1_Problem_description_and_EDA.ipynb

{
 "cells": [
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    "# Ceasarian section classification\n",
    "<img src=\"https://www.todaysparent.com/wp-content/uploads/2017/06/Forceps-deliveries-1024x576-1496764741.jpg\" title=\"Womb of a pregnant woman\" />\n",
    "PHOTO: ISTOCKPHOTO\n",
    "## 1. Aim and objectives\n",
    "In order to help a pregnant woman deliver her baby, ceasarian section (C-section) might be needed. We'd like to assess whether the need for C-section can be predicted, based on 5 main health features of the woman: Age, delivery number, delivery time, blood pressure and heart status. This could help assist medical staff take operational decision, and also provide pregnant women with more information about likelihood of need for C-section.\n",
    "Our objectives is to:\n",
    "1. Understand the data available\n",
    "2. Test rapidly different classification models\n",
    "3. Assess the best model(s) and improve them\n",
    "4. Present the results and what could be further addressed.\n",
    "\n",
    "The source of data is the __[UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Caesarian+Section+Classification+Dataset)__. There are 80 instances, which have 5 attributes: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Attribute | Value | Meaning |\n",
    "| --- | --- |\n",
    "| Age | { 22,26,28,27,32,36,33,23,20,29,25,37,24,18,30,40,31,19,21,35,17,38 } | No of years |\n",
    "| Delivery number | { 1,2,3,4 } | No of prior deliveries |\n",
    "| Delivery time | { 0,1,2 } | {0 = timely , 1 = premature , 2 = latecomer} |\n",
    "| Blood of Pressure | { 2,1,0 } | {0 = low , 1 = normal , 2 = high } |\n",
    "| Heart Problem | { 1,0 } | {0 = apt, 1 = inept } |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the information on the occurence or not of a C-section with the dependent variable Caesarian taking the values of 0 for no C-section and 1 for the occurence of C-section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Exploratory data analysis\n",
    "## Load the dataset\n",
    "We use the pandas library to load the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
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   "source": [
    "import pandas as pd\n",
    "path = \" \" #insert the path to the data\n",
    "filename = \"caesarian.csv\"\n",
    "df = pd.read_csv(path+filename, header = None, skiprows = 17,\n",
    "                 names = ['Age','Delivery number','Delivery time','Blood pressure', 'Heart problem', 'Caesarian'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Delivery number</th>\n",
       "      <th>Delivery time</th>\n",
       "      <th>Blood pressure</th>\n",
       "      <th>Heart problem</th>\n",
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      "text/plain": [
       "   Age  Delivery number  Delivery time  Blood pressure  Heart problem  \\\n",
       "0   22                1              0               2              0   \n",
       "1   26                2              0               1              0   \n",
       "2   26                2              1               1              0   \n",
       "3   28                1              0               2              0   \n",
       "4   22                2              0               1              0   \n",
       "\n",
       "   Caesarian  \n",
       "0          0  \n",
       "1          1  \n",
       "2          0  \n",
       "3          0  \n",
       "4          1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
   ]
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   "execution_count": 3,
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       "80"
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     "execution_count": 3,
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    "len(df)"
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   "cell_type": "markdown",
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   "source": [
    "## Duplicates\n",
    "We look for duplicates to check if there are cases when all the input variables (conditions and history of the pregnant woman) are the same and the output variable is different (occurence of C-section)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['is_duplicated'] = df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    76\n",
       "True      4\n",
       "Name: is_duplicated, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['is_duplicated'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4 duplicates. Let's have a look at them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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      "text/plain": [
       "    Age  Delivery number  Delivery time  Blood pressure  Heart problem  \\\n",
       "47   32                2              0               2              1   \n",
       "61   19                1              0               1              0   \n",
       "71   29                1              2               0              1   \n",
       "72   28                2              0               1              0   \n",
       "\n",
       "    Caesarian  is_duplicated  \n",
       "47          1           True  \n",
       "61          1           True  \n",
       "71          1           True  \n",
       "72          0           True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    "df[df['is_duplicated']==1].head()"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we check if there are duplicates when we look at the dataset without the outcome ('Caesarian' columnn)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    72\n",
       "True      8\n",
       "Name: duplicated_condition, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duplicated_condition'] = df.drop(['Caesarian', 'is_duplicated'], axis = 1).duplicated()\n",
    "df['duplicated_condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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       "    Age  Delivery number  Delivery time  Blood pressure  Heart problem  \\\n",
       "47   32                2              0               2              1   \n",
       "48   26                2              2               1              0   \n",
       "61   19                1              0               1              0   \n",
       "67   29                2              0               1              1   \n",
       "68   25                1              0               0              0   \n",
       "\n",
       "    Caesarian  is_duplicated  duplicated_condition  \n",
       "47          1           True                  True  \n",
       "48          0          False                  True  \n",
       "61          1           True                  True  \n",
       "67          0          False                  True  \n",
       "68          1          False                  True  "
      ]
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     "execution_count": 8,
     "metadata": {},
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    "df[df['duplicated_condition']==1].head()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also 4 duplicates. These duplicates are the same as the ones when we look at the whole dataset, meaning that cases where two pregnant women had the same features (health conditions and delivery history), the occurence or not of C-section was the same.\n",
    "## Visualising health conditions and delivery history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at the main statistics of the quantitative attributes: Age and number of prior deliveries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "      <th>count</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>27.687500</td>\n",
       "      <td>1.662500</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
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       "      <th>std</th>\n",
       "      <td>5.017927</td>\n",
       "      <td>0.794662</td>\n",
       "      <td>0.497462</td>\n",
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       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "             Age  Delivery number  Caesarian\n",
       "count  80.000000        80.000000  80.000000\n",
       "mean   27.687500         1.662500   0.575000\n",
       "std     5.017927         0.794662   0.497462\n",
       "min    17.000000         1.000000   0.000000\n",
       "25%    25.000000         1.000000   0.000000\n",
       "50%    27.000000         1.000000   1.000000\n",
       "75%    32.000000         2.000000   1.000000\n",
       "max    40.000000         4.000000   1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age','Delivery number','Caesarian']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ages range from 17 to 40 years old, with an average of 27.7 years old. All women had at least one prior delivery, the mean being 1.66 prior deliveries and the max being 4 prior deliveries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.575\n",
       "0    0.425\n",
       "Name: Caesarian, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Breakdown of C-section occurences or not\n",
    "df['Caesarian'].value_counts()/80"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we plot the distribution of all the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = df.hist(bins=15,\n",
    "                 color='steelblue',\n",
    "                 #edgecolor='black', linewidth=1.0,\n",
    "                 xlabelsize=10, ylabelsize=10,\n",
    "                 #xrot=45, yrot=0,\n",
    "                 figsize=(10,9),\n",
    "                 grid=False)\n",
    "\n",
    "plt.tight_layout(rect=(0, 0, 1.5, 1.5)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our main comments on the dataset are:\n",
    "* Almost 50% of pregnant women have normal blood pressure. The numbers of women with low and high blood pressure are in the same order of magnitude.\n",
    "* 50% of women had exactly one prior delivery.\n",
    "* More than 50% of pregnant women have timely delivery time. The numbers of women with premature and latecomer delivery times are in the same order of magnitude.\n",
    "* The majority of women has no heart problem.\n",
    "* The majority of women had a Caesarian section (57.5%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age and number of prior deliveries\n",
    "We check the relationship between age and number of prior deliveries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1a1e2d0320>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Age',y='Delivery number',data=df,kind='hex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the younger women are, the lower is the number of prior deliveries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age vs occurence of C-section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "title = fig.suptitle(\"C-section according to age and prior number of deliveries\", fontsize=14)\n",
    "sns.set_style('whitegrid')\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Delivery number\") \n",
    "\n",
    "g = sns.FacetGrid(data=df, \n",
    "                  hue='Caesarian', \n",
    "                  palette={1: \"orange\", 0: \"blue\"})\n",
    "\n",
    "g = g.map(sns.distplot,'Age',bins=20,ax=ax)\n",
    "ax.legend(title='Caesarian')\n",
    "plt.close(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are numerous cases of women aged around 25 years old who didn't have C-section, while there are numerous cases of women aged 32 years old who had a C-section. \n",
    "Women over 36 years old all had C-section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of prior delivery vs occurence of C-section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 7))\n",
    "\n",
    "cp = sns.countplot(data=df, \n",
    "                   x=\"Delivery number\", \n",
    "                   hue=\"Caesarian\", \n",
    "                   palette={1: \"orange\", 0: \"blue\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blood pressure vs occurence of C-section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 7))\n",
    "\n",
    "cp = sns.countplot(data=df, \n",
    "                   x=\"Blood pressure\", \n",
    "                   hue=\"Caesarian\", \n",
    "                   palette={1: \"orange\", 0: \"blue\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The occurence of C-section is greater when pregnant women have low blood pressure (0) or high blood pressure (2) in this sample. These two conditions seem to impact the need for C-section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heart issue vs occurence of C-section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 7))\n",
    "\n",
    "cp = sns.countplot(data=df, \n",
    "                   x=\"Heart problem\", \n",
    "                   hue=\"Caesarian\", \n",
    "                   palette={1: \"orange\", 0: \"blue\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The occurence of C-section is far greater when pregnant women have a heart problem, which seems reasonable.\n",
    "## Delivery time vs occurence of C-section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 7))\n",
    "\n",
    "cp = sns.countplot(data=df, \n",
    "                   x=\"Delivery time\", \n",
    "                   hue=\"Caesarian\", \n",
    "                   palette={1: \"orange\", 0: \"blue\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Occurences of C-section is higher when delivery time is timely (0), while it's almost balanced when it's premature (1) or latecomer (2). I would have thought that latecomer time delivery would have had more C-section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blood pressure and delivery time\n",
    "In order to investigate a little more the delivery time, we use a grid to plot the following data:\n",
    "* by column: blood pressure (0 = low, 1 = normal, 2 = high)\n",
    "* by row: delivery time (0 = timely, 1 = premature , 2 = latecomer) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 706x648 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"Blood pressure\",  row=\"Delivery time\",hue='Caesarian')\n",
    "g = g.map(plt.scatter, \"Age\", \"Delivery number\").add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our main comments on the dataset are:\n",
    "* <b> Low blood pressure seems to be positively highly correlated with the need for C-section.</b>\n",
    "    * All the pregnant women with low blood pressure and timely delivery time had a C-section.\n",
    "    * Most pregnant women with low blood pressure and premature or latecomer delivery time had a C-section.    \n",
    "* Most pregnant women with high blood pressure and premature or timely delivery time had a C-section. The need of C-section is more balanced for pregnant women with high blood pressure and latecomer delivery time.\n",
    " * I'd have thought that pregnant women with latecomer delivery time and high blood pressure would have had the same proportion or even a higher number of C-sections than pregnant women with premature or timely delivery time and high blood pressure.\n",
    "* In case of normal blood pressure, the majority of C-section is observed for timely delivery. This might be linked to other health issues (eg heart problem) or the size of the dataset.\n",
    " * 9 out of 22 pregnant women with normal blood pressure and timely delivery time had a C-section\n",
    " * One out of 6 pregnant women with normal blood pressure and premature delivery time had a C-section.\n",
    " * One out of 4 pregnant women with normal blood pressure and latecomer delivery time had a C-section.\n",
    "\n",
    "\n",
    " \n",
    " \n",
    "## Overview of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sorted_df = df.sort_values(by = [\"Blood pressure\",\n",
    "                                 \"Heart problem\",\n",
    "                                 'Caesarian',\n",
    "                                #\"Age\", \n",
    "                                 \"Delivery time\",\n",
    "                                \"Delivery number\",])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a261f0e10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tp0/HmDFj4OLigpiYmEavzcnJQa9evZCcnKz0vuz1CRDSiD0PM/kOgbQwbXVBiMViBAcHo3fv3igvL8fEiRPh6OiInj171rtOJpNh48aNGDx4sEr3VXtXZI7j8NVXXyE5ORkGBgbw9vbGhx9+qN6nI4QQHdBWAjYzM1Pse2lsbAxra2sUFRW9lID37t2LUaNGqTwrTO1dkX///XcUFhYiKSkJBgYGKC4uVuNjEUKI7uhiEO7+/fvIzc1V7AhUp6ioCGfOnEFMTIz2EnBjuyIfPHgQmzZtgoHBs27kTp06NfdzEEKIbnEilS6LjY2tt8rXy8sLXl5eL11XUVEBPz8/LF++HMbGxvXei4iIQGBgIMRiscrhqb0rckFBAX744QekpKTA1NQUn376KXr06KHygwkhRNdUbQE3lnCfV1tbCz8/P7i5uWHkyJEvvf/rr7/C398fAFBaWorz58/D0NAQI0aMaPSeKiXgul2Rnz59Cl9fX9y8eRMSiQStWrXCsWPHcPr0aSxfvhwHDhxo8j4sbknE4txmQlghl6rWAlaG4ziEhobC2toas2bNavCa1NRUxX8HBwfjgw8+aDL5As2cBVG3K/KPP/4Ic3NzxW8BZ2dnhISENOdWhBCic5yKXRDKXLlyBfHx8bC1tcX48eMBPKuB/vDhQwCAt7e3WvdVmoBLSkpgaGgIExMTxa7IH330EUaMGIGLFy9i0qRJuHTpEnU/EEL0jrYG4QYMGIAbN26ofP3atWtVuk7tXZH/+c9/IjAwEDExMWjbti0iIiJUDo4QQloCJ9dOC1hX1N4V2cTEBFFRUToJihBCtEHPd6WnlXCaYnFgEaDBRcIGfW8Bq1wLQiaTwd3dHfPmzQMAZGZmYsKECRg/fjy8vb1x9+5dnQVJCCHqkMtEKh18UTkB79mzBzY2NorX4eHh2LhxI+Lj4+Hq6opt27bpJEBCCFEXJxepdPBFpQT86NEjpKWlYdKkSfXO1xVqLy8vV6yTJoQQfcFxIpUOvqjU0RcZGYmgoCBUVFQozkVERGDu3Llo1aoVjI2NcejQIZ0FSQgh6tD3guxKW8Dnzp2Dqakp+vTpU+98dHQ0oqKikJ6eDg8PD9qQkxCid+ScSKWDL0pbwFevXkVqairS09NRU1OD8vJyzJ07F7dv31ZUAxo7dizmzJmj82AJIaQ55DL93nNCaXQBAQFIT09HamoqvvjiCwwcOBBbt25FWVkZ8vPzAQAZGRn1BugIIUQfcJxqB1/UmuxpaGiIzz//HH5+fhCJROjQoQMiIyO1HRshhGhE3+cBNysBOzg4wMHBAcCzAjzOzs7NehiLk/tZ3eaG9k8TDvqzahyf/buqYC8jEkLI//A5xUwVlIAJIczS91oQKg0RPn36FH5+fhg9ejTGjBmD7OxsJCUlwcXFBXZ2dirvf0QIIS1JJjdQ6eCLSi3giIgIvP/++9iyZQskEgmqq6thYmKCr7/+GmFhYbqOkRBC1KLvLWClCbi8vByXL19WFBg2MjKCkZERTExMdB4cIYRoQvCDcAUFBTA1NUVISAjy8vLQu3dvhIaGom3bti0RHyGEqE3fB+GUdn5IpVJcv34d3t7eiIuLQ5s2bagQOyFEEAS/FNnCwgIWFhaKZcejR49WOwGzWLx8D98BEEIapeddwMpbwF26dIGFhQVu374N4Fkhdlp2TAgRAn2fBaHSk1esWIHAwEC4ubkhNzcX8+fPR0pKCoYMGYLs7GzMmzcPPj4+uo6VEEKaRa7iwReVpqH16tULx44dq3dOnaXIhBDSkjjo9yAcrYQjhDBLruedwJSACSHMkrPaAr59+zaWLl2qeF1QUAA/Pz/MnDlTG3ERQojGZKwmYGtra8THxwN4tmX9kCFDqE+YEKJX/hZ9wJmZmbCysoKlpWWT1721uKM2HqdfVvAdACGkMXq+J6d2EnBiYiJcXV21cStCCNEafU/AGs9AlkgkSE1NxejRo7URDyGEaA0HkUoHXzRuAaenp6N3797o3LmzNuIhhBCt0fMt4TRPwImJiXBxcdFGLIQQolXMzoIAgKqqKly4cAGrVq1S6fpXfBgcsVpxmu8ICCGN0Pc+YI0ScJs2bZCVlaWtWAghRKvkIoZbwIQQos/0fCVyyybgeQOWteTjWsSH3QbxHQIhpBHa7IIICQlBWloaOnXqhJMnTzZ4TVZWFiIjIyGVStGxY0fs27evyXsqTcA1NTX497//DYlEAplMhlGjRsHPz0/x/urVq3Hs2DFkZ2c38+MQQohuSbXYBeHh4YFp06bhk08+afD9p0+f4rPPPsPOnTvRrVs3FBcXK72n0gRsZGSEmJgYtGvXDrW1tZg6dSqGDBmCfv364ZdffsHTp0+b/0kIIaQFaLMLwt7eHvfv32/0/YSEBDg7O6Nbt24AgE6dOim9p9IELBKJ0K5dOwDP9oeTSqUQiUSQyWRYv349Nm3ahDNnzqj0AVjckohVWw1peIAIn6rzgGNjYxEbG6t47eXlBS8vr2Y9686dO5BKpZg+fToqKirw4Ycfwt3dvcmfUelfmUwmg4eHB+7du4epU6eib9++iImJwfDhw2FmZtasIAkhpKWo2gesTsJ9kUwmw2+//Ybo6GhUV1djypQp6Nu3L15//fVGf0alBCwWixEfH4+nT5/C19cXly9fRnJyMvbu3dusAFlsVe15mMl3CDrB4uAii58JYPfv4G4t3KMlZ0FYWFigY8eOaNu2Ldq2bYsBAwYgLy+vyQTcrFoQJiYmcHBwQFZWFu7du4eRI0fCyckJVVVVVIqSEKJ35CLVDm0YPnw4fv75Z0ilUlRVVSEnJ0fpBsZKm6QlJSUwNDSEiYkJqqurceHCBXz00UfIyMhQXNO/f3+kpKRo/gkIIUSLtDnq5O/vj0uXLqG0tBRDhgzB4sWLIf3fuJa3tzdsbGzw/vvvY9y4cTAwMMCkSZNga2vb5D2VJuDHjx8jODgYMpkMHMdh9OjRGDZsmFofgMVBuD18B0AIaRSnxYVwX3zxhdJr5syZgzlz5qh8T6UJ2M7ODnFxcU1eQ3OACSH6iOlaEIQQos8oAT+HtiQihLQkwdeCKCwsxLJly/Dnn3/CwMAAkydPxowZM5CXl4ewsDBUVlbC0tISGzduhLGxcUvETAghKtH3guxKp6GJxWIEBwcjKSkJsbGxOHDgAG7duoXQ0FAEBAQgISEBI0aMwM6dO1siXkIIUZlUxYMvSlvAZmZmitVuxsbGsLa2RlFREfLz82Fvbw8AcHR0hI+PD5YsWdLkva5/XaqFkPULq5P7CWGBvndBNGshxv3795Gbm4u+ffvC1tYWZ8+eBQAkJyejsLBQJwESQoi6WnIhhjpUHoSrqKiAn58fli9fDmNjY0RERCAiIgJbt26Fk5MTjIyMlN6DxUG4hQy26gE2l42Tvx8mZkHU1tbCz88Pbm5uGDlyJADAxsYGu3c/W62dn5+PtLQ0nQVJCCHqEHwXBMdxCA0NhbW1NWbNmqU4X1dsWC6XY9u2bZgyZYruoiSEEDVIwal08EVpC/jKlSuIj4+Hra0txo8fD+DZmug7d+7gwIEDAABnZ2dMnDhR6cMWbSvTMFz9s+fxZb5D0AkaXBQO+rNqnL63gJUm4AEDBuDGjRsvnR86dChmzJihk6AIIUQbmOgDJoQQIdL3hRgtmoCpGhohpCXJ9bwTQqUE7OTkhHbt2sHAwABisRjHjh1TvLdr1y6sX78emZmZMDU11VmghBDSXPqdfpvRAo6JiXkpwRYWFuLChQuKXUAJIUSf8DnDQRXNWgn3ojVr1iAoKAgikZ53tBBC/pY4FQ++qJyAfXx84OHhodi6+ezZszAzM4OdnZ3OgiOEEE3IVTz4olIXxMGDB2Fubo7i4mLMmjUL1tbW2L59u2IlHCGE6CN9H4RTqQVsbm4OAOjUqROcnZ1x6dIl3L9/H+PHj4eTkxMePXoEDw8P/PHHHzoNlhBCmkPfuyCUtoArKyshl8thbGyMyspKZGRkYOHChcjMzFRc4+TkhCNHjtAsCEKIXhH8Qozi4mL4+voCAGQyGVxdXTFkyBC1HsZiNTTakogQ/SXT8y4IpQnYysoKJ06caPKa1NRUrQVECCHaou99wLQUmRDCLP1Ov5SACSEM0/cWsEYLMdLT0zFq1Cg4OzsjKipKWzERQohWMDEPuCEymQyrVq3Cd999B3Nzc0yaNAlOTk7o2bNnoz/DYj1gVrFYOGkgo7WbWaWNVQb6Pgindgs4JycHr732GqysrGBkZAQXFxfFJp2EEKIPOBX/xxe1E3BRUREsLCwUr83NzVFUVKSVoAghRBuY7YLguJd/a1BRHkKIPpE3kKf0idoJ2MLCAo8ePVK8LioqgpmZmVaCIoQQbdDv9KtBF8Tbb7+NO3fuoKCgABKJBImJiXByctJmbIQQohE5OJUOvqjdAjY0NMTKlSsxZ84cyGQyTJw4EW+88YY2YxMEZnekZXAWBPn70fdZEBotxBg6dCiGDh2qrVgIIUSr9H0hBq2E0xCL82UJYYU2p5ilp6cjIiICcrkcnp6emDt3br33Hz58iE8++QRlZWWQyWQIDAxU2kClBEwIYZa2ppipsvBs27ZtGDNmDKZOnYpbt25h7ty5SguVqb0r8rp163Du3Dm88sor+Mc//oE1a9bAxMREs09JCCFa1NB0WXU8v/AMgGLh2fMJWCQSoby8HABQVlam0qwwtXdFdnR0REBAAAwNDbFhwwZ8++23CAoKavIe3yxor+rjBOP616V8h0AIaYS2+oAbWniWk5NT75pFixbBx8cH+/btQ1VVFb777jul91V7GtrgwYNhaPgsf/fr16/enGBCCNEHMnAqHbGxsfDw8FAcdZsP11Fl4VliYiImTJiA9PR0REVFYdmyZZDLm+4EUbkF7OPjA5FIBC8vL3h5edV77+jRoxgzZoyqtyKEkBahagu4obz2PFUWnh05cgQ7d+4EAPTv3x81NTUoLS1Fp06dGr2v2rsi29vbA3jW8SwWizFu3Dil92GxGtoeRitsXTSz5zsEoiJm56Jrgbb6gJ9feGZubo7ExERs2rSp3jVdu3ZFZmYmPDw88Pvvv6OmpkbpPpkqJeAXd0XOycmBvb09jh8/jrS0NERHR1MdCEKI3tHWLIjGFp5t3rwZffr0wfDhwxEcHIxPP/1UkQ/Xrl2rNC+qvStyeno6duzYgX379qFNmzZa+piEEKI92pwH3NDCs48//ljx3z179sT333/frHuqvSuys7MzJBIJZs2aBQDo27cvVq1a1eS9WJwFsYd2RSZEb8k4/d6YXu1dkVNSUnQSECGEaAstRX4Oi4NwzA6A0BJrwgA+d7tQhdIEfPv2bSxdulTxuqCgAH5+frh27Rry8/MBPFv10b59e8THx+suUkIIaSbBF2S3trZWJFaZTKbo/505c6bimrVr18LY2FhnQRJCiDr0O/02swsiMzMTVlZWsLS0VJzjOA5JSUmIiYlR+vMsVg7bakj1jAjRV0z1AScmJsLV1bXeuZ9//hmdOnVCjx49tBkXIYRoTN9nQahcC0IikSA1NRWjR4+ud/7kyZMvJWVCCNEHzGxJlJ6ejt69e6Nz586Kc1KpFCkpKTh27JhK92Dx6zqL3SqEsELwsyDqJCYmwsXFpd65CxcuwNraul6ZNkII0RfaqgWhKyol4KqqKly4cOGllW4//PDDS0mZEH3G6rztPQ8z+Q5Bq7T156Tvg3AirgV/RczuMamlHtViqAtCOFjsAgPYS751pJIHGt+jr8V7Kl33n0cXNH6WOtj8G0kIIWCoD5g0bCDVAyY8Y7VbRRsEvxKusLAQy5Ytw59//gkDAwNMnjwZM2bMQG5uLsLCwlBTUwOxWIzw8HC88847LREzIYSoRPAtYLFYjODgYPTu3Rvl5eWYOHEiHB0dsWHDBvj6+mLo0KE4f/48NmzYgL1797ZEzIQQohLBt4DNzMwUex8ZGxvD2toaRUVFEIlEqKioAKD6FsyEENKSBN8Cft79+/eRm5uLvn37Yvny5fDx8cG6desgl8ubXQmeEEJ0jZmlyBUVFfDz88Py5cthbGyMgwcPIiQkBOfPn0dISAhCQ0N1GSchhDSbnONUOviiUgKura2Fn5+1Loz9AAAZOUlEQVQf3NzcMHLkSADA8ePHFf89ZswY5OTk6C5KQghRA6fi//iiNAFzHIfQ0FBYW1sr9n8DnvUNX7p0CQBw8eJFqoZGCNE7HCdX6eCL0j7gK1euID4+Hra2thg/fjwAwN/fH6tXr0ZkZCSkUilatWqldENOgM1VY3v4DoAQ0ih9X4qsNAEPGDAAN27caPA9VaugEUIIH5goxkMIIUKk77MgKAETQpil7wsxVJ6GJpPJ4O7ujnnz5tU7v3r1avTv31/rgRFCiKYEPwuizp49e2BjY1Pv3C+//IKnT59qPShCCNEGjuNUOviiUhfEo0ePkJaWhvnz5yM6OhrAsxbx+vXrsWnTJpw5c0alh/W99oXageqtbu/zHQEhpBGCnwUBAJGRkQgKClLUfgCAffv2Yfjw4VQDghCit2Ry/R6EU9oFce7cOZiamqJPnz6Kc0VFRUhOTsa0adN0GhwhhGhC37sglG5JtGnTJsTHx8PQ0BA1NTUoLy+HkZERjIyM0KpVKwDAw4cPYWVlhZSUlCYfxuKWRKxicdEMq1sSsWr3nSMa36ODsY3yiwA8Kf9d42epQ+nfyICAAAQEBAAAsrKysHv3bnz77bf1runfv7/S5EsIIS2NFmI8h1pVhJCWpO/zgJuVPRwcHODg4PDS+ezsbK0FRAgh2sJUQXZCCBESfZ8F0aIJ+K3FHVvycS1i4delfIdACGmENlvA6enpiIiIgFwuh6enJ+bOnVvvfYlEgmXLluG3337Dq6++ii+//BLdu3dv8p4qr4QjhBCh0dY0NJlMhlWrVmHnzp1ITEzEyZMncevWrXrXHD58GCYmJkhJScHMmTOxceNGpfelBEwIYZa2EnBOTg5ee+01WFlZwcjICC4uLjh79my9a1JTUzFhwgQAwKhRo5CZman03i3aBdH2k+9a8nEtYsAnfEdAVLWb7wBIi6uVPFDputjYWMTGxipee3l5wcvLS/G6qKgIFhYWitfm5uYvbcNWVFSErl27AgAMDQ3Rvn17lJaWwtTUtNHn0iAcIeRv78WE+6KGWrIikajZ17yIuiAIIUQJCwsLPHr0SPG6qKjopTo4FhYWKCwsBABIpVKUlZXh1VdfbfK+lIAJIUSJt99+G3fu3EFBQQEkEgkSExPh5ORU7xonJyccP34cAHDq1CkMHDhQaQtYaS0IQgghwPnz5xEZGQmZTIaJEydiwYIF2Lx5M/r06YPhw4ejpqYGQUFByM3NRYcOHfDll1/CysqqyXtSAiaEEJ5QFwQhhPCEEjAhhPCEiQQsk8mwbt06vsMghJBmYWIesFgsxm+//QaO45SOOgpJVVUVdu/ejcLCQnz++ee4c+cO8vPzMWzYML5D09iDBw9w9+5dvPfee6iuroZUKoWxsTHfYWkkLy8PDx48gEwmU5wbOXIkjxERfcdEAgaAt956CwsWLMDo0aPRtm1bxXkh/wMICQlB7969ce3aNQDP5hl+/PHHgk/Ahw4dQmxsLJ48eYIzZ87g0aNHCAsLQ0xMDN+hqS0kJAQ3btzAG2+8AQOD//tiKeS/f8+TyWT4888/6/1y6datG48RsYGZBPzkyRN07NgRWVlZ9c4L+R/AvXv38NVXXyExMREA0Lp1a72v8K+K/fv34/Dhw5g8eTIAoEePHigpKeE5Ks385z//wQ8//MB3GDqxd+9efPPNN+jcuXO9Xy4JCQk8RsUGZhLwmjVr+A5B64yMjFBdXa3oVrl37x6MjIx4jkpzdXsK1pEysFNKv379cOvWLfTs2ZPvULRuz549SE5ORseO7JWT5RszCTg/Px/h4eEoLi7GyZMnkZeXh9TUVCxcuJDv0NS2ePFizJkzB4WFhQgICEB2djYTv2js7e2xfft2VFdXIyMjAwcOHHhpVZHQTJgwAV5eXujcuXO9Xy4stBItLCzQvn17vsNgEjMLMaZNm4Zly5Zh5cqViIuLAwC4urri5MmTPEemmdLSUvznP/8Bx3Ho27dvk5WVhEIul+PIkSP46aefAACDBw+Gp6enoAdQnZ2dERwcDFtb23pf0y0tLXmMSjuWL1+O/Px8fPDBB/V+ucyaNYvHqNjATAu4qqoK77zzTr1zYrGYp2i0p6ioCDKZDDKZDD///DMAYfdry2QyfPLJJ9i4caOiD5gFXbt2xfDhw/kOQye6deuGbt26oba2FrW1tXyHwxRmEnDHjh1x7949RSsqOTkZXbp04TkqzbA4si4Wi1FaWgqJRMJEf3Yda2trBAQEYNiwYfU+l5D/rOosWrSI7xCYxUwCDgsLw4oVK3D79m28//776N69u0pbgugzVkfWLS0t4e3tDScnp3pTBoX8lbampgZGRkbIyMiod56FBFxSUoIdO3bg1q1bqKmpUZzfs2cPj1GxgZkEbGVlhejoaFRWVkIulwt+Uj/A7si6mZkZzMzMwHEcKioq+A5HK1gYHG1MYGAgxowZg7S0NHz22Wc4fvw4E2MR+oCZQbjS0lL8v//3/3DlyhWIRCK8++678PX1FfTUmcuXL2P+/PlMjqyzhsVZOHU8PDxw7NgxuLm5Kf7uTZs2Dfv27eM5MuFjpgXs7++PAQMGYMuWLQCeJamlS5ciOjqa38A0sHz5cqxfv/6lkXWhmz59eoMzHoT8lXbFihWKWTgAYGdnh8DAQCYSsKHhszRhZmaGtLQ0mJmZ1dsdgqiPmQT85MkT+Pr6Kl4vXLgQZ86c4TEizbE6sv7JJ/+3k2lNTQ1Onz4t+BkrrM7CAYAFCxagrKwMn3zyCVavXo2KigqEhITwHRYTmEnADg4OSExMxJgxYwA8mwXxwQcf8BuUhlgdWe/Tp0+91//85z8xbdo0nqLRDhZn4dSpqz3Svn177N27l+do2CL4PuD+/ftDJBKB4zhUVVUpvqrL5XK0bdsWV69e5TlC9TXWyhD6gM9ff/2l+G+5XI7ffvsNn3/+OU6dOsVjVJopKCjAihUrkJ2dDRMTE3Tv3h0bNmxA9+7d+Q5NbTt27MBHH32E1atXN9hl9Omnn/IQFVsEn4CJ8Dg5OSl+aRoaGqJ79+5YuHAhBgwYwHdoGmNpFk5qamq9jSZfNGHChBaOiD1MJWBW6rFSy0M4vvvuuybfF/LcZuDZysWNGzfW67cn2sNMHzBLq8ZsbGwAvNxXyoqkpCS8//77MDY2xtatW3H9+nUsWLAAvXv35ju0ZmNlHnNj6jY7ILrBTAJmadVYXWWw1q1bKwYV6yQlJfERklZt3boVY8aMwc8//4yffvoJs2fPRnh4OA4fPsx3aM32d1im+9Zbb2H+/PlMbXagL5iZXFq3aowlUVFRKp0TmrrpWefPn4e3tzdGjBgh+CIvBQUFmD9/PgYOHIhBgwZhwYIFKCgo4DssrXh+s4Nz584pDqI5ZlrALNVjPX/+PNLT01FUVITPP/9ccb68vJyJuaXm5uZYuXIlLly4gI8++ggSiQRyuZzvsDQSEBCAqVOn4ptvvgEAJCYmwt/fX5Ct+hcJfdaNPmNmEI6leqx5eXnIzc3Fli1b4Ofnpzjfrl07ODg4oEOHDjxGp7mqqir8+OOPsLW1RY8ePfD48WPcvHkTgwcP5js0tXl6er6UbCdPnoxDhw7xFJH21NTU4MiRI/jvf/9brxgPJWbNMdMCZmnVmJ2dHezs7ODq6opXXnmF73C07o8//lAU987KysKNGzfg7u7Od1hqqZvT7ODggKioKIwdOxYikQg//PADhg4dynN02hEUFARra2v89NNP8PX1RUJCAqytrfkOiwnMtIDDw8NRVlbG3KoxFo0fPx5Hjx7FgwcP4OPjAycnJ+Tn52PHjh18h9Zsz89pfpFIJMLZs2d5iEq73N3dERcXpyjGU1tbCx8fH0HX7tAXzLSAWa7HyhoDAwMYGhri9OnTmDFjBqZPny7YFnBqairfIehcXTEeExMT3Lx5E507d8aDBw94jooNzCRgFvujbt68CVtbW77D0DpDQ0OcPHkS8fHx2LZtGwDh74xcW1uLgwcPKraN+te//gUvLy8mupC8vLzw5MkTLFmyBAsWLEBlZWW9sQmiPma6IFism+Dt7Y3a2lpMmDABbm5uMDEx4Tskrbh16xa+//579OvXD66urigoKEBSUhLmzp3Ld2hqCw0NhVQqVbTkT5w4AQMDA0RERPAcGdFnzCTg5wu51NTU4MyZMzAzMxP8st07d+7g6NGjSE5OxjvvvAMPDw84OjryHZbGqqur8fDhQ2YGc8aNG4cTJ04oPSdEMTExmDhxItq1a4dPP/0U169fR0BAgKBnregNjlEymYybPn0632FohVQq5ZKTk7nBgwdzo0eP5kaNGsWdOnWK77DUdvbsWW7kyJHcsGHDOI7juOvXr3Pz5s3jOSrNuLu7c3fv3lW8vnfvHufu7s5jRNrj5ubGcRzHpaenc/PmzeNyc3OZ+Wx8Y6YP+EV37txBYWEh32FoJC8vD8eOHcP58+fx3nvvYfv27ejduzeKioowZcoUwQ4wfvPNNzhy5AimT58OAOjVq5fgB3WWLVuGDz/8EFZWVuA4Dg8fPkRkZCTfYWkF978vyefPn8fEiRNhZ2fX4KwP0nzMJODn6wKLRCJ06dIFgYGBfIelkdWrV8PT0xP+/v5o3bq14ry5uTk+/vhjHiPTjFgsRvv27fkOQ2vkcjlatWqF06dP4/bt2wCeFdN/fjqkkPXp0wezZ8/G/fv3ERAQgPLycqa2yOITMwk4Ozub7xC0SiaTwcLCotHpWUKdtgUAb7zxBhISEiCTyXDnzh3s3bsX/fv35zsstRkYGGDdunWIjY2FnZ0d3+FoXUREBHJzc2FlZYU2bdqgtLSUmdY935j6NVZUVISrV6/i8uXLikOoxGIx/vrrL0gkEr5D0boVK1bg1q1bMDIyQkBAAIyNjREaGsp3WBpxdHTEqVOnmPxqLhKJcOvWLcXCi6qqKib/XvKBmVkQGzZsQFJSEmxsbOoVrNm+fTuPUWlm5cqV+O233+Dk5FSvDKCQi3yzWuC7f//+qKqqglgsRqtWrRRdYULeEqtOWFgYDAwMcPHiRSQlJeHJkyeYPXs2jh49yndogsdMF8SZM2eQnJzMTL8b8GwbcDMzM3Acx0zhb1YLfLPWBfa8nJwcHD9+XNHt1aFDB8GXD9UXzCRgKysr1NbWMpWA64p9V1ZW1msBCx2rBb5Pnz6NK1euQCQSYcCAARgxYgTfIWmFoaEhZDKZYnuskpISGoTTEmYScJs2beDu7o5BgwbVS8JCXoiRnZ2N0NBQVFZWIi0tDXl5efj+++8RHh7Od2gaeb7A9/OEnIDDw8Nx7949uLi4AAAOHjyIjIwMhIWF8RyZ5qZPnw5fX18UFxfjyy+/RHJyMpYsWcJ3WExgpg+YxZ1bPT09sWXLFixYsABxcXEAAFdXV5w8eZLnyMiLXFxccPLkSUUrUS6Xw83NDYmJiTxHph2///47Ll68CI7jMGjQIMW+hUQzzLSAhZxom9K1a9d6r1n46ldQUICIiAhcu3YNIpEI/fr1w/Lly2FlZcV3aGp7/fXX8fDhQ8UGAIWFhXjzzTd5jkp7bGxs8OqrryoKsj98+BDdunXjOSrhYyYBs6hr1664evUqRCIRJBIJ9u7dy0TLg8Xte/766y+MHTsW77zzDgDgl19+Qb9+/TB//nwAwp6Nc/bsWaxbtw6PHz+GqakpHj58CBsbG2Za93yiBKzHwsPDERERgaKiIgwdOhSOjo5YuXIl32FpjOO4egtJxo8fj/379/MYkeZYLs+4efNmxMbGYtasWYiLi8PFixcp+WoJMwmY1dq5mzZt4jsErWts+5667X1effVVniNsvn/96198h6AzhoaG6NixI+RyOeRyOQYOHIiNGzfyHRYTmEnAYWFhzNXOnTJlCrp3744xY8Zg1KhRTHwmAPjhhx8AAN9//32980ePHmVmGx+WmJiYoKKiAvb29ggMDISpqalilwyiGWZmQQBs1s7NyclBYmIizpw5g549e2Ls2LEYP34832GRv4G7d+/izz//RK9evdC6dWvI5XIkJCTgwYMH+OCDD9CnTx++QxQ+Hkpg6hRrtXPrFBcXc0FBQZydnR3foZAGREdHq3ROSObOncvl5ua+dD4nJ0fw9Zv1hfDnNP1PXl4eIiMjMXbsWFy8eBHbt29HUlISYmJiBLstUXl5OY4fP445c+bA29sbXbp0EfRMAZbVzdN+XmNz04XiwYMHDVZ3e/vttwVfv1lfMNORw2Lt3HHjxmHEiBHw9fUVdLlGlp08eRInT55EQUGBYsoZAFRUVAhyMPF5dXN+G1JdXd2CkbCLiQTMau3cs2fPKlZWsUBZEZ7evXu3UCTa079/f3Tp0gWlpaWYPXu24ny7du0EvxDj7bffxqFDhzB58uR65w8fPizIPyt9xEQCfr52LgvFeCIiIhAaGooFCxY0+L5QJ/WvXbsWACCRSPDrr78qEtSNGzfwzjvv4ODBg3yGpxZLS0tYWFigTZs2zE1FW758ORYtWoSEhARFwv31119RW1urWERDNMNEAgae/UPw9vZmonZu3SyH51tULNi7dy8AYOnSpVi1apUiAd+8eRO7d+/mMzSNiMVitG7dGmVlZUxttdS5c2d8//33uHjxIv773/8CAIYOHYpBgwbxHBk7mEnALNXOrZvew1qLqs7t27frfT23tbVFbm4ujxFprlWrVnBzc8N7771XrwEg5Gp8dQYOHIiBAwfyHQaTmJoHDLBRO9fNza3J9xMSElooEt3w9/dHmzZtMG7cOIhEIpw4cQKVlZX44osv+A5NbSxW4yO6x0wCZql2rrIpPnUVt4SqpqYGBw8eVOzZZ29vD29vb7Rq1YrnyAhpWcwkYFZr5z548AB3797Fe++9h+rqakilUhgbG/MdlsYkEgny8/MhEonw+uuv45VXXuE7JI3cuXMHX3zxBW7dulVv+hYtqyZNYWYhBsBe7dxDhw7Bz89PUQHt0aNH8PX15TkqzWVlZWHUqFFYvXo1PvvsM4waNUrQO1gDQEhICLy9vSEWi7Fnzx64u7vTknGilLAz1HNerJ27a9cuwdfO3b9/Pw4ePKho8fbo0QMlJSU8R6W5devWYdeuXdi3bx/279+PXbt2CXa1Yp2amhrF7ABLS0ssXrwYFy9e5Dkqou+YScDh4eHYv3+/onZubm6u4GvnGhkZ1ZvXLJVKeYxGe2pra2Ftba14/frrrwt+l10jIyPI5XK89tpr2LdvH1JSUlBcXMx3WETPMdMHXFJSAlNTU77D0Kr169fDxMQEcXFxWLFiBQ4cOICePXti6dKlfIemkZCQEIhEIsVX9ISEBMhkMkG3gnNycmBjY4OysjJs3rwZ5eXl8PHxQb9+/fgOjegxZhLwyJEjmaudK5fLceTIEfz0008AgMGDB8PT01Pwy5MlEgn279+PK1eugOM42NvbY+rUqUysYmRhGiRpOcwkYIDN2rl1fb6ste5ZmwXB0jRI0nKYSsB1SkpKsHbtWiQkJAhyhRXHcfjmm2+wb98+xWsDAwNMmzYNixYt4jk6zWVlZSE4OBiWlpbgOA6FhYVYt24d7O3t+Q5NbaxOgyS6xcxS5PLycqSkpCAxMREFBQUYMWKEYGvnxsTE4OrVqzhy5Ihiq/aCggKEh4cjOjoaM2fO5DdADdXNgqgbiMvPz0dAQACOHTvGc2SaYW0aJNE9ZhIwS7Vz4+LisHv37nrdDlZWVtiwYQNmz54t+ATM4iyIF6dB7t27V/DTIInuMZOAWaqdK5VKG+zzNTU1ZWIqWp8+fbB8+fJ6syCEvr9YeHg4IiIiFNMgHR0dBT8Nkuie4BMwi7VzmxqQEvpgFQB89tln2L9/P/bu3VtvFoSQmZqaYtOmTXyHQQRG8INwv/76K/r06YNLly41+L4QSzr26tULbdq0eek8x3GQSCRKd5YgLWf16tVNfvNioRwl0R3BJ2AiHCyW2Xy+DOXXX3+NxYsX13ufylGSpgg+AbP4j5pVrJfZdHd3b3B3ZEIaI/g+YCH28f5dNZRgS0pK0LFjRyYGUFn4DKRlCX6ioqWlpeIAgLt378LS0hKdOnVChw4deI6OPO/atWuYPn06Fi1ahOvXr8PV1VWxjU96ejrf4RHS4gTfBVHn0KFDiI2NxZMnT3DmzBncuXMHYWFhiImJ4Ts08j8eHh7w9/dHWVkZVq5ciR07dqBfv374/fffERAQIMiv7/3791e0fKurq9G6dWsAzwZMRSIRrl69ymd4RM8Jvguizv79+3H48GFMnjwZADu1c1kik8kwePBgAMCWLVsUlcKEvGAhOzub7xCIgAm+C6IOq7VzWfL80ty6lmId6j8lf0fMtIDt7e2xfft2VFdXIyMjAwcOHICTkxPfYZHn5OXl4d133wXHcaipqcG7774L4P/mNxPyd8NMHzCrtXMJIexiJgED7NbOJYSwSfBdEKzXziWEsEvwg3DP187NysrCpUuXcPjwYWRnZyM6Oprv8AghpFGC74Jwd3d/qXYu8Kw7Yvbs2YKcW0oI+XsQfAuY9dq5hBB2CT4Bs147lxDCLsEPwtXNLX0RzS0lhOg7wfcBE0KIUAm+C4IQQoSKEjAhhPCEEjAhhPCEEjAhhPCEEjAhhPCEEjAhhPDk/wNz6wY3ztnA1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(sorted_df.drop(['Age','is_duplicated','duplicated_condition'], axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a good understanding of our data, let's take a look at how we could classify the pregnant women and try to guess if they would need a C-section!"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}