Exploratory_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"final_merged_df.csv\")\n",
    "df = df.drop(\"Unnamed: 0\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5982813 entries, 0 to 5982812\n",
      "Data columns (total 26 columns):\n",
      "user_title                       object\n",
      "user_clevel                      int64\n",
      "user_indus                       int64\n",
      "user_disc                        int64\n",
      "user_country                     object\n",
      "user_region                      int64\n",
      "user_premium                     float64\n",
      "user_numb_work_experiences       int64\n",
      "user_years_experience            int64\n",
      "user_years_experience_current    int64\n",
      "user_edu_degree                  int64\n",
      "user_edu_fieldofstudies          object\n",
      "user_wtcj                        int64\n",
      "item_title                       object\n",
      "item_clevel                      int64\n",
      "item_indus                       int64\n",
      "item_disc                        int64\n",
      "item_country                     object\n",
      "item_region                      int64\n",
      "item_is_payed                    int64\n",
      "item_employment                  int64\n",
      "item_tags                        object\n",
      "item_created_at                  object\n",
      "interaction_type                 int64\n",
      "interaction_created_at           int64\n",
      "scores                           int64\n",
      "dtypes: float64(1), int64(18), object(7)\n",
      "memory usage: 1.2+ GB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 982 813 Entries\n",
    "\n",
    "### 27 Features: (20) numeric, (7) objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = df.iloc[:4000000] # train = 4 million datapoints\n",
    "test = df.iloc[4000000:] # test = 1,9 million datapoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>user_title</th>\n",
       "      <th>user_clevel</th>\n",
       "      <th>user_indus</th>\n",
       "      <th>user_disc</th>\n",
       "      <th>user_country</th>\n",
       "      <th>user_region</th>\n",
       "      <th>user_premium</th>\n",
       "      <th>user_numb_work_experiences</th>\n",
       "      <th>user_years_experience</th>\n",
       "      <th>user_years_experience_current</th>\n",
       "      <th>...</th>\n",
       "      <th>item_disc</th>\n",
       "      <th>item_country</th>\n",
       "      <th>item_region</th>\n",
       "      <th>item_is_payed</th>\n",
       "      <th>item_employment</th>\n",
       "      <th>item_tags</th>\n",
       "      <th>item_created_at</th>\n",
       "      <th>interaction_type</th>\n",
       "      <th>interaction_created_at</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3999997</th>\n",
       "      <td>[2923342, 3562023, 2489581]</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>ch</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>de</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[1385885, 2139882, 399503, 3797268, 2103114, 2...</td>\n",
       "      <td>1478214000</td>\n",
       "      <td>4</td>\n",
       "      <td>1478531448</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999998</th>\n",
       "      <td>[3606563, 864194, 3248841]</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>de</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>de</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[1385885, 2139882, 399503, 3797268, 2103114, 2...</td>\n",
       "      <td>1478214000</td>\n",
       "      <td>4</td>\n",
       "      <td>1478596538</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999999</th>\n",
       "      <td>[2134393]</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>ch</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>de</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[1385885, 2139882, 399503, 3797268, 2103114, 2...</td>\n",
       "      <td>1478214000</td>\n",
       "      <td>4</td>\n",
       "      <td>1478682938</td>\n",
       "      <td>-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          user_title  user_clevel  user_indus  user_disc  \\\n",
       "3999997  [2923342, 3562023, 2489581]            3          16         17   \n",
       "3999998   [3606563, 864194, 3248841]            4          15          4   \n",
       "3999999                    [2134393]            4          15          4   \n",
       "\n",
       "        user_country  user_region  user_premium  user_numb_work_experiences  \\\n",
       "3999997           ch            0           0.0                           3   \n",
       "3999998           de           10           0.0                           3   \n",
       "3999999           ch            0           1.0                           3   \n",
       "\n",
       "         user_years_experience  user_years_experience_current   ...    \\\n",
       "3999997                      6                              2   ...     \n",
       "3999998                      6                              1   ...     \n",
       "3999999                      7                              3   ...     \n",
       "\n",
       "         item_disc item_country  item_region item_is_payed  item_employment  \\\n",
       "3999997         17           de            6             1                1   \n",
       "3999998         17           de            6             1                1   \n",
       "3999999         17           de            6             1                1   \n",
       "\n",
       "                                                 item_tags  item_created_at  \\\n",
       "3999997  [1385885, 2139882, 399503, 3797268, 2103114, 2...       1478214000   \n",
       "3999998  [1385885, 2139882, 399503, 3797268, 2103114, 2...       1478214000   \n",
       "3999999  [1385885, 2139882, 399503, 3797268, 2103114, 2...       1478214000   \n",
       "\n",
       "        interaction_type  interaction_created_at  scores  \n",
       "3999997                4              1478531448      -5  \n",
       "3999998                4              1478596538      -5  \n",
       "3999999                4              1478682938     -15  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Exploratory Data Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YZHZzf6ckP2vmd16SpzTtFyf5ZDPur5K8dJD5HJnkqiRXfe/RW9bV4ZQkSVpvWKHvfccB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tu7NW83sr3x0GOjFgbzUCYpjpQ0r3kcBdzaQpyYUEm5iYjekbSN7Vb/2DVL3m8HdqfMyp0HfKF2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\ntat/rKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b592f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = train.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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heKzrg/xCT8Ga/HTt/7+HLmTZ7D9YumM+cVFnade5NXYOtalS04bzcRcL+c5Y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\n3xSr27q9O2v3r2T1nmW6ODpFqWFdnajdS1i5azFBc8bqdEcFD2f1nmW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ITg5OREUFBQsey3t7d/Z115ciqG0tyVa8ViEdmK7Jw8uUaegcyAlFfaF4KCSEuRU0aWW1eUVzdF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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11685bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#saleprice correlation matrix\n",
    "k = 10 #number of variables for heatmap\n",
    "cols = corrmat.nlargest(k, 'scores')['scores'].index\n",
    "cm = np.corrcoef(train[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Univariable Analysis\n",
    "Inspecting the target variables interaction_type & Score.\n",
    "\n",
    "\n",
    "### Interaction-Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "interactions = train[\"interaction_type\"].value_counts()\n",
    "df_train = pd.DataFrame(data=interactions)\n",
    "df_train[\"%\"] = df_train[\"interaction_type\"].apply(lambda x: x / df_train[\"interaction_type\"].sum() * 100)\n",
    "#df_train[\"%\"] = round(df_train[\"%\"])\n",
    "df_train.rename(columns={\"interaction_type\" : 'Sum'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of Interaction-Type:\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196cff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
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       "\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>Sum</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3289882</td>\n",
       "      <td>82.247050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>602331</td>\n",
       "      <td>15.058275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76962</td>\n",
       "      <td>1.924050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29165</td>\n",
       "      <td>0.729125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1660</td>\n",
       "      <td>0.041500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sum          %\n",
       "1  3289882  82.247050\n",
       "4   602331  15.058275\n",
       "2    76962   1.924050\n",
       "3    29165   0.729125\n",
       "5     1660   0.041500"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Distribution of Interaction-Type:\")\n",
    "\n",
    "labels = '1', '2', '3', '4', '5'\n",
    "sizes = [82.15, 2.15, 0.73, 14.91, 0.041]\n",
    "explode = (0.1, 0, 0, 0, 0.1)\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, shadow=True, startangle=0)\n",
    "ax1.axis('equal')\n",
    "plt.legend(labels=labels)\n",
    "plt.show()\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "interactions = train[\"scores\"].value_counts()\n",
    "df_train = pd.DataFrame(data=interactions)\n",
    "df_train[\"%\"] = df_train[\"scores\"].apply(lambda x: x / df_train[\"scores\"].sum() * 100)\n",
    "df_train.rename(columns={\"scores\" : 'Sum'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of Scores:\n"
     ]
    },
    {
     "data": {
      "image/png": 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ATLSSlcAqNTJ9dHLENH205xPs/Wo/qpJOxfyeudOlE3VwHrkcshSzQwkxrSA7Ac/820qt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\nMSorK2EwGHD//ffj7bffhtO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wHAdvYgI6ExPQWVIEi30ACb19sPbbwIvn3kRdLYrG24Ww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\n6BJCSBhR6BJCSBj9/9fbGaX38RG6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196cfc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Sum</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1343916</td>\n",
       "      <td>33.597900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1283215</td>\n",
       "      <td>32.080375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>343393</td>\n",
       "      <td>8.584825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-5</th>\n",
       "      <td>319824</td>\n",
       "      <td>7.995600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319358</td>\n",
       "      <td>7.983950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-15</th>\n",
       "      <td>135176</td>\n",
       "      <td>3.379400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-10</th>\n",
       "      <td>110829</td>\n",
       "      <td>2.770725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57574</td>\n",
       "      <td>1.439350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40647</td>\n",
       "      <td>1.016175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-20</th>\n",
       "      <td>36502</td>\n",
       "      <td>0.912550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7906</td>\n",
       "      <td>0.197650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>709</td>\n",
       "      <td>0.017725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>519</td>\n",
       "      <td>0.012975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>230</td>\n",
       "      <td>0.005750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>202</td>\n",
       "      <td>0.005050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sum          %\n",
       " 1   1343916  33.597900\n",
       " 6   1283215  32.080375\n",
       " 7    343393   8.584825\n",
       "-5    319824   7.995600\n",
       " 2    319358   7.983950\n",
       "-15   135176   3.379400\n",
       "-10   110829   2.770725\n",
       " 5     57574   1.439350\n",
       " 10    40647   1.016175\n",
       "-20    36502   0.912550\n",
       " 15     7906   0.197650\n",
       " 25      709   0.017725\n",
       " 20      519   0.012975\n",
       " 45      230   0.005750\n",
       " 40      202   0.005050"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Distribution of Scores:\")\n",
    "\n",
    "labels = '1', '6', '7', '-5', '2', '-15', '-10', '5', '10', '-20', '15', '25', '20', '45', '40'\n",
    "sizes = [33, 32, 9, 8, 8, 3, 3, 1, 1, 1, 0.2, 0.01, 0.01, 0.005, 0.005]\n",
    "#explode = (0.1, 0, 0, 0, 0.1)\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, shadow=True, startangle=0)\n",
    "ax1.axis('equal')\n",
    "plt.legend(labels=labels)\n",
    "plt.show()\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# one-hot encode the interaction_type variable to inspect every interaction type separately\n",
    "train = pd.get_dummies(train, columns=['interaction_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Univariable Analysis\n",
    "Inspect how the dependent variable and independent variables relate.\n",
    "\n",
    "\n",
    "### user_title & item_title\n",
    "\n",
    "\n",
    "Are there any jobs where people are more actively looking for a new job? Does a match in user and item title contribute to a high score and interaction?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     4000000\n",
       "unique     234079\n",
       "top           [0]\n",
       "freq       187494\n",
       "Name: user_title, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_title\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]                                                                                                          276152\n",
       "[4432048, 4028770]                                                                                           39206\n",
       "[3698446]                                                                                                    29271\n",
       "[3055643]                                                                                                    27787\n",
       "[818603, 3544650, 3020707, 1504937, 531856, 2149973, 1935579, 2994300, 434858, 4302676, 427470, 4238250]     22707\n",
       "[1762890]                                                                                                    22113\n",
       "[3698446, 4191181]                                                                                           21477\n",
       "[4302676, 3020707, 3544650, 434858, 531856, 1504937]                                                         21103\n",
       "[1074124, 4432048, 4028770, 4195757, 373883, 2644164, 721436]                                                21084\n",
       "[131642, 4302676, 3544650]                                                                                   21079\n",
       "[2493893]                                                                                                    20553\n",
       "[864194, 1408054, 3606563, 436042, 2040078, 3853510]                                                         20191\n",
       "[2197227]                                                                                                    19612\n",
       "[3591852, 2649757]                                                                                           19591\n",
       "[3345198, 507322, 821921, 4330980, 3614810, 3609864, 2714822, 2149973]                                       19540\n",
       "[4099592, 4375874, 2504888]                                                                                  19525\n",
       "[4432048, 2197227]                                                                                           19400\n",
       "[3020707, 4302676, 2994300, 3544650, 427470, 1504937, 531856, 434858]                                        19345\n",
       "[2343124, 2109682, 4260413, 1518861, 3614032]                                                                19274\n",
       "[2554980, 1940275, 1408054, 3853510, 4390688, 1370264, 3525794, 2120727, 2635587, 1987601]                   19209\n",
       "[4180570, 4432048, 4028770, 3684404]                                                                         19200\n",
       "[2014588, 3698446]                                                                                           19199\n",
       "[1535940, 4028770, 4432048]                                                                                  19182\n",
       "[1762618]                                                                                                    19167\n",
       "[4352438, 1089980, 4210223]                                                                                  19156\n",
       "[4028770, 4432048, 2637331]                                                                                  19153\n",
       "[2197227, 2662375]                                                                                           19151\n",
       "[371255, 1470943, 4028770, 3223722, 4432048]                                                                 19146\n",
       "[1214514, 1408054, 4313058, 3853510, 668736, 4368523]                                                        19096\n",
       "[2954315, 4509781, 2302462, 4117495, 1159513, 3666663, 1683923, 3292641, 4126776]                            19079\n",
       "                                                                                                             ...  \n",
       "[127655, 2114599, 2304652, 4579146, 2251467, 680979, 2091343, 621565, 3680343, 1285499]                          1\n",
       "[4374971, 391283, 508564]                                                                                        1\n",
       "[3978034, 3353252, 818603, 3706903, 1935579, 2126376]                                                            1\n",
       "[3982413, 910844, 2768303, 841306, 2637331, 2259834, 2022017, 4064327, 2881063]                                  1\n",
       "[3548368, 2451736, 848315, 1776734, 2371784]                                                                     1\n",
       "[1356210, 1583806, 1574645, 1976591]                                                                             1\n",
       "[4437636, 1532345, 792827, 1510524, 4238250, 4290272]                                                            1\n",
       "[2787924, 1359999, 3876666, 2958644, 4098162, 987206, 2945601, 2090086]                                          1\n",
       "[4350281, 292033, 735146]                                                                                        1\n",
       "[3859656, 2563652]                                                                                               1\n",
       "[3393273, 3680343, 127655]                                                                                       1\n",
       "[3614810, 688331, 3460004, 3298787, 579315, 1472314]                                                             1\n",
       "[2304652, 887209]                                                                                                1\n",
       "[519876, 889965, 2714822, 302644, 3818119, 3614810, 4330980, 3491044]                                            1\n",
       "[157228, 3286664]                                                                                                1\n",
       "[4368523, 4260413, 4313058, 4126776, 1429047]                                                                    1\n",
       "[888227, 3995554, 4187316, 1762618]                                                                              1\n",
       "[3698446, 2076310, 4191181]                                                                                      1\n",
       "[17398, 3666814, 792827, 350917, 1027303]                                                                        1\n",
       "[2505961, 4347966, 3859656, 2103114, 1537682]                                                                    1\n",
       "[2398283, 4191156, 1090675, 2038807]                                                                             1\n",
       "[3739444, 478421, 2439010, 3927066, 2599999]                                                                     1\n",
       "[2028066, 2735786, 2180632, 3113130, 2139882, 2337219, 4138314, 3218188]                                         1\n",
       "[1920996, 3018023, 1240666, 2859773, 3859656, 2641683, 2283841, 3328642, 4481635, 896806]                        1\n",
       "[2994300, 901938, 665762, 1920996, 3859656, 792161]                                                              1\n",
       "[1469577, 91042]                                                                                                 1\n",
       "[756283, 2348887, 3529325, 587927, 2070620]                                                                      1\n",
       "[3109290, 1762814, 4272387, 1756458, 3965874, 1729779, 3606563, 3965530, 864194]                                 1\n",
       "[2245165, 1920996, 2103114]                                                                                      1\n",
       "[2432628, 1976591, 2839782, 4051860]                                                                             1\n",
       "Name: item_title, Length: 178897, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"item_title\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two features have a lot of unique values and therefore it makes no sense to plot them. But I think they could contain valuable informatio when we have a ,atch of the same title between an item and a user."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### user_clevel & item_clevel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1922502\n",
       "3    1095927\n",
       "4     551565\n",
       "2     141289\n",
       "5     132302\n",
       "6     124697\n",
       "1      31718\n",
       "Name: user_clevel, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_clevel\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 0 = unknown\n",
    "* 1 = Student/Intern\n",
    "* 2 = Entry Level (Beginner)\n",
    "* 3 = Professional/Experienced\n",
    "* 4 = Manager (Manager/Supervisor)\n",
    "* 5 = Executive (VP, SVP, etc.)\n",
    "* 6 = Senior Executive (CEO, CFO, President)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AwD6NLMuynB6ivrrsuerNWfYPEoxRQ52eADWYsPtlp0e4onV9J9V4DB8zhGAZ+5oSAADf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196fb400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distribution of the categories of user c_level\n",
    "sns.countplot(x=\"user_clevel\", data=train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The User C_Levels that happen the most often are...\n",
    "\n",
    "1. Unknown\n",
    "2. Professional/Experienced\n",
    "3. Manager\n",
    "4. Entry-Level\n",
    "5. Executive & also Senior Executive\n",
    "6. Student/Intern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EYmJiNH/+fONmAAD/CrAsy7I7xNWKc/mNT/6MVLsjNFjsi0vsjgA0iHHXlAAA+CGUEgDA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d8a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets do the same for item item_cleve\n",
    "sns.countplot(x=\"item_clevel\", data=train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Item C_Levels that happen the most often are...\n",
    "\n",
    "1. Professional/Experienced\n",
    "2. Manager\n",
    "3. Entry-Level\n",
    "4. Executive \n",
    "5. Student/Intern\n",
    "6. Senior Executive\n",
    "\n",
    "Note that there is no unknown C_Level within the items."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What insights can we learn from this?\n",
    "* \"Professional/Experienced\" is the c-level that is most second common at the users the most common at items. \n",
    "* most of the user c-levels are unknown, which makes this features less important\n",
    "\n",
    "\n",
    "Although that a lot of user-clevels are unknown and most items and users are \"Professional/Experienced\", I think this feature could be valuable since a person that has the c-level of an intern isn't the appropriate candidate for a Senior Executive Job-Posting and therefore it would be nonsense to show him this posting as an add."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### user_indus & item_indus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1451660\n",
       "20     398703\n",
       "7      392092\n",
       "16     256240\n",
       "15     241372\n",
       "12     168168\n",
       "3      161182\n",
       "6      110588\n",
       "14      96482\n",
       "17      88702\n",
       "8       70440\n",
       "18      66689\n",
       "21      62603\n",
       "5       59137\n",
       "9       52806\n",
       "4       50802\n",
       "2       48376\n",
       "23      40319\n",
       "13      33055\n",
       "11      32653\n",
       "19      31866\n",
       "22      31567\n",
       "1       27983\n",
       "10      26515\n",
       "Name: user_indus, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_indus\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categories: 24\n"
     ]
    }
   ],
   "source": [
    "print(\"Categories:\", len(train[\"user_indus\"].value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### user_disc & item_disc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f68ILL9SNN96omJgYjRw5Ul999ZX27t3r6DgAAACAsxHULSLt27dXv3791KlT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101b9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'user_disc'\n",
    "data = pd.concat([train['scores'], train[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "fig = sns.boxplot(x=var, y=\"scores\", data=data)\n",
    "fig.axis(ymin=-21, ymax=21);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sMTVq1KjS8t27d+u3v/2t7rjjDmcGBgAAANSDo1ewly1bpqysLI0a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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1b0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'item_disc'\n",
    "data = pd.concat([train['scores'], train[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "fig = sns.boxplot(x=var, y=\"scores\", data=data)\n",
    "fig.axis(ymin=-21, ymax=21);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oqKhgtAcAAACcN9sDdUlJiSZMmKCpU6dqyJAhZ5zjcrm0Zs0azZw5Uz6fT1988YVeffXV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\nBgCAAgI1AAAUEKgBAKCAQA0AAAXeAUJlnosIwOVRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e613cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'item_disc'\n",
    "data = pd.concat([train['scores'], train[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "fig = sns.barplot(x=var, y=\"scores\", data=data)\n",
    "fig.axis(ymin=0, ymax=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through these plots we see that some values reach higher scores than other. Because of that I think these features could contain valuable information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### user_country & item_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "de          3434331\n",
       "ch           225212\n",
       "non_dach     172742\n",
       "at           167715\n",
       "Name: user_country, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vH5YsWQI7Ozu88sor9d0jERE1EmbdU9q2bRu2bt1qetghMDAQW7ZswZYtW+qzNyIiamTM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\nImn8H8Hhc2jZF6BqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119758748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"user_country\", data=train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lm77Gjh1b6fEAAKBIxYP6pz/9abp3754jjzwyHTt2zBlnnJGnnnoqzzzzTLN1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\nPP+d5BsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f654da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'user_country'\n",
    "data = pd.concat([train['scores'], train[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "fig = sns.barplot(x=var, y=\"scores\", data=data)\n",
    "fig.axis(ymin=0, ymax=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clearly visible that nearly all users are from Germany. Therefore I think this is one of the features that contains the least valuable information. The only valuable information this feature could give the algorithm would be if the user is \"non_dach\", cause this could result in a high score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### user_region & item_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2031837\n",
       "2      399590\n",
       "9      377867\n",
       "1      369946\n",
       "7      231490\n",
       "10     185173\n",
       "6      178195\n",
       "3      131065\n",
       "4       27193\n",
       "13      26736\n",
       "5       18263\n",
       "8        7166\n",
       "15       5217\n",
       "12       4558\n",
       "11       2590\n",
       "14       2360\n",
       "16        754\n",
       "Name: user_region, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_region\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CuVwupaen+3zG9JYtW7z/K0nTp09XQUGBOnfurBEjRuiRRx5Rjx49/B0PAAAAMOL3Gerx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ff40278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'user_region'\n",
    "data = pd.concat([train['scores'], train[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "fig = sns.barplot(x=var, y=\"scores\", data=data)\n",
    "fig.axis(ymin=0, ymax=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I would say this is also one of the features with the least information, except if we have a match between user and item and the user is only searching for a job in his region."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### user_premium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This feature is important for a high score, since premium users get weighted heavier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### user_numb_work_experiences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1912818\n",
       "1    1168753\n",
       "2     918429\n",
       "Name: user_numb_work_experiences, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_numb_work_experiences\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e19a/ti6NjY1JksbGN/KNOyamQ2cXRyrhssO+1dYjAK2kRVeoL7zwwiTJgAEDcuqpp+bP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\n/wP8GPuPevf5VwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e6237b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'user_numb_work_experiences'\n",
    "data = pd.concat([train['scores'], train[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "fig = sns.barplot(x=var, y=\"scores\", data=data)\n",
    "fig.axis(ymin=0, ymax=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This bar chart makes it clearly visible that a \"user_numb_work_experiences\" of 1 makes a higher score possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### user_years_experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    832089\n",
       "4    772407\n",
       "5    748979\n",
       "0    697291\n",
       "6    641061\n",
       "3    185227\n",
       "2     99193\n",
       "1     23753\n",
       "Name: user_years_experience, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_years_experience\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Izs6GhYWFwbilpSW0Wi20Wi06d+4sbawp1tYd0aGDibJc1OJHa1w9e3ZufiMiFdwvr83m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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ff3a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distribution\n",
    "sns.countplot(x=\"user_years_experience\", data=train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AoAYAAACMENQAAACAAYIaAAAAMEBQAwAAAAYIagAAAMAAQQ0AAAAYIKgBAAAAAwF2D4Dq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\nDQAAABggqAEAAAADBDUAAABg4P8BOe+54Vc+f4IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196e7cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'user_years_experience'\n",
    "data = pd.concat([train['scores'], train[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "fig = sns.barplot(x=var, y=\"scores\", data=data)\n",
    "fig.axis(ymin=0, ymax=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. Different levels of experience have different ranges of scores. Therefore I would say this feature is valuable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### user_years_experience_current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1075920\n",
       "0     795302\n",
       "1     586295\n",
       "4     582853\n",
       "3     549398\n",
       "5     210133\n",
       "6     116469\n",
       "7      83630\n",
       "Name: user_years_experience_current, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_years_experience_current\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiKTBUiIiImmwlIiISBosJSIikgZLiYiIpMFSIiIiabCUiIhIGiwlIiKSBkuJiIikwVIi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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168b76d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distribution\n",
    "sns.countplot(x=\"user_years_experience_current\", data=train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GYWFhrnWhoaE6dOhQTYwHAAAAXDPfmtjJDTfccNXH7Xa7JCkwMNC1zmazyeFw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116821cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'user_years_experience_current'\n",
    "data = pd.concat([train['scores'], train[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "fig = sns.barplot(x=var, y=\"scores\", data=data)\n",
    "fig.axis(ymin=0, ymax=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values are roughly equal distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}