Turnover Guide.ipynb

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    "    \n",
    "# Understanding and Predicting Employee Turnover\n",
    "\n",
    "`A case study in prescriptive analytics`\n",
    "***"
   ]
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    "\n",
    "**\"You don't build a business. You build people, and people build the business.\" - Zig Ziglar**\n",
    "***\n",
    "<!-- ![ET GIF](employee-turnover.png) -->\n",
    "<img src=\"employee-turnover.png\" style=\"width=500px;height:300px\"/>\n",
    "\n",
    "\n",
    "**\"I quit...\"** This is the last thing anybody wants to hear from their employees. In a sense, it’s the employees who make the company. It’s the employees who do the work. It’s the employees who shape the company’s culture. "
   ]
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    "### We'll be covering:\n",
    "- `Descriptive Analytics` - What happened?\n",
    "- `Predictive Analytics` - What might happen?\n",
    "- `Prescriptive Analytics` - What should we do?\n",
    "\n",
    "\n",
    "***\n",
    "### Objective: \n",
    "- To understand what factors contributed most to employee turnover.\n",
    "- To perform clustering to find any meaningful patterns of employee traits.\n",
    "- To create a model that predicts the likelihood if a certain employee will leave the company or not. \n",
    "- To create or improve different retention strategies on targeted employees. "
   ]
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    "## Table of Contents\n",
    "\n",
    "### The Problem\n",
    "- [Why is Employee Turnover a Problem?](#problem)\n",
    "\n",
    "### The Dataset\n",
    "- [Visualizing the dataset](#dataset)\n",
    "\n",
    "### Data Quality Check\n",
    "- [Simple Inspection of Data](#datacleaning)\n",
    "\n",
    "### Descriptive Statistics\n",
    "- [Summary Statistics, Turnover Rate, Size of Data](#descriptive_statistics)\n",
    "- [Correlation Matrix](#correlation_matrix)\n",
    "\n",
    "### Exploratory Data Analysis\n",
    "1. [Employee Satisfaction, Evaluation, and Project Count Distribution](#eda1)\n",
    "2. [Employee Satisfaction VS Evaluation](#eda2)\n",
    "3. [K Means Clustering of Employee](#clustering)\n",
    "4. [Employee Satisfaction Distribution](#satisfaction)\n",
    "5. [Employee Project Count Distribution](#project_count)\n",
    "6. [Employee Average Monthly Hours Distribution](#avghours)\n",
    "\n",
    "### Simple Pre-Processing\n",
    "- [Pre-Processing: Categorical and Numerical Variable](#pre_processing)\n",
    "\n",
    "### Split Train/Test Set\n",
    "- [Splitting the Data into Train/Test Sets](#train_test_split)\n",
    "\n",
    "### Class Imbalance\n",
    "- [How to Treat Class Imbalance Problems](#class_imbalance)\n",
    "\n",
    "### Resample Techniques to Treat Imbalance Data\n",
    "- [Evaluate Original, Upsampled, and Downsampled Data Metrics](#resample)\n",
    "- [Choose Which Sampling Technique to Use For Model - Upsampling](#upsampling)\n",
    "\n",
    "### Model Training and Performance (5-Fold CV)\n",
    "- [Logistic Regression Classifier](#lr)\n",
    "- [Random Forest Classifier](#rf)\n",
    "- [Gradient Boosting Classifier](#svc)\n",
    "\n",
    "### Model Evaluation (F1 Score & ROC/AUC)\n",
    "- [ROC and AUC](#roc)\n",
    "- [Feature Importance](#feature_importance)\n",
    "\n",
    "### Conclusion\n",
    "- [Retention Plan](#retention_plan)\n"
   ]
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   "source": [
    "<a id='problem'></a>\n",
    "# The Problem\n",
    "***"
   ]
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   "source": [
    "One of the most common problems at work is **turnover.** \n",
    "Replacing a worker earning about **50,000 dollars** cost the company about **10,000 dollars** or 20% of that worker’s yearly income according to the Center of American Progress.\n",
    "\n",
    "\n",
    "---\n",
    "**Costs include:**\n",
    "- Cost of off-boarding \n",
    "- Cost of hiring (advertising, interviewing, hiring)\n",
    "- Cost of onboarding a new person (training, management time)\n",
    "- Lost productivity (a new person may take 1-2 years to reach the productivity of an existing person)"
   ]
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   "source": [
    "## Example\n",
    "\n",
    "1. Jobs (earning under 30k a year): the cost to replace a 10/hour retail employee would be **3,328 dollars**.\n",
    "2. Jobs (earning 30k-50k a year) - the cost to replace a 40k manager would be **8,000 dollars**.\n",
    "3. Jobs of executives (earning 100k+ a year) - the cost to replace a 100k CEO is **213,000 dollars**."
   ]
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   "source": [
    "**Annual Cost of Turnover** = (Hiring + Onboarding + Development + Unfilled Time) * Employee Count * Turnover Rate\n",
    "\n",
    "**Annual Cost of Turnover** = (\\\\$500 + \\\\$500 + \\\\$300 + \\\\$200) * 15000 * 0.24\n",
    "\n",
    "**Annual Cost of Turnover** = \\\\$1500 * 3600\n",
    "\n",
    "**Annual Cost of Turnover** = \\\\$5,400,000"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inital Turnover Cost = $5400000\n",
      "Improved Turnover Cost = $5175000\n",
      "Difference = $225000\n"
     ]
    }
   ],
   "source": [
    "# Calculating Turnover rate improvement\n",
    "a = (500+500+300+200)*15000*0.24\n",
    "b = (500+500+300+200)*15000*0.23\n",
    "\n",
    "print (f\"Inital Turnover Cost = ${int(a)}\")\n",
    "print (f\"Improved Turnover Cost = ${int(b)}\")\n",
    "print (f\"Difference = ${int(a-b)}\")"
   ]
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    "<a id='problem'></a>"
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   "source": [
    "<a id='dataset'></a>\n",
    "# Reading the Data\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
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   "source": [
    "<img src=\"IBM.png\" style=\"width=900px;height:400px\"/>"
   ]
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# Import the neccessary modules for data manipulation and visual representation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as matplot\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('HR_comma_sep.csv.txt')"
   ]
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   "execution_count": 4,
   "metadata": {
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       "      <td>low</td>\n",
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       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
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   "source": [
    "# Examine the dataset\n",
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
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   "source": [
    "# Rename Columns\n",
    "# Renaming certain columns for better readability\n",
    "df = df.rename(columns={'satisfaction_level': 'satisfaction', \n",
    "                        'last_evaluation': 'evaluation',\n",
    "                        'number_project': 'projectCount',\n",
    "                        'average_montly_hours': 'averageMonthlyHours',\n",
    "                        'time_spend_company': 'yearsAtCompany',\n",
    "                        'Work_accident': 'workAccident',\n",
    "                        'promotion_last_5years': 'promotion',\n",
    "                        'sales' : 'department',\n",
    "                        'left' : 'turnover'\n",
    "                        })"
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     "execution_count": 6,
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   "source": [
    "df.shape"
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   "execution_count": 7,
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      "text/plain": [
       "   satisfaction  evaluation  projectCount  averageMonthlyHours  \\\n",
       "0          0.38        0.53             2                  157   \n",
       "1          0.80        0.86             5                  262   \n",
       "2          0.11        0.88             7                  272   \n",
       "3          0.72        0.87             5                  223   \n",
       "4          0.37        0.52             2                  159   \n",
       "\n",
       "   yearsAtCompany  workAccident  turnover  promotion department  salary  \n",
       "0               3             0         1          0      sales     low  \n",
       "1               6             0         1          0      sales  medium  \n",
       "2               4             0         1          0      sales  medium  \n",
       "3               5             0         1          0      sales     low  \n",
       "4               3             0         1          0      sales     low  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.76\n",
       "1    0.24\n",
       "Name: turnover, dtype: float64"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "round(df.turnover.value_counts(1), 2)"
   ]
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   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
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   "source": [
    "<img src=\"employee_turnover.png\" style=\"width=900px;height:600px\"/>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
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    {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "turnover = df.turnover.value_counts()\n",
    "sns.barplot(y=turnover.values, x=turnover.index, alpha=0.6)\n",
    "plt.title('Distribution of Employee Turnover')\n",
    "plt.xlabel('Employee Turnover', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='datacleaning'></a>\n",
    "# Data Quality Check\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction           False\n",
       "evaluation             False\n",
       "projectCount           False\n",
       "averageMonthlyHours    False\n",
       "yearsAtCompany         False\n",
       "workAccident           False\n",
       "turnover               False\n",
       "promotion              False\n",
       "department             False\n",
       "salary                 False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can you check to see if there are any missing values in our data set\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction           float64\n",
       "evaluation             float64\n",
       "projectCount             int64\n",
       "averageMonthlyHours      int64\n",
       "yearsAtCompany           int64\n",
       "workAccident             int64\n",
       "turnover                 int64\n",
       "promotion                int64\n",
       "department              object\n",
       "salary                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the type of our features. Are there any data inconsistencies?\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='descriptive_statistics'></a>\n",
    "# Exploratory Data Analysis\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <th>workAccident</th>\n",
       "      <th>turnover</th>\n",
       "      <th>promotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.00</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>14999.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "      <td>3.80</td>\n",
       "      <td>201.05</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.23</td>\n",
       "      <td>49.94</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.36</td>\n",
       "      <td>2.00</td>\n",
       "      <td>96.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>3.00</td>\n",
       "      <td>156.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.72</td>\n",
       "      <td>4.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>310.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction  evaluation  projectCount  averageMonthlyHours  \\\n",
       "count      14999.00    14999.00      14999.00             14999.00   \n",
       "mean           0.61        0.72          3.80               201.05   \n",
       "std            0.25        0.17          1.23                49.94   \n",
       "min            0.09        0.36          2.00                96.00   \n",
       "25%            0.44        0.56          3.00               156.00   \n",
       "50%            0.64        0.72          4.00               200.00   \n",
       "75%            0.82        0.87          5.00               245.00   \n",
       "max            1.00        1.00          7.00               310.00   \n",
       "\n",
       "       yearsAtCompany  workAccident  turnover  promotion  \n",
       "count        14999.00      14999.00  14999.00   14999.00  \n",
       "mean             3.50          0.14      0.24       0.02  \n",
       "std              1.46          0.35      0.43       0.14  \n",
       "min              2.00          0.00      0.00       0.00  \n",
       "25%              3.00          0.00      0.00       0.00  \n",
       "50%              3.00          0.00      0.00       0.00  \n",
       "75%              4.00          0.00      0.00       0.00  \n",
       "max             10.00          1.00      1.00       1.00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the statistical overview of the employees\n",
    "round(df.describe(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Display the mean summary of Employees (Turnover V.S. Non-turnover). What do you notice between the groups?\n",
    "turnover_Summary = df.groupby('turnover')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <th>workAccident</th>\n",
       "      <th>promotion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turnover</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>3.79</td>\n",
       "      <td>199.06</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.72</td>\n",
       "      <td>3.86</td>\n",
       "      <td>207.42</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          satisfaction  evaluation  projectCount  averageMonthlyHours  \\\n",
       "turnover                                                                \n",
       "0                 0.67        0.72          3.79               199.06   \n",
       "1                 0.44        0.72          3.86               207.42   \n",
       "\n",
       "          yearsAtCompany  workAccident  promotion  \n",
       "turnover                                           \n",
       "0                   3.38          0.18       0.03  \n",
       "1                   3.88          0.05       0.01  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(turnover_Summary.mean(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <th>workAccident</th>\n",
       "      <th>promotion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turnover</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.98</td>\n",
       "      <td>45.68</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.82</td>\n",
       "      <td>61.20</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          satisfaction  evaluation  projectCount  averageMonthlyHours  \\\n",
       "turnover                                                                \n",
       "0                 0.22        0.16          0.98                45.68   \n",
       "1                 0.26        0.20          1.82                61.20   \n",
       "\n",
       "          yearsAtCompany  workAccident  promotion  \n",
       "turnover                                           \n",
       "0                   1.56          0.38       0.16  \n",
       "1                   0.98          0.21       0.07  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(turnover_Summary.std(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a id='correlation_matrix'></a>\n",
    "### Correlation Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"heatmap.png\" style=\"width=900px;height:600px\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <th>workAccident</th>\n",
       "      <th>turnover</th>\n",
       "      <th>promotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>satisfaction</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105021</td>\n",
       "      <td>-0.142970</td>\n",
       "      <td>-0.020048</td>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.025605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>evaluation</th>\n",
       "      <td>0.105021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>-0.008684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectCount</th>\n",
       "      <td>-0.142970</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>-0.006064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <td>-0.020048</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>-0.003544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>0.067433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workAccident</th>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>0.039245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turnover</th>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion</th>\n",
       "      <td>0.025605</td>\n",
       "      <td>-0.008684</td>\n",
       "      <td>-0.006064</td>\n",
       "      <td>-0.003544</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>0.039245</td>\n",
       "      <td>-0.061788</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     satisfaction  evaluation  projectCount  \\\n",
       "satisfaction             1.000000    0.105021     -0.142970   \n",
       "evaluation               0.105021    1.000000      0.349333   \n",
       "projectCount            -0.142970    0.349333      1.000000   \n",
       "averageMonthlyHours     -0.020048    0.339742      0.417211   \n",
       "yearsAtCompany          -0.100866    0.131591      0.196786   \n",
       "workAccident             0.058697   -0.007104     -0.004741   \n",
       "turnover                -0.388375    0.006567      0.023787   \n",
       "promotion                0.025605   -0.008684     -0.006064   \n",
       "\n",
       "                     averageMonthlyHours  yearsAtCompany  workAccident  \\\n",
       "satisfaction                   -0.020048       -0.100866      0.058697   \n",
       "evaluation                      0.339742        0.131591     -0.007104   \n",
       "projectCount                    0.417211        0.196786     -0.004741   \n",
       "averageMonthlyHours             1.000000        0.127755     -0.010143   \n",
       "yearsAtCompany                  0.127755        1.000000      0.002120   \n",
       "workAccident                   -0.010143        0.002120      1.000000   \n",
       "turnover                        0.071287        0.144822     -0.154622   \n",
       "promotion                      -0.003544        0.067433      0.039245   \n",
       "\n",
       "                     turnover  promotion  \n",
       "satisfaction        -0.388375   0.025605  \n",
       "evaluation           0.006567  -0.008684  \n",
       "projectCount         0.023787  -0.006064  \n",
       "averageMonthlyHours  0.071287  -0.003544  \n",
       "yearsAtCompany       0.144822   0.067433  \n",
       "workAccident        -0.154622   0.039245  \n",
       "turnover             1.000000  -0.061788  \n",
       "promotion           -0.061788   1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a correlation matrix. What features correlate the most with turnover? What other correlations did you find?\n",
    "corr = df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values, annot=True)\n",
    "plt.title('Heatmap of Correlation Matrix');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a id='eda1'></a>\n",
    "# EDA 1. \n",
    "## Distribution of Satisfaction, Evaluation, and Monthly Hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"Employee Satisfaction.png\" style=\"width:800px;height:500px\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of Employee Satisfaction, Evaluation, and Project Count. What story can you tell?\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, axes = plt.subplots(ncols=3, figsize=(16, 8))\n",
    "\n",
    "# Graph Employee Satisfaction\n",
    "sns.distplot(df.satisfaction, kde=False, color=\"g\", ax=axes[0]).set_title('Employee Satisfaction Distribution')\n",
    "axes[0].set_ylabel('Employee Count');\n",
    "\n",
    "# Graph Employee Evaluation\n",
    "sns.distplot(df.evaluation, kde=False, color=\"r\", ax=axes[1]).set_title('Employee Evaluation Distribution')\n",
    "axes[1].set_ylabel('Employee Count');\n",
    "\n",
    "# Graph Employee Average Monthly Hours\n",
    "sns.distplot(df.averageMonthlyHours, kde=False, color=\"b\", ax=axes[2]).set_title('Employee Average Monthly Hours Distribution')\n",
    "axes[2].set_ylabel('Employee Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a id='eda2'></a>\n",
    "# EDA 2.\n",
    "## Satisfaction VS Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"satisfaction_vs_evaluation.png\" style=\"width=800px;height:500px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"clusters.png\" style=\"width=800px;height:500px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "`There are 3 distinct clusters for employees who left the company`\n",
    " \n",
    "**Cluster 1 (Hard-working and Sad Employee):** Satisfaction was below 0.2 and evaluations were greater than 0.75. Which could be a good indication that employees who left the company were good workers but felt horrible at their job. \n",
    " - **Question:** What could be the reason for feeling so horrible when you are highly evaluated? Could it be working too hard? Could this cluster mean employees who are \"overworked\"?\n",
    "\n",
    "**Cluster 2 (Bad and Sad Employee):** Satisfaction between about 0.35~0.45 and evaluations below ~0.58. This could be seen as employees who were badly evaluated and felt bad at work.\n",
    " - **Question:** Could this cluster mean employees who \"under-performed\"?\n",
    "\n",
    "**Cluster 3 (Hard-working and Happy Employee):** Satisfaction between 0.7~1.0 and evaluations were greater than 0.8. Which could mean that employees in this cluster were \"ideal\". They loved their work and were evaluated highly for their performance. \n",
    " - **Question:** Could this cluser mean that employees left because they found another job opportunity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.lmplot(x='satisfaction', y='evaluation', data=df, fit_reg=False, hue='turnover') # Color by turnover value\n",
    "plt.title('Satisfaction VS Evaluation');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<a id='clustering'></a>\n",
    "##  K-Means Clustering of Employee Turnover\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Cluster 1 (Blue):** Hard-working and Sad Employees\n",
    "\n",
    "**Cluster 2 (Red):** Bad and Sad Employee \n",
    "\n",
    "**Cluster 3 (Green):** Hard-working and Happy Employee "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import KMeans Model\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Graph and create 3 clusters of Employee Turnover\n",
    "kmeans = KMeans(n_clusters=3,random_state=2)\n",
    "kmeans.fit(df[df.turnover==1][[\"satisfaction\",\"evaluation\"]])\n",
    "\n",
    "kmeans_colors = ['green' if c == 0 else 'blue' if c == 2 else 'red' for c in kmeans.labels_]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=\"satisfaction\",y=\"evaluation\", data=df[df.turnover==1],\n",
    "            alpha=0.25,color = kmeans_colors)\n",
    "plt.xlabel(\"Satisfaction\")\n",
    "plt.ylabel(\"Evaluation\")\n",
    "plt.scatter(x=kmeans.cluster_centers_[:,0],y=kmeans.cluster_centers_[:,1],color=\"black\",marker=\"X\",s=100)\n",
    "plt.title(\"Clusters of Employee Turnover\")\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a id='satisfaction'></a>\n",
    "### EDA 3. Employee Satisfaction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"Employee Satisfaction Distribution.png\" style=\"width=800px;height:500px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "There is a **tri-modal** distribution for employees that turnovered\n",
    "- Employees who had really low satisfaction levels **(0.2 or less)** left the company more\n",
    "- Employees who had low satisfaction levels **(0.3~0.5)** left the company more\n",
    "- Employees who had really high satisfaction levels **(0.7 or more)** left the company more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cell_style": "center",
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KDEPlot: Kernel Density Estimate Plot\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 0),'satisfaction'] , color='b',shade=True, label='no turnover')\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 1),'satisfaction'] , color='r',shade=True, label='turnover')\n",
    "plt.title('Employee Satisfaction Distribution - Turnover V.S. No Turnover');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a id='project_count'></a>\n",
    "# EDA 4. Employee Project Count "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"projectCount.png\" style=\"width=800px;height:500px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- More than half of the employees with **2, 6 and 7** projects left the company\n",
    "- Majority of the employees who did not leave the company had **3,4, and 5** projects\n",
    "- All of the employees with **7** projects left the company\n",
    "- There is an increase in employee turnover rate as project count increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "ax = sns.barplot(x=\"projectCount\", y=\"projectCount\", hue=\"turnover\", data=df, estimator=lambda x: len(x) / len(df) * 100)\n",
    "ax.set(ylabel=\"Percent\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a id='department'></a>\n",
    "# EDA 5. Employee Department Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"Department Distribution.png\" style=\"width=900px;height:700px\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>department</th>\n",
       "      <th>left</th>\n",
       "      <th>stay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sales</td>\n",
       "      <td>1014</td>\n",
       "      <td>4140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>technical</td>\n",
       "      <td>697</td>\n",
       "      <td>2720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>support</td>\n",
       "      <td>555</td>\n",
       "      <td>2229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT</td>\n",
       "      <td>273</td>\n",
       "      <td>1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hr</td>\n",
       "      <td>215</td>\n",
       "      <td>739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>accounting</td>\n",
       "      <td>204</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>marketing</td>\n",
       "      <td>203</td>\n",
       "      <td>858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product_mng</td>\n",
       "      <td>198</td>\n",
       "      <td>902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RandD</td>\n",
       "      <td>121</td>\n",
       "      <td>787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>management</td>\n",
       "      <td>91</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    department  left  stay\n",
       "0        sales  1014  4140\n",
       "1    technical   697  2720\n",
       "2      support   555  2229\n",
       "3           IT   273  1227\n",
       "4           hr   215   739\n",
       "5   accounting   204   767\n",
       "6    marketing   203   858\n",
       "7  product_mng   198   902\n",
       "8        RandD   121   787\n",
       "9   management    91   630"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hrleft = df[df['turnover']==1]\n",
    "\n",
    "hrleft = pd.DataFrame(hrleft.department.value_counts()).reset_index()\n",
    "hrstay = pd.DataFrame(df.department.value_counts()).reset_index()\n",
    "\n",
    "hr_merge = pd.merge(hrleft, hrstay, how='inner', on='index')\n",
    "\n",
    "hr_merge = hr_merge.rename(columns={\"department_x\":'left', \"department_y\":'stay', \"index\":'department' })\n",
    "hr_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Initialize the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(20, 12))\n",
    "\n",
    "# Plot the total schools per city\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=\"stay\", y='department', data=hr_merge,\n",
    "            label=\"Total\", color=\"b\")\n",
    "\n",
    "# Plot the total community schools per city\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x=\"left\", y=\"department\", data=hr_merge,\n",
    "            label=\"Left\", color=\"r\")\n",
    "\n",
    "# Add a legend and informative axis label\n",
    "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
    "ax.set( ylabel=\"Department\", title='Employees Per Department',\n",
    "       xlabel=\"# of Employees\")\n",
    "sns.despine(left=True, bottom=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a id='avghours'></a>\n",
    "# EDA 6. Average Monthly Hours\n",
    "\n",
    "**Summary:** \n",
    " - A bi-modal distribution for employees that turnovered \n",
    " - Employees who had less hours of work **(~150hours or less)** left the company more\n",
    " - Employees who had too many hours of work **(~250 or more)** left the company \n",
    " - Employees who left generally were **underworked** or **overworked**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"AverageMonthly Hours.png\" style=\"width=600px;height:400px\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KDEPlot: Kernel Density Estimate Plot\n",
    "fig = plt.figure(figsize=(18, 8))\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 0),'averageMonthlyHours'] , color='b',shade=True, label='no turnover')\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 1),'averageMonthlyHours'] , color='r',shade=True, label='turnover')\n",
    "ax.set(xlabel='Employee Average Monthly Hours', ylabel='Frequency')\n",
    "plt.title('Employee AverageMonthly Hours Distribution - Turnover V.S. No Turnover');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='pre_processing'></a>\n",
    "# Pre-processing \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Apply **get_dummies()** to the categorical variables.\n",
    "- Seperate categorical variables and numeric variables, then combine them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>turnover</th>\n",
       "      <th>promotion</th>\n",
       "      <th>department_RandD</th>\n",
       "      <th>department_accounting</th>\n",
       "      <th>department_hr</th>\n",
       "      <th>department_management</th>\n",
       "      <th>department_marketing</th>\n",
       "      <th>department_product_mng</th>\n",
       "      <th>department_sales</th>\n",
       "      <th>department_support</th>\n",
       "      <th>...</th>\n",
       "      <th>department_nan</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "      <th>salary_nan</th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <th>workAccident</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   turnover  promotion  department_RandD  department_accounting  \\\n",
       "0         1          0                 0                      0   \n",
       "1         1          0                 0                      0   \n",
       "2         1          0                 0                      0   \n",
       "3         1          0                 0                      0   \n",
       "4         1          0                 0                      0   \n",
       "\n",
       "   department_hr  department_management  department_marketing  \\\n",
       "0              0                      0                     0   \n",
       "1              0                      0                     0   \n",
       "2              0                      0                     0   \n",
       "3              0                      0                     0   \n",
       "4              0                      0                     0   \n",
       "\n",
       "   department_product_mng  department_sales  department_support  ...  \\\n",
       "0                       0                 1                   0  ...   \n",
       "1                       0                 1                   0  ...   \n",
       "2                       0                 1                   0  ...   \n",
       "3                       0                 1                   0  ...   \n",
       "4                       0                 1                   0  ...   \n",
       "\n",
       "   department_nan  salary_low  salary_medium  salary_nan  satisfaction  \\\n",
       "0               0           1              0           0          0.38   \n",
       "1               0           0              1           0          0.80   \n",
       "2               0           0              1           0          0.11   \n",
       "3               0           1              0           0          0.72   \n",
       "4               0           1              0           0          0.37   \n",
       "\n",
       "   evaluation  projectCount  averageMonthlyHours  yearsAtCompany  workAccident  \n",
       "0        0.53             2                  157               3             0  \n",
       "1        0.86             5                  262               6             0  \n",
       "2        0.88             7                  272               4             0  \n",
       "3        0.87             5                  223               5             0  \n",
       "4        0.52             2                  159               3             0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_var = ['department','salary','turnover','promotion']\n",
    "num_var = ['satisfaction','evaluation','projectCount','averageMonthlyHours','yearsAtCompany', 'workAccident']\n",
    "categorical_df = pd.get_dummies(df[cat_var], drop_first=True, dummy_na=True)\n",
    "numerical_df = df[num_var]\n",
    "\n",
    "new_df = pd.concat([categorical_df,numerical_df], axis=1)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 21)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='train_test_split'></a>\n",
    "# Split Train/Test Set\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's split our data into a train and test set. We'll fit our model with the train set and leave our test set for our last evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, confusion_matrix, precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# Create the X and y set\n",
    "X = new_df.iloc[:,1:]\n",
    "y = new_df.iloc[:,0]\n",
    "\n",
    "# Define train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=123, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11999, 20)\n",
      "(3000, 20)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='class_imbalance'></a>\n",
    "# Class Imbalance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Employee Turnover Rate: 24%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.76\n",
       "1    0.24\n",
       "Name: turnover, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df.turnover.value_counts(1), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"https://miro.medium.com/max/1240/1*yYMSLDHkxY_4OkPqQt7S0g.png\" style=\"width=800px;height:600px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# How to Treat Imbalanced Datasets\n",
    "\n",
    "There are many ways of dealing with imbalanced data. We will focus in the following approaches:\n",
    "\n",
    "1. Oversampling - `SMOTE`\n",
    "2. Upsampling & Downsampling - `sklearn.utils.resample`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"https://miro.medium.com/max/1400/1*GJY7UnJ4WCzqgtrfkCvkdA.png\" style=\"width=500px;height:300px\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "from imblearn.over_sampling import SMOTE \n",
    "\n",
    "# Upsample minority class\n",
    "X_train_u, y_train_u = resample(X_train[y_train == 1],\n",
    "                                y_train[y_train == 1],\n",
    "                                replace=True,\n",
    "                                n_samples=X_train[y_train == 0].shape[0],\n",
    "                                random_state=1)\n",
    "X_train_u = np.concatenate((X_train[y_train == 0], X_train_u))\n",
    "y_train_u = np.concatenate((y_train[y_train == 0], y_train_u))\n",
    "\n",
    "\n",
    "# Upsample using SMOTE\n",
    "sm = SMOTE(random_state=12, sampling_strategy = 1.0)\n",
    "x_train_sm, y_train_sm = sm.fit_resample(X_train, y_train)\n",
    "\n",
    "\n",
    "# Downsample majority class\n",
    "X_train_d, y_train_d = resample(X_train[y_train == 0],\n",
    "                                y_train[y_train == 0],\n",
    "                                replace=True,\n",
    "                                n_samples=X_train[y_train == 1].shape[0],\n",
    "                                random_state=1)\n",
    "X_train_d = np.concatenate((X_train[y_train == 1], X_train_d))\n",
    "y_train_d = np.concatenate((y_train[y_train == 1], y_train_d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original shape: (11999, 20) (11999,)\n",
      "Upsampled shape: (18284, 20) (18284,)\n",
      "SMOTE sample shape: (18284, 20) (18284,)\n",
      "Downsampled shape: (5714, 20) (5714,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Original shape:\", X_train.shape, y_train.shape)\n",
    "print(\"Upsampled shape:\", X_train_u.shape, y_train_u.shape)\n",
    "print (\"SMOTE sample shape:\", x_train_sm.shape, y_train_sm.shape)\n",
    "print(\"Downsampled shape:\", X_train_d.shape, y_train_d.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<a id='upsampling'></a>\n",
    "# Choose Which Sampling Technique to Use For Model\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Apply 10-Fold Cross Validation for Logistic Regression\n",
    "#### Train on Original, Upsampled, SMOTE, and Downsampled Data\n",
    "\n",
    "**Objective**:Train our Logistic Regression Model to our **original, upsampled, and downsampled data** to see which performs best. \n",
    "\n",
    "**Resut:**\n",
    "- **Original Sample**: F1 Score 44.96%\n",
    "- **Upsample:** F1 Score 77.93%\n",
    "- **SMOTE:** F1 Score 78.8%\n",
    "- **Downsample:** F1 Score 77.85%\n",
    "\n",
    "**SMOTE gives us the best performance**\n",
    "\n",
    "## Upsample\n",
    "You randomly resample the minority class to create new data. \n",
    "\n",
    "## SMOTE\n",
    "You use the nearest neighbors of the minority observations to create new synthetic data\n",
    "\n",
    "## Downsample\n",
    "You remove some samples of the majority class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best F1 Score for Original data:\n",
      "0.4915813541254518\n",
      "The best F1 Score for Upsampled data:\n",
      "0.7658953577236598\n",
      "The best F1 Score for Downsampled data:\n",
      "0.7733963399170387\n",
      "The best F1 Score for SMOTE data:\n",
      "0.7835500386973869\n"
     ]
    }
   ],
   "source": [
    "# Create the Original, Upsampled, and Downsampled training sets\n",
    "methods_data = {\"Original\": (X_train, y_train),\n",
    "                \"Upsampled\": (X_train_u, y_train_u),\n",
    "                \"Downsampled\": (X_train_d, y_train_d),\n",
    "                \"SMOTE\":(x_train_sm, y_train_sm)}\n",
    "\n",
    "# Loop through each type of training sets and apply 5-Fold CV using Logistic Regression\n",
    "# By default in cross_val_score StratifiedCV is used\n",
    "for method in methods_data.keys():\n",
    "    lr_results = cross_val_score(LogisticRegression(), \n",
    "                                 methods_data[method][0], \n",
    "                                 methods_data[method][1], \n",
    "                                 cv=5, \n",
    "                                 scoring='f1')\n",
    "    print(f\"The best F1 Score for {method} data:\")\n",
    "    print (lr_results.mean())\n",
    " \n",
    "\n",
    "# cross_val_score(LogisticRegression(class_weight='balanced'), X_train, y_train, cv=5, scoring='f1').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Model Training and Performance (5-Fold CV)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We're training 3 models:\n",
    "\n",
    "1. Logistic Regression Classifier\n",
    "2. Random Forest Classifier\n",
    "3. Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<a id='lr'></a>\n",
    "## Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr = lr.fit(x_train_sm, y_train_sm)\n",
    "lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply 5-Fold Cross Validation on Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7835500386973869"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_result = cross_val_score(lr, x_train_sm, y_train_sm, cv=5, scoring='f1')\n",
    "lr_result.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Classifier AUC  (0.77)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " ---Logistic Regression Model---\n",
      "Logistic Regression AUC = 0.77\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.76      0.83      2286\n",
      "           1       0.51      0.79      0.62       714\n",
      "\n",
      "    accuracy                           0.77      3000\n",
      "   macro avg       0.71      0.77      0.72      3000\n",
      "weighted avg       0.82      0.77      0.78      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"\\n\\n ---Logistic Regression Model---\")\n",
    "lr_auc = roc_auc_score(y_test, lr.predict(X_test))\n",
    "\n",
    "print (\"Logistic Regression AUC = %2.2f\" % lr_auc)\n",
    "\n",
    "lr2 = lr.fit(x_train_sm, y_train_sm)\n",
    "print(classification_report(y_test, lr.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<a id='rf'></a>\n",
    "# Random Forest Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Random Forest Model\n",
    "rf = RandomForestClassifier()\n",
    "rf = rf.fit(x_train_sm, y_train_sm)\n",
    "rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply 5-Fold Cross Validation on Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9803413695052837"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_result = cross_val_score(rf, x_train_sm, y_train_sm, cv=5, scoring='f1')\n",
    "rf_result.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier AUC (0.98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " ---Random Forest Model---\n",
      "Random Forest AUC = 0.98\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      2286\n",
      "           1       0.97      0.98      0.97       714\n",
      "\n",
      "    accuracy                           0.99      3000\n",
      "   macro avg       0.98      0.98      0.98      3000\n",
      "weighted avg       0.99      0.99      0.99      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "print (\"\\n\\n ---Random Forest Model---\")\n",
    "rf_roc_auc = roc_auc_score(y_test, rf.predict(X_test))\n",
    "print (\"Random Forest AUC = %2.2f\" % rf_roc_auc)\n",
    "print(classification_report(y_test, rf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='svc'></a>\n",
    "# Gradient Boosting Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "gbc = GradientBoostingClassifier()  \n",
    "gbc = gbc.fit(x_train_sm,y_train_sm)\n",
    "gbc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply 5-Fold Cross Validation on Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9583186379021547"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc_result = cross_val_score(gbc, x_train_sm, y_train_sm, cv=5, scoring='f1')\n",
    "gbc_result.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier AUC  (0.96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " ---Gradient Boosting Model---\n",
      "Gradient Boosting Classifier AUC = 0.96\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.98      2286\n",
      "           1       0.91      0.94      0.92       714\n",
      "\n",
      "    accuracy                           0.96      3000\n",
      "   macro avg       0.95      0.96      0.95      3000\n",
      "weighted avg       0.96      0.96      0.96      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "print (\"\\n\\n ---Gradient Boosting Model---\")\n",
    "gbc_auc = roc_auc_score(y_test, gbc.predict(X_test))\n",
    "print (\"Gradient Boosting Classifier AUC = %2.2f\" % gbc_auc)\n",
    "print(classification_report(y_test, gbc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Model Evaluation (F1 Score & ROC/AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<a id='roc'></a>\n",
    "# ROC Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create ROC Graph\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, lr.predict_proba(X_test)[:,1])\n",
    "rf_fpr, rf_tpr, rf_thresholds = roc_curve(y_test, rf.predict_proba(X_test)[:,1])\n",
    "gbc_fpr, gbc_tpr, gbc_thresholds = roc_curve(y_test, gbc.predict_proba(X_test)[:,1])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,12))\n",
    "\n",
    "# Plot Logistic Regression ROC\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % lr_auc)\n",
    "\n",
    "# Plot Random Forest ROC\n",
    "plt.plot(rf_fpr, rf_tpr, label='Random Forest Classifier (area = %0.2f)' % rf_roc_auc)\n",
    "\n",
    "# Plot Decision Tree ROC\n",
    "plt.plot(gbc_fpr, gbc_tpr, label='Gradient Boosting Classifier (area = %0.2f)' % gbc_auc)\n",
    "\n",
    "# Plot Base Rate ROC\n",
    "plt.plot([0,1], [0,1],label='Base Rate')\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Graph')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"confusion_matrix.png\" style=\"width=500px;height:300px\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1741,  545],\n",
       "       [ 153,  561]], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix for Logistic Regresion\n",
    "confusion_matrix(y_test, lr.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2220,   66],\n",
       "       [  43,  671]], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix for GBC\n",
    "confusion_matrix(y_test, gbc.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2262,   24],\n",
       "       [  16,  698]], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix for Random Forest\n",
    "confusion_matrix(y_test, rf.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Recall or Precision?\n",
    "***\n",
    "It depends on how much cost/weight you want on your two types of errors: (1) **False Positives** or (2) **False Negatives**\n",
    "\n",
    "We want our machine learn model to capture as much of the minority class as possible (turnover group). Our objective is to catch ALL of the highly probable turnover employee at the risk of flagging some low-risk non-turnover employee."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"pre_recall.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- Consider employee turnover domain where an employee is given treatment by Human  Resources because they think the employee will leave the company within a month, but the employee actually does not. This is a false positive. This mistake could be expensive, inconvenient, and time consuming for both the Human Resources and employee, but is a good investment for relational growth. \n",
    "\n",
    "\n",
    "- Compare this with the opposite error, where Human Resources does not give treatment/incentives to the employees and they do leave. This is a false negative. This type of error is more detrimental because the company lost an employee, which could lead to great setbacks and more money to rehire. \n",
    "\n",
    "\n",
    "- Depending on these errors, different costs are weighed based on the type of employee being treated. For example, if it’s a high-salary employee then would we need a costlier form of treatment? What if it’s a low-salary employee? The cost for each error is different and should be weighed accordingly. \n",
    "\n",
    "\n",
    "**Optimize Recall When:**\n",
    "- You want to limit **false negatives**\n",
    "- You will get more False Positives\n",
    "- FN > FP\n",
    "- Example: Predicting Cancer Patients or Credit Card Fraud\n",
    "\n",
    "**Optimize Precision When:**\n",
    "- You want to limit **false positives**\n",
    "- FP > FN\n",
    "- Example: Spam VS Not Spam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='feature_importance'></a>\n",
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>index</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>satisfaction</td>\n",
       "      <td>0.250529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yearsAtCompany</td>\n",
       "      <td>0.236979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>averageMonthlyHours</td>\n",
       "      <td>0.163668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>projectCount</td>\n",
       "      <td>0.154021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>evaluation</td>\n",
       "      <td>0.128582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>workAccident</td>\n",
       "      <td>0.025898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>salary_low</td>\n",
       "      <td>0.007449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>salary_medium</td>\n",
       "      <td>0.005158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>department_sales</td>\n",
       "      <td>0.004491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>department_technical</td>\n",
       "      <td>0.004062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>department_RandD</td>\n",
       "      <td>0.003701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>department_support</td>\n",
       "      <td>0.002947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>promotion</td>\n",
       "      <td>0.002827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>department_management</td>\n",
       "      <td>0.002717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>department_product_mng</td>\n",
       "      <td>0.001937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>department_accounting</td>\n",
       "      <td>0.001858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>department_hr</td>\n",
       "      <td>0.001597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>department_marketing</td>\n",
       "      <td>0.001581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>salary_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>department_nan</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     index  importance\n",
       "0             satisfaction    0.250529\n",
       "1           yearsAtCompany    0.236979\n",
       "2      averageMonthlyHours    0.163668\n",
       "3             projectCount    0.154021\n",
       "4               evaluation    0.128582\n",
       "5             workAccident    0.025898\n",
       "6               salary_low    0.007449\n",
       "7            salary_medium    0.005158\n",
       "8         department_sales    0.004491\n",
       "9     department_technical    0.004062\n",
       "10        department_RandD    0.003701\n",
       "11      department_support    0.002947\n",
       "12               promotion    0.002827\n",
       "13   department_management    0.002717\n",
       "14  department_product_mng    0.001937\n",
       "15   department_accounting    0.001858\n",
       "16           department_hr    0.001597\n",
       "17    department_marketing    0.001581\n",
       "18              salary_nan    0.000000\n",
       "19          department_nan    0.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Feature Importances\n",
    "feature_importances = pd.DataFrame(rf.feature_importances_,\n",
    "                                   index = x_train_sm.columns,\n",
    "                                    columns=['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "feature_importances = feature_importances.reset_index()\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1296x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Initialize the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(18, 15))\n",
    "\n",
    "# Plot the Feature Importance\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=\"importance\", y='index', data=feature_importances,\n",
    "            label=\"Total\", color=\"b\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply Random Noise to Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply Random Noise to data set\n",
    "X_train_rnoise = pd.DataFrame(X_train)\n",
    "X_train_rnoise['RANDOM_NOISE'] = np.random.normal(0, 1, X_train_rnoise.shape[0])\n",
    "\n",
    "# Fit Random Forest to DataSet\n",
    "rf_random = RandomForestClassifier()\n",
    "rf_random = rf_random.fit(X_train_rnoise, y_train)\n",
    "\n",
    "# Get Feature Importances\n",
    "feature_importances_random = pd.DataFrame(rf_random.feature_importances_, index = X_train_rnoise.columns,columns=['importance']).sort_values('importance', ascending=False)\n",
    "feature_importances_random = feature_importances_random.reset_index()\n",
    "\n",
    "# Create Seaborn PLot\n",
    "sns.set(style=\"whitegrid\")\n",
    "# Initialize the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(18, 15))\n",
    "\n",
    "\n",
    "clrs = ['red' if (x == 5 ) else 'green' for x in feature_importances_random.index.values ]\n",
    "\n",
    "# Plot the Feature Importance\n",
    "sns.barplot(x=\"importance\", y='index', data=feature_importances_random, label=\"Total\",  palette=clrs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='retention_plan'></a>\n",
    "# Retention Plan\n",
    "\n",
    "******"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Reference:** http://rupeshkhare.com/wp-content/uploads/2013/12/Employee-Attrition-Risk-Assessment-using-Logistic-Regression-Analysis.pdf\n",
    "\n",
    "Since this model is being used for people, we should refrain from soley relying on the output of our model. Instead, we can use it's probability output and design our own system to treat each employee accordingly. \n",
    "\n",
    "1.\t**Safe Zone (Green)** – Employees within this zone are considered safe. \n",
    "2.\t**Low Risk Zone (Yellow)** – Employees within this zone are too be taken into consideration of potential turnover. This is more of a long-term track.\n",
    "3.\t**Medium Risk Zone (Orange)** – Employees within this zone are at risk of turnover. Action should be taken and monitored accordingly. \n",
    "4.\t**High Risk Zone (Red)** – Employees within this zone are considered to have the highest chance of turnover. Action should be taken immediately. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<!-- ![png](employee-retention.png) -->\n",
    "<img src=\"employee-retention.png\" style=\"width=800px;height:600px\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [0.96, 0.04],\n",
       "       [0.  , 1.  ],\n",
       "       [0.  , 1.  ],\n",
       "       [0.86, 0.14],\n",
       "       [0.82, 0.18],\n",
       "       [0.  , 1.  ],\n",
       "       [0.1 , 0.9 ],\n",
       "       [0.99, 0.01]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ranking turnover probability for employees\n",
    "rf.predict_proba(X_test)[175:185]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 0.04, 1.0, 1.0, 0.14, 0.18, 1.0, 0.9, 0.01]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(rf.predict_proba(X_test)[175:185, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[False, False, False, True, True, False, False, True, True, False]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(rf.predict_proba(X_test)[175:185, 1] > 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conclusion\n",
    "\n",
    "******"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## What to Optimize\n",
    "**Binary Classification**: Turnover V.S. Non Turnover\n",
    "\n",
    "**Instance Scoring**: Likelihood of employee responding to an offer/incentive to save them from leaving.\n",
    "\n",
    "**Need for Application**: Save employees from leaving\n",
    "\n",
    "In our employee retention problem, rather than simply predicting whether an employee will leave the company within a certain time frame, we would much rather have an estimate of the probability that he/she will leave the company. \n",
    "We would rank employees by their probability of leaving, then allocate a limited incentive budget to the highest probability instances. \n",
    "\n",
    "-------------\n",
    " \n",
    " **Solution 1:** \n",
    " - We can rank employees by their probability of leaving, then allocate a limited incentive budget to the highest probability instances.\n",
    " - OR, we can allocate our incentive budget to the instances with the highest expected loss, for which we'll need the probability of turnover.\n",
    "\n",
    "**Solution 2:** \n",
    " - Develop learning programs for managers, then use analytics to gauge their performance and measure progress.\n",
    " - Be a good coach. Empower the team and do not micromanage\n",
    " - Express interest for team member success\n",
    " - Have clear vision / strategy for team\n",
    " - Help team with career development    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "# Selection Bias\n",
    "***\n",
    "\n",
    "- One thing to note about this dataset is the turnover feature. We don't know if the employees that left are interns, contractors, full-time, or part-time. These are important variables to take into consideration when performing a machine learning algorithm to it. \n",
    "\n",
    "- Another thing to note down is the type of bias of the evaluation feature. Evaluation is heavily subjective, and can vary tremendously depending on who is the evaluator. If the employee knows the evaluator, then he/she will probably have a higher score. "
   ]
  }
 ],
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