Assignment_Marolla_21062023.ipynb

{
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   "cell_type": "code",
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   "id": "3d4a4c67-1ff3-404b-903c-0404a6aad503",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparatory assignment for the LISS Challenge \n",
    "# Formulating the problem\n",
    "# What kind of problem is this? Classification or regression?\n",
    "#         Definitely a classification problem\n",
    "# What are the inputs (features) and what are the outcomes (target)?\n",
    "#         Background data of the respondents (sociodemographics, sociopolitical attitudes, etc.)        \n",
    "# How many classes are there in the target variable?\n",
    "#         Allegedly two, having children in 2020-2022 vs not having children in 2020-2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af6e62c7-20b9-433b-a60d-f551b04d3f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7657cf87-3948-430d-b250-5af231092570",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading in and exploring the data\n",
    "# Reading the LISS features data\n",
    "data = pd.read_csv('LISS_example_input_data.csv', encoding='cp1252', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c485b40-c5bc-454e-8c3a-d57269e1cf99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the LISS target data\n",
    "outcome = pd.read_csv('LISS_example_groundtruth_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c65037bf-78a9-4f37-8d3f-ea75de752938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9459, 29459)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXPLORING THE DATA\n",
    "# How many features do we have?\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5156e576-8706-4b5b-9560-8e736119f677",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nomem_encr</th>\n",
       "      <th>gebjaar</th>\n",
       "      <th>geslacht</th>\n",
       "      <th>nohouse_encr2007</th>\n",
       "      <th>nohouse_encr2008</th>\n",
       "      <th>nohouse_encr2009</th>\n",
       "      <th>nohouse_encr2010</th>\n",
       "      <th>nohouse_encr2011</th>\n",
       "      <th>nohouse_encr2012</th>\n",
       "      <th>nohouse_encr2013</th>\n",
       "      <th>...</th>\n",
       "      <th>cw19l600</th>\n",
       "      <th>cw19l601</th>\n",
       "      <th>cw19l602</th>\n",
       "      <th>cw19l603</th>\n",
       "      <th>cw19l604</th>\n",
       "      <th>cw19l605</th>\n",
       "      <th>cw19l606</th>\n",
       "      <th>cw19l607</th>\n",
       "      <th>cw19l608</th>\n",
       "      <th>cw19l609</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>800000.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</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>800018.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>516668.0</td>\n",
       "      <td>516668.0</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>2</th>\n",
       "      <td>800021.0</td>\n",
       "      <td>1979</td>\n",
       "      <td>Female</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>3</th>\n",
       "      <td>800033.0</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>583404.0</td>\n",
       "      <td>583404.0</td>\n",
       "      <td>583404.0</td>\n",
       "      <td>583404.0</td>\n",
       "      <td>583404.0</td>\n",
       "      <td>583404.0</td>\n",
       "      <td>583404.0</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>4</th>\n",
       "      <td>800042.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>Female</td>\n",
       "      <td>500277.0</td>\n",
       "      <td>500277.0</td>\n",
       "      <td>500277.0</td>\n",
       "      <td>500277.0</td>\n",
       "      <td>500277.0</td>\n",
       "      <td>500277.0</td>\n",
       "      <td>500277.0</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>5</th>\n",
       "      <td>800057.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>Male</td>\n",
       "      <td>580532.0</td>\n",
       "      <td>580532.0</td>\n",
       "      <td>580532.0</td>\n",
       "      <td>580532.0</td>\n",
       "      <td>580532.0</td>\n",
       "      <td>580532.0</td>\n",
       "      <td>580532.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</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>6</th>\n",
       "      <td>800076.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>Female</td>\n",
       "      <td>578048.0</td>\n",
       "      <td>578048.0</td>\n",
       "      <td>589065.0</td>\n",
       "      <td>589065.0</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>7</th>\n",
       "      <td>800085.0</td>\n",
       "      <td>1977</td>\n",
       "      <td>Male</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>8</th>\n",
       "      <td>800103.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557192.0</td>\n",
       "      <td>557192.0</td>\n",
       "      <td>557192.0</td>\n",
       "      <td>557192.0</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>9</th>\n",
       "      <td>800112.0</td>\n",
       "      <td>1990</td>\n",
       "      <td>Male</td>\n",
       "      <td>587908.0</td>\n",
       "      <td>587908.0</td>\n",
       "      <td>587908.0</td>\n",
       "      <td>587908.0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29459 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nomem_encr  gebjaar geslacht  nohouse_encr2007  nohouse_encr2008  \\\n",
       "0    800000.0     1980   Female               NaN               NaN   \n",
       "1    800018.0     1985     Male               NaN               NaN   \n",
       "2    800021.0     1979   Female               NaN               NaN   \n",
       "3    800033.0     1991     Male          583404.0          583404.0   \n",
       "4    800042.0     1975   Female          500277.0          500277.0   \n",
       "5    800057.0     1975     Male          580532.0          580532.0   \n",
       "6    800076.0     1985   Female          578048.0          578048.0   \n",
       "7    800085.0     1977     Male               NaN               NaN   \n",
       "8    800103.0     1978   Female               NaN               NaN   \n",
       "9    800112.0     1990     Male          587908.0          587908.0   \n",
       "\n",
       "   nohouse_encr2009  nohouse_encr2010  nohouse_encr2011  nohouse_encr2012  \\\n",
       "0               NaN               NaN               NaN               NaN   \n",
       "1               NaN               NaN               NaN          516668.0   \n",
       "2               NaN               NaN               NaN               NaN   \n",
       "3          583404.0          583404.0          583404.0          583404.0   \n",
       "4          500277.0          500277.0          500277.0          500277.0   \n",
       "5          580532.0          580532.0          580532.0          580532.0   \n",
       "6          589065.0          589065.0               NaN               NaN   \n",
       "7               NaN               NaN               NaN               NaN   \n",
       "8          557192.0          557192.0          557192.0          557192.0   \n",
       "9          587908.0          587908.0               NaN               NaN   \n",
       "\n",
       "   nohouse_encr2013  ...  cw19l600  cw19l601  cw19l602  cw19l603  cw19l604  \\\n",
       "0               NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "1          516668.0  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "2               NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "3          583404.0  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "4          500277.0  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "5          580532.0  ...      80.0       NaN       NaN       NaN       NaN   \n",
       "6               NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "7               NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "8               NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "9               NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   cw19l605  cw19l606  cw19l607  cw19l608  cw19l609  \n",
       "0       NaN       NaN       NaN       NaN       NaN  \n",
       "1       NaN       NaN       NaN       NaN       NaN  \n",
       "2       NaN       NaN       NaN       NaN       NaN  \n",
       "3       NaN       NaN       NaN       NaN       NaN  \n",
       "4       NaN       NaN       NaN       NaN       NaN  \n",
       "5       NaN       NaN       NaN       NaN       NaN  \n",
       "6       NaN       NaN       NaN       NaN       NaN  \n",
       "7       NaN       NaN       NaN       NaN       NaN  \n",
       "8       NaN       NaN       NaN       NaN       NaN  \n",
       "9       NaN       NaN       NaN       NaN       NaN  \n",
       "\n",
       "[10 rows x 29459 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check if they are both structured in the same way\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f61985d7-d9a7-47c3-aad0-17cdf1e337cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nomem_encr</th>\n",
       "      <th>new_child</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>800000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>800018.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>800021.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>800033.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>800042.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>800057.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>800076.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>800085.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>800103.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>800112.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   nomem_encr  new_child\n",
       "0    800000.0        NaN\n",
       "1    800018.0        NaN\n",
       "2    800021.0        NaN\n",
       "3    800033.0        NaN\n",
       "4    800042.0        NaN\n",
       "5    800057.0        0.0\n",
       "6    800076.0        NaN\n",
       "7    800085.0        NaN\n",
       "8    800103.0        NaN\n",
       "9    800112.0        NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome.head(10)\n",
    "# they indeed look structured in the same way\n",
    "# The feature dataset contains a combination of numerical and categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fcdc48b7-ae77-418f-97b9-221b1b0c5a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    902.000000\n",
       "mean       0.165188\n",
       "std        0.371557\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        0.000000\n",
       "max        1.000000\n",
       "Name: new_child, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look at the target variable descriptives \n",
    "outcome['new_child'].describe()\n",
    "# Only 16.4 % of the samples are in the 1 class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6329c4eb-b1ab-4754-9151-79535461d9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the data\n",
    "selected_columns = ['burgstat2019', 'leeftijd2019', 'woonvorm2019', 'oplmet2019', 'aantalki2019']\n",
    "features = data[selected_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ae2d1bfd-fb08-4ea1-9ca8-44f70b776fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning the data\n",
    "# The null values in the target variable\n",
    "y_isna = outcome['new_child'].isnull()\n",
    "\n",
    "# The rows where any of the features is NaN\n",
    "X_isna = features.isnull().any(axis=1)\n",
    "\n",
    "# For both datasets drop rows where any of the features is NaN OR the outcome is NaN\n",
    "features = features.drop(features[y_isna | X_isna].index)\n",
    "outcome = outcome.drop(outcome[y_isna | X_isna].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a664f55-819a-4731-b089-0a6dc1f8ed49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(504, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a check whether everything went as it should\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24cebedf-42f2-4ee1-94f6-4c8cc5416a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing (recoding) the variables\n",
    "from sklearn.compose import make_column_selector as selector\n",
    "\n",
    "numerical_columns_selector = selector(dtype_exclude=object)\n",
    "categorical_columns_selector = selector(dtype_include=object)\n",
    "\n",
    "numerical_columns = numerical_columns_selector(features)\n",
    "categorical_columns = categorical_columns_selector(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a4587c0-cf27-4ada-9766-872956ada4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "categorical_preprocessor = OneHotEncoder(handle_unknown=\"ignore\")\n",
    "numerical_preprocessor = StandardScaler()\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "preprocessor = ColumnTransformer([\n",
    "    ('one-hot-encoder', categorical_preprocessor, categorical_columns),\n",
    "    ('standard_scaler', numerical_preprocessor, numerical_columns)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b1f5bca-17b2-49f4-b339-d7dabcc77526",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Machine learning - Step 1: Split data into train and test datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features, outcome['new_child'], test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f9337e0-2ab8-436f-913a-a3636b1b2e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(352, 5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "996d487d-f9b5-4948-b644-a16c313271db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152, 5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7db1d363-cb18-4825-a953-c08f085acfab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(352,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c4efd63c-a608-494f-bd6a-b9949b43156a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e08ef935-d1ca-4886-a20e-e3b045df519e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;one-hot-encoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;burgstat2019&#x27;,\n",
       "                                                   &#x27;woonvorm2019&#x27;, &#x27;oplmet2019&#x27;,\n",
       "                                                   &#x27;aantalki2019&#x27;]),\n",
       "                                                 (&#x27;standard_scaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;leeftijd2019&#x27;])])),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;one-hot-encoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;burgstat2019&#x27;,\n",
       "                                                   &#x27;woonvorm2019&#x27;, &#x27;oplmet2019&#x27;,\n",
       "                                                   &#x27;aantalki2019&#x27;]),\n",
       "                                                 (&#x27;standard_scaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;leeftijd2019&#x27;])])),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;one-hot-encoder&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;burgstat2019&#x27;, &#x27;woonvorm2019&#x27;, &#x27;oplmet2019&#x27;,\n",
       "                                  &#x27;aantalki2019&#x27;]),\n",
       "                                (&#x27;standard_scaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;leeftijd2019&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">one-hot-encoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;burgstat2019&#x27;, &#x27;woonvorm2019&#x27;, &#x27;oplmet2019&#x27;, &#x27;aantalki2019&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standard_scaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;leeftijd2019&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(transformers=[('one-hot-encoder',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['burgstat2019',\n",
       "                                                   'woonvorm2019', 'oplmet2019',\n",
       "                                                   'aantalki2019']),\n",
       "                                                 ('standard_scaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  ['leeftijd2019'])])),\n",
       "                ('logisticregression', LogisticRegression(max_iter=500))])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Machine learning - Step 2: training the model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "model = make_pipeline(preprocessor, LogisticRegression(max_iter=500))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1a2c7d8a-f90e-43de-8580-c0f1facc646f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "cv_result = cross_validate(model, X_train, y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e5b10c6a-77f5-4973-ae57-e16205866924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([0.01695514, 0.01495409, 0.01299644, 0.01392746, 0.01232505]),\n",
       " 'score_time': array([0.00402021, 0.00499272, 0.00498939, 0.00402141, 0.0036304 ]),\n",
       " 'test_score': array([0.85915493, 0.84507042, 0.85714286, 0.85714286, 0.85714286])}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d6a9818a-1513-4f6d-891c-3c4e924a88b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8551307847082494, 0.005090185368480278)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result[\"test_score\"].mean(), cv_result[\"test_score\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0f8f3a46-0023-42a2-93eb-f8c6780841ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "_ = model.fit(X_train, y_train)\n",
    "_ = ConfusionMatrixDisplay.from_estimator(model, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "382fdfdd-1e8c-4c00-aa28-97358a67fed0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}