1s\n",
            " 15600K .......... .......... .......... .......... .......... 27%  202M 1s\n",
            " 15650K .......... .......... .......... .......... .......... 27%  190M 1s\n",
            " 15700K .......... .......... .......... .......... .......... 27%  201M 1s\n",
            " 15750K .......... .......... .......... .......... .......... 27%  175M 1s\n",
            " 15800K .......... .......... .......... .......... .......... 27%  183M 1s\n",
            " 15850K .......... .......... .......... .......... .......... 27%  193M 1s\n",
            " 15900K .......... .......... .......... .......... .......... 27%  201M 1s\n",
            " 15950K .......... .......... .......... .......... .......... 28% 27.6M 1s\n",
            " 16000K .......... .......... .......... .......... .......... 28%  186M 1s\n",
            " 16050K .......... .......... .......... .......... .......... 28%  126M 1s\n",
            " 16100K .......... .......... .......... .......... .......... 28%  122M 1s\n",
            " 16150K .......... .......... .......... .......... .......... 28% 19.8M 1s\n",
            " 16200K .......... .......... .......... .......... .......... 28% 63.7M 1s\n",
            " 16250K .......... .......... .......... .......... .......... 28%  134M 1s\n",
            " 16300K .......... .......... .......... .......... .......... 28%  146M 1s\n",
            " 16350K .......... .......... .......... .......... .......... 28%  165M 1s\n",
            " 16400K .......... .......... .......... .......... .......... 28%  187M 1s\n",
            " 16450K .......... .......... .......... .......... .......... 28%  182M 1s\n",
            " 16500K .......... .......... .......... .......... .......... 28%  190M 1s\n",
            " 16550K .......... .......... .......... .......... .......... 29% 47.5M 1s\n",
            " 16600K .......... .......... .......... .......... .......... 29% 23.0M 1s\n",
            " 16650K .......... .......... .......... .......... .......... 29%  117M 1s\n",
            " 16700K .......... .......... .......... .......... .......... 29%  179M 1s\n",
            " 16750K .......... .......... .......... .......... .......... 29% 39.1M 1s\n",
            " 16800K .......... .......... .......... .......... .......... 29% 38.2M 1s\n",
            " 16850K .......... .......... .......... .......... .......... 29% 40.7M 1s\n",
            " 16900K .......... .......... .......... .......... .......... 29% 51.0M 1s\n",
            " 16950K .......... .......... .......... .......... .......... 29% 41.0M 1s\n",
            " 17000K .......... .......... .......... .......... .......... 29% 25.0M 1s\n",
            " 17050K .......... .......... .......... .......... .......... 29% 50.2M 1s\n",
            " 17100K .......... .......... .......... .......... .......... 30% 32.7M 1s\n",
            " 17150K .......... .......... .......... .......... .......... 30%  158M 1s\n",
            " 17200K .......... .......... .......... .......... .......... 30%  179M 1s\n",
            " 17250K .......... .......... .......... .......... .......... 30%  195M 1s\n",
            " 17300K .......... .......... .......... .......... .......... 30% 84.8M 1s\n",
            " 17350K .......... .......... .......... .......... .......... 30%  165M 1s\n",
            " 17400K .......... .......... .......... .......... .......... 30%  132M 1s\n",
            " 17450K .......... .......... .......... .......... .......... 30%  106M 1s\n",
            " 17500K .......... .......... .......... .......... .......... 30%  109M 1s\n",
            " 17550K .......... .......... .......... .......... .......... 30%  163M 1s\n",
            " 17600K .......... .......... .......... .......... .......... 30%  104M 1s\n",
            " 17650K .......... .......... .......... .......... .......... 30%  188M 1s\n",
            " 17700K .......... .......... .......... .......... .......... 31%  108M 1s\n",
            " 17750K .......... .......... .......... .......... .......... 31%  140M 1s\n",
            " 17800K .......... .......... .......... .......... .......... 31%  122M 1s\n",
            " 17850K .......... .......... .......... .......... .......... 31%  109M 1s\n",
            " 17900K .......... .......... .......... .......... .......... 31%  187M 1s\n",
            " 17950K .......... .......... .......... .......... .......... 31%  161M 1s\n",
            " 18000K .......... .......... .......... .......... .......... 31%  104M 1s\n",
            " 18050K .......... .......... .......... .......... .......... 31%  191M 1s\n",
            " 18100K .......... .......... .......... .......... .......... 31%  191M 1s\n",
            " 18150K .......... .......... .......... .......... .......... 31%  177M 1s\n",
            " 18200K .......... .......... .......... .......... .......... 31%  117M 1s\n",
            " 18250K .......... .......... .......... .......... .......... 32%  191M 1s\n",
            " 18300K .......... .......... .......... .......... .......... 32%  195M 1s\n",
            " 18350K .......... .......... .......... .......... .......... 32%  152M 1s\n",
            " 18400K .......... .......... .......... .......... .......... 32%  194M 1s\n",
            " 18450K .......... .......... .......... .......... .......... 32%  189M 1s\n",
            " 18500K .......... .......... .......... .......... .......... 32%  192M 1s\n",
            " 18550K .......... .......... .......... .......... .......... 32%  160M 1s\n",
            " 18600K .......... .......... .......... .......... .......... 32%  195M 1s\n",
            " 18650K .......... .......... .......... .......... .......... 32%  197M 1s\n",
            " 18700K .......... .......... .......... .......... .......... 32%  180M 1s\n",
            " 18750K .......... .......... .......... .......... .......... 32%  153M 1s\n",
            " 18800K .......... .......... .......... .......... .......... 33%  201M 1s\n",
            " 18850K .......... .......... .......... .......... .......... 33%  191M 1s\n",
            " 18900K .......... .......... .......... .......... .......... 33%  182M 1s\n",
            " 18950K .......... .......... .......... .......... .......... 33%  177M 1s\n",
            " 19000K .......... .......... .......... .......... .......... 33%  183M 1s\n",
            " 19050K .......... .......... .......... .......... .......... 33%  194M 1s\n",
            " 19100K .......... .......... .......... .......... .......... 33%  184M 1s\n",
            " 19150K .......... .......... .......... .......... .......... 33%  164M 1s\n",
            " 19200K .......... .......... .......... .......... .......... 33%  187M 1s\n",
            " 19250K .......... .......... .......... .......... .......... 33%  182M 1s\n",
            " 19300K .......... .......... .......... .......... .......... 33%  177M 1s\n",
            " 19350K .......... .......... .......... .......... .......... 33%  174M 1s\n",
            " 19400K .......... .......... .......... .......... .......... 34%  197M 1s\n",
            " 19450K .......... .......... .......... .......... .......... 34%  134M 1s\n",
            " 19500K .......... .......... .......... .......... .......... 34% 72.4M 1s\n",
            " 19550K .......... .......... .......... .......... .......... 34% 93.7M 1s\n",
            " 19600K .......... .......... .......... .......... .......... 34% 17.4M 1s\n",
            " 19650K .......... .......... .......... .......... .......... 34%  141M 1s\n",
            " 19700K .......... .......... .......... .......... .......... 34%  127M 1s\n",
            " 19750K .......... .......... .......... .......... .......... 34%  140M 1s\n",
            " 19800K .......... .......... .......... .......... .......... 34%  190M 1s\n",
            " 19850K .......... .......... .......... .......... .......... 34%  190M 1s\n",
            " 19900K .......... .......... .......... .......... .......... 34%  178M 1s\n",
            " 19950K .......... .......... .......... .......... .......... 35%  155M 1s\n",
            " 20000K .......... .......... .......... .......... .......... 35%  201M 1s\n",
            " 20050K .......... .......... .......... .......... .......... 35%  184M 1s\n",
            " 20100K .......... .......... .......... .......... .......... 35%  180M 1s\n",
            " 20150K .......... .......... .......... .......... .......... 35%  175M 1s\n",
            " 20200K .......... .......... .......... .......... .......... 35%  190M 1s\n",
            " 20250K .......... .......... .......... .......... .......... 35%  191M 1s\n",
            " 20300K .......... .......... .......... .......... .......... 35%  171M 1s\n",
            " 20350K .......... .......... .......... .......... .......... 35%  161M 1s\n",
            " 20400K .......... .......... .......... .......... .......... 35%  173M 1s\n",
            " 20450K .......... .......... .......... .......... .......... 35%  179M 1s\n",
            " 20500K .......... .......... .......... .......... .......... 35%  185M 1s\n",
            " 20550K .......... .......... .......... .......... .......... 36%  170M 1s\n",
            " 20600K .......... .......... .......... .......... .......... 36%  190M 1s\n",
            " 20650K .......... .......... .......... .......... .......... 36%  157M 1s\n",
            " 20700K .......... .......... .......... .......... .......... 36%  188M 1s\n",
            " 20750K .......... .......... .......... .......... .......... 36%  120M 1s\n",
            " 20800K .......... .......... .......... .......... .......... 36% 34.3M 1s\n",
            " 20850K .......... .......... .......... .......... .......... 36% 46.3M 1s\n",
            " 20900K .......... .......... .......... .......... .......... 36% 46.3M 1s\n",
            " 20950K .......... .......... .......... .......... .......... 36%  162M 1s\n",
            " 21000K .......... .......... .......... .......... .......... 36%  195M 1s\n",
            " 21050K .......... .......... .......... .......... .......... 36%  171M 1s\n",
            " 21100K .......... .......... .......... .......... .......... 37%  184M 1s\n",
            " 21150K .......... .......... .......... .......... .......... 37%  162M 1s\n",
            " 21200K .......... .......... .......... .......... .......... 37%  173M 1s\n",
            " 21250K .......... .......... .......... .......... .......... 37%  186M 1s\n",
            " 21300K .......... .......... .......... .......... .......... 37%  188M 1s\n",
            " 21350K .......... .......... .......... .......... .......... 37%  175M 1s\n",
            " 21400K .......... .......... .......... .......... .......... 37%  179M 1s\n",
            " 21450K .......... .......... .......... .......... .......... 37% 88.7M 1s\n",
            " 21500K .......... .......... .......... .......... .......... 37%  174M 1s\n",
            " 21550K .......... .......... .......... .......... .......... 37%  147M 1s\n",
            " 21600K .......... .......... .......... .......... .......... 37%  197M 1s\n",
            " 21650K .......... .......... .......... .......... .......... 38%  204M 1s\n",
            " 21700K .......... .......... .......... .......... .......... 38%  178M 1s\n",
            " 21750K .......... .......... .......... .......... .......... 38%  167M 1s\n",
            " 21800K .......... .......... .......... .......... .......... 38%  179M 1s\n",
            " 21850K .......... .......... .......... .......... .......... 38%  195M 1s\n",
            " 21900K .......... .......... .......... .......... .......... 38%  183M 1s\n",
            " 21950K .......... .......... .......... .......... .......... 38%  159M 1s\n",
            " 22000K .......... .......... .......... .......... .......... 38%  190M 1s\n",
            " 22050K .......... .......... .......... .......... .......... 38%  188M 1s\n",
            " 22100K .......... .......... .......... .......... .......... 38%  170M 1s\n",
            " 22150K .......... .......... .......... .......... .......... 38% 69.2M 1s\n",
            " 22200K .......... .......... .......... .......... .......... 38%  171M 1s\n",
            " 22250K .......... .......... .......... .......... .......... 39% 63.0M 1s\n",
            " 22300K .......... .......... .......... .......... .......... 39%  184M 1s\n",
            " 22350K .......... .......... .......... .......... .......... 39%  158M 1s\n",
            " 22400K .......... .......... .......... .......... .......... 39%  196M 1s\n",
            " 22450K .......... .......... .......... .......... .......... 39%  176M 1s\n",
            " 22500K .......... .......... .......... .......... .......... 39%  199M 1s\n",
            " 22550K .......... .......... .......... .......... .......... 39%  169M 1s\n",
            " 22600K .......... .......... .......... .......... .......... 39%  193M 1s\n",
            " 22650K .......... .......... .......... .......... .......... 39%  182M 1s\n",
            " 22700K .......... .......... .......... .......... .......... 39%  196M 1s\n",
            " 22750K .......... .......... .......... .......... .......... 39%  161M 1s\n",
            " 22800K .......... .......... .......... .......... .......... 40%  193M 1s\n",
            " 22850K .......... .......... .......... .......... .......... 40%  182M 1s\n",
            " 22900K .......... .......... .......... .......... .......... 40%  164M 1s\n",
            " 22950K .......... .......... .......... .......... .......... 40% 56.3M 1s\n",
            " 23000K .......... .......... .......... .......... .......... 40%  192M 1s\n",
            " 23050K .......... .......... .......... .......... .......... 40%  191M 1s\n",
            " 23100K .......... .......... .......... .......... .......... 40%  185M 1s\n",
            " 23150K .......... .......... .......... .......... .......... 40%  164M 1s\n",
            " 23200K .......... .......... .......... .......... .......... 40%  198M 1s\n",
            " 23250K .......... .......... .......... .......... .......... 40% 47.3M 1s\n",
            " 23300K .......... .......... .......... .......... .......... 40%  176M 1s\n",
            " 23350K .......... .......... .......... .......... .......... 40% 45.3M 1s\n",
            " 23400K .......... .......... .......... .......... .......... 41%  196M 1s\n",
            " 23450K .......... .......... .......... .......... .......... 41%  183M 1s\n",
            " 23500K .......... .......... .......... .......... .......... 41% 46.2M 1s\n",
            " 23550K .......... .......... .......... .......... .......... 41%  149M 1s\n",
            " 23600K .......... .......... .......... .......... .......... 41%  194M 1s\n",
            " 23650K .......... .......... .......... .......... .......... 41%  196M 1s\n",
            " 23700K .......... .......... .......... .......... .......... 41%  152M 1s\n",
            " 23750K .......... .......... .......... .......... .......... 41% 32.7M 1s\n",
            " 23800K .......... .......... .......... .......... .......... 41%  188M 1s\n",
            " 23850K .......... .......... .......... .......... .......... 41%  201M 1s\n",
            " 23900K .......... .......... .......... .......... .......... 41%  175M 1s\n",
            " 23950K .......... .......... .......... .......... .......... 42%  163M 1s\n",
            " 24000K .......... .......... .......... .......... .......... 42%  193M 1s\n",
            " 24050K .......... .......... .......... .......... .......... 42%  187M 1s\n",
            " 24100K .......... .......... .......... .......... .......... 42% 33.8M 1s\n",
            " 24150K .......... .......... .......... .......... .......... 42%  176M 1s\n",
            " 24200K .......... .......... .......... .......... .......... 42%  202M 1s\n",
            " 24250K .......... .......... .......... .......... .......... 42%  179M 1s\n",
            " 24300K .......... .......... .......... .......... .......... 42% 97.9M 1s\n",
            " 24350K .......... .......... .......... .......... .......... 42% 81.1M 1s\n",
            " 24400K .......... .......... .......... .......... .......... 42%  200M 1s\n",
            " 24450K .......... .......... .......... .......... .......... 42% 92.5M 1s\n",
            " 24500K .......... .......... .......... .......... .......... 42%  196M 1s\n",
            " 24550K .......... .......... .......... .......... .......... 43% 95.9M 1s\n",
            " 24600K .......... .......... .......... .......... .......... 43%  179M 1s\n",
            " 24650K .......... .......... .......... .......... .......... 43% 82.3M 1s\n",
            " 24700K .......... .......... .......... .......... .......... 43%  200M 1s\n",
            " 24750K .......... .......... .......... .......... .......... 43%  103M 1s\n",
            " 24800K .......... .......... .......... .......... .......... 43%  192M 1s\n",
            " 24850K .......... .......... .......... .......... .......... 43%  202M 1s\n",
            " 24900K .......... .......... .......... .......... .......... 43%  183M 1s\n",
            " 24950K .......... .......... .......... .......... .......... 43%  102M 1s\n",
            " 25000K .......... .......... .......... .......... .......... 43%  195M 1s\n",
            " 25050K .......... .......... .......... .......... .......... 43%  177M 1s\n",
            " 25100K .......... .......... .......... .......... .......... 44%  200M 1s\n",
            " 25150K .......... .......... .......... .......... .......... 44%  161M 1s\n",
            " 25200K .......... .......... .......... .......... .......... 44%  197M 1s\n",
            " 25250K .......... .......... .......... .......... .......... 44%  181M 1s\n",
            " 25300K .......... .......... .......... .......... .......... 44%  200M 1s\n",
            " 25350K .......... .......... .......... .......... .......... 44%  175M 1s\n",
            " 25400K .......... .......... .......... .......... .......... 44%  187M 1s\n",
            " 25450K .......... .......... .......... .......... .......... 44%  189M 1s\n",
            " 25500K .......... .......... .......... .......... .......... 44%  194M 1s\n",
            " 25550K .......... .......... .......... .......... .......... 44%  162M 1s\n",
            " 25600K .......... .......... .......... .......... .......... 44%  172M 1s\n",
            " 25650K .......... .......... .......... .......... .......... 45%  125M 1s\n",
            " 25700K .......... .......... .......... .......... .......... 45% 75.2M 1s\n",
            " 25750K .......... .......... .......... .......... .......... 45%  175M 1s\n",
            " 25800K .......... .......... .......... .......... .......... 45%  196M 1s\n",
            " 25850K .......... .......... .......... .......... .......... 45%  190M 1s\n",
            " 25900K .......... .......... .......... .......... .......... 45%  178M 1s\n",
            " 25950K .......... .......... .......... .......... .......... 45%  160M 1s\n",
            " 26000K .......... .......... .......... .......... .......... 45% 62.1M 1s\n",
            " 26050K .......... .......... .......... .......... .......... 45%  194M 1s\n",
            " 26100K .......... .......... .......... .......... .......... 45%  191M 1s\n",
            " 26150K .......... .......... .......... .......... .......... 45%  162M 1s\n",
            " 26200K .......... .......... .......... .......... .......... 45%  192M 1s\n",
            " 26250K .......... .......... .......... .......... .......... 46%  202M 1s\n",
            " 26300K .......... .......... .......... .......... .......... 46%  191M 1s\n",
            " 26350K .......... .......... .......... .......... .......... 46%  149M 1s\n",
            " 26400K .......... .......... .......... .......... .......... 46%  198M 1s\n",
            " 26450K .......... .......... .......... .......... .......... 46%  198M 1s\n",
            " 26500K .......... .......... .......... .......... .......... 46%  204M 1s\n",
            " 26550K .......... .......... .......... .......... .......... 46%  161M 1s\n",
            " 26600K .......... .......... .......... .......... .......... 46%  185M 1s\n",
            " 26650K .......... .......... .......... .......... .......... 46%  200M 1s\n",
            " 26700K .......... .......... .......... .......... .......... 46%  189M 1s\n",
            " 26750K .......... .......... .......... .......... .......... 46%  156M 1s\n",
            " 26800K .......... .......... .......... .......... .......... 47%  196M 1s\n",
            " 26850K .......... .......... .......... .......... .......... 47%  196M 1s\n",
            " 26900K .......... .......... .......... .......... .......... 47%  186M 1s\n",
            " 26950K .......... .......... .......... .......... .......... 47%  176M 1s\n",
            " 27000K .......... .......... .......... .......... .......... 47%  195M 1s\n",
            " 27050K .......... .......... .......... .......... .......... 47%  192M 1s\n",
            " 27100K .......... .......... .......... .......... .......... 47%  177M 1s\n",
            " 27150K .......... .......... .......... .......... .......... 47%  165M 1s\n",
            " 27200K .......... .......... .......... .......... .......... 47%  184M 1s\n",
            " 27250K .......... .......... .......... .......... .......... 47%  193M 1s\n",
            " 27300K .......... .......... .......... .......... .......... 47% 24.2M 1s\n",
            " 27350K .......... .......... .......... .......... .......... 47%  163M 1s\n",
            " 27400K .......... .......... .......... .......... .......... 48%  138M 1s\n",
            " 27450K .......... .......... .......... .......... .......... 48%  189M 1s\n",
            " 27500K .......... .......... .......... .......... .......... 48%  158M 1s\n",
            " 27550K .......... .......... .......... .......... .......... 48% 19.7M 1s\n",
            " 27600K .......... .......... .......... .......... .......... 48%  191M 1s\n",
            " 27650K .......... .......... .......... .......... .......... 48%  155M 0s\n",
            " 27700K .......... .......... .......... .......... .......... 48% 66.0M 0s\n",
            " 27750K .......... .......... .......... .......... .......... 48%  168M 0s\n",
            " 27800K .......... .......... .......... .......... .......... 48%  203M 0s\n",
            " 27850K .......... .......... .......... .......... .......... 48%  201M 0s\n",
            " 27900K .......... .......... .......... .......... .......... 48%  203M 0s\n",
            " 27950K .......... .......... .......... .......... .......... 49% 7.74M 0s\n",
            " 28000K .......... .......... .......... .......... .......... 49% 70.2M 0s\n",
            " 28050K .......... .......... .......... .......... .......... 49%  154M 0s\n",
            " 28100K .......... .......... .......... .......... .......... 49%  191M 0s\n",
            " 28150K .......... .......... .......... .......... .......... 49%  175M 0s\n",
            " 28200K .......... .......... .......... .......... .......... 49% 37.6M 0s\n",
            " 28250K .......... .......... .......... .......... .......... 49%  160M 0s\n",
            " 28300K .......... .......... .......... .......... .......... 49%  197M 0s\n",
            " 28350K .......... .......... .......... .......... .......... 49%  163M 0s\n",
            " 28400K .......... .......... .......... .......... .......... 49% 6.12M 0s\n",
            " 28450K .......... .......... .......... .......... .......... 49%  127M 0s\n",
            " 28500K .......... .......... .......... .......... .......... 50%  190M 0s\n",
            " 28550K .......... .......... .......... .......... .......... 50%  179M 0s\n",
            " 28600K .......... .......... .......... .......... .......... 50%  181M 0s\n",
            " 28650K .......... .......... .......... .......... .......... 50% 12.9M 0s\n",
            " 28700K .......... .......... .......... .......... .......... 50%  184M 0s\n",
            " 28750K .......... .......... .......... .......... .......... 50%  161M 0s\n",
            " 28800K .......... .......... .......... .......... .......... 50%  198M 0s\n",
            " 28850K .......... .......... .......... .......... .......... 50% 17.8M 0s\n",
            " 28900K .......... .......... .......... .......... .......... 50%  182M 0s\n",
            " 28950K .......... .......... .......... .......... .......... 50%  169M 0s\n",
            " 29000K .......... .......... .......... .......... .......... 50%  193M 0s\n",
            " 29050K .......... .......... .......... .......... .......... 50%  203M 0s\n",
            " 29100K .......... .......... .......... .......... .......... 51% 22.7M 0s\n",
            " 29150K .......... .......... .......... .......... .......... 51%  102M 0s\n",
            " 29200K .......... .......... .......... .......... .......... 51%  191M 0s\n",
            " 29250K .......... .......... .......... .......... .......... 51%  205M 0s\n",
            " 29300K .......... .......... .......... .......... .......... 51%  198M 0s\n",
            " 29350K .......... .......... .......... .......... .......... 51%  157M 0s\n",
            " 29400K .......... .......... .......... .......... .......... 51%  189M 0s\n",
            " 29450K .......... .......... .......... .......... .......... 51%  190M 0s\n",
            " 29500K .......... .......... .......... .......... .......... 51% 10.5M 0s\n",
            " 29550K .......... .......... .......... .......... .......... 51%  146M 0s\n",
            " 29600K .......... .......... .......... .......... .......... 51% 65.6M 0s\n",
            " 29650K .......... .......... .......... .......... .......... 52%  198M 0s\n",
            " 29700K .......... .......... .......... .......... .......... 52%  152M 0s\n",
            " 29750K .......... .......... .......... .......... .......... 52% 63.5M 0s\n",
            " 29800K .......... .......... .......... .......... .......... 52%  188M 0s\n",
            " 29850K .......... .......... .......... .......... .......... 52%  192M 0s\n",
            " 29900K .......... .......... .......... .......... .......... 52%  207M 0s\n",
            " 29950K .......... .......... .......... .......... .......... 52%  162M 0s\n",
            " 30000K .......... .......... .......... .......... .......... 52%  191M 0s\n",
            " 30050K .......... .......... .......... .......... .......... 52%  197M 0s\n",
            " 30100K .......... .......... .......... .......... .......... 52%  188M 0s\n",
            " 30150K .......... .......... .......... .......... .......... 52%  173M 0s\n",
            " 30200K .......... .......... .......... .......... .......... 52% 9.33M 0s\n",
            " 30250K .......... .......... .......... .......... .......... 53%  201M 0s\n",
            " 30300K .......... .......... .......... .......... .......... 53%  193M 0s\n",
            " 30350K .......... .......... .......... .......... .......... 53%  157M 0s\n",
            " 30400K .......... .......... .......... .......... .......... 53% 17.6M 0s\n",
            " 30450K .......... .......... .......... .......... .......... 53%  200M 0s\n",
            " 30500K .......... .......... .......... .......... .......... 53%  183M 0s\n",
            " 30550K .......... .......... .......... .......... .......... 53%  180M 0s\n",
            " 30600K .......... .......... .......... .......... .......... 53%  195M 0s\n",
            " 30650K .......... .......... .......... .......... .......... 53% 6.63M 0s\n",
            " 30700K .......... .......... .......... .......... .......... 53%  161M 0s\n",
            " 30750K .......... .......... .......... .......... .......... 53% 56.7M 0s\n",
            " 30800K .......... .......... .......... .......... .......... 54%  189M 0s\n",
            " 30850K .......... .......... .......... .......... .......... 54%  194M 0s\n",
            " 30900K .......... .......... .......... .......... .......... 54%  189M 0s\n",
            " 30950K .......... .......... .......... .......... .......... 54%  175M 0s\n",
            " 31000K .......... .......... .......... .......... .......... 54%  199M 0s\n",
            " 31050K .......... .......... .......... .......... .......... 54%  197M 0s\n",
            " 31100K .......... .......... .......... .......... .......... 54%  189M 0s\n",
            " 31150K .......... .......... .......... .......... .......... 54%  164M 0s\n",
            " 31200K .......... .......... .......... .......... .......... 54%  198M 0s\n",
            " 31250K .......... .......... .......... .......... .......... 54% 10.3M 0s\n",
            " 31300K .......... .......... .......... .......... .......... 54%  189M 0s\n",
            " 31350K .......... .......... .......... .......... .......... 54%  158M 0s\n",
            " 31400K .......... .......... .......... .......... .......... 55%  190M 0s\n",
            " 31450K .......... .......... .......... .......... .......... 55%  199M 0s\n",
            " 31500K .......... .......... .......... .......... .......... 55%  192M 0s\n",
            " 31550K .......... .......... .......... .......... .......... 55%  151M 0s\n",
            " 31600K .......... .......... .......... .......... .......... 55%  191M 0s\n",
            " 31650K .......... .......... .......... .......... .......... 55%  204M 0s\n",
            " 31700K .......... .......... .......... .......... .......... 55%  197M 0s\n",
            " 31750K .......... .......... .......... .......... .......... 55%  173M 0s\n",
            " 31800K .......... .......... .......... .......... .......... 55%  189M 0s\n",
            " 31850K .......... .......... .......... .......... .......... 55%  198M 0s\n",
            " 31900K .......... .......... .......... .......... .......... 55%  188M 0s\n",
            " 31950K .......... .......... .......... .......... .......... 56% 44.9M 0s\n",
            " 32000K .......... .......... .......... .......... .......... 56% 93.4M 0s\n",
            " 32050K .......... .......... .......... .......... .......... 56%  193M 0s\n",
            " 32100K .......... .......... .......... .......... .......... 56%  188M 0s\n",
            " 32150K .......... .......... .......... .......... .......... 56%  155M 0s\n",
            " 32200K .......... .......... .......... .......... .......... 56%  193M 0s\n",
            " 32250K .......... .......... .......... .......... .......... 56%  183M 0s\n",
            " 32300K .......... .......... .......... .......... .......... 56%  204M 0s\n",
            " 32350K .......... .......... .......... .......... .......... 56% 18.5M 0s\n",
            " 32400K .......... .......... .......... .......... .......... 56%  173M 0s\n",
            " 32450K .......... .......... .......... .......... .......... 56% 23.3M 0s\n",
            " 32500K .......... .......... .......... .......... .......... 57% 45.8M 0s\n",
            " 32550K .......... .......... .......... .......... .......... 57%  169M 0s\n",
            " 32600K .......... .......... .......... .......... .......... 57%  200M 0s\n",
            " 32650K .......... .......... .......... .......... .......... 57%  134M 0s\n",
            " 32700K .......... .......... .......... .......... .......... 57% 14.7M 0s\n",
            " 32750K .......... .......... .......... .......... .......... 57%  160M 0s\n",
            " 32800K .......... .......... .......... .......... .......... 57%  190M 0s\n",
            " 32850K .......... .......... .......... .......... .......... 57%  177M 0s\n",
            " 32900K .......... .......... .......... .......... .......... 57%  212M 0s\n",
            " 32950K .......... .......... .......... .......... .......... 57%  159M 0s\n",
            " 33000K .......... .......... .......... .......... .......... 57%  197M 0s\n",
            " 33050K .......... .......... .......... .......... .......... 57%  168M 0s\n",
            " 33100K .......... .......... .......... .......... .......... 58%  218M 0s\n",
            " 33150K .......... .......... .......... .......... .......... 58%  173M 0s\n",
            " 33200K .......... .......... .......... .......... .......... 58%  195M 0s\n",
            " 33250K .......... .......... .......... .......... .......... 58%  200M 0s\n",
            " 33300K .......... .......... .......... .......... .......... 58%  196M 0s\n",
            " 33350K .......... .......... .......... .......... .......... 58%  147M 0s\n",
            " 33400K .......... .......... .......... .......... .......... 58%  163M 0s\n",
            " 33450K .......... .......... .......... .......... .......... 58%  180M 0s\n",
            " 33500K .......... .......... .......... .......... .......... 58%  188M 0s\n",
            " 33550K .......... .......... .......... .......... .......... 58%  158M 0s\n",
            " 33600K .......... .......... .......... .......... .......... 58%  161M 0s\n",
            " 33650K .......... .......... .......... .......... .......... 59%  186M 0s\n",
            " 33700K .......... .......... .......... .......... .......... 59% 61.8M 0s\n",
            " 33750K .......... .......... .......... .......... .......... 59%  177M 0s\n",
            " 33800K .......... .......... .......... .......... .......... 59%  185M 0s\n",
            " 33850K .......... .......... .......... .......... .......... 59%  168M 0s\n",
            " 33900K .......... .......... .......... .......... .......... 59%  198M 0s\n",
            " 33950K .......... .......... .......... .......... .......... 59%  158M 0s\n",
            " 34000K .......... .......... .......... .......... .......... 59%  200M 0s\n",
            " 34050K .......... .......... .......... .......... .......... 59%  175M 0s\n",
            " 34100K .......... .......... .......... .......... .......... 59% 14.5M 0s\n",
            " 34150K .......... .......... .......... .......... .......... 59%  161M 0s\n",
            " 34200K .......... .......... .......... .......... .......... 59%  164M 0s\n",
            " 34250K .......... .......... .......... .......... .......... 60%  198M 0s\n",
            " 34300K .......... .......... .......... .......... .......... 60%  190M 0s\n",
            " 34350K .......... .......... .......... .......... .......... 60%  146M 0s\n",
            " 34400K .......... .......... .......... .......... .......... 60%  180M 0s\n",
            " 34450K .......... .......... .......... .......... .......... 60%  204M 0s\n",
            " 34500K .......... .......... .......... .......... .......... 60%  193M 0s\n",
            " 34550K .......... .......... .......... .......... .......... 60%  164M 0s\n",
            " 34600K .......... .......... .......... .......... .......... 60%  201M 0s\n",
            " 34650K .......... .......... .......... .......... .......... 60%  192M 0s\n",
            " 34700K .......... .......... .......... .......... .......... 60%  204M 0s\n",
            " 34750K .......... .......... .......... .......... .......... 60%  150M 0s\n",
            " 34800K .......... .......... .......... .......... .......... 61%  199M 0s\n",
            " 34850K .......... .......... .......... .......... .......... 61%  197M 0s\n",
            " 34900K .......... .......... .......... .......... .......... 61%  194M 0s\n",
            " 34950K .......... .......... .......... .......... .......... 61%  147M 0s\n",
            " 35000K .......... .......... .......... .......... .......... 61% 41.0M 0s\n",
            " 35050K .......... .......... .......... .......... .......... 61%  185M 0s\n",
            " 35100K .......... .......... .......... .......... .......... 61%  177M 0s\n",
            " 35150K .......... .......... .......... .......... .......... 61%  146M 0s\n",
            " 35200K .......... .......... .......... .......... .......... 61%  197M 0s\n",
            " 35250K .......... .......... .......... .......... .......... 61%  151M 0s\n",
            " 35300K .......... .......... .......... .......... .......... 61% 55.2M 0s\n",
            " 35350K .......... .......... .......... .......... .......... 61%  161M 0s\n",
            " 35400K .......... .......... .......... .......... .......... 62%  180M 0s\n",
            " 35450K .......... .......... .......... .......... .......... 62%  167M 0s\n",
            " 35500K .......... .......... .......... .......... .......... 62% 66.9M 0s\n",
            " 35550K .......... .......... .......... .......... .......... 62%  155M 0s\n",
            " 35600K .......... .......... .......... .......... .......... 62%  176M 0s\n",
            " 35650K .......... .......... .......... .......... .......... 62%  162M 0s\n",
            " 35700K .......... .......... .......... .......... .......... 62%  201M 0s\n",
            " 35750K .......... .......... .......... .......... .......... 62%  168M 0s\n",
            " 35800K .......... .......... .......... .......... .......... 62%  178M 0s\n",
            " 35850K .......... .......... .......... .......... .......... 62%  203M 0s\n",
            " 35900K .......... .......... .......... .......... .......... 62%  198M 0s\n",
            " 35950K .......... .......... .......... .......... .......... 63%  163M 0s\n",
            " 36000K .......... .......... .......... .......... .......... 63%  181M 0s\n",
            " 36050K .......... .......... .......... .......... .......... 63%  188M 0s\n",
            " 36100K .......... .......... .......... .......... .......... 63%  203M 0s\n",
            " 36150K .......... .......... .......... .......... .......... 63%  175M 0s\n",
            " 36200K .......... .......... .......... .......... .......... 63%  184M 0s\n",
            " 36250K .......... .......... .......... .......... .......... 63%  194M 0s\n",
            " 36300K .......... .......... .......... .......... .......... 63%  197M 0s\n",
            " 36350K .......... .......... .......... .......... .......... 63%  161M 0s\n",
            " 36400K .......... .......... .......... .......... .......... 63%  179M 0s\n",
            " 36450K .......... .......... .......... .......... .......... 63%  204M 0s\n",
            " 36500K .......... .......... .......... .......... .......... 64%  186M 0s\n",
            " 36550K .......... .......... .......... .......... .......... 64%  158M 0s\n",
            " 36600K .......... .......... .......... .......... .......... 64% 35.7M 0s\n",
            " 36650K .......... .......... .......... .......... .......... 64%  189M 0s\n",
            " 36700K .......... .......... .......... .......... .......... 64% 60.7M 0s\n",
            " 36750K .......... .......... .......... .......... .......... 64%  156M 0s\n",
            " 36800K .......... .......... .......... .......... .......... 64%  186M 0s\n",
            " 36850K .......... .......... .......... .......... .......... 64%  185M 0s\n",
            " 36900K .......... .......... .......... .......... .......... 64%  187M 0s\n",
            " 36950K .......... .......... .......... .......... .......... 64%  170M 0s\n",
            " 37000K .......... .......... .......... .......... .......... 64%  165M 0s\n",
            " 37050K .......... .......... .......... .......... .......... 64%  192M 0s\n",
            " 37100K .......... .......... .......... .......... .......... 65%  201M 0s\n",
            " 37150K .......... .......... .......... .......... .......... 65% 52.9M 0s\n",
            " 37200K .......... .......... .......... .......... .......... 65%  191M 0s\n",
            " 37250K .......... .......... .......... .......... .......... 65%  183M 0s\n",
            " 37300K .......... .......... .......... .......... .......... 65%  201M 0s\n",
            " 37350K .......... .......... .......... .......... .......... 65%  159M 0s\n",
            " 37400K .......... .......... .......... .......... .......... 65%  195M 0s\n",
            " 37450K .......... .......... .......... .......... .......... 65%  172M 0s\n",
            " 37500K .......... .......... .......... .......... .......... 65%  194M 0s\n",
            " 37550K .......... .......... .......... .......... .......... 65%  164M 0s\n",
            " 37600K .......... .......... .......... .......... .......... 65%  190M 0s\n",
            " 37650K .......... .......... .......... .......... .......... 66%  185M 0s\n",
            " 37700K .......... .......... .......... .......... .......... 66%  200M 0s\n",
            " 37750K .......... .......... .......... .......... .......... 66% 67.2M 0s\n",
            " 37800K .......... .......... .......... .......... .......... 66% 66.1M 0s\n",
            " 37850K .......... .......... .......... .......... .......... 66%  123M 0s\n",
            " 37900K .......... .......... .......... .......... .......... 66% 66.1M 0s\n",
            " 37950K .......... .......... .......... .......... .......... 66%  152M 0s\n",
            " 38000K .......... .......... .......... .......... .......... 66%  198M 0s\n",
            " 38050K .......... .......... .......... .......... .......... 66%  190M 0s\n",
            " 38100K .......... .......... .......... .......... .......... 66% 26.8M 0s\n",
            " 38150K .......... .......... .......... .......... .......... 66% 50.7M 0s\n",
            " 38200K .......... .......... .......... .......... .......... 66% 56.8M 0s\n",
            " 38250K .......... .......... .......... .......... .......... 67% 55.6M 0s\n",
            " 38300K .......... .......... .......... .......... .......... 67%  182M 0s\n",
            " 38350K .......... .......... .......... .......... .......... 67% 63.4M 0s\n",
            " 38400K .......... .......... .......... .......... .......... 67%  179M 0s\n",
            " 38450K .......... .......... .......... .......... .......... 67%  167M 0s\n",
            " 38500K .......... .......... .......... .......... .......... 67%  190M 0s\n",
            " 38550K .......... .......... .......... .......... .......... 67%  173M 0s\n",
            " 38600K .......... .......... .......... .......... .......... 67%  194M 0s\n",
            " 38650K .......... .......... .......... .......... .......... 67%  176M 0s\n",
            " 38700K .......... .......... .......... .......... .......... 67%  164M 0s\n",
            " 38750K .......... .......... .......... .......... .......... 67% 27.8M 0s\n",
            " 38800K .......... .......... .......... .......... .......... 68%  108M 0s\n",
            " 38850K .......... .......... .......... .......... .......... 68%  189M 0s\n",
            " 38900K .......... .......... .......... .......... .......... 68%  178M 0s\n",
            " 38950K .......... .......... .......... .......... .......... 68%  161M 0s\n",
            " 39000K .......... .......... .......... .......... .......... 68%  198M 0s\n",
            " 39050K .......... .......... .......... .......... .......... 68% 73.1M 0s\n",
            " 39100K .......... .......... .......... .......... .......... 68%  182M 0s\n",
            " 39150K .......... .......... .......... .......... .......... 68% 99.1M 0s\n",
            " 39200K .......... .......... .......... .......... .......... 68%  106M 0s\n",
            " 39250K .......... .......... .......... .......... .......... 68%  191M 0s\n",
            " 39300K .......... .......... .......... .......... .......... 68% 94.3M 0s\n",
            " 39350K .......... .......... .......... .......... .......... 69%  178M 0s\n",
            " 39400K .......... .......... .......... .......... .......... 69%  112M 0s\n",
            " 39450K .......... .......... .......... .......... .......... 69%  111M 0s\n",
            " 39500K .......... .......... .......... .......... .......... 69%  188M 0s\n",
            " 39550K .......... .......... .......... .......... .......... 69% 96.8M 0s\n",
            " 39600K .......... .......... .......... .......... .......... 69%  118M 0s\n",
            " 39650K .......... .......... .......... .......... .......... 69%  186M 0s\n",
            " 39700K .......... .......... .......... .......... .......... 69%  176M 0s\n",
            " 39750K .......... .......... .......... .......... .......... 69%  183M 0s\n",
            " 39800K .......... .......... .......... .......... .......... 69%  187M 0s\n",
            " 39850K .......... .......... .......... .......... .......... 69%  176M 0s\n",
            " 39900K .......... .......... .......... .......... .......... 69%  189M 0s\n",
            " 39950K .......... .......... .......... .......... .......... 70%  158M 0s\n",
            " 40000K .......... .......... .......... .......... .......... 70%  180M 0s\n",
            " 40050K .......... .......... .......... .......... .......... 70% 85.3M 0s\n",
            " 40100K .......... .......... .......... .......... .......... 70%  197M 0s\n",
            " 40150K .......... .......... .......... .......... .......... 70%  147M 0s\n",
            " 40200K .......... .......... .......... .......... .......... 70% 58.4M 0s\n",
            " 40250K .......... .......... .......... .......... .......... 70%  183M 0s\n",
            " 40300K .......... .......... .......... .......... .......... 70%  199M 0s\n",
            " 40350K .......... .......... .......... .......... .......... 70%  155M 0s\n",
            " 40400K .......... .......... .......... .......... .......... 70%  195M 0s\n",
            " 40450K .......... .......... .......... .......... .......... 70%  172M 0s\n",
            " 40500K .......... .......... .......... .......... .......... 71%  201M 0s\n",
            " 40550K .......... .......... .......... .......... .......... 71%  163M 0s\n",
            " 40600K .......... .......... .......... .......... .......... 71%  176M 0s\n",
            " 40650K .......... .......... .......... .......... .......... 71%  188M 0s\n",
            " 40700K .......... .......... .......... .......... .......... 71%  189M 0s\n",
            " 40750K .......... .......... .......... .......... .......... 71%  160M 0s\n",
            " 40800K .......... .......... .......... .......... .......... 71%  124M 0s\n",
            " 40850K .......... .......... .......... .......... .......... 71%  197M 0s\n",
            " 40900K .......... .......... .......... .......... .......... 71%  197M 0s\n",
            " 40950K .......... .......... .......... .......... .......... 71%  103M 0s\n",
            " 41000K .......... .......... .......... .......... .......... 71%  190M 0s\n",
            " 41050K .......... .......... .......... .......... .......... 71%  178M 0s\n",
            " 41100K .......... .......... .......... .......... .......... 72% 63.9M 0s\n",
            " 41150K .......... .......... .......... .......... .......... 72%  159M 0s\n",
            " 41200K .......... .......... .......... .......... .......... 72%  168M 0s\n",
            " 41250K .......... .......... .......... .......... .......... 72%  187M 0s\n",
            " 41300K .......... .......... .......... .......... .......... 72%  187M 0s\n",
            " 41350K .......... .......... .......... .......... .......... 72%  167M 0s\n",
            " 41400K .......... .......... .......... .......... .......... 72%  181M 0s\n",
            " 41450K .......... .......... .......... .......... .......... 72%  181M 0s\n",
            " 41500K .......... .......... .......... .......... .......... 72%  196M 0s\n",
            " 41550K .......... .......... .......... .......... .......... 72%  159M 0s\n",
            " 41600K .......... .......... .......... .......... .......... 72%  186M 0s\n",
            " 41650K .......... .......... .......... .......... .......... 73%  160M 0s\n",
            " 41700K .......... .......... .......... .......... .......... 73% 61.9M 0s\n",
            " 41750K .......... .......... .......... .......... .......... 73%  172M 0s\n",
            " 41800K .......... .......... .......... .......... .......... 73%  195M 0s\n",
            " 41850K .......... .......... .......... .......... .......... 73%  180M 0s\n",
            " 41900K .......... .......... .......... .......... .......... 73%  189M 0s\n",
            " 41950K .......... .......... .......... .......... .......... 73%  157M 0s\n",
            " 42000K .......... .......... .......... .......... .......... 73%  183M 0s\n",
            " 42050K .......... .......... .......... .......... .......... 73%  196M 0s\n",
            " 42100K .......... .......... .......... .......... .......... 73%  191M 0s\n",
            " 42150K .......... .......... .......... .......... .......... 73%  177M 0s\n",
            " 42200K .......... .......... .......... .......... .......... 73%  179M 0s\n",
            " 42250K .......... .......... .......... .......... .......... 74%  184M 0s\n",
            " 42300K .......... .......... .......... .......... .......... 74%  134M 0s\n",
            " 42350K .......... .......... .......... .......... .......... 74% 51.3M 0s\n",
            " 42400K .......... .......... .......... .......... .......... 74%  193M 0s\n",
            " 42450K .......... .......... .......... .......... .......... 74%  177M 0s\n",
            " 42500K .......... .......... .......... .......... .......... 74%  199M 0s\n",
            " 42550K .......... .......... .......... .......... .......... 74%  180M 0s\n",
            " 42600K .......... .......... .......... .......... .......... 74%  184M 0s\n",
            " 42650K .......... .......... .......... .......... .......... 74%  193M 0s\n",
            " 42700K .......... .......... .......... .......... .......... 74%  193M 0s\n",
            " 42750K .......... .......... .......... .......... .......... 74%  168M 0s\n",
            " 42800K .......... .......... .......... .......... .......... 75%  189M 0s\n",
            " 42850K .......... .......... .......... .......... .......... 75% 19.2M 0s\n",
            " 42900K .......... .......... .......... .......... .......... 75%  192M 0s\n",
            " 42950K .......... .......... .......... .......... .......... 75% 66.8M 0s\n",
            " 43000K .......... .......... .......... .......... .......... 75%  200M 0s\n",
            " 43050K .......... .......... .......... .......... .......... 75%  196M 0s\n",
            " 43100K .......... .......... .......... .......... .......... 75%  170M 0s\n",
            " 43150K .......... .......... .......... .......... .......... 75%  165M 0s\n",
            " 43200K .......... .......... .......... .......... .......... 75%  184M 0s\n",
            " 43250K .......... .......... .......... .......... .......... 75% 17.3M 0s\n",
            " 43300K .......... .......... .......... .......... .......... 75%  186M 0s\n",
            " 43350K .......... .......... .......... .......... .......... 76%  162M 0s\n",
            " 43400K .......... .......... .......... .......... .......... 76%  204M 0s\n",
            " 43450K .......... .......... .......... .......... .......... 76% 3.87M 0s\n",
            " 43500K .......... .......... .......... .......... .......... 76%  154M 0s\n",
            " 43550K .......... .......... .......... .......... .......... 76%  177M 0s\n",
            " 43600K .......... .......... .......... .......... .......... 76%  180M 0s\n",
            " 43650K .......... .......... .......... .......... .......... 76% 46.7M 0s\n",
            " 43700K .......... .......... .......... .......... .......... 76%  186M 0s\n",
            " 43750K .......... .......... .......... .......... .......... 76%  162M 0s\n",
            " 43800K .......... .......... .......... .......... .......... 76%  188M 0s\n",
            " 43850K .......... .......... .......... .......... .......... 76%  178M 0s\n",
            " 43900K .......... .......... .......... .......... .......... 76%  186M 0s\n",
            " 43950K .......... .......... .......... .......... .......... 77%  155M 0s\n",
            " 44000K .......... .......... .......... .......... .......... 77%  183M 0s\n",
            " 44050K .......... .......... .......... .......... .......... 77%  184M 0s\n",
            " 44100K .......... .......... .......... .......... .......... 77%  199M 0s\n",
            " 44150K .......... .......... .......... .......... .......... 77%  172M 0s\n",
            " 44200K .......... .......... .......... .......... .......... 77%  177M 0s\n",
            " 44250K .......... .......... .......... .......... .......... 77%  192M 0s\n",
            " 44300K .......... .......... .......... .......... .......... 77%  194M 0s\n",
            " 44350K .......... .......... .......... .......... .......... 77%  159M 0s\n",
            " 44400K .......... .......... .......... .......... .......... 77%  174M 0s\n",
            " 44450K .......... .......... .......... .......... .......... 77%  187M 0s\n",
            " 44500K .......... .......... .......... .......... .......... 78%  197M 0s\n",
            " 44550K .......... .......... .......... .......... .......... 78%  171M 0s\n",
            " 44600K .......... .......... .......... .......... .......... 78% 5.62M 0s\n",
            " 44650K .......... .......... .......... .......... .......... 78%  176M 0s\n",
            " 44700K .......... .......... .......... .......... .......... 78%  156M 0s\n",
            " 44750K .......... .......... .......... .......... .......... 78%  159M 0s\n",
            " 44800K .......... .......... .......... .......... .......... 78%  201M 0s\n",
            " 44850K .......... .......... .......... .......... .......... 78% 8.99M 0s\n",
            " 44900K .......... .......... .......... .......... .......... 78%  176M 0s\n",
            " 44950K .......... .......... .......... .......... .......... 78%  165M 0s\n",
            " 45000K .......... .......... .......... .......... .......... 78%  176M 0s\n",
            " 45050K .......... .......... .......... .......... .......... 78%  191M 0s\n",
            " 45100K .......... .......... .......... .......... .......... 79%  199M 0s\n",
            " 45150K .......... .......... .......... .......... .......... 79%  165M 0s\n",
            " 45200K .......... .......... .......... .......... .......... 79%  177M 0s\n",
            " 45250K .......... .......... .......... .......... .......... 79%  200M 0s\n",
            " 45300K .......... .......... .......... .......... .......... 79%  196M 0s\n",
            " 45350K .......... .......... .......... .......... .......... 79%  180M 0s\n",
            " 45400K .......... .......... .......... .......... .......... 79%  120M 0s\n",
            " 45450K .......... .......... .......... .......... .......... 79%  299M 0s\n",
            " 45500K .......... .......... .......... .......... .......... 79%  324M 0s\n",
            " 45550K .......... .......... .......... .......... .......... 79%  257M 0s\n",
            " 45600K .......... .......... .......... .......... .......... 79%  322M 0s\n",
            " 45650K .......... .......... .......... .......... .......... 80%  198M 0s\n",
            " 45700K .......... .......... .......... .......... .......... 80% 32.1M 0s\n",
            " 45750K .......... .......... .......... .......... .......... 80%  273M 0s\n",
            " 45800K .......... .......... .......... .......... .......... 80%  177M 0s\n",
            " 45850K .......... .......... .......... .......... .......... 80%  323M 0s\n",
            " 45900K .......... .......... .......... .......... .......... 80%  340M 0s\n",
            " 45950K .......... .......... .......... .......... .......... 80% 26.2M 0s\n",
            " 46000K .......... .......... .......... .......... .......... 80%  316M 0s\n",
            " 46050K .......... .......... .......... .......... .......... 80%  340M 0s\n",
            " 46100K .......... .......... .......... .......... .......... 80%  333M 0s\n",
            " 46150K .......... .......... .......... .......... .......... 80%  305M 0s\n",
            " 46200K .......... .......... .......... .......... .......... 81%  269M 0s\n",
            " 46250K .......... .......... .......... .......... .......... 81%  319M 0s\n",
            " 46300K .......... .......... .......... .......... .......... 81%  324M 0s\n",
            " 46350K .......... .......... .......... .......... .......... 81% 20.1M 0s\n",
            " 46400K .......... .......... .......... .......... .......... 81%  251M 0s\n",
            " 46450K .......... .......... .......... .......... .......... 81%  240M 0s\n",
            " 46500K .......... .......... .......... .......... .......... 81%  340M 0s\n",
            " 46550K .......... .......... .......... .......... .......... 81%  307M 0s\n",
            " 46600K .......... .......... .......... .......... .......... 81%  336M 0s\n",
            " 46650K .......... .......... .......... .......... .......... 81% 16.5M 0s\n",
            " 46700K .......... .......... .......... .......... .......... 81%  190M 0s\n",
            " 46750K .......... .......... .......... .......... .......... 81% 38.1M 0s\n",
            " 46800K .......... .......... .......... .......... .......... 82% 38.7M 0s\n",
            " 46850K .......... .......... .......... .......... .......... 82%  267M 0s\n",
            " 46900K .......... .......... .......... .......... .......... 82%  327M 0s\n",
            " 46950K .......... .......... .......... .......... .......... 82%  135M 0s\n",
            " 47000K .......... .......... .......... .......... .......... 82% 75.2M 0s\n",
            " 47050K .......... .......... .......... .......... .......... 82% 41.9M 0s\n",
            " 47100K .......... .......... .......... .......... .......... 82% 33.4M 0s\n",
            " 47150K .......... .......... .......... .......... .......... 82%  140M 0s\n",
            " 47200K .......... .......... .......... .......... .......... 82%  173M 0s\n",
            " 47250K .......... .......... .......... .......... .......... 82%  191M 0s\n",
            " 47300K .......... .......... .......... .......... .......... 82%  202M 0s\n",
            " 47350K .......... .......... .......... .......... .......... 83%  175M 0s\n",
            " 47400K .......... .......... .......... .......... .......... 83%  173M 0s\n",
            " 47450K .......... .......... .......... .......... .......... 83%  173M 0s\n",
            " 47500K .......... .......... .......... .......... .......... 83%  185M 0s\n",
            " 47550K .......... .......... .......... .......... .......... 83%  137M 0s\n",
            " 47600K .......... .......... .......... .......... .......... 83% 74.1M 0s\n",
            " 47650K .......... .......... .......... .......... .......... 83%  159M 0s\n",
            " 47700K .......... .......... .......... .......... .......... 83%  104M 0s\n",
            " 47750K .......... .......... .......... .......... .......... 83% 75.4M 0s\n",
            " 47800K .......... .......... .......... .......... .......... 83% 71.9M 0s\n",
            " 47850K .......... .......... .......... .......... .......... 83% 73.3M 0s\n",
            " 47900K .......... .......... .......... .......... .......... 83%  244M 0s\n",
            " 47950K .......... .......... .......... .......... .......... 84%  267M 0s\n",
            " 48000K .......... .......... .......... .......... .......... 84%  268M 0s\n",
            " 48050K .......... .......... .......... .......... .......... 84%  217M 0s\n",
            " 48100K .......... .......... .......... .......... .......... 84%  221M 0s\n",
            " 48150K .......... .......... .......... .......... .......... 84%  172M 0s\n",
            " 48200K .......... .......... .......... .......... .......... 84%  176M 0s\n",
            " 48250K .......... .......... .......... .......... .......... 84%  190M 0s\n",
            " 48300K .......... .......... .......... .......... .......... 84%  181M 0s\n",
            " 48350K .......... .......... .......... .......... .......... 84%  247M 0s\n",
            " 48400K .......... .......... .......... .......... .......... 84%  332M 0s\n",
            " 48450K .......... .......... .......... .......... .......... 84%  236M 0s\n",
            " 48500K .......... .......... .......... .......... .......... 85%  293M 0s\n",
            " 48550K .......... .......... .......... .......... .......... 85%  282M 0s\n",
            " 48600K .......... .......... .......... .......... .......... 85%  313M 0s\n",
            " 48650K .......... .......... .......... .......... .......... 85%  330M 0s\n",
            " 48700K .......... .......... .......... .......... .......... 85%  336M 0s\n",
            " 48750K .......... .......... .......... .......... .......... 85%  232M 0s\n",
            " 48800K .......... .......... .......... .......... .......... 85%  291M 0s\n",
            " 48850K .......... .......... .......... .......... .......... 85%  199M 0s\n",
            " 48900K .......... .......... .......... .......... .......... 85%  199M 0s\n",
            " 48950K .......... .......... .......... .......... .......... 85%  164M 0s\n",
            " 49000K .......... .......... .......... .......... .......... 85%  206M 0s\n",
            " 49050K .......... .......... .......... .......... .......... 85%  164M 0s\n",
            " 49100K .......... .......... .......... .......... .......... 86%  107M 0s\n",
            " 49150K .......... .......... .......... .......... .......... 86%  142M 0s\n",
            " 49200K .......... .......... .......... .......... .......... 86%  180M 0s\n",
            " 49250K .......... .......... .......... .......... .......... 86%  187M 0s\n",
            " 49300K .......... .......... .......... .......... .......... 86% 98.8M 0s\n",
            " 49350K .......... .......... .......... .......... .......... 86%  165M 0s\n",
            " 49400K .......... .......... .......... .......... .......... 86%  167M 0s\n",
            " 49450K .......... .......... .......... .......... .......... 86%  180M 0s\n",
            " 49500K .......... .......... .......... .......... .......... 86%  149M 0s\n",
            " 49550K .......... .......... .......... .......... .......... 86%  150M 0s\n",
            " 49600K .......... .......... .......... .......... .......... 86%  163M 0s\n",
            " 49650K .......... .......... .......... .......... .......... 87%  185M 0s\n",
            " 49700K .......... .......... .......... .......... .......... 87%  187M 0s\n",
            " 49750K .......... .......... .......... .......... .......... 87%  158M 0s\n",
            " 49800K .......... .......... .......... .......... .......... 87%  195M 0s\n",
            " 49850K .......... .......... .......... .......... .......... 87%  193M 0s\n",
            " 49900K .......... .......... .......... .......... .......... 87%  192M 0s\n",
            " 49950K .......... .......... .......... .......... .......... 87%  151M 0s\n",
            " 50000K .......... .......... .......... .......... .......... 87%  201M 0s\n",
            " 50050K .......... .......... .......... .......... .......... 87%  189M 0s\n",
            " 50100K .......... .......... .......... .......... .......... 87%  184M 0s\n",
            " 50150K .......... .......... .......... .......... .......... 87%  145M 0s\n",
            " 50200K .......... .......... .......... .......... .......... 88%  181M 0s\n",
            " 50250K .......... .......... .......... .......... .......... 88%  194M 0s\n",
            " 50300K .......... .......... .......... .......... .......... 88%  171M 0s\n",
            " 50350K .......... .......... .......... .......... .......... 88%  143M 0s\n",
            " 50400K .......... .......... .......... .......... .......... 88%  187M 0s\n",
            " 50450K .......... .......... .......... .......... .......... 88%  194M 0s\n",
            " 50500K .......... .......... .......... .......... .......... 88%  184M 0s\n",
            " 50550K .......... .......... .......... .......... .......... 88%  178M 0s\n",
            " 50600K .......... .......... .......... .......... .......... 88%  201M 0s\n",
            " 50650K .......... .......... .......... .......... .......... 88%  195M 0s\n",
            " 50700K .......... .......... .......... .......... .......... 88%  184M 0s\n",
            " 50750K .......... .......... .......... .......... .......... 88%  113M 0s\n",
            " 50800K .......... .......... .......... .......... .......... 89%  184M 0s\n",
            " 50850K .......... .......... .......... .......... .......... 89%  150M 0s\n",
            " 50900K .......... .......... .......... .......... .......... 89%  176M 0s\n",
            " 50950K .......... .......... .......... .......... .......... 89%  159M 0s\n",
            " 51000K .......... .......... .......... .......... .......... 89%  136M 0s\n",
            " 51050K .......... .......... .......... .......... .......... 89%  179M 0s\n",
            " 51100K .......... .......... .......... .......... .......... 89%  174M 0s\n",
            " 51150K .......... .......... .......... .......... .......... 89%  141M 0s\n",
            " 51200K .......... .......... .......... .......... .......... 89%  164M 0s\n",
            " 51250K .......... .......... .......... .......... .......... 89%  186M 0s\n",
            " 51300K .......... .......... .......... .......... .......... 89%  186M 0s\n",
            " 51350K .......... .......... .......... .......... .......... 90%  149M 0s\n",
            " 51400K .......... .......... .......... .......... .......... 90%  187M 0s\n",
            " 51450K .......... .......... .......... .......... .......... 90%  174M 0s\n",
            " 51500K .......... .......... .......... .......... .......... 90%  168M 0s\n",
            " 51550K .......... .......... .......... .......... .......... 90%  136M 0s\n",
            " 51600K .......... .......... .......... .......... .......... 90%  192M 0s\n",
            " 51650K .......... .......... .......... .......... .......... 90%  196M 0s\n",
            " 51700K .......... .......... .......... .......... .......... 90%  176M 0s\n",
            " 51750K .......... .......... .......... .......... .......... 90%  168M 0s\n",
            " 51800K .......... .......... .......... .......... .......... 90%  200M 0s\n",
            " 51850K .......... .......... .......... .......... .......... 90%  183M 0s\n",
            " 51900K .......... .......... .......... .......... .......... 90%  188M 0s\n",
            " 51950K .......... .......... .......... .......... .......... 91%  134M 0s\n",
            " 52000K .......... .......... .......... .......... .......... 91%  215M 0s\n",
            " 52050K .......... .......... .......... .......... .......... 91%  157M 0s\n",
            " 52100K .......... .......... .......... .......... .......... 91%  188M 0s\n",
            " 52150K .......... .......... .......... .......... .......... 91%  169M 0s\n",
            " 52200K .......... .......... .......... .......... .......... 91%  157M 0s\n",
            " 52250K .......... .......... .......... .......... .......... 91%  183M 0s\n",
            " 52300K .......... .......... .......... .......... .......... 91%  199M 0s\n",
            " 52350K .......... .......... .......... .......... .......... 91%  144M 0s\n",
            " 52400K .......... .......... .......... .......... .......... 91%  179M 0s\n",
            " 52450K .......... .......... .......... .......... .......... 91%  186M 0s\n",
            " 52500K .......... .......... .......... .......... .......... 92%  183M 0s\n",
            " 52550K .......... .......... .......... .......... .......... 92%  142M 0s\n",
            " 52600K .......... .......... .......... .......... .......... 92%  175M 0s\n",
            " 52650K .......... .......... .......... .......... .......... 92%  181M 0s\n",
            " 52700K .......... .......... .......... .......... .......... 92%  161M 0s\n",
            " 52750K .......... .......... .......... .......... .......... 92%  142M 0s\n",
            " 52800K .......... .......... .......... .......... .......... 92%  191M 0s\n",
            " 52850K .......... .......... .......... .......... .......... 92%  190M 0s\n",
            " 52900K .......... .......... .......... .......... .......... 92%  180M 0s\n",
            " 52950K .......... .......... .......... .......... .......... 92%  138M 0s\n",
            " 53000K .......... .......... .......... .......... .......... 92%  172M 0s\n",
            " 53050K .......... .......... .......... .......... .......... 92%  173M 0s\n",
            " 53100K .......... .......... .......... .......... .......... 93%  159M 0s\n",
            " 53150K .......... .......... .......... .......... .......... 93%  162M 0s\n",
            " 53200K .......... .......... .......... .......... .......... 93%  185M 0s\n",
            " 53250K .......... .......... .......... .......... .......... 93%  172M 0s\n",
            " 53300K .......... .......... .......... .......... .......... 93%  186M 0s\n",
            " 53350K .......... .......... .......... .......... .......... 93%  159M 0s\n",
            " 53400K .......... .......... .......... .......... .......... 93%  182M 0s\n",
            " 53450K .......... .......... .......... .......... .......... 93%  173M 0s\n",
            " 53500K .......... .......... .......... .......... .......... 93%  197M 0s\n",
            " 53550K .......... .......... .......... .......... .......... 93%  160M 0s\n",
            " 53600K .......... .......... .......... .......... .......... 93%  221M 0s\n",
            " 53650K .......... .......... .......... .......... .......... 94%  178M 0s\n",
            " 53700K .......... .......... .......... .......... .......... 94%  195M 0s\n",
            " 53750K .......... .......... .......... .......... .......... 94%  180M 0s\n",
            " 53800K .......... .......... .......... .......... .......... 94%  169M 0s\n",
            " 53850K .......... .......... .......... .......... .......... 94%  173M 0s\n",
            " 53900K .......... .......... .......... .......... .......... 94%  194M 0s\n",
            " 53950K .......... .......... .......... .......... .......... 94%  161M 0s\n",
            " 54000K .......... .......... .......... .......... .......... 94%  178M 0s\n",
            " 54050K .......... .......... .......... .......... .......... 94%  147M 0s\n",
            " 54100K .......... .......... .......... .......... .......... 94%  173M 0s\n",
            " 54150K .......... .......... .......... .......... .......... 94%  166M 0s\n",
            " 54200K .......... .......... .......... .......... .......... 95%  175M 0s\n",
            " 54250K .......... .......... .......... .......... .......... 95%  197M 0s\n",
            " 54300K .......... .......... .......... .......... .......... 95%  199M 0s\n",
            " 54350K .......... .......... .......... .......... .......... 95%  136M 0s\n",
            " 54400K .......... .......... .......... .......... .......... 95%  173M 0s\n",
            " 54450K .......... .......... .......... .......... .......... 95%  197M 0s\n",
            " 54500K .......... .......... .......... .......... .......... 95%  184M 0s\n",
            " 54550K .......... .......... .......... .......... .......... 95%  103M 0s\n",
            " 54600K .......... .......... .......... .......... .......... 95%  189M 0s\n",
            " 54650K .......... .......... .......... .......... .......... 95%  174M 0s\n",
            " 54700K .......... .......... .......... .......... .......... 95%  165M 0s\n",
            " 54750K .......... .......... .......... .......... .......... 95%  138M 0s\n",
            " 54800K .......... .......... .......... .......... .......... 96%  178M 0s\n",
            " 54850K .......... .......... .......... .......... .......... 96%  164M 0s\n",
            " 54900K .......... .......... .......... .......... .......... 96%  185M 0s\n",
            " 54950K .......... .......... .......... .......... .......... 96%  172M 0s\n",
            " 55000K .......... .......... .......... .......... .......... 96%  176M 0s\n",
            " 55050K .......... .......... .......... .......... .......... 96%  170M 0s\n",
            " 55100K .......... .......... .......... .......... .......... 96%  178M 0s\n",
            " 55150K .......... .......... .......... .......... .......... 96%  154M 0s\n",
            " 55200K .......... .......... .......... .......... .......... 96%  167M 0s\n",
            " 55250K .......... .......... .......... .......... .......... 96%  187M 0s\n",
            " 55300K .......... .......... .......... .......... .......... 96%  178M 0s\n",
            " 55350K .......... .......... .......... .......... .......... 97%  166M 0s\n",
            " 55400K .......... .......... .......... .......... .......... 97%  193M 0s\n",
            " 55450K .......... .......... .......... .......... .......... 97%  195M 0s\n",
            " 55500K .......... .......... .......... .......... .......... 97%  196M 0s\n",
            " 55550K .......... .......... .......... .......... .......... 97%  151M 0s\n",
            " 55600K .......... .......... .......... .......... .......... 97%  200M 0s\n",
            " 55650K .......... .......... .......... .......... .......... 97%  200M 0s\n",
            " 55700K .......... .......... .......... .......... .......... 97%  196M 0s\n",
            " 55750K .......... .......... .......... .......... .......... 97%  158M 0s\n",
            " 55800K .......... .......... .......... .......... .......... 97%  179M 0s\n",
            " 55850K .......... .......... .......... .......... .......... 97%  187M 0s\n",
            " 55900K .......... .......... .......... .......... .......... 97%  155M 0s\n",
            " 55950K .......... .......... .......... .......... .......... 98%  158M 0s\n",
            " 56000K .......... .......... .......... .......... .......... 98%  185M 0s\n",
            " 56050K .......... .......... .......... .......... .......... 98%  183M 0s\n",
            " 56100K .......... .......... .......... .......... .......... 98%  164M 0s\n",
            " 56150K .......... .......... .......... .......... .......... 98%  175M 0s\n",
            " 56200K .......... .......... .......... .......... .......... 98%  196M 0s\n",
            " 56250K .......... .......... .......... .......... .......... 98%  197M 0s\n",
            " 56300K .......... .......... .......... .......... .......... 98%  181M 0s\n",
            " 56350K .......... .......... .......... .......... .......... 98%  158M 0s\n",
            " 56400K .......... .......... .......... .......... .......... 98%  194M 0s\n",
            " 56450K .......... .......... .......... .......... .......... 98%  109M 0s\n",
            " 56500K .......... .......... .......... .......... .......... 99%  166M 0s\n",
            " 56550K .......... .......... .......... .......... .......... 99%  163M 0s\n",
            " 56600K .......... .......... .......... .......... .......... 99%  140M 0s\n",
            " 56650K .......... .......... .......... .......... .......... 99%  177M 0s\n",
            " 56700K .......... .......... .......... .......... .......... 99%  130M 0s\n",
            " 56750K .......... .......... .......... .......... .......... 99%  122M 0s\n",
            " 56800K .......... .......... .......... .......... .......... 99%  136M 0s\n",
            " 56850K .......... .......... .......... .......... .......... 99%  177M 0s\n",
            " 56900K .......... .......... .......... .......... .......... 99% 93.0M 0s\n",
            " 56950K .......... .......... .......... .......... .......... 99%  153M 0s\n",
            " 57000K .......... .......... .......... .......... .......... 99%  180M 0s\n",
            " 57050K .......... .......... .......... .......... ........  100%  179M=0.8s\n",
            "\n",
            "2021-07-02 18:23:43 (73.9 MB/s) - ‘Miniconda3-4.5.4-Linux-x86_64.sh.1’ saved [58468498/58468498]\n",
            "\n",
            "Python 3.6.5 :: Anaconda, Inc.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yf6gne3PPG15",
        "outputId": "dccd45ce-558b-4bc0-875d-5693e6617e69"
      },
      "source": [
        "!which conda # should return /usr/local/bin/conda"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/bin/conda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cadXjCNLPJNW",
        "outputId": "88342ab4-c170-43f7-dcb3-54af129c00fe"
      },
      "source": [
        "!conda --version # should return 4.5.4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "conda 4.5.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvB4oOPwPMCw",
        "outputId": "b344ccd3-866f-42d9-e890-351cb7f56093"
      },
      "source": [
        "!which python # still returns /usr/local/bin/python"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/bin/python\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdn974yaPOf6",
        "outputId": "f32b0eae-85b9-4f18-ec77-965f01f59dfa"
      },
      "source": [
        "!python --version # now returns Python 3.6.5 :: Anaconda, Inc."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 3.6.5 :: Anaconda, Inc.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2DU7uA7OZcs",
        "outputId": "bae6c8da-6406-4418-f7b8-f6d58fa5eda6"
      },
      "source": [
        "%%bash\n",
        "conda install --channel defaults conda python=3.7 --yes\n",
        "conda update --channel defaults --all --yes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs: \n",
            "    - conda\n",
            "    - python=3.7\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    pysocks-1.7.1              |           py37_1          27 KB\n",
            "    conda-4.10.3               |   py37h06a4308_0         3.1 MB\n",
            "    pycosat-0.6.3              |   py37h27cfd23_0         108 KB\n",
            "    setuptools-52.0.0          |   py37h06a4308_0         921 KB\n",
            "    brotlipy-0.7.0             |py37h27cfd23_1003         350 KB\n",
            "    cffi-1.14.5                |   py37h261ae71_0         224 KB\n",
            "    asn1crypto-1.4.0           |             py_0          77 KB\n",
            "    cryptography-3.4.7         |   py37hd23ed53_0         1.0 MB\n",
            "    ruamel_yaml-0.15.87        |   py37h7b6447c_0         271 KB\n",
            "    conda-package-handling-1.7.3|   py37h27cfd23_1         962 KB\n",
            "    python-3.7.10              |       h12debd9_4        51.3 MB\n",
            "    certifi-2021.5.30          |   py37h06a4308_0         141 KB\n",
            "    pip-21.1.3                 |   py37h06a4308_0         2.0 MB\n",
            "    chardet-4.0.0              |py37h06a4308_1003         205 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        60.6 MB\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "    asn1crypto:             0.24.0-py36_0           --> 1.4.0-py_0              \n",
            "    brotlipy:               0.7.0-py36h27cfd23_1003 --> 0.7.0-py37h27cfd23_1003 \n",
            "    ca-certificates:        2018.03.07-0            --> 2021.5.25-h06a4308_1    \n",
            "    certifi:                2018.4.16-py36_0        --> 2021.5.30-py37h06a4308_0\n",
            "    cffi:                   1.11.5-py36h9745a5d_0   --> 1.14.5-py37h261ae71_0   \n",
            "    chardet:                3.0.4-py36h0f667ec_1    --> 4.0.0-py37h06a4308_1003 \n",
            "    conda:                  4.5.4-py36_0            --> 4.10.3-py37h06a4308_0   \n",
            "    conda-package-handling: 1.7.3-py36h27cfd23_1    --> 1.7.3-py37h27cfd23_1    \n",
            "    cryptography:           2.2.2-py36h14c3975_0    --> 3.4.7-py37hd23ed53_0    \n",
            "    idna:                   2.6-py36h82fb2a8_1      --> 2.10-pyhd3eb1b0_0       \n",
            "    libffi:                 3.2.1-hd88cf55_4        --> 3.3-he6710b0_2          \n",
            "    libgcc-ng:              7.2.0-hdf63c60_3        --> 9.1.0-hdf63c60_0        \n",
            "    libstdcxx-ng:           7.2.0-hdf63c60_3        --> 9.1.0-hdf63c60_0        \n",
            "    ncurses:                6.1-hf484d3e_0          --> 6.2-he6710b0_1          \n",
            "    openssl:                1.0.2o-h20670df_0       --> 1.1.1k-h27cfd23_0       \n",
            "    pip:                    10.0.1-py36_0           --> 21.1.3-py37h06a4308_0   \n",
            "    pycosat:                0.6.3-py36h0a5515d_0    --> 0.6.3-py37h27cfd23_0    \n",
            "    pycparser:              2.18-py36hf9f622e_1     --> 2.20-py_2               \n",
            "    pyopenssl:              18.0.0-py36_0           --> 20.0.1-pyhd3eb1b0_1     \n",
            "    pysocks:                1.6.8-py36_0            --> 1.7.1-py37_1            \n",
            "    python:                 3.6.5-hc3d631a_2        --> 3.7.10-h12debd9_4       \n",
            "    readline:               7.0-ha6073c6_4          --> 8.1-h27cfd23_0          \n",
            "    requests:               2.18.4-py36he2e5f8d_1   --> 2.25.1-pyhd3eb1b0_0     \n",
            "    ruamel_yaml:            0.15.37-py36h14c3975_2  --> 0.15.87-py37h7b6447c_0  \n",
            "    setuptools:             39.2.0-py36_0           --> 52.0.0-py37h06a4308_0   \n",
            "    six:                    1.11.0-py36h372c433_1   --> 1.16.0-pyhd3eb1b0_0     \n",
            "    sqlite:                 3.23.1-he433501_0       --> 3.36.0-hc218d9a_0       \n",
            "    tk:                     8.6.7-hc745277_3        --> 8.6.10-hbc83047_0       \n",
            "    tqdm:                   4.61.1-pyhd3eb1b0_1     --> 4.61.1-pyhd3eb1b0_1     \n",
            "    urllib3:                1.22-py36hbe7ace6_0     --> 1.26.6-pyhd3eb1b0_1     \n",
            "    wheel:                  0.31.1-py36_0           --> 0.36.2-pyhd3eb1b0_0     \n",
            "    xz:                     5.2.4-h14c3975_4        --> 5.2.5-h7b6447c_0        \n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ruamel_yaml-0.15.100       |   py37h27cfd23_0         253 KB\n",
            "    yaml-0.2.5                 |       h7b6447c_0          75 KB\n",
            "    zlib-1.2.11                |       h7b6447c_3         103 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         431 KB\n",
            "\n",
            "The following packages will be REMOVED:\n",
            "\n",
            "  asn1crypto-1.4.0-py_0\n",
            "  conda-env-2.6.0-h36134e3_1\n",
            "  libedit-3.1.20170329-h6b74fdf_2\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  libgcc-ng                                9.1.0-hdf63c60_0 --> 9.3.0-h5101ec6_17\n",
            "  libstdcxx-ng                             9.1.0-hdf63c60_0 --> 9.3.0-hd4cf53a_17\n",
            "  ruamel_yaml                        0.15.87-py37h7b6447c_0 --> 0.15.100-py37h27cfd23_0\n",
            "  yaml                                     0.1.7-had09818_2 --> 0.2.5-h7b6447c_0\n",
            "  zlib                                    1.2.11-ha838bed_2 --> 1.2.11-h7b6447c_3\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\rruamel_yaml-0.15.100 | 253 KB    |            |   0% \rruamel_yaml-0.15.100 | 253 KB    | ####4      |  44% \rruamel_yaml-0.15.100 | 253 KB    | ########## | 100% \n",
            "\rzlib-1.2.11          | 103 KB    |            |   0% \rzlib-1.2.11          | 103 KB    | ########## | 100% \n",
            "\ryaml-0.2.5           | 75 KB     |            |   0% \ryaml-0.2.5           | 75 KB     | ########## | 100% \ryaml-0.2.5           | 75 KB     | ########## | 100% \n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rpysocks-1.7.1        |   27 KB |            |   0% \rpysocks-1.7.1        |   27 KB | ########## | 100% \n",
            "\rconda-4.10.3         |  3.1 MB |            |   0% \rconda-4.10.3         |  3.1 MB | ########   |  81% \rconda-4.10.3         |  3.1 MB | ########## | 100% \n",
            "\rpycosat-0.6.3        |  108 KB |            |   0% \rpycosat-0.6.3        |  108 KB | ########## | 100% \n",
            "\rsetuptools-52.0.0    |  921 KB |            |   0% \rsetuptools-52.0.0    |  921 KB | ########   |  80% \rsetuptools-52.0.0    |  921 KB | #########6 |  97% \rsetuptools-52.0.0    |  921 KB | ########## | 100% \n",
            "\rbrotlipy-0.7.0       |  350 KB |            |   0% \rbrotlipy-0.7.0       |  350 KB | ########## | 100% \n",
            "\rcffi-1.14.5          |  224 KB |            |   0% \rcffi-1.14.5          |  224 KB | ########## | 100% \n",
            "\rasn1crypto-1.4.0     |   77 KB |            |   0% \rasn1crypto-1.4.0     |   77 KB | ########## | 100% \n",
            "\rcryptography-3.4.7   |  1.0 MB |            |   0% \rcryptography-3.4.7   |  1.0 MB | #######8   |  78% \rcryptography-3.4.7   |  1.0 MB | #########4 |  94% \rcryptography-3.4.7   |  1.0 MB | ########## | 100% \n",
            "\rruamel_yaml-0.15.87  |  271 KB |            |   0% \rruamel_yaml-0.15.87  |  271 KB | #########6 |  96% \rruamel_yaml-0.15.87  |  271 KB | ########## | 100% \n",
            "\rconda-package-handli |  962 KB |            |   0% \rconda-package-handli |  962 KB | ########7  |  88% \rconda-package-handli |  962 KB | ########## | 100% \n",
            "\rpython-3.7.10        | 51.3 MB |            |   0% \rpython-3.7.10        | 51.3 MB | 4          |   5% \rpython-3.7.10        | 51.3 MB | ##2        |  22% \rpython-3.7.10        | 51.3 MB | ###9       |  40% \rpython-3.7.10        | 51.3 MB | #####6     |  57% \rpython-3.7.10        | 51.3 MB | #######4   |  75% \rpython-3.7.10        | 51.3 MB | ########8  |  88% \rpython-3.7.10        | 51.3 MB | #########7 |  98% \rpython-3.7.10        | 51.3 MB | ########## | 100% \n",
            "\rcertifi-2021.5.30    |  141 KB |            |   0% \rcertifi-2021.5.30    |  141 KB | ########## | 100% \n",
            "\rpip-21.1.3           |  2.0 MB |            |   0% \rpip-21.1.3           |  2.0 MB | #######7   |  77% \rpip-21.1.3           |  2.0 MB | ########## | 100% \n",
            "\rchardet-4.0.0        |  205 KB |            |   0% \rchardet-4.0.0        |  205 KB | ########## | 100% \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_33MoRLPlMQ",
        "outputId": "fce047c5-1400-485b-e2aa-cf245ec9efbd"
      },
      "source": [
        "!conda --version # now returns 4.10.3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "conda 4.10.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpiKesYtPpjC",
        "outputId": "6f4c498c-4fbe-453c-89e4-9290cb0bcde5"
      },
      "source": [
        "!python --version # now returns Python 3.6.13 :: Anaconda, Inc."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 3.7.10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZyOE-LPkPtET",
        "outputId": "433dfb05-5a65-4673-8941-a31ac6aecd53"
      },
      "source": [
        "%%bash\n",
        "conda config --add channels intel"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: 'intel' already in 'channels' list, moving to the top\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwLiHY0KP6EI",
        "outputId": "2f564c9e-c013-48ef-f627-29d23825faec"
      },
      "source": [
        "%%bash\n",
        "conda create -n  idp \n",
        "intelpython3_core python=3.7"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: A conda environment already exists at '/usr/local/envs/idp'\n",
            "Remove existing environment (y/[n])? Invalid choice: intelpython3_core python=3.7\n",
            "WARNING: A conda environment already exists at '/usr/local/envs/idp'\n",
            "Remove existing environment (y/[n])? \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "CondaSystemExit: Exiting.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MczxsbO8QUWn",
        "outputId": "11f76adc-5771-467c-f3e9-153e827f0659"
      },
      "source": [
        "%%bash\n",
        "conda activate idp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.\n",
            "To initialize your shell, run\n",
            "\n",
            "    $ conda init <SHELL_NAME>\n",
            "\n",
            "Currently supported shells are:\n",
            "  - bash\n",
            "  - fish\n",
            "  - tcsh\n",
            "  - xonsh\n",
            "  - zsh\n",
            "  - powershell\n",
            "\n",
            "See 'conda init --help' for more information and options.\n",
            "\n",
            "IMPORTANT: You may need to close and restart your shell after running 'conda init'.\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6atWvdJSieR"
      },
      "source": [
        "> Conda version --> 4.10.3 (up to date)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6X2SGVrkQrXp",
        "outputId": "16329f46-af94-48f1-a53c-5bd92783e717"
      },
      "source": [
        "!conda --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "conda 4.10.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYolXIROSnsK"
      },
      "source": [
        "> Python version --> 3.7.10 (up to date)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltOiFGLGeIWY",
        "outputId": "48c8e040-c46d-40f6-e7e4-ef6958cb0b7b"
      },
      "source": [
        "!python --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 3.7.10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJMEAf68SwjM"
      },
      "source": [
        "> List of packages in conda environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGXBYu5NQywl",
        "outputId": "c438b616-631b-4b8b-d404-000c22567cd7"
      },
      "source": [
        "! conda list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# packages in environment at /usr/local:\n",
            "#\n",
            "# Name                    Version                   Build  Channel\n",
            "_libgcc_mutex             0.1                        main  \n",
            "_openmp_mutex             4.5                       1_gnu  \n",
            "brotlipy                  0.7.0           py37h27cfd23_1003  \n",
            "ca-certificates           2021.5.25            h06a4308_1  \n",
            "certifi                   2021.5.30        py37h06a4308_0  \n",
            "cffi                      1.14.5           py37h261ae71_0  \n",
            "chardet                   4.0.0           py37h06a4308_1003  \n",
            "conda                     4.10.3           py37h06a4308_0  \n",
            "conda-package-handling    1.7.3            py37h27cfd23_1  \n",
            "cryptography              3.4.7            py37hd23ed53_0  \n",
            "idna                      2.10               pyhd3eb1b0_0  \n",
            "ld_impl_linux-64          2.35.1               h7274673_9  \n",
            "libffi                    3.3                  he6710b0_2  \n",
            "libgcc-ng                 9.3.0               h5101ec6_17  \n",
            "libgomp                   9.3.0               h5101ec6_17  \n",
            "libstdcxx-ng              9.3.0               hd4cf53a_17  \n",
            "ncurses                   6.2                  he6710b0_1  \n",
            "openssl                   1.1.1k               h27cfd23_0  \n",
            "pip                       21.1.3           py37h06a4308_0  \n",
            "pycosat                   0.6.3            py37h27cfd23_0  \n",
            "pycparser                 2.20                       py_2  \n",
            "pyopenssl                 20.0.1             pyhd3eb1b0_1  \n",
            "pysocks                   1.7.1                    py37_1  \n",
            "python                    3.7.10               h12debd9_4  \n",
            "readline                  8.1                  h27cfd23_0  \n",
            "requests                  2.25.1             pyhd3eb1b0_0  \n",
            "ruamel_yaml               0.15.100         py37h27cfd23_0  \n",
            "setuptools                52.0.0           py37h06a4308_0  \n",
            "six                       1.16.0             pyhd3eb1b0_0  \n",
            "sqlite                    3.36.0               hc218d9a_0  \n",
            "tk                        8.6.10               hbc83047_0  \n",
            "tqdm                      4.61.1             pyhd3eb1b0_1  \n",
            "urllib3                   1.26.6             pyhd3eb1b0_1  \n",
            "wheel                     0.36.2             pyhd3eb1b0_0  \n",
            "xz                        5.2.5                h7b6447c_0  \n",
            "yaml                      0.2.5                h7b6447c_0  \n",
            "zlib                      1.2.11               h7b6447c_3  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cP5tIH60RVJe"
      },
      "source": [
        "##**Vehicle Loan Prediction Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYKUwSEgc49g"
      },
      "source": [
        "**Problem Statement:**<br>\n",
        "Financial institutions incur significant losses due to the default of vehicle loans. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by these institutions. This warrants a study to estimate the determinants of vehicle loan default. A financial institution has hired you to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date. Following Information regarding the loan and loanee are provided in the datasets: Loanee Information (Demographic data like age, Identity proof etc.) Loan Information (Disbursal details, loan to value ratio etc.) Bureau data & history (Bureau score, number of active accounts, the status of other loans, credit history etc.) Doing so will ensure that clients capable of repayment are not rejected and important determinants can be identified which can be further used for minimising the default rates.\n",
        "<br><br>\n",
        "**Proposed Solution:**<br>\n",
        "1. Deal with null values by filling them using some value\n",
        "2. Removing outliers in data\n",
        "3. Using binning by depth method for numerical data\n",
        "4. Data resampling to deal with data imbalance\n",
        "5. Feature selection to prune highly correlated data\n",
        "6. Create a classifier using Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcqtYfuCRnOj",
        "outputId": "4e4ada32-bb8c-449a-b2b5-23dcc167f0c5"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zAeaPSV_NiEU"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, auc, roc_curve, roc_auc_score, balanced_accuracy_score, classification_report\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.utils import resample\n",
        "from sklearn.metrics import mean_absolute_error, r2_score, log_loss, jaccard_score, mean_squared_error, mean_squared_log_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmwg1rGGSC-f"
      },
      "source": [
        "dt=datetime.today()\n",
        "\n",
        "\n",
        "def removeOutlier(dataSet, feature):    \n",
        "    q1=dataSet[feature].quantile(0.25)\n",
        "    q3=dataSet[feature].quantile(0.75)\n",
        "    IQR=q3-q1\n",
        "    lowerLimit = q1 - 1.5 * IQR\n",
        "    UpperLimit = q3 + 1.5 * IQR \n",
        "    dataSet = dataSet[dataSet[feature]< UpperLimit]\n",
        "    dataSet = dataSet[dataSet[feature]> lowerLimit]\n",
        "    return dataSet\n",
        "\n",
        "def modelEvaluation(model, X_test, y_train,y_test, y_pred,cols=None):\n",
        "    print(\"Accurancy: {:.3f}\".format(accuracy_score(y_test, y_pred)))\n",
        "    print(\"ROC AUC Score: {:.3f}\".format(roc_auc_score(y_test, y_pred)))\n",
        "    print(\"F1 Score:: {:.3f} \".format(f1_score(y_test, y_pred)))\n",
        "    print(\"Balanced Accurancy Score:: {:.3f} \".format(balanced_accuracy_score(y_test, y_pred)))\n",
        "    print('\\n clasification report:\\n', classification_report(y_test,y_pred))\n",
        "    fig = plt.figure(figsize=(10,6))\n",
        "    ax = fig.add_subplot(111)\n",
        "    prediction_probabilities = model.predict_proba(X_test[cols])[:,1]\n",
        "    fpr , tpr , thresholds = roc_curve(y_test,prediction_probabilities)\n",
        "    ax.plot(fpr,tpr,label = [\"Area under curve : \",auc(fpr,tpr)],linewidth=2,linestyle=\"dotted\")\n",
        "    ax.plot([0,1],[0,1],linewidth=2,linestyle=\"dashed\")\n",
        "    plt.legend(loc=\"best\")\n",
        "    plt.title(\"ROC-CURVE and AREA UNDER CURVE\")\n",
        "    #ax.set_facecolor(\"k\")\n",
        "    \n",
        "def calculate_age(row):\n",
        "    try:\n",
        "        yrs, mon = tuple(row['AVERAGE.ACCT.AGE'].split(' '))\n",
        "        age = round(float(yrs.replace('yrs', '')) + float(mon.replace('mon', ''))/12, 2)\n",
        "        row['AVERAGE.ACCT.AGE'] = age\n",
        "\n",
        "        yrs, mon = tuple(row['CREDIT.HISTORY.LENGTH'].split(' '))\n",
        "        age = round(float(yrs.replace('yrs', '')) + float(mon.replace('mon', ''))/12, 2)\n",
        "        row['CREDIT.HISTORY.LENGTH'] = age\n",
        "    except Exception as e:\n",
        "        print(row, e)\n",
        "        raise e\n",
        "    return row\n",
        "\n",
        "\n",
        "def binning_by_depth_factor(df_column, factor):\n",
        "    divs, max_da, min_da= round(np.sqrt(len(df_column))/factor), df_column.max(), df_column.min()\n",
        "    step = (max_da - min_da)/divs\n",
        "    return (df_column/step).astype(int)*divs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "HHhSMZaNR9-p",
        "outputId": "2c18481d-ddf1-4e21-8880-52c20332bd97"
      },
      "source": [
        "loan_df=pd.read_csv('/content/gdrive/My Drive/train.csv') # dataset provided for the assessment\n",
        "loan_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UniqueID</th>\n",
              "      <th>disbursed_amount</th>\n",
              "      <th>asset_cost</th>\n",
              "      <th>ltv</th>\n",
              "      <th>branch_id</th>\n",
              "      <th>supplier_id</th>\n",
              "      <th>manufacturer_id</th>\n",
              "      <th>Current_pincode_ID</th>\n",
              "      <th>Date.of.Birth</th>\n",
              "      <th>Employment.Type</th>\n",
              "      <th>DisbursalDate</th>\n",
              "      <th>State_ID</th>\n",
              "      <th>Employee_code_ID</th>\n",
              "      <th>MobileNo_Avl_Flag</th>\n",
              "      <th>Aadhar_flag</th>\n",
              "      <th>PAN_flag</th>\n",
              "      <th>VoterID_flag</th>\n",
              "      <th>Driving_flag</th>\n",
              "      <th>Passport_flag</th>\n",
              "      <th>PERFORM_CNS.SCORE</th>\n",
              "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
              "      <th>PRI.NO.OF.ACCTS</th>\n",
              "      <th>PRI.ACTIVE.ACCTS</th>\n",
              "      <th>PRI.OVERDUE.ACCTS</th>\n",
              "      <th>PRI.CURRENT.BALANCE</th>\n",
              "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
              "      <th>PRI.DISBURSED.AMOUNT</th>\n",
              "      <th>SEC.NO.OF.ACCTS</th>\n",
              "      <th>SEC.ACTIVE.ACCTS</th>\n",
              "      <th>SEC.OVERDUE.ACCTS</th>\n",
              "      <th>SEC.CURRENT.BALANCE</th>\n",
              "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
              "      <th>SEC.DISBURSED.AMOUNT</th>\n",
              "      <th>PRIMARY.INSTAL.AMT</th>\n",
              "      <th>SEC.INSTAL.AMT</th>\n",
              "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
              "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
              "      <th>AVERAGE.ACCT.AGE</th>\n",
              "      <th>CREDIT.HISTORY.LENGTH</th>\n",
              "      <th>NO.OF_INQUIRIES</th>\n",
              "      <th>loan_default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>420825</td>\n",
              "      <td>50578</td>\n",
              "      <td>58400</td>\n",
              "      <td>89.55</td>\n",
              "      <td>67</td>\n",
              "      <td>22807</td>\n",
              "      <td>45</td>\n",
              "      <td>1441</td>\n",
              "      <td>01-01-84</td>\n",
              "      <td>Salaried</td>\n",
              "      <td>03-08-18</td>\n",
              "      <td>6</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Bureau History Available</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0yrs 0mon</td>\n",
              "      <td>0yrs 0mon</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>537409</td>\n",
              "      <td>47145</td>\n",
              "      <td>65550</td>\n",
              "      <td>73.23</td>\n",
              "      <td>67</td>\n",
              "      <td>22807</td>\n",
              "      <td>45</td>\n",
              "      <td>1502</td>\n",
              "      <td>31-07-85</td>\n",
              "      <td>Self employed</td>\n",
              "      <td>26-09-18</td>\n",
              "      <td>6</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>598</td>\n",
              "      <td>I-Medium Risk</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>27600</td>\n",
              "      <td>50200</td>\n",
              "      <td>50200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1991</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1yrs 11mon</td>\n",
              "      <td>1yrs 11mon</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>417566</td>\n",
              "      <td>53278</td>\n",
              "      <td>61360</td>\n",
              "      <td>89.63</td>\n",
              "      <td>67</td>\n",
              "      <td>22807</td>\n",
              "      <td>45</td>\n",
              "      <td>1497</td>\n",
              "      <td>24-08-85</td>\n",
              "      <td>Self employed</td>\n",
              "      <td>01-08-18</td>\n",
              "      <td>6</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Bureau History Available</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0yrs 0mon</td>\n",
              "      <td>0yrs 0mon</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>624493</td>\n",
              "      <td>57513</td>\n",
              "      <td>66113</td>\n",
              "      <td>88.48</td>\n",
              "      <td>67</td>\n",
              "      <td>22807</td>\n",
              "      <td>45</td>\n",
              "      <td>1501</td>\n",
              "      <td>30-12-93</td>\n",
              "      <td>Self employed</td>\n",
              "      <td>26-10-18</td>\n",
              "      <td>6</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>305</td>\n",
              "      <td>L-Very High Risk</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0yrs 8mon</td>\n",
              "      <td>1yrs 3mon</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>539055</td>\n",
              "      <td>52378</td>\n",
              "      <td>60300</td>\n",
              "      <td>88.39</td>\n",
              "      <td>67</td>\n",
              "      <td>22807</td>\n",
              "      <td>45</td>\n",
              "      <td>1495</td>\n",
              "      <td>09-12-77</td>\n",
              "      <td>Self employed</td>\n",
              "      <td>26-09-18</td>\n",
              "      <td>6</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Bureau History Available</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0yrs 0mon</td>\n",
              "      <td>0yrs 0mon</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   UniqueID  disbursed_amount  ...  NO.OF_INQUIRIES  loan_default\n",
              "0    420825             50578  ...                0             0\n",
              "1    537409             47145  ...                0             1\n",
              "2    417566             53278  ...                0             0\n",
              "3    624493             57513  ...                1             1\n",
              "4    539055             52378  ...                1             1\n",
              "\n",
              "[5 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SeXzqn72NiEk"
      },
      "source": [
        "### **Data Exploration**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "5Eg_pIrwNiEo",
        "outputId": "b7da7afc-8b1b-49b1-a938-f08af1719d40"
      },
      "source": [
        "loan_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UniqueID</th>\n",
              "      <th>disbursed_amount</th>\n",
              "      <th>asset_cost</th>\n",
              "      <th>ltv</th>\n",
              "      <th>branch_id</th>\n",
              "      <th>supplier_id</th>\n",
              "      <th>manufacturer_id</th>\n",
              "      <th>Current_pincode_ID</th>\n",
              "      <th>State_ID</th>\n",
              "      <th>Employee_code_ID</th>\n",
              "      <th>MobileNo_Avl_Flag</th>\n",
              "      <th>Aadhar_flag</th>\n",
              "      <th>PAN_flag</th>\n",
              "      <th>VoterID_flag</th>\n",
              "      <th>Driving_flag</th>\n",
              "      <th>Passport_flag</th>\n",
              "      <th>PERFORM_CNS.SCORE</th>\n",
              "      <th>PRI.NO.OF.ACCTS</th>\n",
              "      <th>PRI.ACTIVE.ACCTS</th>\n",
              "      <th>PRI.OVERDUE.ACCTS</th>\n",
              "      <th>PRI.CURRENT.BALANCE</th>\n",
              "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
              "      <th>PRI.DISBURSED.AMOUNT</th>\n",
              "      <th>SEC.NO.OF.ACCTS</th>\n",
              "      <th>SEC.ACTIVE.ACCTS</th>\n",
              "      <th>SEC.OVERDUE.ACCTS</th>\n",
              "      <th>SEC.CURRENT.BALANCE</th>\n",
              "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
              "      <th>SEC.DISBURSED.AMOUNT</th>\n",
              "      <th>PRIMARY.INSTAL.AMT</th>\n",
              "      <th>SEC.INSTAL.AMT</th>\n",
              "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
              "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
              "      <th>NO.OF_INQUIRIES</th>\n",
              "      <th>loan_default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.0</td>\n",
              "      <td>233154.00000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>2.331540e+05</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "      <td>233154.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>535917.573376</td>\n",
              "      <td>54356.993528</td>\n",
              "      <td>7.586507e+04</td>\n",
              "      <td>74.746530</td>\n",
              "      <td>72.936094</td>\n",
              "      <td>19638.635035</td>\n",
              "      <td>69.028054</td>\n",
              "      <td>3396.880247</td>\n",
              "      <td>7.262243</td>\n",
              "      <td>1549.477148</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.84032</td>\n",
              "      <td>0.075577</td>\n",
              "      <td>0.144943</td>\n",
              "      <td>0.023242</td>\n",
              "      <td>0.002127</td>\n",
              "      <td>289.462994</td>\n",
              "      <td>2.440636</td>\n",
              "      <td>1.039896</td>\n",
              "      <td>0.156549</td>\n",
              "      <td>1.659001e+05</td>\n",
              "      <td>2.185039e+05</td>\n",
              "      <td>2.180659e+05</td>\n",
              "      <td>0.059081</td>\n",
              "      <td>0.027703</td>\n",
              "      <td>0.007244</td>\n",
              "      <td>5.427793e+03</td>\n",
              "      <td>7.295923e+03</td>\n",
              "      <td>7.179998e+03</td>\n",
              "      <td>1.310548e+04</td>\n",
              "      <td>3.232684e+02</td>\n",
              "      <td>0.381833</td>\n",
              "      <td>0.097481</td>\n",
              "      <td>0.206615</td>\n",
              "      <td>0.217071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>68315.693711</td>\n",
              "      <td>12971.314171</td>\n",
              "      <td>1.894478e+04</td>\n",
              "      <td>11.456636</td>\n",
              "      <td>69.834995</td>\n",
              "      <td>3491.949566</td>\n",
              "      <td>22.141304</td>\n",
              "      <td>2238.147502</td>\n",
              "      <td>4.482230</td>\n",
              "      <td>975.261278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.36631</td>\n",
              "      <td>0.264320</td>\n",
              "      <td>0.352044</td>\n",
              "      <td>0.150672</td>\n",
              "      <td>0.046074</td>\n",
              "      <td>338.374779</td>\n",
              "      <td>5.217233</td>\n",
              "      <td>1.941496</td>\n",
              "      <td>0.548787</td>\n",
              "      <td>9.422736e+05</td>\n",
              "      <td>2.374794e+06</td>\n",
              "      <td>2.377744e+06</td>\n",
              "      <td>0.626795</td>\n",
              "      <td>0.316057</td>\n",
              "      <td>0.111079</td>\n",
              "      <td>1.702370e+05</td>\n",
              "      <td>1.831560e+05</td>\n",
              "      <td>1.825925e+05</td>\n",
              "      <td>1.513679e+05</td>\n",
              "      <td>1.555369e+04</td>\n",
              "      <td>0.955107</td>\n",
              "      <td>0.384439</td>\n",
              "      <td>0.706498</td>\n",
              "      <td>0.412252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>417428.000000</td>\n",
              "      <td>13320.000000</td>\n",
              "      <td>3.700000e+04</td>\n",
              "      <td>10.030000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10524.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.678296e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.746470e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>476786.250000</td>\n",
              "      <td>47145.000000</td>\n",
              "      <td>6.571700e+04</td>\n",
              "      <td>68.880000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>16535.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>1511.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>713.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>535978.500000</td>\n",
              "      <td>53803.000000</td>\n",
              "      <td>7.094600e+04</td>\n",
              "      <td>76.800000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>20333.000000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>2970.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1451.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>595039.750000</td>\n",
              "      <td>60413.000000</td>\n",
              "      <td>7.920175e+04</td>\n",
              "      <td>83.670000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>23000.000000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>5677.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2362.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.500650e+04</td>\n",
              "      <td>6.250000e+04</td>\n",
              "      <td>6.080000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.999000e+03</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>671084.000000</td>\n",
              "      <td>990572.000000</td>\n",
              "      <td>1.628992e+06</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>261.000000</td>\n",
              "      <td>24803.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>7345.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>3795.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>890.000000</td>\n",
              "      <td>453.000000</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>9.652492e+07</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.603285e+07</td>\n",
              "      <td>3.000000e+07</td>\n",
              "      <td>3.000000e+07</td>\n",
              "      <td>2.564281e+07</td>\n",
              "      <td>4.170901e+06</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            UniqueID  disbursed_amount  ...  NO.OF_INQUIRIES   loan_default\n",
              "count  233154.000000     233154.000000  ...    233154.000000  233154.000000\n",
              "mean   535917.573376      54356.993528  ...         0.206615       0.217071\n",
              "std     68315.693711      12971.314171  ...         0.706498       0.412252\n",
              "min    417428.000000      13320.000000  ...         0.000000       0.000000\n",
              "25%    476786.250000      47145.000000  ...         0.000000       0.000000\n",
              "50%    535978.500000      53803.000000  ...         0.000000       0.000000\n",
              "75%    595039.750000      60413.000000  ...         0.000000       0.000000\n",
              "max    671084.000000     990572.000000  ...        36.000000       1.000000\n",
              "\n",
              "[8 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "S7EyJQvLNiEp",
        "outputId": "6328cf82-fb22-4c5a-ff6d-a2a820d3dcc9"
      },
      "source": [
        "loan_df['loan_default'].astype(bool).value_counts().plot.pie()\n",
        "loan_df['loan_default'].astype(bool).value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    182543\n",
              "True      50611\n",
              "Name: loan_default, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oD7R6kJkNiEq"
      },
      "source": [
        "> The pie chart of the target class shows the data is skewed towards False (0) \n",
        ">\n",
        "> Oversampling has been applied to True (1) to balance the data distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10dOOm7DNiEr",
        "outputId": "0bff9294-353d-44f8-d6bb-6ee6d2b36dc4"
      },
      "source": [
        "print('Before Resampling:')\n",
        "print(loan_df['loan_default'].value_counts())\n",
        "df_majority = loan_df[loan_df['loan_default']==0]\n",
        "df_minority = loan_df[loan_df['loan_default']==1]\n",
        "\n",
        "\n",
        "df_minority_upsampled = resample(df_minority, \n",
        "                                 replace=True,                  # sample with replacement\n",
        "                                 n_samples=len(df_majority),    # to match majority class\n",
        "                                 random_state=123)              # reproducible results\n",
        "df_upsampled = pd.concat([df_majority, df_minority_upsampled])\n",
        "print('After Resampling:')\n",
        "print(df_upsampled['loan_default'].value_counts())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before Resampling:\n",
            "0    182543\n",
            "1     50611\n",
            "Name: loan_default, dtype: int64\n",
            "After Resampling:\n",
            "1    182543\n",
            "0    182543\n",
            "Name: loan_default, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "33pHczv1NiEs",
        "outputId": "c43e93e6-2768-48b8-ecf5-6b9743cb4145"
      },
      "source": [
        "flags = ['MobileNo_Avl_Flag', 'Aadhar_flag', 'PAN_flag', 'VoterID_flag', 'Driving_flag', 'Passport_flag']\n",
        "fig, axs = plt.subplots(len(flags), 2, figsize=(10, 10))\n",
        "\n",
        "for _i, flag in enumerate(flags):\n",
        "    for i, (vals, group) in enumerate(loan_df.groupby([flag])):\n",
        "        group.groupby(['loan_default']).size().plot.pie(ax=axs[_i][i])\n",
        "        axs[_i][i].set_title(str(flag)+' '+str(vals))\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "DwDlJTAzNiEt",
        "outputId": "52009d37-0da3-44a4-cc7f-bef23deb91c4"
      },
      "source": [
        "cols = ['disbursed_amount', 'asset_cost', 'ltv']\n",
        "fig, axs = plt.subplots(len(cols), figsize=(10, 5*len(cols)))\n",
        "\n",
        "for j, col in enumerate(cols):\n",
        "    for i, (val, group) in enumerate(loan_df.groupby('loan_default')):\n",
        "        column = binning_by_depth_factor(group[col], 10) \n",
        "        column.value_counts().to_frame('counts').reset_index().sort_values('index').plot(x='index', y='counts', label=val, ax=axs[j])\n",
        "        axs[j].set_xlabel(col)\n",
        "        axs[j].set_ylabel('counts')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eu7eAU4SNiEv"
      },
      "source": [
        "> Disbursed amount and asset cost in both both classes showing gaussian distribution but with different distribution frequencies. \n",
        ">\n",
        "> This shows that the defaulters are defaulting mostly in lesser amounts of loan and for lesser asset costs.\n",
        ">\n",
        "> Additionally as expected, lower ltv value customers are also defaulting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0RQLqDBffgJ",
        "outputId": "187de133-1c54-4026-daa4-e2c54761b005"
      },
      "source": [
        "dt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2021, 7, 3, 11, 22, 55, 175076)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "3BhVpjgxgUFn",
        "outputId": "d83c5ed4-7122-40ed-f8b8-652e8cf71cee"
      },
      "source": [
        "loan_df[['Date.of.Birth','DisbursalDate']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date.of.Birth</th>\n",
              "      <th>DisbursalDate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01-01-84</td>\n",
              "      <td>03-08-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31-07-85</td>\n",
              "      <td>26-09-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>24-08-85</td>\n",
              "      <td>01-08-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30-12-93</td>\n",
              "      <td>26-10-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>09-12-77</td>\n",
              "      <td>26-09-18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Date.of.Birth DisbursalDate\n",
              "0      01-01-84      03-08-18\n",
              "1      31-07-85      26-09-18\n",
              "2      24-08-85      01-08-18\n",
              "3      30-12-93      26-10-18\n",
              "4      09-12-77      26-09-18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "2WfT6DWzfWHP",
        "outputId": "a3f0d130-d0dc-49fa-ab78-fd6e7e4956c0"
      },
      "source": [
        "loan_df['DOB'] =  pd.to_datetime(loan_df['Date.of.Birth'], format='%d-%m-%y')\n",
        "loan_df['DD'] =  pd.to_datetime(loan_df['DisbursalDate'], format='%d-%m-%y')\n",
        "loan_df[['DOB','DD']].head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DOB</th>\n",
              "      <th>DD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1984-01-01</td>\n",
              "      <td>2018-08-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1985-07-31</td>\n",
              "      <td>2018-09-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1985-08-24</td>\n",
              "      <td>2018-08-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1993-12-30</td>\n",
              "      <td>2018-10-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1977-12-09</td>\n",
              "      <td>2018-09-26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DOB         DD\n",
              "0 1984-01-01 2018-08-03\n",
              "1 1985-07-31 2018-09-26\n",
              "2 1985-08-24 2018-08-01\n",
              "3 1993-12-30 2018-10-26\n",
              "4 1977-12-09 2018-09-26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTFqByVdhxpY",
        "outputId": "f9d4e4eb-264c-4e68-f48a-d65620fab756"
      },
      "source": [
        "dt=datetime.today()\n",
        "dt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2021, 7, 3, 11, 23, 13, 463798)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "PdFgwRrDfvTR",
        "outputId": "09488e95-c2d2-4176-e5f4-65871f898082"
      },
      "source": [
        "loan_df = pd.DataFrame(loan_df[~loan_df['DOB'].isna()])\n",
        "loan_df = pd.DataFrame(loan_df[~loan_df['DD'].isna()])\n",
        "loan_df['APPLICANT_AGE'] = ((dt - loan_df['DOB']).apply(lambda x: float(x.days)) / 365.0)\n",
        "loan_df['DISBURSAL_AGE'] = ((dt - loan_df['DD']).apply(lambda x: float(x.days)) / 365.0)\n",
        "\n",
        "cols = ['APPLICANT_AGE', 'DISBURSAL_AGE']\n",
        "\n",
        "for j, col in enumerate(cols):\n",
        "    fig, axs = plt.subplots(1, len(cols), figsize=(20, 5), sharey=True)\n",
        "    for i, (val, group) in enumerate(loan_df.groupby('loan_default')):\n",
        "        group[col].plot.box( ax=axs[i], label=val)\n",
        "        axs[i].set_title(col)\n",
        "        axs[i].set_xlabel(\"Loan Default: %s\"%bool(val))\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5a2RLOyNiEx"
      },
      "source": [
        "> As seen from the box plots, Applicant Age seems to have almost no impact on the defaulting, similar behaviour is seen in Disbursal Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "28OrWtemNiEy",
        "outputId": "a2b487e3-d093-463a-f5b3-debc61788364"
      },
      "source": [
        "loan_df.groupby('PERFORM_CNS.SCORE.DESCRIPTION').agg({'PERFORM_CNS.SCORE':[np.min, np.max]}).sort_values(('PERFORM_CNS.SCORE', 'amin'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">PERFORM_CNS.SCORE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>amin</th>\n",
              "      <th>amax</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No Bureau History Available</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not Scored: More than 50 active Accounts found</th>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not Scored: Only a Guarantor</th>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not Scored: Sufficient History Not Available</th>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not Scored: No Activity seen on the customer (Inactive)</th>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not Scored: Not Enough Info available on the customer</th>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not Scored: No Updates available in last 36 months</th>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M-Very High Risk</th>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L-Very High Risk</th>\n",
              "      <td>301</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K-High Risk</th>\n",
              "      <td>351</td>\n",
              "      <td>520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>J-High Risk</th>\n",
              "      <td>521</td>\n",
              "      <td>570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>I-Medium Risk</th>\n",
              "      <td>571</td>\n",
              "      <td>600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H-Medium Risk</th>\n",
              "      <td>601</td>\n",
              "      <td>630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G-Low Risk</th>\n",
              "      <td>631</td>\n",
              "      <td>650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F-Low Risk</th>\n",
              "      <td>651</td>\n",
              "      <td>680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E-Low Risk</th>\n",
              "      <td>681</td>\n",
              "      <td>705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D-Very Low Risk</th>\n",
              "      <td>706</td>\n",
              "      <td>735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C-Very Low Risk</th>\n",
              "      <td>736</td>\n",
              "      <td>760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B-Very Low Risk</th>\n",
              "      <td>761</td>\n",
              "      <td>805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A-Very Low Risk</th>\n",
              "      <td>806</td>\n",
              "      <td>890</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   PERFORM_CNS.SCORE     \n",
              "                                                                amin amax\n",
              "PERFORM_CNS.SCORE.DESCRIPTION                                            \n",
              "No Bureau History Available                                        0    0\n",
              "Not Scored: More than 50 active Accounts found                    11   11\n",
              "Not Scored: Only a Guarantor                                      14   14\n",
              "Not Scored: Sufficient History Not Available                      15   15\n",
              "Not Scored: No Activity seen on the customer (I...                16   16\n",
              "Not Scored: Not Enough Info available on the cu...                17   17\n",
              "Not Scored: No Updates available in last 36 months                18   18\n",
              "M-Very High Risk                                                 300  300\n",
              "L-Very High Risk                                                 301  350\n",
              "K-High Risk                                                      351  520\n",
              "J-High Risk                                                      521  570\n",
              "I-Medium Risk                                                    571  600\n",
              "H-Medium Risk                                                    601  630\n",
              "G-Low Risk                                                       631  650\n",
              "F-Low Risk                                                       651  680\n",
              "E-Low Risk                                                       681  705\n",
              "D-Very Low Risk                                                  706  735\n",
              "C-Very Low Risk                                                  736  760\n",
              "B-Very Low Risk                                                  761  805\n",
              "A-Very Low Risk                                                  806  890"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "navDr1tQNiEy"
      },
      "source": [
        "> PERFORM_CNS.SCORE.DESCRIPTION is a way of labelling values for PERFORM_CNS.SCORE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzu63c54NiEy"
      },
      "source": [
        "###**Data Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGxjleQvNiEz",
        "outputId": "d08f1fce-fd75-467c-ab62-d3681915947c"
      },
      "source": [
        "loan_df = loan_df[~loan_df['loan_default'].isna()]\n",
        "\n",
        "total_records = len(loan_df)\n",
        "\n",
        "analysis = []\n",
        "\n",
        "loan_df = loan_df.apply(calculate_age, axis=1)\n",
        "loan_df['AVERAGE.ACCT.AGE'] = loan_df['AVERAGE.ACCT.AGE'].astype(float)\n",
        "loan_df['CREDIT.HISTORY.LENGTH'] = loan_df['CREDIT.HISTORY.LENGTH'].astype(float)\n",
        "\n",
        "loan_df['Aadhar_flag'] = loan_df['Aadhar_flag'].astype(bool)\n",
        "loan_df['PAN_flag'] = loan_df['PAN_flag'].astype(bool)\n",
        "loan_df['VoterID_flag'] = loan_df['VoterID_flag'].astype(bool)\n",
        "loan_df['Driving_flag'] = loan_df['Driving_flag'].astype(bool)\n",
        "loan_df['Passport_flag'] = loan_df['Passport_flag'].astype(bool)\n",
        "\n",
        "loan_df['Date.of.Birth'] = pd.to_datetime(loan_df['Date.of.Birth'])\n",
        "loan_df['DisbursalDate'] = pd.to_datetime(loan_df['DisbursalDate'])\n",
        "\n",
        "loan_df['APPLICANT_AGE'] = ((dt - loan_df['Date.of.Birth']) / 365).apply(lambda x: float(x.days))\n",
        "loan_df['DISBURSAL_AGE'] = ((dt - loan_df['DisbursalDate']) / 365).apply(lambda x: float(x.days))\n",
        "\n",
        "loan_df = loan_df.drop('Date.of.Birth', axis=1)\n",
        "loan_df = loan_df.drop('DisbursalDate', axis=1)\n",
        "loan_df = loan_df.drop('PERFORM_CNS.SCORE.DESCRIPTION', axis=1)\n",
        "loan_df = loan_df.drop('MobileNo_Avl_Flag', axis=1)\n",
        "loan_df = loan_df.drop('UniqueID', axis=1)\n",
        "\n",
        "loan_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 233154 entries, 0 to 233153\n",
            "Data columns (total 40 columns):\n",
            " #   Column                               Non-Null Count   Dtype         \n",
            "---  ------                               --------------   -----         \n",
            " 0   disbursed_amount                     233154 non-null  int64         \n",
            " 1   asset_cost                           233154 non-null  int64         \n",
            " 2   ltv                                  233154 non-null  float64       \n",
            " 3   branch_id                            233154 non-null  int64         \n",
            " 4   supplier_id                          233154 non-null  int64         \n",
            " 5   manufacturer_id                      233154 non-null  int64         \n",
            " 6   Current_pincode_ID                   233154 non-null  int64         \n",
            " 7   Employment.Type                      225493 non-null  object        \n",
            " 8   State_ID                             233154 non-null  int64         \n",
            " 9   Employee_code_ID                     233154 non-null  int64         \n",
            " 10  Aadhar_flag                          233154 non-null  bool          \n",
            " 11  PAN_flag                             233154 non-null  bool          \n",
            " 12  VoterID_flag                         233154 non-null  bool          \n",
            " 13  Driving_flag                         233154 non-null  bool          \n",
            " 14  Passport_flag                        233154 non-null  bool          \n",
            " 15  PERFORM_CNS.SCORE                    233154 non-null  int64         \n",
            " 16  PRI.NO.OF.ACCTS                      233154 non-null  int64         \n",
            " 17  PRI.ACTIVE.ACCTS                     233154 non-null  int64         \n",
            " 18  PRI.OVERDUE.ACCTS                    233154 non-null  int64         \n",
            " 19  PRI.CURRENT.BALANCE                  233154 non-null  int64         \n",
            " 20  PRI.SANCTIONED.AMOUNT                233154 non-null  int64         \n",
            " 21  PRI.DISBURSED.AMOUNT                 233154 non-null  int64         \n",
            " 22  SEC.NO.OF.ACCTS                      233154 non-null  int64         \n",
            " 23  SEC.ACTIVE.ACCTS                     233154 non-null  int64         \n",
            " 24  SEC.OVERDUE.ACCTS                    233154 non-null  int64         \n",
            " 25  SEC.CURRENT.BALANCE                  233154 non-null  int64         \n",
            " 26  SEC.SANCTIONED.AMOUNT                233154 non-null  int64         \n",
            " 27  SEC.DISBURSED.AMOUNT                 233154 non-null  int64         \n",
            " 28  PRIMARY.INSTAL.AMT                   233154 non-null  int64         \n",
            " 29  SEC.INSTAL.AMT                       233154 non-null  int64         \n",
            " 30  NEW.ACCTS.IN.LAST.SIX.MONTHS         233154 non-null  int64         \n",
            " 31  DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  233154 non-null  int64         \n",
            " 32  AVERAGE.ACCT.AGE                     233154 non-null  float64       \n",
            " 33  CREDIT.HISTORY.LENGTH                233154 non-null  float64       \n",
            " 34  NO.OF_INQUIRIES                      233154 non-null  int64         \n",
            " 35  loan_default                         233154 non-null  int64         \n",
            " 36  DOB                                  233154 non-null  datetime64[ns]\n",
            " 37  DD                                   233154 non-null  datetime64[ns]\n",
            " 38  APPLICANT_AGE                        233154 non-null  float64       \n",
            " 39  DISBURSAL_AGE                        233154 non-null  float64       \n",
            "dtypes: bool(5), datetime64[ns](2), float64(5), int64(27), object(1)\n",
            "memory usage: 65.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UgjTgofRNiEz"
      },
      "source": [
        "#### **Handling Outliers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SR1Le1nRNiE0",
        "outputId": "8f077bf2-6f93-45b8-d6ce-145c4ae8577b"
      },
      "source": [
        "loan_df = removeOutlier(loan_df, 'disbursed_amount')\n",
        "loan_df = removeOutlier(loan_df, 'asset_cost')\n",
        "loan_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(201893, 40)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "qSjFMiP3NiE0",
        "outputId": "824ed94b-8dc1-4c29-fa11-d6d8a359013d"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,8))\n",
        "sns.heatmap(loan_df.corr(), ax=ax, vmin=0, vmax=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60ed8ce890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIHYLqzSNiE0",
        "outputId": "6684a45a-5288-4e54-d6ad-9946dd01430a"
      },
      "source": [
        "# identify features which are highly correlated\n",
        "corr_df = loan_df.corr()\n",
        "for col in corr_df.columns[:]:\n",
        "    correlated_fields = list(filter(lambda x: x!=col, corr_df[(np.abs(corr_df[col])>.75)][col].index.values))\n",
        "    correlated_values = list(filter(lambda x: x!=col, corr_df[(np.abs(corr_df[col])>.75)][col].values))\n",
        "    if correlated_fields:\n",
        "        print(col, ':', ', '.join(map(str, zip(correlated_fields, correlated_values))), sep='\\t')\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Aadhar_flag\t:\t('VoterID_flag', 1.0)\n",
            "VoterID_flag\t:\t('Aadhar_flag', -0.864987577891264)\n",
            "PRI.SANCTIONED.AMOUNT\t:\t('PRI.DISBURSED.AMOUNT', 1.0)\n",
            "PRI.DISBURSED.AMOUNT\t:\t('PRI.SANCTIONED.AMOUNT', 0.9987371096823607)\n",
            "SEC.NO.OF.ACCTS\t:\t('SEC.ACTIVE.ACCTS', 1.0)\n",
            "SEC.ACTIVE.ACCTS\t:\t('SEC.NO.OF.ACCTS', 0.8345477903675187)\n",
            "SEC.CURRENT.BALANCE\t:\t('SEC.SANCTIONED.AMOUNT', 1.0), ('SEC.DISBURSED.AMOUNT', 0.928276607792109)\n",
            "SEC.SANCTIONED.AMOUNT\t:\t('SEC.CURRENT.BALANCE', 0.928276607792109), ('SEC.DISBURSED.AMOUNT', 1.0)\n",
            "SEC.DISBURSED.AMOUNT\t:\t('SEC.CURRENT.BALANCE', 0.9290793014657733), ('SEC.SANCTIONED.AMOUNT', 0.9996587295900329)\n",
            "AVERAGE.ACCT.AGE\t:\t('CREDIT.HISTORY.LENGTH', 1.0)\n",
            "CREDIT.HISTORY.LENGTH\t:\t('AVERAGE.ACCT.AGE', 0.8298416580519211)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQYsY7sjNiE1"
      },
      "source": [
        "> From the Correlation Graph and the code filter above, we see that the below fields are highly correlated\n",
        "- Aadhar_flag  : VoterID_flag\n",
        "- VoterID_flag : Aadhar_flag\n",
        "-\tPRI.SANCTIONED.AMOUNT\t:\tPRI.DISBURSED.AMOUNT\n",
        "-\tPRI.DISBURSED.AMOUNT\t: PRI.SANCTIONED.AMOUNT\n",
        "-\tSEC.NO.OF.ACCTS\t:\tSEC.ACTIVE.ACCTS\n",
        "-\tSEC.ACTIVE.ACCTS\t:\tSEC.NO.OF.ACCTS\n",
        "-\tSEC.CURRENT.BALANCE\t:\tSEC.SANCTIONED.AMOUNT, SEC.DISBURSED.AMOUNT\n",
        "-\tSEC.SANCTIONED.AMOUNT\t:\tSEC.CURRENT.BALANCE, SEC.DISBURSED.AMOUNT\n",
        "-\tSEC.DISBURSED.AMOUNT\t:\tSEC.CURRENT.BALANCE, SEC.SANCTIONED.AMOUNT\n",
        "-\tAVERAGE.ACCT.AGE\t:\tCREDIT.HISTORY.LENGTH\n",
        "-\tCREDIT.HISTORY.LENGTH\t:\tAVERAGE.ACCT.AGE\n",
        ">\n",
        "><br>From the Correlation graph alone, we see the below field are highly correlated\n",
        "-\tPRI.NO.OF.ACCTS\t:\tPRI.ACTIVE.ACCTS\n",
        "-\tPRI.ACTIVE.ACCTS\t:\tPRI.NO.OF.ACCTS\n",
        "-\tPRI.CURRENT.BALANCE\t:\tPRI.SANCTIONED.AMOUNT, PRI.DISBURSED.AMOUNT\n",
        ">\n",
        "><br>From the above, we can remove the below columns from the feature set\n",
        "- PRI.NO.OF.ACCTS\n",
        "- PRI.SANCTIONED.AMOUNT\n",
        "- SEC.SANCTIONED.AMOUNT\n",
        "- AVERAGE.ACCT.AGE\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "O-9pBKFYNiE1"
      },
      "source": [
        "loan_df = loan_df.drop(['PRI.NO.OF.ACCTS', 'PRI.SANCTIONED.AMOUNT', 'SEC.SANCTIONED.AMOUNT', 'AVERAGE.ACCT.AGE', 'VoterID_flag'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "93FF5JWcNiE2",
        "outputId": "ec579f49-6934-4ce8-b7c5-f3d0e3a5e25a"
      },
      "source": [
        "state_counts_df = loan_df.groupby(['State_ID', 'loan_default']).size().to_frame('counts').reset_index()\n",
        "\n",
        "states_counts =[(ld, group) for ld, group in state_counts_df.groupby(['loan_default'])]\n",
        "\n",
        "for ld, state_df in states_counts:\n",
        "    state_df.columns=['State_ID', 'loan_default', 'COUNT_%s'%bool(ld)]\n",
        "    \n",
        "states_counts_df = states_counts[0][1].merge(states_counts[1][1], on='State_ID')\n",
        "states_counts_df = states_counts_df.drop(['loan_default_x', 'loan_default_y'], axis=1)\n",
        "states_counts_df['total']= states_counts_df['COUNT_False']+ states_counts_df['COUNT_True']\n",
        "states_counts_df['COUNT_False']= states_counts_df['COUNT_False']/ states_counts_df['total']\n",
        "states_counts_df['COUNT_True']= states_counts_df['COUNT_True']/ states_counts_df['total']\n",
        "states_counts_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State_ID</th>\n",
              "      <th>COUNT_False</th>\n",
              "      <th>COUNT_True</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.823282</td>\n",
              "      <td>0.176718</td>\n",
              "      <td>8324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.728660</td>\n",
              "      <td>0.271340</td>\n",
              "      <td>3866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.817901</td>\n",
              "      <td>0.182099</td>\n",
              "      <td>24569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.792111</td>\n",
              "      <td>0.207889</td>\n",
              "      <td>41046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.803561</td>\n",
              "      <td>0.196439</td>\n",
              "      <td>8201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.794578</td>\n",
              "      <td>0.205422</td>\n",
              "      <td>31647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.812101</td>\n",
              "      <td>0.187899</td>\n",
              "      <td>5008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.769426</td>\n",
              "      <td>0.230574</td>\n",
              "      <td>12226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.780140</td>\n",
              "      <td>0.219860</td>\n",
              "      <td>14955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.822717</td>\n",
              "      <td>0.177283</td>\n",
              "      <td>3187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>0.794416</td>\n",
              "      <td>0.205584</td>\n",
              "      <td>6304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>0.742019</td>\n",
              "      <td>0.257981</td>\n",
              "      <td>3101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0.696819</td>\n",
              "      <td>0.303181</td>\n",
              "      <td>13675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0.724618</td>\n",
              "      <td>0.275382</td>\n",
              "      <td>8904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0.791987</td>\n",
              "      <td>0.208013</td>\n",
              "      <td>4418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>0.816791</td>\n",
              "      <td>0.183209</td>\n",
              "      <td>2418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>0.753989</td>\n",
              "      <td>0.246011</td>\n",
              "      <td>3760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>0.778117</td>\n",
              "      <td>0.221883</td>\n",
              "      <td>4908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>0.822335</td>\n",
              "      <td>0.177665</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>0.810651</td>\n",
              "      <td>0.189349</td>\n",
              "      <td>169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>0.802632</td>\n",
              "      <td>0.197368</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>0.885714</td>\n",
              "      <td>0.114286</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    State_ID  COUNT_False  COUNT_True  total\n",
              "0          1     0.823282    0.176718   8324\n",
              "1          2     0.728660    0.271340   3866\n",
              "2          3     0.817901    0.182099  24569\n",
              "3          4     0.792111    0.207889  41046\n",
              "4          5     0.803561    0.196439   8201\n",
              "5          6     0.794578    0.205422  31647\n",
              "6          7     0.812101    0.187899   5008\n",
              "7          8     0.769426    0.230574  12226\n",
              "8          9     0.780140    0.219860  14955\n",
              "9         10     0.822717    0.177283   3187\n",
              "10        11     0.794416    0.205584   6304\n",
              "11        12     0.742019    0.257981   3101\n",
              "12        13     0.696819    0.303181  13675\n",
              "13        14     0.724618    0.275382   8904\n",
              "14        15     0.791987    0.208013   4418\n",
              "15        16     0.816791    0.183209   2418\n",
              "16        17     0.753989    0.246011   3760\n",
              "17        18     0.778117    0.221883   4908\n",
              "18        19     0.822335    0.177665    985\n",
              "19        20     0.810651    0.189349    169\n",
              "20        21     0.802632    0.197368    152\n",
              "21        22     0.885714    0.114286     70"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_B5gHExNiE2"
      },
      "source": [
        "#### **Label Encoding the Employment.Type Column values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "AP1TqZGONiE3",
        "outputId": "a3b45e8f-f9cc-480c-9c49-db0f4625f6ec"
      },
      "source": [
        "'Employment Type NULL values: %d'% len(loan_df[loan_df['Employment.Type'].isna()])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Employment Type NULL values: 5901'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLzsSUf9oG-g",
        "outputId": "c25fce83-47cb-4c3e-dd83-5d84cb18ec9d"
      },
      "source": [
        "loan_df['Employment.Type']=loan_df['Employment.Type'].fillna('Missing')\n",
        "loan_df_null_unique=loan_df['Employment.Type'].unique()\n",
        "print(loan_df_null_unique)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Salaried' 'Self employed' 'Missing']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GONFhZyLo3Si",
        "outputId": "b1f260a1-323c-4ed8-db71-036ed4d1f016"
      },
      "source": [
        "label_encoder = preprocessing.LabelEncoder()\n",
        "loan_df['Employment.Type']= label_encoder.fit_transform(loan_df['Employment.Type'])\n",
        "loan_df['Employment.Type'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LiftSviioJ45",
        "outputId": "ab3a2d78-c059-47bd-8370-4162451d06bc"
      },
      "source": [
        "loan_df['Employment.Type'].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToQptKcsNiE5"
      },
      "source": [
        "### **Creating the Prediction Model using Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJaC74v-NiE5",
        "outputId": "e33a12f7-a8b5-4fa9-fe20-387a4e062701"
      },
      "source": [
        "feature_columns = ['disbursed_amount', 'asset_cost', 'ltv', 'branch_id', 'supplier_id',\n",
        "       'manufacturer_id', 'Current_pincode_ID', 'State_ID',\n",
        "       'Employee_code_ID', 'Aadhar_flag', 'PAN_flag',\n",
        "       'Driving_flag', 'Passport_flag', 'PERFORM_CNS.SCORE',\n",
        "       'PRI.ACTIVE.ACCTS', 'PRI.OVERDUE.ACCTS', 'PRI.CURRENT.BALANCE',\n",
        "       'PRI.DISBURSED.AMOUNT', 'SEC.NO.OF.ACCTS', 'SEC.ACTIVE.ACCTS',\n",
        "       'SEC.OVERDUE.ACCTS', 'SEC.CURRENT.BALANCE', 'SEC.DISBURSED.AMOUNT',\n",
        "       'PRIMARY.INSTAL.AMT', 'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS',\n",
        "       'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS', 'CREDIT.HISTORY.LENGTH',\n",
        "       'NO.OF_INQUIRIES', 'Employment.Type']\n",
        "target_column = 'loan_default'\n",
        "\n",
        "print('Before Resampling:')\n",
        "print(loan_df['loan_default'].value_counts())\n",
        "df_majority = loan_df[loan_df['loan_default']==0]\n",
        "df_minority = loan_df[loan_df['loan_default']==1]\n",
        "\n",
        "\n",
        "df_minority_upsampled = resample(df_minority, \n",
        "                                 replace=True,                  # sample with replacement\n",
        "                                 n_samples=len(df_majority),    # to match majority class\n",
        "                                 random_state=123)              # reproducible results\n",
        "loan_df = pd.concat([df_majority, df_minority_upsampled])\n",
        "print('After Resampling:')\n",
        "print(loan_df['loan_default'].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before Resampling:\n",
            "0    158326\n",
            "1     43567\n",
            "Name: loan_default, dtype: int64\n",
            "After Resampling:\n",
            "1    158326\n",
            "0    158326\n",
            "Name: loan_default, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tg4t1OzyGgkd"
      },
      "source": [
        "x = loan_df[feature_columns]\n",
        "y = loan_df[target_column]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=123)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vPR27i-DNiE7"
      },
      "source": [
        "#### **Using RandomForestClassifier**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDrGxd8JZPfR"
      },
      "source": [
        ">**NOTE:** Thought I would try using GridSearch or RandomSearch, but the runtime increases drastically as the dataset is quite big. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "bMy3gRKeNiE7",
        "outputId": "ca66ae26-62f1-40f8-8525-51b918403f05"
      },
      "source": [
        "rfc_clf = RandomForestClassifier()\n",
        "rfc_clf.fit(x_train, y_train)\n",
        "y_predict = rfc_clf.predict(x_test)\n",
        "modelEvaluation(rfc_clf,x_test, y_train, y_test, y_predict, x_train.columns)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accurancy: 0.936\n",
            "ROC AUC Score: 0.936\n",
            "F1 Score:: 0.937 \n",
            "Balanced Accurancy Score:: 0.936 \n",
            "\n",
            " clasification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.92      0.93     31656\n",
            "           1       0.92      0.96      0.94     31675\n",
            "\n",
            "    accuracy                           0.94     63331\n",
            "   macro avg       0.94      0.94      0.94     63331\n",
            "weighted avg       0.94      0.94      0.94     63331\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "u8zwMOgFtlr2",
        "outputId": "74fc883f-c90c-41dc-9653-371b228d703f"
      },
      "source": [
        "y_pred=rfc_clf.predict(x_test)\n",
        "comparison=pd.DataFrame({'Actual loan_default': y_test, 'Predicted loan_default': y_pred[:79163]})\n",
        "comparison.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual loan_default</th>\n",
              "      <th>Predicted loan_default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>130049</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76648</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69033</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164932</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47906</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220778</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133339</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190875</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230983</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178972</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153426</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4708</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72031</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192292</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102567</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119743</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138193</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26828</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17726</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97191</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Actual loan_default  Predicted loan_default\n",
              "130049                    0                       0\n",
              "76648                     0                       0\n",
              "69033                     1                       1\n",
              "164932                    0                       0\n",
              "47906                     1                       1\n",
              "220778                    0                       1\n",
              "133339                    1                       1\n",
              "190875                    0                       0\n",
              "230983                    1                       1\n",
              "178972                    0                       0\n",
              "153426                    1                       1\n",
              "4708                      0                       0\n",
              "72031                     1                       1\n",
              "192292                    0                       0\n",
              "102567                    1                       1\n",
              "119743                    1                       1\n",
              "138193                    0                       0\n",
              "26828                     1                       1\n",
              "17726                     1                       1\n",
              "97191                     0                       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LqbJdUcLsw-B"
      },
      "source": [
        "###**Model Metrics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "i5GafBkhqQsL",
        "outputId": "6b79bf81-9afa-4979-cbb4-703c1f9bbcc3"
      },
      "source": [
        "# Plot non-normalized confusion matrix\n",
        "titles_options = [(\"Confusion matrix, without normalization\", None),\n",
        "                  (\"Normalized confusion matrix\", 'true')]\n",
        "for title, normalize in titles_options:\n",
        "    disp = plot_confusion_matrix(rfc_clf, x_test, y_test,\n",
        "                                 display_labels=['Actual','Predicted'],\n",
        "                                 cmap=plt.cm.Blues,\n",
        "                                 normalize=normalize)\n",
        "    disp.ax_.set_title(title)\n",
        "\n",
        "    print(title)\n",
        "    print(disp.confusion_matrix)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[28972  2684]\n",
            " [ 1360 30315]]\n",
            "Normalized confusion matrix\n",
            "[[0.91521355 0.08478645]\n",
            " [0.04293607 0.95706393]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wEZcPfP3bA-x",
        "outputId": "6cfb76cf-ba61-451a-a374-606896bb83d2"
      },
      "source": [
        "print('MAE(Mean Absolute Error)= ', mean_absolute_error(y_test,y_pred))\n",
        "print('R2 Score = ', round(r2_score(y_test, y_pred), 2))\n",
        "print('Log Loss= ',log_loss(y_test, y_pred))\n",
        "print('IoU (Jaccard Score)= ',jaccard_score(y_test,y_pred))\n",
        "print('MSE(Mean Squared Error', mean_squared_error(y_test,y_pred))\n",
        "print('RMSE(Root Mean Squared Error', np.sqrt(mean_squared_error(y_test,y_pred)))\n",
        "print('MSLE(Mean Squared Log Error', mean_squared_log_error(y_test, y_pred))\n",
        "\n",
        "MAE = mean_absolute_error(y_test,y_pred)\n",
        "NAE = np.sqrt(np.sum(np.square((y_test-y_pred) - MAE))/(len(y_test) - 1))\n",
        "print('NAE(Normalised Absolute Error)= ', NAE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE(Mean Absolute Error)=  0.0638549841309943\n",
            "R2 Score =  0.74\n",
            "Log Loss=  2.205506905946874\n",
            "IoU (Jaccard Score)=  0.8823015803719549\n",
            "MSE(Mean Squared Error 0.0638549841309943\n",
            "RMSE(Root Mean Squared Error 0.25269543749540535\n",
            "MSLE(Mean Squared Log Error 0.030679319579435132\n",
            "NAE(Normalised Absolute Error)=  0.26571312880903064\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIrGi01pfDhb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}