Use Case Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3164a502",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1f718d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data_science_job.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f1718dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   enrollee_id      city  city_development_index gender  \\\n",
       "0         8949  city_103                   0.920   Male   \n",
       "1        29725   city_40                   0.776   Male   \n",
       "2        11561   city_21                   0.624    NaN   \n",
       "3        33241  city_115                   0.789    NaN   \n",
       "4          666  city_162                   0.767   Male   \n",
       "\n",
       "       relevent_experience enrolled_university education_level  \\\n",
       "0  Has relevent experience       no_enrollment        Graduate   \n",
       "1   No relevent experience       no_enrollment        Graduate   \n",
       "2   No relevent experience    Full time course        Graduate   \n",
       "3   No relevent experience                 NaN        Graduate   \n",
       "4  Has relevent experience       no_enrollment         Masters   \n",
       "\n",
       "  major_discipline  experience company_size    company_type  training_hours  \\\n",
       "0             STEM        20.0          NaN             NaN            36.0   \n",
       "1             STEM        15.0        50-99         Pvt Ltd            47.0   \n",
       "2             STEM         5.0          NaN             NaN            83.0   \n",
       "3  Business Degree         0.0          NaN         Pvt Ltd            52.0   \n",
       "4             STEM        20.0        50-99  Funded Startup             8.0   \n",
       "\n",
       "   target  \n",
       "0     1.0  \n",
       "1     0.0  \n",
       "2     0.0  \n",
       "3     1.0  \n",
       "4     0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "822a803c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "enrollee_id                0.000000\n",
       "city                       0.000000\n",
       "city_development_index     2.500261\n",
       "gender                    23.530640\n",
       "relevent_experience        0.000000\n",
       "enrolled_university        2.014824\n",
       "education_level            2.401086\n",
       "major_discipline          14.683161\n",
       "experience                 0.339284\n",
       "company_size              30.994885\n",
       "company_type              32.049274\n",
       "training_hours             3.998330\n",
       "target                     0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9af0177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19158, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92843e5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['city_development_index',\n",
       " 'enrolled_university',\n",
       " 'education_level',\n",
       " 'experience',\n",
       " 'training_hours']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [var for var in df.columns if df[var].isnull().mean() < 0.05 and df[var].isnull().mean() > 0]\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ec1a7af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>city_development_index</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>experience</th>\n",
       "      <th>training_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17053</th>\n",
       "      <td>0.754</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>High School</td>\n",
       "      <td>4.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>0.579</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15605</th>\n",
       "      <td>0.920</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14759</th>\n",
       "      <td>0.624</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12540</th>\n",
       "      <td>0.920</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>10.0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city_development_index enrolled_university education_level  experience  \\\n",
       "17053                   0.754       no_enrollment     High School         4.0   \n",
       "7122                    0.579    Full time course        Graduate         6.0   \n",
       "15605                   0.920       no_enrollment        Graduate        20.0   \n",
       "14759                   0.624       no_enrollment        Graduate         9.0   \n",
       "12540                   0.920       no_enrollment        Graduate        10.0   \n",
       "\n",
       "       training_hours  \n",
       "17053            76.0  \n",
       "7122             21.0  \n",
       "15605            21.0  \n",
       "14759            25.0  \n",
       "12540           153.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cols].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9530e738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduate          11598\n",
       "Masters            4361\n",
       "High School        2017\n",
       "Phd                 414\n",
       "Primary School      308\n",
       "Name: education_level, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education_level'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ecab105f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8968577095730244"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[cols].dropna()) / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8158e979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((19158, 13), (17182, 5))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[cols].dropna()\n",
    "df.shape, new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "895dc3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.hist(bins=50, density=True, figsize=(12, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "929ec46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1RdgeLaWI6s/Bg/1OJWkRzDmsVM4h7AT2A8uAOzPzcETcDExm5jiwB7g7IqaAl2h+4FP63Qc8DhwHrsvMN07StmiJtfuy2/DPAt9f8iiSFllX5xwycx+wb0bbjS3TrwKXd1j2FuCWWdY9AUx0k0P94fMipNOP35CWJFVYHCRJFRYHSVKFxUGSVGFxkCRVWBwkSRUWB0lShU+C05JpjDbatk+O+Iw4adBYHLR0vLWGVBsOK0mSKiwOkqQKi4MkqcLiIEmqsDhIkiosDpKkCouDJKnC4iBJqrA4SJIqLA6SpAqLgySpwuIgSaqwOEiSKroqDhExFBFHI2IqIna3mb88Iu4t8w9ExJqWeTeU9qMRcWlpOz8iHoyIxyPicERcv2hbJElasDmLQ0QsA+4ALgPWA1dExPoZ3YaBlzPzAuB24Nay7HpgB3AhMAR8vqzvOPCvM3M9cDFwXZt1SpL6pJsjh43AVGY+mZmvA2PAthl9tgF3lem9wCUREaV9LDNfy8yngClgY2Y+l5mPAmTmXwNHgPMWvjmSpMUQmTl7h4jtwFBmXlNeXwlsysydLX0OlT7HyusngE3ATcBDmXlPad8D3J+Ze1uWXQP8OfChzPxxm98/AowArFq1asPY2FjPGzk9Pc0zLx3tebl+WnnWal545Vi/Y/RsPrk/+L4NJylNd6anp1mxYkVfM8xHHXPXMTPUM3dr5q1btx7MzPaPYuygr0+Ci4gVwB8Bv9WuMABk5igwCtBoNHLLli09/56JiQn2fH3XApIuveHNt7Hnm/XKDPPLPXnV7P9BOdkmJiaYz/uq3+qYu46ZoZ65F5q5m2GlZ4HzW16vLm1t+0TEGcDZwIuzLRsRb6NZGL6SmV+bT3hJ0snRTXF4BFgXEWsj4kyaJ5jHZ/QZB64u09uBB7I5XjUO7ChXM60F1gEPl/MRe4Ajmfm7i7EhkqTFM+ewUmYej4idwH5gGXBnZh6OiJuBycwcp/lBf3dETAEv0SwglH73AY/TvELpusx8IyI+AlwJfDciHiu/6t9l5r5F3j7VWOPa6Dhv8gv9GYrqlKlfeaSTpatzDuVDe9+Mthtbpl8FLu+w7C3ALTPavgF0/pcvLbZo/3ZrfGEDw+cMs2v0redJJkcmlyKVNLD6ekJaWiqNkQ4zDh6EzVc0/5T0JouDamm2IadFWf9oT1f9Sacci4PUjkcSOs154z1JUoXFQZJU4bCS+u5knz+Q1DuPHCRJFRYHSVKFxUGSVGFxkCRVWBwkSRUWB0lShcVBklRhcZAkVfglOKkPfC6EBp3FQTrNWJjUDYuDdDJ1eMgQnZ4v0UG7W4i/+ZCiDneQ9cNeC2FxkBZDpyfN9VoEernP1BwPKfKeVVoIi4O0CHotAtKg82olSVKFRw6SgP6dqPYE+U8N0r6wOEhaVJ2evz05Mnlyf+8s51gW68N1sT6863A+yOIgaXEt0vO3l+R/0V1eSDC8+TZ2Xbu142rq8GHfq66KQ0QMAZ8BlgFfzMzfmTF/OfBlYAPwIvBrmfl0mXcDMAy8AfxmZu7vZp2SBluvH4iNa2POD9mTpWNWLyToaM7iEBHLgDuAXwaOAY9ExHhmPt7SbRh4OTMviIgdwK3Ar0XEemAHcCHwt4E/i4i/W5aZa52SBsCg/a940PKcqrq5WmkjMJWZT2bm68AYsG1Gn23AXWV6L3BJRERpH8vM1zLzKWCqrK+bdUqS+iQyZx+/i4jtwFBmXlNeXwlsysydLX0OlT7HyusngE3ATcBDmXlPad8D3F8Wm3WdLese4acHfz8HHJ3Hdq4EXpjHcv1Ux8xQz9x1zAz1zF3HzFDP3CcyvwCQmUO9LDzwJ6QzcxQYXcg6ImIyM9tfQjGg6pgZ6pm7jpmhnrnrmBnqmXuhmbsZVnoWOL/l9erS1rZPRJwBnE3zxHSnZbtZpySpT7opDo8A6yJibUScSfME8/iMPuPA1WV6O/BANserxoEdEbE8ItYC64CHu1ynJKlP5hxWyszjEbET2E/zstM7M/NwRNwMTGbmOLAHuDsipoCXaH7YU/rdBzwOHAeuy8w3ANqtc/E3700LGpbqkzpmhnrmrmNmqGfuOmaGeuZe2HD8XCekJUmnH2+8J0mqsDhIkipO+eIQEUMRcTQipiJid7/zdBIRT0fEdyPisYiYLG3nRMSfRsQPyp8/MwA574yI58t3W060tc0ZTZ8t+/47EXHRAGW+KSKeLfv7sYj4eMu8G0rmoxFxaZ8ynx8RD0bE4xFxOCKuL+0Du69nyTzo+/rtEfFwRHy75P73pX1tRBwo+e4tF89QLrC5t7QfiIg1A5T5SxHxVMu+/nBp7/39kZmn7A/Nk91PAO8HzgS+Dazvd64OWZ8GVs5o+4/A7jK9G7h1AHJ+FLgIODRXTuDjNL/0GMDFwIEBynwTsKtN3/XlfbIcWFveP8v6kPm9wEVl+l3A90u2gd3Xs2Qe9H0dwIoy/TbgQNmH9wE7SvsfAP+yTH8S+IMyvQO4d4AyfwnY3qZ/z++PU/3Ioe636Wi9LcldwCf6F6UpM/+c5hVprTrl3AZ8OZseAt4TEe9dkqAtOmTupNMtX5ZUZj6XmY+W6b8GjgDnMcD7epbMnQzKvs7MnC4v31Z+EvhFmrcDguq+bne7oCUzS+ZOen5/nOrF4TzgmZbXx5j9zdpPCfxJRByM5i1DAFZl5nNl+i+BVf2JNqdOOQd9/+8sh9h3tgzZDVzmMmzx8zT/d1iLfT0jMwz4vo6IZRHxGPA88Kc0j2J+mJnH22R7M3eZ/yPg3CUNTDVzZp7Y17eUfX17NO+Y/ZbMxZz7+lQvDnXykcy8CLgMuC4iPto6M5vHhgN/3XFdcgK/D/wd4MPAc8B/6muaDiJiBfBHwG9l5o9b5w3qvm6TeeD3dWa+kZkfpnm3ho3AB/qbaG4zM0fEh4AbaGb/B8A5wL+d7/pP9eJQm9t0ZOaz5c/ngf9K8w36VycO/cqfz/cv4aw65RzY/Z+Zf1X+cf0/4D/z0+GMgckcEW+j+SH7lcz8Wmke6H3dLnMd9vUJmflD4EHgF2gOvZz4onBrtk63C+qLlsxDZWgvM/M14L+wgH19qheHWtymIyLOioh3nZgGPgYc4q23Jbka+OP+JJxTp5zjwFXlSomLgR+1DIn01Yzx1n9Mc39D51u+LHW+oHnngSOZ+bstswZ2X3fKXIN9/bMR8Z4y/Q6az5k5QvMDd3vpNnNft7td0JLpkPl7Lf9xCJrnSFr3dW/vj6U+y77UPzTP0n+f5hjip/qdp0PG99O8auPbwOETOWmOY/5P4AfAnwHnDEDWr9IcGvgbmuOWw51y0rwy4o6y778LNAYo890l03fKP5z3tvT/VMl8FLisT5k/QnPI6DvAY+Xn44O8r2fJPOj7+u8B3yr5DgE3lvb30yxWU8AfAstL+9vL66ky//0DlPmBsq8PAffw0yuaen5/ePsMSVLFqT6sJEmaB4uDJKnC4iBJqrA4SJIqLA6SpAqLgySpwuIgSar4/1avpBcnKD1EAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['training_hours'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "new_df['training_hours'].hist(bins=50, ax=ax, color='green', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c12da018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['training_hours'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "new_df['training_hours'].plot.density(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4cb10af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['city_development_index'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "new_df['city_development_index'].hist(bins=50, ax=ax, color='green', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a9c9af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['city_development_index'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "new_df['city_development_index'].plot.density(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "92c1e7d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['experience'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "new_df['experience'].hist(bins=50, ax=ax, color='green', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14c2992e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['experience'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "new_df['experience'].plot.density(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b18d2417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no_enrollment</th>\n",
       "      <td>0.721213</td>\n",
       "      <td>0.735188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full time course</th>\n",
       "      <td>0.196106</td>\n",
       "      <td>0.200733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Part time course</th>\n",
       "      <td>0.062533</td>\n",
       "      <td>0.064079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  original       cca\n",
       "no_enrollment     0.721213  0.735188\n",
       "Full time course  0.196106  0.200733\n",
       "Part time course  0.062533  0.064079"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([\n",
    "            # percentage of observations per category, original data\n",
    "            df['enrolled_university'].value_counts() / len(df),\n",
    "\n",
    "            # percentage of observations per category, cca data\n",
    "            new_df['enrolled_university'].value_counts() / len(new_df)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "# add column names\n",
    "temp.columns = ['original', 'cca']\n",
    "\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e2c5ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Graduate</th>\n",
       "      <td>0.605387</td>\n",
       "      <td>0.619835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Masters</th>\n",
       "      <td>0.227633</td>\n",
       "      <td>0.234082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High School</th>\n",
       "      <td>0.105282</td>\n",
       "      <td>0.107380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phd</th>\n",
       "      <td>0.021610</td>\n",
       "      <td>0.022116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary School</th>\n",
       "      <td>0.016077</td>\n",
       "      <td>0.016587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                original       cca\n",
       "Graduate        0.605387  0.619835\n",
       "Masters         0.227633  0.234082\n",
       "High School     0.105282  0.107380\n",
       "Phd             0.021610  0.022116\n",
       "Primary School  0.016077  0.016587"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([\n",
    "            # percentage of observations per category, original data\n",
    "            df['education_level'].value_counts() / len(df),\n",
    "\n",
    "            # percentage of observations per category, cca data\n",
    "            new_df['education_level'].value_counts() / len(new_df)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "# add column names\n",
    "temp.columns = ['original', 'cca']\n",
    "\n",
    "temp"
   ]
  }
 ],
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