SAP_burnout_modelling1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SAP-burnout-modelling1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PxsfujDaml1J"
      },
      "source": [
        "## Notebook Description \n",
        "This notebook involves the analysis of HackerEarth employee burnout dataset to create a model for predicting the burnout rate of employees in work-from-home condition."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeW4JP6YttcK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2fa166e-976e-4050-e3c9-56cf9ee97ddd"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzaX_XNEWcrO"
      },
      "source": [
        "# Importing essential libraries \n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQ_t54GeeA1G",
        "outputId": "4b92544e-359d-4e59-834d-97633b647907"
      },
      "source": [
        "# Importing datasets from drive \n",
        "df_train = pd.read_csv('/content/drive/MyDrive/SAP-burnout/train.csv')\n",
        "df_train.head()\n",
        "print(df_train.shape)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(22750, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "89tOuk3oejw3",
        "outputId": "df308407-be79-4839-b8ff-d24868355312"
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": 114,
      "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>Employee ID</th>\n",
              "      <th>Date of Joining</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Company Type</th>\n",
              "      <th>WFH Setup Available</th>\n",
              "      <th>Designation</th>\n",
              "      <th>Resource Allocation</th>\n",
              "      <th>Mental Fatigue Score</th>\n",
              "      <th>Burn Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>fffe32003000360033003200</td>\n",
              "      <td>2008-09-30</td>\n",
              "      <td>Female</td>\n",
              "      <td>Service</td>\n",
              "      <td>No</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fffe3700360033003500</td>\n",
              "      <td>2008-11-30</td>\n",
              "      <td>Male</td>\n",
              "      <td>Service</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fffe31003300320037003900</td>\n",
              "      <td>2008-03-10</td>\n",
              "      <td>Female</td>\n",
              "      <td>Product</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fffe32003400380032003900</td>\n",
              "      <td>2008-11-03</td>\n",
              "      <td>Male</td>\n",
              "      <td>Service</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fffe31003900340031003600</td>\n",
              "      <td>2008-07-24</td>\n",
              "      <td>Female</td>\n",
              "      <td>Service</td>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Employee ID Date of Joining  ... Mental Fatigue Score Burn Rate\n",
              "0  fffe32003000360033003200      2008-09-30  ...                  3.8      0.16\n",
              "1      fffe3700360033003500      2008-11-30  ...                  5.0      0.36\n",
              "2  fffe31003300320037003900      2008-03-10  ...                  5.8      0.49\n",
              "3  fffe32003400380032003900      2008-11-03  ...                  2.6      0.20\n",
              "4  fffe31003900340031003600      2008-07-24  ...                  6.9      0.52\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yn_IMhCWem62"
      },
      "source": [
        "## Dataset Description\n",
        "The following images shows the column description of the dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "VId7joPditYa",
        "outputId": "5106cb5a-9640-455c-c325-33a1243df2ce"
      },
      "source": [
        "# Showing column description \n",
        "from IPython.display import Image\n",
        "Image(filename='/content/drive/MyDrive/SAP-burnout/columns_description.png')"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPiO1nHgjFkU"
      },
      "source": [
        "https://www.kaggle.com/redwankarimsony/hackerearth-employee-burnout-challenge \n",
        "\n",
        "Fields which are going to be automatically filled for a user:\n",
        "  1. Employee ID\n",
        "\n",
        "Fields which are going to be entered by the user:\n",
        "  1. Date of Joining \n",
        "  2. Gender\n",
        "  3. Company Type\n",
        "  4. WFH setup available\n",
        "  5. Resource Allocation\n",
        "  6. Designation\n",
        "  7. Mental Fatigue Score(may be evaluated using other means out of the scope of modelling)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-66f_y3fn8_V",
        "outputId": "bfbb8f32-f758-4cda-bfce-050bd4adf415"
      },
      "source": [
        "# Seeing dataset details \n",
        "df_train.info()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 22750 entries, 0 to 22749\n",
            "Data columns (total 9 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   Employee ID           22750 non-null  object \n",
            " 1   Date of Joining       22750 non-null  object \n",
            " 2   Gender                22750 non-null  object \n",
            " 3   Company Type          22750 non-null  object \n",
            " 4   WFH Setup Available   22750 non-null  object \n",
            " 5   Designation           22750 non-null  float64\n",
            " 6   Resource Allocation   21369 non-null  float64\n",
            " 7   Mental Fatigue Score  20633 non-null  float64\n",
            " 8   Burn Rate             21626 non-null  float64\n",
            "dtypes: float64(4), object(5)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkS0dDdtov2S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c5dfbf7-a228-48d0-f4d8-33cbb435c4df"
      },
      "source": [
        "# Seeing null values\n",
        "df_train.isna().sum()"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Employee ID                0\n",
              "Date of Joining            0\n",
              "Gender                     0\n",
              "Company Type               0\n",
              "WFH Setup Available        0\n",
              "Designation                0\n",
              "Resource Allocation     1381\n",
              "Mental Fatigue Score    2117\n",
              "Burn Rate               1124\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHKcKLnOpqQz"
      },
      "source": [
        "There are null values in the fields 'Resource Allocation', 'Mental Fatigue Score' and 'Burn Rate'.\n",
        "\n",
        "After EDA, the filling of null values will be decided."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "N7Q0JcOap7-C",
        "outputId": "82744785-540e-4098-d91f-d62030ee500d"
      },
      "source": [
        "# Seeing statistical details \n",
        "df_train.describe()"
      ],
      "execution_count": 118,
      "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>Designation</th>\n",
              "      <th>Resource Allocation</th>\n",
              "      <th>Mental Fatigue Score</th>\n",
              "      <th>Burn Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>22750.000000</td>\n",
              "      <td>21369.000000</td>\n",
              "      <td>20633.000000</td>\n",
              "      <td>21626.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.178725</td>\n",
              "      <td>4.481398</td>\n",
              "      <td>5.728188</td>\n",
              "      <td>0.452005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.135145</td>\n",
              "      <td>2.047211</td>\n",
              "      <td>1.920839</td>\n",
              "      <td>0.198226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.900000</td>\n",
              "      <td>0.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Designation  Resource Allocation  Mental Fatigue Score     Burn Rate\n",
              "count  22750.000000         21369.000000          20633.000000  21626.000000\n",
              "mean       2.178725             4.481398              5.728188      0.452005\n",
              "std        1.135145             2.047211              1.920839      0.198226\n",
              "min        0.000000             1.000000              0.000000      0.000000\n",
              "25%        1.000000             3.000000              4.600000      0.310000\n",
              "50%        2.000000             4.000000              5.900000      0.450000\n",
              "75%        3.000000             6.000000              7.100000      0.590000\n",
              "max        5.000000            10.000000             10.000000      1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_M9kT0FqZ6x"
      },
      "source": [
        "Designation and resource allocation(provided in hours) are categorical features, hence they need to be handles as such, otherwise only mental fatigue score the target, burn rate are only numeric fields."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfG1IDjIsJ3x"
      },
      "source": [
        "## EDA\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wHmgT6OtEbB",
        "outputId": "e190f8de-9b5a-4a34-e27b-a7a798fa9848"
      },
      "source": [
        "# Plotting Years in company with burnout rate\n",
        "df_train['Date of Joining']"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2008-09-30\n",
              "1        2008-11-30\n",
              "2        2008-03-10\n",
              "3        2008-11-03\n",
              "4        2008-07-24\n",
              "            ...    \n",
              "22745    2008-12-30\n",
              "22746    2008-01-19\n",
              "22747    2008-11-05\n",
              "22748    2008-01-10\n",
              "22749    2008-01-06\n",
              "Name: Date of Joining, Length: 22750, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJFDUxP6tSsa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0843ca1-0683-464e-8cbd-520d0c994282"
      },
      "source": [
        "pd.to_datetime(df_train['Date of Joining'])"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2008-09-30\n",
              "1       2008-11-30\n",
              "2       2008-03-10\n",
              "3       2008-11-03\n",
              "4       2008-07-24\n",
              "           ...    \n",
              "22745   2008-12-30\n",
              "22746   2008-01-19\n",
              "22747   2008-11-05\n",
              "22748   2008-01-10\n",
              "22749   2008-01-06\n",
              "Name: Date of Joining, Length: 22750, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lIV6xM-vaMT"
      },
      "source": [
        "df_train['Date of Joining'] = pd.to_datetime(df_train['Date of Joining'])"
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUqMNh_YyaIx",
        "outputId": "2ef4a207-6058-4117-f5db-010f060774b2"
      },
      "source": [
        "now = pd.to_datetime('now')\n",
        "now"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2021-04-15 10:00:56.421966')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dK2UmEB0458"
      },
      "source": [
        "df_train['Experience'] = round((now - df_train['Date of Joining']).dt.total_seconds() / (60*60*24*365.25), 1)"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "03nAMhoK1WSI",
        "outputId": "e9598e52-3425-4824-b189-9c9b292ea985"
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": 124,
      "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>Employee ID</th>\n",
              "      <th>Date of Joining</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Company Type</th>\n",
              "      <th>WFH Setup Available</th>\n",
              "      <th>Designation</th>\n",
              "      <th>Resource Allocation</th>\n",
              "      <th>Mental Fatigue Score</th>\n",
              "      <th>Burn Rate</th>\n",
              "      <th>Experience</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>fffe32003000360033003200</td>\n",
              "      <td>2008-09-30</td>\n",
              "      <td>Female</td>\n",
              "      <td>Service</td>\n",
              "      <td>No</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0.16</td>\n",
              "      <td>12.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fffe3700360033003500</td>\n",
              "      <td>2008-11-30</td>\n",
              "      <td>Male</td>\n",
              "      <td>Service</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.36</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fffe31003300320037003900</td>\n",
              "      <td>2008-03-10</td>\n",
              "      <td>Female</td>\n",
              "      <td>Product</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.49</td>\n",
              "      <td>13.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fffe32003400380032003900</td>\n",
              "      <td>2008-11-03</td>\n",
              "      <td>Male</td>\n",
              "      <td>Service</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.20</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fffe31003900340031003600</td>\n",
              "      <td>2008-07-24</td>\n",
              "      <td>Female</td>\n",
              "      <td>Service</td>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.52</td>\n",
              "      <td>12.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Employee ID Date of Joining  ... Burn Rate Experience\n",
              "0  fffe32003000360033003200      2008-09-30  ...      0.16       12.5\n",
              "1      fffe3700360033003500      2008-11-30  ...      0.36       12.4\n",
              "2  fffe31003300320037003900      2008-03-10  ...      0.49       13.1\n",
              "3  fffe32003400380032003900      2008-11-03  ...      0.20       12.4\n",
              "4  fffe31003900340031003600      2008-07-24  ...      0.52       12.7\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usgDicbj2hxI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "6a6169f1-de9c-460e-d46d-e1885cff471a"
      },
      "source": [
        "# Plotting experience with burn rate \n",
        "fig, ax = plt.subplots(figsize = (10, 10))\n",
        "ax.scatter(df_train['Experience'].sample(frac=1, random_state=42).iloc[:500], \n",
        "           df_train['Burn Rate'].sample(frac=1, random_state=42).iloc[:500],\n",
        "           color = 'salmon')\n",
        "ax.set(title = 'Relation between experience and burn rate',\n",
        "       xlabel = 'Experience',\n",
        "       ylabel = 'Burn Rate');"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ClCLNr8bePdLCgeUGb0oLB9SQKheIRRoXIKWc1krKy5HnSbrF3b/g7t+RdLWkC3u+xyV9V/H1JklfSng8E4PyJUgp1PxavGF97RkLNS5AQjmtlZRB2GmSbu+4faRo63SZpJ8xsyNqvQv2S/0eyMx+zswWzGzh6NGjKY41FsqXIKVI88ub62vPWaRxAVLKaK2UvTH/mZKudPetkp4q6R1mtuKY3P2t7l539/qWLVvGfpCVQ/kSpBRpfq083Qxuz1mkcQFSymitpDzTfFHS6R23txZtnZ4v6d2S5O4HJJ0k6dSExzQRKF+ClGqzc9LUVHfj1FQ159fMjvW1Z4x1Dwwnp7WSMgj7uKSHm9k2M7ufpGdIuqbnew5LmpUkM3uUWkEY1xtHNLV9RrULLlqO6jdtVu2Ci9ici43jPvh2RdTO2CaZdTeatdorhnUPDCentZIsO9Ld7zOzF0r6kFofP/F2d/+0mb1a0oK7XyPpUklvM7NfVWuT/nPcK3o2zwzlS5BKc/8+qdmzZ6rZVHP/vsrNueb+fX0Dyir2RWLdA8PKZa0k/Zyw4jO/ru1pe0XH15+R9MSUxwBgg2W0qXVkkfoCoHIquPsUQKky2tQ6skh9AVA5BGEA1iWnTa2jitQXANWT9HIkypNLSYaNEKkvEbSf+whjMrV9Rs3Dh7rLFp1Tr2RfAFQPQVhAOZVkGFWkvkSSy6bWUS0dXJRuXlj+cFZvSjcvaOmMbSH6ByBvXI4MKKeSDKOK1Bfkh/kFoEwEYRFFyviK1Bfkh/kFoEQEYRFFyviK1Bfkh/kFoEQEYQFFyviK1Bfkh/kFoExszA+I7DWkFiVjNdJaQZ6irBWkQRAWFNlrSCVaxmqUtYL8RFsr2HhcjkTWyF7LD2MCDIe1grUQhCFvZK/lhzEBhsNawRoIwpA3stfyw5gAw2GtYA0EYR2WDi6qsXuXGq+6VI3du1r7kVAqstfyw5gAw2GtYC1szC+wgTJPZK/lhzEBhsNawVrM3cs+hnWp1+u+sLCw4Y/b2L2r/3X6TZs1/Ssv3/DfBwAA4jOzRXev97uPy5FtbKAEAABjRBDWxgZKAAAwRgRhBTZQAgCAcWJjfoENlAAAYJwIwjpQvgQAAIwLlyMBAABKQBAGAABQAoIwAACAErAnLKjG3j3S4g2SNyWrSTM7ND2/s+zDOi5LBxfDJExE6gsAYDQEYQE19u6RFg4sN3hTWjighlS5QCxSOalIfQEAjI7LkREt3rC+9ow19+9bDlraGo1We8VE6gsAYHQEYRF5c33tOYtUTipSXwAAIyMIi8hWGdbV2nMWqZxUpL4AAEZWwVdlrGlmx/raMxapnFSkvgAARsfG/ICm53eqcddR6dAty43bzq7cpnyptWG9efhQd6bnOfVKbmSPVBqLLE8AGB1BWEBLBxelI7d1Nx65TUsHFyv3Qrl0cFG6eWF5P5s3pZsXtHTGtsr1RYpRGossTwDYGFyODChSFl6kvkTBmADAxiAIiyhSFl6kvkTBmADAhiAIiyhSFl6kvkTBmADAhiAIC6g2OydNTXU3Tk1VMguPjML8MCb5Wjq4qMbuXWq86lI1du9q7akEkC025kflPvh2RUTKKIyCMckTCRNA9RCEBdTcv09q9nw6frOp5v59lTwZR8gojIYxyc+ghAnGCsgTlyMjYuM0MHlY90DlEIRFxMZpYPKw7oHKIQgLiI3TwORh3QPVw56wgCKV+kGeGnv3dM+vmR2VLIsVCes+T5FKfEXqSy54JyygVUv9kK6ODdDYu0daONA9vxYOtNpRGtZ9fo5lrLb35RUZq1Uck0h9yQlBWECUlUFSizesrx1jwbrPT6QxidSXnBCERUSWFFLy5vraMR6s+/xEGpNIfckIQVhEZEkhJVvltLFaO8aDdZ+fSGMSqS8Z4awZEFlSSGpmx/raMRas+/yEKyFX6wkZarVK9iUnZEcGRFkZpDQ9v1MNiezIzLDuMxWkhJwkyWzwbawbQVhQlJVBStPzOyWCruyw7vMSqYRcc/8+aWmpu3FpqZJ9yQmXIwEASCHSZvZIfckIQRgAAClE2sweqS8ZIQgDACCBSMkSkfqSE/aEBUV5iTwxLsBwIpTGipQsEakvOTGvWKZGvV73hYWFsg8ja8fKS3R+uvH0tGoXXMSCKRHjAgznWGmsXvXzKxeIAWa26O71fvdxOTIgykvkiXEBhkRpLEwIgrCIyGLJE+MCDIfSWJgQBGERkcWSJ8YFGA6lsTAhmNEBkcWSJ8YFGBKlsTAhyI4MaGr7jJqHD3VnFp1TZ/N3ySJlF5HliZSm53eqcevnpTvvWG489aGV3JTPWsEgBGEBLR1clG5eWN4/4U3p5gUtnbGNxV+yCGVlVmR53nN367ZU+b4hD429e7oDMEm68w419u6pVCDGWsFauBwZEFl4SIn5heSCZEeyVrAWgrCIyMJDSswvpBYlO5K1gjUQhEVEFh5SYn4htSjZkawVrIE9YQHVZufUfO9f9m2vogjlSyKpzc71/+T/is6vSBpXXS4dumW5YdvZmr74kvIO6HjN7Oj/ifkVy46szc6p+f6rpWbHO3i1GmsFx1TszwoMo3nTjetqz9mx8iWdSQYLB1rtKMXU9hnVLrho+a/5TZspvZSBFQGYJB26pdVeMbUztklm3Y1mrfaq6dMPoI13wiLqPRGv1Z6zQRt0eTesNBGyPMMJtO6b+/dJvXWN3dXcv69S8665f5+0tNTduLRUuX4gHd4JQ96ibNAFMLwoG9qj9APJEIQhb1E26AIYXpQN7VH6gWR4JYto29nra88Z5UuA4QRa91FKfEXpB9IhCAto+uJLVp54K5olNT2/U6qfv/zOl9Wk+vmVzY5cOrioxu5darzqUjV272pVNwA2wPTFl0gnn9LdePIplVz3U9tnpHPq3eu+gqXXovQD6bAxP6gqnnhXMz2/M8QmfEqYIKXG3j3S1+/tbvz6vZUr9SPFKb0WpR9Ih3fCgDGhhAmSClLqR4qzVqL0A+kQhAHjQqYUUoqUSRxlrUTpB5IhCAPGhUwppBQpkzjKWonSDyTDnjBkb+ngYuvt+3vubn06++xcJfdTRCphEqmUVJT5FaXUjxSnNFa0EnJh1ory6QtBGLIWbjN7gBImx0pJtbVLSUmVC8Qiza/p+Z1qSCGC4/Zzn8OL5Ciahw+t2l61vkRaKzn1hSAMWRu0sbVqCz9MCZNApaQizS8pTiaxFKQ0FmslSzn1pYKbBTBRIm1sjdIXNoADw2Gt5CmjvhCEIW+RNrZG6QsbwIHhsFbylFFfKjgTMEkilf0I05dApaTCjAnyxFrJUk59YU9Yh1yyJbBsavtMa3Nr52bjipb9iNKX6fmdatx1VDp0y3LjtrMruwE8wpi0RcpajSBaskSUtZJTXwjCCjllS2BZpLIfUfqydHBROnJbd+OR27R0cLFS/ZDijIkUK2s1kijJEpHWSk594XJkgfISeYo0LlH6EqUfUqy+RCpbhPxEWis59YUgrC2jbAl0iDQuUfoSpR9SrL5EysRDfiKtlYz6QhDWllG2BDpEGpcofYnSDylWXyJl4iE/kdZKRn1hdRZqs3NSrefpqGhJGal1zbuxe5car7pUjd27WtfAKyinLJZR1WbnpKmp7sapqcr1JdyYRFn3gTLxpDjnsChqs3N9K35Uca3kdA5jY36nACVlpFhJBlHKlxzjPvh2BYQbkyDrPlImXqRzWBTNw4f6nr+qWIIpp3MYQVghTEkZ5VWSYSOEKF+iYlyaPftzms1KjkuoMQmy7qU4mXjRzmEhBCrBJOVzDuNyZFtGG/VGFqkvkTAu+WFM8sS45IfEjyQIwtoy2qg3skh9iYRxyQ9jkifGJT8kfiTBs1fIaaPeqCL1JRLGJT+MSZ4YlwwFS/zIBXvCCjlt1BtVTiUZsCzSHItS4ivSmEQytX1GzZtu7C6NtfXMSo5LlLUSqVxZTswrlp1Vr9d9YWGh7MPI2orMIqn1V+QFF1Vy8SMvzC+ktqIEU1v9/Eq96EdaK5H6Mm5mtuju9X73cTkyoJxKMiAe5heSC1KCKdJaidSXnBCERURmEVJifiG1KJl4kdZKpL5khCAsIjKLkBLzC6lFycSLtFYi9SUjFZvRGMZqGURkFmEjkLmG5IJk4kVaK7yupEEQFlBz71+tqx1Yj6ntM6pdcNHyX8CbNrM5F+gj0lppHj60rnYMh4+oiOjb315fO7BOuZT8QFCBSuSEWSuBxiQnvBMGAMhLlI35kTAmSRCEAQDyEmVjfiSMSRI8exGdeOL62gEgJ0E25ofCmCSRdE+YmT1F0pskTUm6wt3f0Od7fkrSZZJc0s3u/qyUxzQJpl/yOjV2/Ya0tLTcODWl6Ze8rryDGkGUsh8AhhOpRE6U89f0/E41bv28dOcdy42nPrSSY5KTZO+EmdmUpDdLmpP0aEnPNLNH93zPwyW9VNIT3f0xkn4l1fFMkqWDi1KtZ2hrtVZ7xRwrldH+QMB77lbzg++pZF8ADGfp4KJ05LbuxiO3VW7dRzp/Nfbu6Q7AJOnOO1rtOG4pL0eeJ+kWd/+Cu39H0tWSLuz5np+V9GZ3v1uS3L1nhHE8IpWXiNQXAMOJsu6j9ENSmFJSuUkZhJ0m6faO20eKtk6PkPQIM/snM/tYcflyBTP7OTNbMLOFo0ePJjrcQCKVl4jUFwDDibLuo/RDIjsykbI35p8g6eGSniTpmZLeZmYP6v0md3+ru9fdvb5ly5YxH2IFRSovEakvAIYTZd1H6YdEdmQiKZ+9L0o6veP21qKt0xFJ17h7w90PSfqcWkFZKZYOLqqxe5car7pUjd27KnndXirKSPTZE1bF8hKUyshTlLWCPNVm5ySz7kazyq37KP2QRHZkIimDsI9LeriZbTOz+0l6hqRrer7n/Wq9CyYzO1Wty5NfSHhMq4q0gVJS34VfRZTKyE+4tYLsNG+6UXLvbnRvtVdI8/Ch/v2o4Pmrdsa2/gHlGdvKOaAgkgVh7n6fpBdK+pCkz0p6t7t/2sxebWZPK77tQ5LuMrPPSPp7SS9297tSHdMgkTZQNvfv6/54CklaWqpkX9gMmp9IawWZ6vxoimHacxXo/NXcv69/QMm6H0nSzwlz92slXdvT9oqOr13SrxX/yhVpA2WkvrAZND+R5heQUqTzF+s+CXbUtUXaQBmpL2wGzU+k+QWkFOn8xbpPooIzIY3a7Jw0Pd3dOD1dyQ2UkfrCZtD8hJpfyNO2s9fXnqtA5y/WfRpJL0dWSbuMRITyEpH6Mj2/Uw2ptYfCm62/IGd2VLZURmPvnsr3JdL8kqTGVZevLI9z8SXlHdAIwpTIufgSNd78xpUlcio2LpHOX9HWfS7MezfaZa5er/vCwkLZhwGsW2PvHmnhwMo76udX8qQcwYoArK2CgdixrNXOpInpadUuuKhyL5SR+gKY2aK71/vdx+VIYFwCZUqFESULT7GyViP1BRiEIAwYl0iZUshPpOy1SH0BBiAIA8YlUqYU8hMpey1SX4ABOPsD4xIoUyqMKFl4ipW9FqkvwCBkRyJ7YTK+5neqcdfRlZl4bMovTZQsPClW9lqkvkQ5f0mx+pJLpjpBGLK2IkuqqFMoqXKLf+ngonTktu7GI7dp6eBi5foSxdLBxZX7jO65u7JjMrV9ppLH3U+EvkQ7f0Xpy4pMdW9KCwfUkMYeiHE5ElmLlCUVqS9RMCZIKdL8itSXnDLVCcKQt0hZUpH6EgVjgpQiza9IfckoU50gDHmLlCUVqS9RMCZIKdL8itSXjDLV2ROGrNVm59R83zulzsoOZpXMkorUlyilfmqzc2p+4F3S0tJy49RUJcdEymez8UaI0JdI86s2O9e/ikEF+6KZHf2rl5SQqc47Ycha8/Ch7qBFktxb7RUTpS99S/0cuqXVXkV9xqSKjm02bl9SaW823run3AM7DpH6EmV+TW2fUe2Ci5bf+dq0ubJlpKbnd0r185ff+bJaaeXjeCcMeRu0gbJifxWH6Uu0Uj/Nnn0gzaaa+/dV78UlyvySwvQl1PxSjIzVtun5nVnMJd4JQ94y2kA5skh9iYLNxnmK0pdI8wtJEIQhbxltoBxZpL5EwWbjPEXpS6T5hSQqNqMxcSKV+onSFzR7wuEAACAASURBVEr95CnK/JLC9CXU/EIS7AkLKkp5ien5nWpIlc+SkuKULZq++JIw2ZGRyuOwVvIztX2mlXjTOSbn1Cs5v6Q4rytSPn0xr1imRr1e94WFhbIPI2sryktIrb++KprJEgXjAgwnylqJ0g+JvozCzBbdvd7vPi5HBhSqvEQgjAswnChrJUo/JPqSCkFYRGTk5IlxAYYTZa1E6YdEXxIhCIuIjJw8MS7AcKKslSj9kOhLIgRhAdVm56Raz9DWamTklIxMqTwtHVxUY/cuNV51qRq7d2np4GLZhzTxopzDarNz0tRUd2OFyxaF6ksm52KyI6MyG3wbYxcpEy+KFRt077m7dVtiXMoW5RwWpGyRpDB9yelcTBAWUHP/vu6CsZK0tFTZUhmRRCr7EcGgDbqMU3minMMilS2K1Bcpn3MxlyMjymjTIZA11kqeooxLlH5IsfqSEYKwiDLadAhkjbWSpyjjEqUfUqy+ZIQgLKCcNh0COWOt5CnKuETphxSrLzlhT1iHXMoYjGpq+4yaH/076c47lhs3ba5kX6Jp7N0ToqxMlH5QViZPOW2cHkWk+RVlTNpyWSuULSpEKsmwoq5fW0Xr+0XR2LtHWjiw8o76+ZUKYKL0Q4q17iP1JQrGJE+ULcpQTmUMRtYvABvUjvFYvGF97bmK0g/FWveR+hIFY5KnnMaFIKyNzA+k5s31tecqSj+kWOs+Ul+iYEzylNG4EIS1kfmB1GyV5bZae66i9EOKte4j9SUKxiRPGY0LG/MLtdm5/teIq5j5se3sVfeEVVEuGyhHNrOj/16qmR3jP5ZRROmHinX//qu7P4SyguVxpKIv73tn96eYm1WyL1KMdR9pfkkxxkTK6/W+gn+6pjG1fUa1Cy5ajoQ3ba7u5smHbFlfe8aObaBsv01clJWpYn2/2hnb+pZiqZ2xrZwDQkuQ8jjNw4f6lpVpHj5UzgGNINK6jzK/Io1JTq/3vBPWIZcyBiMbtHG6YtlrkcrKNPfv6/8iWbW+RJtfAcrjSIo3LgHWfaT5FWVM2nJ5veedsIjYOJ2nKH1hfuWJcclPlH5IsfqSEYKwiNg4nacofWF+5YlxyU+Ufkix+pKRCq5OrGm1DdIV3TgdpVRGmL4wv/LEuGQnSj+kWH3JCUFYQNPzO6X6+ct/AVutkp9mLuW1gXJUU9tnpK1ndjduPbNyfZme37ky03bb2cyvkkUblxXvsFSw9Fqk+RWpLzmhbBEwJlHK/VCKJU+RxoXSa4iEskVADoKU+8mp5AeWhRoXSq9hQhCEAeMSJXuNLKk8MS5A5RCEAeMSJXuNLKk8MS5A5fBhrUFFKS8hBepLkHI/0UqxRJFTKZaRBSq91ti7p7XlwJutP7hmdlRqD2inSH3JBUFYQCs26BblJSRVLniJ1Jfp+Z1qSDFOYkFKsUTSXg8R/mCpnXuemn2CsNq555VwNMdvRTKON6WFA2pIlVv3kfqSE4KwgCKVl4jUF6k4WVX8hBWpFEs0uZRiGdVqyQSVm2OBSkmF6ktGKrYZBUOJtEE3Ul+iYEyQWpQ5FiUZR4rVl4wQhEUUaYNupL5EwZggtShzLEoyjhSrLxnh2QsoUnmJSH2JgjFBamHmWKBSUqH6khH2hHVovPmN0p13LDec+lBN/+JvlndAx2lq+4ya132wey/ViSdVay9FIdJmYylGpme0MYkkwvySinPY3r/qbqzVKteX6fmdatx1tDvTs6KlpEIlFimftULZosKKAKytgoFYpL5EEqmsDPITaX41fu8y6ev3rrzj5FM0fell4z6c4xZpTCIZ97hQtmgY/YKWQe05i9SXQEKVlUF2Qs2vfgHYoPZMhRqTQHIaF4IwYFyiZHwhT8yv/DAmecpoXAjCgHGJkvGFPDG/8sOY5CmjcSEIazv1oetrz1mkvgQSJuMLWQo1v04+ZX3tmQo1JoHkNC5rBmFm9ggz229mnypuP87MXp7+0MZr+hd/c2WQUtGN7JH6EsnU9hnpnPry5+pYTTqnzgZdbIip7TOqXXDR8l/zmzZXdgP49KWXrQy4KrYpX2LN5yqntbJmdqSZXS/pxZL+2N3PLdo+5e6PHcPxrZAqOxJIjUwpYLKw5iGNnh35AHe/saftvtEPC5gsOWXkAEiPNY+1DBOE3Wlm3yfJJcnMdkr696RHBUSUUUYOgDFgzWMNw3xi/i9KequkR5rZFyUdkvTTSY8KiGjT5v4nXzKlgJhY81jDMEGYu/uTzeyBkmrufq+ZbUt9YBhNLiUZNkJj754QpTJqs3NqfuBd0tLScuPUVCUzpULNr6suX1lW5uJLyjugEURZK1KMOVabnVPz/VdLzWZHY62Sa16KMSa5GeZy5F9Jkrt/w93bH1e8J90hYVTHNoO2/wK75241P/geLR1cLPfAjkNj7x5p4UDrRUVq/b9woNVeRb2JMBUrGyYFm1+9AZgkHbql1V4xkdZKpDkms8G3KyLUmGRk1SDMzB5pZj8paZOZ/a+Of8+RdNLYjhDrFmoz6OIN62vPWHP/vu6/iCWp2azcuISaX70B2FrtOYu2VgLMseb+fd3vfEvS0lLl+iHFGZPcDLoc+f2SflzSgyRd0NF+r6SfTXlQGFGkzaDeXF97zqKMS5R+RMNayU+Ufkix+pKRVYMwd/+ApA+Y2fnufmCMx4RRRdoMarX+LyJWwWIPUcYlSj+iYa3kJ0o/pFh9ycgwq/MmM/tFM/sjM3t7+1/yI8Nxy6kkw8hmdqyvPWNRxiVKPyRJ285eX3vOWCvZidIPKVZfcjJMEPYOSd8j6cckXS9pq1qXJJGpnEoyjGp6fqdUP7+77Ef9/EpmfEUZl6ntM9LWM7sbt55ZuX5IamVBnnhid+OJJ1YyOzLaWlnxDsumzZWbY5HKFkU5f+VmmLJFN7n7uWb2SXd/nJlNS/oHd3/CeA6xG2WLgHIdy8LrVcEX/L7ZkVKlP6YigijjQtkiSKOXLWrPnq+a2WMlbZL00AHfDyCyQFl4obIjIwkyLmQUYi3DfFjrW81ss6SXS7pG0smSfjvpUQHIV6QsPCAlMgqxhjWDMHe/ovjyo5K+V5LM7IyUBwUgY5Gy8ICUyCjEGgYGYWZ2vqTTJH3U3e8ws8dJeomk/ybp9DEcHxCqVEaIsjIzO/rvCatgFp62nb3q3iOUKMi41Gbn+u8Jq2hGIefijTfoE/N/R9LbJf2kpL1mtkvSdZJukPTw8RweJl2kUhlRyspEysKrnXveutoxJg/Zsr72TEXKKORcnMagd8LmJZ3r7t8q9oTdLumx7n7rWI4M0OCNrZU7kQ3a0F6xAGZ6fmfljrmf1TZIV3J+RRJorUxtnwkxlzgXpzFoE8e33P1bkuTud0v6NwIwjF2kja1saM9PpPkVCWslP5HWSkbza9A7Yd9rZtd03N7Wedvdn5busIBCpI2tbGjPT6T5FQlrJT+R1kpG82vQb7xQ0u91/Ou9DSQXqlRGoLIyUYSaX5GwVrITaq1kNL8GFfC+fpwHkoNcsiU2QpQslqntM2p+9O+kO+9Ybqxg+RKptY+qcdfR7qyvbWdXdo5F0J5HEdaKFOccNj2/U41bP9+97k99aCX7EulcLMVYK9PzO1ufQp/BWlmzbFFuUpUtilSKJVKpjCjlS6RY44L8RDqHRekLax7S6GWLJkOgUiyhSmUEKV8iBRsX5CfQOSxKX1jzWAtBWFtG2RIji5TFEgnjgpQincOi9IU1jzWsWbbIzB4h6cWSzuz8fnf/4YTHNX4ZZUuMLFIWSySMC1KKdA6L0hfWPNYwTAHv90h6i6S3SVpKezglClSKpTY7p+YH3iUtdQzX1FQ1s1iClC+RinF571/2bUd5omxmj3QOi9KXUOfiYHJJmBjmz4r73P1yd7/R3Rfb/5If2ZjVztgm1Xqejlqt1V5FvQkXFUvAiKh5043rakd6OZUvGVXtjG2SWXejWSXPYZFKY3Euzk9OJZiGeSfsg2b2C5LeJ+nb7UZ3/0qyoypBc/8+qdnz9nezWcmSDJH6Emljfqi+RJFR+ZJRNffv6/uCX8l1rxilsUKdiwPJqQTTMEHYs4v/X9zR5pK+d+MPp0SRNlBG6guQUpQN4BLrPkeMSZ4yGpeBlyPNrCbpJe6+redfrABMWn2jZBU3UEbqC5DSahu9q7YBXGLd54gxyVNG4zLwTOPuTXW/AxZWpJIMkfqy6gb8Cm7MD9WXKDIqXzKqUOs+CMYkTzmNyzCXI//OzH5d0rskfaPdGG1PWKSSDFPbZ9Q8fKg74+uceiX7Mn3xJWr83mXS1+9dbjz5lMp9Wr5U9KW3AkAFP/lfipNRGKk8TqRzWBTRxiSXjMJR5fQauWbZIjM71KfZy7okmapsUSSRSmVEKV8SSaQxidQXIKVIryvj7stIZYv67AeLuScskFClMoKULwkl0phE6guQUKTXlZz6Mswn5l/cr93dr9r4w8GGyCjzY2SRsteiiDQmkfoCpBTpdSWjvgyzJ+wHO74+SdKspP8riSAsV5FKZUQpXxJJpDGJ1BcgpUivKxn1Zc0gzN1/qfO2mT1I0tXJjggjC1UqI0j5krYQG/MjjUmkvihOwgTyE+l1Jae+HM+fe9+QVL06GJMmSKmMSOVLVgRgknTollZ7hYQq8XXX0fW1ZyxSCSZkKsjriqRs+jLMnrAPqvUJ+VIraHu0pHenPCiMJlqpjAjlSySFKVsUan4FGRNJoUowIT+R1n1OfRlmT9jvdnx9n6Tb3P1IouPBRsho0yECYn7liSQDpBRp3WfUl2H2hF3f/trMTpV0V9Ijwugy2nSIgJhfeSLJAClFWvcZ9WXV1WlmTzCzj5jZe83sXDP7lKRPSfoPM3vK+A4R65VTSQZ0CFK2KNT8CjImkkKVYEJ+Iq37nPoy6E+kP5T0OknvlPRhSS9w9++R9N8lvX6YBzezp5jZv5rZLWb2kgHf95Nm5mbW9xNlx2Xp4KIau3ep8apL1di9S0sHF8s8nOM2tX1G2npmd+PWMyt33T6a1bIgq5YdObV9Rjqn3p0sUeGyWDrxxO7GE0+s3JhIsZJYImlcdXnrNaX9r2KJOG2RXldyOoetWrbIzD7h7o8vvv6suz+q476b3P3cgQ9sNiXpc5J+RNIRSR+X9Ex3/0zP950iaa+k+0l6obsPrEmUqmxRpJIMlGLJU+MNvyV9+9sr7zjxRE2/5HXjP6DjFGqt9MtYlar50SHITqT5Fel1pSplizo3F/xnz33D5HKeJ+kWd/+Cu39Hrc8Wu7DP971G0hslfWuIx0wmpzIGI6MUS576BWCD2jMVaq1Eyo5EfiLNr0CvKzmdwwYFYeeY2dfM7F5Jjyu+bt/ePsRjnybp9o7bR4q2Y8zsBySd7u57Bz2Qmf2cmS2Y2cLRo4k+vyejbImRkSWFlCKtFQDDifS6ktE5bNUgzN2n3P273P0Udz+h+Lp9e3q1nxuWmdUk/b6kS9f6Xnd/q7vX3b2+ZcuWUX91f6tlRVQx82O1bCiypLARIq0VAMOJ9LqS0Tks5bP3RUmnd9zeWrS1nSLpsZI+Yma3SnqCpGvK2pyfU7bEyMiSylPvBvC12jMVaq1Eyo5EfiLNr0CvKzmdw1IGYR+X9HAz22Zm95P0DEnXtO9093vc/VR3P8vdz5L0MUlPW2tjfio5ZUuMiiypPE2/5HX9M/EqtClfaq2V2gUXLf/VuGlzJTflS0V25MmndDeefErlNk23RcnwjmL64kukUx/a3XjqQys5vyK9ruT0er9qduSGPLjZUyXtljQl6e3u/loze7WkBXe/pud7PyLp18mOBDAuZHwhJcYkT1XJjhyZu1/r7o9w9+9z99cWba/oDcCK9ieV9S6YlFe2BIAxIeMLCTEmecppXCq4oy6RjLIlAIwJGV9IiTHJU0bjQhDWllG2BIAxIeMLKTEmecpoXNYs4D0parNz/a8RVzHjK5ilg4utt4nvubu1CXx2rrL7KRp797QudXmz9UI/s6Nye4+kPp8EXsFPAJfUyuzqtyesohlfnMPywpjkqTY7p+b7r5aaHe9412rhsiMrJVLGVyTHNlC23ya+5241P/ieSmZ9HdsE3r7U5U1p4UCrvUL6lmI5dEsla+JFy/jiHJYXxiRjZoNvjwnvhHWY2j7D4sjMoA2UlRurQZvAq/SiH6kUi4pArErP/wCcw/LDmOSnuX+ftLTU3bi0VMrrCu+EIW8ZbaAcWaRN4ABQVRm9rhCEIW8ZbaAcWaRN4ABQVRm9rnD2R9ZyKi8xsihlPyKVYgEwcXJ6XWFPWFBRMgqnts+oedON3fuNtp5Zyb5Mz+9UQ6p8duT0xZfEyY4MJsq6R56izK/2MefQl6Rli1JIVbYokkilMiKVlQFSirTukR/m1/ErrWwRypFTSYaRBSorA6QUat0jO8yvNAjCIsoo82NkZBQCw4m07pEf5lcSBGERZZT5MTIyCoHhRFr3yA/zKwk25gdUm51T8wPv6v4wuqmp6mYUBikrI8XZ2Bql/FIklMjJU5Q1X5udU/N975Q695GbMb9GxNsJUfUmXFQsAaMtUlmZKCWYopRfioYSOfmJsuYlqXn4UN/XlebhQ+UcUBC8ExZQc/++7sKkktRsVrPUj+KUlQlTgilK+aWAKJGTlzBrXmLdJ8I7YRGxgTJPUcaFZAlgOFHWvMS6T4QgLCI2UOYpyriQLAEMJ8qal1j3ifDsBZRTSQYsCzMuUcovAYmFWfMS6z4R9oR1iFKKJaeSDBshSnZRlHGJUn6pLcr8kmJlrUYYlyhrXmLdp0LZosKKAKytooFYFJTKQEqR5lekEl+RxgX5Gff8omzRMPoFYIPaMRaUykBKoeZXoBJfocYF2clpfhGEIW+RsouQn0jzK1L2WqRxQX4yml8EYchbpOwi5CfS/IqUvRZpXJCfjOZXBVdnItvOXl975pYOLqqxe5car7pUjd27KvkJzVKw7KJAQs0vs+7GqpZiCZS9xrpHSjnNL4KwwvTFl6wMuCq6KT9SqQxKseQn0vxq3nRj/1IsN91YzgFBEuseaeU0v/iIig5VDLj6CVUqQ5RiyU2o+RUpISdYWRnWPVLKZX7xTlhEGW06REDMrzxF2pgPTAiCsIgy2nSIgJhfeYq0MR+YEKzOgHLadIh4Qs2vSAk5gTbmA5OCPWEdciljMKqp7TOtjcWd+1q2nlnJvkQToaxMqFIsF1+ixpvfKN15x3LjqQ+t5P7Q6fmdatx1dGXptYrNL2CSEIQVVpQxKDK+JFXuxaWxd8/KjcWHblFj7x5OyCVaUVbGm9LCATWkyo1LLptaR7V0cHHlXrZ77tbSwcXK9W/p4KJ05LbuxiO3VbIvwKTgcmQhpzIGIwtUviQUxiU7kdZ9pL4Ak4IgrC1SxhdZUnliXPITad1H6gswIQjC2iJlfJEllSfGJT+R1n2kvgATgj1hhdrsnJrvv1pqdrwrUatVM+NrZkf33qPOdpSHcclObXauey+oVNlMz0h9keIkSiFPucwvgrBOfWrIVVHtjG1q9nmxr52xrYSjQdv0/E41pMpnR0YSKdMzUl8iJUohPznNL4KwQnP/PmlpqbtxaamSpVhW24hbxb5EMz2/s5IlZCKLkukpxelLqNJYyE5O84vNKG2RNrVG6guAycM5DCllNL8IwtoibWqN1BcAk4dzGFLKaH4RhBUilWKJ1BcAk4dzGFLKaX6xJ6wQaVPr1PYZNQ8f6t4Afk69kn2R8sliAXIXZa1EO4chLzm93hOEdYiyqXXp4KJ088Lyh4B6U7p5QUtnbKtc/3LKYgFyFmmtRDqHIU+5vN5zOTKgSOVLIvUFSCnSWonUF2AQgrCIMsr8GFmkvgApRVorkfoCDEAQFlFGmR8ji9QXIKVIayVSX4ABCMICyinzY1SR+gKkFGmtROoLMAgb8wPKKfNjVGRJIbVIGYUS6z43UeZXNI29e7IoIUcQFlQumR+jIksKKUXKKJRY97mJNr+iaOzdI3XWV/amtHBADWnsgRiXI5E1sqSQEvMrT1HGJUo/wlm8YX3tCRGEIW9kSSEl5leeooxLlH5E036Hddj2hAjCkDeypJAS8ytPUcYlSj+isVVCn9XaE2JPWFC5bDocVW12rntPhVTpLKkom3RDza/3Xy01O/4CrtUqO7+iqM3OqfmBd0lLS8uNU1OVG5do8yvK+UszO7r3hHW2jxlBWEA5bTocVaSMryibdCPNL0mS2eDbKIf74NtVEWR+RTl/Sa3zVEPK4g9JgrCIBm06rOCLZJSMr0GbdCvVv0Dzq7l/X/e7LZK0tFS9MQmmuX9f97tHktRsVm5cIs2vMOevwvT8zizOV+wJiyijTYfoEGWTbqT5FWVMookyLlH6IcXqS0YIwiLKaNMhOkTZpBtpfkUZk2iijEuUfkix+pKRCp41sabVNheWsOkQy8KUYgk0v8KMSTBRxiVKP6RYfckJe8I6RMn8yGnT4UZoXHW5dOiW5YZtZ2v64kvKO6DjFKUUy/T8TjXuOrpyTCo4vyIlfkQytX1GzZtu7J5jW8+s3LhEWfMSayUV84plnNTrdV9YWNjwx12R+SG1ovwLLmKSlWhFANZWwUAsyhyL0g/ka0UGblv9/EoF+6wVSJKZLbp7vd99XI4sUF4iU/0CsEHtGYsyx6L0AxnLqKzMKFgrWAtBWBuZH0gtyhyL0g/kK0oGLmsFayAIayPzA6lFmWNR+oF8RcnAZa1gDWzML0QpkxHOtrNX3RNWNVFKMEXpR1uUhBwpUF8yKiszCsoW5SuXvlTsz4rEopTJCGT64ktWBlwV3JQvtbKLahdctPxX8KbNldygG6UfUsfG6fbloaIUy9LBxXIP7DhE6sv0/E6pfv7yO19Wq9ymfEmtzMh+n/x/+FA5BzSCSPMrp77wTlghSpmMiKoYcK0mSgmmKP2IVIolUl+kfMrKjCRaia8g8yunvvBOWBsbKIHJE2ndR+pLFFESDKRY8yujvhCEtbGBEpg8kdZ9pL5EESXBQIo1vzLqSwVnQhqUZAAmT6R1H6kvYVDiK0s59YU9YQVKMuQrlywWLIsyJpHWfaQSOVFEKiEXba1IefSFskXIGmU/8sOY5IlxAfJE2SJUFmU/8sOY5IlxAaqHIAx5yyiLBQXGJE+MC1A5BGHIW0ZZLCgwJnliXIDKYWN+UFE2Tkcr+xEBJb7yxFpBalFeV3JCEBbQig26RUkGSdVcMGaDb2P8KPGVJ9YKEgn3upIJLkcGFGmDbnP/vu53XCRpaamSfYliUIkvlIe1gpQiva7khCAsokgbdCP1JQrGJE+MC1JifiVBEBZRpA26kfoSBWOSJ8YFKTG/kiAICyinkgyjitSXKBiTPDEuSIn5lQYb8ztEyfzIqSTDqCL1JQrGJE/RyhZFOR9HMbV9Rs2bbpQO3bLcuPVMxmRElC0qUPIDQJVFOodF6ksUjb17pIUDK++on1/JWpjjRNmiIZD5AaDKIp3DIvUljMUb1teOoRCEtZH5AaDKIp3DIvUlCm+urx1DIQhrI/MDQJVFOodF6ksUtkq4sFo7hsLG/EJtdq7/HoSKZn5E2tQaqS9RMCb5iXQOozRWhmZ29N8TNrNj/MeyAXI5hxGEFSJlfEUqLxGpL1EwJnmKdA6TRGmszEzP71RD6s6+ndlRyU35OZ3DCMI6TG2fqe4Jq8OgTa1V61+kvkTBmOQr1DlsldJYEfpXVdPzO6UKBl29cjqHcTE3okibWiP1JQrGBKkxx5BSRvOLICyiSJtaI/UlCsYEqTHHkFJG84sgLKBI5SUi9SUKxgSpMceQUk7ziz1hAUXaoBupL1EwJkgt0hzLJQsPy3KaX5QtAgAgAcovQaJsEQAAY0f5JayFIAwAgBQyysJDngjCAABIIaMsPOSJjflBsRk0T429e0J84nQkrBWkEqmUlMT5KwWCsIByKsmAZY29e7prr3lTWjighsSJrCSsFaSUUxbeqDh/pUEQFlBOJRnQYfGG1ds5iZWCtYLUopSS4vyVBnvCImIzaJ68ub52pMdaAYbD+SsJgrCI2AyaJ1tlua3WjvRYK8BwOH8lwbMXUE4lGdBhZsf62pEcawUYEuevJJLuCTOzp0h6k6QpSVe4+xt67v81SS+QdJ+ko5Ke5+63pTymSRBpM2gk0/M71ZBCZBdFyZJirQDDiXT+ykmyskVmNiXpc5J+RNIRSR+X9Ex3/0zH9/xPSTe4+zfN7BJJT3L3pw96XMoWAeVakSXVVj+fEzIA9CirbNF5km5x9y+4+3ckXS3pws5vcPe/d/dvFjc/JmlrwuMBsBEGZUkBAIaWMgg7TdLtHbePFG2reb6kvgW1zOznzGzBzBaOHj26gYcIYN3IkgKADZHFxnwz+xlJdUm/0+9+d3+ru9fdvb5ly5bxHhyAbmRJAcCGSHnW/KKk0ztuby3aupjZkyW9TNLT3P3bCY8HwEYgSwoANkTK7MiPS3q4mW1TK/h6hqRndX6DmZ0r6Y8lPcXd70h4LAA2CFlSwPAi1SaNkhWdk2RBmLvfZ2YvlPQhtT6i4u3u/mkze7WkBXe/Rq3LjydLeo+ZSdJhd39aqmMCsDGm53dSqgRYQ6TapNSOTCPp54S5+7WSru1pe0XH109O+fsBAChLqNqk1I5Mgp20AACkEKk2KVnRSRCEAQCQQqTapGRFJ5H0cmTVRNpACWA4kTYbcw7LS212rntPmFTd2qQzO/pXyiAreiQEYYVIGygBDCfSZmPOYfmJVJuUrOg0CMIKoTZQAhhOoM3GnMPyNLV9JszzT1b0xuNiblukDZQAhhNpszHnMKByCMLaIm2gBDCcSJuNOYcBlVPBM00atdk5aXq6u7GqGygBDCdQCSbOYUD1sCesEGkDJYDhTM/vVOOuo9KhW5Ybt51dyc3GnMOA6iEI6xBpAyWAtS0dXJSO3NbdeOQ2LR1crOS5gHMYUC1cjgQwsQZlFAJAagRhACYXGYUAZxyctwAADqpJREFUSkQQBmBykVEIoETsCQuK8iV5ilQiJ4La7JyaH3iXtLS03Dg1VdmMwkjrPlJfomBMNh5BWECUL8lTpBI5obgPvl0RkdZ9pL5EwZikweXIgNhsnKlBJXJQiub+fVKz59Pxm81KrpVI6z5SX6JgTNIgCIuIzcZ5ilQiJ4pIa4W+ICXGJAmCsIjYbJynSCVyooi0VugLUmJMkuDsHxDlSzIVqEROFJHWCn1BSoxJGgRhAU1tn5HOqS+/w2I16Zw6mydLNj2/U6qf3z0u9fPZlF+iSGtlavuMahdctPzOxKbNql1wEX3Bhoi0VnJiXrFMoHq97gsLC2UfRtZWZLFIrb9YOIkBXVgrwHBYK8fPzBbdvd7vPt4JC4gsFmA4rBVgOKyVNAjCIiKLBRgOawUYDmslCYKwiMhiAYbDWgGGw1pJgk/M7xClJENtdq7/tXuyWIAurBWkxusKBiEIK0QqydA+3ggLH0iJtYKUeF3BWgjCCoM2HVZxkk1tn6nkcQPjxlpBKryuYC3sCWtj0yEAYCPxuoI1EIS1sekQALCReF3BGgjCCpRkAABsJF5XsBb2hBXYdAgML0rGVzSMS154XcFaKFsEYF0oX5InxgXIE2WLAGwYypfkiXEBqocgDMD6kPGVJ8YFqByCMADrQ8ZXnhgXoHIIwgCsCxlfeWJcgOohOxLAupDxlSfGBagegjAA60b5kjwxLkC1cDkSAACgBARhAAAAJSAIAwAAKAF7wgBMNEr9AJMnl3VPEAZgYq0o9XPP3a3bEoEYEFRO657LkQAmFqV+gMmT07onCAMwuSj1A0yejNY9QRiAyUWpH2DyZLTuCcIATCxK/QCTJ6d1z8Z8ABOLUj/A8BpXXS4dumW5YdvZmr74kvIO6DjltO7N3cf+S0dRr9d9YWGh7MMAAGBirAjA2ioaiI2TmS26e73ffVyOBAAAg/ULwAa1YygEYQAAACUgCAMAACgBG/ORvVzKS2yESH2JgjEBhrDt7FX3hOH48U4YsnasvET7Q/SK8hJLBxfLPbDjEKkvUTAmwHCmL75kZcDFpvyR8U4YsjaovETV3q2I1JcoGBNgeARcG493wpC3jMpLjCxSX6JgTACUiCAMecuovMTIIvUlCsYEQIkIwpC1nMpLjCpSX6JgTACUiT1hyFpO5SVGFakvUTAmAMpE2SIAAIBEKFsEAACQGYIwAACAEhCEAQAAlICN+QAAJEJZLAxCEAYAQALHymK1qzIUZbEkEYhBEpcjAQBIYlBZLEAiCAMAIA3KYmENBGEAAKRAWSysgSAMAIAEKIuFtbAxHwCQnQhZhVPbZ9Q8fEhavEHypmQ16Zx65fqBdHgnDACQlWNZhe29U0VW4dLBxXIPbJ2WDi5KNy+0AjCp9f/NC5XrB9IhCAMAZCVKVmGUfiAdgjAAQF6iZBVG6QeSIQgDAOQlSlZhlH4gGTbmBxVhUyuAyVSbnev+pHmpklmFUfqBdAjCAqJUBoAqa5+nqv6HZJR+IB2CsIAGbQZl8QOogqntMyHOV1H6gTTYExYRm0EBAMgeQVhEbAYFACB7BGEBUSoDAID8sScsIDaDAsMjkxhAWQjCgmIzKLA2MokBlInLkQAmFmVlAJSJIAzA5CKTGECJCMIATC4yiQGUiCAMwMQikxhAmdiYD2BikUkMoEwEYQAmGpnEAMrC5UgAAIASEIQBAACUgCAMAACgBOwJAwAAa6LE18YjCAMAAANR4isNLkcCAICBKPGVBkEYAAAYjBJfSRCEAQCAwSjxlQRBGAAAGIgSX2mwMT8oslgAABuFEl9pEIQFRBYLAGCjUeJr43E5MiCyWAAAyB9BWERksQAAkD2CsIjIYgEAIHtJ94SZ2VMkvUnSlKQr3P0NPfefKOkqSTOS7pL0dHe/NeUxDRJlM3ttdk7N918tNZsdjTWyWDIQZY4BqTX27pEWb5C8KVlNmtmh6fmdZR8WsKGSvRNmZlOS3ixpTtKjJT3TzB7d823Pl3S3u58t6Q8kvTHV8azl2Gb29iW7YjP70sHFsg7puDUPH+oOwCSp2Wy1ozSR5hiQUmPvHmnhQCsAk1r/LxxotQOBpLwceZ6kW9z9C+7+HUlXS7qw53sulPRnxdd7JM2amSU8plWF2sy+eMP62jEWoeYYkBLnMEyIlEHYaZJu77h9pGjr+z3ufp+keyQ9pPeBzOznzGzBzBaOHj2a5mgjbWb35vraMR6R5hiQEucwTIhKbMx397e6e93d61u2bEnzSyJtZrdVhnW1doxHpDkGpMQ5DBMi5Yz+oqTTO25vLdr6fo+ZnSBpk1ob9McuVEmGmR3ra8dYhJpjQEqcwzAhUmZHflzSw81sm1rB1jMkPavne66R9GxJByTtlPRhd/eEx7SqSCUZpud3qiGRWZSZSHMMSIlzGCaFpYx5zOypknar9REVb3f315rZqyUtuPs1ZnaSpHdIOlfSVyQ9w92/MOgx6/W6LywsJDtmAACAjWJmi+5e73df0s8Jc/drJV3b0/aKjq+/JemilMcAAACQI3Y5AgAAlIAgDAAAoAQEYQAAACUgCAMAACgBQRgAAEAJCMIAAABKQBAGAABQAoIwAACAEhCEAQAAlIAgDAAAoAQEYQAAACUgCAMAACgBQRgAAEAJCMIAAABKQBAGAABQAoIwAACAEhCEAQAAlIAgDAAAoAQEYQAAACUwdy/7GNbFzI5Kuq3s46iQUyXdWfZBYAXGJT+MSZ4Yl/wwJutzprtv6XdH5YIwrI+ZLbh7vezjQDfGJT+MSZ4Yl/wwJhuHy5EAAAAlIAgDAAAoAUFYfG8t+wDQF+OSH8YkT4xLfhiTDcKeMAAAgBLwThgAAEAJCMIAAABKQBBWUWb2djO7w8w+1dH2O2b2L2b2STN7n5k9aMDPT5nZTWb21+M54skwyriY2YPMbE/xvZ81s/PHd+RxjTgmv2pmnzazT5nZO83spPEdeWyrjMtrijH5hJldZ2YPW+Vnn21m/1b8e/b4jjq24x0TM3u8mR0o1sonzezp4z3y6iIIq64rJT2lp+1vJT3W3R8n6XOSXjrg518k6bNpDm2iXanjH5c3Sfobd3+kpHPE+GyUK3UcY2Jmp0n6ZUl1d3+spClJz0h7qBPlSq0cl99x98e5++Ml/bWkV/T+kJk9WNIrJe2QdJ6kV5rZ5sTHOimu1HGMiaRvSrrY3R9T/PzuQW8CYBlBWEW5+0clfaWn7Tp3v6+4+TFJW/v9rJltlTQv6YqkBzmBjndczGyTpP8u6U+Kn/mOu3818eFOhFHWiqQTJN3fzE6Q9ABJX0p2oBNmlXH5WsfNB0rqlzn2Y5L+1t2/4u53qxVQ9wYOOA7HOybu/jl3/7fi6y9JukNS30+IRzeCsLieJ2nfKvftlvQbkprjOxwUVhuXbZKOSvrT4jLxFWb2wPEe2sTqOybu/kVJvyvpsKR/l3SPu1835mObOGb2WjO7XdJPq/+7LqdJur3j9pGiDYkMMSad33uepPtJ+vw4jq3qCMICMrOXSbpP0l/0ue/HJd3h7otjP7AJN2hc1HrH5QckXe7u50r6hqSXjPHwJtIaa2WzpAvVCpAfJumBZvYz4z3CyePuL3P309UakxeWfTwYfkzM7L9Ieoek57o7f+QPgSAsGDN7jqQfl/TT3v9D4J4o6WlmdqukqyX9sJn9+fiOcDINMS5HJB1x9xuK23vUCsqQyBBj8mRJh9z9qLs3JL1X0g+N8RAn3V9I+sk+7V+UdHrH7a1FG9JbbUxkZt8laa+kl7n7x8Z6VBVGEBaImT1FrcuMT3P3b/b7Hnd/qbtvdfez1Npk/GF356/7hIYcly9Lut3Mvr9ompX0mTEd4sQZZkzUugz5BDN7gJmZWmNCskRCZvbwjpsXSvqXPt/2IUk/amabi3crf7RoQwLDjImZ3U/S+yRd5e57xnVsERCEVZSZvVPSAUnfb2ZHzOz5kv5Q0imS/rZIJ35L8b0PM7NrSzzciTHiuPySpL8ws09Keryk14358EM63jEp3pXcI+n/Sjqo1vmSci0bZJVxeUPxcSCfVCu4elHxvXUzu0KS3P0rkl4j6ePFv1cXbRjR8Y6JpJ9SK7HoOcV6+oSZPb6MPlQNZYsAAABKwDthAAAAJSAIAwAAKAFBGAAAQAkIwgAAAEpAEAYAAFACgjAA2TKzpY6U90+YWdIqAmb2tNS/AwDa+IgKANkys6+7+8lj+l0ndBT1BoDkeCcMQKWY2SYz+9d2dQEze6eZ/Wzx9dfN7A/M7NNmtt/MthTt32dmf2Nmi2b2D2b2yKL9SjN7i5ndIOn/mNlzzOwPi/u2mNlfmdnHi39PLNovM7O3m9lHzOwLZvbLHcd2sZl90sxuNrN3DHocACAIA5Cz+/dcjny6u9+jVhHhK83sGZI2u/vbiu9/oKQFd3+MpOslvbJof6ukX3L3GUm/LumPOn7HVkk/5O6/1vO73yTpD9z9B9Wql3dFx32PlPRjks6T9Eozmzazx0h6uaQfdvdzVHyy+BqPA2CCnVD2AQDAAP/p7ivKn7j735rZRZLeLOmcjruakt5VfP3nkt5rZierVXj7Pa0SkJKkEzt+5j3uvtTndz9Z0qM7fua7iseSpL3u/m1J3zazOyR9t6QfLh7rzuIYvzLocdz962t3H0BkBGEAKsfMapIeJembkjZLOrLKt7pa7/h/tV8wV/jGKu01SU9w92/1/G5J+nZH05IGn0v7Pg4AcDkSQBX9qqTPSnqWpD81s+mivSZpZ/H1syT9o7t/TdKh4p0zWcs5vQ/Yx3VqFVVX8XNrFST+sKSLzOwhxfc/+DgfB8CEIAgDkLPePWFvKDbkv0DSpe7+D5I+qtZeLKn1rtZ5ZvYptS4Pvrpo/2lJzzezmyV9WtKFQ/zuX5ZULzbaf0bSzw/6Znf/tKTXSrq++D2/fzyPA2By8BEVAMIY50daAMCoeCcMAACgBLwTBgAAUALeCQMAACgBQRgAAEAJCMIAAABKQBAGAABQAoIwAACAEvw/6RZikpB4esAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVtrFkH33Mrg"
      },
      "source": [
        "The plot shows that the burn rate usually lies between 0.2 and 0.8 irrespective of experience. Also, burnout rate increases a bit between 12.6 - 13.0 years of experience, not going below 0.2 for this group."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "Lk3WzgC873KY",
        "outputId": "fb419d2e-815e-402f-cd4a-1054510fa8d1"
      },
      "source": [
        "# Distribution of company type \n",
        "plt.figure(figsize=(7,7))\n",
        "df_train['Company Type'].value_counts().plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1nDjHU289r2"
      },
      "source": [
        "An overwhelming number of employees most work in service sector, rather than in product sector. This will give us more clarity on its impact in burn rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IF_H-8gr8Nt",
        "outputId": "3507ba11-79ad-4996-cd0b-e0c5e9547d17"
      },
      "source": [
        "df_train['Experience'][df_train['Company Type']=='Service']"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        12.5\n",
              "1        12.4\n",
              "3        12.4\n",
              "4        12.7\n",
              "6        13.3\n",
              "         ... \n",
              "22738    12.3\n",
              "22739    13.2\n",
              "22745    12.3\n",
              "22747    12.4\n",
              "22748    13.3\n",
              "Name: Experience, Length: 14833, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "XKv5ZywA-BPZ",
        "outputId": "4ab30657-7926-42ad-dc61-624405866ccf"
      },
      "source": [
        "# Burn rate and experience w.r.t. company type\n",
        "fig, ax = plt.subplots(figsize=(7,7))\n",
        "ax.scatter(df_train['Experience'][df_train['Company Type']=='Service'].sample(frac=1, random_state=42).iloc[:250],\n",
        "           df_train['Burn Rate'][df_train['Company Type']=='Service'].sample(frac=1, random_state=42).iloc[:250],\n",
        "           color='lightgreen', label = 'Service')\n",
        "ax.scatter(df_train['Experience'][df_train['Company Type']=='Product'].sample(frac=1, random_state=42).iloc[:250],\n",
        "           df_train['Burn Rate'][df_train['Company Type']=='Product'].sample(frac=1, random_state=42).iloc[:250],\n",
        "           color='salmon', label = 'Product')\n",
        "ax.set(title = 'Relation between Experience and Burn Rate w.r.t. company',\n",
        "       xlabel = 'Experience in years',\n",
        "       ylabel = 'Burn rate')\n",
        "ax.legend();"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6p7aZDdqsqgf"
      },
      "source": [
        "The plot shows that people employed in the service sector and with more experience are more vulnerable to burnout. Also, the product sector inhabits the lower burnout region densely.\n",
        "\n",
        "This shows that service sector employees are more prone to burnout."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "SpTauxuNvDJl",
        "outputId": "394252b7-19de-48c6-bfaa-c873a54ca521"
      },
      "source": [
        "# Gender distribution \n",
        "plt.figure(figsize=(7,7))\n",
        "df_train['Gender'].value_counts().plot.bar(color=['springgreen', 'orangered'])\n",
        "plt.show()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBMXQqA5vmql"
      },
      "source": [
        "The plot shows that there are slightly more women than men in the workplace, let's try to get a more deep insight on how gender affects burn rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "4DYq3lR7xIfd",
        "outputId": "9d1c2ae6-bce2-40f8-a62e-fb4f32ca2d92"
      },
      "source": [
        "# Making a cross distribution of gender and company type\n",
        "plt.figure(figsize=(7,7))\n",
        "pd.crosstab(df_train['Gender'], df_train['Company Type']).plot.bar()\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jT-x97ikxWBb"
      },
      "source": [
        "The plot shows that an almost same ratio of male and female employees are split between service and ratio."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "0usz2SWV1dtY",
        "outputId": "dfd7ad17-45c3-418c-f38c-e5367e47529d"
      },
      "source": [
        "# Using seaborn for plotting categorical plots \n",
        "import seaborn as sns\n",
        "small_df = df_train.sample(500, random_state=42)\n",
        "plt.figure(figsize=(7,7))\n",
        "sns.catplot(x='Gender', y='Burn Rate', hue='Company Type', data=small_df)\n",
        "plt.show()"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 441x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QgcA6IQs18vz"
      },
      "source": [
        "The plot shows that among the men in product sector the burn rate is higher, while the burn rate being clearly high in service sector employees."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99gqt4xp4BKU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "29cc21d4-6777-4acd-c39f-3ebc6cb22799"
      },
      "source": [
        " df_train.head()"
      ],
      "execution_count": 132,
      "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>Employee ID</th>\n",
              "      <th>Date of Joining</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Company Type</th>\n",
              "      <th>WFH Setup Available</th>\n",
              "      <th>Designation</th>\n",
              "      <th>Resource Allocation</th>\n",
              "      <th>Mental Fatigue Score</th>\n",
              "      <th>Burn Rate</th>\n",
              "      <th>Experience</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>fffe32003000360033003200</td>\n",
              "      <td>2008-09-30</td>\n",
              "      <td>Female</td>\n",
              "      <td>Service</td>\n",
              "      <td>No</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0.16</td>\n",
              "      <td>12.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fffe3700360033003500</td>\n",
              "      <td>2008-11-30</td>\n",
              "      <td>Male</td>\n",
              "      <td>Service</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.36</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fffe31003300320037003900</td>\n",
              "      <td>2008-03-10</td>\n",
              "      <td>Female</td>\n",
              "      <td>Product</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.49</td>\n",
              "      <td>13.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fffe32003400380032003900</td>\n",
              "      <td>2008-11-03</td>\n",
              "      <td>Male</td>\n",
              "      <td>Service</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.20</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fffe31003900340031003600</td>\n",
              "      <td>2008-07-24</td>\n",
              "      <td>Female</td>\n",
              "      <td>Service</td>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.52</td>\n",
              "      <td>12.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Employee ID Date of Joining  ... Burn Rate Experience\n",
              "0  fffe32003000360033003200      2008-09-30  ...      0.16       12.5\n",
              "1      fffe3700360033003500      2008-11-30  ...      0.36       12.4\n",
              "2  fffe31003300320037003900      2008-03-10  ...      0.49       13.1\n",
              "3  fffe32003400380032003900      2008-11-03  ...      0.20       12.4\n",
              "4  fffe31003900340031003600      2008-07-24  ...      0.52       12.7\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "8JfBFL40bTWU",
        "outputId": "ff79e1ab-9410-47f6-ad4a-538f3370304d"
      },
      "source": [
        "# Plotting wfh setup and gender \n",
        "plt.figure(figsize=(7,7))\n",
        "pd.crosstab(df_train['Gender'], df_train['WFH Setup Available']).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cm235A-rbsMN"
      },
      "source": [
        "More females have work-from-home setup arranged whereas an equal number of males have and don't have work-from-home setup."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "VLBpHi3tcBBB",
        "outputId": "2eb5d205-d8a8-4cf0-eb74-2c5db3fa1b67"
      },
      "source": [
        "small_df = df_train.sample(500, random_state=42)\n",
        "plt.figure(figsize=(7,7))\n",
        "sns.catplot(x='Gender', y='Burn Rate', hue='WFH Setup Available', data=small_df)\n",
        "plt.show()"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 471.625x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgjaQ-JmcNA1"
      },
      "source": [
        "This is clear that employees who don't have work from home setup available are more prone to burn out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "qWPkUJ8lcuwr",
        "outputId": "7935a1e7-7a66-47ff-a956-c9327051df64"
      },
      "source": [
        "# Plotting mental fatigue score with burn rate \n",
        "fig, ax = plt.subplots(figsize=(7,7))\n",
        "ax.scatter(df_train['Mental Fatigue Score'][df_train['WFH Setup Available']=='Yes'].sample(frac=1, random_state=42).iloc[:250],\n",
        "           df_train['Burn Rate'][df_train['WFH Setup Available']=='Yes'].sample(frac=1, random_state=42).iloc[:250],\n",
        "           color = 'crimson', label='Yes', marker='x')\n",
        "ax.scatter(df_train['Mental Fatigue Score'][df_train['WFH Setup Available']=='No'].sample(frac=1, random_state=42).iloc[:250],\n",
        "           df_train['Burn Rate'][df_train['WFH Setup Available']=='No'].sample(frac=1, random_state=42).iloc[:250],\n",
        "           color = 'lightseagreen', label='No', marker='x')\n",
        "ax.set(title = 'Relation between Experience and Burn Rate w.r.t. company',\n",
        "       xlabel = 'Mental Fatigue Score',\n",
        "       ylabel = 'Burn rate')\n",
        "ax.legend();"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KIoVSOA8eZEx"
      },
      "source": [
        "This shows that mental fatigue score is directly proportional to burn rate and those who don't have wfh setup have higer mental fatigue score and higher burn rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "_WOLjtAHgGEz",
        "outputId": "4d32e77b-cae2-4156-b0ed-8a9121e1b804"
      },
      "source": [
        "# Plotting wfh designation and resource allocation\n",
        "fig, ax = plt.subplots(figsize=(7,7))\n",
        "ax.scatter(df_train['Resource Allocation'].sample(frac=1, random_state=42).iloc[:500],\n",
        "           df_train['Designation'].sample(frac=1, random_state=42).iloc[:500])\n",
        "ax.set(title='Designation and Resource Allocation',\n",
        "       xlabel='Resource Allocation',\n",
        "       ylabel='Designation');"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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owR73yF02qV1t2H2nRZvUvjUbLMCq6pSq2qeqlgO/DvxHVR0/VH/SfDv2Sct4y/MOZNmSxQRYtmQxb3negQtmFuIlJx6+UVgtlFmIM83mWyiz/BZyfVeedtRGYbWtzkJMVQ3fSXI48HtzTaOfmpqqFStWDF6PJKkNSVZW1dR0ywb/OzCAqroUuHQ++pIkbRs8E4ckqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJ2w+14iQ7A58Cdur7+XBVnTpUf9p6LT/5wo3arjv96AlUsrGFXBss7PoWcm0AR5x5KdfefNcD9x/3yF245MTDJ1eQNjLkCOxu4BlV9UTgYOCoJIcO2J+2QtN9yc3WPp8Wcm2wsOtbyLXBxuEFcO3Nd3HEmZdOpiBNa7ARWFUVcGd/d4f+p4bqT5K2lA3Da652Tcagx8CSLEpyBXAzcElVXTbNY16SZEWSFatXrx6yHEnSVmTQAKuqdVV1MLAP8OQkT5jmMWdV1VRVTS1dunTIciRJW5F5mYVYVWuATwBHzUd/kvRQPO6Ru2xSuyZjsABLsjTJkv72YuAI4OtD9aet00yz0hbCbLWFXBss7PoWcm0Al5x4+EZh5SzEhSfdXIsBVpwcBLwXWEQXlB+qqjfN9pypqalasWLFIPVIktqTZGVVTU23bMhZiFcCTxpq/ZKkbZtn4pAkNWnsEViSRcBeo8+pqu8MUZQkSXMZK8CSvAI4Ffg+cH/fXMBBA9UlSdKsxh2BvRI4oKp+MGQxkiSNa9xjYDcAtw1ZiCRJm2LcEdi3gEuTXEh3kl4AqurMQaqSJGkO4wbYd/qfHfsfSZImaqwAq6rTAJLs2t+/c/ZnSJI0rLGOgSV5QpLLga8CX02yMsnPDVuaJEkzG3cSx1nAiVW1f1XtD5wEvGu4siRJmt24AbZLVX1i/Z2quhTwtMySpIkZexZikv8PeF9//3i6mYmSJE3EuCOw3wSWAuf2P0v7NkmSJmLcWYg/BH534FokSRrbrAGW5G1V9aokH6E79+GDVNVzBqtMkqRZzDUCW3/M60+GLkSSpE0xa4BV1cr+5sFV9fbRZUleCXxyqMIkSZrNuJM4XjRN2wlbsA5JkjbJXMfAjgN+A3h0kgtGFu0G3DpkYZIkzWauY2CfA74L7An86Uj7HcCVQxUlSdJc5joGdj1wPfCU+SlHkqTxjHsy30OTfDHJnUnuSbIuye1DFydJ0kzGncTxDuA44FpgMfBi4J1DFSVJ0lzGDTCq6hvAoqpaV1V/Dxw1XFmSJM1u3JP5/ijJjsAVSd5KN7Fj7PCTJGlLGzeEXtA/9uXAXcC+wP8aqihJkuYy7sl8r+9v/hg4bbhyJEkaz1gBluQw4I3A/qPPqaqfGaYsSZJmN+4xsHcDrwZWAuuGK0eSpPGMG2C3VdW/DVqJJEmbYNwA+0SSM+iuxnz3+saq+tIgVUmSNIdxA+yQ/t+pkbYCnrFly5EkaTzjzkJ8+tCFSJK0KcadhXjiNM23ASur6ootW5IkSXMb9w+Zp4CXAsv6n/9DdyqpdyV57UC1SZI0o3GPge0D/NequhMgyanAhcAv0E2tf+sw5UmSNL1xR2CPZGT2IXAvsFdVrd2gXZKkeTHuCOwc4LIk/9Lffzbw/iS7AF8bpDJJkmYx7izEP0zyb8BhfdNLq2pFf/v5g1QmSdIsZg2wJLtX1e1J9gC+1f+sX7ZHVd06dIGSJE1nrhHY+4Fj6CZq1Eh7+vuezFeSNBGzBlhVHdP/++j5KUeSpPGMNQsxyWH9hA2SHJ/kzCT7DVuaJEkzG3ca/V8BP0ryROAk4JvA+warSpKkOYwbYPdVVQHPBd5RVe8EdhuuLEmSZjfu34HdkeQU4AXAzyfZDthhuLIkSZrduCOwX6M748ZvVtX36E4tdcZgVUmSNIexAqwPrX8GduqbbgHOG6ooSZLmMu4sxN8GPgz8Td+0DDh/qKIkSZrLuLsQX0Z3GqnbAarqWroT/EqSNBHjBtjdVXXP+jtJtufBZ+aQJGlejRtgn0zyB8DiJEcA/wR8ZLiyJEma3bgBdjKwGriK7mrM/wq8fqiiJEmay7iXU7k/yfnA+VW1euCaJEma06wjsHTemOQW4BrgmiSrk7xhfsqTJGl6c+1CfDXd7MP/XlV7VNUewCHAYUlePXh1kiTNYK4AewFwXFV9e31DVX0LOB544ZCFSZI0m7kCbIequmXDxv44mOdClCRNzFwBds9mLpMkaVBzzUJ8YpLbp2kPsPMA9UiSNJZZA6yqFm3uipPsC/wDsBfdWTvOqqq3b+76WnfQqRdx+93rHri/+06LuPK0oyZY0U8sP/nCjdquO/3oCVQyvYVc30KuDRZ+fdJDMe4fMm+O+4CTqurxwKHAy5I8fsD+FqwNwwvg9rvXcdCpF02oop+Y7gtutvb5tpDrW8i1wcKvT3qoBguwqvpuVX2pv30HcDXdWey3ORuG11ztkqS5DTkCe0CS5cCTgMumWfaSJCuSrFi92pN8SJLGM3iAJdmV7mKYr6qqjSaEVNVZVTVVVVNLly4duhxJ0lZi0ABLsgNdeJ1TVecO2ddCtvtO08+FmaldkjS3wQIsSYB3A1dX1ZlD9dOCK087aqOwWiizEGeakbZQZqot5PoWcm2w8OuTHqpUDXNdyiRPAz5NdwmW+/vmP6iqf53pOVNTU7VixYpB6pEktSfJyqqamm7ZWJdT2RxV9Rm6P3iWJGmLm5dZiJIkbWkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJ2w+14iR/BxwD3FxVTxiqn/WWn3zhRm3XnX700N2ObSHXt5Brg4Vd3yFvvoTv33HPA/f32m1HLnvdEROsSNp2DDkCew9w1IDrf8B0X3Cztc+3hVzfQq4NFnZ9G4YXwPfvuIdD3nzJhCqSti2DBVhVfQq4daj1S5O2YXjN1S5py5r4MbAkL0myIsmK1atXT7ocSVIjJh5gVXVWVU1V1dTSpUsnXY4kqRETDzCpVXvttuMmtUvasraKAJtpRtpCmam2kOtbyLXBwq7vstcdsVFYOQtRmj+pqmFWnHwAOBzYE/g+cGpVvXu250xNTdWKFSsGqUeS1J4kK6tqarplg/0dWFUdN9S6JUnaKnYhSpK2PQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUkGmCSpSQaYJKlJBpgkqUmDBliSo5Jck+QbSU4esi9J0rZl+6FWnGQR8E7gCOBG4ItJLqiqrw3R3/KTL9yo7brTjx6iq83y/Hd9ns9+89YH7h/2mD0457efMsGKJKltQ47Angx8o6q+VVX3AB8EnjtER9OF12zt823D8AL47Ddv5fnv+vyEKpKk9g0ZYMuAG0bu39i3bXM2DK+52iVJc5v4JI4kL0myIsmK1atXT7ocSVIjhgywVcC+I/f36dsepKrOqqqpqppaunTpgOVIkrYmQwbYF4HHJXl0kh2BXwcuGLC/Beuwx+yxSe2SpLkNFmBVdR/wcuBi4GrgQ1X11SH6mmm24UKZhXjObz9lo7ByFqIkPTSpqknX8ICpqalasWLFpMuQJC0QSVZW1dR0yyY+iUOSpM1hgEmSmmSASZKaZIBJkppkgEmSmmSASZKaZIBJkppkgEmSmmSASZKaZIBJkppkgEmSmmSASZKatKBO5ptkNXD9pOsY2J7ALZMuolFuu83nttt8brvNs6W22/5VNe3FIhdUgG0LkqyY6czKmp3bbvO57Taf227zzMd2cxeiJKlJBpgkqUkG2Pw7a9IFNMxtt/ncdpvPbbd5Bt9uHgOTJDXJEZgkqUkGmCSpSQbYPEiyb5JPJPlakq8meeWka2pNkkVJLk/y0UnX0pIkS5J8OMnXk1yd5CmTrqkVSV7d/3/9SpIPJNl50jUtVEn+LsnNSb4y0rZHkkuSXNv/+1Nbul8DbH7cB5xUVY8HDgVeluTxE66pNa8Erp50EQ16O3BRVf0X4Im4DceSZBnwu8BUVT0BWAT8+mSrWtDeAxy1QdvJwMer6nHAx/v7W5QBNg+q6rtV9aX+9h10XyLLJltVO5LsAxwN/O2ka2lJkocDvwC8G6Cq7qmqNZOtqinbA4uTbA88DLhpwvUsWFX1KeDWDZqfC7y3v/1e4Ngt3a8BNs+SLAeeBFw22Uqa8jbgtcD9ky6kMY8GVgN/3+9+/dsku0y6qBZU1SrgT4DvAN8Fbquqj022qubsVVXf7W9/D9hrS3dggM2jJLsC/wy8qqpun3Q9LUhyDHBzVa2cdC0N2h74r8BfVdWTgLsYYDfO1qg/XvNcul8C9gZ2SXL8ZKtqV3V/r7XF/2bLAJsnSXagC69zqurcSdfTkMOA5yS5Dvgg8IwkZ0+2pGbcCNxYVetH+x+mCzTN7VnAt6tqdVXdC5wLPHXCNbXm+0l+GqD/9+Yt3YEBNg+ShO44xNVVdeak62lJVZ1SVftU1XK6g+j/UVX+JjyGqvoecEOSA/qmZwJfm2BJLfkOcGiSh/X/f5+JE2A21QXAi/rbLwL+ZUt3YIDNj8OAF9CNHq7of35p0kVpm/AK4JwkVwIHA3884Xqa0I9aPwx8CbiK7rvSU0rNIMkHgM8DByS5MclvAacDRyS5lm5Ee/oW79dTSUmSWuQITJLUJANMktQkA0yS1CQDTJLUJANMktQkA0xbpSTr+j9X+EqSjyRZMumaNkeStyVZlWS7kbYTkryjv/3GJL+3BftbkuR3Ru7vneTDW2r90pZkgGlrtbaqDu7PJH4r8LL5LiDJoof4/O2AXwZuAH5xixQ1tyXAAwFWVTdV1a/MU9/SJjHAtC34PP3Z/5M8JslFSVYm+XSS/9K3/2o/Wvtykk/1bTsn+fskV/Unw3163/7ACKi//9Ekh/e370zyp0m+DDwlyQuTXNmv9339Y5Ym+eckX+x/Dpuh7sOBrwJ/BRw314tMcnCSL/T9nbf++ktJHpvk3/savtRvg12TfLy/f1WS5/arOR14TD96PSPJ8vXXeJpje5zbb9drk7x1/LdG2nzbT7oAaUj9KOiZ9JcUoTubwkur6tokhwB/CTwDeANwZFWtGtnd+DK685Ae2A/1FcQAAALiSURBVAfdx5L87Bxd7gJcVlUnJfk54PXAU6vqliR79I95O/BnVfWZJPsBFwP/zzTrOg74AN0peP44yQ79eflm8g/AK6rqk0neBJwKvAo4Bzi9qs5Ld1HG7YB7gF+uqtuT7Al8IckFdCf7fUJVHdxvv+Uj659texxMd5WFu4FrkvxFVd0wx7aSHhIDTFurxUmuoBt5XQ1c0l8N4KnAP3WntwNgp/7fzwLvSfIhuhO3AjwN+AuAqvp6kuuBuQJsHd1Jm6ELxn+qqlv6day/XtKzgMeP1LB7kl2r6s71DUl2BH4JOLGq7khyGXAkMO0VqdNd+2tJVX2yb3pv/zp3A5ZV1Xl9DT/uH78DXSj+At1lapYx9+UuZtseH6+q2/p1fw3Yn27XpzQYA0xbq7VVdXCSh9GNcF5Gd9XYNetHF6Oq6qX9iOxoYGWS/zbLuu/jwbvfRy81/+OqWjdHbdsBh64PkxkcSXc86qo+6B4GrGWGANsMzweWAv+tqu5Nd7b/nWd/yqzuHrm9Dr9bNA88BqatWlX9iO7S8CcBPwK+neRXobtKQJIn9rcfU1WXVdUb6C4CuS/wabovevpdZfsB1wDXAQcn2S7JvsCTZ+j+P4BfTfKIfh3rdyF+jO4ku/TtGwUq3e7DF1fV8v5M/I+mOzHqw2Z4nbcBP0zy833TC4BP9lcAvzHJsX1fO/XreDjdddbu7Y9l7d8/7w5gtxlez0zbQ5oIA0xbvaq6HLiSLhSeD/xWP8niq3QXLQQ4o5+c8BXgc8CX6Y6PbZfkKuAfgROq6m663Y3fprs0yZ/TnbF8un6/CrwZ+GTf3/pL6fwuMNVPtvga8NLR5/UBcxRw4ci67gI+Azx7lpf6ov51rD/z/Jv69hcAv9u3fw54FN1xsan+tb0Q+Hrfzw+Az/YTWs7YYP0zbQ9pIjwbvSSpSY7AJElNMsAkSU0ywCRJTTLAJElNMsAkSU0ywCRJTTLAJElN+v8BKbYGXsrBsG0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9ChKIoJgtbt"
      },
      "source": [
        "The plot shows that higher the designation, higher are the hours allocated per day."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "yvfrhfPYigRw",
        "outputId": "f384dc16-a0d8-4c29-89dd-cef7536f8c60"
      },
      "source": [
        "# Plot showing relation of resource allocation and burn rate w.r.t. designation\n",
        "plt.figure(figsize=(7,7))\n",
        "sns.catplot(x='Resource Allocation', y='Burn Rate', hue='Designation', data=small_df)\n",
        "plt.show()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 428x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MU0q7C5i57F"
      },
      "source": [
        "The plot shows that employees who have higher positions and give more time on their job are suffering from significantly higher burn rates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "3kRmkL5Kjzvr",
        "outputId": "99394b48-b0f1-4f5d-b7d6-b9e14231e718"
      },
      "source": [
        "# Plotting mental fatigue score with burn rate \n",
        "plt.figure(figsize=(8,8))\n",
        "sns.catplot(x='Designation', y='Burn Rate', hue='WFH Setup Available', data = small_df)\n",
        "plt.show();"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 471.625x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5phm_eUfkL85"
      },
      "source": [
        "It is seen in the plot that those who don't have WHF setup available in every designation level are more prone to burn out."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHrBcaQWlFqd"
      },
      "source": [
        "Now, the EDA part is over. Let's summarize by writing some points about the dataset:\n",
        "\n",
        "\n",
        "1.   Irrespective of gender, employees in service sector are prone to higher burn rates.\n",
        "2.   Employees with higher mental fatigue scores are having proportinally higher burn rates.\n",
        "3.   Employees at higher designation allocate more resources and hence have higher burn rate.\n",
        "4.   At every level of designation and resource allocation, employees who don't have WFH setup available are suffering from higher burn rates.\n",
        "\n",
        "Let's see the correlation matrix to see correlations between fields."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "SV9W6Zi_nHwG",
        "outputId": "8c3fcf78-56e6-4fdf-8725-b54d1311e099"
      },
      "source": [
        "# Plotting correlation matrix\n",
        "corr = df_train.corr()\n",
        "corr.style.background_gradient(cmap='coolwarm')"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            background-color:  #c83836;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row2_col4,#T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col0,#T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col1,#T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col2,#T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col3{\n",
              "            background-color:  #3b4cc0;\n",
              "            color:  #f1f1f1;\n",
              "        }</style><table id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Designation</th>        <th class=\"col_heading level0 col1\" >Resource Allocation</th>        <th class=\"col_heading level0 col2\" >Mental Fatigue Score</th>        <th class=\"col_heading level0 col3\" >Burn Rate</th>        <th class=\"col_heading level0 col4\" >Experience</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >Designation</th>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.878459</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.690983</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.737556</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.001371</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >Resource Allocation</th>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.878459</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.799662</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.856278</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.000478</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >Mental Fatigue Score</th>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.690983</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.799662</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.944546</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row2_col4\" class=\"data row2 col4\" >-0.003483</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >Burn Rate</th>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.737556</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.856278</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.944546</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.001123</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >Experience</th>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.001371</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.000478</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col2\" class=\"data row4 col2\" >-0.003483</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.001123</td>\n",
              "                        <td id=\"T_7bfdd1da_9dd1_11eb_bc34_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe202504b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qzq8vfEunaAi"
      },
      "source": [
        "## Modelling \n",
        "Now, its time to start preprocessing and creating a prediction model pipeline."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zl04789Tn-F0",
        "outputId": "1ecfe726-d9ba-4591-e058-ac5d6ab9fad9"
      },
      "source": [
        "# Shape of dataframe\n",
        "print(df_train.shape)\n",
        "print(df_train.columns)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(22750, 10)\n",
            "Index(['Employee ID', 'Date of Joining', 'Gender', 'Company Type',\n",
            "       'WFH Setup Available', 'Designation', 'Resource Allocation',\n",
            "       'Mental Fatigue Score', 'Burn Rate', 'Experience'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-i-Q3z9hoGWq"
      },
      "source": [
        "# Columns to be removed, 'Employee id', 'Date of joining' and 'Experience'\n",
        "df_train.drop(['Employee ID', 'Date of Joining', 'Experience'], axis=1, inplace=True)"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xnByoiNoiz1",
        "outputId": "a6730431-cc7f-470a-eaf6-932350819cbd"
      },
      "source": [
        "df_train.shape"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22750, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJf5MTOvomIV",
        "outputId": "40aad66b-a5ec-4779-e559-83edaca510c4"
      },
      "source": [
        "# Filling null values \n",
        "df_train.isna().sum()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender                     0\n",
              "Company Type               0\n",
              "WFH Setup Available        0\n",
              "Designation                0\n",
              "Resource Allocation     1381\n",
              "Mental Fatigue Score    2117\n",
              "Burn Rate               1124\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGKgF2FmoxO0"
      },
      "source": [
        "Since knowing the burn rate is very important, there is not point in having rows which don't have burn rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0m3XoKjip3o9",
        "outputId": "064d0ba5-ea5a-4c58-ea04-625a534372d3"
      },
      "source": [
        "df_train['Resource Allocation'].median()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2Yf2O8RpFGF"
      },
      "source": [
        "# Filling null values in 'Resource Allocation' and 'Mental Fatigue Score'\n",
        "df_train['Resource Allocation'].fillna(value=df_train['Resource Allocation'].median(), inplace=True)"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8msTyOKp-Bf",
        "outputId": "f0ed5b3f-b621-4991-96c7-2894cf310a19"
      },
      "source": [
        "df_train.isna().sum()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender                     0\n",
              "Company Type               0\n",
              "WFH Setup Available        0\n",
              "Designation                0\n",
              "Resource Allocation        0\n",
              "Mental Fatigue Score    2117\n",
              "Burn Rate               1124\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "TPUgJ3d2qJRs",
        "outputId": "0ee322d2-cbdd-427d-c805-23ad5bcb32bc"
      },
      "source": [
        "sns.violinplot(x=df_train['Mental Fatigue Score'])"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1c2bb1550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHR474zSr_Ku",
        "outputId": "9d898265-303f-4044-8657-6e0a9d5a638b"
      },
      "source": [
        "df_train.isna().sum()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender                     0\n",
              "Company Type               0\n",
              "WFH Setup Available        0\n",
              "Designation                0\n",
              "Resource Allocation        0\n",
              "Mental Fatigue Score    2117\n",
              "Burn Rate               1124\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4RrPnUGsC-8"
      },
      "source": [
        "# Dropping rows with no burn rate \n",
        "df_train.dropna(inplace=True)"
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3svE-YLDsNX1",
        "outputId": "a77b5b6d-bc8d-45c4-d013-f9b9990d9c14"
      },
      "source": [
        "df_train.shape"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19681, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IvPGfXlsOv8"
      },
      "source": [
        "# Label encoding categorical features \n",
        "cat_features = [x for x in df_train.columns if df_train[x].dtype=='object']"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbH962a_s9Bl",
        "outputId": "5804011e-d4b1-42ea-fe0a-aa1b3d826f01"
      },
      "source": [
        "cat_features"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Gender', 'Company Type', 'WFH Setup Available']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDqPczd2uHr9"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "for cat in cat_features:\n",
        "  le = LabelEncoder()\n",
        "  df_train[cat] = le.fit_transform(df_train[cat])"
      ],
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "fdoI7zfbw6Ee",
        "outputId": "4428158e-7025-4aad-fb01-09ab8ac5344c"
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": 154,
      "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>Gender</th>\n",
              "      <th>Company Type</th>\n",
              "      <th>WFH Setup Available</th>\n",
              "      <th>Designation</th>\n",
              "      <th>Resource Allocation</th>\n",
              "      <th>Mental Fatigue Score</th>\n",
              "      <th>Burn Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Company Type  ...  Mental Fatigue Score  Burn Rate\n",
              "0       0             1  ...                   3.8       0.16\n",
              "1       1             1  ...                   5.0       0.36\n",
              "2       0             0  ...                   5.8       0.49\n",
              "3       1             1  ...                   2.6       0.20\n",
              "4       0             1  ...                   6.9       0.52\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnszVZDjxWvk"
      },
      "source": [
        "# Splitting data into train and test split \n",
        "X = df_train.drop('Burn Rate', axis=1)\n",
        "y = df_train['Burn Rate']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train_full, X_test, y_train_full, y_test = train_test_split(X, y, \n",
        "                                                              test_size=0.2,\n",
        "                                                              random_state=42)"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-1JDEz9yMni",
        "outputId": "982bef51-d43f-49b8-8b8a-5093a21fedd5"
      },
      "source": [
        "X_train_full.shape, X_test.shape"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((15744, 6), (3937, 6))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YShobpySyb58"
      },
      "source": [
        "# Splitting into validation set \n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full,\n",
        "                                                      test_size=0.25,\n",
        "                                                      random_state=42)"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGGUqmfByuVX",
        "outputId": "1730942c-aec4-4913-824a-53b5c8e58d90"
      },
      "source": [
        "X_train.shape, X_valid.shape"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((11808, 6), (3936, 6))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9zs5c8myx6L",
        "outputId": "ff0f7bdc-6789-46ca-cbe8-49efc1267796"
      },
      "source": [
        "# Using Linear Regression on the dataset \n",
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train.values, y_train.values)\n",
        "lr.score(X_valid, y_valid)"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9130281661795171"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfdgijA-2aXh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e25ec45b-6b22-499d-82e7-129d8f0aae58"
      },
      "source": [
        "# Finding other metrics on the model \n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "y_pred = lr.predict(X_valid)\n",
        "print(mean_absolute_error(y_valid, y_pred))"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.047775297276039616\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0w26Deu217F",
        "outputId": "718f091f-c44d-4fc1-85ee-89dd9e39bb9f"
      },
      "source": [
        "print(mean_squared_error(y_valid, y_pred))"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0033849121235445256\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXdN60g6261u"
      },
      "source": [
        "# Making a function to calculate metrics\n",
        "def metrics(model, X, y_true):\n",
        "  \"\"\"\n",
        "  Function to calculate accuracy, mean squared error and mean absolute error\n",
        "\n",
        "  Parameters\n",
        "  -----------\n",
        "  model - model trained with training data\n",
        "  y_true - true label\n",
        "\n",
        "  Return \n",
        "  -----------\n",
        "  Dictionary storing the metrics \n",
        "  \"\"\"\n",
        "  y_pred = model.predict(X)\n",
        "  mae = mean_absolute_error(y_true, y_pred)\n",
        "  acc = model.score(X, y_true)\n",
        "  mse = mean_squared_error(y_true, y_pred)\n",
        "\n",
        "  return {'Accuracy( in %)': round(acc*100, 4),\n",
        "          'Mean Absolute Error': mae,\n",
        "          'Mean Squared Error': mse}"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQqL3H8s5E3L",
        "outputId": "a6905cf3-4d19-4621-ed13-3dbd5ba1111f"
      },
      "source": [
        "metrics(lr, X_valid, y_valid)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy( in %)': 91.3028,\n",
              " 'Mean Absolute Error': 0.047775297276039616,\n",
              " 'Mean Squared Error': 0.0033849121235445256}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnP1MitY5OGQ",
        "outputId": "3d2aeb34-bf2c-4fe4-ebde-398bcec496ee"
      },
      "source": [
        "# Using lasso regression, which gives more importance to some features\n",
        "from sklearn.linear_model import Lasso \n",
        "las = Lasso()\n",
        "las.fit(X_train, y_train)\n",
        "metrics(las, X_valid, y_valid)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy( in %)': -0.007,\n",
              " 'Mean Absolute Error': 0.15864330645338973,\n",
              " 'Mean Squared Error': 0.03892235212358899}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ku0DMXgz6neX",
        "outputId": "1b55b55b-d8f2-4a54-a2c7-2f831b6132b4"
      },
      "source": [
        "# Using ridge regression \n",
        "from sklearn.linear_model import Ridge\n",
        "rid = Ridge()\n",
        "rid.fit(X_train, y_train)\n",
        "metrics(rid, X_valid, y_valid)"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy( in %)': 91.3028,\n",
              " 'Mean Absolute Error': 0.047774964202885774,\n",
              " 'Mean Squared Error': 0.0033849170417458827}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7qq90KV8xEd",
        "outputId": "000b50c6-3572-4cb8-f825-83b69a4e25ea"
      },
      "source": [
        "# Using random forest, lol \n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "forest = RandomForestRegressor()\n",
        "forest.fit(X_train, y_train)\n",
        "metrics(forest, X_valid, y_valid)"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy( in %)': 90.7839,\n",
              " 'Mean Absolute Error': 0.047412634640248835,\n",
              " 'Mean Squared Error': 0.003586889644693737}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OI7Xvk2J0i_O"
      },
      "source": [
        "#Implementing some more models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQkkVn7U0idQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42eaead3-aba4-4327-8f4a-a8c871a56b7b"
      },
      "source": [
        "#KNN Regressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "n1 = KNeighborsRegressor(n_neighbors=2)\n",
        "n1.fit(X_train, y_train)\n"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                    metric_params=None, n_jobs=None, n_neighbors=2, p=2,\n",
              "                    weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dqfc74x0hba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54d22f3d-20a4-49ee-8cac-cb3d4a586a07"
      },
      "source": [
        "metrics(n1, X_valid, y_valid)"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy( in %)': 89.1952,\n",
              " 'Mean Absolute Error': 0.050910823170731714,\n",
              " 'Mean Squared Error': 0.004205189278455285}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VEhnuKWP2Zp_"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCVNhMiG0heR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41792d5e-1b4e-47ff-9eea-f1c82dcd40a8"
      },
      "source": [
        "leaf_size = list(range(1,50))\n",
        "n_neighbors = list(range(1,30))\n",
        "p=[1,2]\n",
        "#Convert to dictionary\n",
        "hyperparameters = dict(leaf_size=leaf_size, n_neighbors=n_neighbors, p=p)\n",
        "#Create new KNN object\n",
        "knn_2 =KNeighborsRegressor()\n",
        "#Use GridSearch\n",
        "clf = GridSearchCV(knn_2, hyperparameters, cv=10)\n",
        "#Fit the model\n",
        "best_model = clf.fit(X_train,y_train)\n",
        "#Print The value of best Hyperparameters\n",
        "print('Best leaf_size:', best_model.best_estimator_.get_params()['leaf_size'])\n",
        "print('Best p:', best_model.best_estimator_.get_params()['p'])\n",
        "print('Best n_neighbors:', best_model.best_estimator_.get_params()['n_neighbors'])"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best leaf_size: 21\n",
            "Best p: 2\n",
            "Best n_neighbors: 29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hclb8j5q7--5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed380ff7-c0aa-46f8-b961-723df9e17658"
      },
      "source": [
        "n2 = KNeighborsRegressor(n_neighbors=29,p=2,leaf_size=21)\n",
        "n2.fit(X_train, y_train)\n",
        "metrics(n2, X_valid, y_valid)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy( in %)': 91.9287,\n",
              " 'Mean Absolute Error': 0.04521613050182226,\n",
              " 'Mean Squared Error': 0.003141309374486432}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggbrE25b9w9H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21bae201-856b-4586-fc00-4e832910ea44"
      },
      "source": [
        "# Hyperparameter tuning in RandomForestRegressor() \n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "rf = RandomForestRegressor()\n",
        "params_dict = {\n",
        "    'n_estimators' : [100, 150, 175, 200],\n",
        "    'criterion' : ['mae', 'mse'],\n",
        "    'min_samples_split': [2, 3, 4, 5],\n",
        "    'min_samples_leaf' : [1, 2, 3],\n",
        "    'max_features' : ['auto', 'sqrt', 'log2']\n",
        "}\n",
        "clf = RandomizedSearchCV(rf, params_dict, cv = 5)\n",
        "best_model = clf.fit(X_train, y_train)\n",
        "clf.best_params_"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'mse',\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 3,\n",
              " 'min_samples_split': 4,\n",
              " 'n_estimators': 200}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbOZDEyiy6Vc",
        "outputId": "3b64bfb4-be3b-4572-a873-07f6f303a55b"
      },
      "source": [
        "metrics(best_model, X_test, y_test)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy( in %)': -449.1967,\n",
              " 'Mean Absolute Error': 0.4230334566108625,\n",
              " 'Mean Squared Error': 0.2148089783965106}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F21mHHd9zBeC",
        "outputId": "9d6b2beb-0514-473a-8d0d-ff0777dfaa65"
      },
      "source": [
        "metrics(best_model, X_valid, y_valid)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Accuracy( in %)': 94.0151,\n",
              " 'Mean Absolute Error': 0.03870177797560813,\n",
              " 'Mean Squared Error': 0.002329311069290185}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUMH-OvIzr1V",
        "outputId": "a80af591-3c23-4a79-bf25-8869af616f2f"
      },
      "source": [
        "final_model = RandomForestRegressor(n_estimators=200, max_features='sqrt',\n",
        "                                    min_samples_leaf=3, min_samples_split=4,\n",
        "                                    criterion='mse')\n",
        "final_model.fit(X_train, y_train)\n",
        "print(metrics(final_model, X_valid, y_valid))"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Accuracy( in %)': 91.8266, 'Mean Absolute Error': 0.04556398467920745, 'Mean Squared Error': 0.00318106191979798}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCH7GDz00a07",
        "outputId": "15b7de7a-ee57-4d38-b518-b6e03418bb7f"
      },
      "source": [
        "pred = final_model.predict(samp.reshape(1,-1))\n",
        "print(samp, pred)"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.  1.  0.  2.  3.  3.8] [0.24562584]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-X8-RkUQ0jum",
        "outputId": "b25fa0fa-bf85-4bbb-ae84-0196f23ab95d"
      },
      "source": [
        "y_test[0]"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAoFipYB1WWW",
        "outputId": "f81ee534-4a2e-412a-f141-7c47ac02d345"
      },
      "source": [
        "pred = n2.predict(samp.reshape(1, -1))\n",
        "print(samp, pred)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.  1.  0.  2.  3.  3.8] [0.26068966]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCQgtQDO-LKH"
      },
      "source": [
        "import pickle as pkl \n",
        "pkl.dump(final_model,open('model_file.pkl','wb'))"
      ],
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJ1VXd-D_htM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}