2. airplane_crashes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d4c1c17-0d48-4e20-ac0c-53b1f13a68ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "pd.set_option('max_colwidth', None)\n",
    "pd.set_option('display.width', None)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore', category=UserWarning)\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f7ab830",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from umap import UMAP\n",
    "from hdbscan import HDBSCAN\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc4a322a-29d4-4739-be35-e951a9c214ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import re\n",
    "\n",
    "STOPWORDS = set(stopwords.words('english'))\n",
    "PUNCT_TO_REMOVE = string.punctuation.replace(\"/\", \"\")\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "\n",
    "def remove_numbers(text):\n",
    "    return re.sub(r'\\d+', '', text)\n",
    "\n",
    "\n",
    "def remove_stopwords(text):\n",
    "    return \" \".join([word for word in str(text).split() if word not in STOPWORDS])\n",
    "\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    return text.translate(str.maketrans('', '', PUNCT_TO_REMOVE))\n",
    "\n",
    "\n",
    "def lemmatize_words(text):\n",
    "    return \" \".join([lemmatizer.lemmatize(word) for word in text.split()])\n",
    "\n",
    "\n",
    "def longest_word(s):\n",
    "    return max(s.split(), key=len)\n",
    "\n",
    "\n",
    "def longest_word_in_list(l):\n",
    "    try:\n",
    "        words_list = []\n",
    "        for s in l:\n",
    "            words = s.strip(\" /\").split()\n",
    "            words_list.append(max(words, key=len))\n",
    "    except Exception as e:\n",
    "        print(l)\n",
    "        raise e\n",
    "    return \" / \".join(words_list)\n",
    "\n",
    "\n",
    "def get_last(s):\n",
    "    return s.split()[-1]\n",
    "\n",
    "\n",
    "def target_encode(df: pd.DataFrame, group_col: str, target_col: str):\n",
    "    target_means = df.groupby(group_col)[target_col].mean().sort_values(ascending=True)\n",
    "    group_ranking = (target_means.rank() - target_means.rank().min()) / (target_means.rank() - target_means.rank().min()).max()\n",
    "    return group_ranking.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3084cb1d",
   "metadata": {},
   "source": [
    "# 1. Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bccd97d0-1982-4d75-8374-1b612e9bea3d",
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>Military - Indian Air Force</td>\n",
       "      <td>Antonov An-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                           Location  \\\n",
       "4830 2009-06-08  State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator           Type  Aboard  Fatalities  Ground  \\\n",
       "4830  Military - Indian Air Force  Antonov An-32    13.0        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes = pd.read_csv('data/clean_crashes.csv', \n",
    "                      usecols=['Date', 'Location', 'Operator', 'Type', 'Aboard', 'Fatalities', 'Ground', 'Summary'])\n",
    "crashes.Date = pd.to_datetime(crashes.Date)\n",
    "crashes.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98740f04",
   "metadata": {},
   "source": [
    "## 1.1. Get summary clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43a1a3fe",
   "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>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Summary_Cluster  Summary_Cluster_name\n",
       "Index                                       \n",
       "4830                15  Disappeared en route"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes_summary_clustered = pd.read_csv('data/clean_crashes.clustered_summary.csv', \n",
    "                                        sep='\\t', usecols=['Unnamed: 0','Cluster','Cluster_name'])\n",
    "crashes_summary_clustered = crashes_summary_clustered.rename(columns={'Unnamed: 0': \"Index\", \n",
    "                                                                      'Cluster': \"Summary_Cluster\", \n",
    "                                                                      'Cluster_name': \"Summary_Cluster_name\"})\n",
    "crashes_summary_clustered = crashes_summary_clustered.set_index('Index')\n",
    "crashes_summary_clustered.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00e2d5e6",
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>Military - Indian Air Force</td>\n",
       "      <td>Antonov An-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                           Location  \\\n",
       "4830 2009-06-08  State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator           Type  Aboard  Fatalities  Ground  \\\n",
       "4830  Military - Indian Air Force  Antonov An-32    13.0        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \\\n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.   \n",
       "\n",
       "      Summary_Cluster  Summary_Cluster_name  \n",
       "4830               15  Disappeared en route  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes = crashes.join(crashes_summary_clustered)\n",
    "crashes.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88044621",
   "metadata": {},
   "source": [
    "# 2. Transform data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ed72ab",
   "metadata": {},
   "source": [
    "## Create 'Mortality' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3c3c9a7",
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4829</th>\n",
       "      <td>2009-06-07</td>\n",
       "      <td>Near Port Hope Simpson, Newfoundland, Canada</td>\n",
       "      <td>Strait Air</td>\n",
       "      <td>Britten-Norman BN-2A-27 Islander</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.</td>\n",
       "      <td>13</td>\n",
       "      <td>Fog</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>Military - Indian Air Force</td>\n",
       "      <td>Antonov An-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                      Location  \\\n",
       "4829 2009-06-07  Near Port Hope Simpson, Newfoundland, Canada   \n",
       "4830 2009-06-08             State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator                              Type  Aboard  \\\n",
       "4829                   Strait Air  Britten-Norman BN-2A-27 Islander     1.0   \n",
       "4830  Military - Indian Air Force                     Antonov An-32    13.0   \n",
       "\n",
       "      Fatalities  Ground  \\\n",
       "4829         1.0     0.0   \n",
       "4830        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \\\n",
       "4829              The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.   \n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.   \n",
       "\n",
       "      Summary_Cluster  Summary_Cluster_name  mortality  \n",
       "4829               13                   Fog        1.0  \n",
       "4830               15  Disappeared en route        1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes['mortality'] = crashes.apply(lambda row: round(row.Fatalities / row.Aboard, 2) if row.Aboard != 0 else 0, axis=1)\n",
    "crashes.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e24b72d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8284, 0.3047)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(crashes.mortality.mean(), 4),  round(crashes.mortality.std(), 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65bec61",
   "metadata": {},
   "source": [
    "## Create 'Military plane' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b05c5f6",
   "metadata": {
    "scrolled": false
   },
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "      <th>mortality</th>\n",
       "      <th>is_military</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4829</th>\n",
       "      <td>2009-06-07</td>\n",
       "      <td>Near Port Hope Simpson, Newfoundland, Canada</td>\n",
       "      <td>Strait Air</td>\n",
       "      <td>Britten-Norman BN-2A-27 Islander</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.</td>\n",
       "      <td>13</td>\n",
       "      <td>Fog</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>Military - Indian Air Force</td>\n",
       "      <td>Antonov An-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                      Location  \\\n",
       "4829 2009-06-07  Near Port Hope Simpson, Newfoundland, Canada   \n",
       "4830 2009-06-08             State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator                              Type  Aboard  \\\n",
       "4829                   Strait Air  Britten-Norman BN-2A-27 Islander     1.0   \n",
       "4830  Military - Indian Air Force                     Antonov An-32    13.0   \n",
       "\n",
       "      Fatalities  Ground  \\\n",
       "4829         1.0     0.0   \n",
       "4830        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \\\n",
       "4829              The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.   \n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.   \n",
       "\n",
       "      Summary_Cluster  Summary_Cluster_name  mortality  is_military  \n",
       "4829               13                   Fog        1.0        False  \n",
       "4830               15  Disappeared en route        1.0         True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes['is_military'] = crashes.Operator.str.lower().str.contains(\"army|navy|military|force|marine|corps\")\n",
    "crashes.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f575f8a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.153, 0.36)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(crashes.is_military.mean(), 4),  round(crashes.is_military.std(), 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4207a9cf-e69e-41d0-a5f1-6812da046da9",
   "metadata": {},
   "source": [
    "## Type column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78d6ff5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "replacement = {'helicopters': 'helicopter',\n",
    "#                \"ilushin\": \"ilyushin\",\n",
    "#                \"illyushin\": \"ilyushin\",\n",
    "#                \"iluyshin\": \"ilyushin\",\n",
    "#                \"illyushin\": \"ilyushin\",\n",
    "#                \"ilysushin\": \"ilyushin\"\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a543e69a-6693-441f-a995-ae177295c6e1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "crashes.Type = crashes.Type.str.lower()\n",
    "crashes.Type = crashes.Type.replace(replacement)\n",
    "# crashes.Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "154c5984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a0c941c0c7a41498795864fda854b77",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4831 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crashes['plane_series'] = crashes.Type.apply(remove_numbers)\n",
    "crashes.plane_series = crashes.plane_series.apply(remove_punctuation)\n",
    "crashes.plane_series = crashes.plane_series.str.strip(\" /.,\")\n",
    "crashes.plane_series = crashes.plane_series.str.split(\"/\").progress_apply(lambda x: longest_word_in_list(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6659a95a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "douglas       895\n",
       "boeing        314\n",
       "havilland     244\n",
       "lockheed      242\n",
       "antonov       230\n",
       "cessna        175\n",
       "mcdonnell     120\n",
       "beechcraft    116\n",
       "curtiss       111\n",
       "piper          95\n",
       "Name: plane_series, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes.plane_series.value_counts().head(10)  # mcdonnell = douglas ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2fca209c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d7c58181c1c41e2914d935a9e30cf58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4831 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "      <th>mortality</th>\n",
       "      <th>is_military</th>\n",
       "      <th>plane_series</th>\n",
       "      <th>series_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4829</th>\n",
       "      <td>2009-06-07</td>\n",
       "      <td>Near Port Hope Simpson, Newfoundland, Canada</td>\n",
       "      <td>Strait Air</td>\n",
       "      <td>britten-norman bn-2a-27 islander</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.</td>\n",
       "      <td>13</td>\n",
       "      <td>Fog</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>brittennorman</td>\n",
       "      <td>0.361963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>Military - Indian Air Force</td>\n",
       "      <td>antonov an-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>antonov</td>\n",
       "      <td>0.453988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                      Location  \\\n",
       "4829 2009-06-07  Near Port Hope Simpson, Newfoundland, Canada   \n",
       "4830 2009-06-08             State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator                              Type  Aboard  \\\n",
       "4829                   Strait Air  britten-norman bn-2a-27 islander     1.0   \n",
       "4830  Military - Indian Air Force                     antonov an-32    13.0   \n",
       "\n",
       "      Fatalities  Ground  \\\n",
       "4829         1.0     0.0   \n",
       "4830        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \\\n",
       "4829              The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.   \n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.   \n",
       "\n",
       "      Summary_Cluster  Summary_Cluster_name  mortality  is_military  \\\n",
       "4829               13                   Fog        1.0        False   \n",
       "4830               15  Disappeared en route        1.0         True   \n",
       "\n",
       "       plane_series  series_encoded  \n",
       "4829  brittennorman        0.361963  \n",
       "4830        antonov        0.453988  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_encoding_dict = target_encode(crashes, group_col='plane_series', target_col='mortality')\n",
    "crashes['series_encoded'] = crashes.plane_series.progress_apply(lambda x: series_encoding_dict[x])\n",
    "crashes.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc32a302-27ee-49f4-b52e-ed27d3553074",
   "metadata": {},
   "source": [
    "## Location column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1e90bcc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "replacements = {  # what am i doing with my life\n",
    "    'ussr': 'russia',\n",
    "    'russian': 'russia',\n",
    "    'minnisota': 'minnesota',\n",
    "    'islands': 'island',\n",
    "    'atlantiocean': 'ocean',\n",
    "    'pacifiocean': 'ocean',\n",
    "    'atlantic': 'ocean',\n",
    "    'baltisea': 'sea',\n",
    "    'democratirepublicongo': 'congo',\n",
    "    'democratirepublicogo': 'congo',\n",
    "    'democtratirepublicongo': 'congo',\n",
    "    'democratirepubliof congo': 'congo',\n",
    "    'somalia': 'somali',\n",
    "    'somaliland': 'somali',\n",
    "    'massachusett': 'massachusetts',\n",
    "    'massachutes': 'massachusetts',\n",
    "    'alaksa': 'alaska',\n",
    "    'alakska': 'alaska',\n",
    "    'nwtcanada': 'canada',\n",
    "    'quebecanada': 'canada',\n",
    "    'indochina': 'vietnam',\n",
    "    'washingon': 'washington',\n",
    "    'inodnesia': 'indonesia',\n",
    "    'indian': 'india',\n",
    "    'airzona': 'arizona',\n",
    "    'baangladesh': 'bangladesh',\n",
    "    'aregntina': 'argentina',\n",
    "    'jamacia': 'jamaica',\n",
    "    'napal': 'nepal',\n",
    "    'afghanstan': 'afghanistan',\n",
    "    'dekota': 'dakota',\n",
    "    'calilfornia': 'california',\n",
    "    'cailifornia': 'california',\n",
    "    'yugosalvia': 'yugoslavia',\n",
    "    'thilandd': 'thailand',\n",
    "    'thiland': 'thailand',\n",
    "    'besarthailand': 'thailand',\n",
    "    'iloilophilippines': 'philippines',\n",
    "    'philipines': 'philippines',\n",
    "    'moscow': 'russia',\n",
    "    'bulgeria': 'bulgaria',\n",
    "    'oklohoma': 'oklahoma',\n",
    "    'romainia': 'romania',\n",
    "    'ilinois': 'illinois',\n",
    "    'herzegovina': 'bosniaherzegovina',\n",
    "    'coloado': 'colorado',\n",
    "    'louisana': 'louisiana',\n",
    "    'stromfjordgreenland': 'greenland',\n",
    "    'labrador': 'canada',\n",
    "    'arazona': 'arizona',\n",
    "    'alalaskaska': 'alaska',\n",
    "    'reunion': 'union',\n",
    "    'tonkindiadia': 'india',\n",
    "    'tonkindia': 'india',\n",
    "    'dc': 'columbia',\n",
    "    'ak': 'alaska',\n",
    "    'ca': 'canada',\n",
    "    'uk': 'kingdom',\n",
    "    'ga': 'georgia',\n",
    "    'nag': 'india',\n",
    "    'in': 'india',\n",
    "    'ny': 'york',\n",
    "    'bo': 'bolivia',\n",
    "    'hi': 'hawaii',\n",
    "    'boliva': 'bolivia',\n",
    "    'bugaria': 'bulgaria',\n",
    "    'columbia': 'colombia',\n",
    "    'cameroons': 'cameroon',\n",
    "    'deleware': 'delaware',\n",
    "    'djbouti': 'djibouti',\n",
    "    'england': 'kingdom',\n",
    "    'indies': 'indonesia',\n",
    "    'malaya': 'malaysia',\n",
    "    'mauritania': 'mauretania',\n",
    "    'morroco': 'morocco',\n",
    "    'morrocco': 'morocco',\n",
    "    'phillipines': 'philippines',\n",
    "    'rico': 'rica',\n",
    "    'surinam': 'suriname',\n",
    "    'wisconson': 'wisconsin'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "270a51da",
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "      <th>mortality</th>\n",
       "      <th>is_military</th>\n",
       "      <th>plane_series</th>\n",
       "      <th>series_encoded</th>\n",
       "      <th>loc_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4829</th>\n",
       "      <td>2009-06-07</td>\n",
       "      <td>Near Port Hope Simpson, Newfoundland, Canada</td>\n",
       "      <td>Strait Air</td>\n",
       "      <td>britten-norman bn-2a-27 islander</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.</td>\n",
       "      <td>13</td>\n",
       "      <td>Fog</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>brittennorman</td>\n",
       "      <td>0.361963</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>Military - Indian Air Force</td>\n",
       "      <td>antonov an-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>antonov</td>\n",
       "      <td>0.453988</td>\n",
       "      <td>india</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                      Location  \\\n",
       "4829 2009-06-07  Near Port Hope Simpson, Newfoundland, Canada   \n",
       "4830 2009-06-08             State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator                              Type  Aboard  \\\n",
       "4829                   Strait Air  britten-norman bn-2a-27 islander     1.0   \n",
       "4830  Military - Indian Air Force                     antonov an-32    13.0   \n",
       "\n",
       "      Fatalities  Ground  \\\n",
       "4829         1.0     0.0   \n",
       "4830        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \\\n",
       "4829              The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.   \n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.   \n",
       "\n",
       "      Summary_Cluster  Summary_Cluster_name  mortality  is_military  \\\n",
       "4829               13                   Fog        1.0        False   \n",
       "4830               15  Disappeared en route        1.0         True   \n",
       "\n",
       "       plane_series  series_encoded loc_clean  \n",
       "4829  brittennorman        0.361963    canada  \n",
       "4830        antonov        0.453988     india  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes['loc_clean'] = crashes.Location.apply(remove_numbers)\n",
    "crashes.loc_clean = crashes.loc_clean.apply(remove_punctuation)\n",
    "crashes.loc_clean = crashes.loc_clean.str.lower().str.strip()\n",
    "crashes.loc_clean = crashes.loc_clean.apply(get_last)\n",
    "crashes.loc_clean = crashes.loc_clean.replace(replacements)\n",
    "crashes.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a1fb87-df20-427f-a622-b5791240fb0a",
   "metadata": {},
   "source": [
    "## Operator column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "caf768f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_operator(input_text: str):\n",
    "    operators_ditry = input_text.split(\"/\")\n",
    "    operators = set()\n",
    "    for operator in operators_ditry:\n",
    "        operator = operator.strip()\n",
    "        operator = operator.replace(\".\", \"\")\n",
    "        operator = operator.replace(\"  \", \" \")\n",
    "        \n",
    "        operator = operator.replace(\"í\", \"i\")\n",
    "        operator = operator.replace(\"ú\", \"u\")\n",
    "        operator = operator.replace(\"é\", \"e\")\n",
    "        \n",
    "        operator = operator.replace(\"air taxi - \", \"\")\n",
    "        operator = operator.replace(\" - taxi\", \"\")\n",
    "        operator = operator.replace(\" - air taxi\", \"\")\n",
    "        operator = operator.replace(\"military - \", \"\")\n",
    "        operator = operator.replace(\"military -\", \"\")\n",
    "        operator = operator.replace(\"mililtary - \", \"\")\n",
    "        operator = operator.replace(\"military \", \"\")\n",
    "        operator = operator.replace(\"miltiary -\", \"\")\n",
    "        \n",
    "        operator = operator.replace(\"bhs - \", \"\")\n",
    "        operator = operator.replace(\" inc\", \"\")\n",
    "        operator = operator.replace(\"private - \", \"\")\n",
    "        operator = operator.replace(\"charter - \", \"\")\n",
    "        operator = operator.replace(\" - charter\", \"\")\n",
    "        \n",
    "        \n",
    "        operator = operator.replace(\"ecuadorean\", \"ecuadorian\")\n",
    "        operator = operator.replace(\"procuradoria\", \"procuraduria\")\n",
    "        operator = operator.replace(\"procurator\", \"procuraduria\")\n",
    "        operator = operator.replace(\"a b aerotransport\", \"ab aerotransport\")\n",
    "        operator = operator.replace(\"u s \", \"us \")\n",
    "        operator = operator.replace(\"\", \"\")\n",
    "        operator = operator.strip()\n",
    "        operators.add(operator)\n",
    "\n",
    "    return \" / \".join(operators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "403935a5-e948-40fd-bb4c-2b2234685eb7",
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "      <th>mortality</th>\n",
       "      <th>is_military</th>\n",
       "      <th>plane_series</th>\n",
       "      <th>series_encoded</th>\n",
       "      <th>loc_clean</th>\n",
       "      <th>Operator_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4829</th>\n",
       "      <td>2009-06-07</td>\n",
       "      <td>Near Port Hope Simpson, Newfoundland, Canada</td>\n",
       "      <td>strait air</td>\n",
       "      <td>britten-norman bn-2a-27 islander</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.</td>\n",
       "      <td>13</td>\n",
       "      <td>Fog</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>brittennorman</td>\n",
       "      <td>0.361963</td>\n",
       "      <td>canada</td>\n",
       "      <td>strait air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>military - indian air force</td>\n",
       "      <td>antonov an-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>antonov</td>\n",
       "      <td>0.453988</td>\n",
       "      <td>india</td>\n",
       "      <td>indian air force</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                      Location  \\\n",
       "4829 2009-06-07  Near Port Hope Simpson, Newfoundland, Canada   \n",
       "4830 2009-06-08             State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator                              Type  Aboard  \\\n",
       "4829                   strait air  britten-norman bn-2a-27 islander     1.0   \n",
       "4830  military - indian air force                     antonov an-32    13.0   \n",
       "\n",
       "      Fatalities  Ground  \\\n",
       "4829         1.0     0.0   \n",
       "4830        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \\\n",
       "4829              The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.   \n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.   \n",
       "\n",
       "      Summary_Cluster  Summary_Cluster_name  mortality  is_military  \\\n",
       "4829               13                   Fog        1.0        False   \n",
       "4830               15  Disappeared en route        1.0         True   \n",
       "\n",
       "       plane_series  series_encoded loc_clean    Operator_clean  \n",
       "4829  brittennorman        0.361963    canada        strait air  \n",
       "4830        antonov        0.453988     india  indian air force  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes.Operator = crashes.Operator.str.lower()\n",
    "crashes['Operator_clean'] = crashes.Operator.apply(lambda x: clean_operator(x))\n",
    "crashes.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1653e324",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "us air force                  180\n",
       "aeroflot                      166\n",
       "air france                     62\n",
       "royal air force                52\n",
       "deutsche lufthansa             50\n",
       "air taxi                       49\n",
       "united air lines               44\n",
       "us army air forces             44\n",
       "us navy                        42\n",
       "pan american world airways     41\n",
       "Name: Operator_clean, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes.Operator_clean.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "04e751ce-0c02-4e66-8b95-20564a6ed048",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unio = crashes.Operator_clean.sort_values(ascending=True).unique()\n",
    "# unio[:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84cbea2",
   "metadata": {},
   "source": [
    "Let's encode rankings by mean death value of operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49ef63a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30870709fd1a44b6bec08b2d48164377",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4831 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "      <th>mortality</th>\n",
       "      <th>is_military</th>\n",
       "      <th>plane_series</th>\n",
       "      <th>series_encoded</th>\n",
       "      <th>loc_clean</th>\n",
       "      <th>Operator_clean</th>\n",
       "      <th>operator_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4829</th>\n",
       "      <td>2009-06-07</td>\n",
       "      <td>Near Port Hope Simpson, Newfoundland, Canada</td>\n",
       "      <td>strait air</td>\n",
       "      <td>britten-norman bn-2a-27 islander</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.</td>\n",
       "      <td>13</td>\n",
       "      <td>Fog</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>brittennorman</td>\n",
       "      <td>0.361963</td>\n",
       "      <td>canada</td>\n",
       "      <td>strait air</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>military - indian air force</td>\n",
       "      <td>antonov an-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>antonov</td>\n",
       "      <td>0.453988</td>\n",
       "      <td>india</td>\n",
       "      <td>indian air force</td>\n",
       "      <td>0.520833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                      Location  \\\n",
       "4829 2009-06-07  Near Port Hope Simpson, Newfoundland, Canada   \n",
       "4830 2009-06-08             State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator                              Type  Aboard  \\\n",
       "4829                   strait air  britten-norman bn-2a-27 islander     1.0   \n",
       "4830  military - indian air force                     antonov an-32    13.0   \n",
       "\n",
       "      Fatalities  Ground  \\\n",
       "4829         1.0     0.0   \n",
       "4830        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \\\n",
       "4829              The air ambulance crashed into hills while attempting to land in heavy fog about 4 miles from the airport.   \n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.   \n",
       "\n",
       "      Summary_Cluster  Summary_Cluster_name  mortality  is_military  \\\n",
       "4829               13                   Fog        1.0        False   \n",
       "4830               15  Disappeared en route        1.0         True   \n",
       "\n",
       "       plane_series  series_encoded loc_clean    Operator_clean  \\\n",
       "4829  brittennorman        0.361963    canada        strait air   \n",
       "4830        antonov        0.453988     india  indian air force   \n",
       "\n",
       "      operator_encoded  \n",
       "4829          1.000000  \n",
       "4830          0.520833  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operator_encoding_dict = target_encode(crashes, 'Operator_clean', 'mortality')\n",
    "crashes['operator_encoded'] = crashes.Operator_clean.progress_apply(lambda x: operator_encoding_dict[x])\n",
    "crashes.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ed7e868",
   "metadata": {},
   "source": [
    "## Date column (removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d176746b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# crashes['date_year'] = crashes.Date.dt.year\n",
    "# crashes['date_month'] = crashes.Date.dt.month\n",
    "# crashes['date_day'] = crashes.Date.dt.day\n",
    "# crashes['date_day_of_week'] = crashes.Date.dt.day_of_week\n",
    "# crashes['date_day_of_year'] = crashes.Date.dt.day_of_year\n",
    "\n",
    "# crashes[['Date', 'date_year', 'date_month', 'date_day', 'date_day_of_week', 'date_day_of_year']].tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "28ef05bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# crashes['Date_is_year_start'] = crashes.Date.dt.is_year_start\n",
    "# crashes['Date_is_quarter_start'] = crashes.Date.dt.is_quarter_start\n",
    "# crashes['Date_is_month_start'] = crashes.Date.dt.is_month_start\n",
    "# crashes['Date_is_month_end'] = crashes.Date.dt.is_month_end\n",
    "\n",
    "# crashes[['Date_is_year_start', 'Date_is_quarter_start', 'Date_is_month_start', 'Date_is_month_end']].tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9f9125e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# crashes['Date_is_weekend'] = np.where(crashes['date_day_of_week'].isin([5,6]), 1,0)\n",
    "# crashes[['Date','date_day_of_week','Date_is_weekend']].tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fde4315",
   "metadata": {},
   "source": [
    "## Categorize by plane size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a9d01007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 100x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crashes.boxplot(['Aboard'], figsize=(1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "584d1592",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Aboard'}>]], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAADaCAYAAACcoWIOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAmwUlEQVR4nO3dfXSU9Z3//9dAJhMSkimBTSY5pDbaLEIDiKAhqCUFEmCJscu26OKmdmUFyp054LFSfnsMWyUstUglW1RMgRpobBVcWiEmqAQ54SZkyQqUw9qK3NQMoZg7IE7G5Pr94ZXr22EIJhCYGXk+zsk5Xtf1zsznmlcQX16Ta2yGYRgCAAAAAKhXoBcAAAAAAMGCggQAAAAAJgoSAAAAAJgoSAAAAABgoiABAAAAgImCBAAAAAAmChIAAAAAmChIAAAAAGCiIAEAAACAiYIEAAiYF154QTabTampqX7HPv74Y9lsNj333HMBWFn35efny2azBXoZAIBrREECAATMr371K0nSkSNHtG/fvgCvBgAAChIAIEAOHDig//3f/9WUKVMkSUVFRQFe0ZW1tbXJ4/EEehkAgOuMggQACIiOQrR8+XKNGTNGJSUlunjxot9ce3u7nn32WX39619XRESERo0apXfeecdvbvfu3Ro/fryio6MVGRmpMWPG6K233vKZOXv2rObMmaMhQ4aob9++iouL07hx4/T+++/7zHW8vW/FihV65plnlJycLIfDoffee0+S9NZbb+mOO+6Qw+FQcnJyyLwNEADw5ShIAIAbrqWlRb/5zW901113KTU1VY8++qiam5v1u9/9zm+2sLBQpaWlWrVqlYqLi9WrVy9NnjxZe/bssWYqKio0btw4NTY2qqioSL/5zW8UHR2t+++/X6+99po19+mnn0qSnn76ab311ltat26dbr31VmVkZGjnzp1+z/3CCy/o3Xff1XPPPaft27fr9ttv1zvvvKMHHnhA0dHRKikp0c9+9jP99re/1bp163r+hQIA3HA2wzCMQC8CAHBzefXVV/WDH/xAL774ombNmqXz588rISFBI0aM0K5duyR9cRUnOTlZiYmJ+vOf/6yIiAhJUnNzs77xjW/ozjvvVHl5uSQpPT1dH330kf785z+rb9++kr54S9wdd9yhhoYGnTx58rI3UGhra5NhGJo0aZJiYmK0efNmn+e+7bbbdPToUdntdut7Ro8erVOnTl12TZ9++qn4axUAQhtXkAAAN1xRUZH69Omjhx56SJLUt29fff/739f777+vDz/80Gd26tSpVhGRZF0Z2rVrl9ra2nThwgXt27dP3/ve96xyJEm9e/dWbm6uTp8+rWPHjln7X3zxRd15552KiIhQWFiY7Ha73nnnHR09etRvnTk5OT7l6MKFC6qqqup0TQCA0EdBAgDcUH/605+0a9cuTZkyRYZhqKGhQQ0NDfre974n6f/d2a6Dy+XyewyXy6XW1ladP39e9fX1MgxDCQkJfnOJiYmSpHPnzkmSVq5cqR/96EdKS0vTG2+8ob1796qqqkqTJk1SS0uL3/df+pj19fVqb2/vdE0AgNAXFugFAABuLr/61a9kGIZef/11vf76637HN2zYoGeeecbadrvdfjNut1vh4eHq27evwsLC1KtXL9XW1vrNffLJJ5KkAQMGSJKKi4uVkZGhNWvW+Mw1Nzdfdq2Xvi2vX79+stlsna4JABD6uIIEALhh2tratGHDBt1222167733/L4WLVqk2tpabd++3fqezZs367PPPrO2m5ub9fvf/1733XefevfuraioKKWlpWnz5s0+V4Ha29tVXFysgQMH6u///u8lfVF4HA6Hz5o++OADnxs+XElUVJTuvvvuTtcEAAh9FCQAwA2zfft2ffLJJ5o5c6YyMjL8vp566ik5HA6fz0Tq3bu3MjMztWXLFr3xxhsaP368mpqatHTpUmumoKBA586d03e+8x29/vrr2rp1q/7hH/5Bhw8f1nPPPWddCcrOzlZZWZmefvppvfvuu1qzZo0mTpyo5OTkLp/DT3/6U7ndbmVmZurNN9+01hQVFdVzLxQAIGAoSACAG6aoqEjh4eH613/918seHzBggP7xH/9Rf/jDH3TmzBlJ0rx585SZmakFCxZo+vTp+vzzz/XWW2/pnnvusb5v7NixevfddxUVFaUf/vCHeuihh9TY2KitW7fqwQcftOaWLFmiRYsWqaioSFOmTNErr7yiF198Uffee2+Xz6GjGDU1NenBBx/UwoUL9U//9E969NFHr/JVAQAEE27zDQAAAAAmriABAAAAgImCBAAAAAAmChIAAAAAmChIAAAAAGCiIAEAAACAiYIEAAAAAKawQC/gemlvb9cnn3yi6Oho6wMCAQAAANx8DMNQc3OzEhMT1avXla8RfWUL0ieffKKkpKRALwMAAABAkDh16pQGDhx4xZmvbEGKjo6W9MWLEBMTE9C1eL1elZWVKSsrS3a7PaBrQdeRW+ghs9BEbqGHzEIPmYUmcus5TU1NSkpKsjrClXxlC1LH2+piYmKCoiBFRkYqJiaGH+4QQm6hh8xCE7mFHjILPWQWmsit53XlV2+4SQMAAAAAmChIAAAAAGCiIAEAAACAiYIEAAAAACYKEgAAAACYvrJ3sQtGqflvy9PGh9Ze6uPlUwK9BAAAAEASV5AAAAAAwEJBAgAAAAATBQkAAAAATBQkAAAAADBdU0EqKCiQzWZTXl6etc8wDOXn5ysxMVF9+vRRRkaGjhw54vN9Ho9H8+fP14ABAxQVFaWcnBydPn3aZ6a+vl65ublyOp1yOp3Kzc1VQ0PDtSwXAAAAAK7oqgtSVVWVXn75ZQ0bNsxn/4oVK7Ry5UoVFhaqqqpKLpdLmZmZam5utmby8vK0ZcsWlZSUaPfu3Tp//ryys7PV1tZmzUyfPl01NTUqLS1VaWmpampqlJube7XLBQAAAIAvdVUF6fz583r44Ye1du1a9evXz9pvGIZWrVqlJUuWaOrUqUpNTdWGDRt08eJFbdq0SZLU2NiooqIi/fznP9eECRM0YsQIFRcX69ChQ9qxY4ck6ejRoyotLdUrr7yi9PR0paena+3atfrDH/6gY8eO9cBpAwAAAIC/q/ocpLlz52rKlCmaMGGCnnnmGWv/8ePH5Xa7lZWVZe1zOBwaO3asKisrNWvWLFVXV8vr9frMJCYmKjU1VZWVlZo4caL27Nkjp9OptLQ0a2b06NFyOp2qrKzUoEGD/Nbk8Xjk8Xis7aamJkmS1+uV1+u9mtPsMR3P7+hlBHQdwSrQ+XSmY13Buj74I7PQRG6hh8xCD5mFJnLrOd15DbtdkEpKSlRdXa0DBw74HXO73ZKk+Ph4n/3x8fE6ceKENRMeHu5z5aljpuP73W634uLi/B4/Li7OmrlUQUGBli5d6re/rKxMkZGRXTiz6++no9oDvYSgtG3btkAv4YrKy8sDvQR0E5mFJnILPWQWesgsNJHbtbt48WKXZ7tVkE6dOqXHH39cZWVlioiI6HTOZrP5bBuG4bfvUpfOXG7+So+zePFiLVy40NpuampSUlKSsrKyFBMTc8Xnvt68Xq/Ky8v17wd6ydN+5dfhZnQ4f2Kgl3BZHbllZmbKbrcHejnoAjILTeQWesgs9JBZaCK3ntPx7rKu6FZBqq6uVl1dnUaOHGnta2tr065du1RYWGj9fpDb7VZCQoI1U1dXZ11Vcrlcam1tVX19vc9VpLq6Oo0ZM8aaOXPmjN/znz171u/qVAeHwyGHw+G33263B80PlKfdJk8bBelSwZJPZ4LpZwhdQ2ahidxCD5mFHjILTeR27brz+nXrJg3jx4/XoUOHVFNTY32NGjVKDz/8sGpqanTrrbfK5XL5XAZsbW1VRUWFVX5Gjhwpu93uM1NbW6vDhw9bM+np6WpsbNT+/futmX379qmxsdGaAQAAAICe1q0rSNHR0UpNTfXZFxUVpf79+1v78/LytGzZMqWkpCglJUXLli1TZGSkpk+fLklyOp2aMWOGFi1apP79+ys2NlZPPPGEhg4dqgkTJkiSBg8erEmTJumxxx7TSy+9JEmaOXOmsrOzL3uDBgAAAADoCVd1F7srefLJJ9XS0qI5c+aovr5eaWlpKisrU3R0tDXz/PPPKywsTNOmTVNLS4vGjx+v9evXq3fv3tbMxo0btWDBAutudzk5OSosLOzp5QIAAACA5ZoL0s6dO322bTab8vPzlZ+f3+n3REREaPXq1Vq9enWnM7GxsSouLr7W5QEAAABAl13VB8UCAAAAwFcRBQkAAAAATBQkAAAAADBRkAAAAADAREECAAAAABMFCQAAAABMFCQAAAAAMFGQAAAAAMBEQQIAAAAAEwUJAAAAAEwUJAAAAAAwUZAAAAAAwERBAgAAAAATBQkAAAAATBQkAAAAADBRkAAAAADAREECAAAAABMFCQAAAABMFCQAAAAAMFGQAAAAAMBEQQIAAAAAEwUJAAAAAEwUJAAAAAAwdasgrVmzRsOGDVNMTIxiYmKUnp6u7du3W8cNw1B+fr4SExPVp08fZWRk6MiRIz6P4fF4NH/+fA0YMEBRUVHKycnR6dOnfWbq6+uVm5srp9Mpp9Op3NxcNTQ0XP1ZAgAAAEAXdKsgDRw4UMuXL9eBAwd04MABjRs3Tg888IBVglasWKGVK1eqsLBQVVVVcrlcyszMVHNzs/UYeXl52rJli0pKSrR7926dP39e2dnZamtrs2amT5+umpoalZaWqrS0VDU1NcrNze2hUwYAAACAywvrzvD999/vs/3ss89qzZo12rt3r4YMGaJVq1ZpyZIlmjp1qiRpw4YNio+P16ZNmzRr1iw1NjaqqKhIr776qiZMmCBJKi4uVlJSknbs2KGJEyfq6NGjKi0t1d69e5WWliZJWrt2rdLT03Xs2DENGjSoJ84bAAAAAPx0qyD9rba2Nv3ud7/ThQsXlJ6eruPHj8vtdisrK8uacTgcGjt2rCorKzVr1ixVV1fL6/X6zCQmJio1NVWVlZWaOHGi9uzZI6fTaZUjSRo9erScTqcqKys7LUgej0cej8fabmpqkiR5vV55vd6rPc0e0fH8jl5GQNcRrAKdT2c61hWs64M/MgtN5BZ6yCz0kFloIree053XsNsF6dChQ0pPT9dnn32mvn37asuWLRoyZIgqKyslSfHx8T7z8fHxOnHihCTJ7XYrPDxc/fr185txu93WTFxcnN/zxsXFWTOXU1BQoKVLl/rtLysrU2RkZPdO8jr56aj2QC8hKG3bti3QS7ii8vLyQC8B3URmoYncQg+ZhR4yC03kdu0uXrzY5dluF6RBgwappqZGDQ0NeuONN/TII4+ooqLCOm6z2XzmDcPw23epS2cuN/9lj7N48WItXLjQ2m5qalJSUpKysrIUExPzped1PXm9XpWXl+vfD/SSp/3Kr8XN6HD+xEAv4bI6csvMzJTdbg/0ctAFZBaayC30kFnoIbPQRG49p+PdZV3R7YIUHh6ub37zm5KkUaNGqaqqSr/4xS/04x//WNIXV4ASEhKs+bq6OuuqksvlUmtrq+rr632uItXV1WnMmDHWzJkzZ/ye9+zZs35Xp/6Ww+GQw+Hw22+324PmB8rTbpOnjYJ0qWDJpzPB9DOEriGz0ERuoYfMQg+ZhSZyu3bdef2u+XOQDMOQx+NRcnKyXC6XzyXA1tZWVVRUWOVn5MiRstvtPjO1tbU6fPiwNZOenq7Gxkbt37/fmtm3b58aGxutGQAAAAC4Hrp1BeknP/mJJk+erKSkJDU3N6ukpEQ7d+5UaWmpbDab8vLytGzZMqWkpCglJUXLli1TZGSkpk+fLklyOp2aMWOGFi1apP79+ys2NlZPPPGEhg4dat3VbvDgwZo0aZIee+wxvfTSS5KkmTNnKjs7mzvYAQAAALiuulWQzpw5o9zcXNXW1srpdGrYsGEqLS1VZmamJOnJJ59US0uL5syZo/r6eqWlpamsrEzR0dHWYzz//PMKCwvTtGnT1NLSovHjx2v9+vXq3bu3NbNx40YtWLDAuttdTk6OCgsLe+J8AQAAAKBT3SpIRUVFVzxus9mUn5+v/Pz8TmciIiK0evVqrV69utOZ2NhYFRcXd2dpAAAAAHDNrvl3kAAAAADgq4KCBAAAAAAmChIAAAAAmChIAAAAAGCiIAEAAACAiYIEAAAAACYKEgAAAACYKEgAAAAAYKIgAQAAAICJggQAAAAAJgoSAAAAAJgoSAAAAABgoiABAAAAgImCBAAAAAAmChIAAAAAmChIAAAAAGCiIAEAAACAiYIEAAAAACYKEgAAAACYKEgAAAAAYKIgAQAAAICJggQAAAAAJgoSAAAAAJi6VZAKCgp01113KTo6WnFxcfrud7+rY8eO+cwYhqH8/HwlJiaqT58+ysjI0JEjR3xmPB6P5s+frwEDBigqKko5OTk6ffq0z0x9fb1yc3PldDrldDqVm5urhoaGqztLAAAAAOiCbhWkiooKzZ07V3v37lV5ebk+//xzZWVl6cKFC9bMihUrtHLlShUWFqqqqkoul0uZmZlqbm62ZvLy8rRlyxaVlJRo9+7dOn/+vLKzs9XW1mbNTJ8+XTU1NSotLVVpaalqamqUm5vbA6cMAAAAAJcX1p3h0tJSn+1169YpLi5O1dXV+va3vy3DMLRq1SotWbJEU6dOlSRt2LBB8fHx2rRpk2bNmqXGxkYVFRXp1Vdf1YQJEyRJxcXFSkpK0o4dOzRx4kQdPXpUpaWl2rt3r9LS0iRJa9euVXp6uo4dO6ZBgwb1xLkDAAAAgI9r+h2kxsZGSVJsbKwk6fjx43K73crKyrJmHA6Hxo4dq8rKSklSdXW1vF6vz0xiYqJSU1OtmT179sjpdFrlSJJGjx4tp9NpzQAAAABAT+vWFaS/ZRiGFi5cqHvvvVepqamSJLfbLUmKj4/3mY2Pj9eJEyesmfDwcPXr189vpuP73W634uLi/J4zLi7OmrmUx+ORx+OxtpuamiRJXq9XXq/3ak6xx3Q8v6OXEdB1BKtA59OZjnUF6/rgj8xCE7mFHjILPWQWmsit53TnNbzqgjRv3jx98MEH2r17t98xm83ms20Yht++S106c7n5Kz1OQUGBli5d6re/rKxMkZGRV3zuG+Wno9oDvYSgtG3btkAv4YrKy8sDvQR0E5mFJnILPWQWesgsNJHbtbt48WKXZ6+qIM2fP19bt27Vrl27NHDgQGu/y+WS9MUVoISEBGt/XV2ddVXJ5XKptbVV9fX1PleR6urqNGbMGGvmzJkzfs979uxZv6tTHRYvXqyFCxda201NTUpKSlJWVpZiYmKu5jR7jNfrVXl5uf79QC952q9cFG9Gh/MnBnoJl9WRW2Zmpux2e6CXgy4gs9BEbqGHzEIPmYUmcus5He8u64puFSTDMDR//nxt2bJFO3fuVHJyss/x5ORkuVwulZeXa8SIEZKk1tZWVVRU6D//8z8lSSNHjpTdbld5ebmmTZsmSaqtrdXhw4e1YsUKSVJ6eroaGxu1f/9+3X333ZKkffv2qbGx0SpRl3I4HHI4HH777XZ70PxAedpt8rRRkC4VLPl0Jph+htA1ZBaayC30kFnoIbPQRG7XrjuvX7cK0ty5c7Vp0yb993//t6Kjo63fB3I6nerTp49sNpvy8vK0bNkypaSkKCUlRcuWLVNkZKSmT59uzc6YMUOLFi1S//79FRsbqyeeeEJDhw617mo3ePBgTZo0SY899pheeuklSdLMmTOVnZ3NHewAAAAAXDfdKkhr1qyRJGVkZPjsX7dunX74wx9Kkp588km1tLRozpw5qq+vV1pamsrKyhQdHW3NP//88woLC9O0adPU0tKi8ePHa/369erdu7c1s3HjRi1YsMC6211OTo4KCwuv5hwBAAAAoEu6/Ra7L2Oz2ZSfn6/8/PxOZyIiIrR69WqtXr2605nY2FgVFxd3Z3kAAAAAcE2u6XOQAAAAAOCrhIIEAAAAACYKEgAAAACYKEgAAAAAYKIgAQAAAICJggQAAAAAJgoSAAAAAJgoSAAAAABgoiABAAAAgImCBAAAAAAmChIAAAAAmChIAAAAAGCiIAEAAACAiYIEAAAAACYKEgAAAACYKEgAAAAAYKIgAQAAAICJggQAAAAAJgoSAAAAAJgoSAAAAABgoiABAAAAgImCBAAAAAAmChIAAAAAmChIAAAAAGDqdkHatWuX7r//fiUmJspms+nNN9/0OW4YhvLz85WYmKg+ffooIyNDR44c8ZnxeDyaP3++BgwYoKioKOXk5Oj06dM+M/X19crNzZXT6ZTT6VRubq4aGhq6fYIAAAAA0FXdLkgXLlzQ8OHDVVhYeNnjK1as0MqVK1VYWKiqqiq5XC5lZmaqubnZmsnLy9OWLVtUUlKi3bt36/z588rOzlZbW5s1M336dNXU1Ki0tFSlpaWqqalRbm7uVZwiAAAAAHRNWHe/YfLkyZo8efJljxmGoVWrVmnJkiWaOnWqJGnDhg2Kj4/Xpk2bNGvWLDU2NqqoqEivvvqqJkyYIEkqLi5WUlKSduzYoYkTJ+ro0aMqLS3V3r17lZaWJklau3at0tPTdezYMQ0aNOhqzxcAAAAAOtXtgnQlx48fl9vtVlZWlrXP4XBo7Nixqqys1KxZs1RdXS2v1+szk5iYqNTUVFVWVmrixInas2ePnE6nVY4kafTo0XI6naqsrLxsQfJ4PPJ4PNZ2U1OTJMnr9crr9fbkaXZbx/M7ehkBXUewCnQ+nelYV7CuD/7ILDSRW+ghs9BDZqGJ3HpOd17DHi1IbrdbkhQfH++zPz4+XidOnLBmwsPD1a9fP7+Zju93u92Ki4vze/y4uDhr5lIFBQVaunSp3/6ysjJFRkZ2/2Sug5+Oag/0EoLStm3bAr2EKyovLw/0EtBNZBaayC30kFnoIbPQRG7X7uLFi12e7dGC1MFms/lsG4bht+9Sl85cbv5Kj7N48WItXLjQ2m5qalJSUpKysrIUExPTneX3OK/Xq/Lycv37gV7ytF/5dbgZHc6fGOglXFZHbpmZmbLb7YFeDrqAzEITuYUeMgs9ZBaayK3ndLy7rCt6tCC5XC5JX1wBSkhIsPbX1dVZV5VcLpdaW1tVX1/vcxWprq5OY8aMsWbOnDnj9/hnz571uzrVweFwyOFw+O232+1B8wPlabfJ00ZBulSw5NOZYPoZQteQWWgit9BDZqGHzEITuV277rx+Pfo5SMnJyXK5XD6XAVtbW1VRUWGVn5EjR8put/vM1NbW6vDhw9ZMenq6GhsbtX//fmtm3759amxstGYAAAAAoKd1+wrS+fPn9ac//cnaPn78uGpqahQbG6uvf/3rysvL07Jly5SSkqKUlBQtW7ZMkZGRmj59uiTJ6XRqxowZWrRokfr376/Y2Fg98cQTGjp0qHVXu8GDB2vSpEl67LHH9NJLL0mSZs6cqezsbO5gBwAAAOC66XZBOnDggL7zne9Y2x2/9/PII49o/fr1evLJJ9XS0qI5c+aovr5eaWlpKisrU3R0tPU9zz//vMLCwjRt2jS1tLRo/PjxWr9+vXr37m3NbNy4UQsWLLDudpeTk9PpZy8BAAAAQE/odkHKyMiQYXR+u2qbzab8/Hzl5+d3OhMREaHVq1dr9erVnc7ExsaquLi4u8sDAAAAgKvWo7+DBAAAAAChjIIEAAAAACYKEgAAAACYKEgAAAAAYKIgAQAAAICJggQAAAAAJgoSAAAAAJgoSAAAAABgoiABAAAAgImCBAAAAAAmChIAAAAAmChIAAAAAGCiIAEAAACAiYIEAAAAACYKEgAAAACYwgK9AOAbT70V6CVclqO3oRV3S6n5b8vTZrvhz//x8ik3/DkBAABudlxBAgAAAAATBQkAAAAATBQkAAAAADBRkAAAAADAREECAAAAABMFCQAAAABMFCQAAAAAMAX95yD98pe/1M9+9jPV1tbqW9/6llatWqX77rsv0MsCrrtg/XyoYMBnRAEAgOslqK8gvfbaa8rLy9OSJUt08OBB3XfffZo8ebJOnjwZ6KUBAAAA+AoK6oK0cuVKzZgxQ//2b/+mwYMHa9WqVUpKStKaNWsCvTQAAAAAX0FB+xa71tZWVVdX66mnnvLZn5WVpcrKSr95j8cjj8djbTc2NkqSPv30U3m93uu72C/h9Xp18eJFhXl7qa3dFtC1oOvC2g1dvNhObkHom0/89rL7Hb0M/X8j2nXHks3ykFnIuBG57Vs8/ro87s2q4++1c+fOyW63B3o56AIyC03k1nOam5slSYZhfOls0Bakv/71r2pra1N8fLzP/vj4eLndbr/5goICLV261G9/cnLydVsjvvqmB3oB6DYyC03XO7cBP7/OTwAACAnNzc1yOp1XnAnagtTBZvP9v4mGYfjtk6TFixdr4cKF1nZ7e7s+/fRT9e/f/7LzN1JTU5OSkpJ06tQpxcTEBHQt6DpyCz1kFprILfSQWeghs9BEbj3HMAw1NzcrMTHxS2eDtiANGDBAvXv39rtaVFdX53dVSZIcDoccDofPvq997WvXc4ndFhMTww93CCK30ENmoYncQg+ZhR4yC03k1jO+7MpRh6C9SUN4eLhGjhyp8vJyn/3l5eUaM2ZMgFYFAAAA4KssaK8gSdLChQuVm5urUaNGKT09XS+//LJOnjyp2bNnB3ppAAAAAL6CgrogPfjggzp37pz+4z/+Q7W1tUpNTdW2bdt0yy23BHpp3eJwOPT000/7vQUQwY3cQg+ZhSZyCz1kFnrILDSRW2DYjK7c6w4AAAAAbgJB+ztIAAAAAHCjUZAAAAAAwERBAgAAAAATBQkAAAAATBSkG+CXv/ylkpOTFRERoZEjR+r9998P9JJuWrt27dL999+vxMRE2Ww2vfnmmz7HDcNQfn6+EhMT1adPH2VkZOjIkSM+Mx6PR/Pnz9eAAQMUFRWlnJwcnT59+gaexc2loKBAd911l6KjoxUXF6fvfve7OnbsmM8MuQWXNWvWaNiwYdYHG6anp2v79u3WcfIKfgUFBbLZbMrLy7P2kVvwyc/Pl81m8/lyuVzWcTILXn/5y1/0L//yL+rfv78iIyN1xx13qLq62jpOdoFFQbrOXnvtNeXl5WnJkiU6ePCg7rvvPk2ePFknT54M9NJuShcuXNDw4cNVWFh42eMrVqzQypUrVVhYqKqqKrlcLmVmZqq5udmaycvL05YtW1RSUqLdu3fr/Pnzys7OVltb2406jZtKRUWF5s6dq71796q8vFyff/65srKydOHCBWuG3ILLwIEDtXz5ch04cEAHDhzQuHHj9MADD1h/uZNXcKuqqtLLL7+sYcOG+ewnt+D0rW99S7W1tdbXoUOHrGNkFpzq6+t1zz33yG63a/v27frjH/+on//85/ra175mzZBdgBm4ru6++25j9uzZPvtuv/1246mnngrQitBBkrFlyxZru7293XC5XMby5cutfZ999pnhdDqNF1980TAMw2hoaDDsdrtRUlJizfzlL38xevXqZZSWlt6wtd/M6urqDElGRUWFYRjkFir69etnvPLKK+QV5Jqbm42UlBSjvLzcGDt2rPH4448bhsGfs2D19NNPG8OHD7/sMTILXj/+8Y+Ne++9t9PjZBd4XEG6jlpbW1VdXa2srCyf/VlZWaqsrAzQqtCZ48ePy+12++TlcDg0duxYK6/q6mp5vV6fmcTERKWmppLpDdLY2ChJio2NlURuwa6trU0lJSW6cOGC0tPTySvIzZ07V1OmTNGECRN89pNb8Prwww+VmJio5ORkPfTQQ/roo48kkVkw27p1q0aNGqXvf//7iouL04gRI7R27VrrONkFHgXpOvrrX/+qtrY2xcfH++yPj4+X2+0O0KrQmY5MrpSX2+1WeHi4+vXr1+kMrh/DMLRw4ULde++9Sk1NlURuwerQoUPq27evHA6HZs+erS1btmjIkCHkFcRKSkpUXV2tgoICv2PkFpzS0tL061//Wm+//bbWrl0rt9utMWPG6Ny5c2QWxD766COtWbNGKSkpevvttzV79mwtWLBAv/71ryXx5y0YhAV6ATcDm83ms20Yht8+BI+ryYtMb4x58+bpgw8+0O7du/2OkVtwGTRokGpqatTQ0KA33nhDjzzyiCoqKqzj5BVcTp06pccff1xlZWWKiIjodI7cgsvkyZOtfx46dKjS09N12223acOGDRo9erQkMgtG7e3tGjVqlJYtWyZJGjFihI4cOaI1a9boBz/4gTVHdoHDFaTraMCAAerdu7dfk6+rq/P7vwIIvI47/1wpL5fLpdbWVtXX13c6g+tj/vz52rp1q9577z0NHDjQ2k9uwSk8PFzf/OY3NWrUKBUUFGj48OH6xS9+QV5Bqrq6WnV1dRo5cqTCwsIUFhamiooKvfDCCwoLC7Ned3ILblFRURo6dKg+/PBD/qwFsYSEBA0ZMsRn3+DBg60beJFd4FGQrqPw8HCNHDlS5eXlPvvLy8s1ZsyYAK0KnUlOTpbL5fLJq7W1VRUVFVZeI0eOlN1u95mpra3V4cOHyfQ6MQxD8+bN0+bNm/Xuu+8qOTnZ5zi5hQbDMOTxeMgrSI0fP16HDh1STU2N9TVq1Cg9/PDDqqmp0a233kpuIcDj8ejo0aNKSEjgz1oQu+eee/w+ruL//u//dMstt0ji77WgcOPvC3FzKSkpMex2u1FUVGT88Y9/NPLy8oyoqCjj448/DvTSbkrNzc3GwYMHjYMHDxqSjJUrVxoHDx40Tpw4YRiGYSxfvtxwOp3G5s2bjUOHDhn//M//bCQkJBhNTU3WY8yePdsYOHCgsWPHDuN//ud/jHHjxhnDhw83Pv/880Cd1lfaj370I8PpdBo7d+40amtrra+LFy9aM+QWXBYvXmzs2rXLOH78uPHBBx8YP/nJT4xevXoZZWVlhmGQV6j427vYGQa5BaNFixYZO3fuND766CNj7969RnZ2thEdHW39NwaZBaf9+/cbYWFhxrPPPmt8+OGHxsaNG43IyEijuLjYmiG7wKIg3QD/9V//Zdxyyy1GeHi4ceedd1q3J8aN99577xmS/L4eeeQRwzC+uLXm008/bbhcLsPhcBjf/va3jUOHDvk8RktLizFv3jwjNjbW6NOnj5GdnW2cPHkyAGdzc7hcXpKMdevWWTPkFlweffRR6995f/d3f2eMHz/eKkeGQV6h4tKCRG7B58EHHzQSEhIMu91uJCYmGlOnTjWOHDliHSez4PX73//eSE1NNRwOh3H77bcbL7/8ss9xsgssm2EYRmCuXQEAAABAcOF3kAAAAADAREECAAAAABMFCQAAAABMFCQAAAAAMFGQAAAAAMBEQQIAAAAAEwUJAAAAAEwUJAAAAAAwUZAAAAAAwERBAgAAAAATBQkAAAAATBQkAAAAADD9/2240TFi/EYTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crashes.hist('Aboard', figsize=(10,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5907db46",
   "metadata": {},
   "source": [
    "Convert number of passengers (Aboard) to [0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8e1f65a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(clip=True)\n",
    "\n",
    "scaler.fit(crashes[crashes.Aboard < 100].Aboard.values.reshape(-1, 1))\n",
    "crashes['Aboard_scaled'] = scaler.transform(crashes.Aboard.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4c46b6f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 100x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crashes.boxplot(['Aboard_scaled'], figsize=(1,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8754b00",
   "metadata": {},
   "source": [
    "# 3. Reviewing the summary column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7cf1750a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fire / explosion        424\n",
       "Land / take off         380\n",
       "Engine failure (v3)     359\n",
       "Cargo (plane)           356\n",
       "Engine failure (v2)     356\n",
       "En route / Mountains    338\n",
       "Pilot error             307\n",
       "Crash                   305\n",
       "Engine failure          288\n",
       "Landing attempt         242\n",
       "Weather / landing       225\n",
       "Crashed into sea        219\n",
       "Weather conditions      210\n",
       "Fog                     204\n",
       "Midair collision        133\n",
       "Disappeared en route    125\n",
       "Shot down               118\n",
       "Helicopter              118\n",
       "Mountains                82\n",
       "Mail plane               42\n",
       "Name: Summary_Cluster_name, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes.Summary_Cluster_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f33a19b9",
   "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>Summary_Cluster_name</th>\n",
       "      <th>Aboard_scaled</th>\n",
       "      <th>mortality</th>\n",
       "      <th>is_military</th>\n",
       "      <th>Aboard_scaled_diff</th>\n",
       "      <th>mortality_diff</th>\n",
       "      <th>is_military_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cargo (plane)</td>\n",
       "      <td>0.067189</td>\n",
       "      <td>0.901826</td>\n",
       "      <td>0.019663</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Crash</td>\n",
       "      <td>0.354694</td>\n",
       "      <td>0.833836</td>\n",
       "      <td>0.167213</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crashed into sea</td>\n",
       "      <td>0.201974</td>\n",
       "      <td>0.854612</td>\n",
       "      <td>0.187215</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Disappeared en route</td>\n",
       "      <td>0.205657</td>\n",
       "      <td>0.982320</td>\n",
       "      <td>0.288000</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>En route / Mountains</td>\n",
       "      <td>0.195236</td>\n",
       "      <td>0.933284</td>\n",
       "      <td>0.162722</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Engine failure</td>\n",
       "      <td>0.199670</td>\n",
       "      <td>0.834826</td>\n",
       "      <td>0.204861</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Engine failure (v2)</td>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.654944</td>\n",
       "      <td>0.084270</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Engine failure (v3)</td>\n",
       "      <td>0.256352</td>\n",
       "      <td>0.786045</td>\n",
       "      <td>0.058496</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fire / explosion</td>\n",
       "      <td>0.395131</td>\n",
       "      <td>0.743986</td>\n",
       "      <td>0.205189</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fog</td>\n",
       "      <td>0.259061</td>\n",
       "      <td>0.807059</td>\n",
       "      <td>0.063725</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Helicopter</td>\n",
       "      <td>0.141585</td>\n",
       "      <td>0.860169</td>\n",
       "      <td>0.296610</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Land / take off</td>\n",
       "      <td>0.297315</td>\n",
       "      <td>0.947053</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Landing attempt</td>\n",
       "      <td>0.365890</td>\n",
       "      <td>0.617025</td>\n",
       "      <td>0.070248</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mail plane</td>\n",
       "      <td>0.028139</td>\n",
       "      <td>0.915952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Midair collision</td>\n",
       "      <td>0.337359</td>\n",
       "      <td>0.837444</td>\n",
       "      <td>0.458647</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mountains</td>\n",
       "      <td>0.154964</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.402439</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pilot error</td>\n",
       "      <td>0.285757</td>\n",
       "      <td>0.770554</td>\n",
       "      <td>0.029316</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shot down</td>\n",
       "      <td>0.303715</td>\n",
       "      <td>0.948729</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Weather / landing</td>\n",
       "      <td>0.208305</td>\n",
       "      <td>0.851867</td>\n",
       "      <td>0.284444</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Weather conditions</td>\n",
       "      <td>0.105339</td>\n",
       "      <td>0.888333</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>mean</td>\n",
       "      <td>0.252241</td>\n",
       "      <td>0.828363</td>\n",
       "      <td>0.152970</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Summary_Cluster_name  Aboard_scaled  mortality  is_military  \\\n",
       "0          Cargo (plane)       0.067189   0.901826     0.019663   \n",
       "1                  Crash       0.354694   0.833836     0.167213   \n",
       "2       Crashed into sea       0.201974   0.854612     0.187215   \n",
       "3   Disappeared en route       0.205657   0.982320     0.288000   \n",
       "4   En route / Mountains       0.195236   0.933284     0.162722   \n",
       "5         Engine failure       0.199670   0.834826     0.204861   \n",
       "6    Engine failure (v2)       0.312025   0.654944     0.084270   \n",
       "7    Engine failure (v3)       0.256352   0.786045     0.058496   \n",
       "8       Fire / explosion       0.395131   0.743986     0.205189   \n",
       "9                    Fog       0.259061   0.807059     0.063725   \n",
       "10            Helicopter       0.141585   0.860169     0.296610   \n",
       "11       Land / take off       0.297315   0.947053     0.150000   \n",
       "12       Landing attempt       0.365890   0.617025     0.070248   \n",
       "13            Mail plane       0.028139   0.915952     0.000000   \n",
       "14      Midair collision       0.337359   0.837444     0.458647   \n",
       "15             Mountains       0.154964   0.950000     0.402439   \n",
       "16           Pilot error       0.285757   0.770554     0.029316   \n",
       "17             Shot down       0.303715   0.948729     0.491525   \n",
       "18     Weather / landing       0.208305   0.851867     0.284444   \n",
       "19    Weather conditions       0.105339   0.888333     0.023810   \n",
       "20                  mean       0.252241   0.828363     0.152970   \n",
       "\n",
       "    Aboard_scaled_diff  mortality_diff  is_military_diff  \n",
       "0                -0.19            0.07             -0.13  \n",
       "1                 0.10            0.01              0.01  \n",
       "2                -0.05            0.03              0.03  \n",
       "3                -0.05            0.15              0.14  \n",
       "4                -0.06            0.10              0.01  \n",
       "5                -0.05            0.01              0.05  \n",
       "6                 0.06           -0.17             -0.07  \n",
       "7                 0.00           -0.04             -0.09  \n",
       "8                 0.14           -0.08              0.05  \n",
       "9                 0.01           -0.02             -0.09  \n",
       "10               -0.11            0.03              0.14  \n",
       "11                0.05            0.12             -0.00  \n",
       "12                0.11           -0.21             -0.08  \n",
       "13               -0.22            0.09             -0.15  \n",
       "14                0.09            0.01              0.31  \n",
       "15               -0.10            0.12              0.25  \n",
       "16                0.03           -0.06             -0.12  \n",
       "17                0.05            0.12              0.34  \n",
       "18               -0.04            0.02              0.13  \n",
       "19               -0.15            0.06             -0.13  \n",
       "20                0.00            0.00              0.00  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stats = crashes.groupby('Summary_Cluster_name') \\\n",
    "                       .agg({\"Aboard_scaled\": np.mean,\n",
    "                             \"mortality\": np.mean,\n",
    "                             \"is_military\": np.mean,\n",
    "                            }) \\\n",
    "                       .reset_index(drop=False)\n",
    "\n",
    "mean_stats = crashes.agg({\"Aboard_scaled\": [np.mean],\n",
    "                          \"mortality\": [np.mean],\n",
    "                          \"is_military\": [np.mean],\n",
    "                         }) \\\n",
    "                    .reset_index(drop=False) \\\n",
    "                    .rename(columns={\"index\": \"Summary_Cluster_name\"})\n",
    "\n",
    "cluster_stats = pd.concat([cluster_stats, mean_stats], ignore_index=True)\n",
    "cluster_stats['Aboard_scaled_diff'] = (cluster_stats.Aboard_scaled - crashes.Aboard_scaled.mean()).round(2)\n",
    "cluster_stats['mortality_diff'] = (cluster_stats.mortality - crashes.mortality.mean()).round(2)\n",
    "cluster_stats['is_military_diff'] = (cluster_stats.is_military - crashes.is_military.mean()).round(2)\n",
    "cluster_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9c363f",
   "metadata": {},
   "source": [
    "Выводы:\n",
    "- В кластерах **Cargo (plane)**, **Mail plane**, **Helicopter** во время полетов было относительно мало пассажиров, потому что это \"малые\" средства полета.\n",
    "- В кластерах **Mountains** и **Weather conditions** тоже было мало пассажиров. Возможно проблемы с погодными условиями чаще встречаются среди малых самолетов, потому что их полеты не так хорошо регламентированы.\n",
    "- Относительно высокая смертность встречается в кластерах **Disappeared en route**, **En route / Mountains**, **Mountains**, **Shot down**. К сожалению, это подтверждает интуицию: в таких случаях действительно гораздо сложнее выжить.\n",
    "- Военный транспорт чаще встречается в кластерах **Disappeared en route**, **Helicopter**, **Midair collision**, **Mountains**, **Shot down**. \n",
    "- Военный транспорт реже встречается в кластерах **Cargo (plane)**, **Mail plane**, **Pilot error**. С первыми двумя понятно, но интересно последнее: наверно в военных самолетах очень опытные пилоты, а основные причины ЧП связаны с внешними факторами."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af75935",
   "metadata": {},
   "source": [
    "Из даты не удалось выжать ничего интересного:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "34882aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cluster_stats = crashes.groupby('Summary_Cluster_name') \\\n",
    "#                        .agg({\"Date_is_weekend\": np.mean,\n",
    "#                             }) \\\n",
    "#                        .reset_index(drop=False)\n",
    "\n",
    "# mean_stats = crashes.agg({\"Date_is_weekend\": [np.mean],\n",
    "#                          }) \\\n",
    "#                     .reset_index(drop=False) \\\n",
    "#                     .rename(columns={\"index\": \"Summary_Cluster_name\"})\n",
    "\n",
    "# cluster_stats = pd.concat([cluster_stats, mean_stats], ignore_index=True)\n",
    "# cluster_stats['is_weekend_diff'] = (cluster_stats.Date_is_weekend - crashes.Date_is_weekend.mean()).round(2)\n",
    "# cluster_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b15196b",
   "metadata": {},
   "source": [
    "One-hot encode summary clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eb2ec06c",
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Type</th>\n",
       "      <th>Aboard</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Summary_Cluster</th>\n",
       "      <th>Summary_Cluster_name</th>\n",
       "      <th>...</th>\n",
       "      <th>Helicopter</th>\n",
       "      <th>Land / take off</th>\n",
       "      <th>Landing attempt</th>\n",
       "      <th>Mail plane</th>\n",
       "      <th>Midair collision</th>\n",
       "      <th>Mountains</th>\n",
       "      <th>Pilot error</th>\n",
       "      <th>Shot down</th>\n",
       "      <th>Weather / landing</th>\n",
       "      <th>Weather conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>State of Arunachal Pradesh, India</td>\n",
       "      <td>military - indian air force</td>\n",
       "      <td>antonov an-32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.</td>\n",
       "      <td>15</td>\n",
       "      <td>Disappeared en route</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                           Location  \\\n",
       "4830 2009-06-08  State of Arunachal Pradesh, India   \n",
       "\n",
       "                         Operator           Type  Aboard  Fatalities  Ground  \\\n",
       "4830  military - indian air force  antonov an-32    13.0        13.0     0.0   \n",
       "\n",
       "                                                                                                                     Summary  \\\n",
       "4830  The military transport went missing while en route and might have crashed due to heavy rain in the mountainous region.   \n",
       "\n",
       "      Summary_Cluster  Summary_Cluster_name  ...  Helicopter  Land / take off  \\\n",
       "4830               15  Disappeared en route  ...         0.0              0.0   \n",
       "\n",
       "     Landing attempt  Mail plane Midair collision Mountains  Pilot error  \\\n",
       "4830             0.0         0.0              0.0       0.0          0.0   \n",
       "\n",
       "      Shot down  Weather / landing  Weather conditions  \n",
       "4830        0.0                0.0                 0.0  \n",
       "\n",
       "[1 rows x 38 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    "summary_cluster_encoded = one_hot_encoder.fit_transform(crashes['Summary_Cluster_name'].values.reshape(-1,1))\n",
    "summary_cluster_encoded_df = pd.DataFrame(summary_cluster_encoded, columns=one_hot_encoder.categories_[0])\n",
    "crashes = crashes.join(summary_cluster_encoded_df)\n",
    "crashes.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "319c2b79",
   "metadata": {},
   "source": [
    "# 4. Clusterize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c640701f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cluster_stats(df: pd.DataFrame, summary_cluster: bool = False):\n",
    "    cluster_stats = df.groupby('cluster') \\\n",
    "                      .agg({\"mortality\": np.mean,\n",
    "                            \"is_military\": np.mean,\n",
    "                            \"series_encoded\": np.mean,\n",
    "                            \"operator_encoded\": np.mean,\n",
    "                            \"Aboard_scaled\": np.mean})\n",
    "    return cluster_stats\n",
    "\n",
    "\n",
    "def create_cluster_name(row):\n",
    "    stats_name = [f\"mor_{round(row.mortality, 1)}\",\n",
    "                  f\"mil_{round(row.is_military, 1)}\",\n",
    "                  f\"ser_{round(row.series_encoded, 1)}\",\n",
    "                  f\"op_{round(row.operator_encoded, 1)}\",\n",
    "                  f\"ab_{round(row.Aboard_scaled, 1)}\"\n",
    "                 ]\n",
    "    return \"/\".join(stats_name)\n",
    "\n",
    "\n",
    "def get_cluster_names(df: pd.DataFrame, clusters, summary_cluster: bool = False):\n",
    "    df_1 = df.copy()\n",
    "    df_1['cluster'] = clusters\n",
    "    cluster_stats = get_cluster_stats(df_1, summary_cluster)\n",
    "    cluster_stats['cluster_name'] = cluster_stats.apply(lambda row: create_cluster_name(row), axis=1)\n",
    "    return cluster_stats['cluster_name'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4489e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_clusters(reduced_embeddings, clusters, cluster_names):\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    scatter = ax.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1], c=clusters, s=0.1, cmap='Spectral')\n",
    "    legend1 = ax.legend(scatter.legend_elements()[0], cluster_names.values(), \n",
    "                        loc=\"best\", fontsize=10, bbox_to_anchor=(1.0, 1.00))\n",
    "    ax.add_artist(legend1)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71687f24",
   "metadata": {},
   "source": [
    "## v1 No summary info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "47087205",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_cluster_1 = ['mortality', 'is_military', 'series_encoded', 'operator_encoded', 'Aboard_scaled']\n",
    "cols_to_show_1 = cols_to_cluster_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b520c33e",
   "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>mortality</th>\n",
       "      <th>is_military</th>\n",
       "      <th>series_encoded</th>\n",
       "      <th>operator_encoded</th>\n",
       "      <th>Aboard_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.453988</td>\n",
       "      <td>0.520833</td>\n",
       "      <td>0.131313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mortality  is_military  series_encoded  operator_encoded  Aboard_scaled\n",
       "4830        1.0         True        0.453988          0.520833       0.131313"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes_cluster_1 = crashes[cols_to_cluster_1]\n",
    "crashes_cluster_1.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9dbe9aac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6594907886565928"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(crashes_cluster_1[crashes_cluster_1.mortality == 1]) / len(crashes_cluster_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f2027051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013040778306768785"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(crashes_cluster_1[crashes_cluster_1.mortality == 0]) / len(crashes_cluster_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e29cb31",
   "metadata": {},
   "source": [
    "### Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0ebf35e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total clusters: 5\n",
      "Clustered: 1.0000\n",
      "Silhouette: 0.3730\n"
     ]
    }
   ],
   "source": [
    "hdbscan_model = HDBSCAN(min_cluster_size=80, min_samples=1, prediction_data=False)\n",
    "clusters_hdbscan_1 = hdbscan_model.fit_predict(crashes_cluster_1)\n",
    "\n",
    "clusters_hdbscan_1_names = get_cluster_names(crashes_cluster_1, clusters_hdbscan_1)\n",
    "clusters_hdbscan_1_names[-1] = -1\n",
    "\n",
    "clustered = (clusters_hdbscan_1 >= 0)\n",
    "print(f\"Total clusters: {len(set(clusters_hdbscan_1))}\")\n",
    "print(f\"Clustered: {(np.sum(clustered) / len(clusters_hdbscan_1)):.4f}\")\n",
    "print(f\"Silhouette: {silhouette_score(crashes_cluster_1, clusters_hdbscan_1):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b122da94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# min_cluster_size=50, min_samples=10\n",
    "# Total clusters: 7\n",
    "# Clustered: 0.9932\n",
    "# Silhouette: 0.3566\n",
    "\n",
    "# min_cluster_size=80, min_samples=10\n",
    "# Total clusters: 6\n",
    "# Clustered: 0.9936\n",
    "# Silhouette: 0.3693\n",
    "\n",
    "# min_cluster_size=80, min_samples=5\n",
    "# Total clusters: 6\n",
    "# Clustered: 0.9983\n",
    "# Silhouette: 0.3707\n",
    "\n",
    "# min_cluster_size=30, min_samples=1\n",
    "# Total clusters: 10\n",
    "# Clustered: 0.9973\n",
    "# Silhouette: 0.3445\n",
    "\n",
    "# ============= BEST =============\n",
    "# min_cluster_size=50, min_samples=1\n",
    "# Total clusters: 6\n",
    "# Clustered: 1.0000\n",
    "# Silhouette: 0.3650\n",
    "\n",
    "\n",
    "# min_cluster_size=80, min_samples=1\n",
    "# Total clusters: 5\n",
    "# Clustered: 1.0000\n",
    "# Silhouette: 0.3730"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6def0f3",
   "metadata": {},
   "source": [
    "### Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "20f58a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "umap_encoder = UMAP(n_neighbors=100, n_components=2, min_dist=0.0, metric='euclidean', random_state=42)\n",
    "reduced_emb_1 = umap_encoder.fit_transform(crashes_cluster_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "65580169",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clusters(reduced_emb_1, clusters_hdbscan_1, clusters_hdbscan_1_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59eeb49",
   "metadata": {},
   "source": [
    "## v2 Cluster every summary cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "687093bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_cluster_2 = ['mortality', 'is_military', 'series_encoded', 'operator_encoded', \n",
    "                   'Aboard_scaled', *one_hot_encoder.categories_[0]]\n",
    "cols_to_show_2 = ['mortality', 'is_military', 'series_encoded', 'operator_encoded', 'Aboard_scaled', 'Summary_Cluster_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5b3908a5",
   "metadata": {
    "scrolled": false
   },
   "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>mortality</th>\n",
       "      <th>is_military</th>\n",
       "      <th>series_encoded</th>\n",
       "      <th>operator_encoded</th>\n",
       "      <th>Aboard_scaled</th>\n",
       "      <th>Cargo (plane)</th>\n",
       "      <th>Crash</th>\n",
       "      <th>Crashed into sea</th>\n",
       "      <th>Disappeared en route</th>\n",
       "      <th>En route / Mountains</th>\n",
       "      <th>...</th>\n",
       "      <th>Helicopter</th>\n",
       "      <th>Land / take off</th>\n",
       "      <th>Landing attempt</th>\n",
       "      <th>Mail plane</th>\n",
       "      <th>Midair collision</th>\n",
       "      <th>Mountains</th>\n",
       "      <th>Pilot error</th>\n",
       "      <th>Shot down</th>\n",
       "      <th>Weather / landing</th>\n",
       "      <th>Weather conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.453988</td>\n",
       "      <td>0.520833</td>\n",
       "      <td>0.131313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mortality  is_military  series_encoded  operator_encoded  Aboard_scaled  \\\n",
       "4830        1.0         True        0.453988          0.520833       0.131313   \n",
       "\n",
       "      Cargo (plane)  Crash  Crashed into sea  Disappeared en route  \\\n",
       "4830            0.0    0.0               0.0                   1.0   \n",
       "\n",
       "      En route / Mountains  ...  Helicopter  Land / take off  Landing attempt  \\\n",
       "4830                   0.0  ...         0.0              0.0              0.0   \n",
       "\n",
       "      Mail plane  Midair collision  Mountains  Pilot error  Shot down  \\\n",
       "4830         0.0               0.0        0.0          0.0        0.0   \n",
       "\n",
       "      Weather / landing  Weather conditions  \n",
       "4830                0.0                 0.0  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes_cluster_2 = crashes[cols_to_cluster_2]\n",
    "crashes_cluster_2.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a525568",
   "metadata": {},
   "source": [
    "### Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "bce5b14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total clusters: 48\n",
      "Clustered: 0.9712\n",
      "Silhouette: 0.4411\n"
     ]
    }
   ],
   "source": [
    "hdbscan_model = HDBSCAN(min_cluster_size=32, min_samples=1, prediction_data=False)\n",
    "clusters_hdbscan_2 = hdbscan_model.fit_predict(crashes_cluster_2)\n",
    "\n",
    "clusters_hdbscan_2_names = get_cluster_names(crashes_cluster_2, clusters_hdbscan_2)\n",
    "clusters_hdbscan_2_names[-1] = -1\n",
    "\n",
    "clustered = (clusters_hdbscan_2 >= 0)\n",
    "print(f\"Total clusters: {len(set(clusters_hdbscan_2))}\")\n",
    "print(f\"Clustered: {(np.sum(clustered) / len(clusters_hdbscan_2)):.4f}\")\n",
    "print(f\"Silhouette: {silhouette_score(crashes_cluster_2, clusters_hdbscan_2):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62b34e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# min_cluster_size=80, min_samples=1\n",
    "# Total clusters: 27\n",
    "# Clustered: 0.9549\n",
    "# Silhouette: 0.4311\n",
    "\n",
    "# min_cluster_size=40, min_samples=1\n",
    "# Total clusters: 43\n",
    "# Clustered: 0.9725\n",
    "# Silhouette: 0.4347\n",
    "\n",
    "# min_cluster_size=35, min_samples=1\n",
    "# Total clusters: 46\n",
    "# Clustered: 0.9725\n",
    "# Silhouette: 0.4398\n",
    "\n",
    "# min_cluster_size=32, min_samples=1\n",
    "# Total clusters: 48\n",
    "# Clustered: 0.9712\n",
    "# Silhouette: 0.4411"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b96cef36",
   "metadata": {},
   "source": [
    "### Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "14ce10b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "umap_encoder = UMAP(n_neighbors=100, n_components=2, min_dist=0.0, metric='cosine', random_state=42)\n",
    "reduced_emb_2 = umap_encoder.fit_transform(crashes_cluster_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fd0b4bf1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clusters(reduced_emb_2, clusters_hdbscan_2, clusters_hdbscan_2_names)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}