takeaway3.ipynb

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    "# Takeaway 3:\n",
    "## Section 1: Business Understanding\n",
    "First, I have check all the questions in the survey and the structure of the answers to know more about the business and also, to know what are the potential question I can ask\n",
    "### Question: \n",
    "Apart of the Career satisfaction, what are other factors (implicit or explicit company factors) that influence Job Satisfaction?\n"
   ],
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   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from utils import *"
   ]
  },
  {
   "source": [
    "## Section 2: Data Understanding"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Gathering"
   ],
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       "(51392, 154)"
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   "source": [
    "df = pd.read_csv('./data/survey-results-public.csv')\n",
    "df.shape"
   ]
  },
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   "execution_count": 3,
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True, fmt=\".2f\");"
   ]
  },
  {
   "source": [
    "There is a high correlation between JobSatisfaction and CareerSatisfaction. So Im going to not include CareerSatisfaction because i want to know if there are other aspects that influence JobSatisfaction"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Getting only the columns that we are interested in "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        InfluenceInternet           InfluenceWorkstation    InfluenceHardware  \\\n",
       "0      Not very satisfied                            NaN                  NaN   \n",
       "1               Satisfied            No influence at all  No influence at all   \n",
       "2          Very satisfied             A lot of influence       Some influence   \n",
       "3                     NaN                            NaN                  NaN   \n",
       "4               Satisfied                            NaN                  NaN   \n",
       "...                   ...                            ...                  ...   \n",
       "51387                 NaN                            NaN                  NaN   \n",
       "51388  Not very satisfied             A lot of influence   Not much influence   \n",
       "51389                 NaN                            NaN                  NaN   \n",
       "51390      Very satisfied  I am the final decision maker  No influence at all   \n",
       "51391           Satisfied                 Some influence       Some influence   \n",
       "\n",
       "          InfluenceServers   InfluenceTechStack    InfluenceDeptTech  \\\n",
       "0                      NaN                  NaN                  NaN   \n",
       "1      No influence at all  No influence at all  No influence at all   \n",
       "2           Some influence       Some influence   A lot of influence   \n",
       "3                      NaN                  NaN                  NaN   \n",
       "4                      NaN                  NaN                  NaN   \n",
       "...                    ...                  ...                  ...   \n",
       "51387                  NaN                  NaN                  NaN   \n",
       "51388  No influence at all   Not much influence   Not much influence   \n",
       "51389                  NaN                  NaN                  NaN   \n",
       "51390  No influence at all   A lot of influence   A lot of influence   \n",
       "51391   A lot of influence   A lot of influence  No influence at all   \n",
       "\n",
       "         InfluenceVizTools    InfluenceDatabase       InfluenceCloud  \\\n",
       "0                      NaN                  NaN                  NaN   \n",
       "1      No influence at all  No influence at all  No influence at all   \n",
       "2           Some influence       Some influence       Some influence   \n",
       "3                      NaN                  NaN                  NaN   \n",
       "4                      NaN                  NaN                  NaN   \n",
       "...                    ...                  ...                  ...   \n",
       "51387                  NaN                  NaN                  NaN   \n",
       "51388       Some influence   A lot of influence   Not much influence   \n",
       "51389                  NaN                  NaN                  NaN   \n",
       "51390  No influence at all   A lot of influence   A lot of influence   \n",
       "51391   A lot of influence   A lot of influence   A lot of influence   \n",
       "\n",
       "      InfluenceConsultants  ...          ShipIt   OtherPeoplesCode  \\\n",
       "0                      NaN  ...             NaN                NaN   \n",
       "1      No influence at all  ...           Agree           Disagree   \n",
       "2           Some influence  ...           Agree           Disagree   \n",
       "3                      NaN  ...  Somewhat agree              Agree   \n",
       "4                      NaN  ...             NaN                NaN   \n",
       "...                    ...  ...             ...                ...   \n",
       "51387                  NaN  ...             NaN                NaN   \n",
       "51388  No influence at all  ...             NaN  Strongly disagree   \n",
       "51389                  NaN  ...             NaN                NaN   \n",
       "51390  No influence at all  ...        Disagree              Agree   \n",
       "51391       Some influence  ...             NaN                NaN   \n",
       "\n",
       "       ProjectManagement  EnjoyDebugging       InTheZone  \\\n",
       "0                    NaN             NaN             NaN   \n",
       "1      Strongly disagree           Agree  Somewhat agree   \n",
       "2               Disagree           Agree           Agree   \n",
       "3         Somewhat agree  Somewhat agree  Strongly agree   \n",
       "4                    NaN             NaN             NaN   \n",
       "...                  ...             ...             ...   \n",
       "51387                NaN             NaN             NaN   \n",
       "51388                NaN             NaN             NaN   \n",
       "51389                NaN             NaN             NaN   \n",
       "51390           Disagree           Agree           Agree   \n",
       "51391                NaN             NaN             NaN   \n",
       "\n",
       "      DifficultCommunication  CollaborateRemote    Salary  \\\n",
       "0                        NaN                NaN       NaN   \n",
       "1                   Disagree  Strongly disagree       NaN   \n",
       "2                   Disagree     Somewhat agree  113750.0   \n",
       "3                   Disagree     Somewhat agree       NaN   \n",
       "4                        NaN                NaN       NaN   \n",
       "...                      ...                ...       ...   \n",
       "51387                    NaN                NaN   58000.0   \n",
       "51388                    NaN                NaN       NaN   \n",
       "51389                    NaN                NaN       NaN   \n",
       "51390               Disagree              Agree   40000.0   \n",
       "51391                    NaN                NaN       NaN   \n",
       "\n",
       "                             Overpaid JobSatisfaction  \n",
       "0                                 NaN             NaN  \n",
       "1                                 NaN             NaN  \n",
       "2      Neither underpaid nor overpaid             9.0  \n",
       "3                                 NaN             3.0  \n",
       "4                                 NaN             8.0  \n",
       "...                               ...             ...  \n",
       "51387               Greatly underpaid             3.0  \n",
       "51388                             NaN             NaN  \n",
       "51389                             NaN            10.0  \n",
       "51390               Greatly underpaid             7.0  \n",
       "51391                             NaN             8.0  \n",
       "\n",
       "[51392 rows x 58 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>InfluenceInternet</th>\n      <th>InfluenceWorkstation</th>\n      <th>InfluenceHardware</th>\n      <th>InfluenceServers</th>\n      <th>InfluenceTechStack</th>\n      <th>InfluenceDeptTech</th>\n      <th>InfluenceVizTools</th>\n      <th>InfluenceDatabase</th>\n      <th>InfluenceCloud</th>\n      <th>InfluenceConsultants</th>\n      <th>...</th>\n      <th>ShipIt</th>\n      <th>OtherPeoplesCode</th>\n      <th>ProjectManagement</th>\n      <th>EnjoyDebugging</th>\n      <th>InTheZone</th>\n      <th>DifficultCommunication</th>\n      <th>CollaborateRemote</th>\n      <th>Salary</th>\n      <th>Overpaid</th>\n      <th>JobSatisfaction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Not very satisfied</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Satisfied</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>...</td>\n      <td>Agree</td>\n      <td>Disagree</td>\n      <td>Strongly disagree</td>\n      <td>Agree</td>\n      <td>Somewhat agree</td>\n      <td>Disagree</td>\n      <td>Strongly disagree</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Very satisfied</td>\n      <td>A lot of influence</td>\n      <td>Some influence</td>\n      <td>Some influence</td>\n      <td>Some influence</td>\n      <td>A lot of influence</td>\n      <td>Some influence</td>\n      <td>Some influence</td>\n      <td>Some influence</td>\n      <td>Some influence</td>\n      <td>...</td>\n      <td>Agree</td>\n      <td>Disagree</td>\n      <td>Disagree</td>\n      <td>Agree</td>\n      <td>Agree</td>\n      <td>Disagree</td>\n      <td>Somewhat agree</td>\n      <td>113750.0</td>\n      <td>Neither underpaid nor overpaid</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>Somewhat agree</td>\n      <td>Agree</td>\n      <td>Somewhat agree</td>\n      <td>Somewhat agree</td>\n      <td>Strongly agree</td>\n      <td>Disagree</td>\n      <td>Somewhat agree</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Satisfied</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>51387</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>58000.0</td>\n      <td>Greatly underpaid</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>51388</th>\n      <td>Not very satisfied</td>\n      <td>A lot of influence</td>\n      <td>Not much influence</td>\n      <td>No influence at all</td>\n      <td>Not much influence</td>\n      <td>Not much influence</td>\n      <td>Some influence</td>\n      <td>A lot of influence</td>\n      <td>Not much influence</td>\n      <td>No influence at all</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>Strongly disagree</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>51389</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>51390</th>\n      <td>Very satisfied</td>\n      <td>I am the final decision maker</td>\n      <td>No influence at all</td>\n      <td>No influence at all</td>\n      <td>A lot of influence</td>\n      <td>A lot of influence</td>\n      <td>No influence at all</td>\n      <td>A lot of influence</td>\n      <td>A lot of influence</td>\n      <td>No influence at all</td>\n      <td>...</td>\n      <td>Disagree</td>\n      <td>Agree</td>\n      <td>Disagree</td>\n      <td>Agree</td>\n      <td>Agree</td>\n      <td>Disagree</td>\n      <td>Agree</td>\n      <td>40000.0</td>\n      <td>Greatly underpaid</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>51391</th>\n      <td>Satisfied</td>\n      <td>Some influence</td>\n      <td>Some influence</td>\n      <td>A lot of influence</td>\n      <td>A lot of influence</td>\n      <td>No influence at all</td>\n      <td>A lot of influence</td>\n      <td>A lot of influence</td>\n      <td>A lot of influence</td>\n      <td>Some influence</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>51392 rows × 58 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "takeaway3 = df[\n",
    "        list(df.columns[df.columns.to_series().str.contains('Influence')]) +\n",
    "        list(df.columns[df.columns.to_series().str.contains('EquipmentSatisfied')]) +\n",
    "        [\n",
    "            'HomeRemote',\n",
    "            'EmploymentStatus',\n",
    "            'FormalEducation',\n",
    "            'JobSeekingStatus',\n",
    "            'Professional',\n",
    "            'CompanySize',\n",
    "            'CompanyType',\n",
    "            'WebDeveloperType',\n",
    "            'MobileDeveloperType',\n",
    "            'NonDeveloperType',\n",
    "             # 'CareerSatisfaction',\n",
    "            'YearsCodedJob',\n",
    "            'ProblemSolving',\n",
    "            'BuildingThings',\n",
    "            'LearningNewTech',\n",
    "            'BoringDetails',\n",
    "            'JobSecurity',\n",
    "            'DiversityImportant',\n",
    "            'FriendsDevelopers',\n",
    "            'RightWrongWay',\n",
    "            'UnderstandComputers',\n",
    "            'SeriousWork',\n",
    "            'InvestTimeTools',\n",
    "            'WorkPayCare',\n",
    "            'KinshipDevelopers',\n",
    "            'ChallengeMyself',\n",
    "            'CompetePeers',\n",
    "            'ChangeWorld',\n",
    "            'HoursPerWeek',\n",
    "            'ImportantBenefits',\n",
    "            'WorkStart',\n",
    "            'AuditoryEnvironment',\n",
    "            'ShipIt',\n",
    "            'OtherPeoplesCode',\n",
    "            'ProjectManagement',\n",
    "            'EnjoyDebugging',\n",
    "            'InTheZone',\n",
    "            'DifficultCommunication',\n",
    "            'CollaborateRemote',\n",
    "            'Salary',\n",
    "            'Overpaid',\n",
    "            'JobSatisfaction'\n",
    "        ]\n",
    "    ]\n",
    "takeaway3"
   ]
  },
  {
   "source": [
    "## Section 3: Data Preparation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Wrangling"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "takeaway3 = takeaway3.dropna(subset=['Overpaid'], axis=0)\n",
    "takeaway3 = takeaway3[takeaway3['EmploymentStatus'].isin(['Employed full-time', 'Employed part-time', 'Independent contractor, freelancer, or self-employed'])]\n",
    "takeaway3 = takeaway3.drop(['EmploymentStatus',], axis=1)"
   ]
  },
  {
   "source": [
    "### Making categorical data into quantitative data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = dict(zip(['Very satisfied', 'Satisfied' ,'Somewhat satisfied', 'Not very satisfied', 'Not at all satisfied'], [10,7,5,3,0]))\n",
    "for k,v in options.items():\n",
    "    takeaway3 = takeaway3.replace(k,v)\n",
    "\n",
    "options = dict(zip(['I am the final decision maker', 'A lot of influence' ,'Some influence', 'Not much influence', 'No influence at all'], [10,7,5,3,0]))\n",
    "for k,v in options.items():\n",
    "    takeaway3 = takeaway3.replace(k,v)\n",
    "\n",
    "options = dict(zip(['Strongly agree', 'Agree' ,'Somewhat agree', 'Disagree', 'Strongly disagree'], [10,7,5,3,0]))\n",
    "for k,v in options.items():\n",
    "    takeaway3 = takeaway3.replace(k,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 13387 entries, 2 to 51390\nData columns (total 57 columns):\n #   Column                      Non-Null Count  Dtype  \n---  ------                      --------------  -----  \n 0   InfluenceInternet           9101 non-null   float64\n 1   InfluenceWorkstation        8886 non-null   float64\n 2   InfluenceHardware           8881 non-null   float64\n 3   InfluenceServers            8846 non-null   float64\n 4   InfluenceTechStack          8860 non-null   float64\n 5   InfluenceDeptTech           8858 non-null   float64\n 6   InfluenceVizTools           8863 non-null   float64\n 7   InfluenceDatabase           8867 non-null   float64\n 8   InfluenceCloud              8862 non-null   float64\n 9   InfluenceConsultants        8846 non-null   float64\n 10  InfluenceRecruitment        8865 non-null   float64\n 11  InfluenceCommunication      8853 non-null   float64\n 12  EquipmentSatisfiedMonitors  9101 non-null   float64\n 13  EquipmentSatisfiedCPU       9102 non-null   float64\n 14  EquipmentSatisfiedRAM       9107 non-null   float64\n 15  EquipmentSatisfiedStorage   9083 non-null   float64\n 16  EquipmentSatisfiedRW        9092 non-null   float64\n 17  HomeRemote                  13378 non-null  object \n 18  FormalEducation             13387 non-null  object \n 19  JobSeekingStatus            9431 non-null   object \n 20  Professional                13387 non-null  object \n 21  CompanySize                 13376 non-null  object \n 22  CompanyType                 13364 non-null  object \n 23  WebDeveloperType            4130 non-null   object \n 24  MobileDeveloperType         560 non-null    object \n 25  NonDeveloperType            0 non-null      float64\n 26  YearsCodedJob               13330 non-null  object \n 27  ProblemSolving              9233 non-null   float64\n 28  BuildingThings              9234 non-null   float64\n 29  LearningNewTech             9235 non-null   float64\n 30  BoringDetails               9224 non-null   float64\n 31  JobSecurity                 9235 non-null   float64\n 32  DiversityImportant          9215 non-null   float64\n 33  FriendsDevelopers           9234 non-null   float64\n 34  RightWrongWay               9214 non-null   float64\n 35  UnderstandComputers         9230 non-null   float64\n 36  SeriousWork                 9225 non-null   float64\n 37  InvestTimeTools             9206 non-null   float64\n 38  WorkPayCare                 9227 non-null   float64\n 39  KinshipDevelopers           9167 non-null   float64\n 40  ChallengeMyself             9208 non-null   float64\n 41  CompetePeers                9196 non-null   float64\n 42  ChangeWorld                 9205 non-null   float64\n 43  HoursPerWeek                5928 non-null   float64\n 44  ImportantBenefits           9301 non-null   object \n 45  WorkStart                   12535 non-null  object \n 46  AuditoryEnvironment         12459 non-null  object \n 47  ShipIt                      9139 non-null   float64\n 48  OtherPeoplesCode            9130 non-null   float64\n 49  ProjectManagement           9089 non-null   float64\n 50  EnjoyDebugging              9142 non-null   float64\n 51  InTheZone                   9065 non-null   float64\n 52  DifficultCommunication      9135 non-null   float64\n 53  CollaborateRemote           9101 non-null   float64\n 54  Salary                      12858 non-null  float64\n 55  Overpaid                    13387 non-null  object \n 56  JobSatisfaction             13350 non-null  float64\ndtypes: float64(44), object(13)\nmemory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "takeaway3.info()"
   ]
  },
  {
   "source": [
    "There are more Categorical columns yet (object)\n",
    "The next functio will treat the data in the following way:\n",
    "- For Quantitative data it will use the mean to fill the NAN\n",
    "- For Categorical data it will use a dummy strategy (create new columns with each option with 0 and 1 and delete the original column)\n",
    "Probably a better solution would be to split some multichoice columns before doing this.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "initial shape: (13387, 57)\n",
      "Shape without NAN JobSatisfaction: (13350, 57)\n",
      "Shape without unnecessary columns: (13350, 56)\n",
      "Quantitative columns: Index(['InfluenceInternet', 'InfluenceWorkstation', 'InfluenceHardware',\n",
      "       'InfluenceServers', 'InfluenceTechStack', 'InfluenceDeptTech',\n",
      "       'InfluenceVizTools', 'InfluenceDatabase', 'InfluenceCloud',\n",
      "       'InfluenceConsultants', 'InfluenceRecruitment',\n",
      "       'InfluenceCommunication', 'EquipmentSatisfiedMonitors',\n",
      "       'EquipmentSatisfiedCPU', 'EquipmentSatisfiedRAM',\n",
      "       'EquipmentSatisfiedStorage', 'EquipmentSatisfiedRW', 'NonDeveloperType',\n",
      "       'ProblemSolving', 'BuildingThings', 'LearningNewTech', 'BoringDetails',\n",
      "       'JobSecurity', 'DiversityImportant', 'FriendsDevelopers',\n",
      "       'RightWrongWay', 'UnderstandComputers', 'SeriousWork',\n",
      "       'InvestTimeTools', 'WorkPayCare', 'KinshipDevelopers',\n",
      "       'ChallengeMyself', 'CompetePeers', 'ChangeWorld', 'HoursPerWeek',\n",
      "       'ShipIt', 'OtherPeoplesCode', 'ProjectManagement', 'EnjoyDebugging',\n",
      "       'InTheZone', 'DifficultCommunication', 'CollaborateRemote', 'Salary'],\n",
      "      dtype='object')\n",
      "Shape after imputing quantitative data: (13350, 56)\n",
      "Categorical columns: Index(['HomeRemote', 'FormalEducation', 'JobSeekingStatus', 'Professional',\n",
      "       'CompanySize', 'CompanyType', 'WebDeveloperType', 'MobileDeveloperType',\n",
      "       'YearsCodedJob', 'ImportantBenefits', 'WorkStart',\n",
      "       'AuditoryEnvironment', 'Overpaid'],\n",
      "      dtype='object')\n",
      "Shape after imputing categorical data: (13350, 2234)\n"
     ]
    }
   ],
   "source": [
    "X, y = imputing_data(takeaway3, y_column='JobSatisfaction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 13350 entries, 2 to 51390\nColumns: 2234 entries, InfluenceInternet to Overpaid_Somewhat underpaid\ndtypes: float64(43), uint8(2191)\nmemory usage: 32.4 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "source": [
    "## Section 4: Modeling"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Reducing the number of levels in Job Satisfaction Column"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (y / 3).apply(np.floor)"
   ]
  },
  {
   "source": [
    " I will use a simple linear regresion model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "cutoffs = [7000, 5000, 3000, 1000, 800, 600, 400, 200, 100, 50, 40]\n",
    "\n",
    "r2_scores_test, r2_scores_train, lm_model, X_train, X_test, y_train, y_test = find_optimal_lm_mod(X, y, cutoffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[0.06230981769271349,\n",
       " 0.09602499480084126,\n",
       " 0.13419484497194978,\n",
       " 0.21676775417621208,\n",
       " 0.22043269108060926,\n",
       " 0.22575170433730218,\n",
       " 0.22667696135241555,\n",
       " 0.22785357552391672,\n",
       " 0.22954729893836145,\n",
       " 0.23066937060991932,\n",
       " 0.23321144881256606]"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "r2_scores_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[0.0649340573108792,\n",
       " 0.10847937464946145,\n",
       " 0.13717626637053926,\n",
       " 0.19442731527405632,\n",
       " 0.19861864202981316,\n",
       " 0.20010160597261806,\n",
       " 0.19865095466261695,\n",
       " 0.19869661897206414,\n",
       " 0.1968676754963813,\n",
       " 0.19542429416790985,\n",
       " 0.1908638888149966]"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "r2_scores_test"
   ]
  },
  {
   "source": [
    "Seems that in some point (~80 features) the model starts to be overfitted\n",
    "Also it seems that the prediction is not good enough. Only 19%."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                              est_int     coefs  abs_coefs\n",
       "49  JobSeekingStatus_I am not interested in new jo...  0.539022   0.539022\n",
       "33                                       HoursPerWeek -0.453918   0.453918\n",
       "78                         Overpaid_Greatly underpaid -0.391503   0.391503\n",
       "26                                        SeriousWork  0.304481   0.304481\n",
       "29                                  KinshipDevelopers  0.265735   0.265735\n",
       "..                                                ...       ...        ...\n",
       "56                CompanySize_Fewer than 10 employees  0.008404   0.008404\n",
       "6                                   InfluenceVizTools -0.007547   0.007547\n",
       "73                                  WorkStart_7:00 AM -0.004286   0.004286\n",
       "75                                  WorkStart_9:00 AM -0.002218   0.002218\n",
       "22                                 DiversityImportant  0.000006   0.000006\n",
       "\n",
       "[82 rows x 3 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>est_int</th>\n      <th>coefs</th>\n      <th>abs_coefs</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>49</th>\n      <td>JobSeekingStatus_I am not interested in new jo...</td>\n      <td>0.539022</td>\n      <td>0.539022</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>HoursPerWeek</td>\n      <td>-0.453918</td>\n      <td>0.453918</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>Overpaid_Greatly underpaid</td>\n      <td>-0.391503</td>\n      <td>0.391503</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>SeriousWork</td>\n      <td>0.304481</td>\n      <td>0.304481</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>KinshipDevelopers</td>\n      <td>0.265735</td>\n      <td>0.265735</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>CompanySize_Fewer than 10 employees</td>\n      <td>0.008404</td>\n      <td>0.008404</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>InfluenceVizTools</td>\n      <td>-0.007547</td>\n      <td>0.007547</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>WorkStart_7:00 AM</td>\n      <td>-0.004286</td>\n      <td>0.004286</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>WorkStart_9:00 AM</td>\n      <td>-0.002218</td>\n      <td>0.002218</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>DiversityImportant</td>\n      <td>0.000006</td>\n      <td>0.000006</td>\n    </tr>\n  </tbody>\n</table>\n<p>82 rows × 3 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# I have copied this function from Udacity Datascience nanodegree program\n",
    "def coef_weights(coefficients, X_train):\n",
    "    '''\n",
    "    INPUT:\n",
    "    coefficients - the coefficients of the linear model \n",
    "    X_train - the training data, so the column names can be used\n",
    "    OUTPUT:\n",
    "    coefs_df - a dataframe holding the coefficient, estimate, and abs(estimate)\n",
    "    \n",
    "    Provides a dataframe that can be used to understand the most influential coefficients\n",
    "    in a linear model by providing the coefficient estimates along with the name of the \n",
    "    variable attached to the coefficient.\n",
    "    '''\n",
    "    coefs_df = pd.DataFrame()\n",
    "    coefs_df['est_int'] = X_train.columns\n",
    "    coefs_df['coefs'] = lm_model.coef_\n",
    "    coefs_df['abs_coefs'] = np.abs(lm_model.coef_)\n",
    "    coefs_df = coefs_df.sort_values('abs_coefs', ascending=False)\n",
    "    return coefs_df\n",
    "\n",
    "coef_df = coef_weights(lm_model.coef_, X_train)\n",
    "\n",
    "coef_df"
   ]
  },
  {
   "source": [
    "However, after checking the coefficients,  it was interesting to see that for instance, more **hours or weeks** have a negative impact on job satisfaction, as well as the feeling of **being underpaid**. And also that makes sense that if you are satisfied with your job you don’t want to change it. Or that if you feel satisfied with the equipment your company has provided you, you are also more motivated to work."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Solution:\n",
    "This question was very frustrating for me. I tried many ways to try to have a better conclusion that the one i have. I tried reducing the number of features or reducing the ones that i consider not importat for this context. I also tried with the full set of columns. I tried changing the levels of choises for the output (y), but nothing. It seems there is not a heavy correlation in the Job satisfaction beyond of the career satisfaction, meaning i was not able to improve the model prediction."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}