exercises/tut1_recap_bads_student.ipynb

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   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Humboldt-WI/delta/blob/master/exercises/tut1_recap_bads_student.ipynb)"
   ]
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  {
   "cell_type": "markdown",
   "id": "7ece0f4b",
   "metadata": {},
   "source": [
    "# BADS Recap: Predictive Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b30d65",
   "metadata": {},
   "source": [
    "In this exercise, you recapitulate the principles of predictive modeling. You will build a predictive model for a travel \n",
    "insurance, which predicts whether a given insurance offer leads to a claim. You can use \"Ex03-Python_Machine_Learning.ipynb\" \n",
    "to lookup on the model building procedure and the required commands in python. \n",
    "\n",
    "In the next cells, we provide the code for importing required packages and for loading the data set (You need to adapt the\n",
    "path to the data.). Afterwards, the exercises begin.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e42767a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load required packages\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, KFold, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.inspection import permutation_importance, partial_dependence, PartialDependenceDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b33cc8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "trav_ins = pd.read_csv('travel insurance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a31ec2ed",
   "metadata": {},
   "outputs": [
    {
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       "<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>Agency</th>\n",
       "      <th>Agency Type</th>\n",
       "      <th>Distribution Channel</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Claim</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Net Sales</th>\n",
       "      <th>Commision (in value)</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CBH</td>\n",
       "      <td>Travel Agency</td>\n",
       "      <td>Offline</td>\n",
       "      <td>Comprehensive Plan</td>\n",
       "      <td>No</td>\n",
       "      <td>186</td>\n",
       "      <td>MALAYSIA</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>9.57</td>\n",
       "      <td>F</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CBH</td>\n",
       "      <td>Travel Agency</td>\n",
       "      <td>Offline</td>\n",
       "      <td>Comprehensive Plan</td>\n",
       "      <td>No</td>\n",
       "      <td>186</td>\n",
       "      <td>MALAYSIA</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>9.57</td>\n",
       "      <td>F</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CWT</td>\n",
       "      <td>Travel Agency</td>\n",
       "      <td>Online</td>\n",
       "      <td>Rental Vehicle Excess Insurance</td>\n",
       "      <td>No</td>\n",
       "      <td>65</td>\n",
       "      <td>AUSTRALIA</td>\n",
       "      <td>-49.5</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CWT</td>\n",
       "      <td>Travel Agency</td>\n",
       "      <td>Online</td>\n",
       "      <td>Rental Vehicle Excess Insurance</td>\n",
       "      <td>No</td>\n",
       "      <td>60</td>\n",
       "      <td>AUSTRALIA</td>\n",
       "      <td>-39.6</td>\n",
       "      <td>23.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CWT</td>\n",
       "      <td>Travel Agency</td>\n",
       "      <td>Online</td>\n",
       "      <td>Rental Vehicle Excess Insurance</td>\n",
       "      <td>No</td>\n",
       "      <td>79</td>\n",
       "      <td>ITALY</td>\n",
       "      <td>-19.8</td>\n",
       "      <td>11.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63321</th>\n",
       "      <td>JZI</td>\n",
       "      <td>Airlines</td>\n",
       "      <td>Online</td>\n",
       "      <td>Basic Plan</td>\n",
       "      <td>No</td>\n",
       "      <td>111</td>\n",
       "      <td>JAPAN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.25</td>\n",
       "      <td>M</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63322</th>\n",
       "      <td>JZI</td>\n",
       "      <td>Airlines</td>\n",
       "      <td>Online</td>\n",
       "      <td>Basic Plan</td>\n",
       "      <td>No</td>\n",
       "      <td>58</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>40.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>F</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63323</th>\n",
       "      <td>JZI</td>\n",
       "      <td>Airlines</td>\n",
       "      <td>Online</td>\n",
       "      <td>Basic Plan</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>MALAYSIA</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.30</td>\n",
       "      <td>M</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63324</th>\n",
       "      <td>JZI</td>\n",
       "      <td>Airlines</td>\n",
       "      <td>Online</td>\n",
       "      <td>Basic Plan</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>VIET NAM</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.30</td>\n",
       "      <td>M</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63325</th>\n",
       "      <td>JZI</td>\n",
       "      <td>Airlines</td>\n",
       "      <td>Online</td>\n",
       "      <td>Basic Plan</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "      <td>HONG KONG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>F</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63326 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Agency    Agency Type Distribution Channel  \\\n",
       "0        CBH  Travel Agency              Offline   \n",
       "1        CBH  Travel Agency              Offline   \n",
       "2        CWT  Travel Agency               Online   \n",
       "3        CWT  Travel Agency               Online   \n",
       "4        CWT  Travel Agency               Online   \n",
       "...      ...            ...                  ...   \n",
       "63321    JZI       Airlines               Online   \n",
       "63322    JZI       Airlines               Online   \n",
       "63323    JZI       Airlines               Online   \n",
       "63324    JZI       Airlines               Online   \n",
       "63325    JZI       Airlines               Online   \n",
       "\n",
       "                          Product Name Claim  Duration Destination  Net Sales  \\\n",
       "0                   Comprehensive Plan    No       186    MALAYSIA      -29.0   \n",
       "1                   Comprehensive Plan    No       186    MALAYSIA      -29.0   \n",
       "2      Rental Vehicle Excess Insurance    No        65   AUSTRALIA      -49.5   \n",
       "3      Rental Vehicle Excess Insurance    No        60   AUSTRALIA      -39.6   \n",
       "4      Rental Vehicle Excess Insurance    No        79       ITALY      -19.8   \n",
       "...                                ...   ...       ...         ...        ...   \n",
       "63321                       Basic Plan    No       111       JAPAN       35.0   \n",
       "63322                       Basic Plan    No        58       CHINA       40.0   \n",
       "63323                       Basic Plan    No         2    MALAYSIA       18.0   \n",
       "63324                       Basic Plan    No         3    VIET NAM       18.0   \n",
       "63325                       Basic Plan    No        22   HONG KONG       26.0   \n",
       "\n",
       "       Commision (in value) Gender  Age  \n",
       "0                      9.57      F   81  \n",
       "1                      9.57      F   71  \n",
       "2                     29.70    NaN   32  \n",
       "3                     23.76    NaN   32  \n",
       "4                     11.88    NaN   41  \n",
       "...                     ...    ...  ...  \n",
       "63321                 12.25      M   31  \n",
       "63322                 14.00      F   40  \n",
       "63323                  6.30      M   57  \n",
       "63324                  6.30      M   63  \n",
       "63325                  9.10      F   35  \n",
       "\n",
       "[63326 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get to know the data\n",
    "trav_ins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "390291cd",
   "metadata": {},
   "source": [
    "**Exercise 1:**<br>\n",
    "Impute missing values with an approach of your own choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8cc0679",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5b1c22c1",
   "metadata": {},
   "source": [
    "**Exercise 2:**<br>\n",
    "Appropriately encode the target \"Claim\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c8986da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2e6cd291",
   "metadata": {},
   "source": [
    "**Exercise 3:**<br>\n",
    "Appropriately encode the categorical variables. We recommend merging categories with few observations in one group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "278e3495",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00fbfc34",
   "metadata": {},
   "source": [
    "**Exercise 4:**<br>\n",
    "Split 80% of the data in the training set and the remaining 20% data in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f965cc56",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e900d40a",
   "metadata": {},
   "source": [
    "**Exercise 5:**<br>\n",
    "Build a random forest model on the training data. Find the best tuning parameters by grid search.  \n",
    "Below is a simple grid, which you could choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "90779e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# example parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [500],\n",
    "    'max_features': [4, 8, 12],\n",
    "    'max_depth': [10],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfaefa0",
   "metadata": {},
   "source": [
    "**Exercise 6:**<br>\n",
    "Evaluate the model by appropriate metrics on the test set. Is the model able to predict claims with certainty?\n",
    "Is the model usefull for application?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca679935",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8ef922d8",
   "metadata": {},
   "source": [
    "**Bonus:**<br>\n",
    "Identify which features are most relevant in predicting claims and how they affect the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "226885d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "610f4ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# partial dependence plot"
   ]
  }
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