Insurance_prediction.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aBPUtx7gcIMh"
   },
   "source": [
    "**Standard libraries for data analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Ik0YonvmcFm9"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HjIviTr6cXKQ"
   },
   "source": [
    "**Load the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "gYQK_IPaJtX2"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/content/drive/MyDrive/Deep Learning/data.csv\")"
   ]
  },
  {
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   "execution_count": 3,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Agency Type</th>\n",
       "      <th>Distribution Channel</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Claim</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Net Sales</th>\n",
       "      <th>Commision (in value)</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3433</td>\n",
       "      <td>CWT</td>\n",
       "      <td>Travel Agency</td>\n",
       "      <td>Online</td>\n",
       "      <td>Rental Vehicle Excess Insurance</td>\n",
       "      <td>0</td>\n",
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       "      <td>75</td>\n",
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       "      <th>3</th>\n",
       "      <td>55816</td>\n",
       "      <td>EPX</td>\n",
       "      <td>Travel Agency</td>\n",
       "      <td>Online</td>\n",
       "      <td>2 way Comprehensive Plan</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>INDONESIA</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>13816</td>\n",
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       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>KOREA, REPUBLIC OF</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
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       "      ID Agency    Agency Type  ... Commision (in value) Gender  Age\n",
       "0   3433    CWT  Travel Agency  ...                17.82    NaN   31\n",
       "1   4339    EPX  Travel Agency  ...                 0.00    NaN   36\n",
       "2  34590    CWT  Travel Agency  ...                11.88    NaN   75\n",
       "3  55816    EPX  Travel Agency  ...                 0.00    NaN   32\n",
       "4  13816    EPX  Travel Agency  ...                 0.00    NaN   29\n",
       "\n",
       "[5 rows x 12 columns]"
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  {
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       "(50553, 12)"
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    "df.shape"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50553 entries, 0 to 50552\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   ID                    50553 non-null  int64  \n",
      " 1   Agency                50553 non-null  object \n",
      " 2   Agency Type           50553 non-null  object \n",
      " 3   Distribution Channel  50553 non-null  object \n",
      " 4   Product Name          50553 non-null  object \n",
      " 5   Claim                 50553 non-null  int64  \n",
      " 6   Duration              50553 non-null  int64  \n",
      " 7   Destination           50553 non-null  object \n",
      " 8   Net Sales             50553 non-null  float64\n",
      " 9   Commision (in value)  50553 non-null  float64\n",
      " 10  Gender                14600 non-null  object \n",
      " 11  Age                   50553 non-null  int64  \n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 4.6+ MB\n"
     ]
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   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "outputId": "c8b23dbc-be45-40b5-b3a6-364c74d3bc74"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'F', 'M'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   "source": [
    "df[\"Gender\"].unique()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
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       "array([0, 1])"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df[\"Claim\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nF-hOOlfcwt3"
   },
   "source": [
    "**Feature target split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "TLLK1XkXJYAe"
   },
   "outputs": [],
   "source": [
    "X = df.drop(\"Claim\",axis=1)\n",
    "y = df[\"Claim\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-GS6s0lTg3Nw"
   },
   "source": [
    "**Label encoding on target column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "5iLawrSgJgE6"
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
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   "source": [
    "**Split categorical and numerical data**"
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "0sCU2w-QL4pz"
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   "outputs": [],
   "source": [
    "df_num = X.select_dtypes([\"int64\",\"float64\"])\n",
    "df_cat = X.select_dtypes(\"object\")"
   ]
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       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      ID  Duration  Net Sales  Commision (in value)  Age\n",
       "0   3433         7        0.0                 17.82   31\n",
       "1   4339        85       69.0                  0.00   36\n",
       "2  34590        11       19.8                 11.88   75\n",
       "3  55816        16       20.0                  0.00   32\n",
       "4  13816        10       15.0                  0.00   29"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dl1OGu2-c9wK"
   },
   "source": [
    "**Standard Scaler**:- A standard scaler converts a distribution, such that it has 0 mean and 1 standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "lWIlRqVdL9Kb"
   },
   "outputs": [],
   "source": [
    "for col in df_num:\n",
    "  ss = StandardScaler()\n",
    "  df_num[col] = ss.fit_transform(df_num[[col]])"
   ]
  },
  {
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   "execution_count": 14,
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    },
    "id": "jNOtIalLMC55",
    "outputId": "9e562029-efa4-464f-e902-3c3badbaabf6"
   },
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       "         ID  Duration  Net Sales  Commision (in value)       Age\n",
       "0 -1.544543 -0.418263  -0.834389              0.400903 -0.640165\n",
       "1 -1.495003  0.350712   0.576677             -0.494132 -0.284961\n",
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    "id": "ZumtNVzOMFNU",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      ],
      "text/plain": [
       "  Agency    Agency Type  ...         Destination Gender\n",
       "0    CWT  Travel Agency  ...            MALAYSIA    NaN\n",
       "1    EPX  Travel Agency  ...           SINGAPORE    NaN\n",
       "2    CWT  Travel Agency  ...            MALAYSIA    NaN\n",
       "3    EPX  Travel Agency  ...           INDONESIA    NaN\n",
       "4    EPX  Travel Agency  ...  KOREA, REPUBLIC OF    NaN\n",
       "\n",
       "[5 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_cat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2jtN0PrQdmd6"
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   "source": [
    "**Categorical data plot**"
   ]
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  {
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   "execution_count": 16,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 1000
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    "id": "rw9BC4M2UDQ9",
    "outputId": "a4478851-f9a0-4e1d-bcad-70611e5e2dc7",
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in df_cat:\n",
    "  plt.figure()\n",
    "  sns.countplot(data=df_cat, x=col)\n",
    "  plt.xticks(rotation=90)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OzZzdk14dXQa"
   },
   "source": [
    "**One hot encoding on categorical column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "8uYE5ht8Mnmm"
   },
   "outputs": [],
   "source": [
    "df_cat = pd.get_dummies(df_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "DH7CF-_wMq7H",
    "outputId": "41c063d1-5665-40ea-8636-425749ac53eb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-f53b7bb3-cc7f-4606-be65-02c55be516d2\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Agency_ADM</th>\n",
       "      <th>Agency_ART</th>\n",
       "      <th>Agency_C2B</th>\n",
       "      <th>Agency_CBH</th>\n",
       "      <th>Agency_CCR</th>\n",
       "      <th>Agency_CSR</th>\n",
       "      <th>Agency_CWT</th>\n",
       "      <th>Agency_EPX</th>\n",
       "      <th>Agency_JWT</th>\n",
       "      <th>Agency_JZI</th>\n",
       "      <th>Agency_KML</th>\n",
       "      <th>Agency_LWC</th>\n",
       "      <th>Agency_RAB</th>\n",
       "      <th>Agency_SSI</th>\n",
       "      <th>Agency_TST</th>\n",
       "      <th>Agency_TTW</th>\n",
       "      <th>Agency Type_Airlines</th>\n",
       "      <th>Agency Type_Travel Agency</th>\n",
       "      <th>Distribution Channel_Offline</th>\n",
       "      <th>Distribution Channel_Online</th>\n",
       "      <th>Product Name_1 way Comprehensive Plan</th>\n",
       "      <th>Product Name_2 way Comprehensive Plan</th>\n",
       "      <th>Product Name_24 Protect</th>\n",
       "      <th>Product Name_Annual Gold Plan</th>\n",
       "      <th>Product Name_Annual Silver Plan</th>\n",
       "      <th>Product Name_Annual Travel Protect Gold</th>\n",
       "      <th>Product Name_Annual Travel Protect Platinum</th>\n",
       "      <th>Product Name_Annual Travel Protect Silver</th>\n",
       "      <th>Product Name_Basic Plan</th>\n",
       "      <th>Product Name_Bronze Plan</th>\n",
       "      <th>Product Name_Cancellation Plan</th>\n",
       "      <th>Product Name_Child Comprehensive Plan</th>\n",
       "      <th>Product Name_Comprehensive Plan</th>\n",
       "      <th>Product Name_Gold Plan</th>\n",
       "      <th>Product Name_Individual Comprehensive Plan</th>\n",
       "      <th>Product Name_Premier Plan</th>\n",
       "      <th>Product Name_Rental Vehicle Excess Insurance</th>\n",
       "      <th>Product Name_Silver Plan</th>\n",
       "      <th>Product Name_Single Trip Travel Protect Gold</th>\n",
       "      <th>Product Name_Single Trip Travel Protect Platinum</th>\n",
       "      <th>...</th>\n",
       "      <th>Destination_NEPAL</th>\n",
       "      <th>Destination_NETHERLANDS</th>\n",
       "      <th>Destination_NEW ZEALAND</th>\n",
       "      <th>Destination_NORTHERN MARIANA ISLANDS</th>\n",
       "      <th>Destination_NORWAY</th>\n",
       "      <th>Destination_OMAN</th>\n",
       "      <th>Destination_PAKISTAN</th>\n",
       "      <th>Destination_PERU</th>\n",
       "      <th>Destination_PHILIPPINES</th>\n",
       "      <th>Destination_POLAND</th>\n",
       "      <th>Destination_PORTUGAL</th>\n",
       "      <th>Destination_QATAR</th>\n",
       "      <th>Destination_ROMANIA</th>\n",
       "      <th>Destination_RUSSIAN FEDERATION</th>\n",
       "      <th>Destination_SAUDI ARABIA</th>\n",
       "      <th>Destination_SINGAPORE</th>\n",
       "      <th>Destination_SLOVENIA</th>\n",
       "      <th>Destination_SOUTH AFRICA</th>\n",
       "      <th>Destination_SPAIN</th>\n",
       "      <th>Destination_SRI LANKA</th>\n",
       "      <th>Destination_SWEDEN</th>\n",
       "      <th>Destination_SWITZERLAND</th>\n",
       "      <th>Destination_TAIWAN, PROVINCE OF CHINA</th>\n",
       "      <th>Destination_TANZANIA, UNITED REPUBLIC OF</th>\n",
       "      <th>Destination_THAILAND</th>\n",
       "      <th>Destination_TUNISIA</th>\n",
       "      <th>Destination_TURKEY</th>\n",
       "      <th>Destination_TURKMENISTAN</th>\n",
       "      <th>Destination_UGANDA</th>\n",
       "      <th>Destination_UKRAINE</th>\n",
       "      <th>Destination_UNITED ARAB EMIRATES</th>\n",
       "      <th>Destination_UNITED KINGDOM</th>\n",
       "      <th>Destination_UNITED STATES</th>\n",
       "      <th>Destination_UZBEKISTAN</th>\n",
       "      <th>Destination_VANUATU</th>\n",
       "      <th>Destination_VENEZUELA</th>\n",
       "      <th>Destination_VIET NAM</th>\n",
       "      <th>Destination_ZAMBIA</th>\n",
       "      <th>Gender_F</th>\n",
       "      <th>Gender_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "   Agency_ADM  Agency_ART  Agency_C2B  ...  Destination_ZAMBIA  Gender_F  Gender_M\n",
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     "metadata": {},
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   "execution_count": 19,
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    "X = pd.concat([df_num, df_cat], axis=1)"
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   "execution_count": 20,
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       "      <th>Product Name_Annual Travel Protect Gold</th>\n",
       "      <th>Product Name_Annual Travel Protect Platinum</th>\n",
       "      <th>Product Name_Annual Travel Protect Silver</th>\n",
       "      <th>Product Name_Basic Plan</th>\n",
       "      <th>Product Name_Bronze Plan</th>\n",
       "      <th>Product Name_Cancellation Plan</th>\n",
       "      <th>Product Name_Child Comprehensive Plan</th>\n",
       "      <th>Product Name_Comprehensive Plan</th>\n",
       "      <th>Product Name_Gold Plan</th>\n",
       "      <th>Product Name_Individual Comprehensive Plan</th>\n",
       "      <th>...</th>\n",
       "      <th>Destination_NEPAL</th>\n",
       "      <th>Destination_NETHERLANDS</th>\n",
       "      <th>Destination_NEW ZEALAND</th>\n",
       "      <th>Destination_NORTHERN MARIANA ISLANDS</th>\n",
       "      <th>Destination_NORWAY</th>\n",
       "      <th>Destination_OMAN</th>\n",
       "      <th>Destination_PAKISTAN</th>\n",
       "      <th>Destination_PERU</th>\n",
       "      <th>Destination_PHILIPPINES</th>\n",
       "      <th>Destination_POLAND</th>\n",
       "      <th>Destination_PORTUGAL</th>\n",
       "      <th>Destination_QATAR</th>\n",
       "      <th>Destination_ROMANIA</th>\n",
       "      <th>Destination_RUSSIAN FEDERATION</th>\n",
       "      <th>Destination_SAUDI ARABIA</th>\n",
       "      <th>Destination_SINGAPORE</th>\n",
       "      <th>Destination_SLOVENIA</th>\n",
       "      <th>Destination_SOUTH AFRICA</th>\n",
       "      <th>Destination_SPAIN</th>\n",
       "      <th>Destination_SRI LANKA</th>\n",
       "      <th>Destination_SWEDEN</th>\n",
       "      <th>Destination_SWITZERLAND</th>\n",
       "      <th>Destination_TAIWAN, PROVINCE OF CHINA</th>\n",
       "      <th>Destination_TANZANIA, UNITED REPUBLIC OF</th>\n",
       "      <th>Destination_THAILAND</th>\n",
       "      <th>Destination_TUNISIA</th>\n",
       "      <th>Destination_TURKEY</th>\n",
       "      <th>Destination_TURKMENISTAN</th>\n",
       "      <th>Destination_UGANDA</th>\n",
       "      <th>Destination_UKRAINE</th>\n",
       "      <th>Destination_UNITED ARAB EMIRATES</th>\n",
       "      <th>Destination_UNITED KINGDOM</th>\n",
       "      <th>Destination_UNITED STATES</th>\n",
       "      <th>Destination_UZBEKISTAN</th>\n",
       "      <th>Destination_VANUATU</th>\n",
       "      <th>Destination_VENEZUELA</th>\n",
       "      <th>Destination_VIET NAM</th>\n",
       "      <th>Destination_ZAMBIA</th>\n",
       "      <th>Gender_F</th>\n",
       "      <th>Gender_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.544543</td>\n",
       "      <td>-0.418263</td>\n",
       "      <td>-0.834389</td>\n",
       "      <td>0.400903</td>\n",
       "      <td>-0.640165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.495003</td>\n",
       "      <td>0.350712</td>\n",
       "      <td>0.576677</td>\n",
       "      <td>-0.494132</td>\n",
       "      <td>-0.284961</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.159134</td>\n",
       "      <td>-0.378829</td>\n",
       "      <td>-0.429475</td>\n",
       "      <td>0.102558</td>\n",
       "      <td>2.485628</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.319781</td>\n",
       "      <td>-0.329535</td>\n",
       "      <td>-0.425385</td>\n",
       "      <td>-0.494132</td>\n",
       "      <td>-0.569124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.976797</td>\n",
       "      <td>-0.388687</td>\n",
       "      <td>-0.527636</td>\n",
       "      <td>-0.494132</td>\n",
       "      <td>-0.782247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 154 columns</p>\n",
       "</div>\n",
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       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-9fcd12ef-7af0-48a6-bb14-2837c1079fb5 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-9fcd12ef-7af0-48a6-bb14-2837c1079fb5');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         ID  Duration  Net Sales  ...  Destination_ZAMBIA  Gender_F  Gender_M\n",
       "0 -1.544543 -0.418263  -0.834389  ...                   0         0         0\n",
       "1 -1.495003  0.350712   0.576677  ...                   0         0         0\n",
       "2  0.159134 -0.378829  -0.429475  ...                   0         0         0\n",
       "3  1.319781 -0.329535  -0.425385  ...                   0         0         0\n",
       "4 -0.976797 -0.388687  -0.527636  ...                   0         0         0\n",
       "\n",
       "[5 rows x 154 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ogYrY6cgQeNI"
   },
   "source": [
    "**Target Claim is highly imbalance**\n",
    "\n",
    "Imbalance means that the number of data points available for different the classes is different:\n",
    "\n",
    "If there are two classes, then balanced data would mean 50% points for each of the class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "3jEajC_bQW6j",
    "outputId": "284a033b-ea1e-4f20-ff1a-e77a94af8a06"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the target variable\n",
    "g = sns.countplot(df['Claim'])\n",
    "g.set_xticklabels([0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rlc-iPFMKfI0",
    "outputId": "fbb7aba1-b418-4ffc-c0b8-b3fd0db69713"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class 0: (49812, 12)\n",
      "class 1: (741, 12)\n"
     ]
    }
   ],
   "source": [
    "# class count for 0 & 1\n",
    "class_count_0, class_count_1 = df['Claim'].value_counts()\n",
    "\n",
    "# Separate class\n",
    "class_0 = df[df['Claim'] == 0]\n",
    "class_1 = df[df['Claim'] == 1]\n",
    "\n",
    "# print the shape of the class\n",
    "print('class 0:', class_0.shape)\n",
    "print('class 1:', class_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "id": "DEG98kfTKo_J",
    "outputId": "caf830b2-aef3-411c-c32e-f9aeeb35b238"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total class of 1 and 0: 1    49812\n",
      "0    49812\n",
      "Name: Claim, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fed38934190>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# oversampling for class 1\n",
    "class_1_over = class_1.sample(class_count_0, replace=True)\n",
    "\n",
    "test_over = pd.concat([class_1_over, class_0], axis=0)\n",
    "\n",
    "print(\"total class of 1 and 0:\",test_over['Claim'].value_counts())\n",
    "\n",
    "# plot the count after under-sampeling\n",
    "test_over['Claim'].value_counts().plot(kind='bar', title='Count (Claim)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ojCVi9XQ4Hp"
   },
   "source": [
    "**Explanation:-**\n",
    "\n",
    "class 0: 49812 data points    and     class 1: 741 data points\n",
    "\n",
    "Number of class 1 are less and class 0 are so accuracy is very high. hence by using oversampling the ratio for class 1 & 0 becomes 50-50%.\n",
    "\n",
    "total class of 1 and 0:\n",
    "\n",
    "1  =  49812\n",
    "\n",
    "0  =  49812"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UG9XHiB2MssN"
   },
   "source": [
    "**Generate training and test dataset into training and test set with an 70%-30% ratio**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "wT5NLgSuMltO"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OyqCPHpRTj_Z"
   },
   "source": [
    "**Random over-sampling with imblearn**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2LXBp2wcK6M-",
    "outputId": "0a8c6631-8d10-4dbf-ecf0-7ef708531e08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape [0 0 0 ... 0 0 0]\n",
      "Original dataset shape [0 0 0 ... 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_train_ros, y_train_ros = ros.fit_resample(X_train,y_train)\n",
    "\n",
    "# fit predictor and target variablex_ros, y_ros = ros.fit_resample(x, y)\n",
    "\n",
    "print('Original dataset shape',(y))\n",
    "print('Original dataset shape',(y_train_ros))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "smfH0b5PNiic",
    "outputId": "dd574a3d-780f-4c7f-b238-fec6a90cd315"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69702, 154)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_ros.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_zGrwd03d-sd"
   },
   "source": [
    "Libraries for Neural network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "7dJKdHITNlsJ"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RrMgM0PpeRRT"
   },
   "source": [
    "**Create model with binary classification**\n",
    "\n",
    "**Input** = Input layer has multiple neurons, one for each feature\n",
    "154 input and 16 neurons in each layer. \n",
    "\n",
    "**Hidden layer** = All the other layers in between input and ouput layer are the Hidden layers. \n",
    "\n",
    "**Drop-out layer** = \n",
    "Drop-out layer used for regularization.\n",
    "\n",
    "**output layer** = Output layer can either have one neuron for regression and binary classification or multiple neurons, one for each class in case of multi-class classification.Sigmoid activation is used in this neural network\n",
    "\n",
    "**Activation function** provides a non-linear output.here, relu is used because more layers present also it reduces vanishing gradient problem.\n",
    "Types of activation functions:\n",
    "1. linear activation = - infinity to + infinity \n",
    "2. tanh = -1 to +1\n",
    "3. sigmoid = 0 to 1\n",
    "4. softmax = multiclass\n",
    "5. relu= 0 to n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "f8RJEN9-NoJq"
   },
   "outputs": [],
   "source": [
    "# create neural nework\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(16, activation=\"relu\", input_dim=154))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(16, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(8, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(4, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation=\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lmEsheHRex_Y"
   },
   "source": [
    "**Optimizer** = Optimizers are algorithms used to change weights and learning rate to reduce the losses. ADAM optimizer used.\n",
    "\n",
    "**Loss Function** =  to minimize the error loss function is used. \n",
    "Here, binary_crossentropy loss function is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "6Sf8wXBgNtjy"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UFb22_Eje6Qx"
   },
   "source": [
    "**Early stopping** = Early stopping is a method that allows you to specify an arbitrary large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset.\n",
    "\n",
    "Here, it will monitor the loss function at 5 patience with minimum 0.01 delta "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "VOQwrBQ8-aFm"
   },
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor=\"loss\", min_delta=0.01, patience=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oW-yvfbte9rY"
   },
   "source": [
    "**Batch size=** Batch size is a hyperparameter that defines the number of samples to work through before updating the internal model parameters.\n",
    "\n",
    "**epochs=** The number of epochs is a hyperparameter that defines the number times that the learning algorithm will work through the entire training dataset.\n",
    "\n",
    "**callbacks=** Callbacks provide a way to execute code and interact with the training model process automatically.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eA2BD7i4Mon5",
    "outputId": "1db01494-685d-4b2b-b3bc-0418ad016906"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1090/1090 [==============================] - 3s 2ms/step - loss: 0.6602\n",
      "Epoch 2/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6328\n",
      "Epoch 3/20\n",
      "1090/1090 [==============================] - 3s 3ms/step - loss: 0.6257\n",
      "Epoch 4/20\n",
      "1090/1090 [==============================] - 3s 2ms/step - loss: 0.6176\n",
      "Epoch 5/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6161\n",
      "Epoch 6/20\n",
      "1090/1090 [==============================] - 3s 2ms/step - loss: 0.6133\n",
      "Epoch 7/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6136\n",
      "Epoch 8/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6064\n",
      "Epoch 9/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6066\n",
      "Epoch 10/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6083\n",
      "Epoch 11/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6072\n",
      "Epoch 12/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6046\n",
      "Epoch 13/20\n",
      "1090/1090 [==============================] - 2s 2ms/step - loss: 0.6039\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7feccc5c6e10>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_ros,y_train_ros, batch_size=64, epochs=20,  callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TcA_2FeHfGSX"
   },
   "source": [
    "**Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "iCOnlP2aN1R1"
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "ojg-VmauN9xL"
   },
   "outputs": [],
   "source": [
    "y_pred = np.where(y_pred >= 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Akqax54jOGcz",
    "outputId": "68e5e35d-d44e-4a90-efd3-cc4a69843551"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.87      0.93     14961\n",
      "           1       0.06      0.56      0.10       205\n",
      "\n",
      "    accuracy                           0.87     15166\n",
      "   macro avg       0.52      0.72      0.52     15166\n",
      "weighted avg       0.98      0.87      0.92     15166\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "o0Xmv1lFZ6HO"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "In6QlnKNfmhF",
    "outputId": "cca59ee9-bd15-4920-be3e-bccdfc6198c3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = SVC(random_state=1)\n",
    "clf.fit(X_train_ros, y_train_ros)\n",
    "SVC(random_state=1)\n",
    "plot_confusion_matrix(clf, X_test, y_test) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YeBaEpv3ATuo"
   },
   "source": [
    "**Conclusion:**\n",
    "\n",
    "Claim for insurance is highly imbalanced, which is balance by using random oversampling technique. For 0 recall value is 0.87 and for 1 its less 0.56. The accuracy for neural network is 87%. From confusion metrics we will understand who will predict for insurance claim."
   ]
  }
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