ct_capstone_1_work.ipynb

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  {
   "cell_type": "markdown",
   "id": "66e532c4",
   "metadata": {},
   "source": [
    "# Coding Temple Capstone I: Caravan Insurance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd95060",
   "metadata": {},
   "source": [
    "## Harry Hill - May 2023"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e1690ee",
   "metadata": {},
   "source": [
    "### Step 1: Data Collection\n",
    "\n",
    "Dataset: Caravan Insurance Challenge, updated 2017 https://www.kaggle.com/datasets/uciml/caravan-insurance-challenge\n",
    "P. van der Putten and M. van Someren (eds) . CoIL Challenge 2000: The Insurance Company Case. Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 2000-09. June 22, 2000.\n",
    "\n",
    "Context: Caravan Insurance protects a mobile-home in the event it is damaged or stolen. Information was collected on a large group of people, a 6% sample of which chose to buy caravan insurance.\n",
    "\n",
    "Format: 1 csv file, tabular\n",
    "\n",
    "Defining Question: Which customers were more likely to be interested in buying Caravan Insurance and why? \n",
    "\n",
    "Hypothesis: If the archetypal retired couple traveling on their savings holds true, then the the average caravan insurance holder will have at least one car, be older and more financially stable, and consist of a family of at least two people who tend to have other insurance policies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4db836e9",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>train</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>train</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>train</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>train</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>train</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>train</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>train</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>train</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>train</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>train</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>train</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0        1         2        3         4         5       6       7   \\\n",
       "0   ORIGIN  MOSTYPE  MAANTHUI  MGEMOMV  MGEMLEEF  MOSHOOFD  MGODRK  MGODPR   \n",
       "1    train       33         1        3         2         8       0       5   \n",
       "2    train       37         1        2         2         8       1       4   \n",
       "3    train       37         1        2         2         8       0       4   \n",
       "4    train        9         1        3         3         3       2       3   \n",
       "5    train       40         1        4         2        10       1       4   \n",
       "6    train       23         1        2         1         5       0       5   \n",
       "7    train       39         2        3         2         9       2       2   \n",
       "8    train       33         1        2         3         8       0       7   \n",
       "9    train       33         1        2         4         8       0       1   \n",
       "10   train       11         2        3         3         3       3       5   \n",
       "11   train       10         1        4         3         3       1       4   \n",
       "12   train        9         1        3         3         3       1       3   \n",
       "13   train       33         1        2         3         8       1       4   \n",
       "14   train       41         1        3         3        10       0       5   \n",
       "15   train       23         1        1         2         5       0       6   \n",
       "16   train       33         1        2         3         8       0       7   \n",
       "17   train       38         1        2         3         9       0       6   \n",
       "18   train       22         2        3         3         5       0       5   \n",
       "19   train       13         1        4         2         3       2       4   \n",
       "\n",
       "        8       9       10      11      12        13        14        15  \\\n",
       "0   MGODOV  MGODGE  MRELGE  MRELSA  MRELOV  MFALLEEN  MFGEKIND  MFWEKIND   \n",
       "1        1       3       7       0       2         1         2         6   \n",
       "2        1       4       6       2       2         0         4         5   \n",
       "3        2       4       3       2       4         4         4         2   \n",
       "4        2       4       5       2       2         2         3         4   \n",
       "5        1       4       7       1       2         2         4         4   \n",
       "6        0       5       0       6       3         3         5         2   \n",
       "7        0       5       7       2       0         0         3         6   \n",
       "8        0       2       7       2       0         0         5         4   \n",
       "9        3       6       6       0       3         3         3         3   \n",
       "10       0       2       7       0       2         2         2         6   \n",
       "11       1       4       7       1       2         0         3         6   \n",
       "12       2       4       7       1       2         2         3         5   \n",
       "13       1       4       6       2       3         3         4         3   \n",
       "14       0       4       7       1       1         1         4         5   \n",
       "15       1       2       1       2       6         5         3         1   \n",
       "16       0       2       7       2       0         0         5         4   \n",
       "17       0       3       7       0       2         0         6         3   \n",
       "18       0       4       7       0       2         0         2         7   \n",
       "19       0       3       7       0       2         1         3         6   \n",
       "\n",
       "          16        17        18        19        20        21        22  \\\n",
       "0   MOPLHOOG  MOPLMIDD  MOPLLAAG  MBERHOOG  MBERZELF  MBERBOER  MBERMIDD   \n",
       "1          1         2         7         1         0         1         2   \n",
       "2          0         5         4         0         0         0         5   \n",
       "3          0         5         4         0         0         0         7   \n",
       "4          3         4         2         4         0         0         3   \n",
       "5          5         4         0         0         5         4         0   \n",
       "6          0         5         4         2         0         0         4   \n",
       "7          0         4         5         0         0         0         4   \n",
       "8          0         3         6         2         0         0         2   \n",
       "9          0         1         8         1         1         0         1   \n",
       "10         0         4         5         2         0         0         3   \n",
       "11         4         3         3         0         0         0         9   \n",
       "12         1         7         1         4         0         0         5   \n",
       "13         1         4         5         1         1         0         3   \n",
       "14         2         4         4         3         0         1         2   \n",
       "15         2         6         2         1         0         0         4   \n",
       "16         0         3         6         2         0         0         2   \n",
       "17         2         6         2         2         0         0         4   \n",
       "18         2         1         7         0         2         0         1   \n",
       "19         5         4         1         6         0         0         3   \n",
       "\n",
       "          23        24    25     26     27    28    29      30      31     32  \\\n",
       "0   MBERARBG  MBERARBO  MSKA  MSKB1  MSKB2  MSKC  MSKD  MHHUUR  MHKOOP  MAUT1   \n",
       "1          5         2     1      1      2     6     1       1       8      8   \n",
       "2          0         4     0      2      3     5     0       2       7      7   \n",
       "3          0         2     0      5      0     4     0       7       2      7   \n",
       "4          1         2     3      2      1     4     0       5       4      9   \n",
       "5          0         0     9      0      0     0     0       4       5      6   \n",
       "6          2         2     2      2      2     4     2       9       0      5   \n",
       "7          1         5     0      1      4     5     0       6       3      8   \n",
       "8          5         2     2      1      2     5     2       0       9      4   \n",
       "9          8         1     1      1      0     8     1       9       0      5   \n",
       "10         3         3     1      2      1     4     2       0       9      6   \n",
       "11         0         0     3      0      6     0     0       0       9      6   \n",
       "12         1         1     2      3      4     1     0       6       3      7   \n",
       "13         2         4     1      2      2     5     1       5       4      6   \n",
       "14         2         2     4      2      1     4     0       5       4      7   \n",
       "15         3         2     1      3      2     4     0       9       0      5   \n",
       "16         5         2     2      1      2     5     2       0       9      4   \n",
       "17         0         4     2      2      4     2     0       6       3      7   \n",
       "18         1         5     2      0      0     7     0       4       5      6   \n",
       "19         0         1     5      2      1     2     0       4       5      7   \n",
       "\n",
       "       33     34       35      36       37        38        39        40  \\\n",
       "0   MAUT2  MAUT0  MZFONDS  MZPART  MINKM30  MINK3045  MINK4575  MINK7512   \n",
       "1       0      1        8       1        0         4         5         0   \n",
       "2       1      2        6       3        2         0         5         2   \n",
       "3       0      2        9       0        4         5         0         0   \n",
       "4       0      0        7       2        1         5         3         0   \n",
       "5       2      1        5       4        0         0         9         0   \n",
       "6       3      3        9       0        5         2         3         0   \n",
       "7       0      1        9       0        4         3         3         0   \n",
       "8       4      2        6       3        2         5         3         0   \n",
       "9       2      3        7       2        7         2         1         0   \n",
       "10      1      2        6       3        2         3         3         1   \n",
       "11      2      1        5       4        0         3         2         2   \n",
       "12      1      2        4       5        3         4         3         1   \n",
       "13      1      3        7       2        3         4         3         1   \n",
       "14      2      0        8       1        3         2         4         1   \n",
       "15      1      3        5       4        4         3         3         0   \n",
       "16      4      2        6       3        2         5         3         0   \n",
       "17      2      0        4       5        0         1         6         2   \n",
       "18      1      2        7       2        0         6         3         0   \n",
       "19      1      2        5       4        2         2         6         0   \n",
       "\n",
       "          41       42        43       44       45       46        47       48  \\\n",
       "0   MINK123M  MINKGEM  MKOOPKLA  PWAPART  PWABEDR  PWALAND  PPERSAUT  PBESAUT   \n",
       "1          0        4         3        0        0        0         6        0   \n",
       "2          0        5         4        2        0        0         0        0   \n",
       "3          0        3         4        2        0        0         6        0   \n",
       "4          0        4         4        0        0        0         6        0   \n",
       "5          0        6         3        0        0        0         0        0   \n",
       "6          0        3         3        0        0        0         6        0   \n",
       "7          0        3         5        0        0        0         6        0   \n",
       "8          0        3         3        0        0        0         0        0   \n",
       "9          0        2         3        0        0        0         5        0   \n",
       "10         0        4         7        2        0        0         0        0   \n",
       "11         2        8         7        0        0        0         6        0   \n",
       "12         0        3         4        2        0        0         5        0   \n",
       "13         0        3         3        0        0        0         6        0   \n",
       "14         0        4         4        0        0        0         0        0   \n",
       "15         0        3         3        2        0        0         5        0   \n",
       "16         0        3         3        2        0        0         0        0   \n",
       "17         0        5         4        0        0        0         0        0   \n",
       "18         0        4         2        2        0        0         6        0   \n",
       "19         0        4         6        2        0        0         5        0   \n",
       "\n",
       "         49       50        51        52      53     54      55        56  \\\n",
       "0   PMOTSCO  PVRAAUT  PAANHANG  PTRACTOR  PWERKT  PBROM  PLEVEN  PPERSONG   \n",
       "1         0        0         0         0       0      0       0         0   \n",
       "2         0        0         0         0       0      0       0         0   \n",
       "3         0        0         0         0       0      0       0         0   \n",
       "4         0        0         0         0       0      0       0         0   \n",
       "5         0        0         0         0       0      0       0         0   \n",
       "6         0        0         0         0       0      0       0         0   \n",
       "7         0        0         0         0       0      0       0         0   \n",
       "8         0        0         0         0       0      3       0         0   \n",
       "9         0        0         0         0       0      0       0         0   \n",
       "10        0        0         0         0       0      0       0         0   \n",
       "11        0        0         0         0       0      0       0         0   \n",
       "12        0        0         0         0       0      0       0         0   \n",
       "13        0        0         0         0       0      0       0         0   \n",
       "14        0        0         0         0       0      0       0         0   \n",
       "15        0        0         0         0       0      0       0         0   \n",
       "16        0        0         0         0       0      0       0         0   \n",
       "17        0        0         0         0       0      0       0         0   \n",
       "18        0        0         0         0       0      0       0         0   \n",
       "19        0        0         0         0       0      0       0         0   \n",
       "\n",
       "         57       58      59       60        61      62       63        64  \\\n",
       "0   PGEZONG  PWAOREG  PBRAND  PZEILPL  PPLEZIER  PFIETS  PINBOED  PBYSTAND   \n",
       "1         0        0       5        0         0       0        0         0   \n",
       "2         0        0       2        0         0       0        0         0   \n",
       "3         0        0       2        0         0       0        0         0   \n",
       "4         0        0       2        0         0       0        0         0   \n",
       "5         0        0       6        0         0       0        0         0   \n",
       "6         0        0       0        0         0       0        0         0   \n",
       "7         0        0       0        0         0       0        0         0   \n",
       "8         0        0       0        0         0       0        0         0   \n",
       "9         0        0       0        0         0       0        0         0   \n",
       "10        0        0       3        0         0       0        0         0   \n",
       "11        0        0       0        0         0       0        0         0   \n",
       "12        0        0       2        0         0       0        0         0   \n",
       "13        0        0       0        0         0       0        0         0   \n",
       "14        0        0       0        0         0       0        0         0   \n",
       "15        0        0       2        0         0       0        0         0   \n",
       "16        0        0       4        0         0       0        0         0   \n",
       "17        0        0       0        0         0       0        0         0   \n",
       "18        0        0       3        0         0       0        0         0   \n",
       "19        0        0       2        0         0       0        0         0   \n",
       "\n",
       "         65       66       67        68       69       70       71        72  \\\n",
       "0   AWAPART  AWABEDR  AWALAND  APERSAUT  ABESAUT  AMOTSCO  AVRAAUT  AAANHANG   \n",
       "1         0        0        0         1        0        0        0         0   \n",
       "2         2        0        0         0        0        0        0         0   \n",
       "3         1        0        0         1        0        0        0         0   \n",
       "4         0        0        0         1        0        0        0         0   \n",
       "5         0        0        0         0        0        0        0         0   \n",
       "6         0        0        0         1        0        0        0         0   \n",
       "7         0        0        0         1        0        0        0         0   \n",
       "8         0        0        0         0        0        0        0         0   \n",
       "9         0        0        0         1        0        0        0         0   \n",
       "10        1        0        0         0        0        0        0         0   \n",
       "11        0        0        0         1        0        0        0         0   \n",
       "12        1        0        0         1        0        0        0         0   \n",
       "13        0        0        0         1        0        0        0         0   \n",
       "14        0        0        0         0        0        0        0         0   \n",
       "15        1        0        0         1        0        0        0         0   \n",
       "16        1        0        0         0        0        0        0         0   \n",
       "17        0        0        0         0        0        0        0         0   \n",
       "18        1        0        0         1        0        0        0         0   \n",
       "19        1        0        0         1        0        0        0         0   \n",
       "\n",
       "          73      74     75      76        77       78       79      80  \\\n",
       "0   ATRACTOR  AWERKT  ABROM  ALEVEN  APERSONG  AGEZONG  AWAOREG  ABRAND   \n",
       "1          0       0      0       0         0        0        0       1   \n",
       "2          0       0      0       0         0        0        0       1   \n",
       "3          0       0      0       0         0        0        0       1   \n",
       "4          0       0      0       0         0        0        0       1   \n",
       "5          0       0      0       0         0        0        0       1   \n",
       "6          0       0      0       0         0        0        0       0   \n",
       "7          0       0      0       0         0        0        0       0   \n",
       "8          0       0      1       0         0        0        0       0   \n",
       "9          0       0      0       0         0        0        0       0   \n",
       "10         0       0      0       0         0        0        0       1   \n",
       "11         0       0      0       0         0        0        0       0   \n",
       "12         0       0      0       0         0        0        0       1   \n",
       "13         0       0      0       0         0        0        0       0   \n",
       "14         0       0      0       0         0        0        0       0   \n",
       "15         0       0      0       0         0        0        0       1   \n",
       "16         0       0      0       0         0        0        0       1   \n",
       "17         0       0      0       0         0        0        0       0   \n",
       "18         0       0      0       0         0        0        0       1   \n",
       "19         0       0      0       0         0        0        0       1   \n",
       "\n",
       "         81        82      83       84        85       86  \n",
       "0   AZEILPL  APLEZIER  AFIETS  AINBOED  ABYSTAND  CARAVAN  \n",
       "1         0         0       0        0         0        0  \n",
       "2         0         0       0        0         0        0  \n",
       "3         0         0       0        0         0        0  \n",
       "4         0         0       0        0         0        0  \n",
       "5         0         0       0        0         0        0  \n",
       "6         0         0       0        0         0        0  \n",
       "7         0         0       0        0         0        0  \n",
       "8         0         0       0        0         0        0  \n",
       "9         0         0       0        0         0        0  \n",
       "10        0         0       0        0         0        0  \n",
       "11        0         0       0        0         0        0  \n",
       "12        0         0       0        0         0        0  \n",
       "13        0         0       0        0         0        0  \n",
       "14        0         0       0        0         0        0  \n",
       "15        0         0       0        0         0        0  \n",
       "16        0         0       0        0         0        0  \n",
       "17        0         0       0        0         0        0  \n",
       "18        0         0       0        0         0        0  \n",
       "19        0         0       0        0         0        0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"\n",
    "    Return a .csv file as a list.\n",
    "    \"\"\"\n",
    "    with open(filename, 'r') as f:\n",
    "        data = list(csv.reader(f))\n",
    "    return data\n",
    "\n",
    "file_path = \"../files/caravan.csv\"\n",
    "\n",
    "data_list = read_csv(file_path)\n",
    "\n",
    "# convert to dataframe\n",
    "df = pd.DataFrame(data_list)\n",
    "pd.set_option('display.max_column',None)\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14067633",
   "metadata": {},
   "source": [
    "### Step 2: Data Cleaning\n",
    "\n",
    "Data Setup:\n",
    "* each observation is an aggregated postal code 'customer'; some categorical variables seem to be the average or mode of the customers in the area\n",
    "* this dataset was originally created as part of a Kaggle competition in two parts, train and test (ORIGIN variable), so that machine learning could be used to predict the target CARAVAN variable in the test dataset. The competition is over and the two parts have been combined for a bigger dataset\n",
    "* header codes\n",
    "    * 'M-' ; demographic statistics\n",
    "    * 'P-' ; product ownership / contribution ; 0-6 with 0 being not having the policy, 1 being low contribution(deductible)/high premium, 6 being high contribution(deductible)/low premium\n",
    "    * 'A-' ; insurance statistics / number of policies ; how many of this policy the 'customer' has (usually 0,1, or 2)\n",
    "        \n",
    "Initial Issues:\n",
    "1. Most columns have an uppercase code instead of a legible header\n",
    "    - convert to concise and descriptive headers\n",
    "2. Several columns' values are numeric keys for short phrases\n",
    "    1. analyze data while referring to keys\n",
    "    2. convert key values to descriptive value (could make it harder to do some analyses on them)\n",
    "        - create dictionaries for keys\n",
    "        - create function that converts each value in a column\n",
    "3. There is no index column for actual postal code, only the index created when the data was converted to a dataframe\n",
    "4. The demograpghic columns could be further organized into demographic aspects (Religion, relationship status, household size, education level, employment type, social class, home ownership, number of cars, health insurance, income level)\n",
    "    - will have to analyze in chunks or aggregate into single columns\n",
    "    - 'M-' header code (M)\n",
    "    - '-DAC-' demographic aspect code (GOD, REL, F, OPL, BER, SK, H, AUT, Z, INK)  \n",
    "5. Hidden aggregation of 'customer' observation leads to questions for data interpretation; average customer in postal code? one customer selected from each postal code?\n",
    "6. Demographic variables have categorical numerical keys pointing to a range which makes for difficult analyses (i.e. customer age bin #1 is 20-30 years). Includes L1, L3, and L4 variables\n",
    "    1. Replace  with the median value of each bin -> customer age #1 is 25 years old\n",
    "    2. Generate a random number within the specified range for each observation\n",
    "\n",
    "1. Postal Code Demographic Statistics    \n",
    "    MOSTYPE: Customer Subtype; see L0\n",
    "        L0: Customer subtype\n",
    "            1: High Income, expensive child\n",
    "            2: Very Important Provincials\n",
    "            3: High status seniors\n",
    "            4: Affluent senior apartments\n",
    "            5: Mixed seniors\n",
    "            6: Career and childcare\n",
    "            7: DINK's (double income no kids)\n",
    "            8: Middle class families\n",
    "            9: Modern, complete families\n",
    "            10: Stable family\n",
    "            11: Family starters\n",
    "            12: Affluent young families\n",
    "            13: Young all american family\n",
    "            14: Junior cosmopolitan\n",
    "            15: Senior cosmopolitans\n",
    "            16: Students in apartments\n",
    "            17: Fresh masters in the city\n",
    "            18: Single youth\n",
    "            19: Suburban youth\n",
    "            20: Etnically diverse\n",
    "            21: Young urban have-nots\n",
    "            22: Mixed apartment dwellers\n",
    "            23: Young and rising\n",
    "            24: Young, low educated\n",
    "            25: Young seniors in the city\n",
    "            26: Own home elderly\n",
    "            27: Seniors in apartments\n",
    "            28: Residential elderly\n",
    "            29: Porchless seniors: no front yard\n",
    "            30: Religious elderly singles\n",
    "            31: Low income catholics\n",
    "            32: Mixed seniors\n",
    "            33: Lower class large families\n",
    "            34: Large family, employed child\n",
    "            35: Village families\n",
    "            36: Couples with teens 'Married with children'\n",
    "            37: Mixed small town dwellers\n",
    "            38: Traditional families\n",
    "            39: Large religous families\n",
    "            40: Large family farms\n",
    "            41: Mixed rurals\n",
    "    MAANTHUI: Number of houses 1 - 10\n",
    "    MGEMOMV: Avg size household 1 - 6\n",
    "    MGEMLEEF: Avg age; see L1\n",
    "        L1: average age keys:\n",
    "            1: 20-30 years\n",
    "            2: 30-40 years\n",
    "            3: 40-50 years\n",
    "            4: 50-60 years\n",
    "            5: 60-70 years\n",
    "            6: 70-80 years\n",
    "    MOSHOOFD: Customer main type; see L2\n",
    "        L2: customer main type keys:\n",
    "            1: Successful hedonists\n",
    "            2: Driven Growers\n",
    "            3: Average Family\n",
    "            4: Career Loners\n",
    "            5: Living well\n",
    "            6: Cruising Seniors\n",
    "            7: Retired and Religeous\n",
    "            8: Family with grown ups\n",
    "            9: Conservative families\n",
    "            10: Farmers\n",
    "    Postal Code Group Percentages\n",
    "        L3: percentage keys:\n",
    "            0: 0%\n",
    "            1: 1 - 10%\n",
    "            2: 11 - 23%\n",
    "            3: 24 - 36%\n",
    "            4: 37 - 49%\n",
    "            5: 50 - 62%\n",
    "            6: 63 - 75%\n",
    "            7: 76 - 88%\n",
    "            8: 89 - 99%\n",
    "            9: 100%\n",
    "        MGODRK: Roman catholic\n",
    "        MGODPR: Protestant …\n",
    "        MGODOV: Other religion\n",
    "        MGODGE: No religion\n",
    "        MRELGE: Married\n",
    "        MRELSA: Living together\n",
    "        MRELOV: Other relation\n",
    "        MFALLEEN: Singles\n",
    "        MFGEKIND: Household without children\n",
    "        MFWEKIND: Household with children\n",
    "        MOPLHOOG: High level education\n",
    "        MOPLMIDD: Medium level education\n",
    "        MOPLLAAG: Lower level education\n",
    "        MBERHOOG: High status\n",
    "        MBERZELF: Entrepreneur\n",
    "        MBERBOER: Farmer\n",
    "        MBERMIDD: Middle management\n",
    "        MBERARBG: Skilled labourers\n",
    "        MBERARBO: Unskilled labourers\n",
    "        MSKA: Social class A\n",
    "        MSKB1: Social class B1\n",
    "        MSKB2: Social class B2\n",
    "        MSKC: Social class C\n",
    "        MSKD: Social class D\n",
    "        MHHUUR: Rented house\n",
    "        MHKOOP: Home owners\n",
    "        MAUT1: 1 car\n",
    "        MAUT2: 2 cars\n",
    "        MAUT0: No car\n",
    "        MZFONDS: National Health Service\n",
    "        MZPART: Private health insurance\n",
    "        MINKM30: Income < 30.000\n",
    "        MINK3045: Income 30-45.000\n",
    "        MINK4575: Income 45-75.000\n",
    "        MINK7512: Income 75-122.000\n",
    "        MINK123M: Income >123.000\n",
    "        MINKGEM: Average income\n",
    "        MKOOPKLA: Purchasing power class\n",
    "    \n",
    "2. Postal Code Product Ownership Statistics\n",
    "    L4: total number keys:\n",
    "        0: 0\n",
    "        1: 1 - 49\n",
    "        2: 50 - 99\n",
    "        3: 100 - 199\n",
    "        4: 200 - 499\n",
    "        5: 500 - 999\n",
    "        6: 1000 - 4999\n",
    "        7: 5000 - 9999\n",
    "        8: 10,000 - 19,999\n",
    "        9: >= 20,000\n",
    "    * PWAPART: Contribution private third party insurance\n",
    "    * PWABEDR: Contribution third party insurance (firms) …\n",
    "    * PWALAND: Contribution third party insurane (agriculture)\n",
    "    * PPERSAUT: Contribution car policies\n",
    "    * PBESAUT: Contribution delivery van policies\n",
    "    * PMOTSCO: Contribution motorcycle/scooter policies\n",
    "    * PVRAAUT: Contribution lorry policies\n",
    "    * PAANHANG: Contribution trailer policies\n",
    "    * PTRACTOR: Contribution tractor policies\n",
    "    * PWERKT: Contribution agricultural machines policies\n",
    "    * PBROM: Contribution moped policies\n",
    "    * PLEVEN: Contribution life insurances\n",
    "    * PPERSONG: Contribution private accident insurance policies\n",
    "    * PGEZONG: Contribution family accidents insurance policies\n",
    "    * PWAOREG: Contribution disability insurance policies\n",
    "    * PBRAND: Contribution fire policies\n",
    "    * PZEILPL: Contribution surfboard policies\n",
    "    * PPLEZIER: Contribution boat policies\n",
    "    * PFIETS: Contribution bicycle policies\n",
    "    * PINBOED: Contribution property insurance policies\n",
    "    * PBYSTAND: Contribution social security insurance policies\n",
    "3. Postal Code Insurance Statistics (Number of Policies)\n",
    "    * AWAPART: Number of private third party insurance 1 - 12\n",
    "    * AWABEDR: Number of third party insurance (firms) …\n",
    "    * AWALAND: Number of third party insurance (agriculture)\n",
    "    * APERSAUT: Number of car policies\n",
    "    * ABESAUT: Number of delivery van policies\n",
    "    * AMOTSCO: Number of motorcycle/scooter policies\n",
    "    * AVRAAUT: Number of lorry policies\n",
    "    * AAANHANG: Number of trailer policies\n",
    "    * ATRACTOR: Number of tractor policies\n",
    "    * AWERKT: Number of agricultural machines policies\n",
    "    * ABROM: Number of moped policies\n",
    "    * ALEVEN: Number of life insurances\n",
    "    * APERSONG: Number of private accident insurance policies\n",
    "    * AGEZONG: Number of family accidents insurance policies\n",
    "    * AWAOREG: Number of disability insurance policies\n",
    "    * ABRAND: Number of fire policies\n",
    "    * AZEILPL: Number of surfboard policies\n",
    "    * APLEZIER: Number of boat policies\n",
    "    * AFIETS: Number of bicycle policies\n",
    "    * AINBOED: Number of property insurance policies\n",
    "    * ABYSTAND: Number of social security insurance policies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "11848026",
   "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>ORIGIN</th>\n",
       "      <th>MOSTYPE</th>\n",
       "      <th>MAANTHUI</th>\n",
       "      <th>MGEMOMV</th>\n",
       "      <th>MGEMLEEF</th>\n",
       "      <th>MOSHOOFD</th>\n",
       "      <th>MGODRK</th>\n",
       "      <th>MGODPR</th>\n",
       "      <th>MGODOV</th>\n",
       "      <th>MGODGE</th>\n",
       "      <th>MRELGE</th>\n",
       "      <th>MRELSA</th>\n",
       "      <th>MRELOV</th>\n",
       "      <th>MFALLEEN</th>\n",
       "      <th>MFGEKIND</th>\n",
       "      <th>MFWEKIND</th>\n",
       "      <th>MOPLHOOG</th>\n",
       "      <th>MOPLMIDD</th>\n",
       "      <th>MOPLLAAG</th>\n",
       "      <th>MBERHOOG</th>\n",
       "      <th>MBERZELF</th>\n",
       "      <th>MBERBOER</th>\n",
       "      <th>MBERMIDD</th>\n",
       "      <th>MBERARBG</th>\n",
       "      <th>MBERARBO</th>\n",
       "      <th>MSKA</th>\n",
       "      <th>MSKB1</th>\n",
       "      <th>MSKB2</th>\n",
       "      <th>MSKC</th>\n",
       "      <th>MSKD</th>\n",
       "      <th>MHHUUR</th>\n",
       "      <th>MHKOOP</th>\n",
       "      <th>MAUT1</th>\n",
       "      <th>MAUT2</th>\n",
       "      <th>MAUT0</th>\n",
       "      <th>MZFONDS</th>\n",
       "      <th>MZPART</th>\n",
       "      <th>MINKM30</th>\n",
       "      <th>MINK3045</th>\n",
       "      <th>MINK4575</th>\n",
       "      <th>MINK7512</th>\n",
       "      <th>MINK123M</th>\n",
       "      <th>MINKGEM</th>\n",
       "      <th>MKOOPKLA</th>\n",
       "      <th>PWAPART</th>\n",
       "      <th>PWABEDR</th>\n",
       "      <th>PWALAND</th>\n",
       "      <th>PPERSAUT</th>\n",
       "      <th>PBESAUT</th>\n",
       "      <th>PMOTSCO</th>\n",
       "      <th>PVRAAUT</th>\n",
       "      <th>PAANHANG</th>\n",
       "      <th>PTRACTOR</th>\n",
       "      <th>PWERKT</th>\n",
       "      <th>PBROM</th>\n",
       "      <th>PLEVEN</th>\n",
       "      <th>PPERSONG</th>\n",
       "      <th>PGEZONG</th>\n",
       "      <th>PWAOREG</th>\n",
       "      <th>PBRAND</th>\n",
       "      <th>PZEILPL</th>\n",
       "      <th>PPLEZIER</th>\n",
       "      <th>PFIETS</th>\n",
       "      <th>PINBOED</th>\n",
       "      <th>PBYSTAND</th>\n",
       "      <th>AWAPART</th>\n",
       "      <th>AWABEDR</th>\n",
       "      <th>AWALAND</th>\n",
       "      <th>APERSAUT</th>\n",
       "      <th>ABESAUT</th>\n",
       "      <th>AMOTSCO</th>\n",
       "      <th>AVRAAUT</th>\n",
       "      <th>AAANHANG</th>\n",
       "      <th>ATRACTOR</th>\n",
       "      <th>AWERKT</th>\n",
       "      <th>ABROM</th>\n",
       "      <th>ALEVEN</th>\n",
       "      <th>APERSONG</th>\n",
       "      <th>AGEZONG</th>\n",
       "      <th>AWAOREG</th>\n",
       "      <th>ABRAND</th>\n",
       "      <th>AZEILPL</th>\n",
       "      <th>APLEZIER</th>\n",
       "      <th>AFIETS</th>\n",
       "      <th>AINBOED</th>\n",
       "      <th>ABYSTAND</th>\n",
       "      <th>CARAVAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
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       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
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       "      <td>7</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>train</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 ORIGIN MOSTYPE MAANTHUI MGEMOMV MGEMLEEF MOSHOOFD MGODRK MGODPR MGODOV  \\\n",
       "1  train      33        1       3        2        8      0      5      1   \n",
       "2  train      37        1       2        2        8      1      4      1   \n",
       "3  train      37        1       2        2        8      0      4      2   \n",
       "4  train       9        1       3        3        3      2      3      2   \n",
       "5  train      40        1       4        2       10      1      4      1   \n",
       "\n",
       "0 MGODGE MRELGE MRELSA MRELOV MFALLEEN MFGEKIND MFWEKIND MOPLHOOG MOPLMIDD  \\\n",
       "1      3      7      0      2        1        2        6        1        2   \n",
       "2      4      6      2      2        0        4        5        0        5   \n",
       "3      4      3      2      4        4        4        2        0        5   \n",
       "4      4      5      2      2        2        3        4        3        4   \n",
       "5      4      7      1      2        2        4        4        5        4   \n",
       "\n",
       "0 MOPLLAAG MBERHOOG MBERZELF MBERBOER MBERMIDD MBERARBG MBERARBO MSKA MSKB1  \\\n",
       "1        7        1        0        1        2        5        2    1     1   \n",
       "2        4        0        0        0        5        0        4    0     2   \n",
       "3        4        0        0        0        7        0        2    0     5   \n",
       "4        2        4        0        0        3        1        2    3     2   \n",
       "5        0        0        5        4        0        0        0    9     0   \n",
       "\n",
       "0 MSKB2 MSKC MSKD MHHUUR MHKOOP MAUT1 MAUT2 MAUT0 MZFONDS MZPART MINKM30  \\\n",
       "1     2    6    1      1      8     8     0     1       8      1       0   \n",
       "2     3    5    0      2      7     7     1     2       6      3       2   \n",
       "3     0    4    0      7      2     7     0     2       9      0       4   \n",
       "4     1    4    0      5      4     9     0     0       7      2       1   \n",
       "5     0    0    0      4      5     6     2     1       5      4       0   \n",
       "\n",
       "0 MINK3045 MINK4575 MINK7512 MINK123M MINKGEM MKOOPKLA PWAPART PWABEDR  \\\n",
       "1        4        5        0        0       4        3       0       0   \n",
       "2        0        5        2        0       5        4       2       0   \n",
       "3        5        0        0        0       3        4       2       0   \n",
       "4        5        3        0        0       4        4       0       0   \n",
       "5        0        9        0        0       6        3       0       0   \n",
       "\n",
       "0 PWALAND PPERSAUT PBESAUT PMOTSCO PVRAAUT PAANHANG PTRACTOR PWERKT PBROM  \\\n",
       "1       0        6       0       0       0        0        0      0     0   \n",
       "2       0        0       0       0       0        0        0      0     0   \n",
       "3       0        6       0       0       0        0        0      0     0   \n",
       "4       0        6       0       0       0        0        0      0     0   \n",
       "5       0        0       0       0       0        0        0      0     0   \n",
       "\n",
       "0 PLEVEN PPERSONG PGEZONG PWAOREG PBRAND PZEILPL PPLEZIER PFIETS PINBOED  \\\n",
       "1      0        0       0       0      5       0        0      0       0   \n",
       "2      0        0       0       0      2       0        0      0       0   \n",
       "3      0        0       0       0      2       0        0      0       0   \n",
       "4      0        0       0       0      2       0        0      0       0   \n",
       "5      0        0       0       0      6       0        0      0       0   \n",
       "\n",
       "0 PBYSTAND AWAPART AWABEDR AWALAND APERSAUT ABESAUT AMOTSCO AVRAAUT AAANHANG  \\\n",
       "1        0       0       0       0        1       0       0       0        0   \n",
       "2        0       2       0       0        0       0       0       0        0   \n",
       "3        0       1       0       0        1       0       0       0        0   \n",
       "4        0       0       0       0        1       0       0       0        0   \n",
       "5        0       0       0       0        0       0       0       0        0   \n",
       "\n",
       "0 ATRACTOR AWERKT ABROM ALEVEN APERSONG AGEZONG AWAOREG ABRAND AZEILPL  \\\n",
       "1        0      0     0      0        0       0       0      1       0   \n",
       "2        0      0     0      0        0       0       0      1       0   \n",
       "3        0      0     0      0        0       0       0      1       0   \n",
       "4        0      0     0      0        0       0       0      1       0   \n",
       "5        0      0     0      0        0       0       0      1       0   \n",
       "\n",
       "0 APLEZIER AFIETS AINBOED ABYSTAND CARAVAN  \n",
       "1        0      0       0        0       0  \n",
       "2        0      0       0        0       0  \n",
       "3        0      0       0        0       0  \n",
       "4        0      0       0        0       0  \n",
       "5        0      0       0        0       0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make the first row the column headers (ONLY DO ONCE)\n",
    "# df.columns = df.iloc[0]\n",
    "\n",
    "# remove first row from DataFrame (ONLY DO ONCE)\n",
    "# df = df[1:]\n",
    "\n",
    "# view updated DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "56021955",
   "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>origin</th>\n",
       "      <th>subtype_L0</th>\n",
       "      <th>num_houses</th>\n",
       "      <th>avg_household_size</th>\n",
       "      <th>avg_age_L1</th>\n",
       "      <th>main_type_L2</th>\n",
       "      <th>roman_catholic</th>\n",
       "      <th>protestant</th>\n",
       "      <th>other_religion</th>\n",
       "      <th>no_religion</th>\n",
       "      <th>married</th>\n",
       "      <th>live_together</th>\n",
       "      <th>other_relation</th>\n",
       "      <th>singles</th>\n",
       "      <th>hh_wo_children</th>\n",
       "      <th>hh_with_children</th>\n",
       "      <th>high_edu</th>\n",
       "      <th>mid_edu</th>\n",
       "      <th>low_edu</th>\n",
       "      <th>high_status</th>\n",
       "      <th>entrepreneur</th>\n",
       "      <th>farmer</th>\n",
       "      <th>middle_management</th>\n",
       "      <th>skilled_labor</th>\n",
       "      <th>unskilled_labor</th>\n",
       "      <th>social_A</th>\n",
       "      <th>social_B1</th>\n",
       "      <th>social_B2</th>\n",
       "      <th>social_C</th>\n",
       "      <th>social_D</th>\n",
       "      <th>renting</th>\n",
       "      <th>owners</th>\n",
       "      <th>one_car</th>\n",
       "      <th>two_cars</th>\n",
       "      <th>no_cars</th>\n",
       "      <th>nhs_ins</th>\n",
       "      <th>private_ins</th>\n",
       "      <th>lt_30k</th>\n",
       "      <th>30k-45k</th>\n",
       "      <th>45k-75k</th>\n",
       "      <th>75k-122k</th>\n",
       "      <th>gt-123k</th>\n",
       "      <th>avg_income</th>\n",
       "      <th>purchasing_power</th>\n",
       "      <th>private_third_party</th>\n",
       "      <th>firm_third_party</th>\n",
       "      <th>agriculture_third_party</th>\n",
       "      <th>personal_car</th>\n",
       "      <th>delivery_van</th>\n",
       "      <th>motorcycle</th>\n",
       "      <th>truck</th>\n",
       "      <th>trailer</th>\n",
       "      <th>tractor</th>\n",
       "      <th>agricultural_machine</th>\n",
       "      <th>moped</th>\n",
       "      <th>life</th>\n",
       "      <th>private_accident</th>\n",
       "      <th>family_accident</th>\n",
       "      <th>disability</th>\n",
       "      <th>fire</th>\n",
       "      <th>surfboard</th>\n",
       "      <th>boat</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>property</th>\n",
       "      <th>social_security</th>\n",
       "      <th>num_private_third_party</th>\n",
       "      <th>num_firm_third_party</th>\n",
       "      <th>num_agriculture_third_party</th>\n",
       "      <th>num_personal_car</th>\n",
       "      <th>num_delivery_van</th>\n",
       "      <th>num_motorcycle</th>\n",
       "      <th>num_truck</th>\n",
       "      <th>num_trailer</th>\n",
       "      <th>num_tractor</th>\n",
       "      <th>num_agricultural_machine</th>\n",
       "      <th>num_moped</th>\n",
       "      <th>num_life</th>\n",
       "      <th>num_private_accident</th>\n",
       "      <th>num_family_accident</th>\n",
       "      <th>num_disability</th>\n",
       "      <th>num_fire</th>\n",
       "      <th>num_surfboard</th>\n",
       "      <th>num_boat</th>\n",
       "      <th>num_bicycle</th>\n",
       "      <th>num_property</th>\n",
       "      <th>num_social_security</th>\n",
       "      <th>caravan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>train</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>train</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>train</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>train</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>train</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>train</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>train</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>train</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>train</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>train</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>train</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>train</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0  origin subtype_L0 num_houses avg_household_size avg_age_L1 main_type_L2  \\\n",
       "1   train         33          1                  3          2            8   \n",
       "2   train         37          1                  2          2            8   \n",
       "3   train         37          1                  2          2            8   \n",
       "4   train          9          1                  3          3            3   \n",
       "5   train         40          1                  4          2           10   \n",
       "6   train         23          1                  2          1            5   \n",
       "7   train         39          2                  3          2            9   \n",
       "8   train         33          1                  2          3            8   \n",
       "9   train         33          1                  2          4            8   \n",
       "10  train         11          2                  3          3            3   \n",
       "11  train         10          1                  4          3            3   \n",
       "12  train          9          1                  3          3            3   \n",
       "13  train         33          1                  2          3            8   \n",
       "14  train         41          1                  3          3           10   \n",
       "15  train         23          1                  1          2            5   \n",
       "16  train         33          1                  2          3            8   \n",
       "17  train         38          1                  2          3            9   \n",
       "18  train         22          2                  3          3            5   \n",
       "19  train         13          1                  4          2            3   \n",
       "20  train         31          1                  2          4            7   \n",
       "\n",
       "0  roman_catholic protestant other_religion no_religion married live_together  \\\n",
       "1               0          5              1           3       7             0   \n",
       "2               1          4              1           4       6             2   \n",
       "3               0          4              2           4       3             2   \n",
       "4               2          3              2           4       5             2   \n",
       "5               1          4              1           4       7             1   \n",
       "6               0          5              0           5       0             6   \n",
       "7               2          2              0           5       7             2   \n",
       "8               0          7              0           2       7             2   \n",
       "9               0          1              3           6       6             0   \n",
       "10              3          5              0           2       7             0   \n",
       "11              1          4              1           4       7             1   \n",
       "12              1          3              2           4       7             1   \n",
       "13              1          4              1           4       6             2   \n",
       "14              0          5              0           4       7             1   \n",
       "15              0          6              1           2       1             2   \n",
       "16              0          7              0           2       7             2   \n",
       "17              0          6              0           3       7             0   \n",
       "18              0          5              0           4       7             0   \n",
       "19              2          4              0           3       7             0   \n",
       "20              0          2              0           7       9             0   \n",
       "\n",
       "0  other_relation singles hh_wo_children hh_with_children high_edu mid_edu  \\\n",
       "1               2       1              2                6        1       2   \n",
       "2               2       0              4                5        0       5   \n",
       "3               4       4              4                2        0       5   \n",
       "4               2       2              3                4        3       4   \n",
       "5               2       2              4                4        5       4   \n",
       "6               3       3              5                2        0       5   \n",
       "7               0       0              3                6        0       4   \n",
       "8               0       0              5                4        0       3   \n",
       "9               3       3              3                3        0       1   \n",
       "10              2       2              2                6        0       4   \n",
       "11              2       0              3                6        4       3   \n",
       "12              2       2              3                5        1       7   \n",
       "13              3       3              4                3        1       4   \n",
       "14              1       1              4                5        2       4   \n",
       "15              6       5              3                1        2       6   \n",
       "16              0       0              5                4        0       3   \n",
       "17              2       0              6                3        2       6   \n",
       "18              2       0              2                7        2       1   \n",
       "19              2       1              3                6        5       4   \n",
       "20              0       0              6                3        0       0   \n",
       "\n",
       "0  low_edu high_status entrepreneur farmer middle_management skilled_labor  \\\n",
       "1        7           1            0      1                 2             5   \n",
       "2        4           0            0      0                 5             0   \n",
       "3        4           0            0      0                 7             0   \n",
       "4        2           4            0      0                 3             1   \n",
       "5        0           0            5      4                 0             0   \n",
       "6        4           2            0      0                 4             2   \n",
       "7        5           0            0      0                 4             1   \n",
       "8        6           2            0      0                 2             5   \n",
       "9        8           1            1      0                 1             8   \n",
       "10       5           2            0      0                 3             3   \n",
       "11       3           0            0      0                 9             0   \n",
       "12       1           4            0      0                 5             1   \n",
       "13       5           1            1      0                 3             2   \n",
       "14       4           3            0      1                 2             2   \n",
       "15       2           1            0      0                 4             3   \n",
       "16       6           2            0      0                 2             5   \n",
       "17       2           2            0      0                 4             0   \n",
       "18       7           0            2      0                 1             1   \n",
       "19       1           6            0      0                 3             0   \n",
       "20       9           0            0      0                 2             4   \n",
       "\n",
       "0  unskilled_labor social_A social_B1 social_B2 social_C social_D renting  \\\n",
       "1                2        1         1         2        6        1       1   \n",
       "2                4        0         2         3        5        0       2   \n",
       "3                2        0         5         0        4        0       7   \n",
       "4                2        3         2         1        4        0       5   \n",
       "5                0        9         0         0        0        0       4   \n",
       "6                2        2         2         2        4        2       9   \n",
       "7                5        0         1         4        5        0       6   \n",
       "8                2        2         1         2        5        2       0   \n",
       "9                1        1         1         0        8        1       9   \n",
       "10               3        1         2         1        4        2       0   \n",
       "11               0        3         0         6        0        0       0   \n",
       "12               1        2         3         4        1        0       6   \n",
       "13               4        1         2         2        5        1       5   \n",
       "14               2        4         2         1        4        0       5   \n",
       "15               2        1         3         2        4        0       9   \n",
       "16               2        2         1         2        5        2       0   \n",
       "17               4        2         2         4        2        0       6   \n",
       "18               5        2         0         0        7        0       4   \n",
       "19               1        5         2         1        2        0       4   \n",
       "20               4        0         0         0        7        2       9   \n",
       "\n",
       "0  owners one_car two_cars no_cars nhs_ins private_ins lt_30k 30k-45k 45k-75k  \\\n",
       "1       8       8        0       1       8           1      0       4       5   \n",
       "2       7       7        1       2       6           3      2       0       5   \n",
       "3       2       7        0       2       9           0      4       5       0   \n",
       "4       4       9        0       0       7           2      1       5       3   \n",
       "5       5       6        2       1       5           4      0       0       9   \n",
       "6       0       5        3       3       9           0      5       2       3   \n",
       "7       3       8        0       1       9           0      4       3       3   \n",
       "8       9       4        4       2       6           3      2       5       3   \n",
       "9       0       5        2       3       7           2      7       2       1   \n",
       "10      9       6        1       2       6           3      2       3       3   \n",
       "11      9       6        2       1       5           4      0       3       2   \n",
       "12      3       7        1       2       4           5      3       4       3   \n",
       "13      4       6        1       3       7           2      3       4       3   \n",
       "14      4       7        2       0       8           1      3       2       4   \n",
       "15      0       5        1       3       5           4      4       3       3   \n",
       "16      9       4        4       2       6           3      2       5       3   \n",
       "17      3       7        2       0       4           5      0       1       6   \n",
       "18      5       6        1       2       7           2      0       6       3   \n",
       "19      5       7        1       2       5           4      2       2       6   \n",
       "20      0       7        2       0       9           0      5       4       0   \n",
       "\n",
       "0  75k-122k gt-123k avg_income purchasing_power private_third_party  \\\n",
       "1         0       0          4                3                   0   \n",
       "2         2       0          5                4                   2   \n",
       "3         0       0          3                4                   2   \n",
       "4         0       0          4                4                   0   \n",
       "5         0       0          6                3                   0   \n",
       "6         0       0          3                3                   0   \n",
       "7         0       0          3                5                   0   \n",
       "8         0       0          3                3                   0   \n",
       "9         0       0          2                3                   0   \n",
       "10        1       0          4                7                   2   \n",
       "11        2       2          8                7                   0   \n",
       "12        1       0          3                4                   2   \n",
       "13        1       0          3                3                   0   \n",
       "14        1       0          4                4                   0   \n",
       "15        0       0          3                3                   2   \n",
       "16        0       0          3                3                   2   \n",
       "17        2       0          5                4                   0   \n",
       "18        0       0          4                2                   2   \n",
       "19        0       0          4                6                   2   \n",
       "20        0       0          3                1                   0   \n",
       "\n",
       "0  firm_third_party agriculture_third_party personal_car delivery_van  \\\n",
       "1                 0                       0            6            0   \n",
       "2                 0                       0            0            0   \n",
       "3                 0                       0            6            0   \n",
       "4                 0                       0            6            0   \n",
       "5                 0                       0            0            0   \n",
       "6                 0                       0            6            0   \n",
       "7                 0                       0            6            0   \n",
       "8                 0                       0            0            0   \n",
       "9                 0                       0            5            0   \n",
       "10                0                       0            0            0   \n",
       "11                0                       0            6            0   \n",
       "12                0                       0            5            0   \n",
       "13                0                       0            6            0   \n",
       "14                0                       0            0            0   \n",
       "15                0                       0            5            0   \n",
       "16                0                       0            0            0   \n",
       "17                0                       0            0            0   \n",
       "18                0                       0            6            0   \n",
       "19                0                       0            5            0   \n",
       "20                0                       0            0            0   \n",
       "\n",
       "0  motorcycle truck trailer tractor agricultural_machine moped life  \\\n",
       "1           0     0       0       0                    0     0    0   \n",
       "2           0     0       0       0                    0     0    0   \n",
       "3           0     0       0       0                    0     0    0   \n",
       "4           0     0       0       0                    0     0    0   \n",
       "5           0     0       0       0                    0     0    0   \n",
       "6           0     0       0       0                    0     0    0   \n",
       "7           0     0       0       0                    0     0    0   \n",
       "8           0     0       0       0                    0     3    0   \n",
       "9           0     0       0       0                    0     0    0   \n",
       "10          0     0       0       0                    0     0    0   \n",
       "11          0     0       0       0                    0     0    0   \n",
       "12          0     0       0       0                    0     0    0   \n",
       "13          0     0       0       0                    0     0    0   \n",
       "14          0     0       0       0                    0     0    0   \n",
       "15          0     0       0       0                    0     0    0   \n",
       "16          0     0       0       0                    0     0    0   \n",
       "17          0     0       0       0                    0     0    0   \n",
       "18          0     0       0       0                    0     0    0   \n",
       "19          0     0       0       0                    0     0    0   \n",
       "20          0     0       0       0                    0     3    0   \n",
       "\n",
       "0  private_accident family_accident disability fire surfboard boat bicycle  \\\n",
       "1                 0               0          0    5         0    0       0   \n",
       "2                 0               0          0    2         0    0       0   \n",
       "3                 0               0          0    2         0    0       0   \n",
       "4                 0               0          0    2         0    0       0   \n",
       "5                 0               0          0    6         0    0       0   \n",
       "6                 0               0          0    0         0    0       0   \n",
       "7                 0               0          0    0         0    0       0   \n",
       "8                 0               0          0    0         0    0       0   \n",
       "9                 0               0          0    0         0    0       0   \n",
       "10                0               0          0    3         0    0       0   \n",
       "11                0               0          0    0         0    0       0   \n",
       "12                0               0          0    2         0    0       0   \n",
       "13                0               0          0    0         0    0       0   \n",
       "14                0               0          0    0         0    0       0   \n",
       "15                0               0          0    2         0    0       0   \n",
       "16                0               0          0    4         0    0       0   \n",
       "17                0               0          0    0         0    0       0   \n",
       "18                0               0          0    3         0    0       0   \n",
       "19                0               0          0    2         0    0       0   \n",
       "20                0               0          0    0         0    0       0   \n",
       "\n",
       "0  property social_security num_private_third_party num_firm_third_party  \\\n",
       "1         0               0                       0                    0   \n",
       "2         0               0                       2                    0   \n",
       "3         0               0                       1                    0   \n",
       "4         0               0                       0                    0   \n",
       "5         0               0                       0                    0   \n",
       "6         0               0                       0                    0   \n",
       "7         0               0                       0                    0   \n",
       "8         0               0                       0                    0   \n",
       "9         0               0                       0                    0   \n",
       "10        0               0                       1                    0   \n",
       "11        0               0                       0                    0   \n",
       "12        0               0                       1                    0   \n",
       "13        0               0                       0                    0   \n",
       "14        0               0                       0                    0   \n",
       "15        0               0                       1                    0   \n",
       "16        0               0                       1                    0   \n",
       "17        0               0                       0                    0   \n",
       "18        0               0                       1                    0   \n",
       "19        0               0                       1                    0   \n",
       "20        0               0                       0                    0   \n",
       "\n",
       "0  num_agriculture_third_party num_personal_car num_delivery_van  \\\n",
       "1                            0                1                0   \n",
       "2                            0                0                0   \n",
       "3                            0                1                0   \n",
       "4                            0                1                0   \n",
       "5                            0                0                0   \n",
       "6                            0                1                0   \n",
       "7                            0                1                0   \n",
       "8                            0                0                0   \n",
       "9                            0                1                0   \n",
       "10                           0                0                0   \n",
       "11                           0                1                0   \n",
       "12                           0                1                0   \n",
       "13                           0                1                0   \n",
       "14                           0                0                0   \n",
       "15                           0                1                0   \n",
       "16                           0                0                0   \n",
       "17                           0                0                0   \n",
       "18                           0                1                0   \n",
       "19                           0                1                0   \n",
       "20                           0                0                0   \n",
       "\n",
       "0  num_motorcycle num_truck num_trailer num_tractor num_agricultural_machine  \\\n",
       "1               0         0           0           0                        0   \n",
       "2               0         0           0           0                        0   \n",
       "3               0         0           0           0                        0   \n",
       "4               0         0           0           0                        0   \n",
       "5               0         0           0           0                        0   \n",
       "6               0         0           0           0                        0   \n",
       "7               0         0           0           0                        0   \n",
       "8               0         0           0           0                        0   \n",
       "9               0         0           0           0                        0   \n",
       "10              0         0           0           0                        0   \n",
       "11              0         0           0           0                        0   \n",
       "12              0         0           0           0                        0   \n",
       "13              0         0           0           0                        0   \n",
       "14              0         0           0           0                        0   \n",
       "15              0         0           0           0                        0   \n",
       "16              0         0           0           0                        0   \n",
       "17              0         0           0           0                        0   \n",
       "18              0         0           0           0                        0   \n",
       "19              0         0           0           0                        0   \n",
       "20              0         0           0           0                        0   \n",
       "\n",
       "0  num_moped num_life num_private_accident num_family_accident num_disability  \\\n",
       "1          0        0                    0                   0              0   \n",
       "2          0        0                    0                   0              0   \n",
       "3          0        0                    0                   0              0   \n",
       "4          0        0                    0                   0              0   \n",
       "5          0        0                    0                   0              0   \n",
       "6          0        0                    0                   0              0   \n",
       "7          0        0                    0                   0              0   \n",
       "8          1        0                    0                   0              0   \n",
       "9          0        0                    0                   0              0   \n",
       "10         0        0                    0                   0              0   \n",
       "11         0        0                    0                   0              0   \n",
       "12         0        0                    0                   0              0   \n",
       "13         0        0                    0                   0              0   \n",
       "14         0        0                    0                   0              0   \n",
       "15         0        0                    0                   0              0   \n",
       "16         0        0                    0                   0              0   \n",
       "17         0        0                    0                   0              0   \n",
       "18         0        0                    0                   0              0   \n",
       "19         0        0                    0                   0              0   \n",
       "20         1        0                    0                   0              0   \n",
       "\n",
       "0  num_fire num_surfboard num_boat num_bicycle num_property  \\\n",
       "1         1             0        0           0            0   \n",
       "2         1             0        0           0            0   \n",
       "3         1             0        0           0            0   \n",
       "4         1             0        0           0            0   \n",
       "5         1             0        0           0            0   \n",
       "6         0             0        0           0            0   \n",
       "7         0             0        0           0            0   \n",
       "8         0             0        0           0            0   \n",
       "9         0             0        0           0            0   \n",
       "10        1             0        0           0            0   \n",
       "11        0             0        0           0            0   \n",
       "12        1             0        0           0            0   \n",
       "13        0             0        0           0            0   \n",
       "14        0             0        0           0            0   \n",
       "15        1             0        0           0            0   \n",
       "16        1             0        0           0            0   \n",
       "17        0             0        0           0            0   \n",
       "18        1             0        0           0            0   \n",
       "19        1             0        0           0            0   \n",
       "20        0             0        0           0            0   \n",
       "\n",
       "0  num_social_security caravan  \n",
       "1                    0       0  \n",
       "2                    0       0  \n",
       "3                    0       0  \n",
       "4                    0       0  \n",
       "5                    0       0  \n",
       "6                    0       0  \n",
       "7                    0       0  \n",
       "8                    0       0  \n",
       "9                    0       0  \n",
       "10                   0       0  \n",
       "11                   0       0  \n",
       "12                   0       0  \n",
       "13                   0       0  \n",
       "14                   0       0  \n",
       "15                   0       0  \n",
       "16                   0       0  \n",
       "17                   0       0  \n",
       "18                   0       0  \n",
       "19                   0       0  \n",
       "20                   0       0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename demographic (M-) column names using dictionary of old:new names\n",
    "df.rename(\n",
    "    columns={\n",
    "        # dataset origin\n",
    "        \"ORIGIN\": \"origin\",\n",
    "        # customer fields\n",
    "        \"MOSTYPE\": \"subtype_L0\",\n",
    "        \"MAANTHUI\": \"num_houses\",\n",
    "        \"MGEMOMV\": \"avg_household_size\",\n",
    "        \"MGEMLEEF\": \"avg_age_L1\",\n",
    "        \"MOSHOOFD\": \"main_type_L2\",\n",
    "        # religions (-GOD-)\n",
    "        \"MGODRK\": \"roman_catholic\",\n",
    "        \"MGODPR\": \"protestant\",\n",
    "        \"MGODOV\": \"other_religion\",\n",
    "        \"MGODGE\": \"no_religion\",\n",
    "        # relationship status (-REL-)\n",
    "        \"MRELGE\": \"married\",\n",
    "        \"MRELSA\": \"live_together\",\n",
    "        \"MRELOV\": \"other_relation\",\n",
    "        # household size (-F-)\n",
    "        \"MFALLEEN\": \"singles\",\n",
    "        \"MFGEKIND\": \"hh_wo_children\",\n",
    "        \"MFWEKIND\": \"hh_with_children\",\n",
    "        # education level (-OPL-)\n",
    "        \"MOPLHOOG\": \"high_edu\",\n",
    "        \"MOPLMIDD\": \"mid_edu\",\n",
    "        \"MOPLLAAG\": \"low_edu\",\n",
    "        # employment type (-BER-)\n",
    "        \"MBERHOOG\": \"high_status\",\n",
    "        \"MBERZELF\": \"entrepreneur\",\n",
    "        \"MBERBOER\": \"farmer\",\n",
    "        \"MBERMIDD\": \"middle_management\",\n",
    "        \"MBERARBG\": \"skilled_labor\",\n",
    "        \"MBERARBO\": \"unskilled_labor\",\n",
    "        # social class (-SK-)\n",
    "        \"MSKA\": \"social_A\",\n",
    "        \"MSKB1\": \"social_B1\",\n",
    "        \"MSKB2\": \"social_B2\",\n",
    "        \"MSKC\": \"social_C\",\n",
    "        \"MSKD\": \"social_D\",\n",
    "        # home ownership (-H-)\n",
    "        \"MHHUUR\": \"renting\",\n",
    "        \"MHKOOP\": \"owners\",\n",
    "        # car ownership (-AUT-)\n",
    "        \"MAUT1\": \"one_car\",\n",
    "        \"MAUT2\": \"two_cars\",\n",
    "        \"MAUT0\": \"no_cars\",\n",
    "        # health insurance (-Z-)\n",
    "        \"MZFONDS\": \"nhs_ins\",\n",
    "        \"MZPART\": \"private_ins\",\n",
    "        # income level (-INK-)\n",
    "        \"MINKM30\": \"lt_30k\",\n",
    "        \"MINK3045\": \"30k-45k\",\n",
    "        \"MINK4575\": \"45k-75k\",\n",
    "        \"MINK7512\": \"75k-122k\",\n",
    "        \"MINK123M\": \"gt-123k\",\n",
    "        \"MINKGEM\": \"avg_income\",\n",
    "        # purchasing power class\n",
    "        \"MKOOPKLA\": \"purchasing_power\"\n",
    "    },\n",
    "    inplace=True\n",
    ")\n",
    "\n",
    "# Rename policy contribution level (P-) column names using dictionary of old:new names\n",
    "df.rename(\n",
    "    columns={\n",
    "        \"PWAPART\": \"private_third_party\",\n",
    "        \"PWABEDR\": \"firm_third_party\",\n",
    "        \"PWALAND\": \"agriculture_third_party\",\n",
    "        \"PPERSAUT\": \"personal_car\",\n",
    "        \"PBESAUT\": \"delivery_van\",\n",
    "        \"PMOTSCO\": \"motorcycle\",\n",
    "        \"PVRAAUT\": \"truck\",\n",
    "        \"PTRACTOR\": \"tractor\",\n",
    "        \"PAANHANG\": \"trailer\",\n",
    "        \"PWERKT\": \"agricultural_machine\",\n",
    "        \"PBROM\": \"moped\",\n",
    "        \"PLEVEN\": \"life\",\n",
    "        \"PPERSONG\": \"private_accident\",\n",
    "        \"PGEZONG\": \"family_accident\",\n",
    "        \"PWAOREG\": \"disability\",\n",
    "        \"PBRAND\": \"fire\",\n",
    "        \"PZEILPL\": \"surfboard\",\n",
    "        \"PPLEZIER\": \"boat\",\n",
    "        \"PFIETS\": \"bicycle\",\n",
    "        \"PINBOED\": \"property\",\n",
    "        \"PBYSTAND\": \"social_security\"        \n",
    "    },\n",
    "    inplace=True\n",
    ")\n",
    "\n",
    "# Rename number of policies (A-) column names using dictionary of old:new names\n",
    "df.rename(\n",
    "    columns={\n",
    "        \"AWAPART\": \"num_private_third_party\",\n",
    "        \"AWABEDR\": \"num_firm_third_party\",\n",
    "        \"AWALAND\": \"num_agriculture_third_party\",\n",
    "        \"APERSAUT\": \"num_personal_car\",\n",
    "        \"ABESAUT\": \"num_delivery_van\",\n",
    "        \"AMOTSCO\": \"num_motorcycle\",\n",
    "        \"AVRAAUT\": \"num_truck\",\n",
    "        \"AAANHANG\": \"num_trailer\",\n",
    "        \"ATRACTOR\": \"num_tractor\",\n",
    "        \"AWERKT\": \"num_agricultural_machine\",\n",
    "        \"ABROM\": \"num_moped\",\n",
    "        \"ALEVEN\": \"num_life\",\n",
    "        \"APERSONG\": \"num_private_accident\",\n",
    "        \"AGEZONG\": \"num_family_accident\",\n",
    "        \"AWAOREG\": \"num_disability\",\n",
    "        \"ABRAND\": \"num_fire\",\n",
    "        \"AZEILPL\": \"num_surfboard\",\n",
    "        \"APLEZIER\": \"num_boat\",\n",
    "        \"AFIETS\": \"num_bicycle\",\n",
    "        \"AINBOED\": \"num_property\",\n",
    "        \"ABYSTAND\": \"num_social_security\"        \n",
    "    },\n",
    "    inplace=True\n",
    ")\n",
    "    \n",
    "# Rename target variable (CARAVAN) column name\n",
    "df.rename(columns={\"CARAVAN\": \"caravan\"}, inplace=True)\n",
    "\n",
    "# view updated \n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51a0b8c",
   "metadata": {},
   "source": [
    "#### Data Collection Summary: \n",
    "\n",
    "1. Moved header codes from first row to column labels\n",
    "2. Converted header codes to descriptive titles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c1ec04",
   "metadata": {},
   "source": [
    "### Step 3: Data Analysis\n",
    "\n",
    "Exploratory Data Analysis:\n",
    "    - info() -> data types? null values?\n",
    "    - describe() -> descriptive statistics\n",
    "    - groupby() -> categorical columns with keys\n",
    "    - matplotlib -> data visualization for key columns\n",
    "    - refine hypothesis?\n",
    "   \n",
    "Diagnostic Data Analysis:\n",
    "    - graphing distributions of variables with Caravan vs No Caravan in Tableau\n",
    "    - identify demographics with most caravan policies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "20280452",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9822 entries, 1 to 9822\n",
      "Data columns (total 87 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   origin                       9822 non-null   object\n",
      " 1   subtype_L0                   9822 non-null   object\n",
      " 2   num_houses                   9822 non-null   object\n",
      " 3   avg_household_size           9822 non-null   object\n",
      " 4   avg_age_L1                   9822 non-null   object\n",
      " 5   main_type_L2                 9822 non-null   object\n",
      " 6   roman_catholic               9822 non-null   object\n",
      " 7   protestant                   9822 non-null   object\n",
      " 8   other_religion               9822 non-null   object\n",
      " 9   no_religion                  9822 non-null   object\n",
      " 10  married                      9822 non-null   object\n",
      " 11  live_together                9822 non-null   object\n",
      " 12  other_relation               9822 non-null   object\n",
      " 13  singles                      9822 non-null   object\n",
      " 14  hh_wo_children               9822 non-null   object\n",
      " 15  hh_with_children             9822 non-null   object\n",
      " 16  high_edu                     9822 non-null   object\n",
      " 17  mid_edu                      9822 non-null   object\n",
      " 18  low_edu                      9822 non-null   object\n",
      " 19  high_status                  9822 non-null   object\n",
      " 20  entrepreneur                 9822 non-null   object\n",
      " 21  farmer                       9822 non-null   object\n",
      " 22  middle_management            9822 non-null   object\n",
      " 23  skilled_labor                9822 non-null   object\n",
      " 24  unskilled_labor              9822 non-null   object\n",
      " 25  social_A                     9822 non-null   object\n",
      " 26  social_B1                    9822 non-null   object\n",
      " 27  social_B2                    9822 non-null   object\n",
      " 28  social_C                     9822 non-null   object\n",
      " 29  social_D                     9822 non-null   object\n",
      " 30  renting                      9822 non-null   object\n",
      " 31  owners                       9822 non-null   object\n",
      " 32  one_car                      9822 non-null   object\n",
      " 33  two_cars                     9822 non-null   object\n",
      " 34  no_cars                      9822 non-null   object\n",
      " 35  nhs_ins                      9822 non-null   object\n",
      " 36  private_ins                  9822 non-null   object\n",
      " 37  lt_30k                       9822 non-null   object\n",
      " 38  30k-45k                      9822 non-null   object\n",
      " 39  45k-75k                      9822 non-null   object\n",
      " 40  75k-122k                     9822 non-null   object\n",
      " 41  gt-123k                      9822 non-null   object\n",
      " 42  avg_income                   9822 non-null   object\n",
      " 43  purchasing_power             9822 non-null   object\n",
      " 44  private_third_party          9822 non-null   object\n",
      " 45  firm_third_party             9822 non-null   object\n",
      " 46  agriculture_third_party      9822 non-null   object\n",
      " 47  personal_car                 9822 non-null   object\n",
      " 48  delivery_van                 9822 non-null   object\n",
      " 49  motorcycle                   9822 non-null   object\n",
      " 50  truck                        9822 non-null   object\n",
      " 51  trailer                      9822 non-null   object\n",
      " 52  tractor                      9822 non-null   object\n",
      " 53  agricultural_machine         9822 non-null   object\n",
      " 54  moped                        9822 non-null   object\n",
      " 55  life                         9822 non-null   object\n",
      " 56  private_accident             9822 non-null   object\n",
      " 57  family_accident              9822 non-null   object\n",
      " 58  disability                   9822 non-null   object\n",
      " 59  fire                         9822 non-null   object\n",
      " 60  surfboard                    9822 non-null   object\n",
      " 61  boat                         9822 non-null   object\n",
      " 62  bicycle                      9822 non-null   object\n",
      " 63  property                     9822 non-null   object\n",
      " 64  social_security              9822 non-null   object\n",
      " 65  num_private_third_party      9822 non-null   object\n",
      " 66  num_firm_third_party         9822 non-null   object\n",
      " 67  num_agriculture_third_party  9822 non-null   object\n",
      " 68  num_personal_car             9822 non-null   object\n",
      " 69  num_delivery_van             9822 non-null   object\n",
      " 70  num_motorcycle               9822 non-null   object\n",
      " 71  num_truck                    9822 non-null   object\n",
      " 72  num_trailer                  9822 non-null   object\n",
      " 73  num_tractor                  9822 non-null   object\n",
      " 74  num_agricultural_machine     9822 non-null   object\n",
      " 75  num_moped                    9822 non-null   object\n",
      " 76  num_life                     9822 non-null   object\n",
      " 77  num_private_accident         9822 non-null   object\n",
      " 78  num_family_accident          9822 non-null   object\n",
      " 79  num_disability               9822 non-null   object\n",
      " 80  num_fire                     9822 non-null   object\n",
      " 81  num_surfboard                9822 non-null   object\n",
      " 82  num_boat                     9822 non-null   object\n",
      " 83  num_bicycle                  9822 non-null   object\n",
      " 84  num_property                 9822 non-null   object\n",
      " 85  num_social_security          9822 non-null   object\n",
      " 86  caravan                      9822 non-null   object\n",
      "dtypes: object(87)\n",
      "memory usage: 6.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# check data types of columns and non-null count\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7fd531f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       subtype_L0   num_houses  avg_household_size   avg_age_L1  \\\n",
      "count  9822.000000  9822.000000         9822.000000  9822.000000   \n",
      "mean     24.253207     1.108735            2.677561     2.996437   \n",
      "std      12.918058     0.412101            0.780701     0.804660   \n",
      "min       1.000000     1.000000            1.000000     1.000000   \n",
      "25%      10.000000     1.000000            2.000000     2.000000   \n",
      "50%      30.000000     1.000000            3.000000     3.000000   \n",
      "75%      35.000000     1.000000            3.000000     3.000000   \n",
      "max      41.000000    10.000000            6.000000     6.000000   \n",
      "\n",
      "0      main_type_L2  roman_catholic   protestant  other_religion  no_religion  \\\n",
      "count   9822.000000     9822.000000  9822.000000     9822.000000  9822.000000   \n",
      "mean       5.779067        0.700672     4.637650        1.050092     3.262981   \n",
      "std        2.874148        1.015107     1.721212        1.011156     1.606287   \n",
      "min        1.000000        0.000000     0.000000        0.000000     0.000000   \n",
      "25%        3.000000        0.000000     4.000000        0.000000     2.000000   \n",
      "50%        7.000000        0.000000     5.000000        1.000000     3.000000   \n",
      "75%        8.000000        1.000000     6.000000        2.000000     4.000000   \n",
      "max       10.000000        9.000000     9.000000        5.000000     9.000000   \n",
      "\n",
      "0          married  live_together  other_relation      singles  \\\n",
      "count  9822.000000    9822.000000     9822.000000  9822.000000   \n",
      "mean      6.188964       0.873142        2.286602     1.887294   \n",
      "std       1.896070       0.961955        1.710674     1.779238   \n",
      "min       0.000000       0.000000        0.000000     0.000000   \n",
      "25%       5.000000       0.000000        1.000000     0.000000   \n",
      "50%       6.000000       1.000000        2.000000     2.000000   \n",
      "75%       7.000000       1.000000        3.000000     3.000000   \n",
      "max       9.000000       7.000000        9.000000     9.000000   \n",
      "\n",
      "0      hh_wo_children  hh_with_children     high_edu      mid_edu  \\\n",
      "count     9822.000000       9822.000000  9822.000000  9822.000000   \n",
      "mean         3.237324          4.302891     1.484525     3.307269   \n",
      "std          1.609139          1.984152     1.645968     1.723377   \n",
      "min          0.000000          0.000000     0.000000     0.000000   \n",
      "25%          2.000000          3.000000     0.000000     2.000000   \n",
      "50%          3.000000          4.000000     1.000000     3.000000   \n",
      "75%          4.000000          6.000000     2.000000     4.000000   \n",
      "max          9.000000          9.000000     9.000000     9.000000   \n",
      "\n",
      "0          low_edu  high_status  entrepreneur       farmer  middle_management  \\\n",
      "count  9822.000000  9822.000000   9822.000000  9822.000000        9822.000000   \n",
      "mean      4.592038     1.898799      0.403278     0.545714           2.877113   \n",
      "std       2.279839     1.814406      0.786792     1.106349           1.846703   \n",
      "min       0.000000     0.000000      0.000000     0.000000           0.000000   \n",
      "25%       3.000000     0.000000      0.000000     0.000000           2.000000   \n",
      "50%       5.000000     2.000000      0.000000     0.000000           3.000000   \n",
      "75%       6.000000     3.000000      1.000000     1.000000           4.000000   \n",
      "max       9.000000     9.000000      5.000000     9.000000           9.000000   \n",
      "\n",
      "0      skilled_labor  unskilled_labor     social_A    social_B1    social_B2  \\\n",
      "count    9822.000000      9822.000000  9822.000000  9822.000000  9822.000000   \n",
      "mean        2.226532         2.291183     1.650682     1.595093     2.204744   \n",
      "std         1.748025         1.684008     1.742410     1.321487     1.534163   \n",
      "min         0.000000         0.000000     0.000000     0.000000     0.000000   \n",
      "25%         1.000000         1.000000     0.000000     1.000000     1.000000   \n",
      "50%         2.000000         2.000000     1.000000     2.000000     2.000000   \n",
      "75%         3.000000         3.000000     2.000000     2.000000     3.000000   \n",
      "max         9.000000         9.000000     9.000000     9.000000     9.000000   \n",
      "\n",
      "0         social_C     social_D      renting       owners      one_car  \\\n",
      "count  9822.000000  9822.000000  9822.000000  9822.000000  9822.000000   \n",
      "mean      3.742211     1.068214     4.187742     4.819487     6.022501   \n",
      "std       1.944900     1.298229     3.093127     3.093541     1.543980   \n",
      "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
      "25%       2.000000     0.000000     2.000000     2.000000     5.000000   \n",
      "50%       4.000000     1.000000     4.000000     5.000000     6.000000   \n",
      "75%       5.000000     2.000000     7.000000     7.000000     7.000000   \n",
      "max       9.000000     9.000000     9.000000     9.000000     9.000000   \n",
      "\n",
      "0         two_cars      no_cars      nhs_ins  private_ins       lt_30k  \\\n",
      "count  9822.000000  9822.000000  9822.000000  9822.000000  9822.000000   \n",
      "mean      1.335980     1.956730     6.254327     2.750662     2.577072   \n",
      "std       1.213627     1.596842     2.000374     2.002960     2.073125   \n",
      "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
      "25%       0.000000     0.000000     5.000000     1.000000     1.000000   \n",
      "50%       1.000000     2.000000     7.000000     2.000000     2.000000   \n",
      "75%       2.000000     3.000000     8.000000     4.000000     4.000000   \n",
      "max       9.000000     9.000000     9.000000     9.000000     9.000000   \n",
      "\n",
      "0          30k-45k      45k-75k     75k-122k      gt-123k  avg_income  \\\n",
      "count  9822.000000  9822.000000  9822.000000  9822.000000  9822.00000   \n",
      "mean      3.505498     2.739462     0.808491     0.208002     3.80452   \n",
      "std       1.871365     1.950625     1.173771     0.561832     1.33093   \n",
      "min       0.000000     0.000000     0.000000     0.000000     0.00000   \n",
      "25%       2.000000     1.000000     0.000000     0.000000     3.00000   \n",
      "50%       4.000000     3.000000     0.000000     0.000000     4.00000   \n",
      "75%       5.000000     4.000000     1.000000     0.000000     4.00000   \n",
      "max       9.000000     9.000000     9.000000     9.000000     9.00000   \n",
      "\n",
      "0      purchasing_power  private_third_party  firm_third_party  \\\n",
      "count       9822.000000          9822.000000       9822.000000   \n",
      "mean           4.260334             0.764915          0.038892   \n",
      "std            1.998913             0.956555          0.356924   \n",
      "min            1.000000             0.000000          0.000000   \n",
      "25%            3.000000             0.000000          0.000000   \n",
      "50%            4.000000             0.000000          0.000000   \n",
      "75%            6.000000             2.000000          0.000000   \n",
      "max            8.000000             3.000000          6.000000   \n",
      "\n",
      "0      agriculture_third_party  personal_car  delivery_van   motorcycle  \\\n",
      "count              9822.000000   9822.000000   9822.000000  9822.000000   \n",
      "mean                  0.073712      2.956424      0.054877     0.170841   \n",
      "std                   0.507818      2.921736      0.566108     0.888518   \n",
      "min                   0.000000      0.000000      0.000000     0.000000   \n",
      "25%                   0.000000      0.000000      0.000000     0.000000   \n",
      "50%                   0.000000      5.000000      0.000000     0.000000   \n",
      "75%                   0.000000      6.000000      0.000000     0.000000   \n",
      "max                   4.000000      9.000000      7.000000     7.000000   \n",
      "\n",
      "0            truck      trailer      tractor  agricultural_machine  \\\n",
      "count  9822.000000  9822.000000  9822.000000           9822.000000   \n",
      "mean      0.008858     0.019344     0.093565              0.011505   \n",
      "std       0.237556     0.200885     0.604350              0.215408   \n",
      "min       0.000000     0.000000     0.000000              0.000000   \n",
      "25%       0.000000     0.000000     0.000000              0.000000   \n",
      "50%       0.000000     0.000000     0.000000              0.000000   \n",
      "75%       0.000000     0.000000     0.000000              0.000000   \n",
      "max       9.000000     5.000000     7.000000              6.000000   \n",
      "\n",
      "0            moped         life  private_accident  family_accident  \\\n",
      "count  9822.000000  9822.000000       9822.000000      9822.000000   \n",
      "mean      0.215027     0.202301          0.011505         0.018733   \n",
      "std       0.810899     0.910574          0.188699         0.213712   \n",
      "min       0.000000     0.000000          0.000000         0.000000   \n",
      "25%       0.000000     0.000000          0.000000         0.000000   \n",
      "50%       0.000000     0.000000          0.000000         0.000000   \n",
      "75%       0.000000     0.000000          0.000000         0.000000   \n",
      "max       6.000000     9.000000          6.000000         3.000000   \n",
      "\n",
      "0       disability         fire    surfboard         boat      bicycle  \\\n",
      "count  9822.000000  9822.000000  9822.000000  9822.000000  9822.000000   \n",
      "mean      0.023315     1.849420     0.001629     0.015272     0.025351   \n",
      "std       0.375350     1.881271     0.057058     0.244210     0.157198   \n",
      "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
      "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
      "50%       0.000000     2.000000     0.000000     0.000000     0.000000   \n",
      "75%       0.000000     4.000000     0.000000     0.000000     0.000000   \n",
      "max       7.000000     8.000000     3.000000     6.000000     1.000000   \n",
      "\n",
      "0         property  social_security  num_private_third_party  \\\n",
      "count  9822.000000      9822.000000              9822.000000   \n",
      "mean      0.016697         0.045408                 0.400020   \n",
      "std       0.211487         0.396983                 0.492001   \n",
      "min       0.000000         0.000000                 0.000000   \n",
      "25%       0.000000         0.000000                 0.000000   \n",
      "50%       0.000000         0.000000                 0.000000   \n",
      "75%       0.000000         0.000000                 1.000000   \n",
      "max       6.000000         5.000000                 2.000000   \n",
      "\n",
      "0      num_firm_third_party  num_agriculture_third_party  num_personal_car  \\\n",
      "count           9822.000000                  9822.000000       9822.000000   \n",
      "mean               0.014050                     0.021279          0.557218   \n",
      "std                0.126058                     0.144319          0.608575   \n",
      "min                0.000000                     0.000000          0.000000   \n",
      "25%                0.000000                     0.000000          0.000000   \n",
      "50%                0.000000                     0.000000          1.000000   \n",
      "75%                0.000000                     0.000000          1.000000   \n",
      "max                5.000000                     1.000000         12.000000   \n",
      "\n",
      "0      num_delivery_van  num_motorcycle    num_truck  num_trailer  \\\n",
      "count       9822.000000     9822.000000  9822.000000  9822.000000   \n",
      "mean           0.011098        0.040216     0.002240     0.011403   \n",
      "std            0.129928        0.223622     0.068402     0.116251   \n",
      "min            0.000000        0.000000     0.000000     0.000000   \n",
      "25%            0.000000        0.000000     0.000000     0.000000   \n",
      "50%            0.000000        0.000000     0.000000     0.000000   \n",
      "75%            0.000000        0.000000     0.000000     0.000000   \n",
      "max            5.000000        8.000000     4.000000     3.000000   \n",
      "\n",
      "0      num_tractor  num_agricultural_machine    num_moped     num_life  \\\n",
      "count  9822.000000               9822.000000  9822.000000  9822.000000   \n",
      "mean      0.034413                  0.005192     0.071065     0.079821   \n",
      "std       0.249706                  0.109954     0.267432     0.384431   \n",
      "min       0.000000                  0.000000     0.000000     0.000000   \n",
      "25%       0.000000                  0.000000     0.000000     0.000000   \n",
      "50%       0.000000                  0.000000     0.000000     0.000000   \n",
      "75%       0.000000                  0.000000     0.000000     0.000000   \n",
      "max       6.000000                  6.000000     3.000000     8.000000   \n",
      "\n",
      "0      num_private_accident  num_family_accident  num_disability     num_fire  \\\n",
      "count           9822.000000          9822.000000     9822.000000  9822.000000   \n",
      "mean               0.004582             0.007941        0.004276     0.574018   \n",
      "std                0.067535             0.088764        0.071224     0.561255   \n",
      "min                0.000000             0.000000        0.000000     0.000000   \n",
      "25%                0.000000             0.000000        0.000000     0.000000   \n",
      "50%                0.000000             0.000000        0.000000     1.000000   \n",
      "75%                0.000000             0.000000        0.000000     1.000000   \n",
      "max                1.000000             1.000000        2.000000     7.000000   \n",
      "\n",
      "0      num_surfboard     num_boat  num_bicycle  num_property  \\\n",
      "count    9822.000000  9822.000000   9822.00000   9822.000000   \n",
      "mean        0.000916     0.005091      0.03146      0.008450   \n",
      "std         0.030258     0.077996      0.20907      0.092647   \n",
      "min         0.000000     0.000000      0.00000      0.000000   \n",
      "25%         0.000000     0.000000      0.00000      0.000000   \n",
      "50%         0.000000     0.000000      0.00000      0.000000   \n",
      "75%         0.000000     0.000000      0.00000      0.000000   \n",
      "max         1.000000     2.000000      4.00000      2.000000   \n",
      "\n",
      "0      num_social_security      caravan  \n",
      "count          9822.000000  9822.000000  \n",
      "mean              0.013846     0.059662  \n",
      "std               0.117728     0.236872  \n",
      "min               0.000000     0.000000  \n",
      "25%               0.000000     0.000000  \n",
      "50%               0.000000     0.000000  \n",
      "75%               0.000000     0.000000  \n",
      "max               2.000000     1.000000  \n"
     ]
    }
   ],
   "source": [
    "# ignore 'origin' column strings when describing\n",
    "# interpret column object values as integers\n",
    "print(df.iloc[:,1:].astype(int).describe())\n",
    "\n",
    "# 9822 observations\n",
    "# average number of houses 1.1\n",
    "# average household size 2.7, median 3\n",
    "# average age 3 (20-30 years), median 3\n",
    "\n",
    "# IDEA: create subset of only buyers and do same; compare (element-wise subtraction?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0a56b803",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export cleaned dataframe to csv file for RStudio and Tableau analysis\n",
    "df.to_csv('../files/caravan_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "131eb951",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l6/k0bks1dx4wd409pkxgr263rm0000gn/T/ipykernel_27541/2531718876.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  df1 = df.drop('origin', 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'subtype_L0'}>,\n",
       "        <AxesSubplot:title={'center':'num_houses'}>,\n",
       "        <AxesSubplot:title={'center':'avg_household_size'}>,\n",
       "        <AxesSubplot:title={'center':'avg_age_L1'}>,\n",
       "        <AxesSubplot:title={'center':'main_type_L2'}>,\n",
       "        <AxesSubplot:title={'center':'roman_catholic'}>,\n",
       "        <AxesSubplot:title={'center':'protestant'}>,\n",
       "        <AxesSubplot:title={'center':'other_religion'}>,\n",
       "        <AxesSubplot:title={'center':'no_religion'}>],\n",
       "       [<AxesSubplot:title={'center':'married'}>,\n",
       "        <AxesSubplot:title={'center':'live_together'}>,\n",
       "        <AxesSubplot:title={'center':'other_relation'}>,\n",
       "        <AxesSubplot:title={'center':'singles'}>,\n",
       "        <AxesSubplot:title={'center':'hh_wo_children'}>,\n",
       "        <AxesSubplot:title={'center':'hh_with_children'}>,\n",
       "        <AxesSubplot:title={'center':'high_edu'}>,\n",
       "        <AxesSubplot:title={'center':'mid_edu'}>,\n",
       "        <AxesSubplot:title={'center':'low_edu'}>],\n",
       "       [<AxesSubplot:title={'center':'high_status'}>,\n",
       "        <AxesSubplot:title={'center':'entrepreneur'}>,\n",
       "        <AxesSubplot:title={'center':'farmer'}>,\n",
       "        <AxesSubplot:title={'center':'middle_management'}>,\n",
       "        <AxesSubplot:title={'center':'skilled_labor'}>,\n",
       "        <AxesSubplot:title={'center':'unskilled_labor'}>,\n",
       "        <AxesSubplot:title={'center':'social_A'}>,\n",
       "        <AxesSubplot:title={'center':'social_B1'}>,\n",
       "        <AxesSubplot:title={'center':'social_B2'}>],\n",
       "       [<AxesSubplot:title={'center':'social_C'}>,\n",
       "        <AxesSubplot:title={'center':'social_D'}>,\n",
       "        <AxesSubplot:title={'center':'renting'}>,\n",
       "        <AxesSubplot:title={'center':'owners'}>,\n",
       "        <AxesSubplot:title={'center':'one_car'}>,\n",
       "        <AxesSubplot:title={'center':'two_cars'}>,\n",
       "        <AxesSubplot:title={'center':'no_cars'}>,\n",
       "        <AxesSubplot:title={'center':'nhs_ins'}>,\n",
       "        <AxesSubplot:title={'center':'private_ins'}>],\n",
       "       [<AxesSubplot:title={'center':'lt_30k'}>,\n",
       "        <AxesSubplot:title={'center':'30k-45k'}>,\n",
       "        <AxesSubplot:title={'center':'45k-75k'}>,\n",
       "        <AxesSubplot:title={'center':'75k-122k'}>,\n",
       "        <AxesSubplot:title={'center':'gt-123k'}>,\n",
       "        <AxesSubplot:title={'center':'avg_income'}>,\n",
       "        <AxesSubplot:title={'center':'purchasing_power'}>,\n",
       "        <AxesSubplot:title={'center':'private_third_party'}>,\n",
       "        <AxesSubplot:title={'center':'firm_third_party'}>],\n",
       "       [<AxesSubplot:title={'center':'agriculture_third_party'}>,\n",
       "        <AxesSubplot:title={'center':'personal_car'}>,\n",
       "        <AxesSubplot:title={'center':'delivery_van'}>,\n",
       "        <AxesSubplot:title={'center':'motorcycle'}>,\n",
       "        <AxesSubplot:title={'center':'truck'}>,\n",
       "        <AxesSubplot:title={'center':'trailer'}>,\n",
       "        <AxesSubplot:title={'center':'tractor'}>,\n",
       "        <AxesSubplot:title={'center':'agricultural_machine'}>,\n",
       "        <AxesSubplot:title={'center':'moped'}>],\n",
       "       [<AxesSubplot:title={'center':'life'}>,\n",
       "        <AxesSubplot:title={'center':'private_accident'}>,\n",
       "        <AxesSubplot:title={'center':'family_accident'}>,\n",
       "        <AxesSubplot:title={'center':'disability'}>,\n",
       "        <AxesSubplot:title={'center':'fire'}>,\n",
       "        <AxesSubplot:title={'center':'surfboard'}>,\n",
       "        <AxesSubplot:title={'center':'boat'}>,\n",
       "        <AxesSubplot:title={'center':'bicycle'}>,\n",
       "        <AxesSubplot:title={'center':'property'}>],\n",
       "       [<AxesSubplot:title={'center':'social_security'}>,\n",
       "        <AxesSubplot:title={'center':'num_private_third_party'}>,\n",
       "        <AxesSubplot:title={'center':'num_firm_third_party'}>,\n",
       "        <AxesSubplot:title={'center':'num_agriculture_third_party'}>,\n",
       "        <AxesSubplot:title={'center':'num_personal_car'}>,\n",
       "        <AxesSubplot:title={'center':'num_delivery_van'}>,\n",
       "        <AxesSubplot:title={'center':'num_motorcycle'}>,\n",
       "        <AxesSubplot:title={'center':'num_truck'}>,\n",
       "        <AxesSubplot:title={'center':'num_trailer'}>],\n",
       "       [<AxesSubplot:title={'center':'num_tractor'}>,\n",
       "        <AxesSubplot:title={'center':'num_agricultural_machine'}>,\n",
       "        <AxesSubplot:title={'center':'num_moped'}>,\n",
       "        <AxesSubplot:title={'center':'num_life'}>,\n",
       "        <AxesSubplot:title={'center':'num_private_accident'}>,\n",
       "        <AxesSubplot:title={'center':'num_family_accident'}>,\n",
       "        <AxesSubplot:title={'center':'num_disability'}>,\n",
       "        <AxesSubplot:title={'center':'num_fire'}>,\n",
       "        <AxesSubplot:title={'center':'num_surfboard'}>],\n",
       "       [<AxesSubplot:title={'center':'num_boat'}>,\n",
       "        <AxesSubplot:title={'center':'num_bicycle'}>,\n",
       "        <AxesSubplot:title={'center':'num_property'}>,\n",
       "        <AxesSubplot:title={'center':'num_social_security'}>,\n",
       "        <AxesSubplot:title={'center':'caravan'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2600x2000 with 90 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df.drop('origin', 1)\n",
    "\n",
    "df1.astype(int).hist(figsize=(26,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ffed9d25",
   "metadata": {
    "scrolled": true
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>origin</td>\n",
       "      <td>subtype_L0</td>\n",
       "      <td>num_houses</td>\n",
       "      <td>avg_household_size</td>\n",
       "      <td>avg_age_L1</td>\n",
       "      <td>main_type_L2</td>\n",
       "      <td>roman_catholic</td>\n",
       "      <td>protestant</td>\n",
       "      <td>other_religion</td>\n",
       "      <td>no_religion</td>\n",
       "      <td>married</td>\n",
       "      <td>live_together</td>\n",
       "      <td>other_relation</td>\n",
       "      <td>singles</td>\n",
       "      <td>hh_wo_children</td>\n",
       "      <td>hh_with_children</td>\n",
       "      <td>high_edu</td>\n",
       "      <td>mid_edu</td>\n",
       "      <td>low_edu</td>\n",
       "      <td>high_status</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>farmer</td>\n",
       "      <td>middle_management</td>\n",
       "      <td>skilled_labor</td>\n",
       "      <td>unskilled_labor</td>\n",
       "      <td>social_A</td>\n",
       "      <td>social_B1</td>\n",
       "      <td>social_B2</td>\n",
       "      <td>social_C</td>\n",
       "      <td>social_D</td>\n",
       "      <td>renting</td>\n",
       "      <td>owners</td>\n",
       "      <td>one_car</td>\n",
       "      <td>two_cars</td>\n",
       "      <td>no_cars</td>\n",
       "      <td>nhs_ins</td>\n",
       "      <td>private_ins</td>\n",
       "      <td>lt_30k</td>\n",
       "      <td>30k-45k</td>\n",
       "      <td>45k-75k</td>\n",
       "      <td>75k-122k</td>\n",
       "      <td>gt-123k</td>\n",
       "      <td>avg_income</td>\n",
       "      <td>purchasing_power</td>\n",
       "      <td>private_third_party</td>\n",
       "      <td>firm_third_party</td>\n",
       "      <td>agriculture_third_party</td>\n",
       "      <td>personal_car</td>\n",
       "      <td>delivery_van</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>truck</td>\n",
       "      <td>trailer</td>\n",
       "      <td>tractor</td>\n",
       "      <td>agricultural_machine</td>\n",
       "      <td>moped</td>\n",
       "      <td>life</td>\n",
       "      <td>private_accident</td>\n",
       "      <td>family_accident</td>\n",
       "      <td>disability</td>\n",
       "      <td>fire</td>\n",
       "      <td>surfboard</td>\n",
       "      <td>boat</td>\n",
       "      <td>bicycle</td>\n",
       "      <td>property</td>\n",
       "      <td>social_security</td>\n",
       "      <td>num_private_third_party</td>\n",
       "      <td>num_firm_third_party</td>\n",
       "      <td>num_agriculture_third_party</td>\n",
       "      <td>num_personal_car</td>\n",
       "      <td>num_delivery_van</td>\n",
       "      <td>num_motorcycle</td>\n",
       "      <td>num_truck</td>\n",
       "      <td>num_trailer</td>\n",
       "      <td>num_tractor</td>\n",
       "      <td>num_agricultural_machine</td>\n",
       "      <td>num_moped</td>\n",
       "      <td>num_life</td>\n",
       "      <td>num_private_accident</td>\n",
       "      <td>num_family_accident</td>\n",
       "      <td>num_disability</td>\n",
       "      <td>num_fire</td>\n",
       "      <td>num_surfboard</td>\n",
       "      <td>num_boat</td>\n",
       "      <td>num_bicycle</td>\n",
       "      <td>num_property</td>\n",
       "      <td>num_social_security</td>\n",
       "      <td>caravan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>train</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9818</th>\n",
       "      <td>9818</td>\n",
       "      <td>test</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9819</th>\n",
       "      <td>9819</td>\n",
       "      <td>test</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9820</th>\n",
       "      <td>9820</td>\n",
       "      <td>test</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9821</th>\n",
       "      <td>9821</td>\n",
       "      <td>test</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9822</th>\n",
       "      <td>9822</td>\n",
       "      <td>test</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9823 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0       1           2           3                   4           5   \\\n",
       "0           origin  subtype_L0  num_houses  avg_household_size  avg_age_L1   \n",
       "1        1   train          33           1                   3           2   \n",
       "2        2   train          37           1                   2           2   \n",
       "3        3   train          37           1                   2           2   \n",
       "4        4   train           9           1                   3           3   \n",
       "...    ...     ...         ...         ...                 ...         ...   \n",
       "9818  9818    test          33           1                   2           4   \n",
       "9819  9819    test          24           1                   2           3   \n",
       "9820  9820    test          36           1                   2           3   \n",
       "9821  9821    test          33           1                   3           3   \n",
       "9822  9822    test           8           1                   2           3   \n",
       "\n",
       "                6               7           8               9            10  \\\n",
       "0     main_type_L2  roman_catholic  protestant  other_religion  no_religion   \n",
       "1                8               0           5               1            3   \n",
       "2                8               1           4               1            4   \n",
       "3                8               0           4               2            4   \n",
       "4                3               2           3               2            4   \n",
       "...            ...             ...         ...             ...          ...   \n",
       "9818             8               0           7               2            0   \n",
       "9819             5               1           5               1            3   \n",
       "9820             8               1           5               1            3   \n",
       "9821             8               1           4               2            3   \n",
       "9822             2               4           3               0            3   \n",
       "\n",
       "           11             12              13       14              15  \\\n",
       "0     married  live_together  other_relation  singles  hh_wo_children   \n",
       "1           7              0               2        1               2   \n",
       "2           6              2               2        0               4   \n",
       "3           3              2               4        4               4   \n",
       "4           5              2               2        2               3   \n",
       "...       ...            ...             ...      ...             ...   \n",
       "9818        5              2               2        2               6   \n",
       "9819        4              2               4        4               4   \n",
       "9820        7              0               2        2               5   \n",
       "9821        7              1               2        2               3   \n",
       "9822        5              2               2        0               6   \n",
       "\n",
       "                    16        17       18       19           20            21  \\\n",
       "0     hh_with_children  high_edu  mid_edu  low_edu  high_status  entrepreneur   \n",
       "1                    6         1        2        7            1             0   \n",
       "2                    5         0        5        4            0             0   \n",
       "3                    2         0        5        4            0             0   \n",
       "4                    4         3        4        2            4             0   \n",
       "...                ...       ...      ...      ...          ...           ...   \n",
       "9818                 2         0        3        6            5             0   \n",
       "9819                 2         2        4        4            2             0   \n",
       "9820                 3         2        3        4            2             0   \n",
       "9821                 4         1        3        5            1             1   \n",
       "9822                 3         8        0        1            8             0   \n",
       "\n",
       "          22                 23             24               25        26  \\\n",
       "0     farmer  middle_management  skilled_labor  unskilled_labor  social_A   \n",
       "1          1                  2              5                2         1   \n",
       "2          0                  5              0                4         0   \n",
       "3          0                  7              0                2         0   \n",
       "4          0                  3              1                2         3   \n",
       "...      ...                ...            ...              ...       ...   \n",
       "9818       0                  1              0                4         2   \n",
       "9819       0                  3              3                3         1   \n",
       "9820       0                  3              4                2         1   \n",
       "9821       1                  2              3                3         2   \n",
       "9822       0                  0              0                1         4   \n",
       "\n",
       "             27         28        29        30       31      32       33  \\\n",
       "0     social_B1  social_B2  social_C  social_D  renting  owners  one_car   \n",
       "1             1          2         6         1        1       8        8   \n",
       "2             2          3         5         0        2       7        7   \n",
       "3             5          0         4         0        7       2        7   \n",
       "4             2          1         4         0        5       4        9   \n",
       "...         ...        ...       ...       ...      ...     ...      ...   \n",
       "9818          0          2         4         2        4       5        4   \n",
       "9819          1          2         5         1        7       2        6   \n",
       "9820          1          3         5         0        7       2        6   \n",
       "9821          2          2         4         1        4       5        6   \n",
       "9822          0          5         1         0        2       7        9   \n",
       "\n",
       "            34       35       36           37      38       39       40  \\\n",
       "0     two_cars  no_cars  nhs_ins  private_ins  lt_30k  30k-45k  45k-75k   \n",
       "1            0        1        8            1       0        4        5   \n",
       "2            1        2        6            3       2        0        5   \n",
       "3            0        2        9            0       4        5        0   \n",
       "4            0        0        7            2       1        5        3   \n",
       "...        ...      ...      ...          ...     ...      ...      ...   \n",
       "9818         4        2        3            6       3        6        0   \n",
       "9819         0        3        7            2       3        3        2   \n",
       "9820         1        2        6            3       2        5        3   \n",
       "9821         2        2        7            2       1        4        4   \n",
       "9822         0        0        2            7       0        0        7   \n",
       "\n",
       "            41       42          43                44                   45  \\\n",
       "0     75k-122k  gt-123k  avg_income  purchasing_power  private_third_party   \n",
       "1            0        0           4                 3                    0   \n",
       "2            2        0           5                 4                    2   \n",
       "3            0        0           3                 4                    2   \n",
       "4            0        0           4                 4                    0   \n",
       "...        ...      ...         ...               ...                  ...   \n",
       "9818         0        0           2                 3                    2   \n",
       "9819         2        0           4                 2                    0   \n",
       "9820         0        0           4                 3                    0   \n",
       "9821         1        0           4                 3                    0   \n",
       "9822         2        0           7                 8                    2   \n",
       "\n",
       "                    46                       47            48            49  \\\n",
       "0     firm_third_party  agriculture_third_party  personal_car  delivery_van   \n",
       "1                    0                        0             6             0   \n",
       "2                    0                        0             0             0   \n",
       "3                    0                        0             6             0   \n",
       "4                    0                        0             6             0   \n",
       "...                ...                      ...           ...           ...   \n",
       "9818                 0                        0             6             0   \n",
       "9819                 0                        0             6             0   \n",
       "9820                 0                        0             6             0   \n",
       "9821                 0                        0             6             0   \n",
       "9822                 0                        0             0             0   \n",
       "\n",
       "              50     51       52       53                    54     55    56  \\\n",
       "0     motorcycle  truck  trailer  tractor  agricultural_machine  moped  life   \n",
       "1              0      0        0        0                     0      0     0   \n",
       "2              0      0        0        0                     0      0     0   \n",
       "3              0      0        0        0                     0      0     0   \n",
       "4              0      0        0        0                     0      0     0   \n",
       "...          ...    ...      ...      ...                   ...    ...   ...   \n",
       "9818           0      0        0        0                     0      0     0   \n",
       "9819           0      0        0        0                     0      0     6   \n",
       "9820           0      0        0        0                     0      0     0   \n",
       "9821           0      0        0        0                     0      0     0   \n",
       "9822           0      0        0        0                     0      0     0   \n",
       "\n",
       "                    57               58          59    60         61    62  \\\n",
       "0     private_accident  family_accident  disability  fire  surfboard  boat   \n",
       "1                    0                0           0     5          0     0   \n",
       "2                    0                0           0     2          0     0   \n",
       "3                    0                0           0     2          0     0   \n",
       "4                    0                0           0     2          0     0   \n",
       "...                ...              ...         ...   ...        ...   ...   \n",
       "9818                 0                0           0     3          0     0   \n",
       "9819                 0                0           0     4          0     0   \n",
       "9820                 0                0           0     3          0     0   \n",
       "9821                 0                0           0     0          0     0   \n",
       "9822                 0                0           0     4          0     0   \n",
       "\n",
       "           63        64               65                       66  \\\n",
       "0     bicycle  property  social_security  num_private_third_party   \n",
       "1           0         0                0                        0   \n",
       "2           0         0                0                        2   \n",
       "3           0         0                0                        1   \n",
       "4           0         0                0                        0   \n",
       "...       ...       ...              ...                      ...   \n",
       "9818        0         0                0                        1   \n",
       "9819        0         0                0                        0   \n",
       "9820        0         1                0                        0   \n",
       "9821        0         0                0                        0   \n",
       "9822        0         0                0                        1   \n",
       "\n",
       "                        67                           68                69  \\\n",
       "0     num_firm_third_party  num_agriculture_third_party  num_personal_car   \n",
       "1                        0                            0                 1   \n",
       "2                        0                            0                 0   \n",
       "3                        0                            0                 1   \n",
       "4                        0                            0                 1   \n",
       "...                    ...                          ...               ...   \n",
       "9818                     0                            0                 1   \n",
       "9819                     0                            0                 1   \n",
       "9820                     0                            0                 1   \n",
       "9821                     0                            0                 1   \n",
       "9822                     0                            0                 0   \n",
       "\n",
       "                    70              71         72           73           74  \\\n",
       "0     num_delivery_van  num_motorcycle  num_truck  num_trailer  num_tractor   \n",
       "1                    0               0          0            0            0   \n",
       "2                    0               0          0            0            0   \n",
       "3                    0               0          0            0            0   \n",
       "4                    0               0          0            0            0   \n",
       "...                ...             ...        ...          ...          ...   \n",
       "9818                 0               0          0            0            0   \n",
       "9819                 0               0          0            0            0   \n",
       "9820                 0               0          0            0            0   \n",
       "9821                 0               0          0            0            0   \n",
       "9822                 0               0          0            0            0   \n",
       "\n",
       "                            75         76        77                    78  \\\n",
       "0     num_agricultural_machine  num_moped  num_life  num_private_accident   \n",
       "1                            0          0         0                     0   \n",
       "2                            0          0         0                     0   \n",
       "3                            0          0         0                     0   \n",
       "4                            0          0         0                     0   \n",
       "...                        ...        ...       ...                   ...   \n",
       "9818                         0          0         0                     0   \n",
       "9819                         0          0         1                     0   \n",
       "9820                         0          0         0                     0   \n",
       "9821                         0          0         0                     0   \n",
       "9822                         0          0         0                     0   \n",
       "\n",
       "                       79              80        81             82        83  \\\n",
       "0     num_family_accident  num_disability  num_fire  num_surfboard  num_boat   \n",
       "1                       0               0         1              0         0   \n",
       "2                       0               0         1              0         0   \n",
       "3                       0               0         1              0         0   \n",
       "4                       0               0         1              0         0   \n",
       "...                   ...             ...       ...            ...       ...   \n",
       "9818                    0               0         1              0         0   \n",
       "9819                    0               0         1              0         0   \n",
       "9820                    0               0         1              0         0   \n",
       "9821                    0               0         0              0         0   \n",
       "9822                    0               0         1              0         0   \n",
       "\n",
       "               84            85                   86       87  \n",
       "0     num_bicycle  num_property  num_social_security  caravan  \n",
       "1               0             0                    0        0  \n",
       "2               0             0                    0        0  \n",
       "3               0             0                    0        0  \n",
       "4               0             0                    0        0  \n",
       "...           ...           ...                  ...      ...  \n",
       "9818            0             0                    0        0  \n",
       "9819            0             0                    0        1  \n",
       "9820            0             1                    0        0  \n",
       "9821            0             0                    0        0  \n",
       "9822            0             0                    0        0  \n",
       "\n",
       "[9823 rows x 88 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# possible work for further work with cleaned csv (linear regressions?)\n",
    "\n",
    "# data_list2 = read_csv(\"../files/caravan_cleaned.csv\")\n",
    "\n",
    "# convert to dataframe\n",
    "# df2 = pd.DataFrame(data_list2)\n",
    "\n",
    "# df2\n",
    "# make the first row the column headers (ONLY DO ONCE)\n",
    "# df2.columns = df.iloc[0]\n",
    "\n",
    "# remove first row from DataFrame (ONLY DO ONCE)\n",
    "# df2 = df2[1:]\n",
    "\n",
    "# df2['45k-75k'].astype(int).corr(df['caravan'].astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce5f660",
   "metadata": {},
   "source": [
    "Summary of work in RStudio:\n",
    "- summarized column datatypes\n",
    "- double checked column statistical summaries to compare to those from Jupyter\n",
    "- outlined some possible linear regression expansions of this work, though Tableau proved more useful and communicable for the purpose of this project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e19a6196",
   "metadata": {},
   "source": [
    "Notes from Tableau:\n",
    "    - 579 total Caravan purchasers, buyers make up just 6% of total customers; not the best sample size which could affect some of these findings' statistical significance\n",
    "    - examined histograms of each variable with side-by-side buyers vs. no-buyers filters to see how the population vs. sample distributions differ\n",
    "    \n",
    "Purchasing Power: buyers had higher purchasing power, with less of 0,1,2,3 and more of 5,6,7\n",
    "\n",
    "Fire: buyers had more fire insurance, with 23 more percentage points at 4 contribution\n",
    "\n",
    "Household Size: buyers had fewer singles, and more households with 2,3, or 4 people\n",
    "\n",
    "Boat: 98% did not have boat insurance vs. 99% ; non-significant\n",
    "\n",
    "Private Third Party Insurance: buyers were 20 percentage points more likely to have PTPI, which mostly went to a low contribution level of 2\n",
    "\n",
    "Social Security: buyers are slightly more likely to have social security, but in general very little SS (1-4%)\n",
    "\n",
    "Age: Buyers are very slightly younger, which could be attributed to differences within age bins (10 years). Even if buyers were on average a few years older (i.e. 61 -> 64), the observed decade might not change. In general, customers are between 30 and 70 years and are most likely to be between 40-50, then 30-40. \n",
    "\n",
    "One Car: more buyers have one car than non-buyers (some caravans can be pulled by one car, and if they are traveling they may sell surplus cars)\n",
    "\n",
    "Two Cars: buyers are about as likely to have two cars as non-buyers, which is not very likely\n",
    "\n",
    "No Car: fewer buyers have no cars than non-buyers. In line with finding that buyers have more purchasing power and are more likely to have at least one car.\n",
    "\n",
    "Cars: summarizing all three car variables: all customers are most likely to have just one car, then no car, then two cars. Buyers are even more likely to have just one car.\n",
    "\n",
    "renters vs. owners: Buyers are less likely to rent and more likely to own. Makes sense if buyers have more purchasing power and are traveling. Better to keep an asset (possibly landlords themselves) than pay monthly for a living space you don't use.\n",
    "\n",
    "main type: changes in distribution seem more in line with wealth than age, makes sense given that customers with more purchasing power are more likely to be buyers as well. 'successful', 'driven', 'living well'\n",
    "- more successful hedonists (+3pp), driven growers (+10pp), average families (+3pp), living well (+7pp)\n",
    "- fewer cruising seniors (-2pp), retired and religious (-3pp), family with grown ups (-2pp), farmers (-3pp)\n",
    "\n",
    "subtype: both are mostly families, with the most customers identifying as lower class large families (13.7%), married with teens (6.5%), or large religious (6.3%). Significant differences are that more buyers identified as middle class families (+7pp) and high income, expensive child (+4pp) and fewer identified as young and rising (-4pp)\n",
    "\n",
    "Further work: transorm data values into their true values according to the keys, then repeat analysis. May be especially useful for age."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794da964",
   "metadata": {},
   "source": [
    "Big Picture: Draws a picture of the mode customer being a wealthy family who own a house, have a single car, and tend to buy other insurance.\n",
    "\n",
    "Hypothesis Review: Exploratory and Diagnostic Data Analysis seem to support hypothesis, except for in regards to the age of the average caravan insurance buyer. Families were also more likely to be buyers than childless couples or couples with adult children."
   ]
  }
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