medical_Insurance_estimation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e28a1a1",
   "metadata": {},
   "source": [
    "# ESTIMATION OF ANNUAL INSURANCE CHARGES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "878f37c0",
   "metadata": {},
   "source": [
    "#PROBLEM STATEMENT\n",
    "\n",
    "> ACME Insurance Inc. offers affordable health insurance to thousands of customer all over the United States.**I need to create an automated system to estimate the annual medical expenditure for new customers**, using information such as their age, sex, BMI, children, smoking habits and region of residence. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9593108f",
   "metadata": {},
   "source": [
    "# 1. DATA ACQUISITION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Dataset source**\n",
    "[Data Set](https://github.com/stedy/Machine-Learning-with-R-datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f0716e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "201518f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data =  pd.read_csv('medical.csv')\n",
    "medical_df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cd12809",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51fe0a5",
   "metadata": {},
   "source": [
    "The dataset contains 1338 rows and 7 columns. Each row of the dataset contains information about one customer. \n",
    "\n",
    "objective is to find a way to estimate the value in the \"charges\" column using the values in the other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec3846db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1338 non-null   int64  \n",
      " 1   sex       1338 non-null   object \n",
      " 2   bmi       1338 non-null   float64\n",
      " 3   children  1338 non-null   int64  \n",
      " 4   smoker    1338 non-null   object \n",
      " 5   region    1338 non-null   object \n",
      " 6   charges   1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 73.3+ KB\n"
     ]
    }
   ],
   "source": [
    "medical_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7daf1ef",
   "metadata": {},
   "source": [
    "Looks like \"age\", \"children\", \"bmi\" ([body mass index](https://en.wikipedia.org/wiki/Body_mass_index)) and \"charges\" are numbers, whereas \"sex\", \"smoker\" and \"region\" are strings (possibly categories). None of the columns contain any missing values.\n",
    "\n",
    "## Here are some statistics for the numerical columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "601dd869",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.207025</td>\n",
       "      <td>30.663397</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>13270.422265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>6.098187</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>12110.011237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.960000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1121.873900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.296250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4740.287150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9382.033000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>34.693750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16639.912515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>53.130000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>63770.428010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               age          bmi     children       charges\n",
       "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
       "mean     39.207025    30.663397     1.094918  13270.422265\n",
       "std      14.049960     6.098187     1.205493  12110.011237\n",
       "min      18.000000    15.960000     0.000000   1121.873900\n",
       "25%      27.000000    26.296250     0.000000   4740.287150\n",
       "50%      39.000000    30.400000     1.000000   9382.033000\n",
       "75%      51.000000    34.693750     2.000000  16639.912515\n",
       "max      64.000000    53.130000     5.000000  63770.428010"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fddbb64",
   "metadata": {},
   "source": [
    "The ranges of values in the numerical columns seem reasonable too (no negative ages!). The \"charges\" column seems to be significantly skewed however, as the median (50 percentile) is much lower than the maximum value.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a03b56",
   "metadata": {},
   "source": [
    "# 2. DATA EXPLORATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2e85bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install plotly matplotlib seaborn --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66d82725",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60caa58a",
   "metadata": {},
   "source": [
    "The following settings will improve the default style and font sizes for our charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ffedac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')\n",
    "matplotlib.rcParams['font.size'] = 14\n",
    "matplotlib.rcParams['figure.figsize'] = (10, 6)\n",
    "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e17a8220",
   "metadata": {},
   "source": [
    "### Age\n",
    "\n",
    "Age is a numeric column. The minimum age in the dataset is 18 and the maximum age is 64. Thus, we can visualize the distribution of age using a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1252a914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1338.000000\n",
       "mean       39.207025\n",
       "std        14.049960\n",
       "min        18.000000\n",
       "25%        27.000000\n",
       "50%        39.000000\n",
       "75%        51.000000\n",
       "max        64.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df.age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa559500",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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         "alignmentgroup": "True",
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           "bgcolor": "#E5ECF6",
           "caxis": {
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            "linecolor": "white",
            "ticks": ""
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          "title": {
           "x": 0.05
          },
          "xaxis": {
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           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
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           "title": {
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           },
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           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Distribution of Age"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "age"
         }
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0,
          1
         ],
         "matches": "x",
         "showgrid": true,
         "showticklabels": false
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
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         "title": {
          "text": "count"
         }
        },
        "yaxis2": {
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         "matches": "y2",
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         "ticks": ""
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(medical_df, \n",
    "                   x='age', \n",
    "                   marginal='box', \n",
    "                   nbins=47, \n",
    "                   title='Distribution of Age')\n",
    "fig.update_layout(bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c5caa6",
   "metadata": {},
   "source": [
    "The distribution of ages in the dataset is almost uniform, with 20-30 customers at every age, except for the ages 18 and 19, which seem to have over twice as many customers as other ages. The uniform distribution might arise from the fact that there isn't a big variation in the number of people of any given age (between 18 & 64) in the USA.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8e283b",
   "metadata": {},
   "source": [
    "### Body Mass Index\n",
    "\n",
    "Distribution of BMI (Body Mass Index) of customers, using a histogram ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ab40b89",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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   "source": [
    "fig = px.histogram(medical_df, \n",
    "                   x='bmi', \n",
    "                   marginal='box', \n",
    "                   color_discrete_sequence=['green'], \n",
    "                   title='Distribution of BMI (Body Mass Index)')\n",
    "fig.update_layout(bargap=0.1)\n",
    "fig.show()"
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  {
   "cell_type": "markdown",
   "id": "028fe64f",
   "metadata": {},
   "source": [
    "The measurements of body mass index seem to form a [Gaussian distribution](https://en.wikipedia.org/wiki/Normal_distribution) centered around the value 30, with a few outliers towards the right. Here's how BMI values can be interpreted ([source](https://study.com/academy/lesson/what-is-bmi-definition-formula-calculation.html)):\n",
    "\n",
    "![](https://i.imgur.com/lh23OiY.jpg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a6b297",
   "metadata": {},
   "source": [
    "### Charges\n",
    "\n",
    "visualize the distribution of \"charges\" i.e. the annual medical charges for customers. This is the column we're trying to predict. Let's also use the categorical column \"smoker\" to distinguish the charges for smokers and non-smokers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "56afc784",
   "metadata": {},
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(medical_df, \n",
    "                   x='charges', \n",
    "                   marginal='box', \n",
    "                   color='smoker', \n",
    "                   color_discrete_sequence=['red', 'green'], \n",
    "                   title='Annual Medical Charges')\n",
    "fig.update_layout(bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc428947",
   "metadata": {},
   "source": [
    "We can make the following observations from the above graph:\n",
    "\n",
    "* For most customers, the annual medical charges are under \\$10,000. Only a small fraction of customer have higher medical expenses, possibly due to accidents, major illnesses and genetic diseases.\n",
    "* There is a significant difference in medical expenses between smokers and non-smokers. While the median for non-smokers is \\$7300, the median for smokers is close to \\$35,000.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87fe7cd2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "810f3059",
   "metadata": {},
   "source": [
    "### Smoker\n",
    "\n",
    "Visualize the distribution of the \"smoker\" column (containing values \"yes\" and \"no\") using a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b742e0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     1064\n",
       "yes     274\n",
       "Name: smoker, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df.smoker.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "131851de",
   "metadata": {},
   "outputs": [
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   "source": [
    "It appears that 20% of customers have reported that they smoke. We can also see that smoking appears a more common habit among males\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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    "# Visualizing the relationship between **\"charges\"** and other columns.\n",
    "\n",
    "## Age and Charges\n",
    "\n",
    "Visualize the relationship between \"age\" and \"charges\" using a scatter plot. Each point in the scatter plot represents one customer."
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   "cell_type": "code",
   "execution_count": 15,
   "id": "5d6d5d24",
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    "fig = px.scatter(medical_df, \n",
    "                 x='age', \n",
    "                 y='charges', \n",
    "                 color='smoker', \n",
    "                 opacity=0.8, \n",
    "                 hover_data=['sex'], \n",
    "                 title='Age vs. Charges')\n",
    "fig.update_traces(marker_size=5)\n",
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    "We can make the following observations from the above chart:\n",
    "\n",
    "* The general trend seems to be that medical charges increase with age, as we might expect. However, there is significant variation at every age, and it's clear that age alone cannot be used to accurately determine medical charges.\n",
    "\n",
    "\n",
    "* We can see three \"clusters\" of points, each of which seems to form a line with an increasing slope:\n",
    "\n",
    "     1. The first and the largest cluster consists primary of presumably \"healthy non-smokers\" who have relatively low medical charges compared to others\n",
    "     \n",
    "     2. The second cluster contains a mix of smokers and non-smokers. It's possible that these are actually two distinct but overlapping clusters: \"non-smokers with medical issues\" and \"smokers without major medical issues\".\n",
    "     \n",
    "     3. The final cluster consists exclusively of smokers, presumably smokers with major medical issues that are possibly related to or worsened by smoking.\n",
    "     \n"
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   "cell_type": "markdown",
   "id": "585f06c7",
   "metadata": {},
   "source": [
    "### BMI and Charges\n",
    "\n",
    "Visualize the relationship between BMI (body mass index) and charges using another scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "745f5099",
   "metadata": {},
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   "source": [
    "fig = px.scatter(medical_df, \n",
    "                 x='bmi', \n",
    "                 y='charges', \n",
    "                 color='smoker', \n",
    "                 opacity=0.8, \n",
    "                 hover_data=['sex'], \n",
    "                 title='BMI vs. Charges')\n",
    "fig.update_traces(marker_size=5)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227a0317",
   "metadata": {},
   "source": [
    "It appears that for non-smokers, an increase in BMI doesn't seem to be related to an increase in medical charges. However, medical charges seem to be significantly higher for smokers with a BMI greater than 30.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d33ac6",
   "metadata": {},
   "source": [
    "## Correlation\n",
    "\n",
    "- As you can tell from the analysis, the values in some columns are more closely related to the values in \"charges\" compared to other columns. E.g. \"age\" and \"charges\" seem to grow together, whereas \"bmi\" and \"charges\" don't.\n",
    "\n",
    "- Let see the correlation between each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53b625c7",
   "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>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109272</td>\n",
       "      <td>0.042469</td>\n",
       "      <td>0.299008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.109272</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012759</td>\n",
       "      <td>0.198341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>0.042469</td>\n",
       "      <td>0.012759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.067998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charges</th>\n",
       "      <td>0.299008</td>\n",
       "      <td>0.198341</td>\n",
       "      <td>0.067998</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       bmi  children   charges\n",
       "age       1.000000  0.109272  0.042469  0.299008\n",
       "bmi       0.109272  1.000000  0.012759  0.198341\n",
       "children  0.042469  0.012759  1.000000  0.067998\n",
       "charges   0.299008  0.198341  0.067998  1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c94049f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IzHmrstYjIiIY0PuUbGVS9A7t2Y03rgLRMUe64cpVrEx6aiqJ+/YSV7lqjn02LpnH1DEjADjzlsF4ysUVW7xuEC7jSAoj32mSMaYnMBuIAk4GvEB14GtjzLWhCU+k4Mp5okhOSc9WlpmseKLD4xeCOOK8MdmSE4DkFGc9cABrOa8n17oeTxQA9/c5k4Tte/l8xt95Hvfpgb1o16wOw//7v8KEL3lITU4iKiYmW1lUtLOelpqS6z5Vj29E72Gv0KHXtfz87ktsW7M85HG6SVkbg/IkMMhaezOQCmCtHQHcDTwagthECiUpJQ1PTPb/4pmJSWJyWm67SAlJTE7NkYh4Ypz1Q4nJ2cqTklNyrXs4MYUWDWty22WnMujFL/I85rN3XkT/S7py8xMfs3zd1kKegRxLVEwMaSnZE5HMxCTa4811n7hKValWrwkdLrqWOqYdy37+LuRxuklEZGShlnAQTBdPS+CHXMp/AF4qmnBEis6W3YlUivMQExVBSpozXqF6pViSUtLYczA5j72lOG3asY/K8eWIiY4iJdVJHmtWjScxOYXd+w/nqFuzany2sppVK7Bl534u6d6WSvGxzH3/fgAicH4J/jHhPu4ZPYmJ0xcSERHBaw9czpVnn8CNj3/Id7OWFsMZlm3lq1Qj+dAB0lJTslpODu/dTVR0DN7yFbLV3bp6GdEeL9XqNc4qq1y7Pnu3avxXWRNMmrQWyDmkHi4C1hRNOCJFZ+nGPaSkptOhyXFZZZ2aHsfi9btJS884xp5S3Bav2kRyahontzky/qdr20YstAmkpWXvppu7dAOdW2cfJ9SlbQPmLt3AfyfP4qQbRtOt3yt06/cKVw19F4DLH3onKxF56o4LuaLHCVw3/AOm/LoktCcmAFQ7vjGR0dFsXb0sq2zrqqVUa9CUyKiobHWXzvyGeV99kK1sx/pVVK5dr1hidYuy1sUzDHjDGPMSTsvLTcaYD4HngMdCEJtIoSQmpzFp9jqeuPZE2jeswtnta9Pv3Oa8+6MzIPK4il68MeHRlFnWHU5K4aNp83jx3kvo2OJ4zj+lJXdddTqvT5oFQI2q8cT6unW++nkx8eW8vHDPxZgGNXh6YC/iy3mZNGMhu/cfZk3Czqxl47Y9APy7dQ8HDifTqVV9Bl5xGk+9+wPz7UZqVI3PWiR0or2xNOvag98/Gse2tZb1C+ew6PvJtOlxMQCH9u4iNTkJgFZnXMi///zFkhlT2Ls1gb++fJ+dG1Zn1ZX8KVMTtVlrvwROA6oB/wC9cAbMnmat/Swk0YkU0qjPFrFo3S4+vK87o/p04NVvlvLtvI0AzH3hInqdpF9l4WLoa98wf/lGprw0gJfvu5Rn35/OFzMXAbBq8qNZ85zsP5TEFQ9NoHPrBvz65t10adOQyx+awIHDeXfbXdy9LQCPDzifVZMfzbZEhcmllW7V5Yp+VG/YnO9eHMqsD1/jxF7X0Pik0wD4aMj1rPnrFwBqNmlFj1sfZtnP3zH58YEkLF3AefeOpHyV44719BLADZcZR/jPJVAcbotoqLb1Umxa/xdKOgQpoJ0r5pZ0CFIIjz7ev6RDkEIY0r1psTZLzDypa6G+a8/4a3aJN6Pke5CsMWYCkNsJZwDJwGZgsrX2nyKKTURERAogXMaRFEYw7Tj7gb5AC2A3sAdoAtwE1AA6A38aY3oVcYwiIiIShIjIiEIt4SCYy4ybAqN8c59kMcY8DHS11vYyxvQDngBy3mBBREREikVkmIwjKYxgzqA7kNt87p8D5/gefw/oVr4iIiIlqExdxQOsBq7MpfxyYIPvcXNge2GDEhERkbItmC6ewcBXxpjzgblABHCSb7nMGHMC8CmgyzxERERKULi0ghRGMPOgfA+0An7GGY9SH/gJ587G04EU4CZr7dMhiFNERETyKTIqslBLOAjmMuPawP1Aa5wJ2iKAM4CeQAtrbUVA80aLiIiUsDLVggK8A5wNzAZOBn4DNgEdgKFFH5qIiIgURGRkRKGWcBBMgnIaThfOUGAR8I219irgEZxp70VERESKRDAJSgSQeb/rpTgtJwATgU5FGZSIiIgUnBvuxRNMFPOAG3yPF+KMPQFnNlkREREJE5FREYVawkEwlxk/CHxjjDkEvAcMMcYsA+oCH4QiOBEREQlemRoka62dDTQAPrTW7sSZ/2QsMAC4KzThiYiISFkUTAsK1toDwAHf403Aa6EISkRERAouXMaRFEZQCYqIiIiEv1CPIzHGeIFXgSuAJOBFa+1zR6l7GvAy0AJYCTzgm/z1mEp/iiUiIiLZRERGFGrJh+eBrjjzo90KDDPGXB1YyRhTA5gCfAa0w7ny90tjTIO8DqAWFBEREZcJ5XT1xpjyQH/gImvtPGCeMeY54E7gk4DqpwJYa5/xrT9ljLkf6AKsP9Zx1IIiIiIiwWgPeHFmlM/0G9DJGBMVUHcnUMkYc4UxJsIYcwlQAWfC12NSC4qIiIjLhPgy49rALmttol/ZVsAD1AA2+5X/inPF76dAOs69/PpZa5fldRAlKCIiIi5T2Kt4jDEDcKYRyTTeWjve9zgOZ2Csv8x1b0B5eaARMAr4AjgHeMUYs8RaO+dYMShBERERcZmIyMIlKL5kZPxRNieSMxHJXD8UUD4E8Fprh/vWFxhjWgPDyOM+fhqDIiIi4jKRUZGFWvKQAFQxxnj8ymrhtKLsCqjbCfg7oGwe0DjPc8irgoiIiIifhUAycIpfWTdgnrU2NaDuJqBVQFlLYHVeB1EXj4iIiMuEciZZa+0hY8x7wDhjzI04rSeDcS49xhhTC9hrrT0MvAnMMsY8AHwOnAncBFyQ13HUgiIiIuIyEVGRhVry4T5gLjADeAMYaa2d6Nu2GbgKwFr7B/Af3/oi4F6gj7V2Rl4HUAuKiIiIyxR2kGxerLWHgL6+JXBbRMD6d8B3wR5DLSgiIiISdtSCIiIi4jIRUYETupY+SlBERERcJpSDZIuLEhQRERGXiQzxGJTioARFRETEZdzQglL6z0BERERcRy0oIiIiLuOGFhQlKCIiIi4T6nlQikOxJyjT+r9Q3IeUItTzzcElHYIU0DsnnFXSIUghfDs/oaRDkEIY0r1psR5PLSgiIiISdtyQoJT+MxARERHXUQuKiIiIy0S6oAVFCYqIiIjLaJCsiIiIhB2NQREREREJAbWgiIiIuIwbWlCUoIiIiLiMxqCIiIhI2ImMiirpEApNCYqIiIjLuKGLp/SfgYiIiLiOWlBERERcxg0tKEpQREREXEaDZEVERCTsqAVFREREwo4bEpTSfwYiIiLiOmpBERERcRmNQREREZGwExGpidpEREQk3LggQSn9bUAiIiLiOmpBERERcRuNQREREZFwE6GbBYqIiEjYccEYFCUoIiIibuOCBKX0d1KJiIiI66gFRURExGU0UZuIiIiEHxd08ShBERERcRslKCIiIhJu3NDFU/rPQERERFxHLSgiIiJuoy4eERERCTtKUERERCTcuGGqe41BERERkbCjBCUPnuhInrquAwtf+g9/Pt+LW3s2z3Ofk5pW47dnLsh128Wd6zHxgTOKOEopCtEeD48unkaLHqeWdChlkicmmnHD+rL151fZ8MNL3N/3vKPWbde8Hr+8N5Q9v7/OnA+H07FVw1zrPXRLL94d1T9bWYXysbz52M1s/mkMW2aOYdywvpQv5y3KU5FcxERFcP/ZzZhy+ylMHtCVq0+qd9S6ZzavzoTrT+J/d3bjres60rVxtWKM1CUiIwu3hIHwiCKMPXxZW05sUo3rXvqFR/5vHgMvaEmvTscftb6pW5Fxt3YlMiLnti6mOk9d3zGE0UpBRXu93PLxGOq2MSUdSpn1zKAr6dKuCefd9gIDn3yPh/tdxJU9O+eoFxfr4euxg/hj0Rq69BnJbwtW8tWr9xIfF5ut3lXnnczw2y7Osf+rD19PqyZ1OP+2F7jgjhfp3LYxLwy+OmTnJY7bTmtM69oVuX/SIkZPX8H1netzVvPqOeq1q1uJoee14PMFG+n3wV98988WnujViqbV40sg6lIsMqpwSxhQgnIM5TxRXH1aY0ZN/Jt/Nuzhh783M36a5YYzm+Za/5rTG/H5g2eyY19ijm1392rJu3d3Y8OOg6EOW4JUu2VTHpzzBdWbNCjpUMqsuFgPt/Q+ncGjP2HBsvVMmbmQ0e/9j9uv6pGj7hU9O5OSmsoDL37K8rWbGfzCx+w9cJgrenYCICoqkleHXs/4ETexZuP2HPsnJqdw99P/x0K7gflL1zHhy185vaMS01CKjY6kV9vavPbzalZsO8CsNTv55K9/6X1C3Rx1e7asyS8rd/DtP1tI2JvI5IUJLNi4h7NMzmRGji4iMqpQSzg45iBZY8zNwIfW2iTf46Oy1r5TpJGFgZbHV8YTHcnclTuyyuau2smdF7YkMgLSM7LXP6NNLQZPmEt8bAz3X9I627ZurWrS95Vf6WJqcGrLGsURvuRTs+5dWPHTbL585HlePbS8pMMpk9qZeng90cxasCKrbNaClQztfxGRkRGk+73ZTm7bhNkLV5GRcaRs9sKVdGnXlAlf/Ep8OS9tmx1PtxtGcc915xIdMFjwtpHvZj1uULsaV593MjP+XBa6kxOaVI8nJiqSxQl7s8oWb9rL9Sc3yPFZOnlhAqkBH64ZGRDv1TUdQQmTbprCyOsVfxT4CkjyPT6aDMB1CUqNyrHsOZRMcmp6VtmOfYl4Y6KoVjGW7Xuzt5TcOm42AJd1zflL/MrnZgLQxSg5CTe/vPF/JR1CmVf7uMrs2nuQpOTUrLJtu/bh9cRQo2pFtuzY61e3Enb9lmz7b9u5j3amPgB7DxzmjJuezvOY7z3Zn2su6MrahO2M+u9XRXQmkptq5T3sT0wlOe1I4rHrUAqe6Egqx3nYdTA5q3x1QCtzw2pxdKxfhZHfLS22eCU8HDNBsdY2yu1xWVHOE0VySnq2ssxkxRNd+rNTkXARF+shKSU1W1lmsuKNyf4xVS7WQ3JyQN2UVLye4H5hP/v2t4z7dAZP33MFU8YO4uRrR2ZrlZGi442JJDkt+2dpim/dE5XLgD2fyuVieOKi1ixK2Msvfi3Zkrdw6aYpjKDe0caY1kALIHDIe4a19uMiiypMJKWk4YnJnohkJiaJyWklEZKIKyUmpeRIRDITjkOJydnrJqfgCUhGvDHROerlZemaTQD0eegN1k59gdM6NOeXeTbY0CUfklPT8URl/yyN8a0nBvwIzFQ93sPzl7YjPT2DEd8sQaljkMpSgmKMeRJ4GNgHHA7YnAG4LkHZsjuRSnEeYqIiSPE1TVavFEtSShp7Dgb3YSgiR5ewfTdVKpYnJjqKlFQn+a9ZrRKJSSns2pe9yX/Ttj3UqlYpW1nN4ypl6wY6mlhvDBec1o6pvy3OSmg2b9/Dnv2HOK6KrhIJlR0Hkon3RhMdGZE1vqRqnIfk1HT2J6bkqF+7UiwvXtaOxNR0Bn3+N/sSU3PUkTy4YAxKMGdwO3C7tbaytbZ2wFInVAGWpKUb95CSmk6HJsdllXVqehyL1+8mLXCErIgU2N/2X5JTUuna/sgVcqee2Iz5y9aRFtA18Mfi1XRpn/1Kuq7tm/LHotX5Otb7T93Kuae0yVpvVLc6VSvFs3zN5kKcgRzLqu0HSE1Pp02dilllbetWxG7bT1rAR2kFbzQvXNqOg0lp3DtxIbsP5UxgJG8RUVGFWsJBMAnKfuC3UAUSjhKT05g0ex1PXHsi7RtW4ez2tel3bnPe/XEVAMdV9OKNKf1ZqkhJO5yYzAffzOLVoddzUutG9Op+AoOu78nYj6YDULNaRWK9MQBMnv4X8XFeXn6wDy0b1+H5+6+mQvlYJk77M8/jJCalMOHLX3n63ivp2r4pHVo15IOnb+WrGfOzunyk6CWlpjNt6VbuPasZLWpW4JTG1biqYz0mLUgAoGpcTFYXUL9TG1GpXAzPfL+cqMgIqsbFUDUuhvKe8PjSlOITzBiUwcBrxpjhwAYg288aa+2GogwsXIz6bBGjrj2RD+/rzoHEFF79ZinfztsIwNwXLmLwhLlMmr2+hKMUKf2GjP6UsUOv5/vxQ9h3MJEn35zC5z/MBeDf6S9zy/C3+WDKLPYfTOTiu15m3LC+3Nz7dBav/Jf/3PUyBw7lnH8o9+N8wpN3X86nLwyknDeGL2fM577nPwrlqQnw2s+rGdSjGS9d3p6Dyam8P2c9M1c489RMvvUUnpm2nKlLt3JG8+rEe6N567qTsu3/w7KtPDlV0wDkmwvGoETkd9S6MeYaYDwQF/gcOINk8/XXaDTgc/WNlGI93xxc0iFIAb1zwlklHYIUwik39C3pEKQQZg7qfvTLlUIgbfmvhfqujWpx2jHjNcZ4gVeBK3CmInnRWvvcUeq2AMYBXYCNwMPW2kl5xRBM/8RonLlO2gPN/ZZmvn9FREQkDERERhZqyYfnga7A2cCtwDBjTI57Rhhj4oHpOIlJe2As8LExplVeBwimiycWeMVauyaIfURERMRFjDHlgf7ARdbaecA8Y8xzwJ3AJwHVbwBSgFustSnASmPMuTjJzTFn3wumBeV5nAwpsItHREREwklobxbYHmc+NP8LZ34DOhljAnc+C/jal5wAYK3tZa19O6+DBNOCcgHQCbjOGLMDyHZhurW2fhDPJSIiIqESEdIrTGsDu6y1/iPTtwIeoAbgf81+E2CBMWYc0Nu3bbi19pu8DhJMgvKWbwGojjMoZl8Q+4uIiEhxKGSCYowZAAzwKxpvrR3vexyHkwP4y1wPnGm+AjAEZ5DsBcC5wJfGmJN93UNHFUyC8gkwDLgNqIYze+xm4AVr7ctBPI+IiIiEUEYhExRfMjL+KJsTyZmIZK4fCihPBRZba4f61hcYY07DSX5uPVYMwZzBGOB64H6gHXAi8DjwgDEm71uHioiIiBskAFWMMR6/slo4rSi7AupuAgInsLFAnsNCgmlBuRroZa391a9skTFmLfApzn16REREpKSFdgzKQiAZOAWY6SvrBsyz1gbeOGk2cF5AWStgXV4HCSZB2YtzqVCg/b5ARUREJBxEhG5eOGvtIWPMe8A4Y8yNOK0ng3EuPcYYUwvYa609DPwXuNsY8yxOl9FFOHOndM7rOMdMUIwxjf1WXwXeM8YMAv7Cmeq+LU7Xz4igzk5ERERCJ/R3M74PeB2YgXPBzEhr7UTfts3ATcC71toNxphzcHKFe4A1wGXW2gV5HSCvFpRVOINhwZnSHuCbXMpe5+iDaURERMRFrLWHgL6+JXBbRMD6HPLRYhIorwSlUbBPKCIiIiWrsFfxhINjJijWWt2mV0REpLRxe4IiIiIipZASFBEREQk7LkhQSv8ZiIiIiOuoBUVERMRlXD9IVkREREohJSgiIiISdkI4k2xxUYIiIiLiNi5oQSn9ZyAiIiKuoxYUERERl9EgWREREQk/ob9ZYMgpQREREXEbF7SglP4zEBEREddRC4qIiIjbuKAFRQmKiIiI2yhBERERkXCjq3hEREQk/LggQSn9ZyAiIiKuoxYUERERt9G9eERERCTsuKCLRwmKiIiIy2iQrIiIiIQfFyQopf8MRERExHWKvQVl54q5xX1IKULvnHBWSYcgBXTzwhklHYIUwqT6LUs6BCmMQd2L9XAZGiQrIiIi4SYjo6QjKDwlKCIiIi6T7oIMRWNQREREJOyoBUVERMRlSn/7iRIUERER10l3QYaiBEVERMRlMlwwBkUJioiIiMu4oQVFg2RFREQk7KgFRURExGVc0ICiBEVERMRt3NDFowRFRETEZTRIVkRERMJOekkHUAQ0SFZERETCjlpQREREXMYFPTxKUERERNxGg2RFREQk7LhhkKzGoIiIiEjYUQuKiIiIy7jhKh4lKCIiIi7jgh4eJSgiIiJuk+6CDEUJioiIiMuU/vREg2RFREQkDKkFRURExGU0D4qIiIiEHRcMQVGCIiIi4jbpLhiFogRFRETEZdzQgqJBsiIiIhJ21IIiIiLiMhokKyIiImHHDV08SlBERERcJtSDZI0xXuBV4AogCXjRWvtcHvtUBZYCD1lr383rGBqDIiIiIsF6HugKnA3cCgwzxlydxz4vAzXzewC1oIiIiLhMKLt4jDHlgf7ARdbaecA8Y8xzwJ3AJ0fZ53ygM7A9v8dRC4qIiIjLpGdkFGrJQ3vAC/zmV/Yb0MkYExVY2RhTAXgDGAAk5/cclKAE8MRE8cr9l7J+yghWTh7GPVd3P2rdNk1qM/21O9gy9Ql+/u9ddDDH51qva9uG7JnxNPVrVckqa1SnKp8+1Zf1U0aw7LOhPHn7hXg9atAqLE9MNOOG9WXrz6+y4YeXuL/veUet2655PX55byh7fn+dOR8Op2OrhrnWe+iWXrw7qn+2sgrlY3nzsZvZ/NMYtswcw7hhfSlfzluUpyJBivZ4eHTxNFr0OLWkQymTPNFRjL7jXFZ+eBdL3r2DO3t3Pmrd1g2r892zfVg/8V6mj76eE5rWyrb93E5N+G3sTayfeC/fPnMtLRscl7XNGxPF0/17sOS9O1j2/kBG33Eu5WNjQnZepVVaeuGWPNQGdllrE/3KtgIeoEYu9Z8DplprfwnmHJSgBBh124V0bl2fi+9/i3tGT2bI9Wdx2Vntc9SLi41h0rM3M3fpBk4f8CqzF6/js2duIr6cJ1s9ryeasUMuJzLyyJ86JjqKT5+6kaSUNM4Z+Dr9Rn1Mr26tGX5Lz5Cfn9s9M+hKurRrwnm3vcDAJ9/j4X4XcWXPnB+UcbEevh47iD8WraFLn5H8tmAlX716L/FxsdnqXXXeyQy/7eIc+7/68PW0alKH8297gQvueJHObRvzwuC8ul8lVKK9Xm75eAx125iSDqXMeuym7pxk6nDZ8IncP+577ruyC5ec1iJHvThvDJ+MuJx5KzZx9n3vM2fpRj569FLKl3OSjPZNazLhwYt5f+rfnHnve6zZtJv3h/YmOsr5DB1y9Smc2rYefZ6YzDUjJ3Fyq7oM73v0H5JlVWFbUIwxA4wxf/ktA/yePg5nYKy/zPVsv9SMMd2Bi4AHgj0HJSh+4mJj6NurMw+/9g0LVyTw3aylvPLJzwzo3TVH3UvPbE9KahpDx33Lig3beGjsFPYdTOTSgGRm6I3nsH3PgWxlHVvWo3Hdatz+zERWbNjGrL/XMuqd77nynBNDen5uFxfr4ZbepzN49CcsWLaeKTMXMvq9/3H7VT1y1L2iZ2dSUlN54MVPWb52M4Nf+Ji9Bw5zRc9OAERFRfLq0OsZP+Im1mzM2WWamJzC3U//HwvtBuYvXceEL3/l9I76ciwJtVs25cE5X1C9SYOSDqXMivPGcN057Xj0nZ9YtHorU/9cxdjJf9LvgpyfaZd0M6SkpjH8nZ9YuXEXw97+if2HkundzUlm7urdma9/t4z/Zj5rNu3mofHTAWhatyoAPTo25sMfFrNw1RYWrtrCu/9bSLe29YvvZMsIa+14a+1Jfst4v82JBCQifuuHMguMMeWAt4C7rLV7g41BCYqfNk3q4I2JYvbidVllsxevo4OpR2RkRLa6nVrV549/1pHh11c35591dG515EPyRFOXq8/twLDXv82278oN27n8oQkcPHykKy4jI4NK8dl/vUtw2pl6eD3RzFqwIqts1oKVnNS6YY7X7+S2TZi9cFW212/2wpV0adcUgPhyXto2O55uN4xizqJVOY5128h3mbd0HQANalfj6vNOZsafy0JwVpKXZt27sOKn2TzbtXdJh1JmtW5UHW9MNH8s3ZhVNmdZAic0q5XjvdfR1OHPZQnZBnH+uSyBk0wdALq1q8+U34+8hw8mptDp1jdZvmEHALv3H6ZX1+ZUjo+lYnkvF3Ztzt+rt4bw7EqntIyMQi15SACqGGP8uwxq4bSi7PIr6ww0BT4wxhwwxhwA6gBvGGPeyOsg+R70YIxpBDwFdAJigGz/66y1pT6FrVWtArv3HyYpOTWrbNuuA3g90VSvHM/WXfuz1V35745s+2/bdYB2TZ03WXRUJGOHXM4j475h175D2ert3HuQmfOOfOlFREQwoPcp2cokeLWPq8yuvQcDXr99eD0x1KhakS079vrVrYRdvyXb/tt27qOdcf4b7z1wmDNuejrPY773ZH+uuaAraxO2M+q/XxXRmUgwfnnj/0o6hDKvZpV4dh84TFJKWlbZ9j0H8cZEU71SHFt3H/SrW55VCbuz7b9tz0HaNq5BhTgP1SrGEQF89OiltG9aiyVrt/Pwm9NZ7dvnsQkzmfDQJdgP7gRg+YYd3PDUF6E/yVImHwNdC2MhzmDXU4CZvrJuwDxrbapfvT+BZgH7/gq8BLyb10GCaUH5AGfk7ivAcODRgKXUi/PGZPtyA0hOcdYDB7CW83pyrevxOAOY7+9zJgnb9/L5jL/zPO7TA3vRrlkdhv/3f4UJv8yLi/WQlJL9Ncl8jbwxAa9frIfkgNcvKSU16IHKz779Laf1fZKErbuZMnYQERERee8k4jLlvNEk+yUnQNa6JyYqoG4MyakBn52paXiio7LG8D3Vvwdfz7JcM3ISO/cdYvLIq7IGwjaoVZnNO/dzxWOf0XvYJ6SkpjH2ngtCdWqlVigHyVprDwHvAeOMMZ2NMf8BBuPkBxhjahljyllrD1trV/kvQBqwzVq7La9zCObTuCPQ0Vq7NIh9SpXE5JxfUB7fF9uhxOxXRiUlp+Ra93BiCi0a1uS2y06lW79X8jzms3deRL9LunL9iP9j+To1UxZGYlJKjkQk8zUKfP0Sk1PwBLx+3pjoHPXysnTNJgD6PPQGa6e+wGkdmvPLPBts6CKlWlJyao5EJHP9cFLOHwKe6IDPzugoDielkpbm/Or/+MfFfDJjCQD3jJnKogm3cW6nJvzw1xrG3H0+Vz32OX8uTwDg5me/Zt6bA2jftCZ/r9JnaKYQt6AA3Ae8DswA9gEjrbUTfds2AzeRj1aSYwkmQVmBc/mQaxOUTTv2UTm+HDHRUaSkOtl/zarxJCansHv/4Rx1a1aNz1ZWs2oFtuzczyXd21IpPpa5798PQISvN+yPCfdxz+hJTJy+kIiICF574HKuPPsEbnz8Q76b5do/a7FJ2L6bKhXLZ3/9qlUiMSmFXfsOZqu7adsealWrlK2s5nGVsnUDHU2sN4YLTmvH1N8WZyU0m7fvYc/+QxxXJT6PvUXcZ/OuA1QuH0tMdCQpqc7P7xpVypOYnJrjs3PzzgPUqFI+W1nNKuXZuvsAO/cdIjkljZUJR4YxJKemsXHbPo6vXpFmx1clvpyHJeuO/PjesG0vu/cfpmHNykpQipGvFaWvbwncdtSmZGtt7vNx5CKYLp4XgDeNMXcYY84zxpzlvwTxPGFr8apNJKemcXKbIwNdu7ZtxEKbQFpAm9fcpRvo3Dr7VQNd2jZg7tIN/HfyLE66YTTd+r1Ct36vcNXQdwG4/KF3shKRp+64kCt6nMB1wz9gyq9LQntiZcTf9l+SU1Lp2r5pVtmpJzZj/rJ1OV6/PxavpotfPYCu7Zvyx6LV+TrW+0/dyrmntMlab1S3OlUrxbN8zeZCnIFI6fTPmm0kp6bRqUXdrLKTWx7PotVbSQu4re48u4lOLepkK+vcsi5/2c2kpWewcNUW2jU+Mhu6NyaK+jUr8e+2vWzZ5VwR2bxetaztNSqXp1L5WNZu2ROCMyu9QjxItlgEk6C8BzQBxgLfAdP9lh+KPrTidzgphY+mzePFey+hY4vjOf+Ultx11em8PmkWADWqxhPr6xb46ufFxJfz8sI9F2Ma1ODpgb2IL+dl0oyF7N5/mDUJO7OWjdv2APDv1j0cOJxMp1b1GXjFaTz17g/MtxupUTU+a5GCO5yYzAffzOLVoddzUutG9Op+AoOu78nYj5zLFGtWq0is1+nHnjz9L+LjvLz8YB9aNq7D8/dfTYXysUyc9meex0lMSmHCl7/y9L1X0rV9Uzq0asgHT9/KVzPmZ3X5iJQlh5NT+fSnJTx329mc2KwWPTs3YeAlnRg/ZR7gJBGZn51f/76C8rExPDOgB83rVeOJW86kfDkPX/zmXAX32pdzuen8E7i8eysa16nC6IE9OZiYwvdz17B55wG+n7uaF+/oSYfmtWndsDpv3H8hc5ZuZJGu5MkmPaNwSziIyCjmTKniGQ+Gyannrpw3hpcG9eY/3duw/2ASr078hbETfwVg38xnue2ZiXw01XnTdTDH8/L9l9KiYQ2WrN7CoJe+YOGKhBzP2bhuNRZ++ABtrn6GDVt2M+r2C7n7qtNzPX6VHg/n+LUfThL35vs2CiWiXKyHsUOvp3ePjuw7mMjLH0zj5Q+mAZC84B1uGf42H0xxEs6OrRoyblhfWjauw+KV/3LnUx+wYNn6HM/51uM3Ex0VxY3D3swqi/XG8OTdl3PFuZ0p543hyxnzue/5j9h/MDHH/uHi5oUzSjqEkHsjYx0vn92H5T/OKulQityk/wws6RCOqZwnmudvP4deXZuz/3Ay476cy+tf/QXA9q+GcNcr32WNKzmhaS1G33EuzetVY+m67Qx544dsCcY1Pdow6Iqu1KpanvkrNjP49R9Y5ev2qRDn4bEbz6BnpyZERUUyY/5aHnlrBnsOhO97D2D7V0OKdQT9NLutUN+1PU2NEh/xH1SCYoyJBS7DuWxoDM5VPcustVuOuaOfcE9Q5NjCPUGRoysLCYqbhXuCIsemBCV4+e7iMcY0xRkoOxIYClQGbgOWGGM6hiQ6ERERCVqIbxZYLIIZgzIG+BJnVrjMOfevAT4HXi7SqERERKTA0jIKt4SDYBKUU4Cx1tqs0K216Th3KdRNZERERMKEG1pQgpkH5QDOLZZXBJS3AXbnrC4iIiIlIfDy7tIomBaUN4D/GmMuxrkPT0tjTH9gPPB2KIITERGRsinfLSjW2lHGmL3Aq0AcMAXYBozGmcRNREREwkC4dNMURjB3M74Z+MRa+6oxpjwQba3Ne15wERERKVbhMtC1MIIZg/Iizm2St1trD+ZVWUREREqGG1pQghmD8gPQ1xgTF6pgRERERCC4FpTjcWaRfdgYsxPINq+wtbZ+UQYmIiIiBZPugqt4gklQ3vAtIiIiEsbK1BgUa+17oQxEREREioYbxqAcM0ExxvwE5OssrbVnFUlEIiIiUihpbk9QgN/8HlcD+uPcj+cvIAVnivvLgddCEZyIiIiUTcdMUKy1j2Y+NsZ8D9xtrX3dv46vlaVfaMITERGRYLlhkGwwlxmfCvyYS/lsoH3RhCMiIiKFVdbuZjwfGGqMKZdZYIypBIzCSVJEREQkDJS1uxkPAL4FthpjVuPcMLApsB64IASxiYiISAGUhUGyWay1y4wxLYBzgJa+4n+A6dba1FAEJyIiImVTXpcZB3YBpQL/8y3Z6llr04s4NhERESmANBcMks2rBSWVfM6DAkQVMhYREREpAmUhQTmL/CcoIiIiEgZcn6BYa2cWUxwiIiIiWfIag7IBONFau9MY8y/HaE3R3YxFRETCg+tbUIBHgQO+x8NCHIuIiIgUAdcnKP53MNbdjEVEREoH1yco/owxVYCHcaa1j8WZqC2Ltfb0og1NRERECqJMJSjA+zh3L54I7A1NOCIiIiLBJSg9gO7W2rmhCkZEREQKr6y1oCQAmi1WREQkzLk+QTHGNPZbHQe8Y4x5AFgDpPnXtdauKfrwREREJFiuT1CAVRyZ+yRzUOx3vsf+5RloqnsREZGwkFoGEpRGAet9gIPAlziJyQhgKc7AWREREZEikdc8KOszHxtjHgYGAwMzy40x64GHgCRgTAjjFBERkXxyQxdPZBB1bweustZ+nFlgrX0MuA4YVMRxiYiISAGlpWcUagkHwVzFUxnYmEv5WqBGfp/k0cf7B3FICTffzk8o6RCkgCbVb1nSIUghXPb1ayUdghTKkGI9WlpGeCQZhRFMC8ovwBPGmPjMAt/jEcBvRR2YiIiIlF3BtKDcCXwPbDbGrPKVNQH+BS4u6sBERESkYMKlm6Yw8p2gWGvXGWPaAOcALYFkYCUwzVqrCdxERETCRJlKUACstcnAt75FREREwlCZS1BEREQk/KWll/6OjWAGyYqIiIgUC7WgiIiIuIy6eERERCTsKEERERGRsFMWbhYoIiIipYwbWlA0SFZERETCjlpQREREXMYNLShKUERERFxGCYqIiIiEHSUoIiIiUuYYY7zAq8AVQBLworX2uaPUvQp4FGgErAKGWWun5HUMDZIVERFxmbT0jEIt+fA80BU4G7gVGGaMuTqwkjHmdOAD4BWgPfA2MNkYc2JeB1ALioiIiMtkhLCLxxhTHugPXGStnQfMM8Y8B9wJfBJQ/QZgkrX2Td/6GGNML+AqYMGxjqMERURExGXSQzsGpT3gBX7zK/sNeNQYE2WtTfMrfxVICdg/A6ic10GUoIiIiLhMRkZIE5TawC5rbaJf2VbAA9QANmcWWmv/9t/RGNMa6AHk6A4KpARFREREsjHGDAAG+BWNt9aO9z2OwxkY6y9z3XuM56wBfAH8CkzOKwYlKCIiIi5T2DEovmRk/FE2J5IzEclcP5TbDsaY44HvgTTgcmttel4x6CoeERERl0lPzyjUkocEoIoxxuNXVgunFWVXYGVjTGOcVpMM4Axr7c78nIMSFBEREZfJSC/ckoeFQDJwil9ZN2CetTbVv6IxpirwA7AX6G6t3Zrfc1AXj4iIiMuEcpCstfaQMeY9YJwx5kac1pPBOJceY4ypBey11h4GngSOAy4Don3bAA5ba/ce6zhqQREREZFg3QfMBWYAbwAjrbUTfds248xzAs5MsxVx5jzZ7Le8ltcB1IIiIiLiMiGeBwVr7SGgr28J3Bbh9/i4gh5DCYqIiIjLhHIm2eKiBEVERMRl3JCgaAyKiIiIhB21oIiIiLhMeminui8WSlBERERcxg1dPEpQREREXEYJioiIiISdUF9mXBw0SFZERETCjlpQgpSWksLvn7zO2nm/ERUdQ5uze9P+vMtzrbti1g8s/O5TDu7ZSbX6TehyZX9qNDLFHLH4i4mK4O4zm3JGs+qkpGUwcf5GPvnr31zrntm8Ojec3IBalWJJ2HOYt39fx+w1+brHlRSAJzqKpwf04D+nGpJT0nj9q78Y+8WfudZt3bA6z99+Lq0bVWflvzsZ/PoPLFy1JWv7uZ2aMLzv6dSrUYl/1mxj8Ovfs2z9DgC8MVE8duMZ/KebITIigu/mrGT4Oz9xMDGlWM5Tcor2eHh43hQ+u3cky3+cVdLhuEIop7ovLmpBCdIfk95m2+rlXDDoSbpddycLv/uE1X/+nKPexqULmPXRODpecgOXjRhHjUaGaWNGkJyY652opZjcdlpjWteuyP2TFjF6+gqu71yfs5pXz1GvXd1KDD2vBZ8v2Ei/D/7iu3+28ESvVjStHl8CUZcNj93UnZNMHS4bPpH7x33PfVd24ZLTWuSoF+eN4ZMRlzNvxSbOvu995izdyEePXkr5cjEAtG9akwkPXsz7U//mzHvfY82m3bw/tDfRUc7H3ZCrT+HUtvXo88Rkrhk5iZNb1WV43+7Feq5yRLTXyy0fj6FuG/14K0ohvllgsVCCEoSUpETsr9PocmV/jmvQjAYndKVdz8tZ+tM3Oeoe3rebDhf1oUmn06lYozYdLupD0sH97E5YV/yBCwCx0ZH0alub135ezYptB5i1Zief/PUvvU+om6Nuz5Y1+WXlDr79ZwsJexOZvDCBBRv3cJbJmcxI4cV5Y7junHY8+s5PLFq9lal/rmLs5D/pd8GJOepe0s2QkprG8Hd+YuXGXQx7+yf2H0qmdzcnmbmrd2e+/t0y/pv5rNm0m4fGTwegad2qAPTo2JgPf1jMwlVbWLhqC+/+byHd2tYvvpOVLLVbNuXBOV9QvUmDkg7FddLTMwq1hIMCJyjGGK8xppMxplJRBhTOdm1cQ1pqCjWbtc4qq9m0FdvXryA9PS1b3WZdzsrq+klNTuKf6V8SW6EyVeo0LM6QxU+T6vHEREWyOOHIDTQXb9qLqVmByIjsdScvTOD9P9ZnK8vIgHivekVDoXWj6nhjovlj6cassjnLEjihWS0iA16cjqYOfy5LwL8F+89lCZxk6gDQrV19pvy+ImvbwcQUOt36Jss3OF08u/cfplfX5lSOj6VieS8Xdm3O36vzfQd4KULNundhxU+zebZr75IOxXUy0jMKtYSDfH/aGmOaAxOAIcA/wO9AK2C/MeZCa+1voQkxfBzasxtvXAWiYzxZZeUqViY9NZXEfXuJq1w1xz4bl8xj6pgRAJx5y2A85eKKLV7Jrlp5D/sTU0lOO/Lm23UoBU90JJXjPOw6mJxVvnrHwWz7NqwWR8f6VRj53dJii7csqVklnt0HDpOUciTR377nIN6YaKpXimPr7oN+dcuzKmF3tv237TlI28Y1qBDnoVrFOCKAjx69lPZNa7Fk7XYefnM6q337PDZhJhMeugT7wZ0ALN+wgxue+iL0Jyk5/PLG/5V0CBLGgmlBGYtzi+SVwE1AdaAe8ALwYtGHFn5Sk5OIionJVhYV7aynpeY+wK7q8Y3oPewVOvS6lp/ffYlta5aHPE7JnTcmkuS07J2rKb51T1REbrsAULlcDE9c1JpFCXv5ZeWOkMZYVpXzRpOckr0VMnPdExMVUDeG5NTU7HVT0/BERxFfzvnx8FT/Hnw9y3LNyEns3HeIySOvonys815tUKsym3fu54rHPqP3sE9ISU1j7D0XhOrUREqEG1pQgklQTgUesNZuBy4GvrLWJgDvA21CEVy4iYqJIS0leyKSmZhEe7y57hNXqSrV6jWhw0XXUse0Y9nP34U8Tsldcmo6nqjs/+VjfOuJKbmPCqse7+HlK9qTnp7BiG+WEB5vW/dJSk7NkYhkrh9Oyp6MJKWk4onO3vjriY7icFIqab7WsY9/XMwnM5awaPVW7hkzFa8ninM7NSG+nIcxd5/PyPd+4Ze/1/P7ko3c/OzXXNClGe2b1gzhGYoUr/SMjEIt4SCYBOUAUNkYcxzQDfjWV94cKBPXXpavUo3kQweytZYc3rubqOgYvOUrZKu7dfUydv67JltZ5dr1STywr1hilZx2HEgm3htNtN+YhqpxHpJT09mfyyWmtSvFMubKE8gA7v38b/YlpuaoI0Vj864DVC4fS0z0kY+kGlXKk5icyu79h7PX3XmAGlXKZyurWaU8W3cfYOe+QySnpLEyYVfWtuTUNDZu28fx1SvS7PiqxJfzsGTdtqztG7btZff+wzSsWTk0JydSAspaC8pk4FNgBrAD+J8x5irgA9/ietWOb0xkdDRbVy/LKtu6ainVGjQlMir7r7+lM79h3lfZ/yw71q+icu16xRKr5LRq+wFS09NpU6diVlnbuhWx2/aTFvB+rOCN5oVL23EwKY17Jy5k9yHNkRFK/6zZRnJqGp1aHLmi6uSWx7No9VbSAj4s59lNdGpRJ1tZ55Z1+ctuJi09g4WrttCu8ZHWEG9MFPVrVuLfbXvZsusAAM3rVcvaXqNyeSqVj2Xtlj0hODMRKahgEpS7gHE4CUoPa20y4AFGAY+EILawE+2NpVnXHvz+0Ti2rbWsXziHRd9Ppk2PiwE4tHcXqclJALQ640L+/ecvlsyYwt6tCfz15fvs3LA6q64Uv6TUdKYt3cq9ZzWjRc0KnNK4Gld1rMekBQkAVI2LyeoC6ndqIyqVi+GZ75cTFRlB1bgYqsbFUN4TdaxDSAEdTk7l05+W8NxtZ3Nis1r07NyEgZd0YvyUeYCTRMR6nG6dr39fQfnYGJ4Z0IPm9arxxC1nUr6chy9+c344vPblXG46/wQu796KxnWqMHpgTw4mpvD93DVs3nmA7+eu5sU7etKheW1aN6zOG/dfyJylG1mkK3nERdzQghIR7GxzxphIoAHwLxDpS1Ty7fmfV4XHmRdQalIisz4ax9r5s/DExtHmnN60O/dSAN4acCGn33gvzU85B4B1C37nry8/YP/2zVQ9vhFdrupPzSatSjL8Qvt2fkJJh1Ao3uhIBvVoRvem1TmYnMrEeRuZON+5tHXmoO48M205U5du5avbTqFSuZgc+/+wbCtPTi2dA52XzMx9VtZwUc4TzfO3n0Ovrs3ZfziZcV/O5fWv/gJg+1dDuOuV7/hkxhIATmhai9F3nEvzetVYum47Q974IVuCcU2PNgy6oiu1qpZn/orNDH79B1b5un0qxHl47MYz6NmpCVFRkcyYv5ZH3prBngOJxX/SQbjs69dKOoSQeiNjHS+f3ce1M8m+kbHu6CPxQ6DZwC8K9V278rXexRpvbvKdoBhjYoCncFpSonHGnjwDpAL9rbUHj7F7ltKeoJR1pT1BKcvCPUGRY3N7guJ2xZ2gNL1jcqG+a1eNu7TEE5RgunhGAj19S+ZPjTHAicDoIo5LRERECsgNXTzBJCjXALdba38G52pL3+RsNwOXhiA2ERERKaOCmbf7OGBbLuUHgXJFE46IiIgUVrjcT6cwgmlBmQ48aIzJ7JfK8N2H52mcK3tEREQkDGSkpxVqCQfBtKAMBL7AaUUphzNRWz1gLXBR0YcmIiIiBREuSUZh5DtB8U1r39kY0wNo4dvXAt9ba3OfJ1xERESKXZlKUDJZa38EfgxBLCIiIiJAEAmKMSYdjnqvtGScOx1PBB611mpecBERkRKSkVb6W1CCGSR7O7DV9+8JOPOf9Ac2Ac8BDwAX4kx9LyIiIiWkrA2SHQLcbK2d6le2yBizAXjdWtvMGJMATAIeLMogRUREJP/CJckojGBaUGoCuc1zvg3IvLXoZqBiLnVERERE8i2YBOUHYJwxpklmge/xGOBHY0wUcAuwuGhDFBERkWCUtS6efsCnwEpjzF4gAqgATAMGABcAtwEXF3WQIiIikn/hkmQURjAJSm3gXKAp0A7nLsZLrbUrAYwx04Ea1trSP7+uiIhIKVbWEpSfgPOttfOAlYEbrbWHiywqERERKbB0FyQowYxB2QjUDVUgIiIiIpmCaUFZBEwyxiwA1gGJ/huttTcUYVwiIiJSQGWtiycD+L9QBSIiIiJFo0wlKNbam0IZiIiIiBQNN0x1H8y9eCKBS4HWQJSvOALwAidaa88p+vBERESkLAqmi2cscBOwAOgM/A40AWoBrxV9aCIiIlIQbujiCeYqniuAPtbaU4BVODcNrA98DMSFIDYREREpADfMJBtMglIRmOt7vBg42VqbBjwNnF/UgYmIiEjBlLUEZTXQwfd4CU43T+Zz6AaBIiIiYSIjPb1QSzgIZgzK88DHxpibce7JM98YkwF0BX4LRXAiIiJSNuW7BcVaOwE4B1hhrV0GXAIcB8zBGTwrIiIiYcANXTzBtKBgrZ3l93gazp2MRUREJIyES5JRGMHMg1IFeBhoD8TizIGSxVp7etGGJiIiIgXhhpsFBtOC8j5wIjAR2BuacERERKSwytRMskAPoLu1dm6eNUVEREQKIZgEJQEIj2uPRERE5KhcPwbFGNPYb3Uc8I4x5gFgDZDt7K21a4o+PBEREQmW6xMUnCntM3yPMwfFfud77F+ewZEbCIqIiEgJKgsJSqOA9T7AQeBLnMRkBLAUZ+CsiIiISJE4ZoJirV2f+dgY8zAwGBiYWW6MWQ88BCQBY0IYp4iIiOSTG1pQIjIyMvKuBRhjNgC3WGt/CCg/HxhnrQ1sbREREREpkGBuFlgZ2JhL+VqgRpFEIyIiIkJwCcovwBPGmPjMAt/jEehmgSIiIlKEgpkH5U7ge2CzMWaVr6wJ8C9wcVEHJiIiImVXvsegABhjPDh3NG4JJAMrgWnWWk3gJiIiIkUmqARFREREpDgEMwZFREREpFgoQRHXM8Y0NMZkGGOaFsFzrTPG9CuKuMSR1+tjjLnRGLPR9/gMX91cx88ZY0YZY2aGMFzxU5TvLZFAwQySFRHoBBwo6SDKmE+Bb0s6CBEpXkpQRIJgrd1e0jGUNdbaw8Dhko5DRIqXEpR8MsZ0BZ4DOuLcHPFXnJl1E4wx5wKjgabATJybLFaw1t7o2/cS4EmcexstB4Zaa6cW8ykIXGqMuQuohPOr/C7gaqAfzk0wh+DctuE+IBV4AagAvG6tHQpOFw8wylr7VrFH7wLGmEbAq0B3YC/OXdI/8m3+jzHmDqAu8CPQ11q70xhzI87f/Phcnq8VMB7oAMzCee9lbrsRuA1ngslzgPuBt4FHgNuBeGA2cJe1dqVvnwygL85tPZoD84AbrLWri+yPUEoV5LXz7XcT8ADOtBT7gM9w/uapxph3ce7r1g6oB5wJbALeBM4FtuF87r5urY3wPd/xwFic13SnL4bh1tpkY0wMzm1XLsN5fWcBd1prbYj+LBJCGoOSD8aYCjhNzNOB1jhvnMbAI8aYxsDXOG+6E4C5wEC/fdsDHwDPAG1xPky/MMacUHxnID79cRKSi4CewDBfeSecL6NOODe+HI8z78+FwMPAw8aYtsUercsYY7w4cymlAF2BW3C+uPr4qtwEXAucAZyI87fP6/m+xZnNugPwBc5r7O9knOkQOgNTcF7XG4DrfdtWATOMMXF++4wABgEnAVWBp4I9V7cp6GtnjOmGk8g8AjTDSRhvAi71e/rrgJHA+cAS4BOgJtAN5/Ua4RdHBM7rvBvnx2IfoBfwtK/KnTifzxfiJD37gXcL/xeQkqAWlPwpj/MhNdpamwGsNcZMAk7B+fU931o70ld3uDHmHL99BwPvWGs/8K2vNsacjPPr/ZbiCV987rPWzgIwxjyK0+o1GIjC+UW33xjzJs5rM8JauxhYbIx5FmgBLC6huN3ibKAOcJK1di/wjzFmIFDdt/1Ba+2fAMaYiUD7fDxfdeB2a+0BYLkx5izguIB6T/q2Y4x5ALjbWjvDt34XcAHOL+7M9+jL1toffdtfB+4t4Pm6SUFfu8M4Lc2TfevrjTH34/zQy7TAWvuFb9/mvmMZa+0KYKEx5jHgDV/ds3B+HHax1qbhvOYDge+NMQ8CDX3HXGet3W6MuR0nMZJSSAlKPlhrt/iaIgf5Wj5a4bwB/8DJ0ucG7DIb55cXOJPatTXG+CcjMcCfoYxZcuX/Os0HquF8wO6w1u73lWeOdVjvV/cw4A19eK7XCljl+4IDwFr7oTGmIfAS4N+NsheIzcfzrc5MPnz+As7zW9/pl5zEA8cDHxpj/CeXjMVpQcvkH8c+nPdrWVeg185aO88Yc9gY8zhOUtIWJ2H40a/+Or/H7YB9vuQk02y/xy1x7gu31xiTWRYBeIAGwH+Bq4BNxpjfgK+ACcGfroQDJSj5YIypi/PBtwCYhtM/eiFOE2QqzhvEn/96NM5YhsA3SVJIgpVj8f9SyuzeTMZ5DY9VV4pGch7bA+8PH/i+yk1gnZSA9US/x5mfd1cDSwPq7fF7HBhnfuJwuwK9dsaYnjhJwvvAVOBxnC4ff/6vUX4+T1fidOsE+tc3DqUhTnfRhThdSwOMMR19g62lFNEYlPzpjZPVX2CtfcVa+ytOM2METp9px4D6/usWaGytXZW54PR/9y6OwCUb/3EknYHNOL/2pHisBJoYYypmFvh+WT9WwOf7B2hqjKniV3bi0Spba/fgDLqs7fdeXIvTfZtXd1JZV9DXrj/wnrV2gG9g+TKcwbJHS/qWAhWMMf7dMoGfp/VwWsYyX8NaOGNQIo0xNwCXWGu/sNb2w/n/0BKnZUZKGbWg5M9OoK5vbMlq4AqcPusFOAMqBxtjhgKf+8pP40iT50vAb8aYP3EG6Z2Nk9X/p1jPQADG+Lra4nEG5b1QwvGUNdNwbi76pm9cQUPgbpwB5AUxHacr7h1jzCNAF+ByYM4x9nkR567sW3ESnCE4V4PcW8AYyoqCvnY7ga7GmHY4rSwPA7U5SpeptXaFMWYa8JYx5m6cLtiRflW+x0kqPzTGPAzEAW8Bf1trE40xlYBhxphdwAqcH4MHfI+llFELSv5MxBlANxHnssMeOKP8W+D8IrscuBFnEOWpwJf4mkSttXNwRpr3x2ltGQTcZK39rjhPQADnEskvca64+gAneZRi4hvUeDHO+Kz5OAMfR+Jc8l2Q50vBGeBaEed9OYCc3QeBXvAd9zVgEdAG6Gmt3VSQGMqKQrx2j+G0VM7GSSiTcf72R23pwrnKZz9OovlfnO7xzM/TNJyr8NKA33F+9P2Kc7ECvuee4FuW+2LuZa3dnd9zlfChmwUWkjGmDRBjrV3gV/YtMNda+1iJBSYiUsr4Lvc+G/ifLwHFGHMF8Ly1tmFJxibFT108hdcEmGCMuQqnGfEcnBaWY87hICIiOSQC7wBvGGPexhlfMgKn1VPKGHXxFJK19iuc+TTexhnAdRdwlbV2UYkGJiJSylhr04FLcFpRluBMyjaVI5MqShmiLh4REREJO2pBERERkbCjBEVERETCjhIUERERCTtKUERERCTsKEERERGRsKMERURERMLO/wNcIeH0nDm01wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(medical_df.corr(), cmap='RdBu_r', annot=True)\n",
    "plt.title('Correlation Matrix');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68de3f2a",
   "metadata": {},
   "source": [
    "Computers can't differentiate between correlation and causation, and decisions based on automated system can often have major consequences on society, so it's important to study why automated systems lead to a given result. Determining cause-effect relationships requires human insight."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f23cfc0",
   "metadata": {},
   "source": [
    "# 3. DATA PREPARATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b69db3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install numpy --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "498d84d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91b67730",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install scikit-learn --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd9464d",
   "metadata": {},
   "source": [
    "## Using Categorical Features\n",
    "\n",
    "If we could use categorical columns like \"smoker\", we can train a single model for the entire dataset.\n",
    "\n",
    "To use the categorical columns, we simply need to convert them to numbers. There are three common techniques for doing this:\n",
    "\n",
    "1. If a categorical column has just two categories (it's called a binary category), then we can replace their values with 0 and 1.(Label encoding)\n",
    "2. If a categorical column has more than 2 categories, we can perform one-hot encoding i.e. create a new column for each category with 1s and 0s.(Nominal)\n",
    "3. If the categories have a natural order (e.g. cold, neutral, warm, hot), then they can be converted to numbers (e.g. 1, 2, 3, 4) preserving the order. (ordinal)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e30d9812",
   "metadata": {},
   "source": [
    "## Binary Categories\n",
    "\n",
    "The \"smoker\" category has just two values \"yes\" and \"no\". Let's create a new column \"smoker_code\" containing 0 for \"no\" and 1 for \"yes\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d5d03bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=medical_df, x='smoker', y='charges');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6256f5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "smoker_codes = {'no': 0, 'yes': 1}\n",
    "medical_df['smoker_code'] = medical_df.smoker.map(smoker_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "684c92db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7872514304984778"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df.charges.corr(medical_df.smoker_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69f219a9",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "      <th>smoker_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges  smoker_code\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400            1\n",
       "1   18    male  33.770         1     no  southeast   1725.55230            0\n",
       "2   28    male  33.000         3     no  southeast   4449.46200            0\n",
       "3   33    male  22.705         0     no  northwest  21984.47061            0\n",
       "4   32    male  28.880         0     no  northwest   3866.85520            0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d2a2c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='charges'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=medical_df, x='sex', y='charges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c3724b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_codes = {'female': 0, 'male': 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "989dbb4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "medical_df['sex_code'] = medical_df.sex.map(sex_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "affe8344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05729206220202533"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df.charges.corr(medical_df.sex_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4e201a",
   "metadata": {},
   "source": [
    "As you might expect, this does have a significant impact on the loss."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7853dc56",
   "metadata": {},
   "source": [
    "\n",
    "### One-hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3041f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=medical_df, x='region', y='charges');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "483627b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['northeast', 'northwest', 'southeast', 'southwest'], dtype=object)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "enc = preprocessing.OneHotEncoder()\n",
    "enc.fit(medical_df[['region']])\n",
    "enc.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "39bfd58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       ...,\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.],\n",
       "       [0., 1., 0., 0.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot = enc.transform(medical_df[['region']]).toarray()\n",
    "one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b456d839",
   "metadata": {},
   "outputs": [],
   "source": [
    "medical_df[['northeast', 'northwest', 'southeast', 'southwest']] = one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "09669544",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "      <th>smoker_code</th>\n",
       "      <th>sex_code</th>\n",
       "      <th>northeast</th>\n",
       "      <th>northwest</th>\n",
       "      <th>southeast</th>\n",
       "      <th>southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>30.970</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10600.54830</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>31.920</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2205.98080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.850</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.83350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>25.800</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.94500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.070</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29141.36030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex     bmi  children smoker     region      charges  \\\n",
       "0      19  female  27.900         0    yes  southwest  16884.92400   \n",
       "1      18    male  33.770         1     no  southeast   1725.55230   \n",
       "2      28    male  33.000         3     no  southeast   4449.46200   \n",
       "3      33    male  22.705         0     no  northwest  21984.47061   \n",
       "4      32    male  28.880         0     no  northwest   3866.85520   \n",
       "...   ...     ...     ...       ...    ...        ...          ...   \n",
       "1333   50    male  30.970         3     no  northwest  10600.54830   \n",
       "1334   18  female  31.920         0     no  northeast   2205.98080   \n",
       "1335   18  female  36.850         0     no  southeast   1629.83350   \n",
       "1336   21  female  25.800         0     no  southwest   2007.94500   \n",
       "1337   61  female  29.070         0    yes  northwest  29141.36030   \n",
       "\n",
       "      smoker_code  sex_code  northeast  northwest  southeast  southwest  \n",
       "0               1         0        0.0        0.0        0.0        1.0  \n",
       "1               0         1        0.0        0.0        1.0        0.0  \n",
       "2               0         1        0.0        0.0        1.0        0.0  \n",
       "3               0         1        0.0        1.0        0.0        0.0  \n",
       "4               0         1        0.0        1.0        0.0        0.0  \n",
       "...           ...       ...        ...        ...        ...        ...  \n",
       "1333            0         1        0.0        1.0        0.0        0.0  \n",
       "1334            0         0        1.0        0.0        0.0        0.0  \n",
       "1335            0         0        0.0        0.0        1.0        0.0  \n",
       "1336            0         0        0.0        0.0        0.0        1.0  \n",
       "1337            1         0        0.0        1.0        0.0        0.0  \n",
       "\n",
       "[1338 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "454a23df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(medical_df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17ddb5c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "22af70a0",
   "metadata": {},
   "source": [
    "# EXTRACTING THE FEATURES THATS REQUIRED FOR THE MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9394790",
   "metadata": {},
   "source": [
    "# Creating a Test Set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99cb8e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ec96a309",
   "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>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker_code</th>\n",
       "      <th>sex_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     bmi  children  smoker_code  sex_code\n",
       "0   19  27.900         0            1         0\n",
       "1   18  33.770         1            0         1\n",
       "2   28  33.000         3            0         1\n",
       "3   33  22.705         0            0         1\n",
       "4   32  28.880         0            0         1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data = medical_df[['age','bmi','children','smoker_code','sex_code']]\n",
    "input_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e553c021",
   "metadata": {},
   "outputs": [],
   "source": [
    "# targeted value \n",
    "output_data = medical_df['charges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1142e391",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(input_data, output_data, test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "31377f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1070, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "len(y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d156710",
   "metadata": {},
   "source": [
    "\n",
    "## Feature Scaling\n",
    "\n",
    "The important feature are:\n",
    "\n",
    "1. Smoker\n",
    "2. Age\n",
    "3. BMI\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c563f2",
   "metadata": {},
   "source": [
    "# Standardization \n",
    "\n",
    "* Standardized the numerical data\n",
    "* Encoded the categorical data\n",
    "* Concatenate the both standardized-numerical and encoded-categorical data to make a single dataset for training and testing.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "56602b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "eb0aeafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_numerical = x_train[['age','bmi','children']].values\n",
    "x_train_std = sc.fit(x_train_numerical)\n",
    "data_mean = x_train_std.mean_\n",
    "data_var = x_train_std.var_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e305005c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_var_df = pd.DataFrame({\"mean\":data_mean,\"variance\":data_var})\n",
    "mean_var_df.to_json(\"mean_variance.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0c1c4836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00228629, -0.66474472, -0.90705771],\n",
       "       [ 1.00228629, -1.51402369, -0.07894188],\n",
       "       [-1.50426607,  1.08117685, -0.90705771],\n",
       "       ...,\n",
       "       [ 0.85905473,  0.70063454,  0.74917395],\n",
       "       [ 0.07128113, -1.38009893,  0.74917395],\n",
       "       [ 1.28874942, -0.44589206, -0.07894188]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_numerical = sc.transform(x_train_numerical)\n",
    "x_train_numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f701ffee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 1],\n",
       "       [0, 1],\n",
       "       ...,\n",
       "       [1, 0],\n",
       "       [1, 0],\n",
       "       [0, 1]], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data = x_train[['smoker_code','sex_code']].values\n",
    "categorical_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b1f463e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00228629, -0.66474472, -0.90705771,  0.        ,  0.        ],\n",
       "       [ 1.00228629, -1.51402369, -0.07894188,  0.        ,  1.        ],\n",
       "       [-1.50426607,  1.08117685, -0.90705771,  0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 0.85905473,  0.70063454,  0.74917395,  1.        ,  0.        ],\n",
       "       [ 0.07128113, -1.38009893,  0.74917395,  1.        ,  0.        ],\n",
       "       [ 1.28874942, -0.44589206, -0.07894188,  0.        ,  1.        ]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_final = np.concatenate((x_train_numerical,categorical_data),axis=1)\n",
    "x_train_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07a79070",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8c5ff394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.43265029,  0.79372858, -0.90705771,  0.        ,  1.        ],\n",
       "       [ 1.28874942,  0.1420703 , -0.90705771,  0.        ,  1.        ],\n",
       "       [ 0.85905473,  1.03381321, -0.90705771,  0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 0.5725916 ,  1.35065959,  0.74917395,  1.        ,  1.        ],\n",
       "       [-1.43265029,  0.96440099, -0.90705771,  0.        ,  0.        ],\n",
       "       [-0.28679778, -0.74232309, -0.90705771,  0.        ,  0.        ]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_numerical = x_test[['age','bmi','children']].values\n",
    "x_test_numerical = sc.transform(x_test_numerical)\n",
    "categorical_data_x_test = x_test[['smoker_code','sex_code']].values\n",
    "x_test_final = np.concatenate((x_test_numerical,categorical_data_x_test),axis=1)\n",
    "x_test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aee1ab21",
   "metadata": {},
   "source": [
    "# 4.  MODELS TRAINING AND TESTING "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8e905801",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install tensorflow --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "60c05567",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sc.transform([[33,22.705,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "21aed9c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred = model.predict([[-0.4419476 , -1.30553108, -0.90861367,0,1]])\n",
    "# print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "242c4c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "85af598f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "        keras.layers.Dense(128, activation = \"relu\",input_shape=(5,),use_bias=True),\n",
    "        keras.layers.Dense(64 , activation='relu'),\n",
    "        keras.layers.Dense(32 , activation='relu'),\n",
    "        keras.layers.Dense(1)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "25785778",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 128)               768       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 11,137\n",
      "Trainable params: 11,137\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8a4605b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import Adam\n",
    "adam = Adam(0.001)\n",
    "model.compile(optimizer=adam,loss=\"mean_squared_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "16df9bbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "34/34 [==============================] - 2s 9ms/step - loss: 320844384.0000 - val_loss: 329550720.0000\n",
      "Epoch 2/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 320441152.0000 - val_loss: 328560320.0000\n",
      "Epoch 3/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 317931712.0000 - val_loss: 323311168.0000\n",
      "Epoch 4/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 308458528.0000 - val_loss: 306111456.0000\n",
      "Epoch 5/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 281538816.0000 - val_loss: 265840480.0000\n",
      "Epoch 6/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 230212768.0000 - val_loss: 198991440.0000\n",
      "Epoch 7/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 159999840.0000 - val_loss: 128474064.0000\n",
      "Epoch 8/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 108461352.0000 - val_loss: 92655528.0000\n",
      "Epoch 9/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 90352944.0000 - val_loss: 83678360.0000\n",
      "Epoch 10/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 83824160.0000 - val_loss: 77741768.0000\n",
      "Epoch 11/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 78157880.0000 - val_loss: 72579504.0000\n",
      "Epoch 12/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 72817128.0000 - val_loss: 67979592.0000\n",
      "Epoch 13/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 67907592.0000 - val_loss: 63412988.0000\n",
      "Epoch 14/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 63335560.0000 - val_loss: 58829036.0000\n",
      "Epoch 15/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 58993416.0000 - val_loss: 54771528.0000\n",
      "Epoch 16/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 54865788.0000 - val_loss: 51181060.0000\n",
      "Epoch 17/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 51276160.0000 - val_loss: 47650180.0000\n",
      "Epoch 18/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 47934028.0000 - val_loss: 44685432.0000\n",
      "Epoch 19/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 45011856.0000 - val_loss: 41883436.0000\n",
      "Epoch 20/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 42651976.0000 - val_loss: 39743048.0000\n",
      "Epoch 21/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 40569812.0000 - val_loss: 37994588.0000\n",
      "Epoch 22/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 39051764.0000 - val_loss: 36689864.0000\n",
      "Epoch 23/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 37843996.0000 - val_loss: 35672404.0000\n",
      "Epoch 24/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 36941240.0000 - val_loss: 35012980.0000\n",
      "Epoch 25/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 36293408.0000 - val_loss: 34339420.0000\n",
      "Epoch 26/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 35760436.0000 - val_loss: 34108456.0000\n",
      "Epoch 27/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 35440360.0000 - val_loss: 33493374.0000\n",
      "Epoch 28/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 35112648.0000 - val_loss: 33229770.0000\n",
      "Epoch 29/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 34856572.0000 - val_loss: 33029830.0000\n",
      "Epoch 30/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 34683040.0000 - val_loss: 33032478.0000\n",
      "Epoch 31/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 34413940.0000 - val_loss: 32569520.0000\n",
      "Epoch 32/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 34212916.0000 - val_loss: 32366470.0000\n",
      "Epoch 33/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 34112480.0000 - val_loss: 32401798.0000\n",
      "Epoch 34/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 33940384.0000 - val_loss: 32179938.0000\n",
      "Epoch 35/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 33706736.0000 - val_loss: 31948522.0000\n",
      "Epoch 36/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 33557712.0000 - val_loss: 31811974.0000\n",
      "Epoch 37/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 33470624.0000 - val_loss: 31705154.0000\n",
      "Epoch 38/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 33213954.0000 - val_loss: 31523366.0000\n",
      "Epoch 39/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 33053530.0000 - val_loss: 31336664.0000\n",
      "Epoch 40/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 32945970.0000 - val_loss: 31213052.0000\n",
      "Epoch 41/300\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 32805066.0000 - val_loss: 30986276.0000\n",
      "Epoch 42/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 32631914.0000 - val_loss: 30915992.0000\n",
      "Epoch 43/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 32451992.0000 - val_loss: 30675980.0000\n",
      "Epoch 44/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 32274990.0000 - val_loss: 30501882.0000\n",
      "Epoch 45/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 32203108.0000 - val_loss: 30318336.0000\n",
      "Epoch 46/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 31960574.0000 - val_loss: 30354308.0000\n",
      "Epoch 47/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 31835516.0000 - val_loss: 30077980.0000\n",
      "Epoch 48/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 31640952.0000 - val_loss: 29952004.0000\n",
      "Epoch 49/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 31503768.0000 - val_loss: 29837040.0000\n",
      "Epoch 50/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 31325266.0000 - val_loss: 29681652.0000\n",
      "Epoch 51/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 31166386.0000 - val_loss: 29492050.0000\n",
      "Epoch 52/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 31044482.0000 - val_loss: 29341842.0000\n",
      "Epoch 53/300\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 30870728.0000 - val_loss: 29075000.0000\n",
      "Epoch 54/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 30685956.0000 - val_loss: 28987410.0000\n",
      "Epoch 55/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 30614964.0000 - val_loss: 28924426.0000\n",
      "Epoch 56/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 30509956.0000 - val_loss: 28599008.0000\n",
      "Epoch 57/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 30249912.0000 - val_loss: 28543894.0000\n",
      "Epoch 58/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 30086406.0000 - val_loss: 28458442.0000\n",
      "Epoch 59/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 29946100.0000 - val_loss: 28215604.0000\n",
      "Epoch 60/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 29827256.0000 - val_loss: 28048794.0000\n",
      "Epoch 61/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 29671218.0000 - val_loss: 27996830.0000\n",
      "Epoch 62/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 29489548.0000 - val_loss: 27838428.0000\n",
      "Epoch 63/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 29345128.0000 - val_loss: 27608630.0000\n",
      "Epoch 64/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 29201906.0000 - val_loss: 27589806.0000\n",
      "Epoch 65/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 29048758.0000 - val_loss: 27267752.0000\n",
      "Epoch 66/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 28836174.0000 - val_loss: 27180600.0000\n",
      "Epoch 67/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 28732908.0000 - val_loss: 27030340.0000\n",
      "Epoch 68/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 28699622.0000 - val_loss: 26945496.0000\n",
      "Epoch 69/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 28480218.0000 - val_loss: 26703702.0000\n",
      "Epoch 70/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 28305296.0000 - val_loss: 26498844.0000\n",
      "Epoch 71/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 28257800.0000 - val_loss: 26302538.0000\n",
      "Epoch 72/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 27977526.0000 - val_loss: 26138104.0000\n",
      "Epoch 73/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 27757612.0000 - val_loss: 26044108.0000\n",
      "Epoch 74/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 27573440.0000 - val_loss: 25806458.0000\n",
      "Epoch 75/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 27429512.0000 - val_loss: 25623740.0000\n",
      "Epoch 76/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 27267576.0000 - val_loss: 25495778.0000\n",
      "Epoch 77/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 27120274.0000 - val_loss: 25309450.0000\n",
      "Epoch 78/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 26967002.0000 - val_loss: 25175140.0000\n",
      "Epoch 79/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 26765488.0000 - val_loss: 24948146.0000\n",
      "Epoch 80/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 26638300.0000 - val_loss: 24774870.0000\n",
      "Epoch 81/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 26510630.0000 - val_loss: 24580546.0000\n",
      "Epoch 82/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 26368590.0000 - val_loss: 24468636.0000\n",
      "Epoch 83/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 26173658.0000 - val_loss: 24295670.0000\n",
      "Epoch 84/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 26026140.0000 - val_loss: 24238822.0000\n",
      "Epoch 85/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 25821142.0000 - val_loss: 23912962.0000\n",
      "Epoch 86/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 25693544.0000 - val_loss: 23725598.0000\n",
      "Epoch 87/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 25615308.0000 - val_loss: 23839708.0000\n",
      "Epoch 88/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 25475988.0000 - val_loss: 23514064.0000\n",
      "Epoch 89/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 25279834.0000 - val_loss: 23270122.0000\n",
      "Epoch 90/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 25056412.0000 - val_loss: 23150238.0000\n",
      "Epoch 91/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 24894778.0000 - val_loss: 22932202.0000\n",
      "Epoch 92/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 24807764.0000 - val_loss: 22872312.0000\n",
      "Epoch 93/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 24644056.0000 - val_loss: 22750600.0000\n",
      "Epoch 94/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 24550876.0000 - val_loss: 22535662.0000\n",
      "Epoch 95/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 24441098.0000 - val_loss: 22403414.0000\n",
      "Epoch 96/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 24317300.0000 - val_loss: 22181798.0000\n",
      "Epoch 97/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 24165570.0000 - val_loss: 22118194.0000\n",
      "Epoch 98/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 24087680.0000 - val_loss: 22010418.0000\n",
      "Epoch 99/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 23901686.0000 - val_loss: 21999780.0000\n",
      "Epoch 100/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 23780822.0000 - val_loss: 21777320.0000\n",
      "Epoch 101/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 23805692.0000 - val_loss: 21707894.0000\n",
      "Epoch 102/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 23689240.0000 - val_loss: 21555470.0000\n",
      "Epoch 103/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 23634834.0000 - val_loss: 21516100.0000\n",
      "Epoch 104/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 23467430.0000 - val_loss: 21412132.0000\n",
      "Epoch 105/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 23339230.0000 - val_loss: 21301838.0000\n",
      "Epoch 106/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 23321670.0000 - val_loss: 21278430.0000\n",
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      "Epoch 267/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20499694.0000 - val_loss: 18706132.0000\n",
      "Epoch 268/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20484004.0000 - val_loss: 18746590.0000\n",
      "Epoch 269/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20464630.0000 - val_loss: 18969228.0000\n",
      "Epoch 270/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20450110.0000 - val_loss: 18732670.0000\n",
      "Epoch 271/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20478562.0000 - val_loss: 18757030.0000\n",
      "Epoch 272/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20415970.0000 - val_loss: 18701442.0000\n",
      "Epoch 273/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20429716.0000 - val_loss: 18649800.0000\n",
      "Epoch 274/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20476238.0000 - val_loss: 18826536.0000\n",
      "Epoch 275/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20453764.0000 - val_loss: 18763552.0000\n",
      "Epoch 276/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20458292.0000 - val_loss: 18939580.0000\n",
      "Epoch 277/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20408234.0000 - val_loss: 18647846.0000\n",
      "Epoch 278/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20482382.0000 - val_loss: 18638768.0000\n",
      "Epoch 279/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20423942.0000 - val_loss: 18717546.0000\n",
      "Epoch 280/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20403404.0000 - val_loss: 18655232.0000\n",
      "Epoch 281/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20392364.0000 - val_loss: 18817282.0000\n",
      "Epoch 282/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20379418.0000 - val_loss: 18703560.0000\n",
      "Epoch 283/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20375322.0000 - val_loss: 18678176.0000\n",
      "Epoch 284/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20403246.0000 - val_loss: 18676610.0000\n",
      "Epoch 285/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20451358.0000 - val_loss: 18795270.0000\n",
      "Epoch 286/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20402892.0000 - val_loss: 18745324.0000\n",
      "Epoch 287/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20503348.0000 - val_loss: 18704680.0000\n",
      "Epoch 288/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20398396.0000 - val_loss: 18617458.0000\n",
      "Epoch 289/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20395988.0000 - val_loss: 18764352.0000\n",
      "Epoch 290/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20490808.0000 - val_loss: 18989456.0000\n",
      "Epoch 291/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20534804.0000 - val_loss: 18615130.0000\n",
      "Epoch 292/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20374638.0000 - val_loss: 18873426.0000\n",
      "Epoch 293/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20404798.0000 - val_loss: 18690894.0000\n",
      "Epoch 294/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20360876.0000 - val_loss: 18708430.0000\n",
      "Epoch 295/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20395332.0000 - val_loss: 18655960.0000\n",
      "Epoch 296/300\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 20376584.0000 - val_loss: 18814562.0000\n",
      "Epoch 297/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20343370.0000 - val_loss: 18674138.0000\n",
      "Epoch 298/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20359914.0000 - val_loss: 18586388.0000\n",
      "Epoch 299/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20451254.0000 - val_loss: 18675666.0000\n",
      "Epoch 300/300\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 20322950.0000 - val_loss: 18600134.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2550a6afca0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_final , y_train , epochs=300, validation_data=(x_test_final,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571af9e9",
   "metadata": {},
   "source": [
    "## SOME CONCLUSION FROM THE ABOVE MODEL\n",
    "\n",
    "* On using optimizes other than Adam the loss was ranging from -inf to +inf \n",
    "* This is because of **exploding gradient** \n",
    "* On using Adam optimizer it shows a finite loss\n",
    "* So tuning the hyperparameter is essential to minimized the loss.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "26167ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# medical_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1bbb1edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred = model.predict(sc.transform([[18,33.770,1,0,1]]))\n",
    "# pred\n",
    "# print(minMax.inverse_transform(pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3792af17",
   "metadata": {},
   "source": [
    "# 5.  FINE TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c4ed65ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install keras-tuner --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "346d0931",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Dense\n",
    "from keras_tuner.tuners import RandomSearch \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0479d4c",
   "metadata": {},
   "source": [
    "## Hyperparameters\n",
    "\n",
    "- How many number of hidden layers we should have?\n",
    "- How many number of neurons we should have in hidden layers?\n",
    "- Learning Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9bc9a60e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from keras import backend as K\n",
    "# def root_mean_squared_error(y_true, y_pred):\n",
    "#         return K.sqrt(K.mean(K.square(y_pred - y_true)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "01c62637",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(hp):\n",
    "    model = keras.Sequential()\n",
    "    for i in range(hp.Int('num_layers', 2, 20)):\n",
    "        model.add(layers.Dense(units=hp.Int('units_' + str(i),\n",
    "                                            min_value=32,\n",
    "                                            max_value=512,\n",
    "                                            step=32),\n",
    "                               activation='relu'))\n",
    "    model.add(layers.Dense(1, activation='linear'))\n",
    "    model.compile(\n",
    "        optimizer=keras.optimizers.Adam(\n",
    "            hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),\n",
    "        loss= hp.Choice('loss' , ['mean_absolute_error','mean_squared_error']),\n",
    "    metrics= hp.Choice('metrics' , ['mean_absolute_error','mean_squared_error']))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "36a9e298",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuner  = RandomSearch(\n",
    "    build_model,\n",
    "    objective=['val_mean_absolute_error','val_mean_squared_error'],\n",
    "    max_trials=5,\n",
    "    executions_per_trial=3,\n",
    "    directory='Hyperparameter_tuning',\n",
    "    project_name='Insurance estimator'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search space summary\n",
      "Default search space size: 6\n",
      "num_layers (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 20, 'step': 1, 'sampling': None}\n",
      "units_0 (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 512, 'step': 32, 'sampling': None}\n",
      "units_1 (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 512, 'step': 32, 'sampling': None}\n",
      "learning_rate (Choice)\n",
      "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}\n",
      "loss (Choice)\n",
      "{'default': 'mean_absolute_error', 'conditions': [], 'values': ['mean_absolute_error', 'mean_squared_error'], 'ordered': False}\n",
      "metrics (Choice)\n",
      "{'default': 'mean_absolute_error', 'conditions': [], 'values': ['mean_absolute_error', 'mean_squared_error'], 'ordered': False}\n"
     ]
    }
   ],
   "source": [
    "tuner.search_space_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc5291f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuner.search(x_train_final , y_train , epochs=500 , validation_data = (x_test_final , y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3c4ff47d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results summary\n",
      "Results in Hyperparameter_tuning\\Insurance estimator\n",
      "Showing 10 best trials\n",
      "<keras_tuner.engine.objective.MultiObjective object at 0x000002550A5290A0>\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 11\n",
      "units_0: 256\n",
      "units_1: 384\n",
      "learning_rate: 0.01\n",
      "loss: mean_absolute_error\n",
      "metrics: mean_absolute_error\n",
      "units_2: 480\n",
      "units_3: 480\n",
      "units_4: 320\n",
      "units_5: 352\n",
      "units_6: 192\n",
      "units_7: 320\n",
      "units_8: 480\n",
      "units_9: 384\n",
      "units_10: 320\n",
      "units_11: 160\n",
      "units_12: 192\n",
      "units_13: 320\n",
      "units_14: 96\n",
      "units_15: 192\n",
      "Score: 1411.4945068359375\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 11\n",
      "units_0: 96\n",
      "units_1: 160\n",
      "learning_rate: 0.0001\n",
      "loss: mean_absolute_error\n",
      "metrics: mean_absolute_error\n",
      "units_2: 32\n",
      "units_3: 32\n",
      "units_4: 32\n",
      "units_5: 32\n",
      "units_6: 32\n",
      "units_7: 32\n",
      "units_8: 32\n",
      "units_9: 32\n",
      "units_10: 32\n",
      "Score: 1566.4409586588542\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 17\n",
      "units_0: 352\n",
      "units_1: 224\n",
      "learning_rate: 0.01\n",
      "loss: mean_absolute_error\n",
      "metrics: mean_squared_error\n",
      "units_2: 96\n",
      "units_3: 64\n",
      "units_4: 64\n",
      "units_5: 352\n",
      "units_6: 160\n",
      "units_7: 320\n",
      "units_8: 384\n",
      "units_9: 416\n",
      "units_10: 352\n",
      "units_11: 256\n",
      "units_12: 192\n",
      "units_13: 384\n",
      "units_14: 160\n",
      "units_15: 96\n",
      "units_16: 32\n",
      "Score: 17874449.333333332\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "num_layers: 16\n",
      "units_0: 480\n",
      "units_1: 128\n",
      "learning_rate: 0.0001\n",
      "loss: mean_squared_error\n",
      "metrics: mean_squared_error\n",
      "units_2: 192\n",
      "units_3: 160\n",
      "units_4: 128\n",
      "units_5: 480\n",
      "units_6: 128\n",
      "units_7: 416\n",
      "units_8: 480\n",
      "units_9: 256\n",
      "units_10: 64\n",
      "units_11: 32\n",
      "units_12: 32\n",
      "units_13: 32\n",
      "units_14: 32\n",
      "units_15: 32\n",
      "Score: 18920461.333333332\n"
     ]
    }
   ],
   "source": [
    "tuner.results_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "        Results summary\n",
    "        Results in Hyperparameter_tuning\\Insurance estimator\n",
    "        Showing 10 best trials\n",
    "        <keras_tuner.engine.objective.MultiObjective object at 0x000002550A5290A0>\n",
    "        Trial summary\n",
    "        Hyperparameters:\n",
    "        num_layers: 11\n",
    "        units_0: 256\n",
    "        units_1: 384\n",
    "        learning_rate: 0.01\n",
    "        loss: mean_absolute_error\n",
    "        metrics: mean_absolute_error\n",
    "        units_2: 480\n",
    "        units_3: 480\n",
    "        units_4: 320\n",
    "        units_5: 352\n",
    "        units_6: 192\n",
    "        units_7: 320\n",
    "        units_8: 480\n",
    "        units_9: 384\n",
    "        units_10: 320\n",
    "        units_11: 160\n",
    "        units_12: 192\n",
    "        units_13: 320\n",
    "        units_14: 96\n",
    "        units_15: 192\n",
    "        Score: 1411.4945068359375 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1da87001",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "        keras.layers.Dense(256, activation = \"relu\",input_shape=(5,)),\n",
    "        keras.layers.Dense(384 , activation='relu'),\n",
    "        keras.layers.Dense(480 , activation='relu'),\n",
    "        keras.layers.Dense(480 , activation='relu'),\n",
    "        keras.layers.Dense(320 , activation='relu'),\n",
    "        keras.layers.Dense(352 , activation='relu'),\n",
    "        keras.layers.Dense(192 , activation='relu'),\n",
    "        keras.layers.Dense(320 , activation='relu'),\n",
    "        keras.layers.Dense(480 , activation='relu'),\n",
    "        keras.layers.Dense(384, activation='relu'),\n",
    "        keras.layers.Dense(320 , activation='relu'),\n",
    "        keras.layers.Dense(160 , activation='relu'),\n",
    "        keras.layers.Dense(1,activation='linear')\n",
    "    ])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9ae1f5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import Adam\n",
    "adam = Adam(0.01)\n",
    "model.compile(optimizer=adam,loss=\"mean_absolute_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b8a0a6b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "34/34 [==============================] - 2s 17ms/step - loss: 11781.7881 - val_loss: 8542.5146\n",
      "Epoch 2/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 5672.1709 - val_loss: 3296.1211\n",
      "Epoch 3/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 3143.6453 - val_loss: 7024.6157\n",
      "Epoch 4/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 4012.8350 - val_loss: 2862.9026\n",
      "Epoch 5/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 3189.1809 - val_loss: 3261.9141\n",
      "Epoch 6/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 3080.4961 - val_loss: 3274.3535\n",
      "Epoch 7/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 3532.8179 - val_loss: 3034.5784\n",
      "Epoch 8/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 3513.9780 - val_loss: 2695.9395\n",
      "Epoch 9/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2507.6655 - val_loss: 2022.4554\n",
      "Epoch 10/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2678.2505 - val_loss: 3323.1128\n",
      "Epoch 11/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 3206.9709 - val_loss: 2274.0188\n",
      "Epoch 12/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2760.7632 - val_loss: 3237.8545\n",
      "Epoch 13/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2624.4058 - val_loss: 2258.1702\n",
      "Epoch 14/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2526.0635 - val_loss: 2419.1506\n",
      "Epoch 15/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2383.5344 - val_loss: 2472.4949\n",
      "Epoch 16/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2953.2185 - val_loss: 1875.3696\n",
      "Epoch 17/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2216.6147 - val_loss: 3157.2866\n",
      "Epoch 18/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 3400.6318 - val_loss: 3500.9541\n",
      "Epoch 19/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 3544.4277 - val_loss: 2750.8701\n",
      "Epoch 20/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 3108.5481 - val_loss: 2445.9980\n",
      "Epoch 21/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2373.0991 - val_loss: 1926.0646\n",
      "Epoch 22/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2885.2805 - val_loss: 2778.3018\n",
      "Epoch 23/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 3219.0398 - val_loss: 3140.5183\n",
      "Epoch 24/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2463.8213 - val_loss: 2846.1875\n",
      "Epoch 25/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2458.9885 - val_loss: 1771.5387\n",
      "Epoch 26/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2305.9346 - val_loss: 2158.6731\n",
      "Epoch 27/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2289.8650 - val_loss: 1843.2412\n",
      "Epoch 28/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2604.7881 - val_loss: 1893.2404\n",
      "Epoch 29/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2382.1689 - val_loss: 2057.9338\n",
      "Epoch 30/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 2423.5974 - val_loss: 1825.3232\n",
      "Epoch 31/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 2242.0435 - val_loss: 1610.4658\n",
      "Epoch 32/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 2066.7402 - val_loss: 1612.0530\n",
      "Epoch 33/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2330.2761 - val_loss: 2791.5649\n",
      "Epoch 34/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 2374.5632 - val_loss: 1954.0776\n",
      "Epoch 35/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2343.4863 - val_loss: 2714.7964\n",
      "Epoch 36/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2352.1077 - val_loss: 2586.8892\n",
      "Epoch 37/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2053.3562 - val_loss: 1541.9202\n",
      "Epoch 38/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2197.9756 - val_loss: 2188.3606\n",
      "Epoch 39/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2353.5449 - val_loss: 3461.7686\n",
      "Epoch 40/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2410.2432 - val_loss: 1713.5072\n",
      "Epoch 41/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 2205.0295 - val_loss: 2148.7839\n",
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      "34/34 [==============================] - 1s 23ms/step - loss: 1877.8832 - val_loss: 1485.9419\n",
      "Epoch 370/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1814.6321 - val_loss: 1799.5668\n",
      "Epoch 371/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1793.4393 - val_loss: 1598.4771\n",
      "Epoch 372/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1677.3818 - val_loss: 1552.2104\n",
      "Epoch 373/500\n",
      "34/34 [==============================] - 1s 25ms/step - loss: 1609.5626 - val_loss: 1528.6405\n",
      "Epoch 374/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1739.3862 - val_loss: 1755.4757\n",
      "Epoch 375/500\n",
      "34/34 [==============================] - 1s 27ms/step - loss: 1697.9291 - val_loss: 1763.9655\n",
      "Epoch 376/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1858.5953 - val_loss: 1906.9750\n",
      "Epoch 377/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1760.6942 - val_loss: 1601.1512\n",
      "Epoch 378/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1658.7908 - val_loss: 1457.3938\n",
      "Epoch 379/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 1638.4844 - val_loss: 1652.9844\n",
      "Epoch 380/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1752.3455 - val_loss: 1518.7267\n",
      "Epoch 381/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1683.3274 - val_loss: 2352.5457\n",
      "Epoch 382/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 2002.5345 - val_loss: 1581.3730\n",
      "Epoch 383/500\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 1757.1019 - val_loss: 1647.4595\n",
      "Epoch 384/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1641.5963 - val_loss: 1438.9382\n",
      "Epoch 385/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1615.9127 - val_loss: 1734.7296\n",
      "Epoch 386/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1775.0070 - val_loss: 1794.9597\n",
      "Epoch 387/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1634.6052 - val_loss: 1675.1842\n",
      "Epoch 388/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1615.4537 - val_loss: 1453.5013\n",
      "Epoch 389/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1619.2189 - val_loss: 1430.7024\n",
      "Epoch 390/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1593.1543 - val_loss: 1773.7867\n",
      "Epoch 391/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1746.3060 - val_loss: 1466.9927\n",
      "Epoch 392/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1604.0459 - val_loss: 1513.7400\n",
      "Epoch 393/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1807.4810 - val_loss: 1528.1725\n",
      "Epoch 394/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1731.6626 - val_loss: 1680.2112\n",
      "Epoch 395/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1659.6251 - val_loss: 1580.6549\n",
      "Epoch 396/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1604.5216 - val_loss: 1778.1252\n",
      "Epoch 397/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1916.6327 - val_loss: 1626.2468\n",
      "Epoch 398/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1817.0356 - val_loss: 2179.8708\n",
      "Epoch 399/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1825.5345 - val_loss: 2150.0688\n",
      "Epoch 400/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1911.1129 - val_loss: 2115.9690\n",
      "Epoch 401/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1798.7509 - val_loss: 1479.4777\n",
      "Epoch 402/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1662.8828 - val_loss: 1483.9019\n",
      "Epoch 403/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1580.8638 - val_loss: 1492.3188\n",
      "Epoch 404/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1631.2534 - val_loss: 1636.7062\n",
      "Epoch 405/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 1621.2428 - val_loss: 1570.4630\n",
      "Epoch 406/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1930.8744 - val_loss: 1843.6418\n",
      "Epoch 407/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1641.0210 - val_loss: 1550.1204\n",
      "Epoch 408/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1759.4718 - val_loss: 1443.8979\n",
      "Epoch 409/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1588.6198 - val_loss: 1460.0995\n",
      "Epoch 410/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1932.7383 - val_loss: 1736.0841\n",
      "Epoch 411/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1745.3553 - val_loss: 1480.8126\n",
      "Epoch 412/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1715.9568 - val_loss: 1619.3124\n",
      "Epoch 413/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1628.3091 - val_loss: 1443.0382\n",
      "Epoch 414/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1640.6699 - val_loss: 1619.6304\n",
      "Epoch 415/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1648.2096 - val_loss: 1583.6141\n",
      "Epoch 416/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1726.3165 - val_loss: 1519.5510\n",
      "Epoch 417/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1649.4717 - val_loss: 1972.5144\n",
      "Epoch 418/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1713.1182 - val_loss: 1504.8953\n",
      "Epoch 419/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1749.4407 - val_loss: 1849.1025\n",
      "Epoch 420/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1666.9246 - val_loss: 1502.3636\n",
      "Epoch 421/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1616.3737 - val_loss: 1939.3240\n",
      "Epoch 422/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1787.5819 - val_loss: 1483.2639\n",
      "Epoch 423/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1770.0280 - val_loss: 1711.3013\n",
      "Epoch 424/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1693.8698 - val_loss: 1588.1052\n",
      "Epoch 425/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1816.7356 - val_loss: 1506.2845\n",
      "Epoch 426/500\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 1688.0762 - val_loss: 1631.7587\n",
      "Epoch 427/500\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 1665.8524 - val_loss: 1803.5620\n",
      "Epoch 428/500\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 1920.4280 - val_loss: 1474.3206\n",
      "Epoch 429/500\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 1734.5162 - val_loss: 1754.6104\n",
      "Epoch 430/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1719.5238 - val_loss: 1498.0405\n",
      "Epoch 431/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1605.9529 - val_loss: 1607.5718\n",
      "Epoch 432/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1638.8063 - val_loss: 1582.1964\n",
      "Epoch 433/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1678.4080 - val_loss: 1728.9735\n",
      "Epoch 434/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1631.9878 - val_loss: 1582.7747\n",
      "Epoch 435/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1752.0553 - val_loss: 1556.5377\n",
      "Epoch 436/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1769.8784 - val_loss: 1866.2170\n",
      "Epoch 437/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1829.9092 - val_loss: 1450.5674\n",
      "Epoch 438/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 1668.7490 - val_loss: 1889.8993\n",
      "Epoch 439/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1706.4363 - val_loss: 1452.9695\n",
      "Epoch 440/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1636.1177 - val_loss: 1573.0883\n",
      "Epoch 441/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1642.8671 - val_loss: 1436.8633\n",
      "Epoch 442/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1593.7203 - val_loss: 1570.6549\n",
      "Epoch 443/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1702.6177 - val_loss: 1733.5834\n",
      "Epoch 444/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1638.3704 - val_loss: 1574.5247\n",
      "Epoch 445/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1618.2290 - val_loss: 1417.7439\n",
      "Epoch 446/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1588.4380 - val_loss: 1637.4286\n",
      "Epoch 447/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 1628.3358 - val_loss: 1450.7372\n",
      "Epoch 448/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1716.6168 - val_loss: 1476.5917\n",
      "Epoch 449/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1629.1744 - val_loss: 1541.0830\n",
      "Epoch 450/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1718.5668 - val_loss: 1457.1206\n",
      "Epoch 451/500\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 1734.2117 - val_loss: 1632.9884\n",
      "Epoch 452/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1608.2214 - val_loss: 1504.4357\n",
      "Epoch 453/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1637.9750 - val_loss: 1409.9136\n",
      "Epoch 454/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1593.1667 - val_loss: 1440.0494\n",
      "Epoch 455/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1615.3135 - val_loss: 1481.8210\n",
      "Epoch 456/500\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 1783.5117 - val_loss: 1710.3633\n",
      "Epoch 457/500\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 1670.1097 - val_loss: 1716.2683\n",
      "Epoch 458/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1913.2332 - val_loss: 1614.5913\n",
      "Epoch 459/500\n",
      "34/34 [==============================] - 1s 25ms/step - loss: 1639.9895 - val_loss: 1679.9415\n",
      "Epoch 460/500\n",
      "34/34 [==============================] - 1s 26ms/step - loss: 1632.9783 - val_loss: 1449.5597\n",
      "Epoch 461/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1682.9359 - val_loss: 1780.8394\n",
      "Epoch 462/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1790.5873 - val_loss: 1747.7058\n",
      "Epoch 463/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1874.0079 - val_loss: 1588.3887\n",
      "Epoch 464/500\n",
      "34/34 [==============================] - 1s 24ms/step - loss: 1691.6853 - val_loss: 1505.2330\n",
      "Epoch 465/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 1633.6100 - val_loss: 1440.5074\n",
      "Epoch 466/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1636.2461 - val_loss: 1490.8190\n",
      "Epoch 467/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1603.3157 - val_loss: 1489.1205\n",
      "Epoch 468/500\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 1677.1929 - val_loss: 1697.8433\n",
      "Epoch 469/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 1772.9469 - val_loss: 1612.5643\n",
      "Epoch 470/500\n",
      "34/34 [==============================] - 1s 26ms/step - loss: 1663.9540 - val_loss: 1834.9169\n",
      "Epoch 471/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 1646.3201 - val_loss: 1715.8820\n",
      "Epoch 472/500\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 1645.9696 - val_loss: 1512.1774\n",
      "Epoch 473/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 1609.8453 - val_loss: 1519.1886\n",
      "Epoch 474/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1598.1919 - val_loss: 1482.2206\n",
      "Epoch 475/500\n",
      "34/34 [==============================] - 1s 25ms/step - loss: 1683.6840 - val_loss: 1454.0214\n",
      "Epoch 476/500\n",
      "34/34 [==============================] - 1s 27ms/step - loss: 1597.5690 - val_loss: 1411.1809\n",
      "Epoch 477/500\n",
      "34/34 [==============================] - 1s 26ms/step - loss: 1591.3060 - val_loss: 1449.1482\n",
      "Epoch 478/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1640.9592 - val_loss: 1581.1011\n",
      "Epoch 479/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1664.1079 - val_loss: 1448.5698\n",
      "Epoch 480/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1645.7101 - val_loss: 1479.1855\n",
      "Epoch 481/500\n",
      "34/34 [==============================] - 1s 24ms/step - loss: 1554.5969 - val_loss: 1440.4025\n",
      "Epoch 482/500\n",
      "34/34 [==============================] - 1s 27ms/step - loss: 1816.9662 - val_loss: 1836.1515\n",
      "Epoch 483/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1875.9784 - val_loss: 2143.0649\n",
      "Epoch 484/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 1706.7167 - val_loss: 1535.9940\n",
      "Epoch 485/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 1593.9442 - val_loss: 1793.0690\n",
      "Epoch 486/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1633.9139 - val_loss: 1668.1523\n",
      "Epoch 487/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1667.6816 - val_loss: 1751.6033\n",
      "Epoch 488/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1695.4370 - val_loss: 1449.4015\n",
      "Epoch 489/500\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 1910.1578 - val_loss: 1693.8448\n",
      "Epoch 490/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1798.5159 - val_loss: 1551.0930\n",
      "Epoch 491/500\n",
      "34/34 [==============================] - 1s 24ms/step - loss: 1651.3353 - val_loss: 1949.4813\n",
      "Epoch 492/500\n",
      "34/34 [==============================] - 1s 24ms/step - loss: 1694.3571 - val_loss: 1848.3967\n",
      "Epoch 493/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1735.3578 - val_loss: 1652.6042\n",
      "Epoch 494/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1667.6986 - val_loss: 1884.6489\n",
      "Epoch 495/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1821.4155 - val_loss: 1502.1281\n",
      "Epoch 496/500\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 1764.7665 - val_loss: 1470.7670\n",
      "Epoch 497/500\n",
      "34/34 [==============================] - 1s 24ms/step - loss: 1639.7068 - val_loss: 1496.3530\n",
      "Epoch 498/500\n",
      "34/34 [==============================] - 1s 25ms/step - loss: 1685.7076 - val_loss: 1527.9674\n",
      "Epoch 499/500\n",
      "34/34 [==============================] - 1s 24ms/step - loss: 1677.9607 - val_loss: 1552.6602\n",
      "Epoch 500/500\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 1553.5503 - val_loss: 1578.9800\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x255131cb040>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_final , y_train , epochs=500, validation_data=(x_test_final,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5c3dcae7",
   "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>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker_code</th>\n",
       "      <th>sex_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>19</td>\n",
       "      <td>35.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>57</td>\n",
       "      <td>31.54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>51</td>\n",
       "      <td>37.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>49</td>\n",
       "      <td>36.63</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>21</td>\n",
       "      <td>22.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age    bmi  children  smoker_code  sex_code\n",
       "559    19  35.53         0            0         1\n",
       "1087   57  31.54         0            0         1\n",
       "1020   51  37.00         0            0         1\n",
       "460    49  36.63         3            0         0\n",
       "802    21  22.30         1            0         1"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "751f694f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of  268 data points  24 has difference of above 2000 .\n",
      "91.04477611940298 %\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict([[x_test_final]])\n",
    "count = 0\n",
    "limit = 2000\n",
    "total_test_size = len(x_test_final)\n",
    "for i in range(total_test_size):\n",
    "    if(abs(pred[i][0]-y_test.values[i]) >= limit):\n",
    "        count = count + 1\n",
    "\n",
    "print(\"Out of \",total_test_size , \"data points \" ,count , \"has difference of above\",limit,\".\")\n",
    "print(((total_test_size-count)*100)/total_test_size , \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62763d45",
   "metadata": {},
   "source": [
    "# 6. SAVING THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3484259d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"Model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55417ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install tensorflowjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "eabdf332",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflowjs as tfjs\n",
    "tfjs.converters.save_keras_model(model, \"Js_Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805352e4",
   "metadata": {},
   "source": [
    "# 7.  CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d46e82cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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