Shark Attack Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "63b6fbb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.6.3.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import string\n",
    "import plotly.express as px\n",
    "from plotly.offline import init_notebook_mode\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pickle\n",
    "\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "3149bdce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"attacks.csv\", encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c4b1e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "0a7c72ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25723, 24)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d19afa2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "c9117d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Unnamed: 22\", \"Unnamed: 23\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dabb72b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "2751818c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Case Number\", \"Date\", \"Year\", \"Area\", \"Location\", \"Name\", \"Injury\",\n",
    "         \"Investigator or Source\", \"pdf\", \"href formula\", \"href\", \n",
    "         \"Case Number.1\", \"Case Number.2\", \"Time\", \"original order\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38d9ba1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "7cad70af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type           19425\n",
       "Country        19471\n",
       "Activity       19965\n",
       "Sex            19986\n",
       "Age            22252\n",
       "Fatal (Y/N)    19960\n",
       "Species        22259\n",
       "dtype: int64"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "36560a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [\"Type\", \"Country\", \"Activity\", \"Sex\", \"Age\", \"Fatal\", \"Species\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "c2c7eb45",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(how=\"any\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "c8bf34b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(r'\\n', ' ', text)\n",
    "    text = re.sub(r'\\d', '', text)\n",
    "    text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
    "    words = text.split()\n",
    "    words = [re.sub(r'(.)\\1{1,}', r'\\1\\1', word) for word in words]\n",
    "    words = [word.strip() for word in words if len(word.strip()) > 1]\n",
    "    \n",
    "    text = \" \".join(words)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "9ea73cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "05264097",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"Country\"] == \"USA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "d211372b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"to\", \"\").strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "0ecbc131",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"feet\", \"\").strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "06ac434b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"less than\", \"\").strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "da2ae08d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"possibly\", \"\").strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "7a4520d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"small\", \"\").strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "5319f748",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"tall\", \"\").strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "3abb1bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"brown\", \"\").strip())\n",
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"cm\", \"\").strip())\n",
    "df[\"Species\"] = df[\"Species\"].apply(lambda text: text.replace(\"greycolored\", \"\").strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "f3c6bdc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def or_split(text):\n",
    "    if \"or\" in text:\n",
    "        return text.split(\"or\")[0].strip()\n",
    "    else:\n",
    "        return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "88dc4891",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(or_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "29fbba4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def len_strip(text):\n",
    "    if len(text.split()) > 2:\n",
    "        words = text.split()[:2]\n",
    "        text = \" \".join(words)\n",
    "        return text\n",
    "    else:\n",
    "        return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "07248129",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].apply(len_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "5c279786",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Species\"] = df[\"Species\"].replace({\n",
    " 'shark': 'unknown',\n",
    " 'juvenile blacktip': 'blacktip shark',\n",
    " 'blacktip': 'blacktip shark',\n",
    " 'tp shark': 'unknown',\n",
    " 'whitetip reef': 'reef shark',\n",
    " 'thought involve': 'unknown',\n",
    " 'shark  blactip': 'blacktip shark',\n",
    " 'p': 'unknown',\n",
    " 'shark involvement': 'unknown',\n",
    " '': 'unknown',\n",
    " 'yearold pound': 'unknown',\n",
    " 'shark tiger': 'tiger shark',\n",
    " 'spinner': 'spinner shark',\n",
    " 'grey reef': 'reef shark',\n",
    " 'juvenile white': 'white shark',\n",
    " 'unknown but': 'unknown',\n",
    " 'unidentified species': 'unknown',\n",
    " '\\x93  shark\\x94': 'unknown',\n",
    " '\\x93\\x94 shark': 'unknown',\n",
    " 'bite diameter': 'unknown',\n",
    " 'species unidentified': 'unknown',\n",
    " 'grey col': 'unknown',\n",
    " 'hammerhead': 'hammerhead shark',\n",
    " 'shark bull': 'bull shark',\n",
    " 'shark oth': 'unknown',\n",
    " 'questionable incident': 'unknown',\n",
    " 'large white': 'white shark',\n",
    " 'shark  blacktip': 'blacktip shark',\n",
    " 'shark was': 'unknown',\n",
    " 'shark probably': 'unknown',\n",
    " 'sh': 'unknown',\n",
    " 'shark seen': 'unknown',\n",
    " 'shark spinner': 'spinner shark',\n",
    " 'young shark': 'unknown',\n",
    " 'unidentified shark': 'unknown',\n",
    " 'said involve': 'unknown',\n",
    " 'bull': 'bull shark',\n",
    " 'lb shark': 'unknown',\n",
    " 'sixgill': 'unknown',\n",
    " 'hammerhead sharko': 'hammerhead shark',\n",
    " 'bull sandbar': 'bull shark',\n",
    " 'maculpinnis': 'unknown',\n",
    " 'local auth': 'unknown',\n",
    " 'hand found': 'unknown',\n",
    " 'h': 'unknown',\n",
    " 'larger shark': 'unknown',\n",
    " 'rep': 'unknown',\n",
    " 'leucas': 'unknown'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "b5160a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Country\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "8f34946f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Activity\n",
       "Surfing            266\n",
       "Swimming           100\n",
       "Wading              49\n",
       "Spearfishing        31\n",
       "Fishing             25\n",
       "Standing            22\n",
       "Snorkeling          14\n",
       "Body boarding       13\n",
       "Boogie boarding     11\n",
       "Scuba diving        10\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Activity\"].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "d1dad328",
   "metadata": {},
   "outputs": [],
   "source": [
    "def activity_split(text):\n",
    "    if \"/\" in text:\n",
    "        text = text.split(\"/\")[0].strip()\n",
    "        return text\n",
    "    \n",
    "    else:\n",
    "        return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "04969ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Activity\"] = df[\"Activity\"].apply(activity_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "139199f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def activity_finder(text):\n",
    "    activities = [\"Surfing\", \"Swimming\", \"Wading\", \"Spearfishing\", \"Standing\", \"Fishing\", \"Snorkeling\", \"Scuba diving\", \"boarding\"]\n",
    "    for i in range(len(activities)):\n",
    "        if activities[i] in text:\n",
    "            return activities[i]\n",
    "        \n",
    "    return \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "15f8a483",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Activity\"] = df[\"Activity\"].apply(activity_finder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "342a9cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"Type\", \"Activity\", \"Sex\", \"Age\", \"Species\", \"Fatal\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6688543",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "f1f8c30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Type', ylabel='count'>"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Type\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "0e6471fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "sns.countplot(x=\"Activity\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "7f01ff2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['57', '52', '15', '29', '25', '54', '40', '18', '69', '13', '34',\n",
       "       '50', '46', '14', '20', '28', '17', '58', '36', '60s', '51', '22',\n",
       "       '21', '43', '61', '59', '42', '6', '19', '48', '23', '26', '71',\n",
       "       '44', '27', '12', '10', '31', '38', '68', '9', '16', '11', '63',\n",
       "       '70', '53', '7', '20s', 'Teen', '39', '37', '30', '45', '32', '60',\n",
       "       '55', '8', '56', '33', '41', '49', '66', '24', '35', '30s', '47',\n",
       "       '\\xa0 ', '6½', '73', '5', '62', ' 28', \"60's\", 'adult', ' 43'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "df8b4b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"] = df[\"Age\"].replace({\n",
    "    \"Teen\": \"18\",\n",
    "    \"adult\": \"25\",\n",
    "    \"\\xa0 \": \"23\",\n",
    "    \"6½\": \"21\",\n",
    "    \"30s\": \"30\",\n",
    "    \"60s\": \"60\",\n",
    "    \"60's\": \"60\",\n",
    "    \"20s\": \"20\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "27e27948",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"] = df[\"Age\"].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "952d92cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "M      693\n",
       "F      140\n",
       "lli      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "b6aad81a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Sex\"] = df[\"Sex\"].replace({\n",
    "    \"lli\": \"F\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "a069fb64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 834 entries, 0 to 6226\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Type      834 non-null    object\n",
      " 1   Activity  834 non-null    object\n",
      " 2   Sex       834 non-null    object\n",
      " 3   Age       834 non-null    int64 \n",
      " 4   Species   834 non-null    object\n",
      " 5   Fatal     834 non-null    object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 45.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "cda4dd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_encoder(column):\n",
    "    le = LabelEncoder().fit(column)\n",
    "    print(column.name, le.classes_)\n",
    "    return le.transform(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "3cb18d0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type ['Boating' 'Invalid' 'Provoked' 'Sea Disaster' 'Unprovoked']\n",
      "Activity ['Fishing' 'Other' 'Scuba diving' 'Snorkeling' 'Spearfishing' 'Standing'\n",
      " 'Surfing' 'Swimming' 'Wading' 'boarding']\n",
      "Sex ['F' 'M']\n",
      "Species ['angel shark' 'blacktip reef' 'blacktip shark' 'blue shark'\n",
      " 'bonita sharkk' 'bull shark' 'dogfish shark' 'foot shark'\n",
      " 'galapagos shark' 'hammerhead shark' 'juvenile shark' 'juvenile tiger'\n",
      " 'lb dog' 'lb reef' 'lb sand' 'lemon shark' 'mako shark' 'nurse shark'\n",
      " 'reef shark' 'sand shark' 'sandbar shark' 'sandshark' 'sandtiger shark'\n",
      " 'sevengill shark' 'shark species' 'shark with' 'sharks' 'spinner shark'\n",
      " 'thresher' 'thresher shark' 'tiger shark' 'unidentified' 'unknown'\n",
      " 'white shark']\n",
      "Fatal ['N' 'Y']\n"
     ]
    }
   ],
   "source": [
    "cols = [\"Type\", \"Activity\", \"Sex\", \"Species\", \"Fatal\"]\n",
    "for col in cols:\n",
    "    df[col] = label_encoder(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "be6af977",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Fatal\", axis=1)\n",
    "y = df[\"Fatal\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "9ba3d505",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "2cc2559b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "103e1f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "c9bdc028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9700598802395209"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "6428f3d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt=\".0f\", cmap=\"Pastel2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "36588bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(rf, open(\"rf.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2940284e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}