Practice_Pycaret_V1.ipynb

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            "outputs": [
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": []
                },
                "image/png": 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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/57/33/565274c28a11af60b7cfc0519d46bde4125fcd7d32ebc0a81b480d0e8da6/zope.interface-5.1.0-cp36-cp36m-manylinux2010_x86_64.whl (234kB)\n",
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            "Collecting combo\n",
            "  Downloading https://files.pythonhosted.org/packages/78/52/e880bd923eba122515307d29ab43c1c356bad60610c27bed2cdec25d0240/combo-0.1.0.tar.gz\n",
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            "Collecting suod\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a1/87/9170cabe1b5e10a7d095c0e28f2e30e7c1886a13f063de85d3cfacc06f4b/suod-0.0.4.tar.gz (2.1MB)\n",
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            "Collecting rsa<=3.5.0,>=3.1.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e1/ae/baedc9cb175552e95f3395c43055a6a5e125ae4d48a1d7a924baca83e92e/rsa-3.4.2-py2.py3-none-any.whl (46kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 5.2MB/s \n",
            "\u001b[?25hCollecting colorama<0.4.4,>=0.2.5; python_version != \"3.4\"\n",
            "  Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n",
            "Collecting botocore==1.16.19\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0c/8c/b8d7e77d9e5a373124dc5817901e454fdde03efcfeadab519dc7be74e3ae/botocore-1.16.19-py2.py3-none-any.whl (6.2MB)\n",
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            "Collecting htmlmin>=0.1.12\n",
            "  Downloading https://files.pythonhosted.org/packages/b3/e7/fcd59e12169de19f0131ff2812077f964c6b960e7c09804d30a7bf2ab461/htmlmin-0.1.12.tar.gz\n",
            "Collecting phik>=0.9.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/01/5a/7ef1c04ce62cd72f900c06298dc2385840550d5c653a0dbc19109a5477e6/phik-0.10.0-py3-none-any.whl (599kB)\n",
            "\u001b[K     |████████████████████████████████| 604kB 37.5MB/s \n",
            "\u001b[?25hCollecting confuse>=1.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f7/64/85dbcea372efee5cba13eaa10a3bfa7019b8fe0c3c8314d8e189116e477a/confuse-1.1.0.tar.gz\n",
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            "Collecting funcy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ce/4b/6ffa76544e46614123de31574ad95758c421aae391a1764921b8a81e1eae/funcy-1.14.tar.gz (548kB)\n",
            "\u001b[K     |████████████████████████████████| 552kB 34.1MB/s \n",
            "\u001b[?25hCollecting chart-studio>=1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/ce/330794a6b6ca4b9182c38fc69dd2a9cbff60fd49421cb8648ee5fee352dc/chart_studio-1.1.0-py3-none-any.whl (64kB)\n",
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            "Building wheels for collected packages: pyod, shap, pandas-profiling, pyLDAvis, cufflinks, combo, suod, htmlmin, confuse, funcy\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Building wheel for combo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Created wheel for suod: filename=suod-0.0.4-cp36-none-any.whl size=2167157 sha256=f6073cd498e20b554b2b2b77410347a8286b9f212b946fd141577be8a7af9be1\n",
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            "  Stored in directory: /root/.cache/pip/wheels/f6/8b/23/41a1b516f6d8d4cc81f5bdb55394a47cdbe9659c53668d3c9e\n",
            "  Building wheel for funcy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for funcy: filename=funcy-1.14-py2.py3-none-any.whl size=32042 sha256=460e0774a5ca15d0258e85575d733c2951f22200c0e5ca809651d69502069d5b\n",
            "  Stored in directory: /root/.cache/pip/wheels/20/5a/d8/1d875df03deae6f178dfdf70238cca33f948ef8a6f5209f2eb\n",
            "Successfully built pyod shap pandas-profiling pyLDAvis cufflinks combo suod htmlmin confuse funcy\n",
            "Installing collected packages: catboost, zope.interface, DateTime, datefinder, scikit-learn, combo, suod, pyod, kmodes, shap, yellowbrick, rsa, colorama, botocore, awscli, htmlmin, phik, confuse, pandas-profiling, funcy, pyLDAvis, chart-studio, cufflinks, lightgbm, pycaret\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: yellowbrick 0.9.1\n",
            "    Uninstalling yellowbrick-0.9.1:\n",
            "      Successfully uninstalled yellowbrick-0.9.1\n",
            "  Found existing installation: rsa 4.0\n",
            "    Uninstalling rsa-4.0:\n",
            "      Successfully uninstalled rsa-4.0\n",
            "  Found existing installation: botocore 1.16.13\n",
            "    Uninstalling botocore-1.16.13:\n",
            "      Successfully uninstalled botocore-1.16.13\n",
            "  Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "  Found existing installation: cufflinks 0.17.3\n",
            "    Uninstalling cufflinks-0.17.3:\n",
            "      Successfully uninstalled cufflinks-0.17.3\n",
            "  Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "Successfully installed DateTime-4.3 awscli-1.18.69 botocore-1.16.19 catboost-0.20.2 chart-studio-1.1.0 colorama-0.4.3 combo-0.1.0 confuse-1.1.0 cufflinks-0.17.0 datefinder-0.7.0 funcy-1.14 htmlmin-0.1.12 kmodes-0.10.1 lightgbm-2.3.1 pandas-profiling-2.3.0 phik-0.10.0 pyLDAvis-2.1.2 pycaret-1.0.0 pyod-0.8.0 rsa-3.4.2 scikit-learn-0.22 shap-0.32.1 suod-0.0.4 yellowbrick-1.0.1 zope.interface-5.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "rsa"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWBT3xW5eA_h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pycaret.utils import version"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFQ33Cbg3op9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "42c909dd-494a-4929-825a-af68f291fd2e"
      },
      "source": [
        "version()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goRNBsuJ4gZD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FSEIDh547bl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "94bb0aaa-cedb-4903-80c3-ea102e6988d4"
      },
      "source": [
        ""
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-81ff94eafa49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_classification\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'C:/Users/P.SridharanSrinivas/Desktop/sai_pycaret/colab/loan_train_data.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    674\u001b[0m         )\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1112\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1114\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1115\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1891\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File C:/Users/P.SridharanSrinivas/Desktop/sai_pycaret/colab/loan_train_data.csv does not exist: 'C:/Users/P.SridharanSrinivas/Desktop/sai_pycaret/colab/loan_train_data.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtPCc3Bd5Iej",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "3c450698-4086-4943-b1be-77a4f4d3bf23"
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-79b246812fff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muploaded\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'C:/Users/P.SridharanSrinivas/Desktop/sai_pycaret/colab/loan_train_data.csv'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'uploaded' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4C8nSw06NMk",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "ca864eed-fdcf-4b92-85ae-1dbf5ce7aac5"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9f070c92-abb9-49d1-843c-9ed3c2a571aa\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-9f070c92-abb9-49d1-843c-9ed3c2a571aa\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving loan_train_data.csv to loan_train_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5sPxGtN_QUg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMl3OTfL6X-M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "8b99e279-6b80-43be-ea3d-b0b14e17214b"
      },
      "source": [
        "print (uploaded['loan_train_data.csv'][:200].decode('utf-8') + '...')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ID,Age,Experience,Income,ZIP Code,Family,CCAvg,Education,Mortgage,Personal Loan,Securities Account,CD Account,Online,CreditCard\n",
            "3510,38,12,61,91330,3,0.9,3,0,0,0,0,0,0\n",
            "1129,30,5,171,94025,2,1.9,2,0,1,...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3P8XQVS6jKf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "b5112ce7-2f81-481b-ae0d-be6c15b5cbee"
      },
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "\n",
        "data_classification = pd.read_csv(io.StringIO(uploaded['loan_train_data.csv'].decode('utf-8')))\n",
        "data_classification"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Experience</th>\n",
              "      <th>Income</th>\n",
              "      <th>ZIP Code</th>\n",
              "      <th>Family</th>\n",
              "      <th>CCAvg</th>\n",
              "      <th>Education</th>\n",
              "      <th>Mortgage</th>\n",
              "      <th>Personal Loan</th>\n",
              "      <th>Securities Account</th>\n",
              "      <th>CD Account</th>\n",
              "      <th>Online</th>\n",
              "      <th>CreditCard</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3510</td>\n",
              "      <td>38</td>\n",
              "      <td>12</td>\n",
              "      <td>61</td>\n",
              "      <td>91330</td>\n",
              "      <td>3</td>\n",
              "      <td>0.9</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1129</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>171</td>\n",
              "      <td>94025</td>\n",
              "      <td>2</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1637</td>\n",
              "      <td>65</td>\n",
              "      <td>39</td>\n",
              "      <td>100</td>\n",
              "      <td>92122</td>\n",
              "      <td>4</td>\n",
              "      <td>1.7</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3165</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>82</td>\n",
              "      <td>95136</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3563</td>\n",
              "      <td>32</td>\n",
              "      <td>8</td>\n",
              "      <td>169</td>\n",
              "      <td>94596</td>\n",
              "      <td>1</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3</td>\n",
              "      <td>272</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3995</th>\n",
              "      <td>3598</td>\n",
              "      <td>56</td>\n",
              "      <td>26</td>\n",
              "      <td>51</td>\n",
              "      <td>92028</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3996</th>\n",
              "      <td>4671</td>\n",
              "      <td>52</td>\n",
              "      <td>26</td>\n",
              "      <td>194</td>\n",
              "      <td>94305</td>\n",
              "      <td>1</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3997</th>\n",
              "      <td>989</td>\n",
              "      <td>63</td>\n",
              "      <td>39</td>\n",
              "      <td>32</td>\n",
              "      <td>94998</td>\n",
              "      <td>1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3998</th>\n",
              "      <td>2038</td>\n",
              "      <td>35</td>\n",
              "      <td>8</td>\n",
              "      <td>52</td>\n",
              "      <td>95616</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3999</th>\n",
              "      <td>2175</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>123</td>\n",
              "      <td>95605</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4000 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        ID  Age  Experience  ...  CD Account  Online  CreditCard\n",
              "0     3510   38          12  ...           0       0           0\n",
              "1     1129   30           5  ...           0       0           0\n",
              "2     1637   65          39  ...           0       0           1\n",
              "3     3165   28           4  ...           0       1           1\n",
              "4     3563   32           8  ...           1       1           0\n",
              "...    ...  ...         ...  ...         ...     ...         ...\n",
              "3995  3598   56          26  ...           0       1           0\n",
              "3996  4671   52          26  ...           0       1           0\n",
              "3997   989   63          39  ...           0       0           0\n",
              "3998  2038   35           8  ...           0       0           1\n",
              "3999  2175   30           5  ...           0       1           0\n",
              "\n",
              "[4000 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdnLcHeN6-KE",
        "colab_type": "code",
        "colab": {
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          "height": 195
        },
        "outputId": "c772c164-b097-46d2-e7bd-119d9d33ef47"
      },
      "source": [
        "data_classification.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>CCAvg</th>\n",
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              "      <th>Securities Account</th>\n",
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            "text/plain": [
              "     ID  Age  Experience  ...  CD Account  Online  CreditCard\n",
              "0  3510   38          12  ...           0       0           0\n",
              "1  1129   30           5  ...           0       0           0\n",
              "2  1637   65          39  ...           0       0           1\n",
              "3  3165   28           4  ...           0       1           1\n",
              "4  3563   32           8  ...           1       1           0\n",
              "\n",
              "[5 rows x 14 columns]"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_dARhrR7WYa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "62fce1cc2b564044bbc9e0acce2ddda9",
            "da337d60577c441e80f1954e40dda165",
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          ]
        },
        "outputId": "0876d16f-478e-4105-e4be-99ad61e49c2c"
      },
      "source": [
        "from pycaret import classification\n",
        "classification_setup = classification.setup(data=data_classification,target='Personal Loan')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62fce1cc2b564044bbc9e0acce2ddda9",
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              "IntProgress(value=0, description='Processing: ', max=13)"
            ]
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              "      <td>17:29:33</td>\n",
              "    </tr>\n",
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              "      <td>Preparing Data for Modeling</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ETC</th>\n",
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              "    </tr>\n",
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              "</table>\n",
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            "text/plain": [
              "                                                                           \n",
              "                                                                           \n",
              "Initiated  . . . . . . . . . . . . . . . . . .                     17:29:33\n",
              "Status     . . . . . . . . . . . . . . . . . .  Preparing Data for Modeling\n",
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
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        },
        {
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          "data": {
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              "        <script type=\"text/javascript\">\n",
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              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
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              "Text(value=\"Following data types have been inferred automatically, if they are correct press enter to continue…"
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        },
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          "output_type": "display_data",
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            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "  <tbody>\n",
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              "      <th>ID</th>\n",
              "      <td>Numeric</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>Numeric</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Experience</th>\n",
              "      <td>Numeric</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>Numeric</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZIP Code</th>\n",
              "      <td>Numeric</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Family</th>\n",
              "      <td>Categorical</td>\n",
              "    </tr>\n",
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              "      <th>CCAvg</th>\n",
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              "      <td>Numeric</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Personal Loan</th>\n",
              "      <td>Label</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Securities Account</th>\n",
              "      <td>Categorical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CD Account</th>\n",
              "      <td>Categorical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Online</th>\n",
              "      <td>Categorical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CreditCard</th>\n",
              "      <td>Categorical</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Data Type\n",
              "ID                      Numeric\n",
              "Age                     Numeric\n",
              "Experience              Numeric\n",
              "Income                  Numeric\n",
              "ZIP Code                Numeric\n",
              "Family              Categorical\n",
              "CCAvg                   Numeric\n",
              "Education           Categorical\n",
              "Mortgage                Numeric\n",
              "Personal Loan             Label\n",
              "Securities Account  Categorical\n",
              "CD Account          Categorical\n",
              "Online              Categorical\n",
              "CreditCard          Categorical"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-948193e1664d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpycaret\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclassification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassification_setup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_classification\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Personal Loan'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pycaret/classification.py\u001b[0m in \u001b[0;36msetup\u001b[0;34m(data, target, train_size, sampling, sample_estimator, categorical_features, categorical_imputation, ordinal_features, high_cardinality_features, high_cardinality_method, numeric_features, numeric_imputation, date_features, ignore_features, normalize, normalize_method, transformation, transformation_method, handle_unknown_categorical, unknown_categorical_method, pca, pca_method, pca_components, ignore_low_variance, combine_rare_levels, rare_level_threshold, bin_numeric_features, remove_outliers, outliers_threshold, remove_multicollinearity, multicollinearity_threshold, create_clusters, cluster_iter, polynomial_features, polynomial_degree, trigonometry_features, polynomial_threshold, group_features, group_names, feature_selection, feature_selection_threshold, feature_interaction, feature_ratio, interaction_threshold, session_id, silent, profile)\u001b[0m\n\u001b[1;32m    929\u001b[0m                                           \u001b[0mdisplay_types\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisplay_dtypes_pass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m#this is for inferred input box\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m                                           \u001b[0mtarget_transformation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m#not needed for classification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m                                           random_state = seed)\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0mprogress\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pycaret/preprocess.py\u001b[0m in \u001b[0;36mPreprocess_Path_One\u001b[0;34m(train_data, target_variable, ml_usecase, test_data, categorical_features, numerical_features, time_features, features_todrop, display_types, imputation_type, numeric_imputation_strategy, categorical_imputation_strategy, apply_zero_nearZero_variance, club_rare_levels, rara_level_threshold_percentage, apply_untrained_levels_treatment, untrained_levels_treatment_method, apply_ordinal_encoding, ordinal_columns_and_categories, apply_cardinality_reduction, cardinal_method, cardinal_features, apply_binning, features_to_binn, apply_grouping, group_name, features_to_group_ListofList, apply_polynomial_trigonometry_features, max_polynomial, trigonometry_calculations, top_poly_trig_features_to_select_percentage, scale_data, scaling_method, Power_transform_data, Power_transform_method, target_transformation, target_transformation_method, remove_outliers, outlier_contamination_percentage, outlier_methods, apply_feature_selection, feature_selection_top_features_percentage, remove_multicollinearity, maximum_correlation_between_features, remove_perfect_collinearity, apply_feature_interactions, feature_interactions_to_apply, feature_interactions_top_features_to_select_percentage, cluster_entire_data, range_of_clusters_to_try, apply_pca, pca_method, pca_variance_retained_or_number_of_components, random_state)\u001b[0m\n\u001b[1;32m   2538\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2539\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2540\u001b[0;31m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2541\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    381\u001b[0m         \"\"\"\n\u001b[1;32m    382\u001b[0m         \u001b[0mlast_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m         \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m         with _print_elapsed_time('Pipeline',\n\u001b[1;32m    385\u001b[0m                                  self._log_message(len(self.steps) - 1)):\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0mmessage_clsname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Pipeline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m                 \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m                 **fit_params_steps[name])\n\u001b[0m\u001b[1;32m    314\u001b[0m             \u001b[0;31m# Replace the transformer of the step with the fitted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m             \u001b[0;31m# transformer. This is necessary when loading the transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/memory.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_and_shelve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    724\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    725\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 726\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    727\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pycaret/preprocess.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, dataset, y)\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m     \u001b[0;31m# since this is for training , we dont nees any transformation since it has already been transformed in fit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m     \u001b[0;31m# additionally we just need to treat the target variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pycaret/preprocess.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, y)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m       \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt_print_out\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Data Type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yz89XwRv7m4f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338,
          "referenced_widgets": [
            "c185b93ccf624a258a465f798114b336",
            "2bbb5075d9f54933b137e3edabb26328",
            "b667f1775dad46b79c1ace59649584fb",
            "8acf9eaa7fbb461695d76da8549b7e83",
            "f41067d791a24f5988b414e8511bbcbb",
            "0e388e4938be4b16a146505def55eb88",
            "7d7fd5818ffb40f5a1c61c4d5b521e70",
            "1420a2854c5642c5be85b54b95690e6b",
            "80dcefc767074df68e968b1c3402b0bc"
          ]
        },
        "outputId": "33d833ac-a7eb-4729-8b8c-7ba6cb1163e7"
      },
      "source": [
        "#build the decision tree\n",
        "from pycaret import classification\n",
        "classification_setup = classification.setup(data= data_classification, target='Personal Loan')\n",
        "classification.compare_models()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            : ;\n",
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              "            : ;\n",
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              "        }    #T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col3 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col4 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col5 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col6 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }</style><table id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.985400</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.997100</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.884600</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.959800</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.920100</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.912000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row1_col0\" class=\"data row1 col0\" >CatBoost Classifier</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.985000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.996900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.869500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.971400</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.916600</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.908400</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row2_col0\" class=\"data row2 col0\" >Extreme Gradient Boosting</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.984600</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.995600</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.880900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.956300</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.916400</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.908000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.982100</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.996000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.854700</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.955400</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.901200</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.891400</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row4_col0\" class=\"data row4 col0\" >Random Forest Classifier</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.979600</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.973800</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.802800</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.982100</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.882500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.871500</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row5_col0\" class=\"data row5 col0\" >Extra Trees Classifier</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.978900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.985000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.798900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.979600</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.878100</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.866700</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row6_col0\" class=\"data row6 col0\" >Decision Tree Classifier</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.978600</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.933100</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.876900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.899500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.886700</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.874900</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row7_col0\" class=\"data row7 col0\" >Ada Boost Classifier</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.963600</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.973300</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.728200</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.874900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.790300</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.770700</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row8_col0\" class=\"data row8 col0\" >Linear Discriminant Analysis</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.940700</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.957500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.591500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.743300</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.655500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.623700</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row9_col0\" class=\"data row9 col0\" >Ridge Classifier</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.937500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.371900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row9_col4\" class=\"data row9 col4\" >0.947800</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.528700</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.502600</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row10_col0\" class=\"data row10 col0\" >Logistic Regression</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.906700</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.922100</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.326800</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.539900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.401200</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.354300</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row11_col0\" class=\"data row11 col0\" >K Neighbors Classifier</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.903900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.600400</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.018700</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.400000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.035500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.029100</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row12_col0\" class=\"data row12 col0\" >SVM - Linear Kernel</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row12_col1\" class=\"data row12 col1\" >0.903900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row12_col2\" class=\"data row12 col2\" >0.000000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row12_col3\" class=\"data row12 col3\" >0.000000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row12_col4\" class=\"data row12 col4\" >0.000000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row12_col5\" class=\"data row12 col5\" >0.000000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row12_col6\" class=\"data row12 col6\" >0.000000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row13_col0\" class=\"data row13 col0\" >Naive Bayes</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row13_col1\" class=\"data row13 col1\" >0.893900</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row13_col2\" class=\"data row13 col2\" >0.929200</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row13_col3\" class=\"data row13 col3\" >0.583800</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row13_col4\" class=\"data row13 col4\" >0.464300</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row13_col5\" class=\"data row13 col5\" >0.513800</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row13_col6\" class=\"data row13 col6\" >0.455500</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col0\" class=\"data row14 col0\" >Quadratic Discriminant Analysis</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col1\" class=\"data row14 col1\" >0.759200</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col2\" class=\"data row14 col2\" >0.756000</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col3\" class=\"data row14 col3\" >0.624400</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col4\" class=\"data row14 col4\" >0.313200</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col5\" class=\"data row14 col5\" >0.373500</td>\n",
              "                        <td id=\"T_0ca53540_a42f_11ea_9817_0242ac1c0002row14_col6\" class=\"data row14 col6\" >0.280500</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe8df14f0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGu0kJSN9hZO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
            "5d25e4744f924a3597789e33c26a7641"
          ]
        },
        "outputId": "6d54715a-1695-4013-8e2e-d45104dbb34f"
      },
      "source": [
        "# AUC-ROC plot\n",
        "classification.plot_model(classification_dt, plot = 'auc')\n",
        "\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6W6_oTR-Ycc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402,
          "referenced_widgets": [
            "50012991f5bc44828a240bf1acb226dd",
            "9284a98767f345d5af681c5fbc4b393f",
            "e007111a2a874359ab13d4d16a2da9f5"
          ]
        },
        "outputId": "0c87006f-f079-46be-f853-fc9a4303e451"
      },
      "source": [
        "classification_dt = classification.create_model('dt')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.9714</td>\n",
              "      <td>0.9180</td>\n",
              "      <td>0.8519</td>\n",
              "      <td>0.8519</td>\n",
              "      <td>0.8519</td>\n",
              "      <td>0.8360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.9893</td>\n",
              "      <td>0.9610</td>\n",
              "      <td>0.9259</td>\n",
              "      <td>0.9615</td>\n",
              "      <td>0.9434</td>\n",
              "      <td>0.9375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.9893</td>\n",
              "      <td>0.9444</td>\n",
              "      <td>0.8889</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9412</td>\n",
              "      <td>0.9353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.9750</td>\n",
              "      <td>0.9200</td>\n",
              "      <td>0.8519</td>\n",
              "      <td>0.8846</td>\n",
              "      <td>0.8679</td>\n",
              "      <td>0.8541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.9929</td>\n",
              "      <td>0.9795</td>\n",
              "      <td>0.9630</td>\n",
              "      <td>0.9630</td>\n",
              "      <td>0.9630</td>\n",
              "      <td>0.9590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.9679</td>\n",
              "      <td>0.8830</td>\n",
              "      <td>0.7778</td>\n",
              "      <td>0.8750</td>\n",
              "      <td>0.8235</td>\n",
              "      <td>0.8059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.9821</td>\n",
              "      <td>0.9736</td>\n",
              "      <td>0.9630</td>\n",
              "      <td>0.8667</td>\n",
              "      <td>0.9123</td>\n",
              "      <td>0.9024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.9821</td>\n",
              "      <td>0.9405</td>\n",
              "      <td>0.8889</td>\n",
              "      <td>0.9231</td>\n",
              "      <td>0.9057</td>\n",
              "      <td>0.8958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.9679</td>\n",
              "      <td>0.9326</td>\n",
              "      <td>0.8889</td>\n",
              "      <td>0.8000</td>\n",
              "      <td>0.8421</td>\n",
              "      <td>0.8243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.9677</td>\n",
              "      <td>0.8787</td>\n",
              "      <td>0.7692</td>\n",
              "      <td>0.8696</td>\n",
              "      <td>0.8163</td>\n",
              "      <td>0.7987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.9786</td>\n",
              "      <td>0.9331</td>\n",
              "      <td>0.8769</td>\n",
              "      <td>0.8995</td>\n",
              "      <td>0.8867</td>\n",
              "      <td>0.8749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0093</td>\n",
              "      <td>0.0326</td>\n",
              "      <td>0.0635</td>\n",
              "      <td>0.0579</td>\n",
              "      <td>0.0505</td>\n",
              "      <td>0.0556</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
              "0       0.9714  0.9180  0.8519  0.8519  0.8519  0.8360\n",
              "1       0.9893  0.9610  0.9259  0.9615  0.9434  0.9375\n",
              "2       0.9893  0.9444  0.8889  1.0000  0.9412  0.9353\n",
              "3       0.9750  0.9200  0.8519  0.8846  0.8679  0.8541\n",
              "4       0.9929  0.9795  0.9630  0.9630  0.9630  0.9590\n",
              "5       0.9679  0.8830  0.7778  0.8750  0.8235  0.8059\n",
              "6       0.9821  0.9736  0.9630  0.8667  0.9123  0.9024\n",
              "7       0.9821  0.9405  0.8889  0.9231  0.9057  0.8958\n",
              "8       0.9679  0.9326  0.8889  0.8000  0.8421  0.8243\n",
              "9       0.9677  0.8787  0.7692  0.8696  0.8163  0.7987\n",
              "Mean    0.9786  0.9331  0.8769  0.8995  0.8867  0.8749\n",
              "SD      0.0093  0.0326  0.0635  0.0579  0.0505  0.0556"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrHbUVd4-nz8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436,
          "referenced_widgets": [
            "4bd4189ccba64dd28e05ca523d16b2e6",
            "ca050c682e6e4f88afe00ca43f7fe321",
            "e27843cdfd084667bfd66ea52c85f6af",
            "8d37ad3027a44b149985cb42e18678dc",
            "4cb55ed15ec942e69f568e27897984df",
            "1dc44a3694e54b8aa940e68ed7b6259e",
            "52c0d83b41e14d7d9b5378cb4fd2adcb",
            "fdf2391398b847cb9a3c41c347372710",
            "3281536ee04249939f4f26ecad632107",
            "01fa0dcf095f47c8a653d161a2fbd04b",
            "21af116eb20c498e8437fe8714a24b1b",
            "be4d5aa95978425a8e14f9869a99d842",
            "2cb30683d53e46b7acaadc8be94c43eb",
            "e7962dc1be3d4cd9811f8de7b913d74c",
            "83548dbee9d74a51b1dff6719fe56d6e",
            "c663cd538e924f5d9916a09aac27e503",
            "b4e20ca8d079412db33f0de03911fa08",
            "dec7f43b78b0458dbdfcbc6d97f8a9b2",
            "9c00946c1072447589a8fdae1db62940",
            "8c805b2f302447b591d5edc6914f30c2",
            "883f540e6b704e17a50e270a26f9dbb5",
            "cb08f554340b4bafb318c54a61021f9b",
            "b1e355a44d174374a042181d3c7b6d7b",
            "145dbb34eb1147e8b07bf8d9af718734",
            "af89fbe62366447ca1023de55a1a83bb"
          ]
        },
        "outputId": "f5c5b5a4-b453-4888-d951-8d6a751678f2"
      },
      "source": [
        "classification.evaluate_model(classification_dt)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4bd4189ccba64dd28e05ca523d16b2e6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4GX1Uut-8Nn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "7031e120-16fa-48ce-8073-c50e5c8f55ff"
      },
      "source": [
        "classification.interpret_model(classification_dt)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ayrZt21i0aJFN2+3atWPq1KkcPXqURx55hC5durB48WI++eQT6zGDBg3i9ddfJzAwkLp16+Lv78/x48fv2f2LiIiUdgpN91CnTp3o1KnTbffff//9TJ8+Pdd9rq6ufPnll7nuc3FxYe/evdbvw4YNY9iwYTmOqVatGh988IH1WU87d+6kSpUqd3oLIiIidkvTc3Ziw4YN9O/fn/T0dMxmM1u3bqVly5bF3S0REZFSQyNNdsLf35/Dhw/Tq1cvHB0dadSoEa+88kpxd0tERKTUUGiyE87OzoSFhRV3N0REREothSbJ0yHf70rkzz7zNWZdkTVdUn8KKyIiRUtrmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExAA9p0ny5B3fAuKzirsbWMaGFHcXwFJ0z34SEZGSTyNNIiIiIgYoNImIiIgYoNAkIiIiYoBCUwEEBATg7+9PcHBwjv/Wrl1baNcIDg7ml19+AcDb25uffvqpUNo9c+YMTz31FAsXLiyU9kREROyFFoIX0OjRo/Hz8yuy9mNiYgq9TYvFwptvvkn16tULvW0REZGyTiNNhWznzp307duX4OBggoKC2Lhxo3VfQEAAq1atYvDgwXTo0IG33nqLAwcOMHDgQDp16kRERIT12NxGlyZMmJDjGIABAwYQHR1tqG+rV6+mevXqtGzZ8i7uUERExD4pNBWi1NRUwsLCmDBhAjExMYwdO5apU6dy9epV6zH79+9nwYIFfPTRR6xbt47PP/+cpUuXsnjxYlauXMm5c+du235AQACff/45mZmZAJw9e5Zjx47RqVOnfPt2+vRpoqKiCA0NvfsbFRERsUOaniugiIgI5s+fn2NbaGgou3btonz58gC0atUKs9nMuXPnqFq1KgA+Pj44OTlRv359ypcvj4+PDyaTyfo9MTGROnXq5HrN1q1bU65cOXbv3o2fnx/btm3jiSeeoEqVKnn29Y9puZdffhk3N7dCuHv7lJCQkOtnuUU1yZ3qYks1saWa2Cqumnh5ed12n0JTAeW2pik7O5ulS5eyY8cOMjIyMJlM1u1/cHV1tX52cHCgUqVKOb6bzebbXtPR0ZGuXbuyYcMGa2gaNmxYvn397LPPqFGjBu3btzd8f2Lrj39ICQkJef6jskeqSe5UF1uqiS3VxFZJrYlCUyHatGkT0dHRLFmyhLp165Kens6TTz5ZqNfo3r07vXv35ttvvyUpKYm2bdvme86OHTs4c+YMAQEBAFy5cgVHR0dOnDjBjBkzCrV/IiIiZZVCUyFKTU3F3d2dOnXqYDabWb58Oc7OzqSlpRXaNRo2bEizZs2YOHEinTt3xskp/z/CRYsW5fg+efJk6tSpw9ChQwutXyIiImWdFoIXIn9/fypVqkRgYCADBw6kefPmdOjQgbCwMH799ddCu0737t05e/Ys3bp1K7Q2RUREJG8aaSqAuLi4XLe7ubkRGRmZY9ufp8/+et6ePXtu+/3QoUO5fgaoV68eDz30EE2aNLmzjv/H5MmTC3SeiIiIPdNIUymTnp7OwoUL6devX3F3RURExK5opKkUiYuLY8GCBbRv3z7H1NzixYv5/PPPb3teUTxdXERExN4oNJUiAQEB1l/A/dngwYMZPHhwMfRIRETEfig0SZ4O+X5XMp6VMWZdcfdARETsnNY0iYiIiBig0CQiIiJigEKTiIiIiAEKTSIiIiIGKDSJiIiIGKDQJCIiImKAQpOIiIiIAXpOk+TJO74FxGcVy7UtY0OK5bq3Lq7nQomISE4aaRIRERExQKFJRERExACFJhEREREDtKapAAICAsjKysLFxSXH9n79+hEUFFQo1wgODiY8PJxGjRrh7e3NihUraNq0aYHb27x5M8uXLyczMxNnZ2cGDhxIly5dCqWvIiIi9kChqYBGjx6Nn59fkbUfExNTaG39/PPPzJw5k2XLltGgQQN++OEHXnjhBZo3b07dunUL7ToiIiJlmabnCtnOnTvp27cvwcHBBAUFsXHjRuu+gIAAVq1axeDBg+nQoQNvvfUWBw4cYODAgXTq1ImIiAjrsd7e3vz000852p4wYUKOYwAGDBhAdHR0nn1ydXVl+vTpNGjQAIBHH30UFxcXTp48ebe3KyIiYjc00lSIUlNTCQsLIzIykubNm7Nv3z5GjRrFU089RdWqVQHYv38/CxYs4OzZs/Ts2ROz2czSpUs5deoUPXv2pG/fvtSpUyfX9gMCApg4cSIjR47E2dmZs2fPcuzYMTp16pRnv+rVq0e9evUAsFgsbN26FWdnZ5o3b164BRARESnDFJoKKCIigvnz5+fYFhoayq5duyhfvjwArVq1wmw2c+7cOWto8vHxwcnJifr161O+fHl8fHwwmUzW74mJibcNTa1bt6ZcuXLs3r0bPz8/tm3bxhNPPEGVKlUM9Xnt2rUsWLAAJycn3n77bSpXrnwXFSjbEhIS7mq/PVJNcqe62FJNbKkmtoqrJl5eXrfdp9BUQLmtacrOzmbp0qXs2LGDjIwMTCaTdfsfXF1drZ8dHByoVKlSju9ms/m213R0dKRr165s2LDBGpqGDRtmuM9BQUEEBQXx/fffM3bsWN555x1atmxp+Hx7ktc/moSEhDz32yPVJHeqiy3VxJZqYquk1kShqRBt2rSJ6OholixZQt26dUlPT+fJJ58s1Gt0796d3r178+2335KUlETbtm3zPefIkSOkpqbSqlUrADw9PfH09GTv3r0KTSIiIgZpIXghSk1Nxd3dnTp16mA2m1m+fDnOzs6kpaUV2jUaNmxIs2bNmDhxIp07d8bJKf/ce/r0aSZOnEhiYiIA58+f5/DhwzRu3LjQ+iUiIlLWaaSpEPn7+xMfH09gYCDVqlVj2LBhdOjQgbCwMBYsWFBo1+nevTvTpk2jW7duho7v2LEj586dY+jQoTg6OgLwj3/8o0gfmSAiIlLWKDQVQFxcXK7b3dzciIyMzLHtz9Nnfz1vz549t/1+6NChXD/DrV/DPfTQQzRp0sRwn5977jmee+45w8eLiIhITpqeK2XS09NZuHAh/fr1K+6uiIiI2BWNNJUicXFxLFiwgPbt2+eYmlu8eDGff/75bc8rzKeLi4iI2CuFplIkICCAgIAAm+2DBw9m8ODBxdAjERER+6HQJHk65Ptd8T0rY8y64rmuiIhILrSmSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDNBzmiRP3vEtID4rz2MsY0PybsSi5y2JiEjpp5EmEREREQMUmkREREQMUGgSERERMUChSURERMSAUr8QfMuWLURFRZGamorZbMbd3Z1+/frRvn17ABYuXMgnn3xCzZo1yc7OJiMjg2bNmjFgwACaNWuWZ9sDBw7k/PnzREdH4+Lici9u547t2LEDT09PatSoke+xZrOZjz76iEWLFvH222/j5+d3D3ooIiJSNpTqkably5fz/vvvM2bMGGJjY1m/fj1Dhw7l7bffZuPGjdbjvLy8iImJYe3atWzYsIG2bdsyYsQI9u/ff9u2jx49SnZ2Nu3atWPr1q334nYKZOHChSQlJRk69tVXX+XSpUtUr169iHslIiJS9pTa0JSSksKiRYsICwvD09PTur1169a8++67NG/ePNfznJycCAoKYvjw4cyaNeu27cfGxtKtWzcCAgJYu3Ztjn0XL15k9OjRBAYG0rNnTz799FPrvg0bNhAcHEzPnj0ZPnw4Z86cASA5OZnQ0FB69uxJcHAwkyZNIjU1FYDJkyczY8YMaxvbt28nICAAgLi4OIYNG8b8+fPp3bs3Xbp04bPPPgNg3LhxnDhxgtDQUJs+5mbgwIGMGzcOJ6dSP8AoIiJyz5Xa/+/5/fffYzKZaNu2rc2+P4eo2/H19WXGjBkkJiZSu3btHPvS0tKIj49nxIgRVK5cmZs3b3L06FEeeeQRAN5++20efPBBIiIiuHz5Mr179+aRRx7BxcWFuXPnEhUVRc2aNVm0aBFTpkwhMjKS6dOnU7VqVaKjo8nOzmbs2LF8+OGHjBs3Lt++Hj58mGeffZYXX3yRPXv2WMNXeHg43t7ezJgxg6ZNm+bbzmOPPZbvMUUhISGhWK5blMriPd0t1SR3qost1cSWamKruGri5eV1232lNjRdu3YNd3f3Ap/v5uYG3Bqx+mto2rJlC61bt6Zy5coA9OjRg9jYWCZMmEBGRgb79u3j5ZdfBqBatWqsW7eOChUqMH/+fFq1akXNmjWBWyM7/fv3Jysriz179hAVFYWDgwMODg4EBwczY8YMQ6GpSpUq+Pj4ANCkSRNu3rxJcnKyTb9Lqrz+ApZGCQkJZe6e7pZqkjvVxZZqYks1sVVSa1JqQ5ObmxtJSUlkZ2fj4HDns4wXLlwAyDV4xcbGcvLkSWtQsVgsmEwmXnnlFa5fv47ZbMbV1dV6/B+LxK9cuWINWnBrKtDJyYmLFy9iNpupVq1ajv5funTJUF//2iZAdna2wTsVERGRwlBq1zQ1b94cBwcHdu7cabPvyy+/ZNu2bXmev3btWpo1a2azKPrHH3/k6tWr7Nmzh127drFr1y52796Nt7c3W7duxc3NDQcHB65cuWI95+LFi6SkpODu7s7ly5et2zMzMzl9+jTVqlXD0dExR0i6fPkyHh4eADg4OOQIQSkpKXdWDBERESlypTY0ubq6Mnz4cGbOnMnBgwet2w8ePMiUKVNuu9g5KyuLmJgY1qxZw+jRo232x8TE5PpTfD8/P2JjY3FycqJdu3asWbMGuDVN+Pzzz3PkyBF8fX05cOAAp06dAmDlypVMnjwZR0dH2rVrx6pVq7BYLGRmZrJmzRp8fX0BqFWrFseOHcNsNpOenn5Hv9ZzcnLi2rVrho8XERGRgim103MAffr0wd3dnffff5+rV6/i7OxMzZo1mT59Ot7e3tbjEhISCA4OxmKxcP36dZo2bcqCBQto3LhxjvZSU1PZtm0bixYtsrlWu3btmDp1KkePHiUsLIypU6fSrVs3ypcvT+/evfmf//kfAEJDQxkxYgSOjo7UrFmTSZMmATBhwgRmzpxJSMitl9t6eXkxZMgQAHr27MnXX39NYGAg9913Hx07dmTFihWGauDv78+YMWPo378/Q4cOve1xiYmJ/POf/wRuTU1GREQwf/58evXqRe/evQ1dS0RExJ6ZLBaLpbg7ISWXaVZWvsdYxobkc8C6QupNyVBSFygWJ9Ukd6qLLdXElmpiq6TWpNROz4mIiIjcS6V6ek7+a/HixXz++ee33R8TE3MPeyMiIlL2KDSVEYMHD2bw4MGF3u4h3+/yHyIdU7am30RERHKj6TkRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhEREREDFJpEREREDNAjByRP3vEtID73p4Ln+STwMvYUcBEREY00iYiIiBig0CQiIiJigEKTiIiIiAEKTSIiIiIGaCE4EBAQQFZWFi4uLjm2u7u7ExkZeU/6EBwcTHh4OI0aNbon1xMREZE7o9D0H6NHj8bPz6/Yrh8TE1Ns1xYREZH8KTTl4/XXX8fFxYXXXnsNgMmTJ1v/b0BAAMHBwcTHx5OYmEiDBg2YPn067u7upKenM2/ePPbu3cvNmzdp0qQJr732GtWqVSMuLo5NmzZRrVo1kpOTiYyMxNvbmxUrVtC0aVOOHTvGrFmzSEpKwmw2ExISQv/+/QEYMmQIbdu25ZtvvuHkyZO4uLgwbdo0HnjgAcxmM++99x67d+/G0dGRFi1aMGHCBMqVK5dnmyIiIpI/rWnKx7hx49i7dy/ff/89hw4dIiEhgTFjxlj3x8fHM3/+fD7//HMqVarEvHnzAJg7dy7Hjh0jKiqKDRs2UKVKFd59913reYcPHyY4ONhm+i89PZ0RI0bQsWNHYmNjWbJkCVFRURw8eNB6zObNm5k6dSrr16+nfv36rFixAoCVK1dy5MgRoqOjWb16NRcuXOCjjz4y1KaIiIjkTSNN/xEREcH8+fNzbAsJCaFPnz6EhYUxbdo0srKymDRpEq6urtZjunXrZl0L1bVrVz744AMAduzYweuvv06FChUA6Nu3L/3797eOVKg9Lc0AACAASURBVLm6uuLt7W3Tj2+++YasrCxCQm49ONLDw4MuXbqwbds2WrVqBcDTTz9N1apVAWjSpAlff/01ADt37qRr1644Oztb78nJyYkDBw7k22ZhS0hIKJJ2S4qyfn8FoZrkTnWxpZrYUk1sFVdNvLy8brtPoek/8lrT1LZtW+bMmUO5cuVsgo6bm5v1c5UqVUhJSQHg2rVrhIeHM2fOHAAsFgsVK1bk6tWrANbQ81fXrl0jLS2N4OBg67abN2/y6KOP5rjOHxwdHTGbzQBcuXKFypUrW/f9EdiMtFnY8vpLV9olJCSU6fsrCNUkd6qLLdXElmpiq6TWRKHJgJiYGNzd3cnIyGDt2rUEBQVZ9125csX6+erVq9ZAU7NmTcaPH8/f//73O7pWrVq1qFq1aoEWhru7u3P58mXr99TUVNLT0++qTREREblFa5rycfr0aRYuXEhYWBhvvPEGH3zwAWfOnLHu/+KLL0hPT8disbBlyxbrdJefnx+rV68mMzMTgN27dzN37tx8r9esWTOcnJzYsmULAFlZWURERHDgwIF8z23fvj0bN24kPT0ds9nMxIkTWbNmzV21KSIiIrdopOk/clvTZLFYyMjI4LnnnqNevXoAPPvss0yZMoUFCxYA8Pjjj/PPf/6TM2fOUL9+fd544w0AXnjhBebOncuzzz6LyWSievXqORaQ3065cuV49913mTlzpnWR+P/8z//w2GOP5XvuP/7xDy5cuEBwcDDly5fH09OTgQMH3lWbIiIicovJYrFYirsTpVVAQAAvv/xysT7fqaiZZmXddp9lbMjtT7SsK4LelAwlda69OKkmuVNdbKkmtlQTWyW1JpqeExERETFAoUlERETEAK1pugtxcXHF3QURERG5RxSaJE+HfL+7/bzymLK7bklEROSvND0nIiIiYoBCk4iIiIgBCk0iIiIiBig0iYiIiBig0CQiIiJigEKTiIiIiAF65IDkyTu+BcTbvkol11eolOFXp4iIiGikSURERMQAhSYRERERAxSaRERERAxQaBIRERExQAvBy6AhQ4bw1FNP0aRJE1588UXq16+PxWIhLS2N+vXrExwcTOfOnYu7myIiIqWKQlMZV6FCBWJiYqzfDx48yPTp0/ntt98YOnRoMfZMRESkdNH0nJ1p1aoVM2fO5KOPPuL8+fPF3R0REZFSQ6HJDjVq1Ii6dety6NCh4u6KiIhIqaHpOTvl5uZGSkpKobaZkJBQqO2VZPZ0r0apJrlTXWypJrZUE1vFVRMvL6/b7lNoslMXLlzAw8OjUNvM6y9aWZKQkGA392qUapI71cWWamJLNbFVUmui6Tk7tH//fi5duoS3t3dxd0VERKTUUGiyM4cOHWLq1KkMHz4cNze34u6OiIhIqaHpuTIuPT2d4OBgAFJTU6lTpw6jRo3Cz8+vmHsmIiJSuig0lUGRkZHWzwcPHizGnoiIiJQdmp4TERERMUChSURERMQAhSYRERERA7SmSfJ0yPe73J+VMWbdve+MiIhIMdJIk4iIiIgBCk0iIiIiBig0iYiIiBig0CQiIiJigEKTiIiIiAEKTSIiIiIG6JEDkifv+BYQnwWAZWzIf3dY9MgBERGxLxppEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGi6AwEBAQQFBdlsz8rKonPnzgQEBNxxm1euXGHLli2F0T0REREpQgpNdygrK4uEhIQc27788kvKlStXoPYOHTqk0CQiIlIK6JEDd8jHx4f169fj5eVl3RYXF8fTTz/N7t27Adi/fz8ffPABN27cwNHRkb59+9KjRw8AvL29GTlyJOvXr6dbt258+umn3Lx5kz59+rBy5Uq2b9/O7NmzqVChAk888QQnT56kVatW9O/fn+PHjxMeHs7ly5dJT0/H19eXUaNGYTKZSEpK4rXXXuPMmTPUq1ePJ554gk2bNrF69Wqys7NZunQpmzdvJisri7p16xIWFka9evWKpYYiIiKlkUaa7lDnzp3ZvXs3qampAFy6dIkffviBJ598EoCLFy8yduxYXnnlFWJiYpg9ezazZ8/mp59+srZx5swZYmJiGDRoEL169cLLy4uVK1eSkpLCpEmTeO2114iNjeXhhx/m0KFD1vOmTp1K27ZtWbNmDcuWLWP9+vXs2bMHgHfffRcPDw82bdrE22+/TWxsrPW8qKgotm7dykcffcSGDRt4/PHHmTJlyr0ol4iISJmhkaY7VL16dby8vNiyZQshISFs2rSJ9u3b4+R0q5RfffUVDzzwAN7e3gDUq1ePtm3bsmfPHpo2bQrcGq3Kzffff0/FihWtASwgIIAPP/zQun/JkiXWz+7u7jRq1Ijff/8duDXNN3HiREwmEzVq1KBTp07Wka/t27cTEhJC1apVAejbty+RkZEkJSVRo0aNAtXhr1OU9sbe7z83qknuVBdbqokt1cRWcdXkzzNJf6XQVADPPPMMS5cuJSQkhLi4OCZPnkxaWhoAycnJuLm55Ti+atWqXLp0Kcf33KSkpNicW6tWLevnXbt2sXLlSi5duoSDgwOJiYn4+voCcO3atRzn/vm8a9eusWzZMlavXm3d5ubmRnJycoFDU15/qcq6hIQEu77/3KgmuVNdbKkmtlQTWyW1JgpNBfDkk08yffp0Nm/ejMlkomnTptZpNA8PDy5fvpzj+MuXL/PQQw/l226lSpW4fv16jm1JSUkAnD59mgkTJjB37lz+/ve/A/Dcc8/lOPePKcM/nwe3ApSfnx/BwcF3eKciIiLyB61pKgAnJye6du3Ke++9R/fu3XPsa9OmDSdPnuT//b//B8Bvv/3GV199xdNPP51rW87OzqSkpGCxWGjWrBmXL1+2BrCNGzdy9epVANLS0jCZTDRp0gSAHTt2cO7cOesI12OPPWb9FV5SUhJffPGF9RodOnRg/fr11lD1448/MmnSJCwWS2GVREREpMzTSFMBPfPMM0RFReHv759ju4eHB+Hh4cyePZsbN27g5OREWFgYDz/8cK7ttG3bllWrVtGlSxfWrFnD+PHjmTJlCi4uLvj6+tK0aVNMJhMPP/wwgYGB9OnTh6pVq+Lv78/QoUP54IMPqF+/Pq+88gqvv/46gYGBNGjQgO7du7N161YAgoKCSE5OZsCAAZhMJipWrMhLL72EyWQq8jqJiIiUFSaLhhtKtGeffZa+ffvyzDPP5HusxWKxBqFPPvmEf/3rX0RGRt7V9U2zsv7b/tiQP11s3V21W5qV1Ln24qSa5E51saWa2FJNbJXUmmh6rgTJzs4mMDDQOkL0888/c/LkSTw9PfM9NzIyklGjRmE2m0lPT2fnzp20bNmyqLssIiJiNzQ9V4I4ODjwxhtvEBERwcKFC3FwcCAsLIyGDRvme27fvn1588036dmzJyaTiZYtW/L8888XfadFRETshEJTCePt7c3KlSvv+DxXV1fCw8OLoEciIiICCk2Sj0O+3/13XnmM/a5jEhER0ZomEREREQMUmkREREQMUGgSERERMUChSURERMQAhSYRERERAxSaRERERAzQIwckT97xLSD+1qtU9BoVERGxZxppEhERETFAoUlERETEAIUmEREREQMUmkREREQM0EJwg86cOcPIkSNzbMvIyCAxMZHVq1fz4IMPEhAQwMsvv4yfnx9xcXFMmzaN++67D4Ds7GwqVqzIkCFD8PHxyfUaSUlJLFiwgISEBBwcHDCbzbRp04YXX3wRNze3O+7zn/sjIiIid0ehyaC6desSExOTY1toaCgtW7bkwQcfzPWc+++/n9WrV1u/f/vtt4wYMYJPPvmEhg0b5jj2ypUrDBw4kKeffppVq1ZRoUIFUlJSmDlzJi+88AKffvop5cqVK/T7EhEREWMUmgooOjqaY8eO8cknnxg+57HHHqNx48bs37/fJjR9+umneHh4MHbsWOu2ypUr88Ybb7Bnzx7rts2bN7N8+XKysrIoX748w4YNo127dgCsWrWKTz75BBcXFzp06JCj/XPnzjFz5kx+++03bt68SceOHXnppZdwcNAMrYiIiBEKTQVw/PhxPvjgAxYsWICLi8sdnZuZmZnriNGBAwdsgg6As7Mz7du3B+Dnn39m+vTpfPzxxzRs2JDvv/+eF198kbVr15KRkcGcOXOIioriwQcfZO3atVy4cAEAi8XCqFGjeOKJJ4iIiOD69esMGTKEhg0bEhgYWIAKiIiI2B+Fpjt048YNxo8fz9ChQ2ncuPEdnbtr1y5+++03nnzySZt9165dw8PDI8/zd+/eTdu2ba2jVJ6enjRq1Iivv/6azMxMHn74YetU4TPPPMPMmTMBOHnyJMePH2fJkiWYTCZcXV3p0aMH27ZtK3BoSkhIKNB5ZYW9339uVJPcqS62VBNbqomt4qqJl5fXbfcpNN2h8PBw7r//fvr06ZPvsadOnSI4OBi4NdpTt25d5s2bR82aNW2OdXNzIzExMc/2kpOTqVatms15ycnJWCwWqlatat3u6OhIlSpVgFuBzGQy8dxzz1n3Z2Vl4e7unu893E5ef6nKuoSEBLu+/9yoJrlTXWypJrZUE1sltSYKTXfg888/58CBA3z66aeGjv/rQvC8tG7dmi+++IIBAwbkWGdkNpv58MMP6devHx4eHhw/fjzHeZcvX8bDw4P09HRSU1Ot27Oysrhy5QqANaRFRUVRvnx5Q/0RERGRnLQK2KDff/+d8PBwpk6dWqCf/+enb9++pKWlMXXqVNLS0gBITU3lzTff5NChQ7i6uuLj48NXX33FyZMnAfjmm284efIkbdq04fHHH+fnn3+27lu3bh0WiwWA2rVr06xZM6KiooBbjz9YvHgxW7ZsKfT7EBERKas00mTQihUryMjI4O2337bZ16dPH0JCQnI5y7gqVaqwdOlS5s+fT58+fXBycsLR0ZF27doxfvx4nJ2defjhhwkLCyM0NJTMzExcXFwIDw/Hw8MDDw8Phg8fzvDhw3FxccHHx4cGDRpY258+fTrh4eH07NkTgMaNG9OrV6+76rOIiIg9MVn+GI4QyYVpVpb1s2Xsn4KhZV0x9KZkKKlz7cVJNcmd6mJLNbGlmtgqqTXR9JyIiIiIAQpNIiIiIgZoTZPk6ZDvd/8dIh1jv1NyIiIiGmkSERERMUChSURERMQAhSYRERERAxSaRERERAxQaBIRERExQKFJRERExACFJhERERED9JwmyZN3fAuIv/UqFb1GRURE7JlGmkREREQMUGgSERERMUChSURERMQAhSYRERERA+xiIXhAQABZWVm4uLjk2O7u7k5kZGSObWfPnuWZZ55h+/btuLm5FUl/duzYgaenJzVq1OCzzz7jxIkTTJgwoUiuBXD69GlmzZrFqVOnyMrKwtvbm3HjxlG+fPkiu6aIiEhZYxehCWD06NH4+fkVdzcAWLhwIZMnT6ZGjRr07t27yK83fvx4nnzySebMmcONGzcYOnQoUVFRDBw4sMivLSIiUlbYTWi6nczMTGbMmMG+ffuoVq0a3bp1s+7LbdSpV69e9O/fn4CAAE6ePMm0adM4f/485cqVY9CgQXTp0gWAyMhItm7dSnZ2NpUrVyY0NJRmzZoxbtw4Tpw4QWhoKIMGDeLChQscOXKEOXPmkJWVxfz589m1axcmkwkPDw9CQ0N54IEHiIuLY9OmTbRo0YJdu3Zx9epVBg4cmG/oys7OZsCAAbRp0waAihUr4u3tzbFjx4qooiIiImWT3a9p2rRpE9988w1r1qzh448/5t///rfhc0NDQ+nYsSPr1q1j1qxZTJ06lVOnTrF3716io6NZvnw5a9eupVWrVkyfPh2A8PBwAGbMmEFQUFCO9tasWcNXX33F8uXLiY6Opk2bNkyYMAGLxQLA4cOHadKkCZ999hlhYWG89957ZGZm5tlHBwcH/Pz8cHV1BeDmzZvs3buXli1bGr5PERERsaORpoiICObPn59jW0hICD/88ANPP/00lSpVAqBHjx5s3Lgx3/Z+//13jh8/TkBAAAD169dn27ZtuLi4cP/99xMXF2ddM9SqVSuio6PzbTM+Pp4ePXpYA07v3r2ZN28e586dA6BKlSr4+PgA0KRJE27evElycjK1a9c2VIObN2/y+uuv4+HhYRPY7lRCQsJdnV/a2fv950Y1yZ3qYks1saWa2Cqumnh5ed12n92EptutaRoxYgQPPfSQ9Xu1atUMtXflyhXKlSuXYzH1H8Hr2rVrzJkzh++++w6LxUJGRgbZ2dn5tpmcnJzj+hUrVqRcuXJcunQJgMqVK1v3OTnd+qMz0u4f/R0zZgzVq1dn9uzZ1vMLKq+/VGVdQkKCXd9/blST3KkutlQTW6qJrZJaE7ufnqtcuTKpqanW78nJydbPDg63ymM2m63bUlJSAKhevTo3b97k+vXr1n2JiYmkpaUxe/Zszp8/z4oVK4iNjTX8yzgPDw9rQAK4fv06GRkZeHh4FOzm/uPatWu8+OKLtGjRghkzZuhXcyIiIgVg96HJy8uLf/3rX9y4cYPs7GzWrfvvO9Xc3d1xdnbm559/BrAuwAaoV68eDz/8MGvWrAFuLRrv3bs3SUlJpKam8sADD1CpUiVSUlJYv349mZmZZGXdeoebk5MT165ds+mLr68v69evtwaxlStX0rRpU2rVqnVX9zhjxgxatmzJSy+9hMlkuqu2RERE7JXdTM/ltqYJYNasWRw+fJjg4GDc3Nzo2bMnmzdvBsDZ2ZmRI0cydepU3NzcaNeuHc2bN7eeGx4ezptvvklsbCzly5dn3LhxNGjQgAEDBjBp0iR69uxJ7dq1efXVVzlx4gT9+vVj1apV+Pv7M2bMGPr375+jLyEhISQnJ/P8889jsVioW7cu06ZNu6ugc/HiRbZu3cp9993H119/bd1et25d5s6dW+B2RURE7I3J8sdPs0RyYZqVZf1sGRvy3x2WdbkcbR9K6lx7cVJNcqe62FJNbKkmtkpqTex+ek5ERETECLuZniurfvnlF8aNG3fb/SNGjMDX1/ce9khERKRsUmgq5Ro1akRMTEyRtX/I97v/DpGOsd8pOREREU3PiYiIiBig0CQiIiJigEKTiIiIiAEKTSIiIiIGKDSJiIiIGKDQJCIiImKAQpOIiIiIAXpOk+TJO74FxN96lYr1NSp2/AoVERGxXxppEhERETFAoUlERETEAIUmEREREQMUmkqh999/Hx8fHxISEoq7KyIiInZDoamUyczMZMuWLbz00kusXbu2uLsjIiJiNxSaSpn4+HgeffRRunfvzqFDh7h69SoAqampjB49ms6dO/P8888THR3NU089ZT0vOjqakJAQAgMDef755zly5Ehx3YKIiEippNBUysTExNCjRw/Kly9Phw4d2LhxIwDLli3j6tWrbNy4kXnz5hEXF2c9Z8eOHURGRvL++++zfv16/vGPfxAaGkp2dnZx3YaIiEipo9BUivz222+cOXOG1q1bA9CjRw/Wrbv1zKSDBw/SqVMnnJ2dcXV1JTAw0Hre9u3b6dq1K3Xq1AGge/fupKWlcfjw4Xt/EyIiIqWUHm5ZisTGxnLp0iXat29v3Xbjxg2++eYbUlJScHNzs26vXbu29XNKSgqHDh1iz5491m2Ojo5cvny5QP3QAnTVIDeqSe5UF1uqiS3VxFZx1cTLy+u2+xSaSomMjAw2bdrE6tWrqVevnnX7ypUrWbt2LZUqVeL69evW7RcuXLB+rlmzJo0bN2bEiBGF0pe8/kLZg4SEBLuvwV+pJrlTXWypJrZUE1sltSaanisltm/fTt26dXMEJgA/Pz/i4+Px9PRk27ZtmM1mUlNT2bBhQ45jtm7dysWLFwE4ffo0oaGhpKen39N7EBERKc000lRKxMbG0qlTJ5vtNWrUoHHjxtSqVYvffvuNwMBA6tati7+/P8ePHwegbdu29O/fn+HDh5OdnY2zszODBg2iQoUK9/o2RERESi2FplJiyZIlt923ePFiAPr374/JZAJg586dVKlSxXpMr1696NWrV9F2UkREpAzT9FwZsWHDBvr37096ejpms5mtW7fSsmXL4u6WiIhImaGRpjLC39+fw4cP06tXLxwdHWnUqBGvvPJKcXdLRESkzFBoKiOcnZ0JCwsr7m6IiIiUWQpNkqdDvt/992efY9YVb2dERESKkdY0iYiIiBig0CQiIiJigEKTiIiIiAEKTSIiIiIGKDSJiIiIGKDQJCIiImKAQpOIiIiIAQpNkifv+BaYZmWBqcet/0REROyUQpOIiIiIAQpNIiIiIgYoNImIiIgYoNAkIiIiYoBC013Yvn07gwYNokePHvTs2ZO+ffsSHR1t6NwhQ4bw8ccfAzBx4kQ2bNhQlF0FYNeuXXTo0IEZM2YU+bVERETKGqfi7kBp9cknnxAVFcXUqVPx8vIC4OjRo4SFhXHy5ElGjx5tuK0333yzqLpptXz5cvbt20fTpk2L/FoiIiJlkUaaCiAlJYUFCxYQFhZmDUwAjzzyCNOnT2f16tWcPn0ab29vNm/ezKBBg/D392fkyJGkp6fbtPfnUachQ4awbNkyRo4cSWBgIH369OHXX38FIDs7m8WLF9OzZ0+eeeYZXnzxRU6fPm2ozy1btmT+/Pm4u7sXQgVERETsj0aaCuD777/HZDLxxBNP2Ox76KGHqF+/Pl9//TUAX331FYsWLSIzM5Pg4GC2b99O9+7d82x/8+bNLFq0iKpVqxIaGsqKFSuYNGkSUVFRbN26lY8++oiqVauyaNEipkyZwqJFi/Lts6enZ8Fu9i8SEhIKpZ3STnWwpZrkTnWxpZrYUk1sFVdN/jwY8lcKTQVw7dq1PEdsatSowdWrVwHo1q0bjo6OODo68uCDD5KYmJhv+08//TRVq1YFoEmTJtYAtn37dkJCQqz7+vbtS2RkJElJSdSoUeNub8uQvP4y2YuEhATV4S9Uk9ypLrZUE1uqia2SWhOFpgKoVq0aSUlJWCwWTCaTzf6LFy9SvXp1AKpUqWLd7ujoiNlszrf9251z7do1li1bxurVq6373dzcSE5OvmehSURExF4pNBWAp6cnTk5O7N69Gx8fnxz7fvnlF06dOsXf//73Qr9urVq18PPzIzg4uNDbFhERkbxpIXgBuLi48M9//pN33nmHQ4cOWbf/+uuvhIWFMWDAAGrVqlXo1+3QoQPr168nNTUVgB9//JFJkyZhsVgK/VoiIiKSk0aaCqhXr17UqFGDDz/8kEuXLuHg4ICrqyv9+vXLd6F3QQUFBZGcnMyAAQMwmUxUrFiRl156Kdcpwr8aMmQIycnJXLx4EQcHBw4cOECzZs3uyeMOREREygKTRcMUkgfTrCwALGNDbm2wrCvG3pQMJXWBYnFSTXKnuthSTWypJrZKak00PSciIiJigKbnyoD9+/czc+bM2+6fMmUKjz766D3skYiISNmj0FQGtGnThpiYmCJp+5Dvd7eGSMdoWk5EROybpudEREREDFBoEhERETFAoUlERETEAIUmEREREQMUmkREREQMUGgSERERMUChSURERMQAhSbJk3d8i1uvUjH1uPWfiIiInVJoEhERETFAoUlERETEAIUmEREREQP07rkCCAgIICsrCxcXlxzb+/XrR1BQUKFcIzg4mPDwcBo1aoS3tzcrVqygadOmBW5v27ZtLFu2jPT0dBwdHfm///s/unTpUih9FRERsQcKTQU0evRo/Pz8iqz9wnwB79GjR3n77bdZsmQJf/vb3zh8+DBDhgzhscceo3bt2oV2HRERkbJM03OFbOfOnfTt25fg4GCCgoLYuHGjdV9AQACrVq1i8ODBdOjQgbfeeosDBw4wcOBAOnXqREREhPVYb29vfvrppxxtT5gwIccxAAMGDCA6OjrPPlWuXJl33nmHv/3tbwA0b96cypUr8+uvv97t7YqIiNgNjTQVotTUVMLCwoiMjKR58+bs27ePUaNG8dRTT1G1alUA9u/fz4IFCzh79iw9e/bEbDazdOlSTp06Rc+ePenbty916tTJtf2AgAAmTpzIyJEjcXZ25uzZsxw7doxOnTrl2a/77ruP++67z/r922+/5caNGzRu3Ljwbl5ERKSMU2gqoIiICObPn59jW2hoKLt27aJ8+fIAtGrVCrPZzLlz56yhycfHBycnJ+rXr0/58uXx8fHBZDJZvycmJt42NLVu3Zpy5cqxe/du/Pz82LZtG0888QRVqlQx3O/jx48zYcIExo0bR7Vq1e74vhMSEu74nLJIdbClmuROdbGlmthSTWwVV028vLxuu0+hqYByW9OUnZ3N0qVL2bFjBxkZGZhMJuv2P7i6ulo/Ozg4UKlSpRzfzWbzba/p6OhI165d2bBhgzU0DRs2zHCf9+/fzxtvvMGoUaPo2rWr4fP+LK+/TPYiISFBdfgL1SR3qost1cSWamKrpNZEoakQbdq0iejoaJYsWULdunVJT0/nySefLNRrdO/end69e/Ptt9+SlJRE27ZtDZ23b98+pkyZwsyZM3nssccKtU8iIiL2QKGpEKWmpuLu7k6dOnUwm80sX74cZ2dn0tLSCu0aDRs2pFmzZkycOJHOnTvj5JT/H+GlS5eYOHEic+bM4dFHHy20voiIiNgT/XquEPn7+1OpUiUCAwMZOHAgzZs3p0OHDoSFhRXqL9W6d+/O2bNn6datm6Hj169fT1paGpMmTSI4ONj639q1awutTyIiImWdRpoKIC4uLtftbm5uREZG5tj25+mzv563Z8+e234/dOhQrp8B6tWr9//bu/P4mK7/8eOvmSRCCCEI5WNr7aSU0hLRRFbEkqSUJrWvtZaKrSgllSpabe1qqz2h1BKl1tRSO1WtpVVrRIgIkWVyfn/k6/4yJsvQkEjez8cjj0fues59z5mZ95x77r1UrVqVmjVrmlXfbt260a1bN7PWFUIIIUT6pKfpJfPo0SPmzp1LQEBATldFCCGEyFek2nOVrQAAIABJREFUp+klsmnTJubMmYOrq6vRqbkFCxawdevWDLfLzruLCyGEEPmVJE0vER8fH3x8fEzm9+zZk549e+ZAjYQQQoj8Q07PiUwdcTmJGm4JakPqnxBCCJFPSdIkhBBCCGEGSZqEEEIIIcwgSZMQQgghhBkkaRJCCCGEMIMkTUIIIYQQZpCkSQghhBDCDJI0CSGEEEKYQZImIYQQQggzSNIkhBBCCGEGSZqEEEIIIcwgSZMQQgghhBkkaXoGPj4+eHt74+fnZ/S3fv36bCvDz8+PixcvAtCwYUPOnj37n/ZnMBhYsGABjRs3ZseOHdlRRSGEECJfsczpCryshg0bhpub23Pbf2hoaLbu76OPPqJcuXKUKFEiW/crhBBC5BfS05TNfvnlFzp37oyfnx/t27fnp59+0pb5+PiwatUqevbsSYsWLfjss884fPgw3bp1w8PDgy+//FJbN73epVGjRhmtA9C1a1fWrVuXZb26devGiBEjsLSUPFkIIYR4FvINmo3i4uIYM2YM8+bNo27duvz6668MHTqUZs2aUaxYMQAOHjzInDlzuH79Or6+vhgMBhYtWsSVK1fw9fWlc+fOlC1bNt39+/j4MG7cOAYNGoSVlRXXr1/n/PnzeHh4ZFm3evXqZeuxCiGEEPmNJE3P6Msvv2T27NlG84KCgti9ezfW1tYAvPnmmxgMBm7cuKElTe+88w6WlpZUqFABa2tr3nnnHXQ6nTZ98+bNDJOmxo0bU6BAAfbs2YObmxvbt2+nadOmFC1a9Lke69GjR5/r/l9GEhNTEpP0SVxMSUxMSUxM5VRMGjRokOEySZqeUXpjmlJSUli0aBE7d+4kISEBnU6nzX+sSJEi2v96vZ7ChQsbTRsMhgzLtLCwoGXLlmzcuFFLmvr27Ztdh5ShzBpQfnT06FGJyRMkJumTuJiSmJiSmJjKrTGRpCkbbd68mXXr1rFw4ULKlSvHo0ePcHJyytYyWrduTceOHTlx4gRRUVE0adIkW/cvhBBCiPTJQPBsFBcXh729PWXLlsVgMLBkyRKsrKx4+PBhtpVRqVIlateuzbhx4/D09JSB3UIIIcQLIklTNvL29qZw4cK0bduWbt26UbduXVq0aMGYMWP4+++/s62c1q1bc/36dVq1amXW+jdv3tTuJXXr1i2+/PJL/Pz8WL16dbbVSQghhMjrpJviGWzatCnd+XZ2dsybN89oXtrTZ09ut2/fvgynjxw5ku7/AOXLl6dq1arUrFnTrPqWKVMm2+/7JIQQQuQ30tP0knn06BFz584lICAgp6sihBBC5CvS0/QS2bRpE3PmzMHV1dXo1NyCBQvYunVrhttJL5MQQgjx30nS9BLx8fHBx8fHZH7Pnj3p2bNnDtRICCGEyD/k9JwQQgghhBkkaRJCCCGEMIMkTUIIIYQQZpCkSQghhBDCDJI0CSGEEEKYQZImIYQQQggzSNIkhBBCCGEGSZqEEEIIIcwgSZMQQgghhBkkaRJCCCGEMIMkTUIIIYQQZpCkSQghhBDCDJI0CSGEEEKYwTKnK/Ai+Pj4kJycjI2NjdF8e3t75s2bZzTv+vXrtGnThh07dmBnZ/dc6rNz504cHR0pVaoUq1ev5tKlS4waNeq5lAVw5coVQkJCuHbtGgANGjRg2LBhFCxY8LmVKYQQQuQ1+SJpAhg2bBhubm45XQ0A5s6dy4QJEyhVqhQdO3Z87uUFBQXh4eHBrFmziI+Pp0+fPixfvpyePXs+97KFEEKIvCLfJE0ZSUpKYurUqfz6668UL16cVq1aacvS63Xq0KEDgYGB+Pj4cPnyZaZMmUJkZCQFChSge/fueHl5ATBv3jzCw8NJSUnB1taWoKAgateuzYgRI7h06RJBQUF0796dW7du8ccffzBz5kySk5OZPXs2u3fvRqfTUbJkSYKCgqhcuTKbNm1i8+bNvP766+zevZt79+7RrVu3LJMug8FAly5dcHZ2BqBQoULUr1+fixcvPqeICiGEEHlTvk+aNm/ezLFjx1i7di2FChVi4sSJZm8bFBSEv78//v7+/Pvvv3Tq1InatWvz77//sm7dOsLCwihSpAizZs0iODiY5cuXExISQsOGDZk6dSq1atVi7ty52v7Wrl3LgQMHWLJkCUWKFGHx4sWMGjWKlStXAnD69Gnee+89+vXrx759+wgKCsLX1xcrK6sM62hhYYGnp6c2ff36dfbt20efPn3MPs6jR4+avW5+ITExJTFJn8TFlMTElMTEVE7FpEGDBhkuyzdJ05dffsns2bON5vn7+3PmzBmaN29O4cKFAWjXrh0//fRTlvv7999/uXDhAj4+PgBUqFCB7du3Y2Njw//+9z82bdqEtbU1AG+++Sbr1q3Lcp+7du2iXbt2FClSBICOHTvy7bffcuPGDQCKFi3KO++8A0DNmjVJTEwkOjqaMmXKZLnvmJgYunbtSlRUFO+//z4eHh5ZbvNYZg0oPzp69KjE5AkSk/RJXExJTExJTEzl1pjkm6QpozFNAwYMoGrVqtp08eLFzdpfTEwMBQoU0BIjQEu8YmNjmTlzJidPnkQpRUJCAikpKVnuMzo62qj8QoUKUaBAAe7cuQOAra2ttszSMvWlM2e/AHZ2dmzYsIGYmBg+/fRTgoODGT16tFnbCiGEEEJuOYCtrS1xcXHadHR0tPa/Xp8aHoPBoM27f/8+ACVKlCAxMZEHDx5oy27evMnDhw+ZPn06kZGRLF26lLCwMLOvjCtZsqSWIAE8ePCAhIQESpYs+WwH93/13bBhA0opIDV5ateuHXv27HnmfQohhBD5Ub5Pmho0aMDevXuJj48nJSWFDRs2aMvs7e2xsrLi3LlzANoAbIDy5ctTrVo11q5dC6SOFerYsSNRUVHExcVRuXJlChcuzP379/nxxx9JSkoiOTkZSO0lio2NNamLi4sLP/74o5aIrVy5klq1auHg4PDMx2dlZcWsWbPYuHEjkJoA7t69mxo1ajzzPoUQQoj8KN+cnktvTBPAtGnTOH36NH5+ftjZ2eHr68uWLVuA1IRj0KBBTJo0CTs7O5ydnalbt662bUhICBMnTiQsLAxra2tGjBhBxYoV6dq1K+PHj8fX15cyZcrw0UcfcenSJQICAli1ahXe3t4MHz6cwMBAo7r4+/sTHR1Nly5dUEpRrlw5pkyZgk6ne+bjLliwIF999RXTp09n4cKF6HQ6qlatKqfmhBBCiKekU4/P2wiRjtw6GC8nSUxMSUzSJ3ExJTExJTExlVtjku9PzwkhhBBCmCPfnJ7Lqy5evMiIESMyXD5gwABcXFxeYI2EEEKIvEmSppfcq6++SmhoaE5XQwghhMjz5PScEEIIIYQZJGkSQgghhDCDJE1CCCGEEGaQpEkIIYQQwgySNAkhhBBCmEGSJiGEEEIIM0jSJIQQQghhBkmahBBCCCHMIEmTEEIIIYQZJGkSQgghhDCDJE1CCCGEEGaQpEkIIYQQwgxZJk03b95k9OjR+Pr64ufnR9u2bZk4cSJxcXEvon4A3Lp1Cz8/P+7fvw/Azp07iYqKAmD16tUEBwdne5m//vor77zzDvPmzcv2fWeXmJgYtm3bZvb6Fy9epHPnznTo0OE51koIIYTIm7JMmkaMGMErr7zC2rVrCQ0NZcWKFURGRhISEvIi6gdA6dKlCQ0NxdbWFoC5c+dqSVPHjh0ZNWpUtpcZGhrKkCFD+Omnn0hJScn2/WeHI0eOmJ00HTt2jKCgIBwdHZ9zrYQQQoi8yTKrFS5dukSvXr2wsLAAoHDhwkydOhWdTgdASkoKixYtYsuWLSQnJ1OuXDnGjBlD+fLlAdi4cSNLlixBKUWZMmUYM2YM5cqVo3fv3jRr1ozAwEAAli1bxr59+5g3bx5z587l8uXLxMTEUKJECfr370+bNm3YsWMHU6ZM4dKlSwQFBdG9e3du3brFH3/8wcyZMzOtS3x8PBMnTuTPP/8EoFSpUnzyySdaPdO6desW586dY+rUqezatYtff/0VJycnbXlGx3T79m2Cg4O5cOECFhYW+Pn58f777wOwZcsWlixZQnJyMtbW1vTt2xdnZ2euX7+uHZudnR0AHTp0IDAwEB8fH3x8fOjSpQvbt2/n2rVrODg4EBISwj///ENwcDCJiYl06tSJlStXZvo6FitWjEWLFrFnzx6OHTuW1csuhBBCiCdkmTS5uLgwceJE/P39ady4MbVq1aJIkSLa8hUrVhAeHs73339PsWLFmD9/Pp9++inz58/n7NmzfP3116xYsYLSpUtry8w55RUREcHSpUupWLEi169f1+aHhITQsGFDpk6dSq1atZg7d65Zdfnpp5+4e/cuoaGh6HQ6wsLC2Lt3L507dzYpe8OGDXh7e2NpaUnbtm0JCwvTkqbMjmny5MlUqVKFL7/8krt379KxY0eqV69OkSJFCA4OZtmyZVSqVIlTp07Rr18/1q9fn2UcIPV05Ndff42VlRU9evQgLCyM3r1706FDBy1hzMqrr75qVlnpOXr06DNvm1dJTExJTNIncTElMTElMTGVUzFp0KBBhsuyTJrGjx/Pxo0b+fnnn1m+fDkpKSk4OTkxaNAgypUrx44dO/D396dYsWIAdO7cmXnz5hEVFcWuXbt48803KV26NADdunXTepayUqVKFSpWrGjWuo9lVpeSJUvy999/s2PHDho1aoSvr2+6+zAYDPz444/Mnj0bAGdnZ7744gtu3bpF6dKlMzymhIQEfv31VwYPHgxA8eLF2bBhAwULFmT+/Pk0adKESpUqAeDo6Mirr77KoUOHMn1xHvPw8KBgwYIAVKtWjZs3bz5VXP4rc+qYnxw9elRi8gSJSfokLqYkJqYkJqZya0yyTJosLS3x9fXF19eX5ORkzpw5w5w5cxgyZAhr164lNjaWxYsXs2bNGm0bOzs7oqOjiYmJ0cYhPd6XpWWWRQJoic/TyKwuLi4uxMfHs3btWsaPH4+joyNBQUFUrlzZaB/79+/n9u3bfPDBB9q8xMREfvzxR3r16pXhMd2+fRuDwWDUC2djYwNAdHQ0xYsXNyrncb3MkbY8CwsLEhMTzdpOCCGEENkn0wzm7t27nD17lqZNm6aubGlJvXr1GDJkiNa74uDggJubG35+fibb29vbc/HiRW06KSmJyMhIypcvj4WFBQaDQVsWGxv7nw8ms7oAtGzZkpYtWxIXF8eMGTOYMmUK8+fPN1onLCyMMWPG0KZNG23eX3/9xUcffUSPHj0yPKYyZcqg1+uJiYmhZMmSANy+fRtra2tKlizJhQsXjMq5e/cuJUuWRK9PHYufNhaPrxIUQgghRO6R6dVzDx8+ZMSIEUZXkCUkJLB582Zq1qyJtbU1LVq04Mcff9RuQfD7778zfvx4lFK4uLhw+PBhrly5AsDKlSuZMGECkJrgPB6UHRMTw549e8yutKWlZbpJVmZ1WbBgAYsXLwagSJEi1KhRw2T7GzducOTIEVxcXIzmV6tWDWtra3799dcMj8nS0hJnZ2fWrl0LpCaBXbp04Y8//uCdd97hwIEDXL58GUi9ku3y5cu89dZb2NvbY2Vlxblz5wDYvXs39+7dMysOVlZW3L9/H6WUWesLIYQQ4tll2tNUrlw55syZw/z581m4cCE6nQ6DwcAbb7zB9OnTAWjfvj3R0dF07doVnU5HoUKFGDhwIDqdjurVqxMUFMSAAQOwsLCgdOnSjB8/HoDAwEDGjBlDu3btqFChAt7e3hw4cMCsSnt7ezN8+HCT8VGZ1aV169Z89tlnbNiwASsrK+zs7AgKCjLafv369TRq1MjodNhjbm5urF+/ni+//DLDYxozZgyTJk2iVatWWFtb07FjRxo1aqQtCwoKIikpCRsbG0JCQrQeqUGDBjFp0iTs7Oxwdnambt26ZsWhSZMmrFq1Ci8vL9auXUvRokUzXPerr75i7969xMXFERsbq/XGhYaGmlWWEEIIkd/plHRTiEzk1sF4OUliYkpikj6JiymJiSmJiancGhN5jIoQQgghhBnMu5RN5Gr379+na9euGS7v0KEDHTt2fHEVEkIIIfIgSZryAFtbWxmblIZSisWLFxMaGkpSUhIGgwEnJyeGDRuGra0tI0eOpEKFCvTv3/+51SE2NpbRo0dz/vx5rKys6N+/Py1btnxu5QkhhHj+JGkS2UY3Lfm57l8NN6+5Tps2jcOHD7Nw4UIcHBx4+PAhkydPpk+fPvzwww/PtY5p61C2bFm++eYbbt68Sfv27WnQoAEODg4vpHwhhBDZT5ImkafExMSwbNky1q9fryUoNjY2jBs3joiICJPbMxw/fpxJkybx8OFD9Ho9Y8eOpUmTJiQnJzN+/HiOHDlCSkoK1atX5/PPP6dgwYLMmzePf/75x2h+2puaAoSHh7NixQoAypQpQ6NGjdi5c2e6j+0RQgjxcpCB4CJPOXnyJGXKlDF51p61tTWurq7azUQfGzduHD169GDbtm307t1bu33E/v37uXr1Ktu2bWP79u289tprHD9+nP379xMVFWUyP627d+8SExNDhQoVtHkVKlTg0qVLz+mohRBCvAjS0yTylJiYGOzt7c1ef8OGDeh0OiD1GXuPb1paokQJLl68yM8//4yTkxNDhgwB4NSpU1y7ds1kflqPHj1Cr9djZWWlzbO2tubOnTv/5dCEEELkMOlpEnlK8eLFiYyMNHv9TZs24e/vj6enJ927d9dO3zk6OjJ27FiWLVtG06ZNGTZsGLGxsTg6OtKlSxeT+WkVKlSIlJQUo2cEPnr0SHsWoRBCiJeTJE0iT6lXrx7R0dH8/vvvRvOTkpKYMWMG8fHx2rzIyEjGjh3L5MmTCQ8PN3kOoZeXF8uWLWPXrl3Ex8ezcOFCABo3bpzu/Mfs7OwoUaKE1msFcPnyZV577bXsPlwhhBAvkCRNIk8pWrQoPXv2JCgoSHvWX3x8POPGjePs2bMUKlRIW/fOnTvY2NhQpUoVkpOTWb16NQAPHjwgNDSUb7/9FkhNgqpUqQKkPnYmLCzMZP6TvL29WbJkCQAXLlzg8OHDtGjR4vkctBBCiBdCxjSJPGfgwIEUK1aMfv36YTAY0Ov1tGjRQntY9GM1atTA2dkZT09P7O3tGTlyJMeOHSMwMJBFixYxevRoPDw8sLCwoGLFinz++edAauKU3vy0PvroI0aOHIm7uzvW1tZMnjxZe9agEEKIl5M8e05kKrc+/ycnSUxMSUzSJ3ExJTExJTExlVtjIqfnhBBCCCHMIEmTEEIIIYQZJGkSQgghhDBDnk2atm3bxgcffICvry9t27ale/fu/PLLL9mybx8fH3bs2AFAv379OHjwIAB//PEHJ0+e1NZTSrFq1So6deqk1aN///4cOXIkW+oB0LBhQ86ePWvWuhcvXqRz58506NAh28oXQggh8os8efXckiVLWLNmDcHBwTg6OgJw6NAhRo8ezcOHD2ndunW2lTV79mzt/40bN/LKK6/w+uuvAzB16lROnDjB559/TqVKlVBKER4eztChQ5kxYwYNGzbMtnpk5dixY0yZMoWGDRty7NixF1auEEIIkVfkuaTp/v37zJ8/n5CQEC1hgtQbEs6YMYNixYrRu3dvHB0diYiIwMfHh86dO7Nu3TpWrVpFUlISdnZ2jBw5kpo1awKwatUqli9fjo2Njcm9dnx8fBg8eDDXrl1j48aN2NjYcPnyZQICAli/fj0rVqygUqVKAOh0Ory8vChRogTly5cHUu/hExISwt27d3n06BEuLi4MHToUnU7HhAkTKFSoEGfPnuWNN95g8ODBfPvtt2zZsoWiRYvi5+dndlyKFSvGokWL2LNnjyRNQgghxDPIc0nTqVOn0Ol0NGnSxGRZ2iTq4MGDfP/99xQsWJCdO3cyb948lixZQtmyZfnpp58ICgpiw4YNXLt2jZkzZ7JixQqqVKnC+vXruXXrlsm+u3TpQkREBM2aNSMwMJB169ZRsWJFkwfHAjRq1Ej7f9KkSbi4uNC1a1eio6Px9fWlYcOGODs7A7B3716WLVtGiRIlOHz4MGFhYaxevZqSJUsyZ84cs+OSXj3MdfTo0WfeNq+SmJiSmKRP4mJKYmJKYmIqp2KS2a0O8lzSFBsba9YDW9966y0KFiwIwI4dO2jZsiVly5YFoHXr1sycOZPTp09z8eJFqlWrpt35uU2bNnzxxRdZ7v/evXtm3cww7SM47O3tefXVV/n333+1ea+//jolSpQA4LfffuPNN9/U9uvr68uCBQuyLOO/yo33yshJufX+ITlJYpI+iYspiYkpiYmp3BqTPJc02dnZERUVRUpKCnp9xuPcixUrpv1///59jhw5wr59+7R5FhYW3L17l3v37hmta2FhQdGiRc2qx82bN7Ncb/fu3axcuZI7d+6g1+u5efMmLi4u6dYzJibGqOzixYtnuf/8SCnF4sWLCQ0NJSkpCYPBgJOTE8OGDcPW1paRI0dSoUIF+vfv/1zrsX//fj7++GMCAwOfe1lCCCGevzyXNNWtWxe9Xs8vv/yCm5ub0bL9+/fz8OFDk21Kly5NjRo1GDBggMmy27dvExcXp00nJycTExOTZT0aNWrE1KlTOXv2LLVq1TJatnnzZkqVKsUrr7zCqFGj+Prrr3n77bcB+OCDDzLcZ9GiRblx44Y2HR0dnWU9Xihdu+e7f7XBrNWmTZvG4cOHWbhwIQ4ODjx8+JDJkyfTp08ffvjhh+dbx/+zadMmfvjhB5PXXgghxMsrz91yoEiRIvTv358vvviC3377TZv/22+/8emnn2JpaZonurm5ER4ezu3btwG4evUqQUFBPHr0iDfeeINz585pD3/dsGEDGT15xsrKitjYWAD+97//0bFjR8aOHcuff/6prbN9+3a+/PJLChYsyMOHD9HpdNqA8507d3Ljxo10EzuAN954g99++407d+4Aqc9AE8ZiYmJYtmwZn3/+OQ4ODgDY2Ngwbtw4evbsafLaHT9+HF9fX7y8vGjZsiW//vorkJocjxkzBk9PT9zd3RkwYABxcXEkJyczb948k/lPqlKlCkuXLqVUqVLP/6CFEEK8EHmupwmgU6dO2NvbM2vWLO7du4eVlRWlS5cmODiYhg0bsmrVKqP1mzRpop1CSUlJwcrKiu7du1OwYEGqVKlC//796d+/PzY2NrzzzjtUrFgx3XLd3d2ZNm0aZ8+e5dtvv+Wjjz7if//7H+PHjycxMRGdTkelSpWYPXs21atXB6Bt27Z06tSJYsWK4e3tTZ8+ffjmm2+oUKGCyf6bNGlCy5YtCQgIwNbWFl9fXwoVKmRWTL766iv27t1LXFwcsbGx2pV3eS3xOnnyJGXKlDEZ+G5tbY2rq6vJ+uPGjaNv3760atWKDRs2MH78eH7++Wf279/P1atX2bZtG5Aav+PHj2MwGIiKijKZ36xZM6P91q5d+zkdoRBCiJwiD+wVmXqqwXi54PTcjz/+yKpVq1i5cmWG66Qd02QwGNDpdOj1eq5cuYK7uzvnzp3j1KlT9O/fn3HjxuHk5ISNjQ2QenVmr169mDRpktF8c8rKy3LroM2cJnExJTExJTExlVtjkudOz4n8rXjx4kRGRpq9/qZNm/D398fT05Pu3btrp+8cHR0ZO3Ysy5Yto2nTpgwbNozY2FgcHR3p0qWLyXwhhBB5nyRNecD9+/fx8/PL8G/16tU5XcUXpl69ekRHR/P7778bzU9KSmLGjBnEx8dr8yIjIxk7diyTJ08mPDyc+fPnG23j5eXFsmXL2LVrF/Hx8drtIRo3bpzufCGEEHlbnhzTlN/Y2trmubFJz6po0aL07NmToKAgvv32WypWrEh8fDwTJ07k9u3bRmPA7ty5g42NDVWqVCE5OVlLLh88eMC2bdu4efMmH374IXZ2dtp9ukJDQzl27BgNGjQwmi+EECLvk6RJ5DkDBw6kWLFi9OvXD4PBgF6vp0WLFkyYMMFovRo1auDs7Iynpyf29vaMHDmSY8eOERgYyKJFixg9ejQeHh5YWFhQsWJFPv/8cyA1cUpvflqjRo3i+PHjREVFYWVlxcaNGwkICCAgIOBFhEAIIcRzIAPBRaZy62C8nCQxMSUxSZ/ExZTExJTExFRujYmMaRJCCCGEMIMkTUIIIYQQZpCkSQghhBDCDJI0CSGEEEKYQZImIYQQQggzSNIkhBBCCGEGSZqEEEIIIcwgSZMQQgghhBkkaRJCCCGEMIMkTUIIIYQQZpCkSQghhBDCDJI0CSGEEEKYwTKnKyByr8fPck5ISMjhmuQ+EhNTEpP0SVxMSUxMSUxM5WRMChQogE6nM5mvU4+/GYV4QkJCAmfOnMnpagghhBAvVJ06dbC2tjaZL0mTyJBSisTExJyuhhBCCPFCSU+TEEIIIcR/IAPBhRBCCCHMIEmTEEIIIYQZJGkSQgghhDCDJE1CCCGEEGaQ+zTlYwsWLGD//v0opXBycqJXr15Gyzds2EBYWBiWlpZUq1aNESNGoNfr2bdvHwsWLMDKyoqSJUsyYcIEChYsmENHkb2eNSZNmjShTp062nodOnTAzc3tRVf/ucgqJg8ePOCzzz7j5MmTbNmyRZufn9tJRjHJy+0Eso7L6tWr2bx5M3q9nvLlyzN+/HisrKwyfF/lBc8Sk6ioKDp37ky1atW09fr370+9evVedPWfi8xiopTiu+++4/Dhw1hZWVGiRAnGjx9P4cKFc8dnihL50unTp1WnTp1UYmKiSkxMVIGBgerEiRPa8ps3b6pWrVqp2NhYlZKSooYOHaq2bt2qHj16pDw9PdX169eVUkqFhISohQsX5tRhZKtnjYlSSrVu3Tqnqv1cZRUTpZQaMWKEWr16tfL29tbm5ed2olT6MVEq77YTpbKOy/nz51X79u1VQkKCUkqp4cOHq02bNmX6vnrZPWtMrl27pnr16pVT1X6usorJ2bNn1ccff6xSUlKUUkp98slsN23HAAAY90lEQVQn6ocffsg1nyl5I5UXTy0iIoLmzZtjZWWFlZUVzs7OREREaMsPHz5Mw4YNsbW1RafT4ebmRkREBKdPn6ZChQqULVsWAHd3d6PtXmbPGpO8LKuYAHzyySc4OTkZzcvP7QTSj0lel1VcqlSpwrJlyyhQoAAAxYsXJyYmJk+/r541JnlZVjGpWbMmISEh6HQ6kpOTuXPnDmXKlMk1nymSNOVTt2/fxt7eXpsuWbIkUVFR2nRUVJTRcnt7e6Kioky2s7e35/bt2y+m0s/Zs8YEUk/HjB49mh49ejBhwgTu3r374ir+HGUVE4AiRYpkuV1+aieQfkwg77YTyDouer2ewoULA3Dt2jUiIiJwc3PL9H31snvWmABERkby8ccf0717d7744gsePXr0Yiv/nJjz/gH45ptvaN26NZUqVcLV1TXXfKZI0iSA1PPI6d39NKvlKg/fG/VpYjJgwADGjBnDwoULKVGiBNOnT39R1XyhsopJZtvlVU8Tk/zSTiDjuPz9998MHDiQMWPGUKZMGbO3ywvMjUmxYsXo1asXkydPZt68edy7d4/vv/8+B2r8/GUUkwEDBrBp0yZiYmJYvnx5utvlBEma8ikHBwejLD0qKgoHBwej5Wmz/9u3b1O6dOkst3uZPWtMAHx9fbVfjF5eXvz5558vqNbP17O+3vm5nWQmr7YTMC8uly5d4qOPPmLcuHE0adJE2y6j99XL7lljUrhwYVq3bk2BAgWwtLTEzc0tz7SVrGLyzz//cO7cOQCsrKxwc3Pjt99+yzWfKZI05VNOTk7s3r2bhIQEEhIS2LVrF82aNdOWN27cmGPHjhETE0NKSgrh4eE4OztTp04drl+/ztWrVwHYunUrzs7OOXUY2epZY3Lp0iWGDBlCcnIykDr2qXr16jl1GNkqq5hkJD+3k4zk5XYCWcclKSmJ0aNHM2XKFKOrwDJ6X+UFzxqTQ4cO8emnn2q9KXmprWQVk2vXrjFlyhSSkpIAOHnyJFWqVMk1nyny7Ll8bOnSpezYsUMbfBkYGMioUaMYOnQopUuXZsuWLaxatQoLCwscHR0ZMmQIOp2OgwcPMnv2bCwsLChfvjxjx47VBjK+7J41JnPnzmXv3r3Y2NhQpEgRxowZQ8mSJXP6cLJFZjEpXrw4H374IYmJifz555/UrVuXmjVrMnTo0HzbTjKLSV5uJ5B5XE6dOsVnn31mdBl948aN6dGjR4bvq7zgWWLSpUsXQkJC+OOPP7CysqJcuXKMGjUKGxubHDyS7JPV5+y8efOIiIjA0tKS4sWLM27cOIoWLZorPlMkaRJCCCGEMIOcnhNCCCGEMIMkTUIIIYQQZpCkSQghhBDCDJI0CSGEEEKYQZImIYQQQggzSNIkxFMICgpi1qxZALi6umr3DElMTCQkJISWLVvi5eVFixYt6NmzJ+fPn9e2rV69OqdPnzbZZ/369Tl06JDRvL1799KwYUO++eYbk/WrV6+Oq6srXl5eeHp64unpSXBwMAkJCdl2nLNmzaJPnz7/eT+BgYEcOnSIM2fO4OnpyZ07dzJd/8GDB/j4+HDgwIH/XHZuMmLECEJDQ3O6GgCcOXOGY8eO5XQ1nklYWBitW7fOcr2rV69SvXr1LNvb0woPDycyMvI/7+fAgQNcuHAhG2qU/U6dOoWvr682vXbtWuLj4596P+a+Br/88gt+fn7Z+vn1PEnSJISZNmzYwKVLl/jwww9Nls2YMYPff/+dNWvWsG3bNrZv307t2rXp27cvBoPhqctatWoVQUFBhIWFkZKSYrL8q6++Ytu2bYSHh7N27VpOnDjBd99990zH9SLUqVMHPz8/xowZk+l6n332GU5OTrz99tsvqGYvRkhICH5+fjldDQBCQ0M5fvx4TlfjpfT1119z69at/7yf77//PtcmTY6OjoSFhQFgMBgIDg5+pqTJXK6urtSuXZsvvvjiuZWRnSRpErne1atXqVGjBhs3bqRdu3a89dZbrF27lsWLF+Pj44OTkxObNm3S1t++fTtt2rTB3d0dHx8f9u3bpy377bff8Pf3p2XLlri6ujJ//nxtWWBgIPPmzaNXr164ubnRpk0bLl68CKT2JM2cOZNBgwah15u+bS5evEitWrW0B7VaWFgwYMAAVq9ejYWFxVMdb2RkJL///jvt27fntddeY+/evZmuX7RoUfz9/Y2OEyAlJQVnZ2d27typzbt37x516tThn3/+4ebNm/Tu3Rtvb29cXV35+OOP0/2192Sv0+nTp43uTpxZvNMKDAzk5MmTGX5hnz9/nu3bt9O7d29t3jfffIOXlxceHh74+flx6tQpAN577z2WLl2qrWcwGGjatCkRERE8evSIyZMn4+npiYuLCwMGDNB+7YaFhfHBBx8wZMgQAgMDgczbxIULF2jTpg0uLi706dOHadOmabHIrJz0jn3hwoXa/9999x3du3enadOm9O3bl9OnTxMQEICTkxODBw/WEmVXV1fmzZvHu+++i5OTEwEBAdqjJOLi4hg2bJj2+vXt29eo/Llz5+Lu7o6npycDBgwgJiaG+fPnExYWxoIFCxg7dqxJPePi4vjkk0/w9PTEy8uLfv36aT0rs2bNYsSIEYwbNw5vb2+aN29OeHh4usc7cuRIgoOD+fDDD3F2dua9997j3Llz9OjRg+bNm9OlSxcePHgApD42ZdCgQVqZQUFB3L9/H0h9L3zwwQe4urrSuXNn/v77b6NyVq5cibe3N25ubrz77rucOXMm3fqklZSUxLRp0/Dy8sLb25sPPvhAe58/2ZN1584dqlevztWrVxk0aBAXLlxg8ODBrFmzhlmzZjFo0CCGDx+Ou7s7zZo1Y8eOHUDqHb3r169vVO7jHuXPPvuMiIgIJk+erPVap/U07eOvv/4iICBAa7vBwcHaXcQjIyN5//33cXZ2JjAwkPnz52vHltlrmbbu7777Lg8ePOC9995jz549jBw5kokTJ2p13bZtG66urkDqZ+TYsWNxdnamffv2WiweO3z4MP7+/lrv+IYNG7RlH374IaGhoVy7di3L1y/HKSFyuStXrqhq1aqpuXPnKqWUWr16tXr99dfV8uXLtWkXFxellFInT55Ur7/+ujpz5oxSSqmDBw+q+vXrq9jYWJWSkqKaNm2qfvrpJ6WUUufPn1c1atRQ586dU0opFRAQoFq1aqXu3r2rlFJq4MCBauTIkUoppfbv368aNWqkDAaDVi8XFxd15coVpZRSa9asUbVq1VLjxo1Tu3btUvfu3TM5jmrVqqlTp06ZzK9Xr546ePCgNj1r1iw1bdo0pZRS4eHhqm/fvlnu54cfflAdO3Y02ffnn3+uhg8frk2vWbNGdejQQSml1ODBg9Unn3yilFLqwYMHys3NTS1dulQppdTXX3+tevfubfK/UkqdOnVKVatWTSmVebwfxzTtsY0aNUp9+umnJvVUSqnp06erDz/8UJvevXu3evvtt7V9ffHFF6p9+/ZKKaWWL1+uOnXqpK0bERGhmjVrpgwGg5o4caIKDAxUDx8+VAaDQY0ePVp9/PHHSimlQkNDlaOjozpw4IBSSmXZJjp06KCmTp2qLXvzzTe1WGRWzpMCAgLUggULtP/bt2+vHjx4oO7du6fq1aununfvrhISErTpQ4cOKaVS25i/v7+Ki4tTBoNB9enTR40aNUoppVRISIjq2bOnMhgMKjExUXXq1EkFBwcrpVLbjYeHh9buR40apYKCgkzq8qTJkyerHj16qISEBJWSkqLGjRun+vTpo5RKbQf16tVTp0+fVkoptWzZMu1996SgoCDVvHlzdfv2bZWQkKBcXV2Vj4+Pio2N1abDwsKUUkr1799fjR49WhkMBpWUlKT69OmjJk6cqJRSavjw4eqjjz5SKSkp6t69e6p169aqVatWSimltm3bpt5++2119epVpZRS69evVy4uLspgMGifGdHR0SZ1W7x4sWrbtq3WrubOnatat26tUlJSVGhoqLZ/pZSKjo5W1apV097nad97X3/9tapdu7Y6cuSIVp/69eurBw8eqIMHD6p69eoZlZv2fe7i4qK2bt2abuyepn34+/trn4tRUVHqjTfeUDt37lRKKTV06FA1ZMgQlZKSom7evKlcXV21Y8vstUxb9yfjGBQUZPT+3bp1q7bdmjVrlIeHh7p//74yGAwqKChI2/bGjRvq9ddfV7t371ZKKXXhwgVVr1499c8//2j7+uCDDzJsl7mJ9DSJl4aXlxeQOqYnPj6etm3batM3btwAUns9nJ2dqV27NpD6SILKlSuze/dudDodv/zyC97e3gC89tpr2Nvbc/nyZa2MFi1aYGdnB/z/56cBHD16lPr166fbywSpv8jmzJnD3bt3GT16NI0bN6Zz584mY3MGDx6Ml5eX0V/arm+DwcC6deu0UzkuLi6cOXMm03EUkZGRrFixgpYtW5osa9OmDb/88guJiYkAbN68WYvb9OnTtd4GGxsbHB0djWJhjszinZ769etz5MiRdJcdPXqUN954Q5tu3rw5u3btwtbWFoC33nqLf/75BwBvb29OnTqlxWXz5s34+Pig1+vZtm0b3bp1o1ChQuj1erp27crWrVu1X+e2tra89dZbAJm2iUePHnHixAnat2+vLUv7jKysysmMs7MzNjY2FC1alDJlytC8eXMKFCigTT9udwDt2rWjcOHC6PV62rRpw+HDhwEYPnw43377LXq9HisrKxo2bKjFJzw8HHd3d2xtbdHpdEyYMMGohyAjO3bsICAggAIFCqDT6QgMDGTv3r3a8/KqV69OnTp1gNT3x+P3XXrefPNN7O3tKVCgAJUrV+btt9/G1tZWm75+/TrJycns2rWLrl27otfrsbS0pFOnTuzatQtIHfvTpk0bdDodRYsWNWrj27Zto23btpQrV06L08OHDzlx4kSWx/juu+9q7SogIIDz588/Uy9H9erVadCgAQDu7u4opTh79uxT7+dJ5raPlStX0r17dwBKlixJ1apVtffwwYMHadu2LTqdDgcHB1q1amVSd3NfS3McPHiQFi1aUKRIEfR6Pe+++662bNeuXVSpUoXmzZsD8Oqrr9KsWTO2bt2qrZPZZ0NuYpnTFRDCXI9PfT1OXNJOP/6iio2N5cCBA1qCBamDi2NiYoDU8Rzr1q0jLi4OnU7H3bt3jb7kihYtqv1vYWGhLYuOjqZEiRKZ1q9Zs2bal+rFixdZvnw5vXr14ueff6Zs2bJA6likunXrGm2Xtht/9+7d3Lp1C39/f21eQkICa9euZcCAAdq8wYMHU6BAAZRSFC5cGD8/PwICAkzqVLNmTcqWLcu+ffuoV68ex48fZ+bMmUBqd/ns2bO5ceMGer2eqKgoLaEyV1bxflKpUqWMnlSe1pMxvnfvHlOnTuXYsWMopUhISNBOPZQoUYKmTZsSHh5Op06d2LFjB8uXL9e2mzRpElOnTgVAKYWNjY1Wp8dJ8WMZtYl79+4BULx4cW3dsmXLaoP7Mysnq7by+AsbUtvZ47b8eDptm0xbvp2dHbGxsQD8+eefzJw5k7///lur9+OHvsbExBidQjX3+VxRUVFGdbezs8NgMGixK1asWIb1fJZjvHv3LgaDwaTMx20kJibGqMy068XGxnLw4EEtwXq83zt37lC6dOlMjzFtTG1sbChQoADR0dEZbpORtPvR6/XY2tpy7949o2N9Fua2jx07drBkyRKio6PR6/XcuHEDd3d3IDU+T7bdtJ7mtTTHk23uydfq77//NvqciI+P1xJeSP1siIiI+E91eBEkaRJ5ioODA82aNWP69Okmyw4dOkRwcDCrV6+mZs2aANky4DgxMZH9+/fz9ttvU6hQISD1l9T48ePZuHEjFy9eNPnAysjq1auZNGmS0aDhc+fO0a9fP/r3768ljOklXxnx8fEhPDycmzdv0qxZM+zs7IiPj6dv376MHj0af39/9Ho9gwcPTnd7vV5vNJj98Zc2ZB7v/yo4OJhbt26xbt06ihQpwp49exgyZIjRca1cuZKKFSvyyiuvULVqVQDKlCnD+PHjjXqFMpJZm3j8RRUXF6c9VDdtj9/TlPNf3L17V/s/bRLRv39/2rRpwzfffIOlpSUhISHa2Bx7e3uj8U0PHz4kJiaGV155JdOySpUqZZQ83LlzR3to6vNQokQJLC0tiY6Oxt7eXiuzVKlSQOoX++PxTZCa8Dzm4OBArVq1GDZsmMl+H1/Vmp5SpUoZxSYuLo6EhARKlSrF33//nWFbT0/a1yYlJYX79+9rr0/a/SQmJvLo0aNM9/W0rly5wpAhQ5g/f77WBtP+2CpcuDBxcXHadHZc9ZfZZ0HRokWNXqu0P44cHByoVq0aq1ev/s91yGlyek7kKR4eHuzbt49Lly4BqR/AQ4YM4datW9y/f58iRYrw6quvArBs2TKSkpJ4+PBhlvt98ksoLUtLS6ZNm0ZISIh2qk0pxZYtW0hJSaFWrVpm1f3atWscPHhQ+6X4WI0aNbC2ts5wgHVWfHx8iIiIYPv27bRr1w6A5ORk4uPjqVOnDnq9nqNHj3Ly5Ml0Y1G2bFkuXbpEQkICBoNBu7IGMo93eqKiorQE5ElPxvj+/ftUqVKFIkWKEBsby7p160hKSiIpKQlIPZX6119/ERoaatRD5uXlxQ8//KCdkty5c2eGV+Zk1iYKFy6sXYAAqb2HaV+Dpynnv9iyZQuPHj1CKcWmTZu0pO7+/fvUrl0bS0tLLl68yJ49e7TXz8PDg23btmnxnDZtGjNmzADAyspK60V7koeHBytWrCApKQmlFEuXLqVFixZPfTGDuSwsLHB1dWXp0qUopUhMTGTFihV4eHgAqaf4fvrpJyD1C3rbtm3atl5eXmzevFlra1euXGHQoEFZJiceHh5azyLAkiVLqFOnDmXLlqVs2bLcuHFDi9u6deuMtn0ydn/99Zc2+HzHjh1YWFhQt25dypQpQ0JCgpbErlq1yiiGmb0G5nrw4AE6nU47xRYeHs61a9e0NtCgQQPtApnIyEijU2HmsrKyAtDqWrZsWf78808MBgPx8fHaawOpr9Uvv/zCw4cPSUlJYe3atdoyZ2dnLl26pJ1+e/DgAaNGjeLPP//U1snssyE3kZ4mkadUrVqVSZMmMXToUBITE9Hr9XTq1InSpUvj7OxMvXr1cHd3p3jx4nTt2pXOnTszderULH+BN2jQgBUrVqCUQqfTGS3T6/UsXryYGTNmaGMIkpKSqFy5MkuWLMnyVM1ja9asoUmTJkanCB/z8vJi9erV2piAp/HKK69QqVIlzp07p21va2vLwIED6d27N3Z2djg5OTF27FiCgoK0BCJt2Vu2bMHd3Z1XXnmF9957T/uwzCze6Tlx4gQNGzZMd1mDBg2Mrqzr3bs3QUFBeHp6UrZsWUaNGsX58+fx9fVl06ZNFCpUCBcXFzZv3sz48eO17T788EOmTZumjYWxt7dP90oxIMs2MWnSJEaPHs3mzZupVasW7dq108YNPU05/8Wbb75Jt27duHLlCpUrV2by5MlA6r2fJk2axIwZM3B0dOTTTz+lX79+TJo0iU8++YTLly/j5+dHgQIFqFKliradt7c3kydP5syZMyxatMiorIEDBzJ16lTatGmDUooaNWoYxfZ5mDBhApMmTdLGlTVq1Eg7Ff3xxx8zYsQIXFxccHBwwMvLS0ucnJ2d6dGjB926dSMlJQUrKyv69u1LwYIFMy2vU6dOREVF8e6776KUonz58syYMQOdTkejRo20K2cdHBzo2bOn0fvdx8eHAQMG0KNHD62uy5cv5/jx48THx/PFF19gbW1N+fLl6dGjB126dKFUqVL4+fkZvSdatWrFlClTOHXqlPa6PK0aNWrw7rvv0qZNG+zs7GjTpg0DBw5k+vTpVKxYkaCgIIYPH46bmxuVK1emffv2bN68+anKKF26NE2aNMHX15fRo0fToUMHIiIicHNzo3z58nh5eWlXhfr5+XHy5Em8vLwoXrw47733Hj/++COQ2qP47bff8vnnn2vJqre3t9Y7DKmfDc+71zZb5MjwcyFeMgkJCap58+Zq//792ry0V8+J9KW9ei4+Pl41adJEHTt2LN11//rrL/XGG29oVy/mFikpKdr/kyZNUiNGjHhhZWd2lZXIWU9eVZobpW27ixYtUu+//34O1iZjkZGRqn79+tqVkLmZnJ4TwgwFChRg0KBBzJo1SxuMLJ7ODz/8QN26dU3uX/NY1apVcXd313655gajRo1iwoQJQOopit27d2fYUyZEbvLNN99oN9d99OgR4eHhubbtzpkzh/bt2xsNDM+tJGkSwky+vr5UqFAhV995O7c6e/Ysa9asYcqUKZmuN3bsWHbv3m3yWJmcMmjQIP7991/c3d3x9/fHzc3N6BETQuRWXbp0wdraGg8PD3x8fKhcuTK9evXK6WqZ2L17NydOnODjjz/O6aqYRafkZ7MQQgghRJakp0kIIYQQwgySNAkhhBBCmEGSJiGEEEIIM0jSJIQQQghhBkmahBBCCCHMIEmTEEIIIYQZ/h9sDq/OthTX4wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x626.4 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9jjClAZ6_RuY",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "295ab471-9a29-4c8f-f26e-aa59249b75e3"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0ac9f612-b4fa-414d-96d1-48e27e879ee7\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-0ac9f612-b4fa-414d-96d1-48e27e879ee7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving loan_test_data.csv to loan_test_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZuYjcg-_Vrg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "0668817a-ba88-433a-bf10-7dc8e416c2a3"
      },
      "source": [
        "print (uploaded['loan_test_data.csv'][:200].decode('utf-8') + '...')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ID,Age,Experience,Income,ZIP Code,Family,CCAvg,Education,Mortgage,Personal Loan,Securities Account,CD Account,Online,CreditCard\n",
            "118,58,33,61,92833,2,2.3,3,193,0,0,0,1,0\n",
            "1817,45,19,91,92373,2,1.7,2,0,0...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWYPf2dw_bHd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "2db4ef37-1ada-4163-a117-907eafd98831"
      },
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "\n",
        "test_data_classification = pd.read_csv(io.StringIO(uploaded['loan_test_data.csv'].decode('utf-8')))\n",
        "test_data_classification"
      ],
      "execution_count": 36,
      "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>ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Experience</th>\n",
              "      <th>Income</th>\n",
              "      <th>ZIP Code</th>\n",
              "      <th>Family</th>\n",
              "      <th>CCAvg</th>\n",
              "      <th>Education</th>\n",
              "      <th>Mortgage</th>\n",
              "      <th>Personal Loan</th>\n",
              "      <th>Securities Account</th>\n",
              "      <th>CD Account</th>\n",
              "      <th>Online</th>\n",
              "      <th>CreditCard</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>118</td>\n",
              "      <td>58</td>\n",
              "      <td>33</td>\n",
              "      <td>61</td>\n",
              "      <td>92833</td>\n",
              "      <td>2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>193</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1817</td>\n",
              "      <td>45</td>\n",
              "      <td>19</td>\n",
              "      <td>91</td>\n",
              "      <td>92373</td>\n",
              "      <td>2</td>\n",
              "      <td>1.7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>671</td>\n",
              "      <td>23</td>\n",
              "      <td>-1</td>\n",
              "      <td>61</td>\n",
              "      <td>92374</td>\n",
              "      <td>4</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1</td>\n",
              "      <td>239</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2994</td>\n",
              "      <td>65</td>\n",
              "      <td>40</td>\n",
              "      <td>20</td>\n",
              "      <td>92647</td>\n",
              "      <td>3</td>\n",
              "      <td>0.1</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3265</td>\n",
              "      <td>67</td>\n",
              "      <td>41</td>\n",
              "      <td>114</td>\n",
              "      <td>95616</td>\n",
              "      <td>4</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>4331</td>\n",
              "      <td>62</td>\n",
              "      <td>37</td>\n",
              "      <td>44</td>\n",
              "      <td>90401</td>\n",
              "      <td>1</td>\n",
              "      <td>1.1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>3221</td>\n",
              "      <td>61</td>\n",
              "      <td>35</td>\n",
              "      <td>28</td>\n",
              "      <td>93302</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>3</td>\n",
              "      <td>135</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>1932</td>\n",
              "      <td>28</td>\n",
              "      <td>2</td>\n",
              "      <td>140</td>\n",
              "      <td>92122</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>4960</td>\n",
              "      <td>51</td>\n",
              "      <td>27</td>\n",
              "      <td>55</td>\n",
              "      <td>93014</td>\n",
              "      <td>1</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2</td>\n",
              "      <td>197</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>3985</td>\n",
              "      <td>34</td>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>95741</td>\n",
              "      <td>4</td>\n",
              "      <td>0.3</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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       ID  Age  Experience  ...  CD Account  Online  CreditCard\n",
              "0     118   58          33  ...           0       1           0\n",
              "1    1817   45          19  ...           0       1           0\n",
              "2     671   23          -1  ...           0       1           0\n",
              "3    2994   65          40  ...           0       0           1\n",
              "4    3265   67          41  ...           0       1           0\n",
              "..    ...  ...         ...  ...         ...     ...         ...\n",
              "995  4331   62          37  ...           0       0           0\n",
              "996  3221   61          35  ...           0       1           0\n",
              "997  1932   28           2  ...           0       1           0\n",
              "998  4960   51          27  ...           0       1           0\n",
              "999  3985   34           8  ...           0       0           0\n",
              "\n",
              "[1000 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3uCIc6U_l_5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "1a126aca-38e1-4335-e81e-39e4d5624444"
      },
      "source": [
        "# make predictions\n",
        "predictions = classification.predict_model(classification_dt, data=test_data_classification)\n",
        "# view the predictions\n",
        "predictions"
      ],
      "execution_count": 37,
      "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>ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Experience</th>\n",
              "      <th>Income</th>\n",
              "      <th>ZIP Code</th>\n",
              "      <th>Family</th>\n",
              "      <th>CCAvg</th>\n",
              "      <th>Education</th>\n",
              "      <th>Mortgage</th>\n",
              "      <th>Personal Loan</th>\n",
              "      <th>Securities Account</th>\n",
              "      <th>CD Account</th>\n",
              "      <th>Online</th>\n",
              "      <th>CreditCard</th>\n",
              "      <th>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>118</td>\n",
              "      <td>58</td>\n",
              "      <td>33</td>\n",
              "      <td>61</td>\n",
              "      <td>92833</td>\n",
              "      <td>2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>193</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1817</td>\n",
              "      <td>45</td>\n",
              "      <td>19</td>\n",
              "      <td>91</td>\n",
              "      <td>92373</td>\n",
              "      <td>2</td>\n",
              "      <td>1.7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>671</td>\n",
              "      <td>23</td>\n",
              "      <td>-1</td>\n",
              "      <td>61</td>\n",
              "      <td>92374</td>\n",
              "      <td>4</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1</td>\n",
              "      <td>239</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2994</td>\n",
              "      <td>65</td>\n",
              "      <td>40</td>\n",
              "      <td>20</td>\n",
              "      <td>92647</td>\n",
              "      <td>3</td>\n",
              "      <td>0.1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3265</td>\n",
              "      <td>67</td>\n",
              "      <td>41</td>\n",
              "      <td>114</td>\n",
              "      <td>95616</td>\n",
              "      <td>4</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>4331</td>\n",
              "      <td>62</td>\n",
              "      <td>37</td>\n",
              "      <td>44</td>\n",
              "      <td>90401</td>\n",
              "      <td>1</td>\n",
              "      <td>1.1</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>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>3221</td>\n",
              "      <td>61</td>\n",
              "      <td>35</td>\n",
              "      <td>28</td>\n",
              "      <td>93302</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>3</td>\n",
              "      <td>135</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>1932</td>\n",
              "      <td>28</td>\n",
              "      <td>2</td>\n",
              "      <td>140</td>\n",
              "      <td>92122</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>4960</td>\n",
              "      <td>51</td>\n",
              "      <td>27</td>\n",
              "      <td>55</td>\n",
              "      <td>93014</td>\n",
              "      <td>1</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2</td>\n",
              "      <td>197</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>3985</td>\n",
              "      <td>34</td>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>95741</td>\n",
              "      <td>4</td>\n",
              "      <td>0.3</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       ID  Age  Experience  Income  ...  Online  CreditCard  Label  Score\n",
              "0     118   58          33      61  ...       1           0      0    0.0\n",
              "1    1817   45          19      91  ...       1           0      0    0.0\n",
              "2     671   23          -1      61  ...       1           0      0    0.0\n",
              "3    2994   65          40      20  ...       0           1      0    0.0\n",
              "4    3265   67          41     114  ...       1           0      0    0.0\n",
              "..    ...  ...         ...     ...  ...     ...         ...    ...    ...\n",
              "995  4331   62          37      44  ...       0           0      0    0.0\n",
              "996  3221   61          35      28  ...       1           0      0    0.0\n",
              "997  1932   28           2     140  ...       1           0      0    0.0\n",
              "998  4960   51          27      55  ...       1           0      0    0.0\n",
              "999  3985   34           8      18  ...       0           0      0    0.0\n",
              "\n",
              "[1000 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ru5Ikg8r_sXQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cc0e7f8b-630d-48e5-c1af-16c58c246616"
      },
      "source": [
        "# save the model\n",
        "classification.save_model(classification_dt, 'decision_tree_1')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Succesfully Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QYR31a0_uFp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "507c7534-d109-43c9-8f50-db9f10678519"
      },
      "source": [
        "# load model\n",
        "dt_model = classification.load_model(model_name='decision_tree_1')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Sucessfully Loaded\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}