SLC_In_Class_Lab_Exercise_Day3_DT_RF.ipynb

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   "source": [
    "<table align=\"center\" width=100%>\n",
    "    <tr>\n",
    "        <td width=\"15%\">\n",
    "            <img src=\"in_class.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"center\">\n",
    "                <font color=\"#21618C\" size=8px>\n",
    "                    <b> Inclass - Lab <br>(Day 3)\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uouclbIkDWHH"
   },
   "source": [
    "### About the data set (Employee data)\n",
    "\n",
    "The dataset contains information about employees. The aim is to find which employees might undergo attrition.<br> Attribute information: \n",
    "\n",
    "**Age:** Age of the employee \n",
    "\n",
    "**BusinessTravel:** How much travel is involved in the job for the employee:No Travel, Travel Frequently, Tavel Rarely\n",
    "\n",
    "**Department:** Department of the employee: Human Resources, Reserach & Development, Sales\n",
    "\n",
    "**Commute:** Number of miles of daily commute for the employee\n",
    "\n",
    "**Education:** Employee education field: Human Resources, Life Sciences, Marketing, Medical Sciences, Technical, Others\n",
    "\n",
    "**EnvironmentSatisfaction:** Satisfaction of employee with office environment\n",
    "\n",
    "**Gender:** Employee gender\n",
    "\n",
    "**JobInvolvement:** Job involvement rating\n",
    "\n",
    "**JobLevel:**  Job level for employee designation\n",
    "\n",
    "**JobSatisfaction:** Employee job satisfaction rating\n",
    "\n",
    "**MonthlyIncome:** Employee monthly salary\n",
    "\n",
    "**OverTime:** Has the employee been open to working overtime: Yes or No\n",
    "\n",
    "**PercentSalaryHike:** Percent increase in salary\n",
    "\n",
    "**PerformanceRating:** Overall employee performance rating\n",
    "\n",
    "**YearsAtCompany:** Number of years the employee has worked with the company\n",
    "\n",
    "**Attrition:** Employee leaving the company: Yes or No"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "colab": {
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    "id": "8WEXx6eRhE-a",
    "outputId": "2215c315-b647-44f5-cb11-aaf859d2381d"
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       "1.0"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "-(.5*np.log2(.5)+.5*np.log2(.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "id": "NBiSqngpkLsz",
    "outputId": "b8c4c6aa-082d-4fdf-9b3a-635b10d43d6f"
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   "outputs": [
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       "0.8112781244591328"
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     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "-(.25*np.log2(.25)+.75*np.log2(.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.8112781244591328*(4/7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-xEy1B4xhUwv",
    "outputId": "9ffa652c-2c19-47f3-9c02-24191e763cab"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9182958340544896"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-((1/3)*np.log2(1/3)+(2/3)*np.log2(2/3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8jeSfQMrhLlH",
    "outputId": "50f5a2b6-d041-45a2-921c-86a850e5a4d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39355535745192405"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.9182958340544896*(3/7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D529XV9UkdAq",
    "outputId": "17ce708f-2911-476e-e80d-e7627fe488a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8571428571428571"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.39355535745192405+0.46358749969093305"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O0_KsgTm965k"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HyN3t3ZB9vPU",
    "outputId": "d669664c-ab55-4f1f-d5c5-0e96a8876003"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#entropy at root\n",
    "-(.5*np.log2(.5)+.5*np.log2(.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LWjWCuyw_rkH",
    "outputId": "eaf4f648-0120-4872-b8d2-e3ad13cf22d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8112781244591328"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#entropy at left branch depth2\n",
    "-((1/4)*np.log2(1/4)+(3/4)*np.log2(3/4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rCM7HgSnGJLP",
    "outputId": "57dcc9dd-351e-42ad-da81-4747dd63b40a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4056390622295664"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.8112781244591328*(.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dzS8kJV_AOQs",
    "outputId": "81fdf06d-373f-434d-afb4-47c97e0c74f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#weighted entropy\n",
    "0.9182958340544896*(3/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9OlxGOpU_5id",
    "outputId": "498629a6-c5c6-4fd4-9b7f-11687272b696"
   },
   "outputs": [
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     "data": {
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       "0.9852281360342515"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#entropy at right branch depth1\n",
    "-((3/7)*np.log2(3/7)+(4/7)*np.log2(4/7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "My4XmJI9BlBK",
    "outputId": "6ec2d761-e124-4008-8847-9de806d9c1eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9544340029249649"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-((3/8)*np.log2(3/8)+(5/8)*np.log2(5/8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Ddf1T0kBrLg",
    "outputId": "8ff16dee-42ae-475e-ad64-c32cf2d86b73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.763547202339972"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.9544340029249649*(8/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CHTyMz5SAUjs",
    "outputId": "d0f8aa42-7249-49e4-9e53-50b376e9e069"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6896596952239761"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.9852281360342515*(7/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wfNp_FWUAXo4",
    "outputId": "905b4107-b8fe-4691-f88c-f6fb87940c15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.965148445440323"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tot entropy at depth1\n",
    "0.6896596952239761+0.27548875021634683"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OEvs27-bAdh8",
    "outputId": "1b640a8f-ac05-4f0f-ea21-3d3cdb352b47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.034851554559677034"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Information gain\n",
    "1-0.965148445440323"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VVP-FNRwDWHI"
   },
   "source": [
    "## Table of Content\n",
    "\n",
    "1. **[Decision tree](#dtree)**\n",
    "2. **[Random forest](#rf)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uEWA3ftlDWHI"
   },
   "source": [
    "**Import the required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bJw4C3A2DWHJ"
   },
   "outputs": [],
   "source": [
    "# import 'Pandas' \n",
    "import pandas as pd \n",
    "\n",
    "# import 'Numpy' \n",
    "import numpy as np\n",
    "\n",
    "# import subpackage of Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# import 'Seaborn' \n",
    "import seaborn as sns\n",
    "\n",
    "# to suppress warnings \n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "# display all columns of the dataframe\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# display all rows of the dataframe\n",
    "pd.options.display.max_rows = None\n",
    " \n",
    "# to display the float values upto 6 decimal places     \n",
    "pd.options.display.float_format = '{:.6f}'.format\n",
    "\n",
    "# import train-test split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# import StandardScaler to perform scaling\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "# resample the dataset\n",
    "#from sklearn.utils import resample\n",
    "#from sklearn.utils import shuffle\n",
    "\n",
    "# import various functions from sklearn \n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# import the functions for visualizing the decision tree\n",
    "import pydotplus\n",
    "from IPython.display import Image  \n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BAXQJQOIDWHL"
   },
   "outputs": [],
   "source": [
    "# set the plot size using 'rcParams'\n",
    "# once the plot size is set using 'rcParams', it sets the size of all the forthcoming plots in the file\n",
    "# pass width and height in inches to 'figure.figsize' \n",
    "plt.rcParams['figure.figsize'] = [15,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "py44M35VF68T",
    "outputId": "1ff22bb6-5652-4282-cc17-128c9e4251f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FnQff0syDWHM"
   },
   "source": [
    "#### Load the csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "XvJ_U4gVDWHM",
    "outputId": "aa562305-a899-4f55-f5cc-1a9d695486be"
   },
   "outputs": [
    {
     "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",
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       "    }\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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>1</td>\n",
       "      <td>3348</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>10400</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>9</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>7</td>\n",
       "      <td>Medical</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>2</td>\n",
       "      <td>2372</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>27</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>1</td>\n",
       "      <td>10820</td>\n",
       "      <td>8</td>\n",
       "      <td>No</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel              Department  DistanceFromHome  \\\n",
       "0   33       Yes  Travel_Frequently  Research & Development                 3   \n",
       "1   32       Yes      Travel_Rarely                   Sales                 4   \n",
       "2   40       Yes      Travel_Rarely  Research & Development                 9   \n",
       "3   42        No      Travel_Rarely  Research & Development                 7   \n",
       "4   43        No  Travel_Frequently  Research & Development                27   \n",
       "\n",
       "  EducationField  EnvironmentSatisfaction  Gender  JobInvolvement  JobLevel  \\\n",
       "0  Life Sciences                        1    Male               3         1   \n",
       "1        Medical                        4    Male               1         3   \n",
       "2  Life Sciences                        4    Male               3         1   \n",
       "3        Medical                        2  Female               4         2   \n",
       "4  Life Sciences                        3  Female               3         3   \n",
       "\n",
       "                  JobRole  JobSatisfaction  MonthlyIncome  NumCompaniesWorked  \\\n",
       "0      Research Scientist                1           3348                   1   \n",
       "1         Sales Executive                4          10400                   1   \n",
       "2   Laboratory Technician                1           2018                   3   \n",
       "3      Research Scientist                2           2372                   6   \n",
       "4  Manufacturing Director                1          10820                   8   \n",
       "\n",
       "  OverTime  PercentSalaryHike  PerformanceRating  YearsAtCompany  \n",
       "0      Yes                 11                  3              10  \n",
       "1       No                 11                  3              14  \n",
       "2       No                 14                  3               5  \n",
       "3      Yes                 16                  3               1  \n",
       "4       No                 11                  3               8  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the csv file\n",
    "df_employee = pd.read_csv('/content/drive/My Drive/SLC/emp_attrition.csv')\n",
    "\n",
    "# display the first two observations\n",
    "df_employee.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VzHxvjtaDWHO",
    "outputId": "7b82ad76-eac9-4eee-c624-4aa50289972a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         int64\n",
       "Attrition                  object\n",
       "BusinessTravel             object\n",
       "Department                 object\n",
       "DistanceFromHome            int64\n",
       "EducationField             object\n",
       "EnvironmentSatisfaction     int64\n",
       "Gender                     object\n",
       "JobInvolvement              int64\n",
       "JobLevel                    int64\n",
       "JobRole                    object\n",
       "JobSatisfaction             int64\n",
       "MonthlyIncome               int64\n",
       "NumCompaniesWorked          int64\n",
       "OverTime                   object\n",
       "PercentSalaryHike           int64\n",
       "PerformanceRating           int64\n",
       "YearsAtCompany              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types of variables\n",
    "df_employee.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wiABDH5-DWHP",
    "outputId": "f3565c7f-b5bb-40c3-b7db-d5cc70b0b90f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1580, 18)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the size of data\n",
    "df_employee.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "hAiiqkOODWHP"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>1. Write a code to detect the outliers in the dataset. Remove the outliers using IQR method, if present.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ptqivtb1DWHQ"
   },
   "source": [
    "**Check the outliers in all the variables and treat them using IQR technique.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "2y27FVmrDWHQ",
    "outputId": "bd6aa069-437a-477a-f407-dc0f7ce1eea9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a boxplot to visualize the outliers in all the numeric variables\n",
    "df_employee.boxplot()\n",
    "\n",
    "# set plot label\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Distribution of all Numeric Variables', fontsize = 15)\n",
    "\n",
    "# xticks() returns the x-axis ticks\n",
    "# 'rotation = vertical' rotates the x-axis labels vertically\n",
    "# set text size using 'fontsize'\n",
    "plt.xticks(rotation = 'vertical', fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0t0R-6CJDWHR"
   },
   "source": [
    "**Notice that the variable 'MonthlyIncome' has a quite large range as compared to the other variables. Thus, it is difficult to see the outliers in other variables. So, we plot the boxplots of independent variables again by not considering the variable 'MonthlyIncome'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 676
    },
    "id": "kdcwzEwJDWHR",
    "outputId": "40ec409f-3dd9-4246-8634-2023fc58df7a",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot boxplots of all the numeric variables except 'MonthlyIncome'\n",
    "df_employee[['Age', 'DistanceFromHome', 'EnvironmentSatisfaction', 'JobInvolvement', 'JobLevel', 'JobSatisfaction',\n",
    "            'NumCompaniesWorked', 'PercentSalaryHike', 'PerformanceRating', 'YearsAtCompany']].boxplot()\n",
    "\n",
    "# set plot label\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Distribution of Independent Variables (except \"MonthlyIncome\")', fontsize = 15)\n",
    "\n",
    "# xticks() returns the x-axis ticks\n",
    "# 'rotation = vertical' rotates the x-axis labels vertically\n",
    "plt.xticks(rotation = 'vertical', fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HkA2NdPODWHR"
   },
   "source": [
    "**Interpretation:** From the above plots, we notice that some of the variables have points above the upper extreme or below the lower extreme - these points are outliers in the data. However, the exact location of these points is not precisely seen.\n",
    "\n",
    "Let us use the IQR method to remove the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9qL_m-B5DWHS",
    "outputId": "01e22a94-4d7f-404e-f21b-d5b3802babcd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1487, 18)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the first quartile\n",
    "Q1 = df_employee.quantile(0.25)\n",
    "\n",
    "#calculate the third quartile\n",
    "Q3 = df_employee.quantile(0.75)\n",
    "\n",
    "# The Interquartile Range (IQR) is defined as the difference between the third and first quartile\n",
    "# calculate IQR\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# remove the outliers from the dataframe\n",
    "df_employee = df_employee[~((df_employee < (Q1 - 1.5 * IQR)) | (df_employee > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
    "\n",
    "# reset the row index\n",
    "df_employee = df_employee.reset_index(drop=True)\n",
    "\n",
    "# check the shape of the data\n",
    "df_employee.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fO40SwEJDWHS"
   },
   "source": [
    "### Let's begin with some hands-on practice exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bSCFi3sPDWHS"
   },
   "source": [
    "<a id=\"dtree\"> </a>\n",
    "## 1. Decision tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bBe8FzjCDWHT"
   },
   "source": [
    "## We shall use the employee dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "FOSZHY8eDWHT"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>2. Build a model to predict whether or not an employee will leave the company based on employee's environment satisfaction and monthly income. Calculate the accuracy for the model.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RgPtTEjJDWHT"
   },
   "source": [
    "#### Split the dependent and independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FDKzbE4UDWHU"
   },
   "outputs": [],
   "source": [
    "# store the target variable \"Attrition\" in a dataframe 'df_target'\n",
    "df_target = df_employee['Attrition']\n",
    "\n",
    "# store all the independent variables in a dataframe 'df_feature' \n",
    "# drop the column \"Attrition\" using drop()\n",
    "# 'axis = 1' drops the specified column\n",
    "df_feature = df_employee.drop('Attrition', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TbysEmVyDWHU",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# if the value in the target variable is 'Yes' then replace it with 1 else with 0\n",
    "for i in range(len(df_target)):\n",
    "    if df_target[i] == 'Yes':\n",
    "        df_target[i] = 1\n",
    "    else:\n",
    "        df_target[i] = 0\n",
    "        \n",
    "# change the datatype of the target variable to integer\n",
    "df_target = df_target.astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0XpOV1biDWHU"
   },
   "source": [
    "#### Filter numerical and categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EJBkzTwSDWHV",
    "outputId": "63cc70b6-eee8-4c1f-e895-7edcab87fee4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'DistanceFromHome', 'EnvironmentSatisfaction', 'JobInvolvement',\n",
       "       'JobLevel', 'JobSatisfaction', 'MonthlyIncome', 'NumCompaniesWorked',\n",
       "       'PercentSalaryHike', 'PerformanceRating', 'YearsAtCompany'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the numerical features in the dataset\n",
    "# 'select_dtypes' is used to select the variables with given data type\n",
    "# 'include = [np.number]' will include all the numerical variables\n",
    "df_num = df_feature.select_dtypes(include = [np.number])\n",
    "\n",
    "# display numerical features\n",
    "df_num.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RiKYt80yDWHV",
    "outputId": "1ccad7f3-ea7a-4381-9e21-e4a520df6a99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole',\n",
       "       'OverTime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the categorical features in the dataset\n",
    "# 'select_dtypes' is used to select the variables with given data type\n",
    "# 'include = [np.object]' will include all the categorical variables\n",
    "df_cat = df_feature.select_dtypes(include = [np.object])\n",
    "\n",
    "# display categorical features\n",
    "df_cat.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1tKRf5vaDWHV"
   },
   "source": [
    "**Encode the each categorical variable and create (n-1) dummy variables for n categories of the variable.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nqXOzZP9DWHV"
   },
   "outputs": [],
   "source": [
    "# use 'get_dummies' from pandas to create dummy variables\n",
    "# use 'drop_first' to create (n-1) dummy variables\n",
    "dummy_var = pd.get_dummies(data = df_cat, drop_first = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_VzQ17C1DWHW"
   },
   "source": [
    "#### Concatenate numerical and dummy encoded categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 243
    },
    "id": "PR-mG-fbDWHW",
    "outputId": "3c9324d8-48b0-4bd5-efe3-b123d9786d03",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>BusinessTravel_Travel_Frequently</th>\n",
       "      <th>BusinessTravel_Travel_Rarely</th>\n",
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       "      <th>Department_Sales</th>\n",
       "      <th>EducationField_Life Sciences</th>\n",
       "      <th>EducationField_Marketing</th>\n",
       "      <th>EducationField_Medical</th>\n",
       "      <th>EducationField_Other</th>\n",
       "      <th>EducationField_Technical Degree</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>JobRole_Human Resources</th>\n",
       "      <th>JobRole_Laboratory Technician</th>\n",
       "      <th>JobRole_Manager</th>\n",
       "      <th>JobRole_Manufacturing Director</th>\n",
       "      <th>JobRole_Research Director</th>\n",
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       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
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       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
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       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
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      ],
      "text/plain": [
       "   Age  DistanceFromHome  EnvironmentSatisfaction  JobInvolvement  JobLevel  \\\n",
       "0   33                 3                        1               3         1   \n",
       "1   32                 4                        4               1         3   \n",
       "2   40                 9                        4               3         1   \n",
       "3   42                 7                        2               4         2   \n",
       "4   43                27                        3               3         3   \n",
       "\n",
       "   JobSatisfaction  MonthlyIncome  NumCompaniesWorked  PercentSalaryHike  \\\n",
       "0                1           3348                   1                 11   \n",
       "1                4          10400                   1                 11   \n",
       "2                1           2018                   3                 14   \n",
       "3                2           2372                   6                 16   \n",
       "4                1          10820                   8                 11   \n",
       "\n",
       "   PerformanceRating  YearsAtCompany  BusinessTravel_Travel_Frequently  \\\n",
       "0                  3              10                                 1   \n",
       "1                  3              14                                 0   \n",
       "2                  3               5                                 0   \n",
       "3                  3               1                                 0   \n",
       "4                  3               8                                 1   \n",
       "\n",
       "   BusinessTravel_Travel_Rarely  Department_Research & Development  \\\n",
       "0                             0                                  1   \n",
       "1                             1                                  0   \n",
       "2                             1                                  1   \n",
       "3                             1                                  1   \n",
       "4                             0                                  1   \n",
       "\n",
       "   Department_Sales  EducationField_Life Sciences  EducationField_Marketing  \\\n",
       "0                 0                             1                         0   \n",
       "1                 1                             0                         0   \n",
       "2                 0                             1                         0   \n",
       "3                 0                             0                         0   \n",
       "4                 0                             1                         0   \n",
       "\n",
       "   EducationField_Medical  EducationField_Other  \\\n",
       "0                       0                     0   \n",
       "1                       1                     0   \n",
       "2                       0                     0   \n",
       "3                       1                     0   \n",
       "4                       0                     0   \n",
       "\n",
       "   EducationField_Technical Degree  Gender_Male  JobRole_Human Resources  \\\n",
       "0                                0            1                        0   \n",
       "1                                0            1                        0   \n",
       "2                                0            1                        0   \n",
       "3                                0            0                        0   \n",
       "4                                0            0                        0   \n",
       "\n",
       "   JobRole_Laboratory Technician  JobRole_Manager  \\\n",
       "0                              0                0   \n",
       "1                              0                0   \n",
       "2                              1                0   \n",
       "3                              0                0   \n",
       "4                              0                0   \n",
       "\n",
       "   JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               1                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           1                        0   \n",
       "1                           0                        1   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  OverTime_Yes  \n",
       "0                             0             1  \n",
       "1                             0             0  \n",
       "2                             0             0  \n",
       "3                             0             1  \n",
       "4                             0             0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat the dummy variables with numeric features to create a dataframe of all independent variables\n",
    "# 'axis=1' concats the dataframes along columns \n",
    "X = pd.concat([df_num, dummy_var], axis = 1)\n",
    "\n",
    "# display first five observations\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zxeGmPSjYatk"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V9jNnl5VDWHX",
    "outputId": "c4d9b249-11af-4da2-ac85-794333cb5f3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_Accuracy: 0.8568232662192393\n"
     ]
    }
   ],
   "source": [
    "# split the target and independent variables\n",
    "# store all the relevant independent variables in a dataframe 'X_subset' \n",
    "X_subset = X.iloc[:,[2,6]]\n",
    "\n",
    "# split data into train subset and test subset\n",
    "# set 'random_state' to generate the same dataset each time you run the code \n",
    "# 'test_size' returns the proportion of data to be included in the test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_subset, df_target, random_state = 10, test_size = 0.3)\n",
    "\n",
    "# instantiate the 'DecisionTreeClassifier' object using 'gini' criterion\n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "decision_tree = DecisionTreeClassifier(random_state=10)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "decision_tree = decision_tree.fit(X_train, y_train)\n",
    "\n",
    "# predict the attrition for test set\n",
    "y_pred = decision_tree.predict(X_test)\n",
    "\n",
    "# pass the actual and predicted target values to the accuracy_score()\n",
    "print(\"Test_Accuracy:\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OcRgGq8LxA-N",
    "outputId": "f95f51fa-47ba-4bb2-de91-57140dbe2ece"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_Accuracy: 0.9951923076923077\n"
     ]
    }
   ],
   "source": [
    "#calculate training accuracy\n",
    "y_pred_train = decision_tree.predict(X_train)\n",
    "\n",
    "# pass the actual and predicted target values to the accuracy_score()\n",
    "print(\"Test_Accuracy:\",accuracy_score(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9F_UdQ00Z21T",
    "outputId": "b41d659d-66b5-4400-b21e-bff66d0b0ede"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Accuracy: 0.9951923076923077\n"
     ]
    }
   ],
   "source": [
    "#What is the acc of training records\n",
    "y_pred_train=decision_tree.predict(X_train)\n",
    "print(\"Training_Accuracy:\",accuracy_score(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QRwdrX4Fer3D",
    "outputId": "6d046590-ec2a-48f8-f9bf-119a81e7a2ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1487, 30)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "3VSywTEwDWHX"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>3. Build a full model to predict if an employee will leave the company. Find three features that impact the model prediction the most.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_R_Gmh4DDWHY",
    "outputId": "1ac846fd-cc04-4022-d9a6-06442031b717"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_Accuracy: 0.901565995525727\n"
     ]
    }
   ],
   "source": [
    "# split data into train subset and test subset\n",
    "# set 'random_state' to generate the same dataset each time you run the code \n",
    "# 'test_size' returns the proportion of data to be included in the test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, df_target, random_state = 10, test_size = 0.3)\n",
    "\n",
    "# instantiate the 'DecisionTreeClassifier' object using 'gini' criterion\n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "decision_tree_full = DecisionTreeClassifier(random_state=10)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "decision_tree_full = decision_tree_full.fit(X_train, y_train)\n",
    "\n",
    "# predict the attrition for test set\n",
    "y_pred_full = decision_tree_full.predict(X_test)\n",
    "\n",
    "# predict the attrition for test set\n",
    "y_pred = decision_tree_full.predict(X_test)\n",
    "\n",
    "# pass the actual and predicted target values to the accuracy_score()\n",
    "print(\"Test_Accuracy:\",accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 989
    },
    "id": "cqUJFJfdyYfm",
    "outputId": "3783ced3-4df1-42fc-dc2d-9c838c9fc64a"
   },
   "outputs": [
    {
     "data": {
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       "<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>Features</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.150081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MonthlyIncome</td>\n",
       "      <td>0.134097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DistanceFromHome</td>\n",
       "      <td>0.097001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>YearsAtCompany</td>\n",
       "      <td>0.068787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>OverTime_Yes</td>\n",
       "      <td>0.068532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JobInvolvement</td>\n",
       "      <td>0.065690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PercentSalaryHike</td>\n",
       "      <td>0.059935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NumCompaniesWorked</td>\n",
       "      <td>0.058395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JobSatisfaction</td>\n",
       "      <td>0.048514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EnvironmentSatisfaction</td>\n",
       "      <td>0.043252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>EducationField_Technical Degree</td>\n",
       "      <td>0.028705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>JobRole_Sales Representative</td>\n",
       "      <td>0.026778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JobLevel</td>\n",
       "      <td>0.020528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>JobRole_Sales Executive</td>\n",
       "      <td>0.017673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Gender_Male</td>\n",
       "      <td>0.016988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BusinessTravel_Travel_Frequently</td>\n",
       "      <td>0.015660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>JobRole_Research Scientist</td>\n",
       "      <td>0.013949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>EducationField_Life Sciences</td>\n",
       "      <td>0.013934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>JobRole_Manufacturing Director</td>\n",
       "      <td>0.012400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Department_Sales</td>\n",
       "      <td>0.010232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BusinessTravel_Travel_Rarely</td>\n",
       "      <td>0.008272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JobRole_Laboratory Technician</td>\n",
       "      <td>0.007970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EducationField_Other</td>\n",
       "      <td>0.006140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>EducationField_Medical</td>\n",
       "      <td>0.003327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>EducationField_Marketing</td>\n",
       "      <td>0.003160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>JobRole_Human Resources</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>JobRole_Manager</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>JobRole_Research Director</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Department_Research &amp; Development</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PerformanceRating</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Features  Importance\n",
       "0                                 Age    0.150081\n",
       "6                       MonthlyIncome    0.134097\n",
       "1                    DistanceFromHome    0.097001\n",
       "10                     YearsAtCompany    0.068787\n",
       "29                       OverTime_Yes    0.068532\n",
       "3                      JobInvolvement    0.065690\n",
       "8                   PercentSalaryHike    0.059935\n",
       "7                  NumCompaniesWorked    0.058395\n",
       "5                     JobSatisfaction    0.048514\n",
       "2             EnvironmentSatisfaction    0.043252\n",
       "19    EducationField_Technical Degree    0.028705\n",
       "28       JobRole_Sales Representative    0.026778\n",
       "4                            JobLevel    0.020528\n",
       "27            JobRole_Sales Executive    0.017673\n",
       "20                        Gender_Male    0.016988\n",
       "11   BusinessTravel_Travel_Frequently    0.015660\n",
       "26         JobRole_Research Scientist    0.013949\n",
       "15       EducationField_Life Sciences    0.013934\n",
       "24     JobRole_Manufacturing Director    0.012400\n",
       "14                   Department_Sales    0.010232\n",
       "12       BusinessTravel_Travel_Rarely    0.008272\n",
       "22      JobRole_Laboratory Technician    0.007970\n",
       "18               EducationField_Other    0.006140\n",
       "17             EducationField_Medical    0.003327\n",
       "16           EducationField_Marketing    0.003160\n",
       "21            JobRole_Human Resources    0.000000\n",
       "23                    JobRole_Manager    0.000000\n",
       "25          JobRole_Research Director    0.000000\n",
       "13  Department_Research & Development    0.000000\n",
       "9                   PerformanceRating    0.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# create a dataframe that stores the feature names and their importance\n",
    "important_features = pd.DataFrame({'Features': X_train.columns, \n",
    "                                   'Importance': decision_tree_full.feature_importances_})\n",
    "\n",
    "# print the dataframe\n",
    "important_features.sort_values(by='Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6q2ClHs2DWHZ"
   },
   "source": [
    "Thus we can see that `Age`, `MonthlyIncome` and `DistanceFromHome` are the most import features when predicting whether or not an employee will leave the company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "GTBNEzz5DWHZ"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>4.  Find the area under the receiver operating characteristic curve for full model built in question 3. </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "RL7lg2EmDWHZ",
    "outputId": "d62ab188-c365-4b15-e804-71c4b9c11d3f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the roc_curve() returns the values for false positive rate, true positive rate and threshold\n",
    "# pass the actual target values and predicted probabilities to the function\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_full)\n",
    "\n",
    "# plot the straight line showing worst prediction for the model\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "\n",
    "# plot the ROC curve\n",
    "plt.plot(fpr, tpr)\n",
    "\n",
    "# add the AUC score to the plot\n",
    "# 'x' and 'y' gives position of the text\n",
    "# 's' is the text \n",
    "# use round() to round-off the AUC score upto 4 digits\n",
    "plt.text(x = 0.02, y = 0.8, s = ('AUC Score:',round(metrics.roc_auc_score(y_test, y_pred_full),4)))\n",
    "                               \n",
    "# plot the grid\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "uCAkmJCjDWHa"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>5. Plot a confusion matrix for the full model built in Q3.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "mkjDjp3QDWHa",
    "outputId": "1a47875f-8a4f-4292-fb2b-fd8af6b16844"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a confusion matrix\n",
    "# pass the actual and predicted target values to the confusion_matrix()\n",
    "cm = confusion_matrix(y_test, y_pred_full)\n",
    "\n",
    "# label the confusion matrix  \n",
    "# pass the matrix as 'data'\n",
    "# pass the required column names to the parameter, 'columns'\n",
    "# pass the required row names to the parameter, 'index'\n",
    "conf_matrix = pd.DataFrame(data = cm,columns = ['Predicted Attrition: No','Predicted Attrition: Yes'], \n",
    "                           index = ['Actual Attrition :No','Actual Attrition :Yes'])\n",
    "\n",
    "# plot a heatmap to visualize the confusion matrix\n",
    "# 'annot' prints the value of each grid \n",
    "# 'fmt = d' returns the integer value in each grid\n",
    "# 'cmap' assigns color to each grid\n",
    "# as we do not require different colors for each grid in the heatmap,\n",
    "# use 'ListedColormap' to assign the specified color to the grid\n",
    "# 'cbar = False' will not return the color bar to the right side of the heatmap\n",
    "# 'linewidths' assigns the width to the line that divides each grid\n",
    "# 'annot_kws = {'size':25})' assigns the font size of the annotated text \n",
    "sns.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = ListedColormap(['lightskyblue']), cbar = False, \n",
    "            linewidths = 0.1, annot_kws = {'size':25})\n",
    "\n",
    "# set the font size of x-axis ticks using 'fontsize'\n",
    "plt.xticks(fontsize = 10)\n",
    "\n",
    "# set the font size of y-axis ticks using 'fontsize'\n",
    "plt.yticks(fontsize = 10)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zecKxXcD1guJ",
    "outputId": "4209c151-81b7-4d3e-885c-69d269272381"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    245\n",
       "1    202\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1hDNB3a21DBq",
    "outputId": "931e60a0-6274-4fb0-f3cf-d92821f04a25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9752475247524752"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sensitivity\n",
    "197/(202)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VFBmNDW91JLd",
    "outputId": "47163c0f-8e1b-4971-c5ae-f3b4d1e3e4f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8408163265306122"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#specificity\n",
    "206/245"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S2eSP8Qh1ZQl",
    "outputId": "fdf871b9-d1e8-4402-b1a8-9449f727a54b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09843400447427293"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "44/(447)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "Gl_BGfj_DWHa"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>6. Calculate the specificity, sensitivity, % of misclassified and correctly classified observations. What can you say about the model by looking at the sensitivity and specificity values? Is this a good model?</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WWtVXpqVDWHb",
    "outputId": "773498f3-9efa-4a7a-8f50-07088a06a236"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of correctly classified observations:  90.16\n",
      "Percentage of misclassified observations:  9.84\n",
      "\n",
      "Specificity:  0.8408\n",
      "Sensitivity:  0.9752\n"
     ]
    }
   ],
   "source": [
    "# True Negative from confusion matrix\n",
    "TN = cm[0][0]\n",
    "# True Positive from confusion matrix\n",
    "TP = cm[1][1]\n",
    "# False Positive from confusion matrix\n",
    "FP = cm[0][1]\n",
    "# False Negative from confusion matrix\n",
    "FN = cm[1][0]\n",
    "\n",
    "# add all the values of a confusion matrix\n",
    "total = TN + TP + FP + FN\n",
    "\n",
    "# find the percentage of correctly classified observations\n",
    "# round the result upto 2 decimal points\n",
    "correct_classified_percentage = ((TN+TP)/total)*100\n",
    "print(\"Percentage of correctly classified observations: \", round(correct_classified_percentage, 2))\n",
    "\n",
    "# find the percentage of  misclassified observations\n",
    "# round the result upto 2 decimal points\n",
    "misclassified_percentage = ((FP+FN)/total)*100\n",
    "print(\"Percentage of misclassified observations: \", round(misclassified_percentage, 2))\n",
    "\n",
    "# find the specificity\n",
    "# round the result upto 4 decimal points\n",
    "specificity = TN/(TN+FP)\n",
    "print(\"\\nSpecificity: \", round(specificity, 4))\n",
    "\n",
    "# find the sensitivity\n",
    "# round the result upto 4 decimal points\n",
    "sensitivity = TP/(TP+FN)\n",
    "print(\"Sensitivity: \", round(sensitivity, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mrUOh1-xDWHb"
   },
   "source": [
    "**Intrepretation:** The is a difference between the sensitivity and the specificity values. But we can say that the model performance is good. For a better model, the values of sensitivity and specificity should be balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "N4ZM0qjfDWHb"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>7. Build and plot a decision tree with maximum 5 terminal nodes.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z_FsrrU5nvkf",
    "outputId": "b3ae6855-8cdc-4c05-9e28-e09392abde66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    570\n",
       "1    470\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TFDNLggii604"
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import pydotplus\n",
    "from IPython.display import Image  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "8EbrNhEqDWHb",
    "outputId": "fbd0e81b-c0ce-44a9-eade-9c615f6ec81a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate the 'DecisionTreeClassifier' object using 'entropy' criterion\n",
    "# set 'max_leaf_nodes = 5' to build the tree with maximim 5 leaf/terminal nodes\n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "decision_tree_classification = DecisionTreeClassifier(max_depth=4)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "dt_model = decision_tree_classification.fit(X_train, y_train)\n",
    "\n",
    "# plot a decision tree\n",
    "# save the column names in 'labels'\n",
    "labels = X_train.columns\n",
    "\n",
    "# export a decision tree in DOT format\n",
    "# pass the 'decision_tree' to export it to Graphviz\n",
    "# pass the column names to 'feature_names'\n",
    "# pass the required class labels to 'class_names'\n",
    "dot_data = tree.export_graphviz(dt_model, feature_names = labels, class_names = [\"No\",\"Yes\"])  \n",
    "\n",
    "# plot the decision tree using DOT format in 'dot_data'\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "\n",
    "# display the decision tree\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fwe_N18plW5N"
   },
   "outputs": [],
   "source": [
    "train_char_label = ['No', 'Yes']\n",
    "DT_Tree_File = open('/content/drive/My Drive/Python for ML-Mahesh Anand/Attr_tree.dot','w')\n",
    "dot_data = tree.export_graphviz(decision_tree_full, out_file=DT_Tree_File, feature_names = list(X_train), class_names = list(train_char_label))\n",
    "DT_Tree_File.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "6ndYy_RTDWHc"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>8. Build a Decision Tree model with the predefined parameters and find the average accuracy by performing the 10 fold cross validation.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dnexzpQdDWHc"
   },
   "source": [
    "#### Split the data into train and test set for upcoming questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MRCZp0c7DWHc"
   },
   "outputs": [],
   "source": [
    "# split data into train subset and test subset\n",
    "# set 'random_state' to generate the same dataset each time you run the code \n",
    "# 'test_size' returns the proportion of data to be included in the test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, df_target, random_state = 10, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UKUi6SqWDWHc",
    "outputId": "a19901db-9592-4df6-db22-6bf97778fee5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy after 10 fold cross validation:  0.89\n"
     ]
    }
   ],
   "source": [
    "# instantiate the 'DecisionTreeClassifier'\n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "decision_tree_model = DecisionTreeClassifier(random_state=10)\n",
    "\n",
    "# using cross_val_score() for k-fold cross validation\n",
    "# estimator: pass the machine learning function. Here we are performing Decision Tree Classification\n",
    "# pass the X_train and y_train sets\n",
    "# cv: stands for number of folds. Similar to k in KFold\n",
    "# scoring: pass the scoring parameter e.g. 'accuracy' for accuracy\n",
    "scores = cross_val_score(estimator = decision_tree_model, \n",
    "                         X = X_train, \n",
    "                         y = y_train, \n",
    "                         cv = 10, \n",
    "                         scoring = 'roc_auc')\n",
    "\n",
    "# get the average of the accuracy obtained after 10 fold cross validation\n",
    "# round the result upto 2 decimal points\n",
    "print(\"Mean accuracy after 10 fold cross validation: \", round(scores.mean(), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YcRs2ZbFZG9p",
    "outputId": "928a1d7d-5d38-4fe9-a769-bb03f6cd2c0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028720352768094003"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(scores,ddof=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "0fR1bTFCDWHc"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>9. For a decision tree model, find the best value of hyperparameters \"criterion\", \"max_depth\", and \"min_samples_split\".</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L6BzOGP2DWHd"
   },
   "source": [
    "Consider the values as:\n",
    "        \n",
    "     criterion = ['entropy', 'gini'],\n",
    "     max_depth = range(2, 10),\n",
    "     min_samples_split = range(2,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nGCnWiMgDWHd",
    "outputId": "f3ea8828-f551-46f2-c036-356fb861f0d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for Decision Tree Classifier:  {'criterion': 'gini', 'max_depth': 9, 'min_samples_split': 2} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create a dictionary with hyperparameters and its values\n",
    "# pass the criteria 'entropy' and 'gini' to the parameter, 'criterion' \n",
    "# pass the range of values to 'max_depth' that assigns maximum depth of the tree\n",
    "# pass the range of values to 'min_samples_split' that assigns minimum number of samples to split an internal node\n",
    "hyp_paramaters = [{'criterion': ['entropy', 'gini'],\n",
    "                     'max_depth': range(2, 10),\n",
    "                     'min_samples_split': range(2,10)}]\n",
    " \n",
    "# instantiate the 'DecisionTreeClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "decision_tree_model = DecisionTreeClassifier(random_state = 10)\n",
    "\n",
    "# use GridSearchCV() to find the optimal value of the hyperparameters\n",
    "# estimator: pass the decision tree classifier model\n",
    "# param_grid: pass the list 'tuned_parameters'\n",
    "# cv: number of folds in k-fold i.e. here cv = 5\n",
    "tree_grid = GridSearchCV(estimator = decision_tree_model, \n",
    "                         param_grid = hyp_paramaters, \n",
    "                         cv = 5)\n",
    "\n",
    "# fit the model on X_train and y_train using fit()\n",
    "tree_grid.fit(X_train, y_train)\n",
    "\n",
    "# get the best parameters\n",
    "print('Best parameters for Decision Tree Classifier: ', tree_grid.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "n67fzkfXDWHd"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                        <b>10. Build a decision tree model using the tuned hyperparameters, perform 10 fold cross validation and get the average ROC-AUC score.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LlhnW9ylDWHd",
    "outputId": "47bd7353-825d-4c10-a6de-581674be3bbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean ROC-AUC score after 10 fold cross validation:  0.87\n"
     ]
    }
   ],
   "source": [
    "# instantiate the 'DecisionTreeClassifier' object using 'gini' criterion\n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "decision_tree_tuned_model = DecisionTreeClassifier(criterion = tree_grid.best_params_['criterion'], \n",
    "                                                   max_depth = tree_grid.best_params_['max_depth'], \n",
    "                                                   min_samples_split = tree_grid.best_params_['min_samples_split'])\n",
    "\n",
    "# using cross_val_score() for k-fold cross validation\n",
    "# estimator: pass the machine learning function. Here we are performing Decision Tree Classification\n",
    "# pass the X_train and y_train sets\n",
    "# cv: stands for number of folds. Similar to k in KFold\n",
    "# scoring: pass the scoring parameter e.g. 'accuracy' for accuracy\n",
    "scores = cross_val_score(estimator = decision_tree_tuned_model, \n",
    "                         X = X_train, \n",
    "                         y = y_train, \n",
    "                         cv = 10, \n",
    "                         scoring = 'roc_auc')\n",
    "\n",
    "# get the average of the ROC-AUC scores obtained after 10 fold cross validation\n",
    "# round the result upto 2 decimal points\n",
    "print(\"Mean ROC-AUC score after 10 fold cross validation: \", round(scores.mean(), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LBW_vS0VZWsh",
    "outputId": "bb0ba6b0-615f-463c-f999-57960bb79248"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.023231105590818115"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(scores,ddof=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mx4HFsYsawHo",
    "outputId": "afa8761d-e352-454d-f4e6-9c8383cd96a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02247191011235957"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(.89-.87)/.89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SIzF0yo5a3vz",
    "outputId": "7735c0ff-3b5c-4c94-b60f-024842e01671"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19115598885793866"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.02872-0.02323)/0.02872"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J5tNowERLLMh",
    "outputId": "62f63221-371f-4f56-ab12-821fceea061d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'DistanceFromHome', 'EnvironmentSatisfaction', 'JobInvolvement',\n",
       "       'JobLevel', 'JobSatisfaction', 'MonthlyIncome', 'NumCompaniesWorked',\n",
       "       'PercentSalaryHike', 'PerformanceRating', 'YearsAtCompany',\n",
       "       'BusinessTravel_Travel_Frequently', 'BusinessTravel_Travel_Rarely',\n",
       "       'Department_Research & Development', 'Department_Sales',\n",
       "       'EducationField_Life Sciences', 'EducationField_Marketing',\n",
       "       'EducationField_Medical', 'EducationField_Other',\n",
       "       'EducationField_Technical Degree', 'Gender_Male',\n",
       "       'JobRole_Human Resources', 'JobRole_Laboratory Technician',\n",
       "       'JobRole_Manager', 'JobRole_Manufacturing Director',\n",
       "       'JobRole_Research Director', 'JobRole_Research Scientist',\n",
       "       'JobRole_Sales Executive', 'JobRole_Sales Representative',\n",
       "       'OverTime_Yes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "Fr78NFRvDWHd"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>11. Build 5 decision trees each with 20 random features. Also predict the attrition for test set for each model.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vAsmEIMYHNth",
    "outputId": "2e7d0f25-0895-4fc1-dfe2-c12ba9386c89"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['JobRole_Human Resources',\n",
       " 'PercentSalaryHike',\n",
       " 'BusinessTravel_Travel_Rarely',\n",
       " 'Department_Research & Development',\n",
       " 'EducationField_Life Sciences',\n",
       " 'JobRole_Manager',\n",
       " 'JobRole_Sales Representative',\n",
       " 'DistanceFromHome',\n",
       " 'Department_Sales',\n",
       " 'Department_Sales',\n",
       " 'DistanceFromHome',\n",
       " 'JobRole_Human Resources',\n",
       " 'JobRole_Human Resources',\n",
       " 'YearsAtCompany',\n",
       " 'EducationField_Technical Degree',\n",
       " 'JobRole_Sales Representative',\n",
       " 'JobRole_Manufacturing Director',\n",
       " 'EducationField_Marketing',\n",
       " 'EducationField_Other',\n",
       " 'EducationField_Marketing']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = list(X_train.columns)\n",
    "\n",
    "# select random features from the columns\n",
    "random.choices(columns, k=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TQ-ZnvKnDWHe"
   },
   "outputs": [],
   "source": [
    "# get the columns from X_train\n",
    "columns = list(X_train.columns)\n",
    "\n",
    "# select random features from the columns\n",
    "sample_features = random.choices(columns, k=20)\n",
    "\n",
    "# Decision Tree Model 1\n",
    "# instantiate the 'DecisionTreeClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "dt_model_1 = DecisionTreeClassifier(random_state = 10)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "dt_model_1 = dt_model_1.fit(X_train[sample_features], y_train)\n",
    "\n",
    "# predict the attrition for test set using dt_model_1\n",
    "y_pred_1 = dt_model_1.predict(X_test[sample_features])\n",
    "###################################################################################\n",
    "\n",
    "# select random features from the columns\n",
    "sample_features = random.choices(columns, k=20)\n",
    "\n",
    "# Decision Tree Model 2\n",
    "# instantiate the 'DecisionTreeClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "dt_model_2 = DecisionTreeClassifier(random_state = 10)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "dt_model_2 = dt_model_2.fit(X_train[sample_features], y_train)\n",
    "\n",
    "# predict the attrition for test set using dt_model_2\n",
    "y_pred_2 = dt_model_2.predict(X_test[sample_features])\n",
    "###################################################################################\n",
    "\n",
    "# select random features from the columns\n",
    "sample_features = random.choices(columns, k=20)\n",
    "\n",
    "# Decision Tree Model 3\n",
    "# instantiate the 'DecisionTreeClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "dt_model_3 = DecisionTreeClassifier(random_state = 10)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "dt_model_3 = dt_model_3.fit(X_train[sample_features], y_train)\n",
    "\n",
    "# predict the attrition for test set using dt_model_3\n",
    "y_pred_3 = dt_model_3.predict(X_test[sample_features])\n",
    "###################################################################################\n",
    "\n",
    "# select random features from the columns\n",
    "sample_features = random.choices(columns, k=20)\n",
    "\n",
    "# Decision Tree Model 4\n",
    "# instantiate the 'DecisionTreeClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "dt_model_4 = DecisionTreeClassifier(random_state = 10)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "dt_model_4 = dt_model_4.fit(X_train[sample_features], y_train)\n",
    "\n",
    "# predict the attrition for test set using dt_model_4\n",
    "y_pred_4 = dt_model_4.predict(X_test[sample_features])\n",
    "###################################################################################\n",
    "\n",
    "# select random features from the columns\n",
    "sample_features = random.choices(columns, k=20)\n",
    "\n",
    "# Decision Tree Model 5\n",
    "# instantiate the 'DecisionTreeClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "dt_model_5 = DecisionTreeClassifier(random_state = 10)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "dt_model_5 = dt_model_5.fit(X_train[sample_features], y_train)\n",
    "\n",
    "# predict the attrition for test set using dt_model_5\n",
    "y_pred_5 = dt_model_5.predict(X_test[sample_features])\n",
    "###################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "jsKHQYHkDWHe"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>12. Create a new dataframe \"model_predictions_df\" by appending each prediction made in question 8. There will be 5 columns in the dataframe for each prediction using the decision tree models built in above question.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "pgcrtXd9DWHe",
    "outputId": "c0757a22-30f3-43d5-977e-e96ced388d72"
   },
   "outputs": [
    {
     "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",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_pred_1</th>\n",
       "      <th>y_pred_2</th>\n",
       "      <th>y_pred_3</th>\n",
       "      <th>y_pred_4</th>\n",
       "      <th>y_pred_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y_pred_1  y_pred_2  y_pred_3  y_pred_4  y_pred_5\n",
       "0         0         0         0         0         0\n",
       "1         1         0         1         0         1\n",
       "2         1         1         1         1         1\n",
       "3         0         1         0         1         0\n",
       "4         0         0         1         1         0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe \"model_predictions_df\" using the predictions obtained from the decision tree models\n",
    "model_predictions_df = pd.DataFrame({\"y_pred_1\": y_pred_1, \n",
    "                                     \"y_pred_2\": y_pred_2,\n",
    "                                     \"y_pred_3\": y_pred_3,\n",
    "                                     \"y_pred_4\": y_pred_4,\n",
    "                                     \"y_pred_5\": y_pred_5})\n",
    "model_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8_qiO3IBL4fn",
    "outputId": "3dfcf554-646c-4649-8a4d-917937d09d24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    2\n",
       "Name: 3, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_predictions_df.iloc[3].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "wEi6LJi1DWHf"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>13. Create a new column \"Voted_Result\" in the dataframe \"model_predictions_df\" that contains the maximum occuring value (mode) of the 5 columns in the dataframe (row-wise).<br></b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "qNoG9x3ODWHf",
    "outputId": "26f30007-a435-444b-976c-d08c884cfa79"
   },
   "outputs": [
    {
     "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>y_pred_1</th>\n",
       "      <th>y_pred_2</th>\n",
       "      <th>y_pred_3</th>\n",
       "      <th>y_pred_4</th>\n",
       "      <th>y_pred_5</th>\n",
       "      <th>Voted_Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y_pred_1  y_pred_2  y_pred_3  y_pred_4  y_pred_5  Voted_Result\n",
       "0         0         0         0         0         0             0\n",
       "1         1         0         1         0         1             1\n",
       "2         1         1         1         1         1             1\n",
       "3         0         1         0         1         0             0\n",
       "4         0         0         1         1         0             0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# empty list to store the votes result \n",
    "votes = []\n",
    "\n",
    "# for loop to find the maximum occuring values from each row and append it to the list \"votes\"\n",
    "for i in range(model_predictions_df.shape[0]):\n",
    "    votes.append(model_predictions_df.iloc[i].value_counts().index[0])\n",
    "    \n",
    "# create a column \"Voted_Result\" and store the votes\n",
    "model_predictions_df['Voted_Result'] = votes\n",
    "\n",
    "model_predictions_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "et2uFnZADWHf"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>14. Consider the values of \"Voted_Result\" as our new predictions and store its values in a variable \"new_y_pred\" and find the accuracy and the roc-auc score using new_y_pred.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dizyow4ADWHf",
    "outputId": "46422d13-4d54-4278-af5b-8735ec894374"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ROC-AUC Score is:  0.9369064457466155\n",
      "The Accuracy is:  0.9351230425055929\n"
     ]
    }
   ],
   "source": [
    "# store the voted result in the variable y_pred_voted\n",
    "y_pred_voted = model_predictions_df['Voted_Result']\n",
    "\n",
    "# print the roc-auc score \n",
    "print(\"The ROC-AUC Score is: \", roc_auc_score(y_test, y_pred_voted))\n",
    "\n",
    "# print the accuracy\n",
    "print(\"The Accuracy is: \", accuracy_score(y_test, y_pred_voted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1JZxH3heDWHf"
   },
   "source": [
    "<a id=\"rf\"> </a>\n",
    "## 2. Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "ofLScrXnDWHg"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>15. Build a random forest full model to predict if an employee will leave the company or not and generate a classification report.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "rkNkagcsYFcl"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier,BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ikXYkyOvYN3S"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg=LogisticRegression() \n",
    "from "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "OF-4BeZqYYSg"
   },
   "outputs": [],
   "source": [
    "LR_bag=BaggingClassifier(base_estimator=logreg,n_estimators=10,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "8LvjQl69rlEk"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cross_val_score' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-68ebf695ca93>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m scores = cross_val_score(estimator = LR_bag, \n\u001b[0m\u001b[0;32m      2\u001b[0m                          \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m                          \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m                          \u001b[0mcv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                          scoring = 'roc_auc')\n",
      "\u001b[1;31mNameError\u001b[0m: name 'cross_val_score' is not defined"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator = LR_bag, \n",
    "                         X = X_train, \n",
    "                         y = y_train, \n",
    "                         cv = 10, \n",
    "                         scoring = 'roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ta_IOgFRrpAT"
   },
   "outputs": [],
   "source": [
    "np.mean(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r6nmE9J_DWHg",
    "outputId": "14ee18dd-2fc8-4f9d-a675-f81a5f18f27d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.93      0.94       245\n",
      "           1       0.91      0.95      0.93       202\n",
      "\n",
      "    accuracy                           0.94       447\n",
      "   macro avg       0.94      0.94      0.94       447\n",
      "weighted avg       0.94      0.94      0.94       447\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# instantiate the 'RandomForestClassifier'\n",
    "# pass the required number of trees in the random forest to the parameter, 'n_estimators'\n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "rf_classification = RandomForestClassifier(n_estimators = 10,random_state = 10)\n",
    "\n",
    "# use fit() to fit the model on the train set\n",
    "rf_model = rf_classification.fit(X_train, y_train)\n",
    "\n",
    "# predict the attrition for test set\n",
    "y_pred = rf_model.predict(X_test)\n",
    "\n",
    "# generate a classification report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vk-XfrtNmPsa",
    "outputId": "e87ee0c3-bca2-47b6-ed2e-aa1445f33909"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9274398868458273"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "jSgQ9IYlDWHh"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>16. For a random forest model, find the best value of hyperparameters \"n_estimators\", \"criterion\", \"max_depth\", and \"min_samples_split\".</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0z0tEu4pDWHh"
   },
   "source": [
    "Given list of values:\n",
    "\n",
    "    criterion = ['entropy', 'gini'],\n",
    "    n_estimators = [90, 100, 150, 200],\n",
    "    max_depth = [10, 15, 20],\n",
    "    min_samples_split = [2, 5, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0l3oMJtLDWHh",
    "outputId": "2b94a277-bad9-4ba4-dcd6-27560e445d51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for random forest Classifier:  {'criterion': 'gini', 'max_depth': 15, 'min_samples_split': 2, 'n_estimators': 90} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create a dictionary with hyperparameters and its values\n",
    "# pass the criteria 'entropy' and 'gini' to the parameter, 'criterion' \n",
    "# pass a list of values to 'n_estimators' to build the different number of trees in the random forest\n",
    "# pass a list of values to 'max_depth' that assigns maximum depth of the tree\n",
    "# pass a list of values to 'min_samples_split' that assigns minimum number of samples to split an internal node\n",
    "hyp_paramaters = [{'criterion': ['entropy', 'gini'],\n",
    "                     'n_estimators': [90, 100, 150, 200],\n",
    "                     'max_depth': [10, 15, 20],\n",
    "                     'min_samples_split': [2, 5, 8]}]\n",
    " \n",
    "# instantiate the 'RandomForestClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "random_forest_classification = RandomForestClassifier(random_state = 10)\n",
    "\n",
    "# use GridSearchCV() to find the optimal value of the hyperparameters\n",
    "# estimator: pass the random forest classifier model\n",
    "# param_grid: pass the list 'tuned_parameters'\n",
    "# cv: number of folds in k-fold i.e. here cv = 5\n",
    "rf_grid = GridSearchCV(estimator = random_forest_classification, \n",
    "                       param_grid = hyp_paramaters, \n",
    "                       cv = 5,scoring='roc_auc')\n",
    "\n",
    "# use fit() to fit the model on the train set\n",
    "rf_model = rf_grid.fit(X_train, y_train)\n",
    "\n",
    "# get the best parameters\n",
    "print('Best parameters for random forest Classifier: ', rf_model.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "1jiiCnhsDWHi"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>17. Build a random forest model using the tuned hyperparameters, perform 10 fold cross validation and get the average ROC-AUC score.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HXJRSPy7DWHi",
    "outputId": "d5cc61aa-6505-438c-8be8-716a58ecf734"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean ROC-AUC score after 10 fold cross validation:  0.99\n"
     ]
    }
   ],
   "source": [
    "# instantiate the 'RandomForestClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "random_forest_tuned_model = RandomForestClassifier(criterion = rf_model.best_params_['criterion'], \n",
    "                                                   max_depth = rf_model.best_params_['max_depth'], \n",
    "                                                   min_samples_split = rf_model.best_params_['min_samples_split'], \n",
    "                                                   n_estimators = rf_model.best_params_['n_estimators'],\n",
    "                                                   random_state = 10)\n",
    "\n",
    "# using cross_val_score() for k-fold cross validation\n",
    "# estimator: pass the machine learning function. Here we are performing Decision Tree Classification\n",
    "# pass the X_train and y_train sets\n",
    "# cv: stands for number of folds. Similar to k in KFold\n",
    "# scoring: pass the scoring parameter e.g. 'accuracy' for accuracy\n",
    "scores = cross_val_score(estimator = random_forest_tuned_model, \n",
    "                         X = X_train, \n",
    "                         y = y_train, \n",
    "                         cv = 10, \n",
    "                         scoring = 'roc_auc')\n",
    "\n",
    "# get the average of the ROC-AUC scores obtained after 10 fold cross validation\n",
    "# round the result upto 2 decimal points\n",
    "print(\"Mean ROC-AUC score after 10 fold cross validation: \", round(scores.mean(), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dEE2icKNblJC",
    "outputId": "c4c5468d-93de-4573-d8d9-d68397bfddf7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.95       245\n",
      "           1       0.93      0.95      0.94       202\n",
      "\n",
      "    accuracy                           0.94       447\n",
      "   macro avg       0.94      0.94      0.94       447\n",
      "weighted avg       0.94      0.94      0.94       447\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_forest_tuned_model.fit(X_train,y_train)\n",
    "y_pred_tuned_model=random_forest_tuned_model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred_tuned_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "q7YV4hpcDWHi"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>18. Using the tuned random forest model, find the features that impact the model prediction the most.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361
    },
    "id": "e0DGH-cPDWHj",
    "outputId": "d11b7f56-6b98-4948-eb34-e3a94ee2c49f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# instantiate the 'RandomForestClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "random_forest_tuned_model = RandomForestClassifier(criterion = 'gini', max_depth = 20, \n",
    "                                                   min_samples_split = 2, n_estimators = 90,\n",
    "                                                   random_state = 10)\n",
    "\n",
    "# use fit() to fit the model on the train set\n",
    "random_forest_tuned_model = random_forest_tuned_model.fit(X_train, y_train)\n",
    "\n",
    "# create a dataframe that stores the feature names and their importance\n",
    "important_features = pd.DataFrame({'Features': X_train.columns, \n",
    "                                   'Importance': random_forest_tuned_model.feature_importances_})\n",
    "\n",
    "# sort the dataframe in the descending order according to the feature importance\n",
    "important_features = important_features.sort_values('Importance', ascending = False)\n",
    "\n",
    "# create a barplot to visualize the features based on their importance\n",
    "sns.barplot(x = 'Importance', y = 'Features', data = important_features)\n",
    "\n",
    "# add plot and axes labels\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Feature Importance', fontsize = 15)\n",
    "plt.xlabel('Importance', fontsize = 15)\n",
    "plt.ylabel('Features', fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oQO4jjrODWHj"
   },
   "source": [
    "Thus we can see that `MonthlyIncome`, `Age` and `YearsAtCompany` are the most import features when predicting whether or not an employee will leave the company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "rdfzkwrdDWHj"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>19. Build a random forest model using the top 10 most important features.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RBdIjtX-DWHk"
   },
   "outputs": [],
   "source": [
    "# get the top 10 important columns from the dataframe 'important_features' by using iloc\n",
    "important_columns = list(important_features['Features'].iloc[:10])\n",
    "\n",
    "# instantiate the 'RandomForestClassifier' \n",
    "# pass the 'random_state' to obtain the same samples for each time you run the code\n",
    "random_forest_tuned_model = RandomForestClassifier(criterion = 'gini', max_depth = 20, \n",
    "                                                   min_samples_split = 2, n_estimators = 90,\n",
    "                                                   random_state = 10)\n",
    "\n",
    "# use fit() to fit the model on the train set\n",
    "random_forest_tuned_model = random_forest_tuned_model.fit(X_train[important_columns], y_train)\n",
    "\n",
    "# predict the attrition for test set\n",
    "y_pred = random_forest_tuned_model.predict(X_test[important_columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "Y07Yiuf4DWHk"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>20. Plot a confusion matrix and area under the receiver operating characteristic curve for the model built in question 19. </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "CBsulUObDWHk",
    "outputId": "cd936d4c-480f-4503-820d-059b5f86d9d5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5JFNL4QMAALARZg1k07XWbmitndxae9KwCgIAAJgU6xXIAAAAmDsCGQAAQEcEMgAAgI4IZAAAAB0RyAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOCGQAAAAdEcgAAAA6IpABAAB0RCADAADoiEAGAADQEYEMAACgIwIZAABARwQyAACAjghkAAAAHRHIAAAAOiKQAQAAdEQgAwAA6IhABgAA0BGBDAAAoCMCGQAAQEcEMgAAgI4IZAAAAB0RyAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOCGQAAAAdEcgAAAA6IpABAAB0RCADAADoiEAGAADQEYEMAACgIwIZAABARwQyAACAjghkAAAAHRHIAAAAOiKQAQAAdEQgAwAA6IhABgAA0BGBDAAAoCMCGQAAQEcEMgAAgI4IZAAAAB0RyAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOVGtt2PcY+g0AAADuxWqmA5uM4u7vuGTlKG4DAL/nuKULtEMAdOq4pQtmPGbIIgAAQEcEMgAAgI4IZAAAAB0RyAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOCGQAAAAdEcgAAAA6IpABAAB0RCADAADoiEAGAADQEYEMAACgIwIZAABARwQyAACAjghkAAAAHRHIAAAAOiKQAQAAdEQgAwAA6IhABgAA0BGBDAAAoCMCGQAAQEcEMgAAgI4IZAAAAB0RyAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOCGQAAAAdEcgAAAA6IpABAAB0RCADAADoiEAGAADQEYEMAACgIwIZAABARwQyAACAjghkAAAAHRHIAAAAOiKQAQAAdEQgAwAA6IhABgAA0BGBDAAAoCMCGQAAQEcEMgAAgI4IZAAAAB0RyAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOCGQAAAAdWa9AVlXzqmrxsIoBAACYJLMGsqr6eFUtrqotk1yeZFlVvWH4pQEAAIy3QXrIlrTWlid5TpIvJNkjyUuGWhUAAMAEGCSQLaiqBZkKZJ9tra1M0oZbFgAAwPgbJJCdlORnSbZM8vWq2i3J8mEWBQAAMAk2me2E1tr7krxv2q6fV9UThlcSAADAZBhkUY+dquqUqvpCb3tJkpcNvTIAAIAxN8iQxVOTfCnJLr3tf09yzLAKAgAAmBSDBLLtW2v/lGRVkrTW7kxy11CrAgAAmACDBLJbq2q79FZWrKoDk9w01KoAAAAmwIyLelTVC1prZyZ5fZLPJtmzqr6RZIckh46oPgAAgLHVb5XFl1TVK5K8JskfJ/nDJJXkR71nkQEAALARZhyy2Fp7RpIPJvl8kjcmuSbJVUkWVdW2oykPAABgfPV9Dllr7TNVdWWSryf5s/TmkfV+P2jItQEAAIy1fnPINkvylkzNF3tRa+1zI6sKAABgAvRbZfGyJPOTPFIYAwAAmHv9hiw+t7W2bGSVAAAATJh+i3qsCWNVdcT0Y2tvAwAAsP4GeTB0MrXcfb9tAAAA1tNAgay1dlK/bQAAANbfoD1kAAAAzDGBDAAAoCMCGQAAQEf6LXu/RlU9Jsnu089vrX10SDUBAABMhFkDWVWdnmTPJJcmuau3uyURyAAAADbCID1k+yVZ0lprwy4GAABgkgwyh+zyJDsPuxAAAIBJM0gP2fZJllXVBUlWrN7ZWnvW0KoCAACYAIMEsuOHXQQAAMAkmjWQtda+VlU7Jdm/t+uC1tq1wy0LAABg/M06h6yqDktyQZLnJzksyXeq6tBhFwYAADDuBhmy+OYk+6/uFauqHZJ8Jcknh1kYAADAuBtklcV5aw1RvH7A6wAAAOhjkB6yL1bVl5Kc0dt+QZJ/GV5JAAAAk2GQRT3eUFWHJHlsb9fJrbVPD7csAACA8TdID1laa2cnOXvItQAAAEyUGQNZVZ3fWjuoqm5O0qYfStJaa4uHXh0AAMAYmzGQtdYO6v1eNLpyYLzdueK2/HrZRbnuymW5vvdz63VXJ0n2PeSoLH3+n8947Ypbl+fXV1yU639697W33XhdkuSgV/9t9nr8c0byGQC4d9uYtma1X1x0bv79q5/MdT+5PCtuuSmbLdo62+/58Oz95MPyB0sfN+N1v7vxulxzxUW5/sorcv2VP8j1V16RFbfclCR56l//Y+6/zwFz8yFhjMw6ZLGqTm+tvWS2fcDsfvMf38+X33nUBl37iwu/mvM/+JY5rgiAcbMxbc2qVXflvP/1pvz0/M9P7ajKplssyu3Lb8gvLz43v7z43Dz0qX+aA1/+pnVe/6Mvn5lLz/7AhpYOE2mQOWT7TN+oqk2SPGo45cD423TLxdlujyXZbo+HZrs9HpoLPvp3a3q6ZrP51ttn292nrtt+jyX56j8cM+RqAbgv2tC25rtnvm9NGFvytBfnEc97dRYu2jorb/9dfvSVf8pFZ7w3V3zx41m8065Z8rQX//4bVGXL7Xbu3XtJtthm+3zj5OPn+NPBeOk3h+yNSd6UZPOqWr56d5I7kpw8gtpg7Oz00EflRad88x77Lv74ewe6ds+Dn2lYIgCz2tC25vblN2TZv5yeJNl1/yfm0S87bs2xBQu3yMOe8fLcvvyGfP+zp+SST/6vPPiPn5NNt7jfPd7jEc87MksPfc2a7Zuv/dXGfBSYCDM+4Lm19vbe/LF3tdYW934Wtda2a629cYQ1wtiYN29+J9cCMDk2tL246vJv566VdyRJHvaMV6zznIc/65VJkjtuXZ5fXHTOnN0bJtmMgayq9u69PKuqHrn2z4jqAwBgBFYv/JEkW//Bnus8Z7P7bZWFW22bJPnVZd9c5znA+uk3h+z1SY5I8u51HGtJnjiUigAA6FRbtWrWYzf84sejKgfGWr9l74+oqnlJ3tJa+8YIawIAYMTut8Mua17f+MsfZ+cl+//eOb+78bqsuPnGqdc3/GZktcE4m3HIYpK01lYlOXFEtQAA0JH77/PozF+waZLke59e9/ptl03bv/K2W0ZSF4y7voGs55yqOqSqaujVAADQiYWLt8lDn/qiJMlV3/9WvnbisbnxVz/NqjtX5pbrrs5FH39PrvjXMzJv/tQAq6mBVMDGGuQ5ZEdmaj7ZnVV1e6aWvm+ttcVDrQwAgJF61Atfm1uv/3Wu/OYX8tPzP3/3A6J7dtjrEdl2tz/Mj77yT9n0fv4pCHNh1kDWW/p+vVTVEZlaECQnnXRSsv+6l04FAODeY978TfL4o9+VPR/3zPzk65/Nb3/+o9x1x4psuf39s/uBT8neTz4s55/81iTJVjvv1nG1MB5mDWRVdU5r7Umz7ZuutXZy7n54dHvHJSs3rkoAAEbmgUsPzgOXHrzOY9f/9AdJkh0fsu8oS4KxNWMgq6qFSbZIsn1VbZOpoYpJsjjJA0ZQGwAA9yLXX3lFbvzPnyRJ9jz4WR1XA+OhXw/ZkUmOSbJLkotzdyBbHisvAgBMlDtX3JZvnfL/Jkl2f/RTsvUDHtRxRTAe+j2H7IQkJ1TV0a21900/VlWbDb0yGFMrbrnpHg/cnHq6RHLnHbfn9uU3rNk/f9PNsmDhFve4dvrx6Vbe/rt7HNtks4XZZLPN57JsAO5DNrSt+c2PL8tVl387u+3/pCzaedfM32RB7rpzZa6+/Du5+BMn5Lc/uyJbbrdzDnzlm9d537ZqVVbcctOa7TtuXX7369tuuce9F2y+5Zpl9mGSVWut/wlV322tPXK2fX2YQwbTnPUXT8kt110163kPPvjZedxr/sc99n3khQ8b6B77HnJUlj7/zzeoPhg3xy1dEO0Qk2ZD25qfX3hOvvru105tVGWzLRfnjt/dkrbqriTJ1g/cK09+w4lZtOO6Z6/cfO2v8smj/++Bajzo1X+bvR7/nIHOhfu645YumPERYv3mkO2cqblim1fV0txzDtkWM10HAMB903Z7LMnDnvmKXHPFxbnlN7/KiltuymaLtsq2uz4kux/41Oz1+OeseQ4ZMDdm7CGrqpcleXmS/ZJcmLsD2c1JTm2tfWrAe+ghA6AzesgA6NoG9ZC11k5LclpVHdJaO3v6sarafw7rAwAAmEiDPBj67CSpqiVJDu/93JipnjMAAAA2UN9AVlW75+4QtjLJbkn2a639bNiFAQAAjLt5Mx2oqm8l+XymQtshrbVHJblZGAMAAJgbMwayJNckWZRkpyQ79Pb1XyMfAACAgc0YyFprz0ny8CQXJzm+qq5Msk1VHTCq4gAAAMZZ3zlkrbWbknwkyUeqasckhyV5T1Xt2lp74CgKBAAAGFf9hizeQ2vt2tbaia21xyY5aIg1AQAATISBA9l0rbWfz3UhAAAAk2aDAhkAAAAbTyADAADoyIyLelTV+9NnmfvW2tFDqQgAAGBC9Ftl8aKRVQEAADCBZgxkrbXTRlkIAADApOn7HLIkqaodkhybZEmShav3t9aeOMS6AAAAxt4gi3p8LMkVSfZI8rYkP0ty4RBrAgAAmAiDBLLtWmunJFnZWvtaa+2VSfSOAQAAbKRZhywmWdn7fXVVPT3JVUm2HV5JAAAAk2GQQPa3VbVVkv+W5P1JFid53VCrAgAAmACzBrLW2ud6L29K8oThlgMAADA5Blll8SNZxwOie3PJAAAA2ECDDFn83LTXC5M8N1PzyAAAANgIgwxZPHv6dlWdkeT8oVUEAAAwIQZZ9n5teyXZca4LAQAAmDSDzCG7OfecQ/brJMcOrSIAAIAJMciQxUWjKAQAAGDSzDpksarOGWQfAAAA62fGHrKqWphkiyTbV9U2Sap3aHGSB4ygNgAAgLHWb8jikUmOSbJLkotzdyBbnuTEIdcFAAAw9mYMZK21E5KcUFV/2Vp7/whrAgAAmAiDLHu/qqq2Xr1RVdtU1WuGWBMAAMBEGCSQvaq1duPqjdbaDUleNbySAAAAJsMggWx+Va2eP5aqmp9k0+GVBAAAMBlmfQ5Zki8mObOqTuptH9nbBwAAwEYYJJAdm+SIJEf1tr+c5ENDqwgAAGBCzDpksbW2qrX2wdbaoa21Q5MsS2LVRQAAgI00SA9ZqmppksOTHJbkyiSfGmZRAAAAk2DGQFZVD8lUCDs8yXVJzkxSrbUnjKg2AACAsdavh+yHSc5L8ozW2n8kSVW9biRVAQAATIB+c8iel+TqJOdW1Yeq6klJqs/5AAAArIcZA1lr7TOttRcm2TvJuUmOSbJjVX2gqp4yqgIBAADG1SCrLN7aWvt4a+2ZSf4gySWZWgofAACAjTBrIJuutXZDa+3k1tqThlUQAADApFivQAYAAMDcEcgAAAA6IpABAAB0RCADAADoiEAGAADQEYEMAACgIwIZAABARwQyAACAjghkAAAAHRHIAAAAOiKQAQAAdEQgAwAA6IhABgAA0BGBDAAAoCMCGQAAQEcEMgAAgI4IZAAAAB0RyAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOCGQAAAAdEcgAAAA6IpABAAB0RCADAADoiEAGAADQEYEMAACgIwIZAABARwQyAACAjghkAAAAHRHIAAAAOiKQAQAAdEQgAwAA6IhABgAA0BGBDAAAoCMCGQAAQEcEMgAAgI4IZAAAAB0RyAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOCGQAAAAdEcgAAAA6IpABAAB0RCADAADoiEAGAADQEYEMAACgIwIZAABARwQyAACAjghkAAAAHanW2rDvMfQbAAAA3IvVTAc26fLmwOyq6ojW2sld1wHA5NIWwfAYsgj3fkd0XQAAE09bBEMikAEAAHREIAMAAOiIQAb3fsbsA9A1bREMyShWWQQAAGAd9JABAAB0RCBjbFTVXVV1aVVdXlVnVdUWG/Fep1bVob3XH66qJX3OfXxVPWYD7vGzqtp+hmP7VlWrqqdO27d7Vf3pWuf8lz7vv19VvW9dNVbVq6vqpetb82x6n+nsaduHVtWpc30fgHsbbdDvvcdI26Cq+pxwU0YAAAVASURBVFhVHTVt+9FVdVlVLZjL+8AwCGSMk9taa/u21h6W5I4kr55+sKo26Ll7rbX/2lpb1ueUxydZ78ZwFocnOb/3e7Xdk/zptO19k6yzMayqTVprF7XWjl5Xja21D7bWPjqXBU/zqH7/eAAYU9qgno7aoNcneUNV7VBV85KcmOQ1rbWVc3wfmHMCGePqvCQP7n0rd15VfTbJsqqaX1XvqqoLe9+cHZkkNeXEqvpRVX0lyY6r36iq/q2q9uu9fmpVfbeqvldV51TV7plqdF/X+2b0cb3G4OzePS6sqsf2rt2uqv61qn5QVR/ODA9Nr6pK8vwkL0/yJ1W1sHfoHUke17vPsUn+JskLetsvqKrjq+r0qvpGktN7n/1zM9R4fFX9Ve9++1bVt3t/j09X1TbTPvc7q+qCqvr3qnrcgH/7dyd58zo+17ZV9Znefb5dVX804PsB3Ndog0bcBrXWrkny90n+rne/y5J8a4a/9/2r6ut1d4/moO0bDIVAxtipqW8hn5bk+71dj0zy2tbaQ5L8WZKbWmv7J9k/yauqao8kz03yh0mWJHlp1vFtY1XtkORDSQ5prT0iyfNbaz9L8sEk7+l9M3pekhN62/snOSTJh3tv8dYk57fW9kny6SS7zvARHpPkytbaT5L8W5Kn9/Yfl+S83n3emeT/SXJmb/vM3jlLkjy5tbbmW80Zapzuo0mOba39Ue9v9tZpxzZprR2Q5JjV+6tql6r6lxlqT5J/SvLIqnrwWvvfluSS3n3e1LsvwFjRBnXaBn2wV8Mbkvz3zPz3/tMkX2qt7ZvkEUkuneH9YCQ2qPsc7qU2r6rV/1M9L8kpmWpYLmitXdnb/5Qkf1S9sflJtkqyV5KDk5zRWrsryVVV9dV1vP+BSb6++r1aa7+doY4nJ1ky9SVjkmRxVd2vd4/n9a79fFXdMMP1hyf5RO/1JzLVOJ89w7lr+2xr7bYBz01VbZVk69ba13q7Tkty1rRTPtX7fXGmhquktXZVZhim0nNXkncleWOSL0zbf1Cm/nGQ1tpXe9/WLm6tLR+0XoB7MW1Qx21Qa21VVZ2UZL/W2vVVNdPf+8Ik/1hT88s+01oTyOiUQMY4ua33bdcavQbp1um7kvxla+1La53XL2Csr3lJDmyt3b6OWvqqqvmZCi3Prqo3Z6re7apq0YD3vnX2U9bLit7vu7J+/784PVOB7PI5rgfg3kobdO9og1b1fpIZ/t5JUlUHZ6r379Sq+ochzquGWRmyyKT5UpKjet+KpaoeUlVbJvl6psbCz6+q+yd5wjqu/XaSg3vDHVJV2/b235xkemP1r0n+cvVGVa1uoL+e3oToqnpakm3WcY8nJbmstfbA1trurbXdMvXN5HPXcZ+1t/tZ57mttZuS3DBt/PxLknxt7fPWV28S9XuSvG7a7vOSvCiZWnEryXV6x4AJow2aZlht0DTr/HtX1W5JrmmtfShTQzofOYf3hPUmkDFpPpxkWZLvVtXlSU7K1Ldun07y496xjyb51toXttZ+k+SIJJ+qqu8lWT1m/p+TPHf1ZOUkRyfZrzeBeFnuXmnrbZlqTH+QqWEjv1hHfYf3apnu7N7+y5Lc1ZvM/bok52ZqWMqlVfWCWT732jVO97Ik76qqyzK1atbf9HujAeaQrXZK7vmN5vGZWoHxskxNDn/ZAO8BME60QaNrg5KZ/96PT/K9qrokyQsyNe8OOlOtta5rAAAAmEh6yAAAADoikAEAAHREIAMAAOiIQAYAANARgQwAAKAjAhkAAEBHBDIAAICOCGQAAAAd+f8B5NidECLIkMoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a confusion matrix\n",
    "# pass the actual and predicted target values to the confusion_matrix()\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# label the confusion matrix  \n",
    "# pass the matrix as 'data'\n",
    "# pass the required column names to the parameter, 'columns'\n",
    "# pass the required row names to the parameter, 'index'\n",
    "conf_matrix = pd.DataFrame(data = cm,columns = ['Predicted Attrition: No','Predicted Attrition: Yes'], \n",
    "                           index = ['Actual Attrition :No','Actual Attrition :Yes'])\n",
    "\n",
    "# plot a heatmap to visualize the confusion matrix\n",
    "# 'annot' prints the value of each grid \n",
    "# 'fmt = d' returns the integer value in each grid\n",
    "# 'cmap' assigns color to each grid\n",
    "# as we do not require different colors for each grid in the heatmap,\n",
    "# use 'ListedColormap' to assign the specified color to the grid\n",
    "# 'cbar = False' will not return the color bar to the right side of the heatmap\n",
    "# 'linewidths' assigns the width to the line that divides each grid\n",
    "# 'annot_kws = {'size':25})' assigns the font size of the annotated text \n",
    "sns.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = ListedColormap(['lightskyblue']), cbar = False, \n",
    "            linewidths = 0.1, annot_kws = {'size':25})\n",
    "\n",
    "# set the font size of x-axis ticks using 'fontsize'\n",
    "plt.xticks(fontsize = 10)\n",
    "\n",
    "# set the font size of y-axis ticks using 'fontsize'\n",
    "plt.yticks(fontsize = 10)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "plgkg6B9d-oJ",
    "outputId": "2f96fd79-b49d-42e8-b120-0c9c51a52c22"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9095238095238095"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "191/210"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "41cVVlOUeBMY",
    "outputId": "e3c746f1-2394-46f3-a820-c53163d21198"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9020408163265307"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "221/245"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HSCyXCDedzyD",
    "outputId": "02c4804c-331c-4b3c-c5f2-6eff2b89fca7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     245\n",
       "Yes    202\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "0ndq2UdFDWHl",
    "outputId": "4729889f-1477-4f9e-ebf9-d8dd5153837f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the roc_curve() returns the values for false positive rate, true positive rate and threshold\n",
    "# pass the actual target values and predicted probabilities to the function\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "\n",
    "# plot the straight line showing worst prediction for the model\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "\n",
    "# plot the ROC curve\n",
    "plt.plot(fpr, tpr)\n",
    "\n",
    "# add the AUC score to the plot\n",
    "# 'x' and 'y' gives position of the text\n",
    "# 's' is the text \n",
    "# use round() to round-off the AUC score upto 4 digits\n",
    "plt.text(x = 0.02, y = 0.8, s = ('AUC Score:',round(metrics.roc_auc_score(y_test, y_pred),4)))\n",
    "                               \n",
    "# plot the grid\n",
    "plt.grid(True)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "SLC - In-Class Lab Exercise_Day3_DT_RF.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}