Human Resources Ananlytics; Employee retention problem.ipynb

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   "source": [
    "<img src=\"http://cliparts.co/cliparts/6iy/oBb/6iyoBbdpT.gif\"/>"
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   },
   "source": [
    "# Human Resources Analytics - Milestone Report\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "**\"Yeah, they all said that to me...\"**, *Bob replied as we were at Starbucks sipping on our dark roast coffee. Bob is a friend of mine and was the owner of a multi-million dollar company, that's right, \"m-i-l-l-i-o-n\". He used to tell me stories about how his company's productivity and growth has sky rocketed from the previous years and everything has been going great. But recently, he's been noticing some decline within his company. In a five month period, he lost one-fifth of his employees. At least a dozen of them throughout each department made phone calls and even left sticky notes on their tables informing him about their leave. Nobody knew what was happening. In that year, he was contemplating about filing for bankruptcy. Fast-forward seven months later, he's having a conversation with his co-founder of the company. The conversation ends with, **\"I quit...\"**"
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   "source": [
    "That 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. Long-term success, a healthy work environment, and high employee retention are all signs of a successful company. But when a company experiences a high rate of employee turnover, then something is going wrong. This can lead the company to huge monetary losses by these innovative and valuable employees."
   ]
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  {
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   "source": [
    "Companies that maintain a healthy organization and culture are always a good sign of future prosperity. Recognizing and understanding what factors that were associated with employee turnover will allow companies and individuals to limit this from happening and may even increase employee productivity and growth. These predictive insights give managers the opportunity to take corrective steps to build and preserve their successful business."
   ]
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   "source": [
    "** \"You don't build a business. You build people, and people build the business.\" - Zig Ziglar**\n",
    "***\n",
    "\n",
    "<img src=\"https://static1.squarespace.com/static/5144a1bde4b033f38036b7b9/t/56ab72ebbe7b96fafe9303f5/1454076676264/\"/>"
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   "source": [
    "## About This Kernel\n",
    "***\n",
    "I will be covering my analysis and approach through different process flows in the data science pipeline, which includes statistical inference and exploratory data analysis. The main goal is to understand the reasoning behind employee turnover and to come up with a model to classify an employee’s risk of attrition. A recommendation for a retention plan was created, which incorporates some best practices for employee retention at different risk levels of attrition."
   ]
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    "_uuid": "e786ac2910dbd798f5ef96dc7227f0500d3bda57"
   },
   "source": [
    "## Business Problem\n",
    "***\n",
    "*Bob's multi-million dollar company is about to go bankrupt and he wants to know why his employees are leaving.*"
   ]
  },
  {
   "cell_type": "markdown",
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    "_uuid": "24ee82de2b89cba4476270d70f85124a2ca54e29"
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   "source": [
    "## Client\n",
    "***\n",
    "*Bob the Boss*"
   ]
  },
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   "source": [
    "## Objective\n",
    "***\n",
    "*The company wants to understand what factors contributed most to employee turnover and to create a model that can predict if a certain employee will leave the company or not. The goal is to create or improve different retention strategies on targeted employees. Overall, the implementation of this model will allow management to create better decision-making actions. *"
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   "source": [
    "## OSEMN Pipeline\n",
    "****\n",
    "\n",
    "*I’ll be following a typical data science pipeline, which is call “OSEMN” (pronounced awesome).*\n",
    "\n",
    "1. **O**btaining the data is the first approach in solving the problem.\n",
    "\n",
    "2. **S**crubbing or cleaning the data is the next step. This includes data imputation of missing or invalid data and fixing column names.\n",
    "\n",
    "3. **E**xploring the data will follow right after and allow further insight of what our dataset contains. Looking for any outliers or weird data. Understanding the relationship each explanatory variable has with the response variable resides here and we can do this with a correlation matrix. \n",
    "\n",
    "4. **M**odeling the data will give us our predictive power on whether an employee will leave. \n",
    "\n",
    "5. I**N**terpreting the data is last. With all the results and analysis of the data, what conclusion is made? What factors contributed most to employee turnover? What relationship of variables were found? \n",
    "\n",
    "**Note:** *The data was found from the “Human Resources Analytics” dataset provided by Kaggle’s website. https://www.kaggle.com/ludobenistant/hr-analytics*\n",
    "\n",
    "**Note:** THIS DATASET IS **SIMULATED**."
   ]
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   "source": [
    "# Part 1: Obtaining the Data \n",
    "***"
   ]
  },
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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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "#Read the analytics csv file and store our dataset into a dataframe called \"df\"\n",
    "df = pd.DataFrame.from_csv('../input/HR_comma_sep.csv', index_col=None)"
   ]
  },
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    "_cell_guid": "88cf1176-a3c4-49a9-915c-3bea5c8a3a4e",
    "_uuid": "b5b800ca16faed7e7317413bd6fe0c4c44d082fb"
   },
   "source": [
    "# Part 2: Scrubbing the Data \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b25ecb0e-e300-4a72-b6fd-35ae93e020b8",
    "_uuid": "f664a189a3b1d04813b5223274b31ea7a14e8438"
   },
   "source": [
    "*Typically, cleaning the data requires a lot of work and can be a very tedious procedure. This dataset from Kaggle is super clean and contains no missing values. But still, I will have to examine the dataset to make sure that everything else is readable and that the observation values match the feature names appropriately.*"
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       "satisfaction_level       False\n",
       "last_evaluation          False\n",
       "number_project           False\n",
       "average_montly_hours     False\n",
       "time_spend_company       False\n",
       "Work_accident            False\n",
       "left                     False\n",
       "promotion_last_5years    False\n",
       "sales                    False\n",
       "salary                   False\n",
       "dtype: bool"
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   "source": [
    "# Check to see if there are any missing values in our data set\n",
    "df.isnull().any()"
   ]
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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": [
    "# Get a quick overview of what we are dealing with in our dataset\n",
    "df.head()"
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   "source": [
    "# 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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       "      <th></th>\n",
       "      <th>turnover</th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
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      "text/plain": [
       "   turnover  satisfaction  evaluation  projectCount  averageMonthlyHours  \\\n",
       "0         1          0.38        0.53             2                  157   \n",
       "1         1          0.80        0.86             5                  262   \n",
       "2         1          0.11        0.88             7                  272   \n",
       "3         1          0.72        0.87             5                  223   \n",
       "4         1          0.37        0.52             2                  159   \n",
       "\n",
       "   yearsAtCompany  workAccident  promotion department  salary  \n",
       "0               3             0          0      sales     low  \n",
       "1               6             0          0      sales  medium  \n",
       "2               4             0          0      sales  medium  \n",
       "3               5             0          0      sales     low  \n",
       "4               3             0          0      sales     low  "
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     "execution_count": 6,
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   "source": [
    "# Move the reponse variable \"turnover\" to the front of the table\n",
    "front = df['turnover']\n",
    "df.drop(labels=['turnover'], axis=1,inplace = True)\n",
    "df.insert(0, 'turnover', front)\n",
    "df.head()"
   ]
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   "source": [
    "# Part 3: Exploring the Data\n",
    "*** \n",
    " <img  src=\"https://s-media-cache-ak0.pinimg.com/originals/32/ef/23/32ef2383a36df04a065b909ee0ac8688.gif\"/>"
   ]
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   "source": [
    "##  3a. Statistical Overview \n",
    "***\n",
    "The dataset has:\n",
    " - About 15,000 employee observations and 10 features \n",
    " - The company had a turnover rate of about 24%\n",
    " - Mean satisfaction of employees is 0.61"
   ]
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    "_uuid": "a7eabf82ee6adbd09aca9e878112f8525ea50417",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The dataset contains 10 columns and 14999 observations\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "25bc8dfe-5d24-4249-bab0-0424a20b1bd5",
    "_uuid": "4be89ba5d2e7f693b0cd228830423a700b6196fa",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "turnover                 int64\n",
       "satisfaction           float64\n",
       "evaluation             float64\n",
       "projectCount             int64\n",
       "averageMonthlyHours      int64\n",
       "yearsAtCompany           int64\n",
       "workAccident             int64\n",
       "promotion                int64\n",
       "department              object\n",
       "salary                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the type of our features. \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "bbac7b26-fc6d-47ad-8b15-b1f61191a471",
    "_uuid": "e4a3109a02643d0a8388553ee176f7fc112f321a",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.761917\n",
       "1    0.238083\n",
       "Name: turnover, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looks like about 76% of employees stayed and 24% of employees left. \n",
    "# NOTE: When performing cross validation, its important to maintain this turnover ratio\n",
    "turnover_rate = df.turnover.value_counts() / len(df)\n",
    "turnover_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "bc3d99b1-7bc5-4ba2-a34f-0a721c51075f",
    "_uuid": "a9ae5674c17ee2b88035aef24d674c56d0b89a03",
    "collapsed": true,
    "scrolled": false
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>turnover</th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
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       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
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       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
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       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.021268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.144281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           turnover  satisfaction    evaluation  projectCount  \\\n",
       "count  14999.000000  14999.000000  14999.000000  14999.000000   \n",
       "mean       0.238083      0.612834      0.716102      3.803054   \n",
       "std        0.425924      0.248631      0.171169      1.232592   \n",
       "min        0.000000      0.090000      0.360000      2.000000   \n",
       "25%        0.000000      0.440000      0.560000      3.000000   \n",
       "50%        0.000000      0.640000      0.720000      4.000000   \n",
       "75%        0.000000      0.820000      0.870000      5.000000   \n",
       "max        1.000000      1.000000      1.000000      7.000000   \n",
       "\n",
       "       averageMonthlyHours  yearsAtCompany  workAccident     promotion  \n",
       "count         14999.000000    14999.000000  14999.000000  14999.000000  \n",
       "mean            201.050337        3.498233      0.144610      0.021268  \n",
       "std              49.943099        1.460136      0.351719      0.144281  \n",
       "min              96.000000        2.000000      0.000000      0.000000  \n",
       "25%             156.000000        3.000000      0.000000      0.000000  \n",
       "50%             200.000000        3.000000      0.000000      0.000000  \n",
       "75%             245.000000        4.000000      0.000000      0.000000  \n",
       "max             310.000000       10.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the statistical overview of the employees\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "d61efd5d-5c2c-4b3c-b2ac-22d562c9e91a",
    "_uuid": "cfb764723207606d3e7fe991b17cd648e04c0a7a",
    "collapsed": true
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   "outputs": [
    {
     "data": {
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       "  <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",
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       "    <tr>\n",
       "      <th>turnover</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>0.666810</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>3.786664</td>\n",
       "      <td>199.060203</td>\n",
       "      <td>3.380032</td>\n",
       "      <td>0.175009</td>\n",
       "      <td>0.026251</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.440098</td>\n",
       "      <td>0.718113</td>\n",
       "      <td>3.855503</td>\n",
       "      <td>207.419210</td>\n",
       "      <td>3.876505</td>\n",
       "      <td>0.047326</td>\n",
       "      <td>0.005321</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "          satisfaction  evaluation  projectCount  averageMonthlyHours  \\\n",
       "turnover                                                                \n",
       "0             0.666810    0.715473      3.786664           199.060203   \n",
       "1             0.440098    0.718113      3.855503           207.419210   \n",
       "\n",
       "          yearsAtCompany  workAccident  promotion  \n",
       "turnover                                           \n",
       "0               3.380032      0.175009   0.026251  \n",
       "1               3.876505      0.047326   0.005321  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview of summary (Turnover V.S. Non-turnover)\n",
    "turnover_Summary = df.groupby('turnover')\n",
    "turnover_Summary.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "918f20ff-ed2c-43dd-849c-b7d751aeed86",
    "_uuid": "bed79decbf6495197f8f0219212c41c0042e196e"
   },
   "source": [
    "##  3b. Correlation Matrix & Heatmap\n",
    "***\n",
    "**Moderate Positively Correlated Features:** \n",
    "- projectCount vs evaluation: 0.349333\n",
    "- projectCount vs averageMonthlyHours:  0.417211\n",
    "- averageMonthlyHours vs evaluation: 0.339742\n",
    "\n",
    "**Moderate Negatively Correlated Feature:**\n",
    " - satisfaction vs turnover:  -0.388375\n",
    "\n",
    "**Questions I've asked myself:**\n",
    "- What features affect our target variable the most (turnover)?\n",
    "- What features have strong correlations with each other?\n",
    "- Can we do a more in depth examination of these features?\n",
    "\n",
    "**Summary:**\n",
    "\n",
    "From the heatmap, there is a **positive(+)** correlation between projectCount, averageMonthlyHours, and evaluation. Which could mean that the employees who spent more hours and did more projects were evaluated highly. \n",
    "\n",
    "For the **negative(-)** relationships, turnover and satisfaction are highly correlated. I'm assuming that people tend to leave a company more when they are less satisfied. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "20d2f962-fcfa-434c-88ab-12dc05071b03",
    "_uuid": "1e2cb2cce7d8edf2ecd13a7197063ef3eea8d073",
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    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>turnover</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.023787</td>\n",
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       "      <td>-0.388375</td>\n",
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       "      <td>-0.142970</td>\n",
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       "      <th>evaluation</th>\n",
       "      <td>0.006567</td>\n",
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       "      <td>-0.007104</td>\n",
       "      <td>-0.008684</td>\n",
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       "      <th>projectCount</th>\n",
       "      <td>0.023787</td>\n",
       "      <td>-0.142970</td>\n",
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       "      <td>0.196786</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>-0.006064</td>\n",
       "    </tr>\n",
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       "      <th>averageMonthlyHours</th>\n",
       "      <td>0.071287</td>\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>-0.003544</td>\n",
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       "    <tr>\n",
       "      <th>yearsAtCompany</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>0.067433</td>\n",
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       "    <tr>\n",
       "      <th>workAccident</th>\n",
       "      <td>-0.154622</td>\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>0.039245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion</th>\n",
       "      <td>-0.061788</td>\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>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     turnover  satisfaction  evaluation  projectCount  \\\n",
       "turnover             1.000000     -0.388375    0.006567      0.023787   \n",
       "satisfaction        -0.388375      1.000000    0.105021     -0.142970   \n",
       "evaluation           0.006567      0.105021    1.000000      0.349333   \n",
       "projectCount         0.023787     -0.142970    0.349333      1.000000   \n",
       "averageMonthlyHours  0.071287     -0.020048    0.339742      0.417211   \n",
       "yearsAtCompany       0.144822     -0.100866    0.131591      0.196786   \n",
       "workAccident        -0.154622      0.058697   -0.007104     -0.004741   \n",
       "promotion           -0.061788      0.025605   -0.008684     -0.006064   \n",
       "\n",
       "                     averageMonthlyHours  yearsAtCompany  workAccident  \\\n",
       "turnover                        0.071287        0.144822     -0.154622   \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",
       "promotion                      -0.003544        0.067433      0.039245   \n",
       "\n",
       "                     promotion  \n",
       "turnover             -0.061788  \n",
       "satisfaction          0.025605  \n",
       "evaluation           -0.008684  \n",
       "projectCount         -0.006064  \n",
       "averageMonthlyHours  -0.003544  \n",
       "yearsAtCompany        0.067433  \n",
       "workAccident          0.039245  \n",
       "promotion             1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9421711b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Matrix\n",
    "corr = df.corr()\n",
    "corr = (corr)\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)\n",
    "\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7d936e7c-b7dd-4b32-9dd3-8e2c55313a30",
    "_uuid": "2126a99f138eb0ae59598629d1b7c4cf6686b8c9"
   },
   "source": [
    "## 3b2. Statistical Test for Correlation\n",
    "***\n",
    "\n",
    "### One-Sample T-Test (Measuring Satisfaction Level)\n",
    "A one-sample t-test checks whether a sample mean differs from the population mean. Since satisfaction has the highest correlation with our dependent variable turnover, let's test to see whether the average satisfaction level of employees that had a turnover differs from the those that had no turnover.\n",
    "\n",
    "**Hypothesis Testing:** Is there significant difference in the **means of satisfaction level** between employees who had a turnover and temployees who had no turnover?\n",
    "\n",
    " - **Null Hypothesis:** *(H0: pTS = pES)* The null hypothesis would be that there is **no** difference in satisfaction level between employees who did turnover and those who did not..\n",
    "\n",
    " - **Alternate Hypothesis:** *(HA: pTS != pES)* The alternative hypothesis would be that there **is** a difference in satisfaction level between employees who did turnover and those who did not.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "2e1c6ff1-1140-4892-b928-03df51d88fae",
    "_uuid": "90b9a8841cbc9c404923acf05a7f607c6df05bb6",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean satisfaction for the employee population with no turnover is: 0.666809590479516\n",
      "The mean satisfaction for employees that had a turnover is: 0.44009801176140917\n"
     ]
    }
   ],
   "source": [
    "# Let's compare the means of our employee turnover satisfaction against the employee population satisfaction\n",
    "#emp_population = df['satisfaction'].mean()\n",
    "emp_population = df['satisfaction'][df['turnover'] == 0].mean()\n",
    "emp_turnover_satisfaction = df[df['turnover']==1]['satisfaction'].mean()\n",
    "\n",
    "print( 'The mean satisfaction for the employee population with no turnover is: ' + str(emp_population))\n",
    "print( 'The mean satisfaction for employees that had a turnover is: ' + str(emp_turnover_satisfaction) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1a35ccd8-f60d-42d0-afb5-f8c32e01925a",
    "_uuid": "797103751f3468f19e22b0b9bdfb97a46737a30c"
   },
   "source": [
    "### Conducting the T-Test\n",
    "***\n",
    "Let's conduct a t-test at **95% confidence level** and see if it correctly rejects the null hypothesis that the sample comes from the same distribution as the employee population. To conduct a one sample t-test, we can use the **stats.ttest_1samp()** function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "3ce92ea4-829d-45bc-8547-ab4b1f261684",
    "_uuid": "7d9177f88ffe8bf88148390454b00abc00195467",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=-51.3303486754725, pvalue=0.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.ttest_1samp(a=  df[df['turnover']==1]['satisfaction'], # Sample of Employee satisfaction who had a Turnover\n",
    "                  popmean = emp_population)  # Employee Who Had No Turnover satisfaction mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fe8233cd-83c4-45a8-82bc-89d20a241e13",
    "_uuid": "a254f97a16de3fe1a3491d349ca574e243cf6b11"
   },
   "source": [
    "### T-Test Result\n",
    "***\n",
    "The test result shows the **test statistic \"t\" is equal to -51.33**. This test statistic tells us how much the sample mean deviates from the null hypothesis. If the t-statistic lies **outside** the quantiles of the t-distribution corresponding to our confidence level and degrees of freedom, we reject the null hypothesis. We can check the quantiles with **stats.t.ppf()**:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9a021234-21cb-4194-9179-b9ba373912cd",
    "_uuid": "a4abedc33125e59fd0fb368cd10d612ff09c6b80"
   },
   "source": [
    "### T-Test Quantile\n",
    "***\n",
    "If the t-statistic value we calculated above **(-51.33)** is outside the quantiles, then we can reject the null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "207f67ca-f12f-4b20-a649-85b8dfeac3f2",
    "_uuid": "da9473ae9e6fc971e89ac291e57f208526ab58d1",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The t-distribution left quartile range is: -1.9606285216\n",
      "The t-distribution right quartile range is: 1.9606285216\n"
     ]
    }
   ],
   "source": [
    "degree_freedom = len(df[df['turnover']==1])\n",
    "\n",
    "LQ = stats.t.ppf(0.025,degree_freedom)  # Left Quartile\n",
    "\n",
    "RQ = stats.t.ppf(0.975,degree_freedom)  # Right Quartile\n",
    "\n",
    "print ('The t-distribution left quartile range is: ' + str(LQ))\n",
    "print ('The t-distribution right quartile range is: ' + str(RQ))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "27019d76-3e7f-4076-b0fc-48f22f4e3281",
    "_uuid": "1b4b02a9d1b86d6b386a36495d20d0059c25f715"
   },
   "source": [
    "### One-Sample T-Test Summary\n",
    "***\n",
    "#### **T-Test = -51.33**       |        **P-Value = 0.000_**       |       **Reject Null Hypothesis**\n",
    "\n",
    "## Question: How come the P-Value is literally 0.0? \n",
    "\n",
    "**Reject the null hypothesis because:**\n",
    " - T-Test score is outside the quantiles\n",
    " - P-value is lower than confidence level of 5%\n",
    "\n",
    "Based on the statistical analysis of a one sample t-test, there seems to be some significant difference between the mean satisfaction of employees who had a turnover and the entire employee population. The super low P-value of **0.00_** at a 5% confidence level is a good indicator to **reject the null hypothesis**. \n",
    "\n",
    "But this does not neccessarily mean that there is practical significance. We would have to conduct more experiments or maybe collect more data about the employees in order to come up with a more accurate finding.\n",
    "\n",
    "<img src=\"https://static1.squarespace.com/static/5144a1bde4b033f38036b7b9/t/56714b05c647ad9f555348fa/1450265419456/PresentWIP.gif?format=500w\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fd10e1bc-1cca-4bdd-a8b4-73a6b7f5ddd7",
    "_uuid": "06467f1691005e605af9084ee142fd2c86b4f324"
   },
   "source": [
    "##  3c. Distribution Plots (Satisfaction - Evaluation - AverageMonthlyHours)\n",
    "***\n",
    "**Summary:** Let's examine the distribution on some of the employee's features. Here's what I found:\n",
    " - **Satisfaction** - There is a huge spike for employees with low satisfaction and high satisfaction.\n",
    " - **Evaluation** - There is a bimodal distrubtion of employees for low evaluations (less than 0.6) and high evaluations (more than 0.8)\n",
    " - **AverageMonthlyHours** - There is another bimodal distribution of employees with lower and higher average monthly hours (less than 150 hours & more than 250 hours)\n",
    " - The evaluation and average monthly hour graphs both share a similar distribution. \n",
    " - Employees with lower average monthly hours were evaluated less and vice versa.\n",
    " - If you look back at the correlation matrix, the high correlation between evaluation and averageMonthlyHours does support this finding.\n",
    " \n",
    "**Questions to ponder on:** \n",
    " - Is there a reason for the high spike in low satisfaction of employees?\n",
    " - Could employees be grouped in a way with these features?\n",
    " - Is there a correlation between evaluation and averageMonthlyHours?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "1899ace3-af9c-45aa-a351-affc1268f6fd",
    "_uuid": "965942ee9773a202172d7d855495089165b5ddac",
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Employee Count')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941e286438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the matplotlib figure\n",
    "f, axes = plt.subplots(ncols=3, figsize=(15, 6))\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": {
    "_cell_guid": "59946601-f880-492f-bba0-2abb6fa3349c",
    "_uuid": "0a7acbd87733d69cd9415694ecf9207bd3a351d8"
   },
   "source": [
    "##  3d. Salary V.S. Turnover\n",
    "***\n",
    "**Summary:** This is not unusual. Here's what I found:\n",
    " - Majority of employees who left either had **low** or **medium** salary.\n",
    " - Barely any employees left with **high** salary\n",
    " - Employees with low to average salaries tend to leave the company.\n",
    " \n",
    "**Questions to ponder on** \n",
    " - What is the work environment like for low, medium, and high salaries?\n",
    " - What made employees with high salaries to leave?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "9949ba1f-4427-422b-99ca-8dac582a02b1",
    "_uuid": "df172af0f3fc0fdcf837b6f94c0f477fd831dc04",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9421711c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(15, 4))\n",
    "sns.countplot(y=\"salary\", hue='turnover', data=df).set_title('Employee Salary Turnover Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0a57deee-74dd-40cc-9fb2-3536ebae7e50",
    "_uuid": "833b1d1e28f3ca30b9f5a675134034fb7cdcc358"
   },
   "source": [
    "<img src=\"https://cdn.dribbble.com/users/20727/screenshots/2118641/video-producer-motion-graphics-designer-animator-jobs-manchester-uk.gif\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "259d4f6a-d194-4ad6-8344-169c8cf6ecc4",
    "_uuid": "4789e0df5a115e9c7404928b5095c9949dd23d9c"
   },
   "source": [
    "##  3e. Department V.S. Turnover \n",
    "***\n",
    "**Summary:** Let's see more information about the departments. Here's what I found:\n",
    " - The **sales, technical, and support department** were the top 3 departments to have employee turnover\n",
    " - The management department had the smallest amount of turnover\n",
    " \n",
    "**Questions to ponder on:** \n",
    " - If we had more information on each department, can we pinpoint a more direct cause for employee turnover?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "d3b1f718-f4d4-4e4f-8e64-e110e65416d6",
    "_uuid": "2fa4f8c2ee0138b1a32a5b64611ae2fc1362227d",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XXlER8TKwcyfrOho4uqfzaGZm9fnOcTMzq8WBw8zManHgMDOzWhw4zMysFgcOMzOrxYHDzMxqceAwM7NaHDjMzKyWZt45bjZXp/9yTMu2NfHj01q2LbOBwiUOMzOrxYHDzMxqceAwM7NaHDjMzKwWBw4zM6vFgcPMzGpx4DAzs1qa+ejYEZKulXS3pLskHVDSD5M0S9Jt5bVt5TMHS5op6V5JW1XSty5pMyUd1Kw8m5lZ15p5A+CrwIER8WdJywDTJU0t750QEcdXF5a0Nvm42HWAVYHfS/qf8vYp5DPLHwFulTQlIu5uYt7NzKwTzXx07KPAo2X6OUn3AMPm8ZHxwHkR8QrwQHn2eOMRszPLI2eRdF5Z1oHDzKwNWtLGIWkk+fzxm0vS/pJmSJokafmSNgx4uPKxR0paZ+kdtzFR0jRJ02bPnt3De2BmZg1NDxySlgZ+A3wxIp4FTgVWB0aTJZLv98R2IuL0iBgTEWOGDBnSE6s0M7O5aOogh5IWIYPGLyLiQoCIeKzy/hnApWV2FjCi8vHhJY15pJuZWYs1s1eVgDOBeyLiB5X0VSqLfQS4s0xPAXaVtJik1YBRwC3ArcAoSatJWpRsQJ/SrHybmdm8NbPEsQmwB3CHpNtK2iHAbpJGAwE8COwDEBF3STqfbPR+FdgvIl4DkLQ/cCUwCJgUEXc1Md9mZjYP3Qockq6OiC27SquKiBsBzeWty+fxmaOBo+eSfvm8PmdmZq0zz8AhaXFgSWCl0vupEQiWZd5da83MrJ/qqsSxD/BF8oa86cwJHM8CJzcxX2Zm1kvNM3BExInAiZI+HxE/alGezMysF+tWG0dE/EjSxsDI6mci4uwm5cvMzHqp7jaO/5y8ae824LWSHIADh5nZANPd7rhjgLUjIpqZGTMz6/26ewPgncBbmpkRMzPrG7pb4lgJuFvSLcArjcSI2LEpuTIzs16ru4HjsGZmwqwdfv7zQ1u2rT32OLxl2zJrtu72qvpDszNiZmZ9Q3d7VT1H9qICWBRYBHghIpZtVsbMzKx36m6JY5nGdBn1djywYbMyZWZmvVftYdUj/RbYqgn5MTOzXq67VVUfrcwuRN7X8XJTcmRmZr1ad3tV7VCZfpV8jsb4Hs+NmZn1et1t49ir2RkxM7O+oVttHJKGS7pI0uPl9RtJw7v4zAhJ10q6W9Jdkg4o6StImirpvvJ3+ZIuSSdJmilphqT3VtY1oSx/n6QJC7LDZma2YLrbOP4z8jnfq5bXJSVtXl4FDoyItckeWPtJWhs4CLg6IkYBV5d5gG3I54yPAiYCp0IGGuBQYANgLHBoI9iYmVnrdTdwDImIn0XEq+U1GRgyrw9ExKMR8ecy/RxwD/nUwPHAWWWxs4APl+nxwNml19afgMGSViF7b02NiCcj4ilgKrB193fRzMx6UncDxxOSPiFpUHl9AniiuxuRNBJ4D3AzMDQiHi1v/RMYWqaHAQ9XPvZISessveM2JkqaJmna7Nmzu5s1MzOrqbuB41PALuSJ/lFgJ2DP7nxQ0tLAb4AvRsSz1ffKMO09MlR7RJweEWMiYsyQIfMsDJmZ2QLobuA4ApgQEUMiYmUykHQ5apukRcig8YuIuLAkP1aqoCh/Hy/ps4ARlY8PL2mdpZuZWRt0N3C8q7QvABART5JVT50qQ5OcCdwTET+ovDUFaPSMmgBcXEn/ZOldtSHwTKnSuhIYJ2n50ig+rqSZmVkbdPcGwIUkLd8IHqWnU1ef3QTYA7hD0m0l7RDgWOB8SXsDD5FVYACXA9sCM4EXgb0gg5SkI4Fby3JHlMBlZmZt0N3A8X3gfyX9uszvDBw9rw9ExI2AOnl7y7ksH8B+naxrEjCpm3k1M7Mm6u6d42dLmgZsUZI+GhF3Ny9bZmbWW3W3xEEJFA4WZmYDXO1h1c3MbGBz4DAzs1ocOMzMrBYHDjMzq8WBw8zManHgMDOzWhw4zMysFgcOMzOrxYHDzMxqceAwM7NaHDjMzKwWBw4zM6vFgcPMzGpx4DAzs1qaFjgkTZL0uKQ7K2mHSZol6bby2rby3sGSZkq6V9JWlfStS9pMSQc1K79mZtY9zSxxTAa2nkv6CRExurwuB5C0NrArsE75zI8lDZI0CDgF2AZYG9itLGtmZm3S7Qc51RUR10sa2c3FxwPnRcQrwAOSZgJjy3szI+J+AEnnlWX9QCkzszZpRxvH/pJmlKqs5UvaMODhyjKPlLTO0v+LpImSpkmaNnv27Gbk28zMaH3gOBVYHRgNPAp8v6dWHBGnR8SYiBgzZMiQnlqtmZl10LSqqrmJiMca05LOAC4ts7OAEZVFh5c05pFuZmZt0NISh6RVKrMfARo9rqYAu0paTNJqwCjgFuBWYJSk1SQtSjagT2llns3M7M2aVuKQdC7wfmAlSY8AhwLvlzQaCOBBYB+AiLhL0vlko/erwH4R8VpZz/7AlcAgYFJE3NWsPJuZWdea2atqt7kknzmP5Y8Gjp5L+uXA5T2YNTMzWwC+c9zMzGpx4DAzs1ocOMzMrBYHDjMzq8WBw8zManHgMDOzWhw4zMysFgcOMzOrpaVjVZlZ7/Tg9dt2vVAPGLm57+XtD1ziMDOzWhw4zMysFgcOMzOrxYHDzMxqceAwM7NaHDjMzKwWBw4zM6ulaYFD0iRJj0u6s5K2gqSpku4rf5cv6ZJ0kqSZkmZIem/lMxPK8vdJmtCs/JqZWfc0s8QxGdi6Q9pBwNURMQq4uswDbEM+Z3wUMBE4FTLQkI+c3QAYCxzaCDZmZtYeTQscEXE98GSH5PHAWWX6LODDlfSzI/0JGCxpFWArYGpEPBkRTwFT+e9gZGZmLdTqNo6hEfFomf4nMLRMDwMeriz3SEnrLP2/SJooaZqkabNnz+7ZXJuZ2Rva1jgeEQFED67v9IgYExFjhgwZ0lOrNTOzDlodOB4rVVCUv4+X9FnAiMpyw0taZ+lmZtYmrQ4cU4BGz6gJwMWV9E+W3lUbAs+UKq0rgXGSli+N4uNKmpmZtUnThlWXdC7wfmAlSY+QvaOOBc6XtDfwELBLWfxyYFtgJvAisBdARDwp6Ujg1rLcERHRscHdzMxaqGmBIyJ26+StLeeybAD7dbKeScCkHsyamfVCM646qOuFesi7xh3bsm31R75z3MzManHgMDOzWvzoWDOzikd+cUlLtjN89x1asp1mcInDzMxqceAwM7NaXFVlZtbLXLjFpi3b1kevubH2Zxw4WuTeo7dp2bbW/MYVLduWmQ08rqoyM7NaXOIYYA457cGWbeuYfUe2bFtm1joucZiZWS0OHGZmVosDh5mZ1eLAYWZmtbhx3KyNTtnhlJZta79L5joAtVltLnGYmVktDhxmZlZLWwKHpAcl3SHpNknTStoKkqZKuq/8Xb6kS9JJkmZKmiHpve3Is5mZpXaWOD4QEaMjYkyZPwi4OiJGAVeXeYBtgFHlNRE4teU5NTOzN/SmqqrxwFll+izgw5X0syP9CRgsaZV2ZNDMzNoXOAK4StJ0SRNL2tCIeLRM/xMYWqaHAQ9XPvtISXsTSRMlTZM0bfbs2c3Kt5nZgNeu7ribRsQsSSsDUyX9tfpmRISkqLPCiDgdOB1gzJgxtT5rZmbd15YSR0TMKn8fBy4CxgKPNaqgyt/Hy+KzgBGVjw8vaWZm1gYtDxySlpK0TGMaGAfcCUwBJpTFJgAXl+kpwCdL76oNgWcqVVpmZtZi7aiqGgpcJKmx/V9GxO8k3QqcL2lv4CFgl7L85cC2wEzgRWCv1mfZzMwaWh44IuJ+4N1zSX8C2HIu6QF4rAQzs16iN3XHNTOzPsCBw8zManHgMDOzWhw4zMysFgcOMzOrxYHDzMxqceAwM7NaHDjMzKwWBw4zM6vFgcPMzGpx4DAzs1ocOMzMrBYHDjMzq8WBw8zManHgMDOzWhw4zMyslj4TOCRtLeleSTMlHdTu/JiZDVR9InBIGgScAmwDrA3sJmnt9ubKzGxg6hOBAxgLzIyI+yPi38B5wPg258nMbEDqK4FjGPBwZf6RkmZmZi2miGh3HrokaSdg64j4dJnfA9ggIvavLDMRmFhm1wTuXcDNrgT8awHX0RN6Qz56Qx6gd+SjN+QBekc+ekMeoHfkozfkARY8H2+LiCFdLbTwAmyglWYBIyrzw0vaGyLidOD0ntqgpGkRMaan1teX89Eb8tBb8tEb8tBb8tEb8tBb8tEb8tDKfPSVqqpbgVGSVpO0KLArMKXNeTIzG5D6RIkjIl6VtD9wJTAImBQRd7U5W2ZmA1KfCBwAEXE5cHkLN9lj1V4LqDfkozfkAXpHPnpDHqB35KM35AF6Rz56Qx6gRfnoE43jZmbWe/SVNg4z66UkrdTuPAwkklZodx4cOCokqd156K0kbSdp4x5e5yhJK/fkOq21JO0ATJM0uN15GQgkbQf8S9LW7cyHAwcgaQmAiAgHj/8m6UPAycBbe2h9a0g6nhxGpss+4/1Ff/ttSdoG+B6wKfAlSTvW+GyvPRa9NW/leH+XHDXjcEnj2pWXAR04JC1ebhw8V9I+kMGjB9c/XNKGZbpX/hi7Imk88APgrIg4r6Qt6O9mI/Jmzdm9oXecpKUlvUPS6DLfo9+VpHGSNujJ31a7lZPYycBGEfEI8HvgMElbdfG5D0ka2xuPRW/+niR9EDiJPN6XAGcDn5W0WDvOLQM6cADrAp8FTgU+LelzPbz+VYAfSdqhN/4Yu1KKxWcAfwKGS/o8QES8vgDr3BH4MvA1YElJ76u8167guifwdWCypB17+OJhPNkb8KhGVV9fvYhoqASNMRHxFEBE3ECO1vCLzoJHORa/A47rbceiN39P5XhfBfwuIp6VNBxYGbgtIl5py7klIgbcC1iSDJorkDcSrkjemb4/sFZlOc3Hupclb1A8GhhHfsG3A6u3e79r7sc2wAxg1zK/OnA/MGJ+jw+wNXAHsH2Z/wh5wh63IMd8AfZxELAocBOwD/AO4M/Aaj2RF2Bb4C4yUK5Lnlg3aPd3u4D7NB74K7B8JW1h4AjgO2Vf/wxs2+FzOwAzgUOADYF7gE3bvT+9/Xsqx/tuYChwBXAZcBZ5QfextuWr3QemDV+EgL2Avcv8d4F9y/TbgKOAz1SXr7n+CcC1wAHkHe+7AxcD72j3vtfYhw+Rg0o2gsZiwL5kffYIYKu6x4dsy5gBbFkC9pnAzeV4/QH4cov3cSlgvTL9UeDbZXoo2Rd+4wVc/47AdODjlbTLyBLu4Pn5bbX7BSxdgsIKlbRBwOFke9UWJW1j4LsdjsWzwG6VtF+QFydtPQa9+XuqHO+Vy/xHgBtL4F1jLsu3LJ8Drqoq8gj/CfiipN3Jq+ihksYCiwPHA3s22jzIQNMlpYXJH+JlEXEiWfI4EXgG+KekDSS9s7F8T+5XD/s/4EsRcV5pz9gJGAw8DfwE+JqkLaB2m9CrZEnvg+RYYy8Ci0fE+4DNJI2Y14d72MrAjyWtT15Bb1bSdyFLis/P74olDQGOBX4UEb+UtIikI8s2VyKrxD5S89i1XUQ8T1ZPPQlvPCfn2+Q+/Rq4oSx3U0R8rSwzjiyNXBUR55ZjsQY5wvVbgOmS3luWben/RG//nirH+/GStDIwPSL+FBEzyznnAEn7leVb1rlnwN4AWE7gPwKuAw4kf8h/By4hSwqfBz4bEc9LWgZYJSL+1o31bgd8AzgN2IMsnv+DPPlOAzYBvhgRv+vpfeppJWjsRf5glyefi3IfGWy3AU6IiItqrG8tYDfgPcA5EXG+pCvJq//7gIkR8Z/K8mrmP22p1z6a/B1sQl4krEdeGd9eToxLRsRz87HuVSPiH+Vi4ltkKevCiLhW0m7Ax4GdIuKVntqfVirH5jiyqu9C4MbIoYHe+M5Kb7yfkRdP65OlksHkIKW7k6XQJ8j/t0si4qo27Eef+J7K/+LNwE/JEvFSZBX768Bk4IqIOL5V+ekzQ470tIi4U9K2wFrkFfAWEfEfSdeTPX5+WoLGsuTJ5TVJh0bEM52ts/zTXCbpaeAE4HHgmrL+L0fE1ZI2I6907+9OIGqzpZnzj74mWcc6OSIek3QysE6dlUXEXyV9l6wOXLIkjyev+u6AN4LLKxHxQLmCWigWoDG+M+W7uljS82T0B375AAAaWElEQVRgDLKqcpfy2xhEBrjDJH0+Ih6ouYnHyt8jyCvrs8i2FMjj+gAwRNJSEXFvs4NkEyxPHrNLKEED/qsE+n/AFyLiwtIp4gTgUrK9bA3gSPI3tQowTtKNEfFiC/cB+s73tAJZ1XsJ2ZljS2BVslptHNkpYZn5uciZL62qE+utL+Y0Xu9QvohvAz8HRpEnt+PJuv1NqFGHWD4/hgwc+5S0hRrvtXu/ax6fS8h67JV7aJ3rAn+h0q5BlmyOBW4j2zy+2OL93AU4skwvCrybvJI+rKQNm8/1rgN8AFikzO9U9nEz8op7OrBdu7/n+dy3JYCFu7Fco2ZjOWA1sgS+f4f3PkreDzKmmt7Cfen13xOwDNneeBrZNrg0cD5wPVlNuMTcjntT8tLuH19veAHvAi4ie3r8qvyAFyOL4j8iq2hUWb67DcKLkL0f3l/mFy9/lwO2I4vB6zf7S+6B47MiMKSH1/lOssi9Gnml/z2yQXIl8urqJmDnsuyGwLpN3sctyHaXcWSJcwpwUHlvT7I6YN0F+a7IarrpZHvANsDmwNvJHj1j2v09N/HYVv93NgUO7/D+V8mq4qPJKstxrcxfX/qeyBqAe4C9yvzngIcq55GPUOlt1azzyoBt4+hIOfTFV8gAMp28wl4c+HlE/Ln0nV4qIu4ty3eryFqK6IcDh0TEFZJWBC4gey09T/ZA+XZEDLjni0haJLJ6cHfyZP2RmNPwujbwAhlkv0V2aDg1mliVIWlzsupsPHBSRJwk6VNkgF+IvBA4PiIuno91DwOmAg+SbUQPl/X+gAzM10TEbT2xH73V3P5nJH2BPPntEBH3lf+XfckS4Avd+R/r4Tz2+u9J0jpk1+G1ySrkXSPiKkm7AL8kS+ynRMSFTctEO6Nnb3uRRe9FyTs0f0he6a4N/JYsHl5B1tl2d32NwLwt8EWy8fUM4JjKMmOB3wBD273/bTzuezKnO+wg5lTprQOcQ3Y2WKl6TJuYl42AA8v0J4AfAxPK/LvIqsfl5nPdKwKfBr5S5pcmr2L/Qp4AhjKfVWJ98UWW6qcAmzW+W/Jenx+1OV/d/p4av9U25PFtZIDbrsyPB+4k70dagezGu0eztj/guuPOS0S8RF7hvkiesG4nT1obAg9HxDbALuVOzu6sL8pV1uUR8cPIb3gh4LLSlW7RiLiFLKq/8ZzgXt5Vtxn+COwuaUJEvBYRr0vaADiYvG/kf8g6Z8oxbApJgyLif4GTJO1F3hD6CnBeWeStZD3zf0rjeS0R8QTZYeKzknYlq+aWJNtz7iW7b39X0nolPwPtdzCYLHk2upoOlzQSWnssuvk9Ha8cOuX1kr/av4cFzONDwI6RnXF2AQ4lqwB/Ellqv41mjgPXzsjeW19kyUPkD+bSknYp2RvoAGBsh+W7vAomA8aK5JAL7+/w3pCy3q8w5yaqXtvm0aRj/g6yU8KiZCnsbOALZDfOFcgeOpu0IB+Ll+/h+2TA+FBJ34gsLX6yzH8ceFfNdTdKoO8hb4B8Bti6pO1DVsedA1xNubu+v7/I+57+QpboLyFL+yuSjb2/Jns1Na6qdwNGtiBPXX1P08m20OuBrciLzX1oQ2mRLA39EfhEmV+4/DbPBN4zl+V7pITU9h9Ob36RVxaHleklyDvA/0p2Tx0OvL3jj60b69yunAQ3LsFpQ/Lu9evInkUPAO9r97636XgvQnbTvKgE0lGV9y7rGDiaEVzJbqbfAf4fWW3yANmzbgZZAhldvqcXyRECPjCf29kC2KZMf6YEpWpngKlU7tDuj6/KCXrDcqzfS17ZXwd8v7y3CTmW3AFkA/oC3dHfA9/TTyvf09jyW9iHbFeodSHRg3lcpvxdpATXk5gzMsbXyfvU9q4sP3iBt9nuH09vfpFVJNOBPcv8QmQ329HA38grw6/WWF/jH2Wzyo/xFOAYSr05eePhcWVbXXZ17G8vskv0frx5zLALgf8t0yOA0R2PaQ/nYVnmdMvcnGyD2Yi8svx/5IBz+5Nddu+hZk+bap7JUu0veXNPmOOBKe3+Ltr0/X8XOLsyP5K8sp/RCBq0qF2hk+/pw5W05Ur6FOC9HT/TwmPWOK/sRN5wOaEE4DPJUuyu5Xe6PXmD61/K/9F857XtP5Te/iLvObixnDAa3WmPBU4s028UE+t+2eUkeQvZWwuy0e0MYL8yv3i7979Nx3yRyvQFlaCxODnG0V1kD6w3jmWT8jGoMr0YecX5SIeT/G/JmwYX5Pe1S2X+ELIaZO0yvwmwYTP3sze9yJsCdy/TC5djPqMcp8UoA1C2IV/rdvjeVy0XDzcxpyvs0m0+dkOBT5fpU4Gplfc+QJaWppHDCQEsO9/bavcPpS+8yDaIJZnTk+LdJbIvTfb8+eB8rncVchiBNYBh5crgDPIqawR5V+satKnnRrtfJUjcXaaXJa9G/xfYmTIQXXmvqceHbLRtlDQml7RBZMnxHEqVGnN6g83vfR6Hk1e1mzfWR1aH3ASMb/f30aLvfAdyNNhPk+0eM8oxWISsdjmTbANpWxAtQeOz5X/3NbLG4A/kRWBbqqsqeWuUPn4NrFj5HR1AVrtOKGmblP+l+WpLG7BDjtQREbMlLQ6cI+lXEXFa6eWxYUT8nrwCnp/1PlqG4Dib7L1zN3myfI78UZ4TETN7Zi/6pG9ExIOSlgMOIoPpxyLHFvoP2evlnIh4TvkUx7Ui4i89mYHyPX+IrEK8ANhO+QyRdcgLin8CC0k6nOxtdXNETK07NIWk1cnuvicBd0n6InkxcQ1ZxXC5pDsi4v6e3L/epByzSyQ9TAaJseRFQqN96e3ARZG9ntqVx0XJC7xtyLu2b4+IQyQtTwa3f83r860gaSmyu+52ki4mO5h8khwZ4Szlw+X2IcfmO1TSaxFxRa2NtPsKoy+9yLud7yQbxJ6g0uC0gFcHb6f0FiGLmw/w5ns93ksfGqakh4+5yCvxy5hT4lsTOJfSvkRWaQwluzUv1oQ8jGBOr5odyYbrScCnyBNbI387kzeNzddzJijPuCB7dd1O9s2/hQyaF9PHnumygMd8M+bc3/EZclTm6nD+36XJownMI29jyv/khsC5lfRR5QT9eeaUGts1JPu7yFqRi8gL0sawRxuSAe/QMj+W7PCzfq31t/sH0tdeZLfRb1Oe4dHD616ULGIeW0l7D9k19FQqde4D6UVeea9SptcgB8s7ihxHaynyxsx3Nfv4VAJ99SFGZ5B96BuN6YdU/ilr54esEr2U8uwKsm76YeCoMr8QC1gl1ttfvLlRekWyjXGbStp3yJtm2z38x6bA98r0BiXI/5YsId5C3mfRzvytTNZcTCzzjaDx7Q7LfYvKs1K683JVVU0RcY+ko6IJI7aSJ4Uge2tRbgT7CFkEPhjYVNJTETGjCdvutSJiFoCkt5NVGI+RV1PPkifuh4F/RcRrjc80YxTTyvqeLtt4B9mI/ZkyP4IcbXlqWW4Jaj7XIyJelHQa8O1y89s4csTWX0v6H3I8pyckXR79dJiaDt/b8uV1H4Ck48k2p6MjhwJasXzmiTaMXLsIsETJw1fJqsztyaHOP0aekKeUfK8MPBMtHJ49Ih6XtFVEPC1pU/Lmyjsj4oiSJ5Gl9bHk/1K3OXDMhyYFDSLiZUnnkc893oq8up5FBo13kkM/3ylpckTc2geH4l5QL5BB4lzyuEwiR1o9AXhU0ruA/0TEPRFv3LXf48enss7nyeemjyJPIhsC/ybbI94N/F7SNhExrbvrLnm+VDk0/9fJNpQzyVLXEeRjTaeSdy4/2N8vIiIfWPR18o76NcmLhoOBpSSdSnZUCUnHRMRlLc7btZJuIDtK/Jv8ni4gO0wE2WUfSTuTJckHJd3QrPNHJ3l8urTPfovs3v/JkieV/5G3lrzfXXfFfvWSF3OqQt5FNpL/hPzBbUH2tjkZ2Js8eWzd7vy26Rgtwpwxjn5IPtthW/Jej+vIKoL9W5ifj5H3GVxDVimOJu//eZqsYjmNMtor2aWz2z3AyLav95C9926r7hfZrnIOc7k7uD++yKqgSWTbwkrkvU7HkL3tNiI7qDTaRIbSwq7sZXvXABuV+ZGULtXA+8gnX14GLFreb8e9HuuS93TsUuaXKL/TS8lOOLXW59Fxe6kyAuZDZD3qx8mrzGsj4hFJnwXeEhGHlmVXAp6PiJfbluEWkrQq8CVyKPaFgMPIbrsnSXoL2eZxRNR4OuF85qN61fZqZG+vNcl7e44o+dmErL5YlGw8/VREdNnzRpUHWCkfrXpIROxU5keSo7UuSQ7Ed2cTdq/XkbRcRDwj6UvAh8l2jxfLex8iSyPDyB5FtwG3RqX6ssl524FsezkuIn6ufGLfesD7yV5NY8iur3eXXnn3RsQ/W5G3Sh7fSQ6jcwVZPTUbeDFKKaTWuhw4eq9Sd/pjMmhcFtl9dxHyn+IUsspmO3L4kz9FxHXtymurlQEJX5P0DWCDiNix8t6awGMR8XQlrenVesrnl99EDoZ3SiWwHEVWr3wxIn7U3bwoH2e6JlkldgNZulmavMJen+xS+TrZQWCpclLq99WXkj4HvBwRkyQt3rhgkvQB8sr+UvJGuFr19guQn8b3vC3ZTfdLZNXyR8m7y79JlozeQ5ZCh5IN+79oRf465PVtZEloKBm8/ne+VtTqIpNftYuYw4BVy/SiZHfgM8v8DuQ9H1dUlu+XPW3mcXz2ozwtkFIVUDlWe1B6lLTi2JAN2Z/vkHYgeR/Cp8p3994a6xtMVlFuSp6IziZLMxeSPWY+RF5U7E+Om7VDu7+PFn3nW5J18ltU0saSJ+hJZEm9pT2umHMRPqj8/RpZKlyz8T75jJGT6QdjkLlxvJeLOT2KFiEb266KiInlqnoccCWwhqS1IuKvwGbK55k/0r5ct9Q1wHmSXoqIn8AbVXcCLgcuLFelJ0X5D26GctV5FXl3eSPtK2Sb1CciYrqkm6nRCBnZsHksWbK8gixdzCQbOtciq+peK+m7AFdKmhE55Ha/FRFXS/oacKCkP5IN5DsBL5EdClYGVlSTnlffSZ4av62QtAJZvfzDyOeULwV8kGzvmBkRT0p6a0T8vRV5awY/j6Pv2Igcs2lipVsqkfXeOwBvKz/QZ8gG2gEhIu4h/0nHAkg6iHzc72TyQUy7AVs3um1KWljSsk3Ix5uCUuk6uwNZrz29VK3dFRFR6r+7u94ZZH3+fWSJ41vlhLMReW/P9uRNZ1uQnSZa1mOnXRo9z8h7D14hbxZ8FrgwImZHxF3Aja0KGlUR8Xrk8zAOBcaWLtpbkFVYd0TECaWN8gflN9Inn7viwNEHlH+U6yNit5J0CBkgDizzj5H13OcB95d/qgGjnCgmltk1yG6RnyP/YY8lu7S+UEpt6wOnSlqjydn6N1mvvUrJ4xuNtHVPaBFxX+QDeq6pXKWuQLZrPEoGzsPJEZYflrR16SLcL1WC9POSliED9NMRcXtlmbYF0PL/ejF5k+pGZFvHnyOHKvo8GexfA34oaXwzS8LN4sDRBzR+WErDySLvhRHxb+U4TluTQymfC7xYet0MNK+Xk8h6wBqluuYYsh3gj5ENqKPJBuVHo8ljgEXEg+SJY8cuFp1fVwKfkrR55PhVo8mxuzYlLyxOlLQZ9M0r2u4oV/fPAScCm0sa3HGZOqW7HsxXlL//INu1pkfE6aVR/93ko3H/H3lfTmOcqz7Fvar6IEnbkSfEE8gf4kRgUkRcKOlgsn/2CRExYyD0sqkqN959gSyBrQ+cERHHKu/C/zLwQER8syzbtGPTol5c25N3ku8TEX9SPtJ4G7I0ci45YOIeEXFTM/PRLh26LL8FeDIi/t3J+/sB10fEHW3K6wHkvShnkRcyz0vanBxG/mPRjS7avYkDRx9T6fr3FvLq+stknf6lZDfALcn+2W8ln6LWL4elmBdJQ4DfA7+IiO+W+yC+QlbjNT1otELld7BaRDxQgsY4cnTWEyKHLjkOeBC4IHKE5z69z3MjaSjwbES81CFQVKc/Q/Zqu5282e3GVh8LSeuS3eZvKEFjY7Lzxj4R8StJi5G9rR7tC9+TA0cfVq6iVye7Zx5Adt29LiKmlHsKjiTvFG1Jf/beRNIKpffKBmR31Yf6S9DoqPTiOYa8YDg6cuiaXcnxvM4mh8S4MCIub2M2m0I5HP3xwF6lF9ogsraoETQOIEvlD5L3P51GPvr1j23I60IR8XqpQryIrDa9lny2x4bkgI5HR4uHTpkf7o7bh0XEdGC6ciyf1YBLKw3j25J3Fv+7s8/3ZyVoLEY+EOhf/TVowBv7ejzwSAkau5BVWDtHjnu1HTm21S3AE/1p/yPi/yT9ELhA0sci4pnGe6U79JrkSLrXRz63ZRtgsKShEfFYG0oeg8mu1V8mg8cp5FD8PwIeJ7+nx6LG+Gbt4MDRP0whn9dxKbzxD7MV+bSvl0uXwNcjYlZ/PHF2JiJekXRIRMyG/hk0GhqN/ZJ2I9s29oyIy0qHgSHAlX2tHr27IuIPkl4HLpW0feSwJCPJ8Zl+S5bCX5C0JdmJ5CXgC5JObGUprJSCnpa0dkT8XdIHyZEALouIu+GN0QIOkHRgRDzeqrzV5V5V/UDkaLCN4ZsPIYdi/ywwRNKR5BXXRZJ26K8nzs4MhKDRwT/J8bAuk7QkeePZBuRQF0haprQL9KveVhFxA3lv02dLldCD5FDnV5WgsQVZpXVARHyZHFfqIEkrtuo4lF6Rg2JOl+oxwFOVoLEf2YX7UvKJoL2W2zj6idLt8G1k8fez5PDj+wNPkk+Pe50cbfcLMUAGxRtoqsFReTPoduSDoKaRo7VOJm8S3IQcjK/e40L7AElLRhn4sJL2QbJt49iI+GkJFD8ABkfEXpXlWnJxobyfaGPyKaLnkU843IW84/035BBCLzU7HwvCJY5+IrJP+wPA+yIHLtucHMzsgoi4lxy/ZwXgc6VHlvUzHU56S5OljTvJBvKLmHMH+p7k8y3Wa3Uem61j0CheIZ/U99My/02yu/b3ACSdLGmLFpZIVyXbNpYjA8ZxZG/IC4GpvT1ogNs4+pVyxdRoHHwHOezC/cohNj5NPpz+arIRzvqx0vD7ndJVdz/y+SDLkYMiPkdegbdkyPF2K9VYNwAoRypemeyWe7ekHwP7kiMLvBwtuOclIh4qbVHnA38hO7AcSP6/9vqgAQ4c/Urp178meQ/Hb4BzJc0Edia7ap5Ddst0/eQAUEqgkFfc00pd/55k9chIYD1JY4GXok03xrWScpiZYcDZEXF9CRrjyOHOg3xy48ci4uZm5yUi7lAOAz8U+FtfCRgNbuPoZ5QDIJ4LfJ4sjn+JHGL8NGBKtHEMH2sPSe8nRxnYJyJuKVWVg4GnyGe2PwqcFhF/6e+dCCQtHxFPSTqTvHdivdLzcEnyru4TWlHqmEu++tRxdxtHPxM5btFEcuTUr5NtGycCFztoDEyRD/g6DDhZ0kcjnzy3HNnL6FlyqPbJpStrnzl5zY8SNNYhn8w3tgSNVciBCF8ibxR8Q6t6XPW14+4SRz+lHHZjMDnMxoCoy7a5a1zNlhPmw+RYZvuR9eu/i4i/lTr3zSPis+Uzy5CPI+6XJ4jKXdyrkKMob0c+ZOlBspfT0uSjmvvd3fY9wSWOfiryuQT3OWhY4+QfOfz86+Rdy38FLo+Iv5XFPkGWPih3V+9KXpX3W5IWJXuefYwsoc8iuy4vRpbUj5b0jrJsv7nnpSe4xGE2wJS7qheu3G1+KbBsRGxeGsuvAu6OiI3bl8vWUI6mvBbZXflm8rHMJ5f3diPvifpmRFzfvlz2Pu5VZTbAlLuqAZB0DXkBubnyuRDbALcAI8rQGN1+1G1fU6rwbgduLx0Gbo+IkzVnZN3x5L1Q/fpRvPPDVVVmA5SkMcAyEfGB0qbxDWDNiBhH1vmv3tYMNllp91lI+XC0hYAtlA/CWlrSYeQD09Yr9134XFnhqiozQ9IR5GN3vxL55LoBQdIS5KCQvyRvkvw4GTBWIUdhuL9SArHCUdRsAJM0SNJKZLXMryPiH2WE1gGh3Hh3Mjl21ZbASsDiwCYOGp1zicPMGo+h3QX4akQ81u78tFrpPbUD8Dzw2xJAHTQ64cBhNsBV7vNYdSBVU82Lg8a8OXCYmVktbuMwM7NaHDjMzKwWBw4zM6vFgcPMzGpx4DCrkHSYpK80cf2DJX2uWetv9XZsYHLgMGuRcmPdYKAVJ/RWbccGIAcOG/AkfUPS3yTdCKxZ0laX9DtJ0yXdIGmtkj5Z0mmSppXPbF/SR5bl/lxeG5f095f0KcDdwLHA6pJuk/S98v4fJF0s6X5Jx0raXdItku6QtHpZzxBJv5F0a3ltUtIPkzRJ0nXl818ou/Wm7bTyeNoAEBF++TVgX8B6wB3AksCy5NPwvgJcDYwqy2wAXFOmJwO/Iy+6RgGPkENULAksXpYZRT7jG/KZFi8Aq5X5kcCdle2/nxwjaRXyORCzgMPLewcAPyzTvwQ2LdNvBe4p04cBN5XPrgQ8ASzScTt++dWTrwEzJo1ZJzYDLoqIFwFKyWBxYGPg15Xn9yxW+cz5kXcV3yfpfvJ5Dg+Qj2YdDbxGPmWv4ZaIeGAeebg1Ih4t2/8/8nkYkAHtA2X6g8DalfwsK2npMn1ZRLwCvCLpcWBot/febD44cJj9t4WApyNidCfvdxxuIYAvAY8B7y6ff7ny/gtdbO+VyvTrlfnXmfM/uhCwYURU10sJJNXPv4b/r63J3MZhA931wIclLVGeSbED8CLwgKSdIcdyKk+Ka9i5PMdhdeDtwL3AcsCjpSSyBzCok+09BywzH/m8Cvh8Y6aUbOZlfrdj1iUHDhvQIuLPwK+A24ErgFvLW7sDe0u6HbiLHHa84e/kU/KuAPYtpYAfAxPK8mvRSSkjIp4A/ijpzpqN1l8AxkiaIeluYN8u9mt+t2PWJQ9yaFaDpMnApRFxQbvzYtYuLnGYmVktLnGYmVktLnGYmVktDhxmZlaLA4eZmdXiwGFmZrU4cJiZWS0OHGZmVsv/B/Sws3beOyu9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941e32f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Employee distri\n",
    "# Types of colors\n",
    "color_types = ['#78C850','#F08030','#6890F0','#A8B820','#A8A878','#A040A0','#F8D030',  \n",
    "                '#E0C068','#EE99AC','#C03028','#F85888','#B8A038','#705898','#98D8D8','#7038F8']\n",
    "\n",
    "# Count Plot (a.k.a. Bar Plot)\n",
    "sns.countplot(x='department', data=df, palette=color_types).set_title('Employee Department Distribution');\n",
    " \n",
    "# Rotate x-labels\n",
    "plt.xticks(rotation=-45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "199dbab9-5628-43d0-b82c-0f061de15fcd",
    "_uuid": "851c00d7a5a61128b2a3928967b8ba3c8f138d1d",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941df48390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.countplot(y=\"department\", hue='turnover', data=df).set_title('Employee Department Turnover Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c070de56-b7b3-4b09-a773-4e4a34910d6e",
    "_uuid": "fe0a392af640faeb8e015ff7f1189e65fa8b82ee"
   },
   "source": [
    "##  3f. Turnover V.S. ProjectCount \n",
    "***\n",
    "**Summary:** This graph is quite interesting as well. Here's what I found:\n",
    " - 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\n",
    " \n",
    "**Questions to ponder on:** \n",
    " - Why are employees leaving at the lower/higher spectrum of project counts?\n",
    " - Does this means that employees with project counts 2 or less are not worked hard enough or are not highly valued, thus leaving the company?\n",
    " - Do employees with 6+ projects are getting overworked, thus leaving the company?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "d46616dd-a54d-4c50-ad5e-25dd6f3adb71",
    "_uuid": "496f694864d11a9fb03be8fe0ca9eb716801be59",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Percent')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941e3a1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {
    "_cell_guid": "b94a2476-5042-4d95-ac00-cab1fe478923",
    "_uuid": "5d6fa0ad02bf997ec83c5f9310d8e5d44735c48a"
   },
   "source": [
    "##  3g. Turnover V.S. Evaluation \n",
    "***\n",
    "**Summary:** \n",
    " - There is a biomodal distribution for those that had a turnover. \n",
    " - Employees with **low** performance tend to leave the company more\n",
    " - Employees with **high** performance tend to leave the company more\n",
    " - The **sweet spot** for employees that stayed is within **0.6-0.8** evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "c83e7515-15b7-42c5-b4a2-79d4b7c40086",
    "_uuid": "ad1e101b036b2bd2ac1ee0eb148b46ea8978ef3c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Employee Evaluation Distribution - Turnover V.S. No Turnover')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941dd77860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kernel Density Plot\n",
    "fig = plt.figure(figsize=(15,4),)\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 0),'evaluation'] , color='b',shade=True,label='no turnover')\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 1),'evaluation'] , color='r',shade=True, label='turnover')\n",
    "ax.set(xlabel='Employee Evaluation', ylabel='Frequency')\n",
    "plt.title('Employee Evaluation Distribution - Turnover V.S. No Turnover')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "23cfc886-46ef-4fbd-891d-3b1f20c42edb",
    "_uuid": "f9ecb9833eba01293ce72c97885092181adb91bd"
   },
   "source": [
    "##  3h. Turnover V.S. AverageMonthlyHours \n",
    "***\n",
    "**Summary:** \n",
    " - Another 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**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "856a761b-b0a1-46e2-a751-58ad923bd5b3",
    "_uuid": "7e431f421027da4cd00322c0f206ce2840cc42a3",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Employee AverageMonthly Hours Distribution - Turnover V.S. No Turnover')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941dd33be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KDEPlot: Kernel Density Estimate Plot\n",
    "fig = plt.figure(figsize=(15,4))\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": {
    "_cell_guid": "e3c6f8ba-37de-4af9-b3bc-d41d8b55f7c5",
    "_uuid": "1e1de25645e5ebd949b5e36c528a755594ea4c85"
   },
   "source": [
    "##  3i. Turnover V.S. Satisfaction \n",
    "***\n",
    "**Summary:** \n",
    " - 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": 23,
   "metadata": {
    "_cell_guid": "be7ff8b9-1523-43d4-8243-1443cb3b8186",
    "_uuid": "68bd32416c8feaf927c20bd17beaa8133e8836c6",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Employee Satisfaction Distribution - Turnover V.S. No Turnover')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941dcabb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KDEPlot: Kernel Density Estimate Plot\n",
    "fig = plt.figure(figsize=(15,4))\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": {
    "_cell_guid": "5c692223-97b5-49e0-91b6-110b14267dad",
    "_uuid": "82a9fc8e7ca4c2d4d7f89921bbf25863ab1b5e0b"
   },
   "source": [
    "##  3j. ProjectCount VS AverageMonthlyHours \n",
    "***\n",
    "\n",
    "**Summary:**\n",
    " - As project count increased, so did average monthly hours\n",
    " - Something weird about the boxplot graph is the difference in averageMonthlyHours between people who had a turnver and did not. \n",
    " - Looks like employees who **did not** have a turnover had **consistent** averageMonthlyHours, despite the increase in projects\n",
    " - In contrast, employees who **did** have a turnover had an increase in averageMonthlyHours with the increase in projects\n",
    "\n",
    "**Questions to ponder on:** \n",
    " - What could be the meaning for this? \n",
    " - **Why is it that employees who left worked more hours than employees who didn't, even with the same project count?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "7af16e4e-4ede-40cf-a9aa-480ddae10861",
    "_uuid": "8111e8f9f4a45ae68a0d81bdf20ef0f35563514c",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f941e037cc0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941e1d7a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ProjectCount VS AverageMonthlyHours [BOXPLOT]\n",
    "#Looks like the average employees who stayed worked about 200hours/month. Those that had a turnover worked about 250hours/month and 150hours/month\n",
    "\n",
    "import seaborn as sns\n",
    "sns.boxplot(x=\"projectCount\", y=\"averageMonthlyHours\", hue=\"turnover\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c13c46d9-10d9-4bc6-83ff-4e8f18e6f51d",
    "_uuid": "795734acfd702fab1d2a861d1fb9a4c37ba67f55"
   },
   "source": [
    "##  3k. ProjectCount VS Evaluation\n",
    "***\n",
    "**Summary:** This graph looks very similar to the graph above. What I find strange with this graph is with the turnover group. There is an increase in evaluation for employees who did more projects within the turnover group. But, again for the non-turnover group, employees here had a consistent evaluation score despite the increase in project counts. \n",
    "\n",
    "**Questions to think about:**\n",
    " - **Why is it that employees who left, had on average, a higher evaluation than employees who did not leave, even with an increase in project count? **\n",
    " - Shouldn't employees with lower evaluations tend to leave the company more? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "f71fe174-dd36-43a2-b882-99976b1fbb8d",
    "_uuid": "8cb12be2d8182a91b87bb91a11edf9864677976d",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f941db4cef0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941dd2a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ProjectCount VS Evaluation\n",
    "#Looks like employees who did not leave the company had an average evaluation of around 70% even with different projectCounts\n",
    "#There is a huge skew in employees who had a turnover though. It drastically changes after 3 projectCounts. \n",
    "#Employees that had two projects and a horrible evaluation left. Employees with more than 3 projects and super high evaluations left\n",
    "import seaborn as sns\n",
    "sns.boxplot(x=\"projectCount\", y=\"evaluation\", hue=\"turnover\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9620f86f-5620-48fd-b2c3-99268e58e2a7",
    "_uuid": "0713ae7edc3f945e5446fa8b52ee3314180b4eac"
   },
   "source": [
    "##  3l. Satisfaction VS Evaluation\n",
    "***\n",
    "**Summary:** This is by far the most compelling graph. This is what I found:\n",
    " - 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": 26,
   "metadata": {
    "_cell_guid": "b8f87f7f-4914-43a0-9874-61faef17311f",
    "_uuid": "44d6e53bb0793ab87470bdcb821ff9c1315227a6",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f941e32f5f8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941e35d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='satisfaction', y='evaluation', data=df,\n",
    "           fit_reg=False, # No regression line\n",
    "           hue='turnover')   # Color by evolution stage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "78601de6-6ea0-4e54-9f9e-c1f2b8b7fbc6",
    "_uuid": "262cca697d2a9692014de75e5dfa9f815d64a753"
   },
   "source": [
    "##  3m. Turnover V.S. YearsAtCompany \n",
    "***\n",
    "**Summary:** Let's see if theres a point where employees start leaving the company. Here's what I found:\n",
    " - More than half of the employees with **4 and 5** years left the company\n",
    " - Employees with **5** years should **highly** be looked into \n",
    " \n",
    "**Questions to ponder on:** \n",
    " - Why are employees leaving mostly at the **3-5** year range?\n",
    " - Who are these employees that left?\n",
    " - Are these employees part-time or contractors? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "e107272c-1ab1-4aed-a7cc-75135fede617",
    "_uuid": "0bcea16190679921613a3aaf7e8bb1fd4dc9cec4",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Percent')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941dc46eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=\"yearsAtCompany\", y=\"yearsAtCompany\", hue=\"turnover\", data=df, estimator=lambda x: len(x) / len(df) * 100)\n",
    "ax.set(ylabel=\"Percent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2c6573d7-83b6-49a5-8b91-e769bdcafe35",
    "_uuid": "115172607d3265eca2e443bacf0a5a035a5021f9"
   },
   "source": [
    "## 3n. K-Means Clustering of Employee Turnover\n",
    "***\n",
    "**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 \n",
    "\n",
    "**Clustering PROBLEM:**\n",
    "    - How do we know that there are \"3\" clusters?\n",
    "    - We would need expert domain knowledge to classify the right amount of clusters\n",
    "    - Hidden uknown structures could be present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "18b5345f-d8bd-4651-bf9d-83ea9f31b44e",
    "_uuid": "4c56e8854ca95e95a632731a9dc98e186c95602f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f941c16d048>"
      ]
     },
     "metadata": {},
     "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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "07c80149-7a46-403c-b64a-ab536de68d01",
    "_uuid": "6bb2ec011a232193b96ff01e3852bef48a712e88"
   },
   "source": [
    "# Feature Importance\n",
    "***\n",
    "**Summary:**\n",
    "\n",
    "By using a decision tree classifier, it could rank the features used for the prediction. The top three features were employee satisfaction, yearsAtCompany, and evaluation. This is helpful in creating our model for logistic regression because it’ll be more interpretable to understand what goes into our model when we utilize less features. \n",
    "\n",
    "**Top 3 Features:**\n",
    "1. Satisfaction\n",
    "2. YearsAtCompany\n",
    "3. Evaluation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "a5211dc8-0597-488a-957f-d5f0712b3a42",
    "_uuid": "1e07c422676ee87d60be433746896bb7c3a63946",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94140ff710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['figure.figsize'] = (12,6)\n",
    "\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",
    "                        })\n",
    "\n",
    "# Convert these variables into categorical variables\n",
    "df[\"department\"] = df[\"department\"].astype('category').cat.codes\n",
    "df[\"salary\"] = df[\"salary\"].astype('category').cat.codes\n",
    "\n",
    "# Create train and test splits\n",
    "target_name = 'turnover'\n",
    "X = df.drop('turnover', axis=1)\n",
    "\n",
    "\n",
    "y=df[target_name]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.15, random_state=123, stratify=y)\n",
    "\n",
    "dtree = tree.DecisionTreeClassifier(\n",
    "    #max_depth=3,\n",
    "    class_weight=\"balanced\",\n",
    "    min_weight_fraction_leaf=0.01\n",
    "    )\n",
    "dtree = dtree.fit(X_train,y_train)\n",
    "\n",
    "## plot the importances ##\n",
    "importances = dtree.feature_importances_\n",
    "feat_names = df.drop(['turnover'],axis=1).columns\n",
    "\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Feature importances by DecisionTreeClassifier\")\n",
    "plt.bar(range(len(indices)), importances[indices], color='lightblue',  align=\"center\")\n",
    "plt.step(range(len(indices)), np.cumsum(importances[indices]), where='mid', label='Cumulative')\n",
    "plt.xticks(range(len(indices)), feat_names[indices], rotation='vertical',fontsize=14)\n",
    "plt.xlim([-1, len(indices)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ccf20c1a-d8da-4001-90f3-ae314d1f87a0",
    "_uuid": "90367f2beb5a2313ca2eee5d7f78d4afd49fc0d2"
   },
   "source": [
    "# 4a. Modeling the Data: Logistic Regression Analysis\n",
    "***\n",
    "**NOTE:** This will be an in-depth analysis of using logistic regression as a classifier. I do go over other types of models in the other section below this. **This is more of a use-case example of what can be done and explained to management in a company.**\n",
    "\n",
    "Logistic Regression commonly deals with the issue of how likely an observation is to belong to each group. This model is commonly used to predict the likelihood of an event occurring. In contrast to linear regression, the output of logistic regression is transformed with a logit function. This makes the output either 0 or 1. This is a useful model to take advantage of for this problem because we are interested in predicting whether an employee will leave (0) or stay (1). \n",
    "\n",
    "Another reason for why logistic regression is the preferred model of choice is because of its interpretability. Logistic regression predicts the outcome of the response variable (turnover) through a set of other explanatory variables, also called predictors. In context of this domain, the value of our response variable is categorized into two forms: 0 (zero) or 1 (one). The value of 0 (zero) represents the probability of an employee not leaving the company and the value of 1 (one) represents the probability of an employee leaving the company.\n",
    "\n",
    "**Logistic Regression models the probability of ‘success’ as: **\n",
    "<img src=\"http://i64.tinypic.com/m95a9k.jpg\"/>\n",
    " \n",
    "The equation above shows the relationship between, the dependent variable (success), denoted as (θ) and independent variables or predictor of event, denoted as xi. Where α is the constant of the equation and, β is the coefficient of the predictor variables\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "f409a259-99cc-4776-aab6-8da342c35494",
    "_uuid": "6dc886150a8f6a2d0867d9a40d57db38cf087b5c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>yearsAtCompany</th>\n",
       "      <th>int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9003</th>\n",
       "      <td>0.59</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10691</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.93</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.91</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13572</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.48</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction  evaluation  yearsAtCompany  int\n",
       "9003           0.59        1.00               3    1\n",
       "5697           0.81        0.98               2    1\n",
       "10691          1.00        0.93               2    1\n",
       "1884           0.87        0.91               5    1\n",
       "13572          0.87        0.48               3    1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "%matplotlib inline\n",
    "#Read the analytics csv file and store our dataset into a dataframe called \"df\"\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "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\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "df = pd.DataFrame.from_csv('../input/HR_comma_sep.csv', index_col=None)\n",
    "\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",
    "                        })\n",
    "\n",
    "# Convert these variables into categorical variables\n",
    "df[\"department\"] = df[\"department\"].astype('category').cat.codes\n",
    "df[\"salary\"] = df[\"salary\"].astype('category').cat.codes\n",
    "\n",
    "\n",
    "# Move the reponse variable \"turnover\" to the front of the table\n",
    "front = df['turnover']\n",
    "df.drop(labels=['turnover'], axis=1,inplace = True)\n",
    "df.insert(0, 'turnover', front)\n",
    "\n",
    "# Create an intercept term for the logistic regression equation\n",
    "df['int'] = 1\n",
    "indep_var = ['satisfaction', 'evaluation', 'yearsAtCompany', 'int', 'turnover']\n",
    "df = df[indep_var]\n",
    "\n",
    "# Create train and test splits\n",
    "target_name = 'turnover'\n",
    "X = df.drop('turnover', axis=1)\n",
    "\n",
    "y=df[target_name]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.15, random_state=123, stratify=y)\n",
    "\n",
    "X_train.head()\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b6e2fa8c-2481-41d9-8944-f5cf517722f9",
    "_uuid": "eeddb19d412d48cf4991817acf113ebadf494ed6"
   },
   "source": [
    "## Using Logistic Regression Coefficients \n",
    "***\n",
    "With the elimination of the other variables, I’ll be using the three most important features to create our model: Satisfaction, Evaluation, and YearsAtCompany.\n",
    "\n",
    "Following overall equation was developed: \n",
    "\n",
    "**Employee Turnover Score** = Satisfaction*(**-3.769022**) + Evaluation*(**0.207596**) + YearsAtCompany*(**0.170145**) + **0.181896**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "bbfc7a7b-7123-4e4d-a8f6-87c306c465dc",
    "_uuid": "1d629bd3599a51c1b6c013a2217a809c2717843c",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.467233\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "satisfaction     -3.769022\n",
       "evaluation        0.207596\n",
       "yearsAtCompany    0.170145\n",
       "int               0.181896\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "iv = ['satisfaction','evaluation','yearsAtCompany', 'int']\n",
    "logReg = sm.Logit(y_train, X_train[iv])\n",
    "answer = logReg.fit()\n",
    "\n",
    "answer.summary\n",
    "answer.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9892e948-68f9-46ca-992f-b5fb8e1d5357",
    "_uuid": "fe172c172253338976af341443178d4fcadd31aa"
   },
   "source": [
    "## Explanation of Coefficients\n",
    "*** \n",
    "**Employee Turnover Score** = Satisfaction*(**-3.769022**) + Evaluation*(**0.207596**) + YearsAtCompany*(**0.170145**) + **0.181896**\n",
    "\n",
    "The values above are the coefficient assigned to each independent variable. The **constant** 0.181896 represents the effect of all uncontrollable variables. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "296b9b41-70d6-462f-bca4-35c5212760d8",
    "_uuid": "2583480b6174d82b7b2608eb38dd43be9ff086a6",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14431462559738251"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create function to compute coefficients\n",
    "coef = answer.params\n",
    "def y (coef, Satisfaction, Evaluation, YearsAtCompany) : \n",
    "    return coef[3] + coef[0]*Satisfaction + coef[1]*Evaluation + coef[2]*YearsAtCompany\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# An Employee with 0.7 Satisfaction and 0.8 Evaluation and worked 3 years has a 14% chance of turnover\n",
    "y1 = y(coef, 0.7, 0.8, 3)\n",
    "p = np.exp(y1) / (1+np.exp(y1))\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b9e181aa-2602-4b05-8b6c-010e873ecce4",
    "_uuid": "ad6e5b490b5617f6f5e8a1be96cbd84757c8e5be",
    "collapsed": true
   },
   "source": [
    "## Intepretation of Score\n",
    "***\n",
    "If you were to use these employee values into the equation:\n",
    "- **Satisfaction**: 0.7\n",
    "- **Evaluation**: 0.8\n",
    "- **YearsAtCompany**: 3\n",
    "\n",
    "You would get:\n",
    "\n",
    "**Employee Turnover Score** = (**0.7**)*(-3.769022) + (**0.8**)*(0.207596) + (**3**)*(0.170145) + 0.181896 = 0.14431 = **14%***\n",
    "\n",
    "**Result**: This employee would have a **14%** chance of leaving the company. This information can then be used to form our retention plan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b3269fcd-283b-43ff-bf67-be5900214c89",
    "_uuid": "1e506366a833f0a732716d1593d2c1cb7330fe5f"
   },
   "source": [
    "## Retention Plan Using Logistic Regression\n",
    "***\n",
    "\n",
    "**Reference:** http://rupeshkhare.com/wp-content/uploads/2013/12/Employee-Attrition-Risk-Assessment-using-Logistic-Regression-Analysis.pdf\n",
    "\n",
    "With the logistic regression model, we can now use our scores and evaluate the employees through different scoring metrics. Each zone is explain here:\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. \n",
    "\n",
    "So with our example above, the employee with a **14%** turnover score will be in the **safe zone**. \n",
    "\n",
    "<img src=\"http://i64.tinypic.com/somk9s.jpg\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a319378d-ce76-4057-a30c-f4b20f91f5ea",
    "_uuid": "70a52b375eee3504b8b0e93d2f94d3d430ebf4e4"
   },
   "source": [
    "# 4b. Using Other Models\n",
    "***\n",
    "**NOTE:** I'll be using four other models in this section to measure the accuracy of the different models\n",
    "\n",
    " The best model performance out of the four (Decision Tree Model, AdaBoost Model, Logistic Regression Model, Random Forest Model) was **Random Forest**! \n",
    " \n",
    " **Remember:** Machines can predict the future, as long as the future doesn't look too different from the past.\n",
    " \n",
    " **Note: Base Rate** \n",
    " ***\n",
    " - A **Base Rate Model** is a simple model or heuristic used as reference point for comparing how well a model is performing. A baseline helps model developers quantify the minimal, expected performance on a particular problem. In this dataset, the majority class that will be predicted will be **0's**, which are employees who did not leave the company. \n",
    " - If you recall back to *Part 3: Exploring the Data*, 24% of the dataset contained 1's (employee who left the company) and the remaining 76% contained 0's (employee who did not leave the company). The Base Rate Model would simply predict every 0's and ignore all the 1's. \n",
    " - **Example**: The base rate accuracy for this data set, when classifying everything as 0's, would be 76% because 76% of the dataset are labeled as 0's (employees not leaving the company)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1614eec3-e1a9-4ed9-bf36-fb8cce0c5d49",
    "_uuid": "348716f3753c3029faad03cb6e871eca4c5fc04c"
   },
   "source": [
    "**Note: Evaluating the Model**\n",
    "***\n",
    "**Precision and Recall / Class Imbalance**\n",
    "\n",
    "This dataset is an example of a class imbalance problem because of the skewed distribution of employees who did and did not leave. More skewed the class means that accuracy breaks down. \n",
    "\n",
    "In this case, evaluating our model’s algorithm based on **accuracy** is the **wrong** thing to measure. We would have to know the different errors that we care about and correct decisions. Accuracy alone does not measure an important concept that needs to be taken into consideration in this type of evaluation: **False Positive** and **False Negative** errors. \n",
    "\n",
    "**False Positives (Type I Error)**: You predict that the employee will leave, but do not\n",
    "\n",
    "**False Negatives (Type II Error)**: You predict that the employee will not leave, but does leave\n",
    "\n",
    "In this problem, what type of errors do we care about more? False Positives or False Negatives?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4f3f59ff-8d4d-47a2-b8c5-6cda97a7bed1",
    "_uuid": "4e2ec8b4e73d53188dca0c4fe53e66e63c45a7c6"
   },
   "source": [
    " **Note: Different Ways to Evaluate Classification Models**\n",
    " ***\n",
    "   1.  **Predictive Accuracy:** How many does it get right?\n",
    "   2. **Speed:** How fast does it take for the model to deploy? \n",
    "   3. **Scalability:** Can the model handle large datasets?\n",
    "   4. **Robustness:** How well does the model handle outliers/missing values?\n",
    "   5. **Interpretability:** Is the model easy to understand?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a2b5b0b4-9e51-42bc-982b-2e85a4f30e3d",
    "_uuid": "cdebeb8304a7173fa3d548c3929df9f98299576d"
   },
   "source": [
    "<img src=\"http://i68.tinypic.com/qsts7k.jpg\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "928c8cd9-4ae0-4f7a-b029-dea37ea19472",
    "_uuid": "22f78fd2f10e6d9f87b83e74a2c89ef21c2051bf",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "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\n",
    "from sklearn.preprocessing import RobustScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "54285c25-10dd-403b-96da-ef7c4d23c9c3",
    "_uuid": "44d34f016c77d80e0cef016d52a744d63bd5079f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create base rate model\n",
    "def base_rate_model(X) :\n",
    "    y = np.zeros(X.shape[0])\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "52a6380e-2cb1-41d9-add1-7fbb7c11a447",
    "_uuid": "2e0c1dcc07bd95aad1bd21f8068625fa656866b1",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create train and test splits\n",
    "target_name = 'turnover'\n",
    "X = df.drop('turnover', axis=1)\n",
    "#robust_scaler = RobustScaler()\n",
    "#X = robust_scaler.fit_transform(X)\n",
    "y=df[target_name]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.15, random_state=123, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "5d51ef8f-edb2-434e-8eff-e381798cd134",
    "_uuid": "213db6b9c482d69e33b8914952bc45f9aa68bb52",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base rate accuracy is 0.76\n"
     ]
    }
   ],
   "source": [
    "# Check accuracy of base rate model\n",
    "y_base_rate = base_rate_model(X_test)\n",
    "from sklearn.metrics import accuracy_score\n",
    "print (\"Base rate accuracy is %2.2f\" % accuracy_score(y_test, y_base_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "9ad8cfad-6a99-4b9c-b71d-d14c9cf63cba",
    "_uuid": "1fa992b574e7e7206602d4eccb1df747a362f252",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic accuracy is 0.77\n"
     ]
    }
   ],
   "source": [
    "# Check accuracy of Logistic Model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(penalty='l2', C=1)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "print (\"Logistic accuracy is %2.2f\" % accuracy_score(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "981e3a3b-f3ac-4cae-806b-a79f4478557e",
    "_uuid": "087405fc49d296fe9670fe949f2fa44a0338bd28",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.793 (0.014)\n"
     ]
    }
   ],
   "source": [
    "# Using 10 fold Cross-Validation to train our Logistic Regression Model\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=7)\n",
    "modelCV = LogisticRegression(class_weight = \"balanced\")\n",
    "scoring = 'roc_auc'\n",
    "results = model_selection.cross_val_score(modelCV, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "print(\"AUC: %.3f (%.3f)\" % (results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2a9855af-4efd-4692-8b60-84d6c8ddafea",
    "_uuid": "ce1f37f6997bba100650cb23814faef02d9945ea"
   },
   "source": [
    "## Logistic Regression V.S. Random Forest V.S. Decision Tree V.S. AdaBoost Model\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "5a76ae8a-f8af-4241-aae5-c7cd5cced2ad",
    "_uuid": "3e3565867e9559ab635430bf2045927a5f56d31a",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Base Model---\n",
      "Base Rate AUC = 0.50\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.76      1.00      0.86      1714\n",
      "          1       0.00      0.00      0.00       536\n",
      "\n",
      "avg / total       0.58      0.76      0.66      2250\n",
      "\n",
      "\n",
      "\n",
      " ---Logistic Model---\n",
      "Logistic AUC = 0.74\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.76      0.82      1714\n",
      "          1       0.48      0.73      0.58       536\n",
      "\n",
      "avg / total       0.80      0.75      0.76      2250\n",
      "\n",
      "\n",
      "\n",
      " ---Decision Tree Model---\n",
      "Decision Tree AUC = 0.94\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.96      0.97      1714\n",
      "          1       0.87      0.91      0.89       536\n",
      "\n",
      "avg / total       0.95      0.95      0.95      2250\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " ---Random Forest Model---\n",
      "Random Forest AUC = 0.97\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.98      0.99      1714\n",
      "          1       0.95      0.96      0.95       536\n",
      "\n",
      "avg / total       0.98      0.98      0.98      2250\n",
      "\n",
      "\n",
      "\n",
      " ---AdaBoost Model---\n",
      "AdaBoost AUC = 0.90\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.97      0.96      1714\n",
      "          1       0.90      0.82      0.86       536\n",
      "\n",
      "avg / total       0.93      0.94      0.93      2250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compare the Logistic Regression Model V.S. Base Rate Model V.S. Random Forest Model\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "\n",
    "print (\"---Base Model---\")\n",
    "base_roc_auc = roc_auc_score(y_test, base_rate_model(X_test))\n",
    "print (\"Base Rate AUC = %2.2f\" % base_roc_auc)\n",
    "print(classification_report(y_test, base_rate_model(X_test)))\n",
    "\n",
    "# NOTE: By adding in \"class_weight = balanced\", the Logistic Auc increased by about 10%! This adjusts the threshold value\n",
    "logis = LogisticRegression(class_weight = \"balanced\")\n",
    "logis.fit(X_train, y_train)\n",
    "print (\"\\n\\n ---Logistic Model---\")\n",
    "logit_roc_auc = roc_auc_score(y_test, logis.predict(X_test))\n",
    "print (\"Logistic AUC = %2.2f\" % logit_roc_auc)\n",
    "print(classification_report(y_test, logis.predict(X_test)))\n",
    "\n",
    "# Decision Tree Model\n",
    "dtree = tree.DecisionTreeClassifier(\n",
    "    #max_depth=3,\n",
    "    class_weight=\"balanced\",\n",
    "    min_weight_fraction_leaf=0.01\n",
    "    )\n",
    "dtree = dtree.fit(X_train,y_train)\n",
    "print (\"\\n\\n ---Decision Tree Model---\")\n",
    "dt_roc_auc = roc_auc_score(y_test, dtree.predict(X_test))\n",
    "print (\"Decision Tree AUC = %2.2f\" % dt_roc_auc)\n",
    "print(classification_report(y_test, dtree.predict(X_test)))\n",
    "\n",
    "# Random Forest Model\n",
    "rf = RandomForestClassifier(\n",
    "    n_estimators=1000, \n",
    "    max_depth=None, \n",
    "    min_samples_split=10, \n",
    "    class_weight=\"balanced\"\n",
    "    #min_weight_fraction_leaf=0.02 \n",
    "    )\n",
    "rf.fit(X_train, y_train)\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)))\n",
    "\n",
    "\n",
    "# Ada Boost\n",
    "ada = AdaBoostClassifier(n_estimators=400, learning_rate=0.1)\n",
    "ada.fit(X_train,y_train)\n",
    "print (\"\\n\\n ---AdaBoost Model---\")\n",
    "ada_roc_auc = roc_auc_score(y_test, ada.predict(X_test))\n",
    "print (\"AdaBoost AUC = %2.2f\" % ada_roc_auc)\n",
    "print(classification_report(y_test, ada.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f37f3cd2-8dcc-41c0-a56f-91e8df1d9adb",
    "_uuid": "4eba4ea0d57b8b74f708a0ccea08c21bb72dc803"
   },
   "source": [
    "## ROC Graph\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "2ad3ed61-963c-430a-b150-aa0dacfa9f8f",
    "_uuid": "0eca62b259ac194a0aef17617a01ee929120bd8c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f940c064710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create ROC Graph\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logis.predict_proba(X_test)[:,1])\n",
    "rf_fpr, rf_tpr, rf_thresholds = roc_curve(y_test, rf.predict_proba(X_test)[:,1])\n",
    "dt_fpr, dt_tpr, dt_thresholds = roc_curve(y_test, dtree.predict_proba(X_test)[:,1])\n",
    "ada_fpr, ada_tpr, ada_thresholds = roc_curve(y_test, ada.predict_proba(X_test)[:,1])\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "# Plot Logistic Regression ROC\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "\n",
    "# Plot Random Forest ROC\n",
    "plt.plot(rf_fpr, rf_tpr, label='Random Forest (area = %0.2f)' % rf_roc_auc)\n",
    "\n",
    "# Plot Decision Tree ROC\n",
    "plt.plot(dt_fpr, dt_tpr, label='Decision Tree (area = %0.2f)' % dt_roc_auc)\n",
    "\n",
    "# Plot AdaBoost ROC\n",
    "plt.plot(ada_fpr, ada_tpr, label='AdaBoost (area = %0.2f)' % ada_roc_auc)\n",
    "\n",
    "# Plot Base Rate ROC\n",
    "plt.plot([0,1], [0,1],label='Base Rate' 'k--')\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_guid": "05b9bf5d-7f3a-4944-a38a-390eb9ba6454",
    "_uuid": "bd5f222c57120e2d10a2530d834fd227de4d24ca"
   },
   "source": [
    "# 5. Interpreting the Data\n",
    "***\n",
    "\n",
    "**Summary:** \n",
    "With all of this information, this is what Bob should know about his company and why his employees probably left:\n",
    " 1. Employees generally left when they are **underworked** (less than 150hr/month or 6hr/day)\n",
    " 2. Employees generally left when they are **overworked** (more than 250hr/month or 10hr/day)\n",
    " 3. Employees with either **really high or low evaluations** should be taken into consideration for high turnover rate\n",
    " 4. Employees with **low to medium salaries** are the bulk of employee turnover\n",
    " 5. Employees that had **2,6, or 7 project count** was at risk of leaving the company\n",
    " 6. Employee **satisfaction** is the highest indicator for employee turnover.\n",
    " 7. Employee that had **4 and 5 yearsAtCompany** should be taken into consideration for high turnover rate\n",
    " 8. Employee **satisfaction**, **yearsAtCompany**, and **evaluation** were the three biggest factors in determining turnover."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "268545cc-eab3-4a7d-95ad-228d39d0a3dd",
    "_uuid": "b00bcd1f6771849605815724764feb5289938f53"
   },
   "source": [
    "**\"You don't build a business. You build people, and people build the business.\" - Zig Ziglar**\n",
    "***\n",
    "<img src=\"http://www.goldbeck.com/hrblog/wp-content/uploads/2015/11/giphy-3.gif\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3828b436-358c-40ed-97db-db5ddcdd867d",
    "_uuid": "65bb542ed029a214dc085a4e3450eb404badecb5"
   },
   "source": [
    "## Potential Solution\n",
    "***\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",
    "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",
    "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",
    "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",
    " **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. Some advice:\n",
    " - Be a good coach\n",
    " - 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    "
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    "## What Now?\n",
    "***\n",
    "This problem is about people decision. When modeling the data, we should not be using this predictive metric as a solution decider. But, we can use this to arm people with much better relevant information for better decision making.\n",
    "\n",
    "We would have to conduct more experiments or collect more data about the employees in order to come up with a more accurate finding. I would recommend to gather more variables from the database that could have more impact on determining employee turnover and satisfaction such as their distance from home, gender, age, and etc.\n",
    "\n",
    "**Reverse Engineer the Problem**\n",
    "***\n",
    "After trying to understand what caused employees to leave in the first place, we can form another problem to solve by asking ourselves \n",
    "1. **\"What features caused employees stay?** \n",
    "2. **\"What features contributed to employee retention?**\n",
    "**\n",
    "There are endless problems to solve!"
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