Haberman Dataset EDA.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "haberman_df=pd.read_csv('haberman.csv', names=['age', 'operation_year', 'num_axilliary_nodes', 'survival_status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset description\n",
    "\n",
    "This dataset was generated as result of a study conducted at University of Chicago's Billings hospital on survival status of patients who had undergone cancer surgery between the year 1958-1970 <br>\n",
    "\n",
    "Number of instances = 306 <br>\n",
    "Number of independant variables / features = 3 <br>\n",
    "\n",
    "### Class labels\n",
    "1 : patient survived 5 years or longer, Count = 225 <br>\n",
    "2 : patient died within 5 years of surgery, Count = 81 <br>\n",
    "\n",
    "### Features\n",
    "Age : Numerical <br>\n",
    "Year of operation - 1900 : Numerical <br>\n",
    "Number of positive axillary nodes detected : Numerical <br>\n",
    "\n",
    "### Axillary nodes\n",
    "Lymphatic system in human body is responsible for detection of chemical changes and indication of infection. Lymph nodes are part of this system and are placed in parts of body including axils (armpits). The first spread of cancer takes place to these axillary nodes. <br>\n",
    "\n",
    "One study conducted in past emphasises the role of number of positive axillary nodes detected as primary prognostic discriminant for cancer survival.\n",
    "<a href=\"https://www.ncbi.nlm.nih.gov/pubmed/6352003\">Relation of number of positive axillary nodes to the prognosis of patients with primary breast cancer. An NSABP update.</a>\n",
    "\n",
    "### Objective\n",
    "Determine the best feature of the 3 that can be used to predict survival status of a new patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transformation_dict={1:'survived', 2: 'not_survived'}\n",
    "haberman_df['survival_status']=haberman_df.survival_status.apply(lambda x: transformation_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>operation_year</th>\n",
       "      <th>num_axilliary_nodes</th>\n",
       "      <th>survival_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>survived</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>4</td>\n",
       "      <td>survived</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age  operation_year  num_axilliary_nodes survival_status\n",
       "0   30              64                    1        survived\n",
       "1   30              62                    3        survived\n",
       "2   30              65                    0        survived\n",
       "3   31              59                    2        survived\n",
       "4   31              65                    4        survived"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haberman_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>operation_year</th>\n",
       "      <th>num_axilliary_nodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>306.000000</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>306.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.457516</td>\n",
       "      <td>62.852941</td>\n",
       "      <td>4.026144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.803452</td>\n",
       "      <td>3.249405</td>\n",
       "      <td>7.189654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.750000</td>\n",
       "      <td>65.750000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>52.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "              age  operation_year  num_axilliary_nodes\n",
       "count  306.000000      306.000000           306.000000\n",
       "mean    52.457516       62.852941             4.026144\n",
       "std     10.803452        3.249405             7.189654\n",
       "min     30.000000       58.000000             0.000000\n",
       "25%     44.000000       60.000000             0.000000\n",
       "50%     52.000000       63.000000             1.000000\n",
       "75%     60.750000       65.750000             4.000000\n",
       "max     83.000000       69.000000            52.000000"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haberman_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subjects_survived=haberman_df[haberman_df['survival_status']=='survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subjects_not_survived=haberman_df[haberman_df['survival_status']=='not_survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
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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>operation_year</th>\n",
       "      <th>num_axilliary_nodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.679012</td>\n",
       "      <td>62.827160</td>\n",
       "      <td>7.456790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.167137</td>\n",
       "      <td>3.342118</td>\n",
       "      <td>9.185654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>52.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "             age  operation_year  num_axilliary_nodes\n",
       "count  81.000000       81.000000            81.000000\n",
       "mean   53.679012       62.827160             7.456790\n",
       "std    10.167137        3.342118             9.185654\n",
       "min    34.000000       58.000000             0.000000\n",
       "25%    46.000000       59.000000             1.000000\n",
       "50%    53.000000       63.000000             4.000000\n",
       "75%    61.000000       65.000000            11.000000\n",
       "max    83.000000       69.000000            52.000000"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjects_not_survived.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>operation_year</th>\n",
       "      <th>num_axilliary_nodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.017778</td>\n",
       "      <td>62.862222</td>\n",
       "      <td>2.791111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.012154</td>\n",
       "      <td>3.222915</td>\n",
       "      <td>5.870318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age  operation_year  num_axilliary_nodes\n",
       "count  225.000000      225.000000           225.000000\n",
       "mean    52.017778       62.862222             2.791111\n",
       "std     11.012154        3.222915             5.870318\n",
       "min     30.000000       58.000000             0.000000\n",
       "25%     43.000000       60.000000             0.000000\n",
       "50%     52.000000       63.000000             0.000000\n",
       "75%     60.000000       66.000000             3.000000\n",
       "max     77.000000       69.000000            46.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjects_survived.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 75% of subjects who survived have less than 3 axillary nodes whereas 75% of those who didn't survive have less than 11 nodes.\n",
    "2. Mean value of nodes among subjects who survived is about 4.66 units less than in those who didn't survive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 87,
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     "output_type": "execute_result"
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32dZk2j0zLgAAjJfgwrasrbgkyez0VDrdpX1cEQAAB4HgwrbMt3s5sia4zLUm0+ktZnl5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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff33c0ecac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(haberman_df, hue='survival_status', size=10).map(sns.distplot, 'num_axilliary_nodes').add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate analysis using number of axillary nodes\n",
    "There is lot of overlap between 2 pdfs and clearly the 2 classes cannot be seperated on basis of number of positive axillary nodes. Following conclusions can be drawn<br>\n",
    "1. For number of axillary nodes < 2 there is higher number of patients surviving as compared to those not surviving<br>\n",
    "2. For axillary nodes lying between 2 and 4 there is higher probability of patient not surviving<br>\n",
    "3. For axillary nodes between 4 and 5 there is more chance of survival<br>\n",
    "4. For axillary nodes more than 5 there is higher probability of patient not surviving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff3402c2828>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o6n0EgG0rK3G74Nn2YYcrExERhU2RxUjRTPRIDD6zs4wCd4xPtPRo+HwBji6/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/w0hf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3402c2c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(haberman_df, hue='survival_status', size=8).map(sns.distplot, 'age').add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate analysis using age of patient at time of operation\n",
    "This feature again does not offer any clear seperation between the 2 classes. However the following conclusions can be drawn\n",
    "1. Patients in age bracket of 30-40 have higher chances of survival as compared to those in 40-50\n",
    "2. Age bracket 65-70 has lower probability of survival as compared to lower age brackets\n",
    "3. No specific correlation exists between age and survival of patient. Trends are highly sporadic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff33c06f550>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z4zAZmLXzJ6fXqaVTRNYEBU5Zc853p+tVloXTOHWQ4Nw5TrV/zOpS1ryovwsn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\nEzLrXytlTmIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff33c06f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(haberman_df, hue='survival_status', size=8).map(sns.distplot, 'operation_year').add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate analysis using year of operation\n",
    "The 2 pdfs are overlapping like crazy. Not much inference can be drawn from this feature. However one can see that the time 1963-1966 was not good for cancer surgery. Although it is expected that with passage of time and development of medical science, survival rates should go up, but no such clear trend is observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'subjects_survived' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-8bf22a1b4492>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# CDF for num_axilliary_nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcounts_nodes_survived\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin_edges_nodes_survived\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubjects_survived\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'num_axilliary_nodes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mpdf_nodes_survived\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcounts_nodes_survived\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcounts_nodes_survived\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mcdf_nodes_survived\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdf_nodes_survived\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'subjects_survived' is not defined"
     ]
    }
   ],
   "source": [
    "# CDF for num_axilliary_nodes\n",
    "counts_nodes_survived, bin_edges_nodes_survived=np.histogram(subjects_survived['num_axilliary_nodes'], bins=100, density=True)\n",
    "pdf_nodes_survived=counts_nodes_survived/sum(counts_nodes_survived)\n",
    "cdf_nodes_survived=np.cumsum(pdf_nodes_survived)\n",
    "\n",
    "counts_nodes_not_survived, bin_edges_nodes_not_survived=np.histogram(subjects_not_survived['num_axilliary_nodes'], bins=100, density=True)\n",
    "pdf_nodes_not_survived=counts_nodes_not_survived/sum(counts_nodes_not_survived)\n",
    "cdf_nodes_not_survived=np.cumsum(pdf_nodes_not_survived)\n",
    "\n",
    "counts_age_survived, bin_edges_age_survived=np.histogram(subjects_survived['age'], bins=10, density=True)\n",
    "pdf_age_survived=counts_age_survived/sum(counts_age_survived)\n",
    "cdf_age_survived=np.cumsum(pdf_age_survived)\n",
    "\n",
    "counts_age_not_survived, bin_edges_age_not_survived=np.histogram(subjects_not_survived['age'], bins=10, density=True)\n",
    "pdf_age_not_survived=counts_age_not_survived/sum(counts_age_not_survived)\n",
    "cdf_age_not_survived=np.cumsum(pdf_age_not_survived)\n",
    "\n",
    "counts_year_survived, bin_edges_year_survived=np.histogram(subjects_survived['operation_year'], bins=10, density=True)\n",
    "pdf_year_survived=counts_year_survived/sum(counts_year_survived)\n",
    "cdf_year_survived=np.cumsum(pdf_year_survived)\n",
    "\n",
    "counts_year_not_survived, bin_edges_year_not_survived=np.histogram(subjects_not_survived['operation_year'], bins=10, density=True)\n",
    "pdf_year_not_survived=counts_year_not_survived/sum(counts_year_not_survived)\n",
    "cdf_year_not_survived=np.cumsum(pdf_year_not_survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1740ee65f8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UUhXGs8M94i3wrQl9HrG7EqWUqlCeuyho7AHYuxwGPgMB9eyuRrmIzMxMoqOj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\nuSENd6WUckP/Hw0KdFbEkOwGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17410f5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bin_edges_nodes_survived[1:], cdf_nodes_survived, label='cdf_nodes_survived')\n",
    "plt.plot(bin_edges_nodes_not_survived[1:], cdf_nodes_not_survived, label='cdf_nodes_not_survived')\n",
    "plt.xlabel('num_axillary_nodes')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.   0.   3.   8.  14.  46.]\n",
      "[  1.   4.  11.  20.  23.  52.]\n"
     ]
    }
   ],
   "source": [
    "# percentiles\n",
    "print(np.percentile(subjects_survived['num_axilliary_nodes'], [25,50,75, 90, 95,100]))\n",
    "print(np.percentile(subjects_not_survived['num_axilliary_nodes'], [25,50,75, 90, 95, 100]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "<li>95% of subjects who survived had num_axillary_nodes less than 14</li>\n",
    "<li>95% of subjects who didn't survive had num_axillary nodes less than 23</li>\n",
    "</ul>\n",
    "This is again indicative of high death rates among patients with higher number of positive axillary nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 38.  41.  43.  45.  52.  60.  67.  70.  77.]\n",
      "[ 42.  45.  46.  47.  53.  61.  67.  70.  83.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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vS7m0CI61+oFKbUZauiQhCrW8hPsBoIZSqopSygXoDwRmG7MeaAOglPLCOE1z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\n/w8IwsEYhCRO5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1738d74e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bin_edges_age_survived[1:], cdf_age_survived, label='cdf_age_survived')\n",
    "plt.plot(bin_edges_age_not_survived[1:], cdf_age_not_survived, label='cdf_age_not_survived')\n",
    "plt.xlabel('age')\n",
    "plt.legend()\n",
    "\n",
    "# percentiles\n",
    "print(np.percentile(subjects_survived['age'], [10,20,25,30,50,75, 90, 95,100]))\n",
    "print(np.percentile(subjects_not_survived['age'], [10,20,25,30,50,75, 90, 95, 100]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In age groups above 50, percentile values of surviving and non surviving subjects are almost identical and hence no prediction can be made about survival status based on age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 58.  60.  60.  61.  63.  66.  67.  68.  69.]\n",
      "[ 58.  59.  59.  60.  63.  65.  67.  68.  69.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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1cOf5TnWpWs7x/wfcuJDRvtcVhmXfvlcIS5BwF8ICklLT+GjjaRbtCaVxtbJ8\nNbzFnR0aE2Ng8WBISYQn/cGlinWKFcWChLsQ9yg0Mp7xSw5y7GIsozq4M/nhRnfeUZqaDL5PQNQZ\nGLkSKjeyTrGi2JBwF+IerD50kbdWHcXergTzn2hFjyZV7xykNax7FUJ3Qv9voO6DBV+oKHYk3IW4\nCwnJqUxdc5zlB8Jp7X4fnw9tQfXypbMfvOsTOPwrPDgZmt9xc7cQ+ULCXYg8OhERy7glBwmNjOfl\nrvV5uVsD7E31gDniC3+8D55DwWdywRYqijUJdyHMpLXm173neW/9ScqXdmDxc23pUM/V9BvO7YY1\nL4F7J+j7JSjpFyMKjoS7EGaISUjhjRVH2Hz8Cj4elZgzyIuKzjmsgnTtNCwdDhXqwpBfwL5kwRUr\nBBLuQuQq4Fw0E5Ye5urNRN7ufT/PdKyTc9fGmHBYPAjsSsKIZVD6voIrVogMEu5CmJCWrvl251nm\n/h5EjfKlWf5iB7xq5bLu+/FVsPYVSE+Fp/zhvtoFU6wQWUi4C5GNq7GJvOp3mD3BUTzqVZ0PHmuK\ni2MO65Ym3YSNk41ZMTVaweMLoGK9gitYiCwk3IXIYsfpq7zmd4T45FQ+GtCMwd61UDldDA3bDyuf\nhxvnofPr8OCbYCcLWAvrknAXIkNyajpztpzmu10heFRxYenwdkYHR1PSUmH3XNgxC8rWgFHroXaH\ngitYiByYFe5KqV7A5xgrMS3UWs/KZsxgYDrG4tlHtNZyt4YoMi5EJTB+6SGOhN1gRFs33unTOOdF\nqa+fg5UvQNheaDYYen8CjuUKrF4hcpNruCul7IB5QHeMxbL3K6X8tdYnMo1pAEwBOmqtryulKudX\nwUJY2rrACKasOAoKvh7RkkeaVTM9WGsI9IP1rxnz1h9fAJ6DC65YIcxkzpF7GyBYax0CoJRaCvQD\nTmQa8zwwT2t9HUBrfdXShQphabeS05ix7jhL/gmjhVt5vhjagloVyuTwhhtGqB9bDm7t4bHvZDaM\nKLTMCfcaQFimx+FA2yxjGgIopfZgnLqZrrXelHVDSqnRwGgANze3u6lXCIsIunKTcb8dJOhKHGN8\n6jGxe0McTLUQADi3B1a9ALER0PVteGCiLLIhCjVzwj27aQI6m+00AHyAmsCfSqmmWusb/3mT1vOB\n+QDe3t5ZtyFEvtNas3R/GNP9j+PiaM/Pz7Shc8NKpt+QlgI7PoQ/58J97vDs71CzVYHVK8TdMifc\nw4FamR7XBCKyGbNXa50ChCqlTmOE/X6LVCmEBcQmpjBl5VHWB16iUwNX5gz2orKLo+k3RJ2FFc8a\ny+K1eAJ6zYJSzqbHC1GImBNMf6qYAAAbG0lEQVTu+4EGSqk6wEVgKJB1JsxqYBjwo1LKFeM0TYgl\nCxXiXhy6cJ3xSw5xKSaRN3p58GLneqZbCGgNB3+GTZONFgKDf4bG/Qq2YCHuUa7hrrVOVUqNAzZj\nnE9fpLU+rpSaAQRorf0zXuuhlDoBpAGva62j8rNwIcyRnq5Z8GcIH28+TZWyjvi90J5WtXPo9ZIQ\nDf7j4dQ6qNMZ+n8L5WoUXMFCWIjS2jqnvr29vXVAQIBVPlsUD5FxSUz0O8KuoGs83LQqswZ4Uq50\nDneOnv0DVo+B+Eh4aBq0ewlK5HCRVQgrUEod0Fp75zZO7lAVNmn3mUhe9TtMzK0U3u/flBFt3Uy3\nEEhNgm0z4O+vwNUDhvtBNc+CLVgIC5NwFzYlNS2dT7cG8fWOs9Sr5Mwvz7ahUdWypt9w9SSseA6u\nHIPWz0H396BkDnPdhSgiJNyFzQiLTuAV38McOH+dId61mNa3MWVKmvgR1xr+WQC/vwMlnWGYL3j0\nKtiChchHEu6iyNNas/LgRab5H0cBnw9tTr/mOVwEjbtqLH93ZgvU7w79vwZn6ZghbIuEuyjSbiQk\n89aqY6w/eok27hWYM9gr5xYCQZth9VhIjoOHP4Y2z8vapsImSbiLImv3mUheW3aYqLhk3ujlwQud\n62Fnau56coJxCmb/QqjSFAYshMr3F2zBQhQgCXdR5CSmpPHx5tN8vzuUepWc+P6p1jStkUO73UuB\nxkXTyNPQfhx0mwr2OSxuLYQNkHAXRcrJS7G8svQwp6/c5Kn2tZn88P2ULmmigVd6ujG9cdsMKFMR\nnlgF9boWbMFCWImEuygS0tM1i/aEMnvTacqWduCHp1vTxSOHi6CxEbDqRQjdCY36QN8voUyFgitY\nCCuTcBeFXsSNW0xadoS/zkbRo3EVPny8GRWdczitcmIN+L8MaclGqLd4Qi6aimJHwl0UamuPRPDW\nqqOkpuvcF6tOijOafR36Baq3gMcXgmv9gi1YiEJCwl0USrGJKUxbc5xVhy7SvFZ5PhvSHHdXJ9Nv\nCD8AK5+D6FDo9Br4TAG7HPrICGHjJNxFobMvJIqJfke4HJvIKw81YFyX+tibWiUpPc1YSGPHh1C2\nOoxaD+4dC7ZgIQohCXdRaCSnGn1hvt15ltoVyrD8xfa0cMuhPe/188bSdxf+hqYDofccKF2+4AoW\nohCTcBeFQvDVm0xYepjjEbEMa1OLt3s3xqlUDj+egX7GYtUAjy8Az8EFU6gQRYRZzaqVUr2UUqeV\nUsFKqcnZvD5KKXVNKXU4489zli9V2CKtNT//fY7eX+zmUkwi859oxYePe5oO9sQY44aklc9D5cbw\n4p8S7EJkI9cjd6WUHTAP6I6xVup+pZS/1vpElqG+Wutx+VCjsFFXYxN5fXkgO4Ou4eNRidkDPXNe\n0/T8X7DyBYi9CF3eggcmgp3841OI7Jjzf0YbIFhrHQKglFoK9AOyhrsQZtt07DJTVgaSkJzGe/2a\nMLJdbdNTHNNSYMcs2D0XyteGZ7dAzVwXohGiWDMn3GsAYZkehwNtsxk3QCnVGQgCXtVah2UzRhRz\n8UmpzFh7At+AMJrWKMtnQ5pTv7KL6TdEnTVOw0QchBYjodcsKJXDeCEEYF64Z3c4lXXh1bXAEq11\nklLqReAn4I4mHkqp0cBoADc3tzyWKoq6A+evM9HvMBeiExjrU49XHmpISXsTl320Nm5G2jjZmK8+\n6Cdo0r9gCxaiCDMn3MOBWpke1wQiMg/QWkdlergA+Ci7DWmt5wPzwVggO0+ViiIrJS2dL7cH89X2\nM1QrVxrf0e1pUyeHPi8J0bD2ZTi5Fup0hv7fQrkcFt8QQtzBnHDfDzRQStUBLgJDgeGZByilqmmt\nL2U87AuctGiVosgKjYznVd/DHA67weMtazC9bxPKOuZw5+jZP2D1GIiPNNYzbT8OSpg1qUsIkUmu\n4a61TlVKjQM2A3bAIq31caXUDCBAa+0PvKyU6gukAtHAqHysWRQBWmt894cxY90JHOxK8NXwFvTx\nrG76DalJRmvev78C14Yw3BeqeRVcwULYGKW1dc6OeHt764CAAKt8tshfUXFJvLniKFtPXqFj/Yp8\nMsiLauVKm37D1VPGRdMrR6H1c8YRe8kclsoTohhTSh3QWuc6XUwmCQuL+uPUVV5fHkjsrRTe7n0/\nz3SsQwlTS99pbSx7t+VtKOkMw3zBo1fBFiyEjZJwFxZxKzmNDzac5Je952lU1YVfn2tDo6plTb8h\n7iqseQnObIH6D0G/r8GlSsEVLISNk3AX9yQ9XbPu6CXmbjnNuagEnnugDpN6euDoYGLpO4CgzbB6\nLCTdhIc/hjbPy2IaQliYhLu4K1prNh+/zKe/n+H0lZt4VHFh8XNt6Vjf1fSbUm7Blndg/wKo0hRG\nrYPK9xdc0UIUIxLuIk+01mw7eZVPtwZxPCKWupWc+GJYC/o0q2b63DrApUDjomnkaWj3EnSbCg45\n9JERQtwTCXdhFq01u85EMvf3II6E3aB2xTLMHexFX6/qphfSAEhPN6Y3bpsBZSrCE6ug3h03Lwsh\nLEzCXeTqr7ORzN0SRMD569QoX5qPBjTj8ZY1ccgp1AFiI2DVixC6Exr1gUe/AKeKBVO0EMWchLsw\naf+5aOZuCeLvkCiqlnXkvf5NGeJdy3Q/mMxOrIG1E4ybkx79Alo+KRdNhShAEu7iDofDbjBny2n+\nPBOJq3Mppj3amGFt3HKeAfOvpDjY9CYc+hWqt4DHF4Jr/fwvWgjxHxLu4rZjF2P49Pcgtp26SgWn\nkvzvkUY80c6d0iXNCHWA8AOw8jmIDoVOr4HPFKOjoxCiwEm4C05fvsmnvwex6fhlypV24PWeHjzV\nwR3nnNYwzSwtBXZ/aiyo4VINRq0H9475W7QQIkcS7sVY8NU4Pt92hnWBETiXtGdCtwY826lOzl0b\ns7qwF9a+AtdOQtMB0HsulC6ff0ULIcwi4V4MnYuM54ttZ1h9+CKODnaM9anH853qUr5MSfM3khAN\nW6fBwZ+hXC0YthQ8Hs6/ooUQeSLhXoyEX0/gy23BLD8YjoOd4rlOdXmhc10qOpcyfyNaQ6AvbH4L\nbl2HDuPhwclQyjn/ChdC5JmEezFwKeYWX20Pxi8gDKUUT7Srzdgu9ajsksc7RCODYf2rELoLanjD\nk6uharP8KVoIcU/MCnelVC/gc4zFOhZqrWeZGDcQWAa01lpLs3Yru3ozka//OMtv/1xAa82Q1rV4\nqUv9nHurZycl0bhgunsu2Jc2zqu3elpWSBKiEMs13JVSdsA8oDvGeqr7lVL+WusTWca5AC8D+/Kj\nUGG+qLgkvtsVws9/nyMlTTOwZU3Gda1PrQp3sQBGyE5YPxGigqHpQOj5gbTmFaIIMOfIvQ0QrLUO\nAVBKLQX6ASeyjHsPmA1MsmiFwmw3EpKZvyuEH/86R2JKGv1b1ODlrg1wd3XK+8birsGWt4zz6/fV\ngZEroX43yxcthMgX5oR7DSAs0+NwoG3mAUqpFkAtrfU6pZSEewGLTUzh+z9DWbQ7lLjkVPp4VmdC\ntwbUr3wXFznT0+HQz/D7NEiOh86vGzckOeTxVI4QwqrMCffsGoLcXnhVKVUC+BQzFsVWSo0GRgO4\nubmZV6EwKT1ds2hPKF9uDybmVgq9mlTlle4Ncl4BKSdXTsC6VyFsL9TuCH0+hUoeli1aCFEgzAn3\ncKBWpsc1gYhMj12ApsAOZTSGqgr4K6X6Zr2oqrWeD8wHY4Hse6i72Lt6M5GJvkfYHRyJj0clJvXw\noGmNcne3seQE2PmR0Zq3VFljybvmw6XRlxBFmDnhvh9ooJSqA1wEhgLD/31Rax0D3F5+Rym1A5gk\ns2Xyz86ga7zmd5i4pFRmPd6MIa1roe42iIO2wIbX4MYFaD4Sus+QtrxC2IBcw11rnaqUGgdsxpgK\nuUhrfVwpNQMI0Fr753eRwpCSls4nW07z3c4QGlZx5rfn29GwisvdbSz2ktG98cQacPWAURukH4wQ\nNsSsee5a6w3AhizPTTUx1ufeyxJZhUUnMH7JIQ6H3WBYGzem9mlsfrfGzNLTYP9C2PYepKdA17eh\nwwSwz0PrASFEoSd3qBYB6wMvMXlFIADzhrekt2e1u9tQxGFY9wpEHDKWuus9ByrUtWClQojCQsK9\nEEtMSWPGuhP8tu8CXrXK89WwFnd3I1LSTdg+E/75Dsq4woDvjQ6OcsFUCJsl4V5IBV25ybjfDhJ0\nJY4XHqzLpB4eua9ZmpXWcHItbHwTbl4C72eg21RpyStEMSDhXshorfHdH8b0tcdxKmnPT8+04cGG\nlfK+oRsXYMPrELQJqjSDIb9ATW/LFyyEKJQk3AuR2MQU/rfyKOsCL9GxfkU+HdycymXz2LkxLQX2\nfm2sigTQ431oOwbs5FstRHEi/8cXEofDbjB+yUEibiTyek8PxjxYjxIl8nhOPOwfY1Wkq8fB4xF4\neDaUr5X7+4QQNkfC3crS0zULd4cwe9NpqpR1xO+FdrSqXSFvG7l1Hba+Cwd+hLLVYchiuL9PvtQr\nhCgaJNytKDIuidf8jrAz6Bo9m1Rh9gAvypXJw/ql18/Bmd+N1gEJUdBuLHSZAqXu8sYmIYTNkHC3\nkj3Bkbzie5iYWym8168JI9vVzr2FQEK0sQpSyB8QssMId4AarWDkCqjmld9lCyGKCAn3Apaals5n\nW88wb0cwdVyd+OnpNjSubqKLY8otuLDXCPKQHXDpCKChpAvU6WQcqdf1AdeGMmddCPEfEu4F6OKN\nW0xYcoiA89cZ1Kom7/ZrQpmSmb4F6WlwOdAI8rN/GMGelgQl7KFmG+jyPyPMq7eU2S9CiBxJQhSQ\nTccu8+aKQOPIfUhz+reoYbwQHfL/R+ahu4yLowCVm0Dr54wwr90BSt3FwhtCiGJLwj2fJaak8cGG\nk/z893ma1ijL1/1r4xazD/x3GIF+47wxsGwN8OhthHmdzrJOqRDinki456Oz1+J4bfHfuFwN4Lfa\nYbRTRynxvdEAjFLljPPmHcYbgV6xvpw3F0JYjIS7paWnoSMOc2y3P/Ent+KnTlGyZCpccwC3dkaL\n3bpdoFpzOW8uhMg3ki73SuuM8+bG9EQduguVGEMz4JxDXVK8nqdko4egdnso6WTtaoUQxYRZ4a6U\n6gV8jrES00Kt9awsr78IvASkAXHAaK31CQvXWnikJkHwVji9EUJ2QswFAJKda/B7ams2pzSiScdH\nea5XW+zy2kJACCEsINdwV0rZAfOA7hiLZe9XSvlnCe/ftNbfZozvC8wFeuVDvdaTng4X/oJAP2Np\nusQb4FgO6nRGd5zAihv1mbIznopOjnz2bHPa1ZV1SIUQ1mPOkXsbIFhrHQKglFoK9ANuh7vWOjbT\neCdAW7JIq9EarhyDo8vg6AqIDQcHJ6NvS7PBUNeH6MR03lh+hK0nr9KtUWU+HuRFBSdZsk4IYV3m\nhHsNICzT43CgbdZBSqmXgIlASaBrdhtSSo0GRgO4ubnltdaCc+OCEeiBy+DaSeMmonrdoPu74PHw\n7XPn+0KimLD0MFHxSUzt05inO7rn3kJACCEKgDnhnl1a3XFkrrWeB8xTSg0H3gaeymbMfGA+gLe3\nd+E6uk+IhuOrjFC/8LfxXK12xjqjjR8Dp/8/zZKervnqj2A+2xqEW4UyrBzTkWY1y1mpcCGEuJM5\n4R4OZG4KXhOIyGH8UuCbeymqwCQnwOkNRqAHb4X0VKjUCLq+A80Gwn3ud7wlOj6ZV3wPsyvoGv2a\nV2fmY81wLiWTjoQQhYs5qbQfaKCUqgNcBIYCwzMPUEo10FqfyXjYGzhDYZWWCqE7jFMup9ZBchy4\nVId2Y4zz6FWbmbyZ6NCF67y0+CCRccm8378pI9q6yWkYIUShlGu4a61TlVLjgM0YUyEXaa2PK6Vm\nAAFaa39gnFLqISAFuE42p2SsSmu4eMCY6XJ8JcRfM2a6NH3cCPTaHaGE6cWntdb89Nc5Zm44SWUX\nR5aPaY9nTVlkWghReJl1PkFrvQHYkOW5qZm+nmDhuiwjMhiO+hmnXaJDwK4UNOwJnoOhQQ+wL5Xr\nJuKSUpm8IpB1gZfo2qgycwd7Ub6MzIYRQhRutney+OZlOLbCCPSIQ4AyGnF1eg3uf9Q4YjdT0JWb\nvPjrAc5Fxt/9uqZCCGEFthHuibFwcq1xlB66C3S6sSpRj5nQdACUrZbnTa46FM7/Vh7DqZQdvz7X\nlg71XPOhcCGEyB9FN9xTk4z1Q48ug6BNkJpozG7pNAmaDYJKDe9qs4kpaby37gSL912gjXsFvhze\ngiplHS1buxBC5LOiF+4RhyFg0f+3ACjjCi2fNC6M1vS+p7a5YdEJjF18kKMXY3ihc11e7+mBvZ3p\nC61CCFFYFb1wD9sHR5dDo97GhdG6PmDncM+b3XbyChP9jpCuNd890YqeTare8zaFEMJail64txhp\n/LFQ+9zUtHTm/h7E1zvO0rhaWb4Z2ZLaFaU1rxCiaCt64W7BnuhXbyby8pJD7A2JZlibWkx7tAmO\nDnYW274QQlhL0Qt3C9kXEsX4JYeITUzhk0FeDGxV09olCSGExRS7cNdaM39XCLM3n8atQhl+eqYN\n91cra+2yhBDCoopVuMfcSmHSsiP8fuIKjzSrykcDPHFxvPeLsUIIUdgUm3A/djGGsYsPEnHjFu/0\nacwz0ntdCGHDbD7ctdb47g9jqv9xKpQpie8L7WhVu4K1yxJCiHxl0+F+KzmNt1cfY8XBcDo1cOWz\nIc2p6Jx7szAhhCjqbDbcQ67FMXbxQU5fucmEbg14uVsD7KTplxCimLDJcN9w9BJvLA/EwU7xw6jW\n+HhUtnZJQghRoMxqnKKU6qWUOq2UClZKTc7m9YlKqRNKqUCl1DalVG3Ll5q7lLR03lt3grGLD1K/\nsjPrXu4kwS6EKJZyPXJXStkB84DuGOup7ldK+WutT2Qadgjw1lonKKXGALOBIflRsCmXYm4x7rdD\nHDh/nVEd3PnfI/dT0l6afgkhiidzTsu0AYK11iEASqmlQD/gdrhrrf/INH4vMNKSRebmzzPXmLD0\nMEkpaXw1vAV9PKsX5McLIUShY0641wDCMj0OB9rmMP5ZYOO9FGWu9HTNl9uD+WxbEA0qO/P1iFbU\nr+xcEB8thBCFmjnhnt0UE53tQKVGAt7AgyZeHw2MBnBzczOzxOxFxyfziu9hdgVd47EWNZj5WFPK\nlLTJ68NCCJFn5qRhOFAr0+OaQETWQUqph4C3gAe11knZbUhrPR+YD+Dt7Z3tLwhzHLpwnZcWHyQy\nLpkPHmvGsDa15G5TIYTIxJxw3w80UErVAS4CQ4HhmQcopVoA3wG9tNZXLV5lJssCwvjfqqNUKevI\nijEdaFbT/AWvhRCiuMg13LXWqUqpccBmwA5YpLU+rpSaAQRorf2BjwFnYFnGEfQFrXXf/Ci4jqsT\nXRtVZvYAL8qVkaZfQgiRHaX1XZ8duSfe3t46ICDAKp8thBBFlVLqgNbaO7dxMhFcCCFskIS7EELY\nIAl3IYSwQRLuQghhgyTchRDCBkm4CyGEDZJwF0IIGyThLoQQNshqNzEppa4B563y4XnnCkRau4gC\nVhz3GYrnfhfHfYaiu9+1tdaVchtktXAvSpRSAebcEWZLiuM+Q/Hc7+K4z2D7+y2nZYQQwgZJuAsh\nhA2ScDfPfGsXYAXFcZ+heO53cdxnsPH9lnPuQghhg+TIXQghbJCEexZKqfJKqeVKqVNKqZNKqfZK\nqQpKqd+VUmcy/nufteu0NBP7/XHG40Cl1CqlVHlr12lJ2e1zptcmKaW0UsrVmjXmB1P7rZQar5Q6\nrZQ6rpSabe06LcnEz3dzpdRepdRhpVSAUqqNteu0JAn3O30ObNJaNwK8gJPAZGCb1roBsC3jsa3J\nbr9/B5pqrT2BIGCKFevLD9ntM0qpWkB34IIVa8tPd+y3UqoL0A/w1Fo3AT6xZoH5ILvv9WzgXa11\nc2BqxmObIeGeiVKqLNAZ+B5Aa52stb6B8UP/U8awn4D+1qkwf5jab631Fq11asawvRiLo9uEHL7X\nAJ8CbwA2d0Eqh/0eA8z6d3H7/F4LuSDlsM8aKJsxrBwQYZ0K84eE+3/VBa4BPyilDimlFiqlnIAq\nWutLABn/rWzNIvOBqf3O7BlgY8GXlm+y3WelVF/gotb6iJXryy+mvtcNgU5KqX1KqZ1KqdbWLdOi\nTO3zK8DHSqkwjH+p2NS/TCXc/8seaAl8o7VuAcRjm6dgsspxv5VSbwGpwGLrlJcvstvn6cBbGP9E\nt1Wmvtf2wH1AO+B1wE9lrHZvA0zt8xjgVa11LeBVMo7sbYWE+3+FA+Fa630Zj5dj/FBcUUpVA8j4\nr838kzWDqf1GKfUU0AcYoW1r3qypfa4DHFFKncM4DXVQKVXVOiXmC1P7HQ6s1IZ/gHSM3iu2wNQ+\nPwWszHhuGSAXVG2V1voyEKaU8sh4qhtwAvDH+EEg479rrFBevjG130qpXsCbQF+tdYLVCswHJvb5\noNa6stbaXWvtjhEKLTPG2oQcfsZXA10BlFINgZIUzaZad8hhnyOABzOe6wqcsUJ5+UZuYspCKdUc\nWIjxwx0CPI3xS9APcMOYQTFIax1ttSLzgYn93g+UAqIyhu3VWr9onQotL7t91lpfz/T6OcBba20T\nIfcvE9/reGAR0BxIBiZprbdbrUgLM7HPTTBm0dgDicBYrfUBqxVpYRLuQghhg+S0jBBC2CAJdyGE\nsEES7kIIYYMk3IUQwgZJuAshhA2ScBdCCBsk4S7EPVBK2Vm7BiGyI+Euig2l1HtKqQmZHs9USr2s\nlHpdKbU/o2/9u5leX62UOpDR33x0pufjlFIzlFL7gPYIUQhJuIvi5Hsy2kgopUoAQ4ErQAOMviLN\ngVZKqc4Z45/RWrcCvIGXlVIVM553Ao5prdtqrXcX5A4IYS57axcgREHRWp9TSkUppVoAVYBDQGug\nR8bXAM4YYb8LI9Afy3i+VsbzUUAasKIgaxciryTcRXGzEBgFVMXopdIN+FBr/V3mQUopH+AhoL3W\nOkEptQNwzHg5UWudVlAFC3E35LSMKG5WAb0wjtg3Z/x5RinlDKCUqqGUqoyxMs/1jGBvhNHnXIgi\nQ47cRbGitU5WSv0B3Mg4+t6ilLof+DtjbYo4YCSwCXhRKRUInMZYZlCIIkO6QopiJeNC6kGMts02\n1b9biMzktIwoNpRSjYFgYJsEu7B1cuQuhBA2SI7chRDCBkm4CyGEDZJwF0IIGyThLoQQNkjCXQgh\nbJCEuxBC2KD/A+sCvGtnCEwsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1738edf048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bin_edges_year_survived[1:], cdf_year_survived, label='cdf_year_survived')\n",
    "plt.plot(bin_edges_year_not_survived[1:], cdf_year_not_survived, label='cdf_year_not_survived')\n",
    "plt.xlabel('year')\n",
    "plt.legend()\n",
    "\n",
    "# percentiles\n",
    "print(np.percentile(subjects_survived['operation_year'], [10,20,25,30,50,75, 90, 95,100]))\n",
    "print(np.percentile(subjects_not_survived['operation_year'], [10,20,25,30,50,75, 90, 95, 100]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is clearly not a good feature for prediction as the percentile values are not differing very much for instance 95% of survived people got operated before and the same is true for those who didn't survive. Hence this feature will not be considered for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1740af4da0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kuZroyxHxeESsjYgXRcSREfHFkdcldY/fR8l/nq8AD0TENaNeWg6MtO8Gbt2z8C2L+fPn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\nmeFkYGZmwP8H/D3q95PeGx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17390e3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='survival_status', y='num_axilliary_nodes', data=haberman_df, width=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Almost all survived subjects have less than 10 axilliary nodes and almost all of non surviving subjects have less than 26 nodes\n",
    "2. 75% of surviving subjects have less than 3 nodes and 50% of non survivors had less than 4 nodes i.e. 50% of non survivors had more than 4 nodes\n",
    "\n",
    "These observations make number of axillary nodes an ideal choice for cancer survival prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1738f65a58>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nOXuKtvXY5K0kzWQGwRyVZMKZZavqsqo6bzrr0dyV5NeTHDyC5z04ycWTtxxqW1clafa0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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1738ce6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='survival_status', y='age', data=haberman_df, width=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is huge overlap in age groups of surviving and non surviving subjects. Even the 50%, 25% and 75% values are almost identical. A clear determination of survival status of a new subject based on age is thus not feasible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7ff34010c438>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UupzCWhX2AgsyNULhCY4LES3WmD9FiIgotArh2GfPTqItGQvvL5tCAM4lYGQA\nxsFuYGTA/1oIaU3k8wLTThZ7h0/ilgMvYu/wSUw7WeTz8toArlXb2nPkBG755IvYc+QEptJZqeXb\nXVdgKl3al6l0Fq4rty+6FOaxaRhK5rEQHqad3KKxz0kdE50ic9+HWLn1xM27C2sZFK1lspSsVQfU\nrFVhpXpNWg7HhYiCcDOJiIgoSM4BxgaB8y8DIu8/jg36xyWZzbvYP3oG4+emkBcexs9NYf/oGczm\n5eZgFFfbKrSzb+Q0nJzEDKBccF9kthElOsaEmku59cTIq1/LZOF90Zg4LkQUhJtJREREQRI2cGG8\n9NiFcf+4JLqqL+moBMRKUtVhdSaSrdw9aCRTytcyWXhfNCaOCxEF4WYSERFRkKwDrOsvPbau3z8u\nia7qSzoqAbGSVHVYnYlkK3cPepm08rVMFt4XjYnjQkRBuJlERERSCSEg5q6ir68XYu4qhIpcDiH8\nQGxv/lFFG3Eb2DEE9GwFzJj/uGPIPy6JrupLfiWgjYsqAW2UXp0sqC+hrTakeI7pGBNqLq0xC8/s\n2oTjj9+Jc5+/G8cfvxPP7NoEL6Z+LSsmhIeZTB7Cm3+sIldHWdUyHT8zwnAOK8RqckQUhO89JyIi\naYQQMNKXYBwbBC6Mw1jXD2/7EESqG6asykGFYOwxvw2s6/dfGNndcqsTmab/nDtH4SVsGAoqIC1b\nfUmyhGXiyY/chrWdNl6ddpCQXGXNskx0pRJLqpOFsZqbjjnmeR7ii8YkbpnwPA8Aw6upegY8rHJf\nh/FNf952F9ZfrF5Yy1RXcysENe8bOY3j56expacTT+/chK5UoqLAaCVVy3T9zGj0c6hBI1STI6LG\n0/irFxERhUc27W8kFQW9GscGgWxaXhsagrEXmCaQbMPk5Fkg2abkl/5YzER7SxymYaC9Ja5kI8nJ\nuXj46Cm866nv4+ZPfBvveur7ePjoKenhqZZV2pdQbiQBWuaYk3PxyKIxeUTBmFDzMPJO4Ppr5J2F\ntQyGqWwtA+QENUuvWqbzZ0Yjn0ON6llNjogaU0h/yyMiokZULujVSKbkNaIhGDtqGJ5apQiFr1Pz\n0LL+XkdDrjWN8DOjEc6BiEgybiYREZE05YJevYzEdyZpCMaOGoanVilC4evUPLSsv9fRkGtNI/zM\naIRzICKSjJtJREQkTyIFb3tp0Ku3fQhISPzLuIZg7KhheGqVNMyxyAWWU915MTtw/fVi+tbGhlxr\nGuFnRiOcAxGRZHwvNRERSWOaJkSqG97ACIxkyv+LeCIlL3zbbwRuy2oYRW14MRtWCEJM68UPT43j\nq7tvLRqXeGgzL4Tw4ORc9PYQQ8XXAAAgAElEQVT2YWY+6FtqX0wTwl4NY+B5P18mMwNP8jyOVGA5\n/PB9ZNMLVRyl3/d0XVbMgmjtAornbcyGFbP8AOicIzeAO+A5TdOsOKi5cB8rD3QuKqagOoC8oc+B\niEgyrmBERCSVaZowW9oxOXkWZku79BeU+bzAlJPDfcOv4B0HvoP7hl/BlJNDPh+eMsvaCQHTuQRz\ndCeMg90wR3fCdC6FqjR1QaFa1J4jJ3DLJ1/EniMnMJXOVlV+/PptCBjpyzBGPwbjYLf/mL7sb5hI\nVAgsPzs5GerA8kIVx+L5ZaQvSb9etDw378KYnSqdt7NTEPm8X0lsZAA42O0/1nr/F6qTBTxnJUHN\nJffxATX3cQlNAeQNfw5ERBJxFSMiolCZzbvYP3qmpFrQ/tEzmM0z/6esCFQSKpBRLeq6dFQljBJe\nr4awbDU32fd/jWuKlvuYiIiU4mYSERGFCqtgrUCEKgnpqBbVCFWxwoTXqzGUGwck2+Tf/zWuKQ1Z\n9Y2IiKrCzSQiIgoVVsFagQhVEtJRLaoRqmKFCa9XYyg3DsjMyL//a1xTGrLqGxERVUX5ZtKbb76J\nffv24QMf+ADuvvtunD59Gq+//joefPBBvO9978ODDz6IN954Q/VpEBE1naiuv60xC8/s2oTjj9+J\nc5+/G8cfvxPP7NqE1lg4/6LtugJX53IQnoerczm4roKcmQhVEvKrRW1cVC1KchU0HVUJNRLCw0wm\nD+HNP8rOpYnY9ZJF9xq8XDU3b9H97+0YgojbK58XNa4pDVn1bSWE8DfrvPnHBs4JU74OEFHTUf6Z\ngM997nPYunUrnn76aWSzWczNzeGZZ55Bf38/9u7di8OHD+Pw4cN4/PHHVZ8KEVFTier6GzOBVeJ1\nGN8cBC6Mo3tdP7wdQzDM7nqfWtVcV2AqncX+0TM4fn4aW3o6cWhgI7pSCblhzEWVhLyEDSPklYQS\nloknP3Ib1nbaeHXaQUJycLUJwIslgHueBjpuAq78HIglQvl27kLQ8b6R0wtz7Omdm9CVSkirnKWl\nimMI6V6DDdMArEXz1koAhoGrZgcy9xxBV0cHpq5cQSrWjnQ6i30jZ1Y2L2qsTuZXmKys6lvDKoSQ\nj/k/i7Cu399Qs7sbbm3VsQ4QUfNRutLNzMzg+PHj2LFjBwAgkUhg1apVeOmll7Bt2zYAwLZt2/C9\n731P5WkQETWdSK+/OQfGouBXI8Rh0kFh4kpCaOcrCU1Ong11JSEn5+Lho6fwrqe+j5s/8W2866nv\n4+Gjp+Res5wD41/vB764CfhMJ/DFTf7XIZ1jOoKOVVdxDJu6rMHZNIx/WzRv/82ftx8/egpb/uHH\nWP+JF7HlH36MizM57Bs5U9u8qLE6WSVV3xpaiAobMPCciFRQ+s6kV199FZ2dnXjiiScwOTmJW2+9\nFQcOHMDU1BTWrFkDAFizZg2mp6cDv39iYqLmc5ibm5PyPI0iav0B2KewiFqfGqU/GzZsUPK8ta6/\nQO1rsKpr3NfXCyMg+NVL2JhUNKaq+tLb11c2TFzV/GyUub9Svb3B18yOW9L6pXuOqRwTHderWJjm\nl6r1F6jP78B9fb1lg9AXz4G1nbbWeSFLI80vmeuE6n7pXgcKGmm8ZIlinwD9/VK5/pI+SjeT8vk8\nfvrTn+JTn/oU3vnOd+Kzn/0sDh8+XPH3y5hkExMTkZqsUesPwD6FRdT6FLX+LFbr+gvUvgYru8aF\nMNnzL187tq4fRtZRNqaq+nJ1LoctPZ0YPze1cKwQJh62vugyMx/AvviaOTlXXr80zzGVY6LlehUJ\n+/ySpR6/A4u5qzAC5q2XSS+ZA69OO1rnhSwNNb8krhOq+6V7HShoqPGSJIp9AqLbL1JL6XuQb7jh\nBtxwww145zvfCQD4wAc+gJ/+9Kfo6urCxYsXAQAXL15EZ2fnck9DRERVivT6qzFMuhBY2tvbpySw\n1I5bODRQGiZ9aEBymHTBfFBsX1+vsqBYHQGvWgK4GVhONarLGlwuCD1uL5kDHXY8YF74AdhB93Gt\n93Ykw59DtE5EJvA8SIhC0ImiRuk7k7q7u3HDDTfg3LlzWL9+PcbHx3HzzTfj5ptvxgsvvIC9e/fi\nhRdewLvf/W6Vp0FE1HQivf7WGPxaKR2BpZZloiuVwOHdm5FKxpDO5GHHLbnh20BJUKyhKChWV8Cr\nEB7iiwK445YJITx57WiaY7qoDiynpeqxBgsBZOIdSN77PKyWNrhzM8gYLUh6xpI5AACd9tIAbAAB\n9/FGJCwTDx89taJ7O7LhzyFaJyIReB4kRCHoRFGkvJrbpz71KfzVX/0Vcrkc1q5diyeffBJCCDz6\n6KMYGxvDjTfeiEOHDqk+DSKiphPp9bcQ/Apce5SsOLAUwEJg6XMP3IG2pLwfn5Zlon3+xX17S1za\n85YoDooFrgXF7hyVdv10Xa/ZvItHjp4q+bhG//ouHN69Ge0xyRXwFM8xHQqB5Yuvl+xxoaV0r8Gz\neRd7h4PvjevNgcLjTCYfcB+fwZMfuW3F97autaEuQrROFALPAYT/uhdo+NlGROUpX0k2bNiAr3/9\n60uOHzlyRHXTRERNjetvbeyEFRxYmgjhxwISdmAwLxLyPo6h63qlkrGyoeW0VKTmccjoXoOXuzcq\nnQPl5svaTnvJsUrnEOcgKaPhZxsRlcf3/xEREQVwsi629JTmmWzp6YSTDWEp5azjv/2/2Lp+/7gk\nuq5Xej5IdnE76UxeajtREal5TMta7t6odA6Umy+Fj8Zd7/uDcA6SMhp+thFRedxMIiIiChCpwFIN\nQbG6rldrLDi0vDUWwnHRIFLzmJa13L1R6RwIni8b0WHHVzyHOAdJmRCFoBNFEd8TTkREUrmugJNz\n0dvXh6tzOSWB0kJ4cHKu0iBR0zTQaceXhGPLbqdwvZQGcBcFxXoJG4aCoFhdAa+xmIlOuzS0vDVm\nISYzLylCdM1jqr/l7o2gOQD4GUmL79eO1viS5zBNY8X3dmTDn6n+QhSCThRF3EwiIiJpXFdgKp3F\n/tEzC1V7Dg1sRFcqIW2DRGfVsGknp7QdHddrwXxQ7OTEBDZs2CD3uRea0BPwGouZaI+ZmFDYl6jQ\nMY+pMeTzAtPO0vWk007gymzpHHhm1+3IugL7Rs6UzIuO1njgc3SlEjXd25EMf6bGEKIQdKKo4bYt\nERFJ4+Rc7B89g/FzU8gLD+PnprB/9AycnLxsjOLKQIU29o2cltqGrnZ0XC9qbrruF6q/2XzwejKb\nXzoHrjg57Bs5s2RelHsOzhciIlqMm0lERCSNjkpbuioD6WiHlclINVbSah7VVHNb22lXVfmNaxIR\nES1W9WaS4zAdn4iIgumotKWrMpCOdliZjFRjJa3mUU01t1ennaoqv3FNIiKixSreTDp16hQ++MEP\n4oMf/CAAYHJyEp/+9KdVnRcREYWQHQ+uJiSzao+uykA62rHjFp7ZtQnHH78T5z5/N44/fiee2aWm\nypEQAmLuKvr6eiHmrkIIoaANDzOZPIQ3/yg86W0AgJt3S/ri5rkxUo4/jzcuqc4V1kpauuZYGC1X\nze3ZXbeXrDNr2uIB82JT2ecInC9CAJkZwJt/lLSmaB1jRX0gImoGFb9n9cknn8TQ0BAeeeQRAEBf\nXx9OnDih7MSIiCh8LMtEVyqxpGqQzDBpXZWBStqJW371OMntGAawyn0dxjcHgQvj6F7XD2/7EDyj\nW1obgL+RZKQvwTjmt2PMtyNS3TAlVb3RFYzu5l2Ys5eX9MVtXQ0rFs4NEtUSloknP3Ib1nbaeHXa\nQUJ2uLsmuuZYmLUlY3j2/s1oa4lhZi6PmGnAgId2cQWriteZHUNoSXUHrqMVreFCAM4lYMx/Tqzr\n90uy2901VdLSOsaK+kBE1CyqWilvvPHG0m/mQktERItYlon2ljjOTk6ivSUuvyoZrlUGMo35R0Uv\nJAvtnD07qaadbNrfFDn/MiDywPmX/a+z6dC1oyvo2cg7gX0x8vwYfhAn5+Lho6fwrqe+j5s/8W28\n66nv4+Gjp0IZqMww8eXN5l0MHjmB3/y772L9E9/Gb/7ddzF45IR/z4wtumfGBmHmnMB1tLCGm4ZR\nfg3POf4mTNFzYmzQP14DrWOsqA9ERM2i4ncm3XjjjTh16hQMw0A2m8VXvvIV3HzzzSrPjYiIKNKM\nZMr/i3ixC+P+8ZC1oyvoWdc1i4ooBXBHqS8qlAvPLnfPIGGvvLGELf85oXmMFfWBiKhZVPzn4k9/\n+tP46le/itdeew2/93u/h4mJCfzt3/6tynMjIiKKNC+T9j9aUWxdv388ZO3oCnrWdc2iIkoB3FHq\niwrlwrPL3TPI1vAOnKwj/zmheYwV9YGIqFlUvJnU2dmJf/zHf8SPfvQjjI+P46mnnkJHR4fKcyMi\nIoq2RAre9iGgZytgxoCerf7XCcnvstHQjq5gdC9mB/bFi/HdBEF0jYsOUeqLCuXCs72Y7WcBFd0z\n2DEExGu4Z+IKnhOax1hRH4iImkXFH3P77Gc/u+RYW1sbfuM3fgPvec97pJ4UERFRI3BdASfnorev\nD1fncgrCxE2IVDe8gREYyZT/DoJESnomoY52dAWjWzELbutqGEV98WK2/PBtIfzslITtv1MhbksP\n5RWuC+Schap0iNswLbn9ME0DnXZ8SaByGAOrdc2xsIrFTHTapeHZrTELVsyEsLuBRfc/YMDJ5Jde\ny0rmvmn6QdU7R6XeI/4Yx/HV3bcWnWtczRgr6gMRUbOoeLXMZDKYmJjATTfdhJtuuglnz57FG2+8\ngbGxMXzuc59TeY5ERETaua7AVDqLvcMnccuBF7F3+CSm0lm4rtzS0aZpwmxph2H4j6qKWxTamZw8\nq6wdXcHoVswq6YuSjSTnEjAyABzs9h+dS1LLhgvXheFchjm6E8bBbv/RuexvMEkkhIdpJ1cyj6ed\nnNpy6wrpmmNhlM8LTDvZRWOdRT4vMJXO4b7hV/COA9/BfcOvYCqdxdW5HPYcOYFbDryIPUdOYCqd\nhahm7psmkGwDjPlHGWuKEDCdSyX3hSn53iuhog9ERE2i4hXz5z//OY4cOYL7778f999/P/75n/8Z\nP/vZz/ClL30JP/zhD1WeIxERkXZOzsX+0TMlVYX2j55h5ahmoKPKUy64Kp3sSlKsgNY8ZvPBa9Zs\nPmgOnMEVJ7dkXiCbrm+FM1ZYIyIKjYo3k1577TXMzs4ufD07O4uLFy/CsiwkEgklJ0dERFQv5Soj\npZIVf0KcwkpDlSddVelYAa15LLdmBR1f22kvOaak8ls1WGGNiCg0Kt5Meuihh/CHf/iHeOKJJ/A3\nf/M32LZtGwYHB+E4Dvr7+6//BERERCFSrjJSOpOv0xmRNhqqPOmqSscKaM1juTUr6Pir086SY0oq\nv1WDFdaIiEKj4s2kj370oxgZGcHb3/52vOc978Gjjz6Knp4e2LaNv/7rv1Z5jkREFCL5vMDVudxC\naHU+ryjrQjE7HlwZiZWjliEEkJkBvPlHVTknqumo8hQPrkonu5KUXx1r46LqWArm8fzY9/X1Kh17\n1/XXF+F5uDqXk55hFmblqrm1xoLnQIcdX1I1DYkUvEVz36t27teyDjRChbUa1zEhPMxk8hDe/KOk\nfDJVz0tEtFIVv1f/a1/7GoaHh/HLX/4SfX19+MlPfoKNGzdieHhY5fkREVGIFAJg94+ewfHz09jS\n04lDAxvRaScQi4Ur2NSyTHSlEkuqYMms5hYpheDesUH/Yynr+v0XgXZ3+EJtNVR58mAgm+xEfGAE\nZjIFkUkjZ7YgDvmB0gnLxJMfuQ1rO228Ou0gIXsOF429oXDsC6H4i9eXrlSC9yUAwwDakjE8e/9m\ntLXEMDOXR2w+oDy+aA7ELROpRGxJZTwAuGp2IHPPEXR1dGDqyhUkzXa0wajsL9C1rgP1rrBW4/kL\n4WEqncW+kdMLc/TpnZvQlUrUFBav6nmJiGpR8WbS8PAwxsbG8Ed/9Ef4yle+gp/97Gf44he/qPLc\niIgoZIoDYAEsBMAe3r0Z7SHbTAL8DaV2y8TExAQ2bNhQ79NpbMXBucC14Nydo36VpLApVHkClJy/\nk3Oxd/jkwr0CAP3ru/x7ReLGiJNz8fDRU0vaee6BO9AmK/9L09gXh+IDi9YXbiYtO6ceCZgDh3dv\nRntLHAAW5sJMJo+P1zJfZMwFxffesmo8/+LAewALwea13m+qnpeIqBYV/+RNJBJIJpMAgGw2i5tv\nvhn/93//p+zEiIgofBha3cQYnFsVXfeKlgBuTWPP9WV51QZwB123mudL2NeBGs9f1f3GIH0iakQV\nbybdcMMNePPNN/Ge97wHDz74IB555BGsWbNG5bkREVHIMLS6iTE4tyq67hUtAdyaxp7ry/KqDeAO\num41z5ewrwM1nr+q+41B+kTUiCreTPrSl76EVatW4c///M+xf/9+7NixA1/60pdUnhsREYXMcgGw\nFHGNEJwbIroC3v0A7k1LgpaltqNp7BmKv7xy16eadbnm+RL2daDG81d1v2m5j4mIqrSi9wX/1m/9\nluzzICKiCIjFTHTapaHVrTFLevi2EB6cnFsSHKsihFQIAWTT6OvrhZi7CiRSMMMWJj3PdQWcnLtQ\nZU96mLhpQtjdwMAIjGTKLzEe4usF4QLZtJ+TkpkBEinAlPfCzbKC7xXZQdKmaaDTji8Jkpd6v5gm\nYK8GBp6Hl2yDsXC95PZF1zULq3LXp5p1uar5IoSfMbQ4KDsgQFt4HjB39draELdhWhLup3LnsFI1\nBoCbpoGuVGJJsLlpGjX93Cr7vPCATLo+YeWkna7ffYgqxQ+ZExGRNEJ4uDKbU1pxRldVGyEEjPQl\nGMf8qj7Gun5424cgUt2h2yDRUQXLH5cc9o28Ev5qQ8IF0peAYw9dq+i0/ctAqlvahpKOe6XQzrSj\nuB0hAOey8mpuuq5ZWC1XTbPS61bxfLle1bOiAG3hujCcy0vXUnt1bRtKqipI1hgAbprGQih24VHG\nz60lzxulCpp0XazoR42IKw0REUlTXHEmL7yFijNOTl6ug442AADZtP/i5/zLgMgD51/2v86m5baj\nQXEVrMI12z96JpzjokM27W8kFY09jj0kdex1XS8t7RRXwCpcr7FB/7hEkZpjChRX0yy+z2fzlV+3\niq9xNWOec4LX0lrnh6Z5J4OSuRui/lPtuP5RI+JmEhERSaOj4oyuqjZGMhVY1cdIpqS2o4OOKliR\nqjaUbAuu6CSxTLmu6xWlam6RmmMKVFvNLei6VXyNqxhzZWtpiCrHKZm7Ieo/1Y7rHzUibiYREZE0\nOirO6Kpq42XSgVV9vEz43pmkowpWpKoNZWaCKzplZqQ1oet6RamaW6TmmALVVnMLum4VX+MqxlzZ\nWhqiynFK5m6I+k+14/pHjYiZSUREJI0dt/Dsrk3IzM6gq6MDU1euINnaJrXijN/G7cjMXi1qo11+\nVZtECt72oYWcD8znfCAh951JOgI1C1WeFmepyB8XtWOvTSLlZyQtzkySOPaF6kyL8y9UVXNT2k6h\nAtbi7BYF1dx0XLOwKlRtW3yft8aC783WmImZTL5k7Sm/vprzQfSFoOfWysc8bgevpeXmR6Wh2mXn\nXeuic61/KLWSuRui/lPtuP5RI+JmEhERSeSh3X0dq77p/3LbPf+iwUM3ADkbJCY8tIsrpW3sGIIh\nsQ0AgOcBVgK452mg4ybgys/9rz1PWhO6AjUty0RXKrGkQpPcKljqx16reAq49ytAy1uAuTcAQ+6v\nTCXVmeKWv6GoYCNxuepSEhtZqIDlJWwYil7AaulLiBUCmp+9fzPaWmKYmcsjZhowytybs7kO7Bk+\ntWjtiQevr5kE8K/3Lwp6Xl1R1TPTsiDs1fCKKz2Wq+ZWTah0YOW11oUw+EYKpVYyd0PUf6od1z9q\nRFxViIhIHh2h1TkHxqLQUUNF6GjOgfFv9wNf3AR8phP44ib/a4nt6AzUtCwT7S1xnJ2cRHtLXH45\n9QgFliObBkZ3Av9fD/B3Hf7j6E7pfSm8+D97dhJtyZiyFwWFdkzDUNfOfAWsycmzfraUoheuWvoS\nUk7OxeCRE/jNv/su1j/xbfzm330Xg0dOwMgHB2AnvLklaw+y6eD11bkSEPQ864+1YV53zE3LgtnS\nDsMwYba0l6/iVm2odKHyWuEccrMNG0qtZO6GqP9UO65/1Gi4mURERNJoCa3WFDqqoy9RCtSMUmC5\njgBuItnKrSfl7k2rpa3if4uOm5YeUxH0XOv63uyh1M3efyLSiptJREQkjZbQak2hozr6EqVAzSgF\nlusI4CaSrdx6Uu7edOdmKv63uPLzpcdUBD3Xur43eyh1s/efiLTiZhIREckzH1qNnq2AGQN6tsoP\nrS6Ejha1oSLstxAau6QvEtvxAzU3on99F2Kmgf71XXh6p9xg7ALhuhBzV9HX1wsxdxXClbxhpWPs\n5wnhYSaTR29vH2YyeQghL8cKwHxfvryoL3IDuAH4+TCZGfT19fobVULIfX6NlI/JQjsCYu4qPM9/\nFCG+ZrKVW0+8WPBa5lotOP74nTj3+btx/PE78eyu2/05HrC+enZH6ffvGIKI2/5Ye568Ma91fdf1\n86FRNXv/iUgrBnATEZE0pmlCpLpLg1YTKZiy81OCgrEl82Agm+xEfGAEZjIFkUkjZ7YgLjlMOmGZ\nePIjt2Ftp41Xpx0kZGcZwd9IMpzLC9WUjPkAXmGvLp9dUjUDs/EOJO59HlZLG9y5GWSNFrRIvl46\nQssFTMzFO5G896swW9oh5q4iY7SiBaa8v8IVBQ0bIQ/K1RUkL4SAkb60dB6nuuWvMSEkhIf4ovUk\nbpnza1kX4kXrsmu1IpGZQveSQgZdgFm6vnox28/fKV5zzQRm5nL4+FHJYx4YKl1FmHut3x92zd5/\nItKKm0lERCSVaZpASzsmJiawYcMG+Q3kHL+q0PmXrx3r2er/8iwx08bJudg7fBLj56YWjvWv78Lh\n3ZvRLmnDx8m5ePjoqSVtPPfAHWhLSvwRnSsK4AUWAni9gRHAapfShJNzsWdYfV+KQ8sBLAQHy2xH\nS1+Kg4aBa0G5kuexDjrGBEBpyDtQOo9b5MzjMJvNu3gkYD05vHvzkrXs+ON3+htJxddybBAYeB74\nt9L11fjz08A395Ue69mKzD1H1Ix5IVQaWNm9UOv3h12z95+ItOE2NRERhYumgNFUMhYYZpuS+OJY\nVwB3lMLEdbSjpS8RCsqN0jwOs+XWrMXHuzo6yofMLz7ecVPgv+3q6FjSVhiLBxAR0cpwM4mIiMJF\nU8BoOpMPDLNNZ/LS2tAVwB2lMHEd7WjpS4SCcqM0j8NsuTVr8fGpK1fKh8wvPn7l54H/durKlSVt\nhbF4ABERrQw3k4iIKFw0BYzacQuHBkrDbA8NyA3H9gNzNy0KzN0kP4BbW5i4+r7oaEdLXyIUlKtt\nHmsMeQ+j1ljwmtUaWzo+ydZ2eEHzL2EDi8PnWzsCr3uytU39mBMRUcNiZhIREYWLaULYXTAGnvc/\nkpGZgZewpQfwWpaJrlQCh3dvRioZQzqThx23YEkMyDZNA12pBJ574A7YCQtO1oUdt6SGFgOAaVkQ\n9urSYPS4LTF8W2NfTAOddnzJuMhsR0cb/jzuBlSH1WtQMvZxC05O1dhrCvgPqVjMRKdduma1xizE\nYiY6WuNLjhtmmaDm1Go/O2l+fTUSNoRnLFk/2gyz4vtdCM+fFwrXhmUJ4eeUMZSaiEgarqJERBQq\nQrgw0pdhjH4MxsFu/zF9GULI/3iFZZlob4nj7OQk2lviUjeSCkzTQFsyBtOYf1T0Asu0LJgt7Zic\nPAuzpV3qRtJCGxr6IoSHaSeHvcMnccuBF7F3+CSmnZzUUvS62phK53Df8Ct4x4Hv4L7hVzCVltuG\nToWxP3t2Uu08Nk2YLe0wDP+RG0nXCOHhymzpvL0ym0M+LzDtZBfN5yxcD/6GkTEf2Gya81UGp4DR\njwEHu/1HZwqmYZRed8uq+H4vVPvbc+QEbjnwIvYcOYGpdFbfXC9UThwZ8Ps0MuB/LYSe9omIIoo/\ngYmIKFSMbBrGsYf8ykIiP1/R6SEYWeamNIPiymF54S1UkXJyEjOTItIGNZdyc2o272L/6JmS4/tH\nzwTPteIqg/PrK8YG/eOSz0vbXFfQJyIi4mYSERGFTVC1oUIVIoq8qFRz01UBjZpHuTlVVWVKBVUG\n6z7XI1Q5kYiokXAziYiIwiWo2lChChFFXlSquemqgEbNo9ycqqoypYIqg3Wf6xGqnEhE1EiUB3Df\nddddSKX8cETLsvD1r38dr7/+Oh577DH84he/wNve9jZ84QtfwFve8hbVp0JE1FTqtf4WglZ7e/sw\noyC42EukgO1f9j/qdmHcLw2+/cvwEilojHOVJp8XmM27SwJzw0gIAWTTSsOR7biFZ3dtQmZ2Bl0d\nHZi6cgXJ1jYF1dw2Yt/IGRw/P40tPZ14eqeKSn5q26D60r0Gl7s3ClXe9o9em2uFKm8zmXxpKHah\nyuDY4ML6ih1DEHEbzuJ/W2HYdmvMxNM7N2HfyOmiua6x8luZPoWxciIRUSPRUs3tyJEj6Oy89heJ\nw4cPo7+/H3v37sXhw4dx+PBhPP744zpOhYioqehefwtBq4tfNHSlEtI2lDzPQDbZiWRRtaGM2YK4\nF76tpEIw7uIXeZ12InQbSkIIGOlLMI75L9iMdf3wtg9BpLolbyh5aHdfx6pv+u10z7fjoRuQuJ2Y\nsEw8+ZHbsLbTxqvTDhIKwtd1tEH1pXMN9oQIvDcEVsNOWPinXbdjVWscb87mEDMNpLN5PHz01NK1\n2i6t8ibiNqbSuYrW9XI/AzrtuPJKj8uyEsA9TwMdNwFXfu5/TURENanLby0vvfQStm3bBgDYtm0b\nvve979XjNIiImo7q9VdXcPGD/3IKb//0y3j7Ey/i7Z9+GQ/+y6lQBheXC8adzYevL8im/Y2kkmD0\nQUB2MLqGdpyci4ePnjPXwxcAACAASURBVMK7nvo+bv7Et/Gup76Ph4/KnWM62qDGo3INNvJO4L1h\n5B3sGT6JjZ/5L6x/4tvY+Jn/wp7hk7ji5ILXatMsqfLm5ETF63r5EHChpWploJwD/Ov9wBc3AZ/p\n9B//9X4GcBMR1UjLO5MGBwdhGAbuvfde3HvvvZiamsKaNWsAAGvWrMH09HTg901MTNTc9tzcnJTn\naRRR6w/APoVF1PrUKP3ZsGGD0udf6foLrGwN7u3tCw5ajVvSrndvX3AbqWRM2Ziqmi9R6ktfX29g\nyK2RTEltT0c7WuaxhjbqoVHW1kqoXn8Bvb8DL3dvBM21tZ32kmNB86+auap6Xq9kfvX19cIIuC5e\nwsZkg8zVMN031Yhiv6LYJ0B/v3Ssv6Se8s2kkZERvPWtb8XU1BQefPBBrF+/vuLvlTHJJiYmIjVZ\no9YfgH0Ki6j1KWr9CVLL+gusbA2emQ96HT83tXBsS08nnJwr7XpfncsFtpHO5JWNqar5EqW+iLmr\nMNb1+++KKFjXDy+TltqejnZ0zGMdbdRDM6ytldL9O/By90bQXHt1uvSdOeXmXzVzVfW8XtH8KhRt\nWHRdjKzTMHM1qvdNFPsVxT4B0e0XqaX8Y25vfetbAQBdXV1473vfi//93/9FV1cXLl68CAC4ePFi\nyWfJiYhIjnqsv36o8Cb0r+9CzDTQv75LetCqHffDZIvbODQQzuDiQjDu4r60xsLXFyRS8LYPAT1b\nATMG9Gz1v06kQteOrnmsug2qL91rsBezA+8NL2YvXWd2bkSHHa9o/lUzVxtyXhcCuIuuCwO4iYhq\np/SdSY7jQAiBtrY2OI6DH/7wh/jTP/1T3HXXXXjhhRewd+9evPDCC3j3u9+t8jSIiJpOvdZf0zTQ\nlUr4Qatxy6/oIzlo1bJMdKUSOLx780IFNDtuwVIQXqy6Ml0sZqLTLu2Lqmpuriv8vvT14epcTvo1\nM00Twu4CioLRvbgtvZqbaZoQqdWl7UiuGlcyjxUFBpumgU47vmQeq8iSWVxdS3v4cROqxxpsxSyI\n1kX3YMyGFbPQaRtL1hnTNALneNB8qfR+UHbvCAHkHP+jfJkZfyOo0nveNIFFoeJVfT8REQVSupk0\nNTWFP/uzPwMAuK6LD33oQ/jd3/1d3HbbbXj00UcxNjaGG2+8EYcOHVJ5GkRETaee669p+gGrKt8y\nbVkm2uc3Qtpb4kra0FGZznWDq7l1pRJSN3pcV2AqrbYdIQQMZ2qhmhvW9QMqqrkJAdO5XFLm29gx\n5L9YlLyh1Jb0f00qPMokhIdpp7IKWbW2o3oe01L1WIPdvAtzduk96LauxrSTC6wauXiOLzdfKr0f\npN87QgDOJWBs0M8+Wtfvv7Oomnu+ECoOXHskIqKaGJ7nefU+iSAnT57E5s2ba36eqH3+M2r9Adin\nsIhan6LWH9lkrMFhv8YzmTz2HDlRkv3Rv74Lzz1wh7TNhatzOewdPrmkjcO7N0vdJNPRjpi7CnN0\nZ2kuSc9WiIERmC3tUtoA4L8rYWRgSTvYORqqF4k65pfOdgrCft83gpWuv8vdg/cNv1LR/a97vlQk\nIvf8cqJ630SxX1HsExDdfpFafH8nERFRADthBVclSsjL/kglY2Wrucmkox0jmSpbSUqqhB3YDhLh\nyj/RMb90tkP1t9w9WOn935DzJSL3PBFR1HAziYiIKICTdbGlpzQcd0tPJ5ysK62N9Hzlo8VtpDN5\naW3oasfLpP2PnxSbryQlVdYJbAdZJ/jfNygd80tnO1R/y92Dld7/DTlfInLPExFFDTeTiIhILuEC\nc2/6Qalzb/pfh1CUKtNpaUdXNbe4DW9RZSYvhJWZ/Pm1cdH8UjP2z+66HccfvxPnPn83jj9+J57d\ndTurxkVQVdXc5qtGzmTyEJ7nPwpP3bonhP9xNW/+UQgI4S1pP9By1djmf97AE6H+eUNEFEZ1+vAz\nERFFknCB9CXg2EPXglK3fxlIdQNmuF686qhMZxgG7ISFf9p1O1a1xvHmbA4x04BhyA1G1tGOX2Wt\nG97ACIxkyn+XhOQqawAgYGDG7EDmniPo6ujA1JUrSJrtaIMRur+QJSwTT37kNqzttPHqtIOEgoqE\nJjy0iytY9U0/lLl7XT+8HUMw0A2AAdyRYhjIJrsQL7oHc2YrYoYRWDXyymxwALz0amxFAdqFYHBv\nxxBmzA58/Oip6wfDF1Vj8xI2jEI1NngLP28Q8p83RERhxM0kIiKSJ5v2f7EvBKWef9n/euB5oGVV\nfc9tBVRXpnNyLvYEBGPLDrvV1Y5pmsB82LYhM3S7iJNz8fGjpxorIHgFnJyLh3X0I+fAGBssuSeN\nscFIhReTz8m5ywbtt8euVcCcyeSxb+T0wr8dPzeFfSOnF+af1GpsOcffSFo0BzP3HCnb/hLz1dgm\ni9fiuTcj9fOGiChswvZHPCIiamTJtuCgVL5oDcQQ5upFpS/a+sHw4qZRTdC+1vuozBzs6uiorX3+\nvCEiqituJhERkTyZmeCg1MxMfc6nwTGEuXpR6Yu2fjC8uGlUE7Sv9T4qMwenrlyprX3+vCEiqitu\nJhERkTyJlJ9ZURyUuv3L8kOYI0JHyLfOdnSISl+09WO58GKKlGqC9rXeRwFz0NsxhGRre23t8+cN\nEVFdhSdcgIiIGp9pQaS6YQw873/UIDMDL5GCGdIwVCE8ODkXvb19mMnkpQdwm6aBTjteEowru41C\nO12pOL66+9aicOy49HaEEEA2rTSAW0cwOgA/NDjn+B/RKQT+SuyLaRroaI0vCUWW3g/ThLC7AdXB\n6IrvFbo+yzKx2o7h+d2/fm39jcVgBgS7l9xHi4K2C2NZfNzz/GPF65RVJjA+6PvN+QDtwv1kxG20\nwagt6Nu0/LDtop83SKQgYMLJ5OUFiDcyxesUEdFyuNoQEZE0QniYSufxseGf4h0HvoOPDf8UU+ll\nSj43ML8vWew5cgK3fPJF7DlyAlPprNS+COFh2slh7/BJ3HLgRewdPolpJyf/egkB07kEc3QnjIPd\nMEd3wnQu+S9EpDUhYKRL2zDSl/wNJskKwehnz06iLRlTs5HkXAJGBoCD3f6j5OvlugLTTnbR2Gfh\nunKvlz+Pc7hv+BW848B3cN/wK5hKy51jOu4Vuj6Rz8OYvQxj9GMwDnb7j7OXIfJLP+YGXLuPTMNY\nuI9KxvKAP5ZONo+pdOlcnUoHz9Wg759KZyFg+Bs+hh+kDdMMbL9qpuWHbRsm0LIKAmZw+1GcixrW\nKSKi5XAziYiIpHFy7kKFoLzwFir0OLlw5dkAevqi7XoVV1MSef9xbNA/Lks2DeNYaRvGsUG/wl/Y\naLheTs7F/tEzJWO/f/SM9LGP1DymZRl5B0ahutnCPfgQjHzl8zZoLPPCq3iu1nsu1Lt9rXSs60RE\ny+BmEhERSROVSluAnr5EqaKXkUwFtmEkQ5hfouF6VVN5qxaRmse0PAnVzYLGclVrvDGrxAWod/ta\nsVIjEdUZN5OIiEiaqFTaAvT0JUoVvbxMOrANLxPCdyZpuF7VVN6qRaTmMS1PQnWzoLF8czbXmFXi\nAtS7fa1YqZGI6oybSUREJI0dt/Dsrk04/vidOPf5u3H88Tvx7K7wVdoC9FQ70lnRywuopiS1olci\nBW/7oja2D4WzspKGCmjVVN6qtZ3IzGPAz4PJzADe/CPzYRZ4MRveoupm3vYvw4uVmbcB1zJoDW+J\nGYFzNRU3A7+/ntUW691+1ebHoK+vt/r5zEqNRFRnrOZGREQSeWh3X8eqbw4CF8bRva4f3vYheOgG\nEK5qOjqqhi1XUUkmAcAwE8A9TwMdNwFXfg6YCQjI/KuSgdl4BxL3Pg+rpQ3u3AyyRgtaQjbuC6xF\n18tKyH16y0RXKrGkkl+5ClkrpWOOaa2w51zyc2EujPvvwtgxBNjdrGAFwDNMuC2rESuqbpa3bJhG\nwLUpey1XB67hXfbqkrmaipswZy8v+X7T7tayppWja02VomgMjJXMZ9P0/21RlTxWcyMinbiZRERE\n8hSHMAMLIczewAjQ0l7fc1uBQrWhiYkJbNiwQWkbABYepcumYfzb/dfGBYDRs1XquDg5F3uGT2H8\n3NTCsf71XXjugTvU9UuVnAP8a+n1Qs9W/0VbFfkz12NZJtotU+n8AvTMMR33SkngMHAtcFjyuISV\nk3Oxd/jkknvw8O7NaF+8SVnmWhoDzweu4cbACNrn14r2lrj/LpoyY2Em29SvacvQsqbKIGM+m+a1\nf8t7gIg049Y1ERFJE6kQ5gjRMS6RCr5lsG1j4rgsq6pQ93LXskyI95K1gmNRO15DIgo5biYREZE0\nkQphjhAd4xKp4FsG2zYmjsuyqgp1L3cty4R4L1krOBa14zUkopDjZhIREcmjKYRZCA8zmTyEN/8o\nPKnPv7id3t4+pe0op2FcdAbfKh8XXcG2tYTvVtOM60LMXYXn/f/s3X+UHFWd//9XVf+Yme5M3Mww\nIbtuYjYomYgLCZCD2V2OEpUfB91FE9YEENRIXI+7E0DhCIGPfFRgRVwlu+cowfiVqEyWDb/WH6hH\nEOGsE0lCsqwyQc8nZhN3BWFmJDPTM/2r6vtHzwzTM9WT6umq6q6a5+OfJkV33fuuuvf27Tvd72vJ\nGh2UVfR+gS+QvkLC4RmlEjF9dUry7K+ObYBgWVZ5G0i0OF5L2+1YMdO9CFGS9KDeSxzRngGEXAP/\nkBgAEDamacpKd8je0C2jKV36a3YyLdPDhKCWZatvOKeu7gPae6Rfq5e2advGVWpPJz1NshpUOUEI\n4r4Elkw8qPvicwLumpPvui2mWJSReaWUB+doj4yxhMpW6iSZMW8W+gK7JyQcnplta37xDzKmboBg\nt8vI9E1vA+mTZE65lq7Hikr3QgpNkvS6j/GTrqGdTMmgPQMIGUYrAICnTNOU2dyqQ4dekNnc6umC\nhVRKMtvVfUA9h/tUsGz1HO5TV/cBZfLeftsiqHKC4vd9KZVRSnxrGmOPPnwgC+S+jCfg/udV0mfa\nSo//+oHScS/LGE++axVeS77rZRlj5UwkVB4rx3jQ23IC7SvjCYeNsUc+eE8wCs73utJx5TKO13J8\nrDAMc+axwuleBNWuPdAQY/zYNTx06AXaM4DQYcQCAIRKUImeI5VQOkICuS9BJMYNKPkuydfnjkr3\n2nVSbS+EKKk07RYAasNiEgAgVIJK9ByphNIREsh9CSIxbkDJd0m+PndUuteuk2p7IURJpWm3AFAb\nFpMAAKESVKLnIBNKw71A7ksQiXGDSr6bSDknVPawHPpKY7Djzve60nGvN0aQFKqk0rRbAKgNCbgB\nAN6yLCmfeW2HKo8TigaV6LmsnERMmbw/5RSLljL5otJNcQ1nC0olYorFwvm3HsuypNywb0m+pfH7\nktC3rzxtUjmJ8CV6Dij5rhmLqdhykoxJCZXteEoxj5JvS8H1FcwsFq9wr+MxWamTypNqjyXb9pxp\nSqmTpA33l35elx0qLVpVU9bYe4jfSdareS+xLLvUrmt5zwkoLgAICiMYAMA74ztUdW+Q8dkOqXtD\n6d8ebw0dRKLnyeW88MIhX8opFi31Dee0eed+nbr1MW3euV99wzkVi427lXYllmXJGH5Z5q6NMj7b\nUXocfrm0wORtQTIz5eWYPrSxQBI9B5B817Js9Y8UdPnOX+pNW3+gy3f+Uv0j3m+B7ndfwYkVi5b6\nMvmye92XyatQsNSXKUw57n0bkDT2HvCKtOsy6bMdpcfMK+7756T3EPn4HjLOzXvJ+K5vV9+3T6du\nfUxX37dPfcO56q5fwHEBQBBYTAIAeCdEO/k0gky+qC27DpbtJrRl18Fw7hiXG66wY5THeVloY1Vp\niB2rEIhK48lIIcA2UGv/bMD+7UkfasC4AKBWLCYBALwTop18GkG6Ke64m1C6KXy/Qg9i1zBJtLEq\nsWPV3DHTeBJYG6i1fzZg//akDzVgXABQKxaTAADeCdFOPo1gOFtw3E1oOFuoU41mL4hdwyTRxqrE\njlVzx0zjSWBtoNb+2YD925M+1IBxAUCtWEwCAHgnqJ18LKuU2NUee/Qp74Rl2RrKFrR8eaeGst7n\nGEklYrp7w8qy3YTu3rAynLsJJdPB7BiVSMme0sZsH9rY+L23bNuXez+5DL/al8SOVXNJKhHTjivP\n1P/79F/pN3dcpP/36b/SjivPVEvcuQ20xAxZo4OybUvW6KCsYmlxxKntu+4Ptb4HBLwbnJu4POlD\nIdrlDqgooLkXwiN836MHADS2WFJ6zzZpwRukgf8u/dtL44lMd28q/UxgyZrSpDzV4WkC4/Gkq13d\nB7T3SL9WL23Tto2r1J5OepZcOBYz1Z5OavuVZ4V+NzfblkYSC9T0/vsVa56n4uiQskazmj1eH7Fs\nS4Y5pY2ZSVm2JdOjv5EFce+DKEMKbvdD1J8hSy35fhkPfkQ62qPYkjVqWfc12YmTprWBlpghc+SV\nUl6zoz0ylqyRvW6Hii0nqX+kUNYuv3rFmcoVLXV1HzxxW611J8QgdlIc47YPetKHAowL8EVAcy+E\nC3ceAOCdfEb61w9I/7xK+kxb6fFfP+BtktGAEpkGlbg4FjPV2pyQaRhqbU6EciFJKl2vj+x8Vm/8\nv0/rz258TG/8v0/rIzuf9fx6GfmMjAfK25jxwAdkeHj/g7j3QSbGDmr3Q9SXkc+UFpLKkuB/REY+\nM60NGIWMY8J8o5CZ1i4HMnl1dR9031Zr3QkxiJ0UVV0f9KQPBRQX4AuSyMMBoxgAwDtBJBkNKJEp\niYurE1gy8aZ5zve/aZ5nRQRx72lf8FwVfWOmhPlT2+XitlQk2yp9EKgCSeThgMUkAIB3gkgyGlAi\nUxIXVyewZOLZIef7nx3yrIgg7j3tC56rom/MlDB/ars81p+JZFulDwJVIIk8HLCYBADwThBJRgNK\nZEri4uoElUzcTqRkr/valETfX5Pt4f0P4t7TvuC1qvpGIuWYMN+Op6a1ywWphLZtXBm5tkofBKpA\nEnk4IAE3AMA7pikr1SFt6JbRlC799TuZlullbgjTlJU6ScaG+0s/38gOyfa6DJVyZLSlEtOSY3ud\nb8aybGXyRd+TI4+XM75zmNflxGKm2lLlycRb4t4nEzdjcVmpk6TJ9z+RkhnzbkoTxL0vS+qbiJXa\nAImxT8yySjk6SGI8jRmLy2qZ0jfipb5hFYtSPvPauJxIyWo5ScaksdqOpxSLx9SeNqclm7Zt2/ex\nMGiNkJze73EZ8AxJ5OGAuw8A8Expd5y8Lt/5S71p6w90+c5fqm847+mW55ZlyRh+Rcauy2R8tqP0\nOPyKLI+3qLUsW/2ZvDbv3K9Ttz6mzTv3qz/jdSyl3YSuvm+fTt36mK6+b5/6hnOebxFfVs7N/pRj\nWbYGRsqv18CIt9drnBmLy2ier0OHXpDRPN/ThSQpmHsvvZbU94UXDpEY243x3YS6N0if7Sg9Zl5m\ne+oxVrEoY6SvfGwc6ZNVKMjIvCJz10YZn+0oPWZeUbZQLBur+0cKsix7WrJpSYH0h3qoZ3L6IMZl\nwFMkkccUtAAAgGcC2aEqN+y4C5Fyw96VoWjt6BWlWIIQpVgihd2EZpavvEOb0/GkPeqqjdMf/MF1\nBRB2LCYBADwTxO44M+1C5KUo7egVpViCEKVYIoXdhGZUaWystMtbrLl8l7dKbZz+4A+uK4CwYzEJ\nAOCZIHbHmWkXIi9FaUevKMUShCjFEinsJjSjSmNjpV3eiqPlu7xVauP0B39wXQGEHYtJAADPlHbH\nmbrrj8c7eiXTjrsQKenxN5MSMd1zxZnae/1bdfj2i7T3+rfqnivODOWOXlHbncyyLFmjg+rsXC5r\ndNDzfFmBtOMgWVZpQcEeewxrjqFESvaU3YRsdhN6zQw7tDkdzxnNrvqrX327WLQ0OJqXZdsaHM2r\nWAxpu5wlT65rVPo2gFBiNzcAgKeSMVN3vO/PtbgtpWP9GSU93s3LtqWRxAI1vf9+xZrnqTg6pKzR\nrGaPc5aastVqDWj+dzZJR3vUsWSN7PU7ZKhDkjdJWoPaTSiIncOCiqWUgP3lUg6Yoz0ylqyRvW6H\nrHSHZzv62batxJR2nIiZsm1bXt37wIwnrd5dul5asqa0nXOqI3TJUy0ZGjIXKPue+9S+YIH6BgbU\nZLZqngz+OiqVBsdYUnrPNmnBG6SB/y79W5KdOkn25F02Eyk1G9N3bXPqr3707WLRUt9wTlt2HdTe\nI/1avbRNd29YqfZ00vMdIBtVzeNyhPo2gHBiMQkA4JlMvqi/+9az6jncN3FszbJ23XvV2RO7AnlR\nxuad08vYfuVZavXyQ0g+I2M82a9USlq7e1NpW9ymeTO/tgrjuwlJ8uwazVROb2+vVqxY4WsZko+x\nTE7ALk0kE7Y3dEvNrZ4UkckX9TGHdux5GwvC5KTV0mtJqz1ux0HI5Iv6qM/jS5gZhYyMBz7w2r2W\nZCw9V9pwv4zm+VKs1D+MSf3EbX/1um9n8kVt2XVw4l72HO7Tll0Hw9nHalDTuByhvg0gnObOaA0A\n8F0QCUXTTXHHMtJef5gk2W9DCiIBe2BtLAgRasckLD6BCom2G3FhIVJ9rF4i1LcBhBOLSQAAzwSR\nUHQ4W3AsYzhb8KwMSST7bVBBJGAPrI0FIULtmITFJ1Ah0bayQ87Pr6NI9bF6iVDfBhBOgSwmFYtF\nXXLJJfroRz8qSTp27JguvfRSnX/++brmmmuUy+WCqAYAzDlBj79BJXq+e0N5cuS7N/iQHDmRKuWf\nmJS0ViT7rb8AErAH1saCEKF2HGSSd68EOQaXEm1/bUrf+JrseOPd60j1sXqJUN8GEE6BfJd0586d\nOuWUUzQ0VPrLyF133aUPfvCDuvjii/V//s//0e7du3XZZZcFURUAmFOCHn9N09CCloS2X3mW0k1x\nDWcLaol7m4Q5FjPVnk6WlZFKxLxP2mqapUSmG3fJTqZk5DKlSTqJTevKNE1Z6Y7yZMLJtGfJt6VS\nG2tLJae141AmBp7UjpVMlb61ENJ2HFSSdy8FOQab8bislpOkDfeXftqWHZIdT8mMO0/3LcsuJX2u\nw7UMbByPsgj1bQDh5Pto8+KLL+rJJ5/U+vXrJZV2SNmzZ48uuOACSdJ73/tePf74435XAwDmnHqM\nv8Wipf5MTpt37tepWx/T5p371Z/Jeb7lcyxmqrU5IdMw1Nqc8O8DiGlKTfN06NALpQ9nTNIbgmma\nMptbdejQCzKbWz1dSJJKH7IHRvJl7XhgJC/L8njLwKCMtWMZZujb8XjCYtMYe2zghaSgx+Bi0dIr\nmYIu2/m83rT1B7ps5/N6JVNwHH8ty1bfcE5X37dPp259TFfft099w7lA23hg43iURahvAwgf30ec\n22+/Xddff/3ERG9gYEDz589XfOyvJIsWLdJLL73kdzUAYM6px/g7eYeegmVP7NCTyZPTBOGRyRfV\n1X2grB13dR+gHaMqQY/B1Yy/tHEAQK18/ZnbT37yE7W1tektb3mLfv7zn1d8nmE4/1Wpt7e35jqM\njo56cp5GEbV4pMoxrfjXtwZaj9737/HsXHPpPoVVo8Tj1xbttY6/0uzG4OWdnRV36GmE6z1bjdJe\nvEAsJ7Z8uXM7TiVivpTHPakPv8ZfqT5z4GrG36DbuFfC1L6qQVzhEcWYpODj8nP8RXB8XUx69tln\n9cQTT+ipp55SNpvV0NCQbrvtNh0/flyFQkHxeFwvvviiFi5c6Ph6LxpZb29vpBpr1OKRGicmL+vQ\nKDF5KWoxRS2eqWodf6XZ9YnB0bxWL21Tz+G+iWPjO/SE+XpHqb0Qy4kNje00NbUdZ/JFX8rjnkRP\nPebA1Yy/Qbdxr0S1fRFXeEQxJim6ccFfvi4mfeITn9AnPvEJSdLPf/5zff3rX9cXv/hFdXV16Yc/\n/KEuvvhiPfzww1q7dq2f1YCXbn2d56dk2AK8V6/xd3yHni27DmrvkX6tXtrmyw499Uwci8qKRUuZ\nfDH0CXXHdw3r6j4w0Y592TXMsqR8Rp2dy0vbt/uUPJf+Erx6jMGpRExfvWKVciNDal+wQH0DA0q2\nzHNst6U2vlJd3QcntfHKYzVtCAAwVSC7uU11/fXX69prr9WXv/xlrVixQpdeemk9qgEAc47f469h\nGEolY/rKFWdqfktCx0fyipvGjD+nq9Z44tipH/Tb00k+3NRRsWipbzg3bSGxPZ0M3YJSILuGWZaU\neVnavUnG0R5pyZrStt6pDk8XlOgvjcXXMdi2Nb/4Bxnf2SQd7VHHkjWy1+2QZZ/k+PRkzNQd7/tz\nLW5L6Vh/RskK/ZQ2BABwEthi0jnnnKNzzjlHkrR48WLt3r07qKIBYE4LcvzN5Iu6euf+sp9OrFnW\nrnuvOlvzmrx5y5mcOFbSROJYL8tA9SYn/5U0kfx3+5VnqTVki0nSa7uGSfKnXeUz0u5N0pGnS/8+\n8nTp3xt3lXZl8gj9pf6CGoONQkbGg+Vtynhwk4wN3VK8tey5mXxRf/etZ12N1bQhAICT8M3uAAAN\nK5WMOSd1TXr386AgykD10k3xisl/4SCZko72lB872lM67iH6y9xhNKUd25TRlJ723GraBW0IAOCE\nxSQAgGcyuaJWL20rO7Z6aZsyOe+2mw6iDFRveCyh72TjyX/hIJcp/bRtsiVrSsc9RH+ZO+zssGOb\nsrPD055bTbugDQEAnLCYBADwzHji4jXL2hU3Da1Z1u554uLxxLHlZXif5FuSCgVLg6N5Le/s1OBo\nXoWC5XkZQbEsW0PZgpYv79RQtiDLsj09/3jy9cn3xY/k65L/sQQikZK9foe09FzJjEtLzy39O+Hx\nN5MC7C+BsKxSsnJ77NEKb5/0mh1PyV43pU2t2yE7nlKxWBrLLNvW4GhezTGzYrsY71+WXXpsiZuO\n43pL3Cx7Xj364dS6hnIsmIo2DiAk+O45AMAzZYmLE7HS7j8+7PrjNnFsLQoFS/2Z6Qml21JJxePh\n+ltMEAl0YzFTSHnHaQAAIABJREFU7emktl95lq+7uUUlGXDRlobMP1LuPfe9tvOWOU/zbMnrZZ4g\n+ksgJiUtl49Jy8PKlqFcU7sSG7plNKVlZ4eVN1sUk6F+h+T485ri09qFbdvqz+Sn9a+2VKIsIX1L\n3HR8XpD9MCpjQRnaOIAQYTEJAOCp8cTFvb29WrFihefnryZxbC1GCjMklA7ZYlJQCXRjMXMi2XZr\nc8Kz804WlWTApXZ8YFo79jpheVD9JRABJS0Pq5FCUZsdNkDYfuVZjmPZV644U2+/68lpzz1R/5rX\nFNdQtlD3fhiVsaAMbRxAiIRrNgwAmPOCSgYbpYTSUUqgG5VYgmpfUblekgJLWh5WM7Upp+PzWxKu\nnzu1vTRCu2qEOniONg4gRFhMAgCESlDJYKOUUDpKCXSjEktQ7Ssq10tSYEnLw2qmNuV0/PhI3vVz\np7aXRmhXjVAHz9HGAYRI+P68inK3vq7eNQCAQI0n+Z6aJ8PrhMIt8VJC6al5Rlri4furd1DXLAhR\niWU8YfnU9uV1HFG5XpJKycnX75ieT8bjpOVhNdOY5XR8PJn21Hbhpr00QrtqhDp4jjYOIERYTAIA\neMqybGXyxYmdtrxOwG2ahtpSiWmJnr1OuBqPm2pLlSeUbonHQpd8WwouMXqxaCmTL/qagDuoWPwW\nVMLysus1ljw5jNdLUikBcaqjlD8mmSp9WyORIjHxmJnGLKe2ZhiGY7tw015met74e4Df7c2vth1U\n/R3RxgGECCMTAMAz47vrXH3fPp1682O6+r596hvOebpds2WVdhvavHO/Tt36mDbv3K/+TN7zLaGL\nxdJubuXl5FQshnOb5vHE6C+8cEjzmuK+LCT1DZdfr75hf66X37EEJRYz1dqc0AuHDqm1OeH5QtK4\n8etlGkaor5ek0ofqpnmSMfbIh+wJ4ztQTh2zCgVroq2ZhjHR1iq1C7ftxel5Ze8BW/15DzhRHWoR\ndP0d0cYBhASjEwDAM5N31ylY9sTuOpm8dzksgihjvJzxHZDGy9my66Dn5UQF1wuor8k7UE7ugyOF\nAPMYBTQ++yXs9QeAILGYBADwTBC767CbW2PiegH11Qh9MOw7rIW9/gA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mtzk2eq2te5b9/+znXKsgw5vwiSZH7KLrddqset6PIZ/f5I07GTfDx4sD+JiIgGENnn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\nKa8g+fPmFfR4Xrv7oz+pxNTfe5eflzPoa00v6/94GOixAx13/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bY2h/GJRlRqusOxeITMZKY8iothhSjhFZwwBj5vQsu24QERERZRsuJhkk6zKQqGVe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff33c07ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.pairplot(haberman_df, hue='survival_status', size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff3370e06d8>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kHhaySlJdi1WxejMAAAAA9B+hN4n58ryEXAAAAAA4B1zeDAAAAAAwFqEXAAAAAGAsQi8A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\nfWae9ky60bbdmxcuuV87Z6xUyJys06ahkDlZO2esHHL35sp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HH32kaDSqNWvW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\nOBahFwAAAADgWP8/eDOkUzIN9kcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3371f46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(haberman_df, hue='survival_status', size=12, aspect=1).map(plt.scatter, 'num_axilliary_nodes', 'age').add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of three features num_axilliary_nodes and age seem most appropriate from univariate analysis. On performing bivariate analysis on these 2 features, clusters can be identified.\n",
    "1. There is fairly large number of patients who survived in the age range 30-40 with only 4 patients not surviving\n",
    "2. Another cluster of cancer survivors can be seen in age bracket 50-60 having axilliary nodes less than 4\n",
    "\n",
    "Therefore, although a linear separation is not possible, a non linear activation function (sigmoid) will be able to learn this kind of mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Conclusion\n",
    "The order of relevance of 3 features for cancer survival prediction is<br>\n",
    "num_axillary_nodes > age > operation_year<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=[(1,2,3),(4,5,6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-5d6e48d7cae5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "a[:1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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