BreastCancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns #for confusion matrix\n",
    "#For data visualization\n",
    "import plotly.plotly as py\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"breastCancer.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      "id                   699 non-null int64\n",
      "clump_thickness      699 non-null int64\n",
      "size_uniformity      699 non-null int64\n",
      "shape_uniformity     699 non-null int64\n",
      "marginal_adhesion    699 non-null int64\n",
      "epithelial_size      699 non-null int64\n",
      "bare_nucleoli        699 non-null object\n",
      "bland_chromatin      699 non-null int64\n",
      "normal_nucleoli      699 non-null int64\n",
      "mitoses              699 non-null int64\n",
      "class                699 non-null int64\n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 60.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "clump_thickness      0\n",
       "size_uniformity      0\n",
       "shape_uniformity     0\n",
       "marginal_adhesion    0\n",
       "epithelial_size      0\n",
       "bare_nucleoli        0\n",
       "bland_chromatin      0\n",
       "normal_nucleoli      0\n",
       "mitoses              0\n",
       "class                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(\"id\",axis=1,inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>size_uniformity</th>\n",
       "      <th>shape_uniformity</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>epithelial_size</th>\n",
       "      <th>bare_nucleoli</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clump_thickness  size_uniformity  shape_uniformity  marginal_adhesion  \\\n",
       "0                5                1                 1                  1   \n",
       "1                5                4                 4                  5   \n",
       "2                3                1                 1                  1   \n",
       "3                6                8                 8                  1   \n",
       "4                4                1                 1                  3   \n",
       "\n",
       "   epithelial_size bare_nucleoli  bland_chromatin  normal_nucleoli  mitoses  \\\n",
       "0                2             1                3                1        1   \n",
       "1                7            10                3                2        1   \n",
       "2                2             2                3                1        1   \n",
       "3                3             4                3                7        1   \n",
       "4                2             1                3                1        1   \n",
       "\n",
       "   class  \n",
       "0      2  \n",
       "1      2  \n",
       "2      2  \n",
       "3      2  \n",
       "4      2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() #id parameter dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(699, 10)\n",
      "(699, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>size_uniformity</th>\n",
       "      <th>shape_uniformity</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>epithelial_size</th>\n",
       "      <th>bare_nucleoli</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clump_thickness  size_uniformity  shape_uniformity  marginal_adhesion  \\\n",
       "0                5                1                 1                  1   \n",
       "1                5                4                 4                  5   \n",
       "2                3                1                 1                  1   \n",
       "3                6                8                 8                  1   \n",
       "4                4                1                 1                  3   \n",
       "\n",
       "   epithelial_size bare_nucleoli  bland_chromatin  normal_nucleoli  mitoses  \\\n",
       "0                2             1                3                1        1   \n",
       "1                7            10                3                2        1   \n",
       "2                2             2                3                1        1   \n",
       "3                3             4                3                7        1   \n",
       "4                2             1                3                1        1   \n",
       "\n",
       "   class  \n",
       "0      2  \n",
       "1      2  \n",
       "2      2  \n",
       "3      2  \n",
       "4      2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=data.dropna(axis=0)\n",
    "print(data.shape)\n",
    "print(df.shape) \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>size_uniformity</th>\n",
       "      <th>shape_uniformity</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>epithelial_size</th>\n",
       "      <th>bare_nucleoli</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clump_thickness  size_uniformity  shape_uniformity  marginal_adhesion  \\\n",
       "0                5                1                 1                  1   \n",
       "1                5                4                 4                  5   \n",
       "2                3                1                 1                  1   \n",
       "3                6                8                 8                  1   \n",
       "4                4                1                 1                  3   \n",
       "\n",
       "   epithelial_size bare_nucleoli  bland_chromatin  normal_nucleoli  mitoses  \\\n",
       "0                2             1                3                1        1   \n",
       "1                7            10                3                2        1   \n",
       "2                2             2                3                1        1   \n",
       "3                3             4                3                7        1   \n",
       "4                2             1                3                1        1   \n",
       "\n",
       "   class  \n",
       "0      2  \n",
       "1      2  \n",
       "2      2  \n",
       "3      2  \n",
       "4      2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index=range(0,len(df),1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 10 columns):\n",
      "clump_thickness      699 non-null int64\n",
      "size_uniformity      699 non-null int64\n",
      "shape_uniformity     699 non-null int64\n",
      "marginal_adhesion    699 non-null int64\n",
      "epithelial_size      699 non-null int64\n",
      "bare_nucleoli        699 non-null object\n",
      "bland_chromatin      699 non-null int64\n",
      "normal_nucleoli      699 non-null int64\n",
      "mitoses              699 non-null int64\n",
      "class                699 non-null int64\n",
      "dtypes: int64(9), object(1)\n",
      "memory usage: 54.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bare_nucleoli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bare_nucleoli\n",
       "0             1\n",
       "1            10\n",
       "2             2\n",
       "3             4\n",
       "4             1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtype_object=df.select_dtypes(include=['object'])\n",
    "dtype_object.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"bare_nucleoli\"] = df[\"bare_nucleoli\"][df[\"bare_nucleoli\"]!='?']\n",
    "#removed_those_rows_for_which_bare_nuclei_=_'?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222e92c518>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cbar=False,yticklabels=False,cmap='viridis') #visualizing missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222ea3a7b8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAFRCAYAAABDrNc+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XmUpVV57/Hvr0EFlGaIaCQ3OBCFIIIyBFDMNQ5JTEAJAglxxAmHCIYszXUKitMCZ0iiQQVR1IgSIxINKiiDiF6aQVDwOoZcrsEJBQcU8Hf/2O/pPnW6qhrX9TzvvtTvs9ZZ3XWqi/1Qdeo577v3s58t20RExPhWjR1AREQ0ScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiExv/Kv/4UasOzra+iIhf0Sd/+UHdln+XK+SIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiE4kIUdEdCIJOSKiE0nIERGdSEKOiOhEEnJERCeSkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohNJyBERnUhCjojoRBJyREQnkpAjIjqRhBwR0Ykk5IiITiQhR0R0Igk5IqITScgREZ1IQo6I6EQSckREJ5KQIyI6kYQcEdGJJOSIiF7YLnsAz6wcr+c4eoihlzh6iKGXOHqIoZc4eoihOo7qK+RnFo+3lB7i6CEG6COOHmKAPuLoIQboI44eYoDCODJlERHRiSTkiIhOVCfkE4vHW0oPcfQQA/QRRw8xQB9x9BAD9BFHDzFAYRwaJq0jImJkmbKIiOhEEnJERCeSkCMiOjH3hCxpe0l3Gv7+MElHSNpy3uMuEsfrJd2/etyZGPaT1MWboKSNJG0rabvJY4QYNpW0Q/W4sZCkHYc/d1vsURzL6PlizBjmvqgn6TJgD+BewFnAGcAOtv9krgOvH8fTgcOAjYGTgffb/lFxDKcC+wCnAyfbvqpy/Kk4ngccDVwH/HJ42rZ3KYxhf+D1wB1t31vSA4FjbD+maPw3236+pI8C6/0SVMUxFc+BwLHA3QAND9teXTD2220/Q9KnF/m0bT983jFMxTJ6vhgzhoqEfInt3SS9ALjJ9gmSLrX9oLkOvHQ8O9AS86HAZ4G3217shTiv8VcPYx9GSwSTN4cbC2P4GrCX7e9XjblIDGuAhwOfmbwWJH2x6k1B0u6210j674t93va5FXFMxfM1YP+x3qR70UO+GDOGjec9AHCzpEOBJwP7D8/doWDc9UjaCNhxeHwPuBw4StLhtv+iIgbbN0g6HdgUeD7wZ8ALJB1v+4SKGID/BErvDhZxi+0fSRplcNtrhj9LE+8yrhvxjunA5T5v+1+qYqGPfDFaDBUJ+TDgWcCrbX9T0r2BUwvGXUDSG2nf3HOA19j+wvCpYyV9pSiGx9C+H9sD7wF+z/Z3JG0GXAVUJeRvAJ+R9G/AzydP2n5j0fgAV0r6S2AjSfcFjgAurBpc0hUsMlXBuqmCsumbwcWSPgD8Kwt/JhXJcP9lPmegMiH3kC9Gi6F0Y4ikrYDftv3FskHXjf1U4J9t/3SRz21RMZ8s6d3AO2yft8jnHmH77HnHMIx19GLP235FxfhDDJsBLwH+cHjqLOBVtm8qGv+ey33e9n9UxDEh6eTFw/BTK+PogaRNge1sl1wobSCW2pxV0LruM8BqYGvgGmAN8MaqdnZTcZx9W56bcwzH3pbnCuPZHLjLSGM/FNho5rndRorl7sB+w+NuY/08xn4AWwBvBC4eHm8AtiiOYX/gK8A3h48fCJxRHMNoOauiBGsL2zcAB9IqC3YHHlkwLgCSNpG0NXBXSVtJ2np43AvYtiqOwaMWee7RxTEgaWdJlwJXAl+StGaEksCzgHMk3X3quXcUx4CkQ4AvAAcDhwCfl3RQ4fgvHP48QdLxs4+qOAYnATfSvg+HADfQFp0rvRz4PeCHALYvA+5dHMNoOatiDnljSfeg/YBfUjDerMNpi2fbApdMPX8D8A8VAUh6NvAcYHtJ07c+m9MqPaqdCBzlobpE0sOAtwMPLozhK8DraHPZT7N9IW3+ttpLgD1tfwdA0jbAp4APFY0/Wci7uGi85Wxv+3FTH79iKAGrtNhib3XDndFyVkVCPoZ2NfRZ2/9T0n2ArxaMC4DttwBvkfQ811UxzHof8HHgtcD/mHr+Rts/GCGeO3uq1M/2ZyTduTgG2z5zWFD9gKSTqP/FA1g1ScaD71O4g9X2R4e//tT2B6c/J+ngqjgGP5O0r+0LhvEfAvysOIZRF3sHk5x1QXXOut13e5P0cNvnLFXa44JVbEmr3crdtl4ihtKkLOnDtLuF9wxPPQHYw/YBhTGsresc3gxOBg60XXGRMB3H64BdgPcPT/05cIXtFxbHcYnt3Tb03JxjeCBwCm0uGeB64Cm2Ly+MYXqxV7TE+EoXLfaOrWJjyP2AtwJ3t72zpF2Ax9h+1VwHXjf+K2wfPeYqtqQzbe8n6Zu0q8Dp+zHbvs+8Y5iJZyvgFcC+QyznAS+3fX1lHIvEtZ3ta0YY90Cmvhe2P1w49qOBP6HdHn9g6lOrgZ1s/15VLFMxrYZWM1899kwcG9Hu5krjkLQJ8DTg/sAmk+dLckVBQj4XeAHwT1NXRFfa3nmuA0eXJL3Q9nFLLVjZPmKEmO4J3Nf2p4YrtI1ctHNS0q60SoJjgL+b+tSNwKcr3ySHBdbXANvafrSknYB9bL+zMIb30WqAb6VVN2xBq3B4XWEMHwSuBv6S9nN5PHCV7SPnPXbF7eFmtr8wM0l/S8G4C6g1B3kSbX/62v/v6gQw3CHMxlBSeK8++jdMFrHWFIy1QZKeQTvEcmvahp3fAt4GPKJi/GE64HJJ77N9c8WYy3gXbepospD1v2hX7WUJmXZXcIOkxwMfA/6W9lopS8jA79g+WNJjbZ8yvEmcVTFwRUL+nqTtGRLAUFL07YJxZ30MuAi4gnUNdUoNC1e7AF+aiqFyJ9Rkzvj1ReOtZ7KIZfuUyXNqHfDuMtIt8nNpZVafH+L6qqS7jRDHvSS9FtiJhbfJldNZd7V9mqQXDWPfIunWwvEB7iDpDsABwN/bvllS9ULX5I3xh5J2Bv6LdhE1dxUJ+bm0MqsdJV0LfJO2iFRtE9tHjTDutL1t7zTW4F6kf8NYuycXuzWVVHprOvi57V9M7uAkbcw41R4n0zrwvQn4A9r23eoywJ9I+g3WXTztTX3Pk38CvkXrM3PeMJ1U/UZ94vB78TJap7e7sHA6aW7KqiyGlfRVVXNzi4z/18CPgTNZ2CugrMJB0juBN9j+ctWYS8TxGeAxtDfky4DvAudWvmFJusz2A4db090Zbk1d3ENC0nG0TQhPAp5Hqxf/su3S+lNJa2zvLukK2w8Ynjvf9kMLY9iN1k9lZ9qmoW2Ag6rfrBeJa2Pb5dOcY5j7FbJao+fHMcybTq5EbB8z77Fn/II2D/US1l0BGai8JTwF+Jyk/6K9KYzVyGaLYZ7u6bSdSEfPbFip0MOtKbS68KfRprIOp01tle8YBG4apm6+KumvgGtpvZHL2L5ErR3pDrTX5leq57UlbUG7U/j94alzaQtrFb1mlr0gcUHzrYopi4/QvplrmLoyHcFRtMn6740Yw0nAExlxHnsw9u5J6OPWFNu/pO1SfHv12DOeD2xG2wjxSlqv6CdXDLxUjT5wP0lli86Dk2hX54cMHz+RoUa9YOzNhz9nS1Mnz81dRdlbFyVuks4A/sKLdHsrjOEcF56+sEwcB9Pmxz5r+9nDTqTXzWybrY5JtHKzW4aPnzy98DeH8ZZqvwnACHcto1miRn+ipFZ/KpbLbD9wQ8/NOYZTgCNt/3D4eCvaVOPcvw8VV8gXSnqA7SsKxlrOrcBlasfUTM8hV5a9XT0sZn2U+p63aw1bdD849fE3aNNKo3G7MpieJzySNsUzL/vN8b/9K5O0B+1u5Z4sLImc+xuD7cPmPcavoIft27tMkjGA7esllZxYUpGQ9wWeMuxSG3Pe9F+Hx5g2pX0P/nDqueoG4KPvnryN5lph4KHfsVrz8W9Ptuaq9eK9+3JfOyfvpW2gGrMs8zXAcTNXhn9j+6WFYTwLePcwlwxt+3bJ1M2UVZK2mmzKGVoelGzpr5iyWLQRuAsbgA9bME+xPUa53XQMR9h+01gxTMXS/e7Jqj4Oki4GHmz7F8PHd6RN5ew577Fn4rjA9r6VYy4Sw3rnxo3QT+Pebqd0rN2+PXmuMIYnAS+idfwzbT771bbfs+wX/hpUdLV6pO3/mH4Azy4Ydy3btwLbDL9soxhiKD3JeBmbed0RVhO9lRVV1eBuPEnGAMPfx3idHC3pHZIOlXTg5FEcw0ZDVRSw9m7hTsv8+3k4HVointooVNUKlWHsd9Om8K6jlYQeWJGMoeYy/CBJN9l+L4Ckf6T+hwxtRf+zw+LeTyZPVpSyTLlQ0t/TtqNOx3DJ0l8yF73snlxOVZ/o70p6jO0zACQ9lnYAbrXDaIfv3oFxdnFCOzfu7GGRz8BTme88/lqSdqQ189li5o1oNVM7F6sMewXK9wtUTFlsStvtchLtdIwf2H7+XAddPI4ezpH79CJPu7ryYqiqOJHWkP56ht2Ttr9VMPbotZ7Thjem99IOMBDtRO4n2f5acRxrN4SMSa373CNo34tP2C7p4TC8ER5Au4s8Y+pTN9LOwqzuiTyKuSVkLez9uzltQe2zDFsQK3fIzcS1eRvePx5j/J6MsXtyqTfGico3yGmS7kL7fRhrJ+nbgTeNvYtzbJL2sf25seMYyzwT8nTv3x56AO9Ma64zeaP4Hu1K6EuFMSy6C8kFJ17PxLFg9+Tk+RF2T45O0qI9Cqq/F5KuonWbG60aSdKNrKvNviNt+uQntlcXjD1py3rCVAxrFZenjmZuc8i2qw8m3JAezpEbcxfStNF3T2rEJuAzfjL1901o9clXLfFv5+mPRxhzAdubT38s6QBaJ7wK02cL3r6PMVpGxRzyc4H3ztQ2Hmr7H+c68PpxXG571w09N+cYRt+FNIw5eombRmwCvoG47kQ7dv6PRhh7V2DSTOh8Fx6dtBRJF9neu3C8PYEXs/DubYx9C6OoqLJ4hu21pzsPu16eAZQmZOAbkl7GwnPkymobBz3sQoI+dk+O1gR8AzajtuEUAJKOBJ7BuqqKUyWd6MKDeWeqG1YBe1B/tXoqI2+QGVNFQl4lScPW2MkGiTHqPJ9KO0du8oI/j1ZqVGnUXUhT/Rs2Bg6T9A3G2z05WhPwaTM9LTaitZwcYy79acBetn8yxHUs8DlaO8wq+0/9/RZaqehjC8cH+O6kBHElqkjIZwGnSXob7YX/LODfC8YFQNJ7bD+RtoA3ysKApCNtv4V2KsauGu8QyZ76N4zWBHzG9PfkFuA6j9N7V7R+KxO3UtygvpOeFkdLegdwNiP2exlLxRzyKlqf2bW1jcA7hp1rcyfpy7T65zOAhzHzIq8ov9O6Zuyl21AXiWPr5T4/Vini2Ia7truzsOKk9PTroT77ycDkxOsDgHfZfnNhDNvQpk3uxcLvRWW3t1NpG2QWHHM2wmLvKMpODBmLpCNoW7XvQ2v6XV5+J+n9wD602+GvT3+KwqmCmVLE7WhTJgK2BK6pqIyR9ATbpy61QWSEjSHPo5UiXsfCBFC+iKR2Yse+tJ/JebYvLR7/QuB8WvXN2gsm26cXxtDFBpmxzG3KQtJptg/REn1nq17wto8Hjpf0VtulPTSmYjhU0m/Spm9G62cxSbjD9NEZtj82fPxo4JFFYdx5+HPzZf9VnSOBHWx/f6wAhrvILw6VL9Xb6KdtZvtvRxwf4CJJO63UDTLz3BhyD9vfHrvbm6TVbh2jFr1dX4m36RrOb5t57mLbe4wV01iG7eyPGmneeDqO9wIvqp4qmYnhVcCFkzfqkWIYfYPMmFbClMWZtvebuV2fqJqyWOpuYZQXm6SzaLempw7xPAH4/YraW0nHL/f56oVXtYNndwD+jYWLSNVTJ+cAewJfYGHjqbI7qmGn3p1p34ebWff6nPtOvakYRm/XO6aKQ04PBI6lHdgoin/Itvcb/hxz5+Bks0MvVQ6H0uZNJwtI5w3PVVhTNM5tdc3wuCPjlGNOjNLDY9rsTr1Zku4/71YDKyXxLqWiyuJrwP62x9iOOhvLb7H+ETnnjRdRSLrzpPa2R5JOsP28sePowdhVQitBRR3ydZ0k42OBP6f1OJ2sIJt2dVgVw6h3C1NxbAO8kPX7SJS1AZW0D/BOWv3xdsO24cNtP6cqhtvoIRWD9PLa2IDSuuiVaJ5VFpNtmBdL+gCt/eaYhd4H0FbTR2mmMziOPu4W3ktrkr8fbaPOk2knI1R6M/BHDL1vbV8u6feX/5LbtV5eG8u5fS84dWCeV8jT2zB/ysgHewLfoLUTHDMhd3G3APyG7XcOOwjPBc5VO2evlO3/lBZcdJVsFupUL6+NGNE8228eBq2Bju0Fx/EMTXWq/RS4TNLslszKVf1e7hYmfSS+LelPgf8D/LfiGP5T0oMBq511eATjtL3ckLnepnd4J7mcX2z4n8T/i4pFvfUWAsZYHJC0aBMf2yVnhg0xnLx4CLXbQiXtRyt7+21a85rVwCsqm7pIuivwFtqGlMmW+iPH2qCx1OKipKfYftccx13sNTFR8toYdgguF8SYm1VWlHluDNmH1vz9+cCbpj61GvgzF/Yhjl+NpBfZfu3YcVQYrtLfQWv81PPi4txo8bMeJ1y52LvSzXMO+Y60FfSNWbhN9gbgoDmOu6ipjSELVGwMmYphcprvbAy9NU45GJhrQu6hkc3gTXSwuCjpFNodwvRBDm+o+H7Y/oN5jxG3zTznkCeLRe9arti7sM5zelvwJrSks2z3szk4cyaGP6PN3/amorzpI7Rpk08x8mJeJ4uLu0ySMaw9yOFB1UEMval3YmE55Lur41ip5l6HfBt23pQs8C0yN/lmSRdQ2IN3tmvW0AXuU1Xj/woqypt6aGQD/SwurpK0le3rYW2r1Ip9AmupnQj+MFpC/hitbe0FQBJykdIf+JhmFi4mx9OM3XHsvrQ2mL2puEI+U9KfjNnIZvAs2uLibwH/m7a4+NwR4ngD7WitD9HeEA8BXl0cw0HArsCltg+TdHfa/HoUWTEJmfaCn7iF1k3qkCX+7Vxo4THr0I4t6uEqcdYH5/UfnvoeCHixpDEb2WwEPNH246vGXIrtd0u6GHg47Xtx4HQLyumr5zn6me1fSrplONXmO4xwvuBK1kNCLtmOuaGFC0lPnncJ3NjNWySdwDLTEZOabNuvmVcMG/oeVLJ9q6THsrAKaDRDAl6qD/DZwLxLRS+WtCXwdloTqB/Tus9FkbL2m8M7rm3fOPP8XOs8b6seGqfMO4alarEnimuyz7b9iA09VxDHq4EtaFvJp9tedlV7K+lS22WLfJLuBay2/cWqMaOm/eYewMm0+VpJ+iHwVNtrAHpIxoMeGqfMNYbKhLsUSZvQeu7edSjtmvw/rwa2HSGkBw9/Tp80bdrUQU9Krpwk7cJUKaKk3+lst+DtWsWUxUnAc2yfDyBpX1qC7u0EgB4ap1T90m1Dm7ueLW+qSEKH0zYLbcvC44puAP6hYPwFUoO7jqSTaL+XCw4Ypb7vzIpVkZBvnCRjANsXDAs7venhCrnKpNvbn1Lc7c32W4C3SHqe7RMqxtyQoZ/HbCvSY5b+ilFUvD73tr1TwTixhIqE/AVJ/wS8n/Zu++fAZyZlaB3N1X12w/9k7qqat4zW7U3Sw22fA1w71VhnrerbY7UDXzcD/oBW4nUQhQtZWuKsxwmvO/OxYm79c1rBB4z2oKK5UBf75IeaytcA29p+tKSdgH1sv7Ni/CEGAY8H7mP7GEnbAb9pu3QlW9JFtvceztY7nrZb8EO2ty8Y+xW2j+6o0dIXbe8y9eddgH+x/Ycb/OJfz/jTZz1uB1w//H1L4BoXHj02bBn/KK0cc8UdMNqD2/0hpxOSPk6bu36J7V0lbUwrgH9AYQxvpc3NPdz27w6LWp+wvWdVDEMco3d764Wkz9veS9JFwIHA94Erbd+3OI63AWdMNspIejTwSNt/UxjD14CjgCtYN4e84s+5q1RRZbEl8CTWbyJTerowcFfbp0l60TD+LZKqexbsZXs3SZcOMVw/bNctZXvSU+NHtFv1cpJ+g3bQ6r60K8QLgGNGaL955vAaPY51B7COsTttT9vPmnxg++OSXlkcwzUr8U25JxVzyB8DLmLmXXcEPxmSgAEk7U1LSJVuHnaHTWLYhhG+J510Wvtn2nmGjxs+fjxtofGRhTEAvB54NvBQ4HO0O4e3FscA8D1JLwVOpb0+nkC7Wq90taT30aYtem2Sf7s2SoP6MUjanTZfujNwJbANcLDtywtjeDxtUXN34F20BaSX2p7bVuUl4riQlnjWMNXZbLb50ZxjWGN795nnLra9x1JfM6c4TgNupCVCgEOBLW1Xb6vfmnbHMGn9eR5tGukHS3/Vrz2GLub1V7KKhPzXtC2YZ7LwXbfshTYVy8bADrTFiq/YvnkDXzKPGHZk3Yr5OR7hHDVJl9l+YPW4MzG8HrgYOG146iDg/raPLo7jcs8clrDYc7d3w53bEba72Ea+Uq0qGOMXwOtot4NrhsfFBeMuIOnrwNNtf8n2lbZvlnTmBr/w128zYCPa937TEcaHodPaSGNPHE6rh/758Phn4ChJN0q6oTCOS4fpKwAk7cUIJZCS7ifpREmfkHTO5FE1vu1bgcdUjReLq7hC/jptMet7cx1ow3FcDVxOO+z0cNu/GKE/wN/RGuOfTrtKPwD4oO1XVcUwxHEjbfvymJ3WVtHmje89VQJ4D9ufLxr/Ctpc7R1od03XDB/fE/iy7Z0r4piK53Lgbaw/jbRmyS/69cfw/0Vfj9uzioR8BvAXtn8614E2HMclQ4XDC2kLSYcAH66c35Z0FfAg2zcNH28KXGL7d6ti6MXYJYCS7rnc56tLvRabU6+2xJ6Bsr0CUVNlcStw2fDDnp5Dri570zDucZLWAGdRf4TTt2jbc28aPr4T8PWqwSXtaPtqLXHKcPGV0KglgB3W1n5U0nOADzPSWkv6eoyvIiH/6/AY29qjmmyfLemPaD0cKv0c+JKkT9Jujx8FXCDp+CGueb9JHQU8k4XN+ieqO5x1UQLYkclr8QVTz5nCBvGStmBhpce5tNrw6vLQFet2v1Ovp6tCddSPeGxTJYC7AacwUglgrCPpdFpJ6OR1+ERgV9vr9RyJ+aiYQ57s1V/Adsk7v6QTbT9zZn5sbTwrcX5ssaY+tE0yV9j+TmEckxJAAWePUQLYE4184vNi5ZA9lEiuJBVTFtOF/pvQqgzK5m5tP3P461uBf7d9g6SX0a7MSremSrov8FrW/6WrPrfsacA+wORN6mG03ZT3k3SM7fdUBGH7auDqirF6pz5OfP6ZpH1tXzDE9BDgZ4Xjr3hzr0O2/f2px7W238w4pzG8dEjG+9Lmbt9F/RbZk4cxb6H1kHg3UJL8ZvwS+F3bj7P9OFoS+DmwF30euroSHES7W/gv24fRTn++U3EMzwb+QdK3JH0L+HtavXgUqWguND13u4p2xTzGQZeT2s4/Bd5m+yOSXl4cw6bDgqKGVf6XSzqftpBS6V62r5v6+DvA/Wz/QFL57sUA+jjx+Spak6Xtae0/f0Srlc+5ekUqpiymV/RvoZV+lfYJGFyr1ij/kcCxku5EzU7FaTcNGyK+KumvgGuBuxXHAHD+sEtxsoD2OOA8SXcGfjhCPNHHic8fof3q8IvfAAACqUlEQVT8L6G9NqPY7b7KYkLSZsAf0xauvirpHsADbH+iMIY9aVchW9Lmr7cAjrN9UVUMQxyi9f7dl7agdgFwulfKi6FzGunEZ0lXVu9QjIXmlpAlHbXc522/cS4Dx7KG2t+zbFe3uYxFLFWOOVFclnkicILtK6rGjIXmOWWx3DzxirwSk3Q/WuH/PVnYh7hskdP2rZJ+KmmLFPx3YXpKb/r3QtRv1tkXeMpQqpojnEZQUYd8CnCk7R8OH28FvGEl9ljtoYHMEMdpwN7AJ1nYRKZ6O3sMhr4mz2HdCSrnA2+d9D0pimHR/h4dbjO/3apIyOt1VKvustaLHhrIDHEsumNwJe0U7M3wJnkDrSUpjNQoP8ZVUWWxStJWtq+HtScjVIzbDa076n30BjLDeEm8/dlhpin+p4c7qlhBqsreLpT0Idqt2CHAqwvG7cka1h31Dm0eefrWpLTetKMdg7HOpZL2nlTcjNUoP8ZVUvYmaSfa4sSkZ8GX5z5ohyQdwiLbt6sbgEu6gLYZ5U3A/sBhtNdC9QaVFa+3RvkxrhVTh9wDSV+0vcuwffs1tLuHF9veqziONbZ3l3SF7QcMz51v+6GVcUR/jfJjXCtqLrcDPWzfhn52DK54SbgxLVfIhYbtytfStm/vTuuk9QUXn3C8yI7B1bQdgyXn2UXE4pKQC/WwfXuIYw/gJbR5yjsMT2cDQMTIkpBXIElfoVV6XMHUsUm5fY4YV+aQV6bv2j5j7CAiYqFcIa9Akh5B2wl2Ngs3qPzLaEFFRK6QV6jDgB1p88eTKQsDScgRI0pCXpl2ndQfR0Q/qk/MiD5cNOyejIiOZA55BZJ0Fe3ctPS9jehIEvIKlL63EX1KQo6I6ETmkCMiOpGEHBHRiSTkiIhOJCFHRHQiCTkiohP/F22JtaD3JWi0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cbar=False,yticklabels=False,cmap='viridis') #no_missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 683 entries, 0 to 698\n",
      "Data columns (total 10 columns):\n",
      "clump_thickness      683 non-null int64\n",
      "size_uniformity      683 non-null int64\n",
      "shape_uniformity     683 non-null int64\n",
      "marginal_adhesion    683 non-null int64\n",
      "epithelial_size      683 non-null int64\n",
      "bare_nucleoli        683 non-null object\n",
      "bland_chromatin      683 non-null int64\n",
      "normal_nucleoli      683 non-null int64\n",
      "mitoses              683 non-null int64\n",
      "class                683 non-null int64\n",
      "dtypes: int64(9), object(1)\n",
      "memory usage: 58.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(683, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(699, 10)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print(df.shape)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"bare_nucleoli\"] = df[\"bare_nucleoli\"].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#HEAT MAP #correlation of parameters \n",
    "f,ax=plt.subplots(figsize=(15,15))\n",
    "sns.heatmap(df.corr(),annot=True,fmt=\".2f\",ax=ax,linewidths=0.5,linecolor=\"orange\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "score=[] #these variables will be used to show the algorithm name and its successes.\n",
    "algorithms=[] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy = 96.58536585365853\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "y=df[\"class\"].values\n",
    "x_data=df.drop([\"class\"],axis=1)\n",
    "\n",
    "#Normalization\n",
    "x=(x_data-np.min(x_data))/(np.max(x_data)-np.min(x_data))\n",
    "\n",
    "#Preparing the test and training set\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=1,test_size=0.3)\n",
    "\n",
    "#model and accuracy\n",
    "knn=KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(x_train,y_train)\n",
    "knn.predict(x_test)\n",
    "score.append(knn.score(x_test,y_test)*100)\n",
    "algorithms.append(\"KNN\")\n",
    "print(\"KNN accuracy =\",knn.score(x_test,y_test)*100)\n",
    "\n",
    "#Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred=knn.predict(x_test)\n",
    "y_true=y_test\n",
    "cm=confusion_matrix(y_true,y_pred)\n",
    "\n",
    "#Confusion Matrix on Heatmap\n",
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidths=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\" KNN Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Navie Bayes accuracy = 97.07317073170731\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Navie-Bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb=GaussianNB()\n",
    "\n",
    "#Training\n",
    "nb.fit(x_train,y_train)\n",
    "#Test\n",
    "score.append(nb.score(x_test,y_test)*100)\n",
    "algorithms.append(\"Navie-Bayes\")\n",
    "print(\"Navie Bayes accuracy =\",nb.score(x_test,y_test)*100)\n",
    "\n",
    "#Confusion Matrix \n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred=nb.predict(x_test)\n",
    "y_true=y_test\n",
    "cm=confusion_matrix(y_true,y_pred)\n",
    "\n",
    "#Confusion Matrix on Heatmap\n",
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidths=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\"Navie Bayes Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm test accuracy = 97.07317073170731\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Support Vector Machine\n",
    "from sklearn.svm import SVC\n",
    "svm=SVC(random_state=1)\n",
    "svm.fit(x_train,y_train)\n",
    "score.append(svm.score(x_test,y_test)*100)\n",
    "algorithms.append(\"Support Vector Machine\")\n",
    "print(\"svm test accuracy =\",svm.score(x_test,y_test)*100)\n",
    "\n",
    "#Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred=svm.predict(x_test)\n",
    "y_true=y_test\n",
    "cm=confusion_matrix(y_true,y_pred)\n",
    "\n",
    "#Confusion Matrix on Heatmap\n",
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidths=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\"Support Vector Machine Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy: 95.60975609756098\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Decision Tree \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt=DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)\n",
    "print(\"Decision Tree accuracy:\",dt.score(x_test,y_test)*100)\n",
    "score.append(dt.score(x_test,y_test)*100)\n",
    "algorithms.append(\"Decision Tree\")\n",
    "\n",
    "#Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred=dt.predict(x_test)\n",
    "y_true=y_test\n",
    "cm=confusion_matrix(y_true,y_pred)\n",
    "\n",
    "#Confusion Matrix on Heatmap\n",
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidths=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\"Decision Tree Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy = 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ronyse\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning:\n",
      "\n",
      "numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RANDOM FOREST\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier(n_estimators=100,random_state=1)\n",
    "rf.fit(x_train,y_train)\n",
    "score.append(rf.score(x_test,y_test)*100)\n",
    "algorithms.append(\"Random Forest\")\n",
    "print(\"Random Forest accuracy =\",rf.score(x_test,y_test))\n",
    "\n",
    "#Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred=rf.predict(x_test)\n",
    "y_true=y_test\n",
    "cm=confusion_matrix(y_true,y_pred)\n",
    "\n",
    "#Confusion Matrix on Heatmap\n",
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidths=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\"Random Forest Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test accuracy 0.9609756097560975\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "score.append(lr.score(x_test,y_test)*100)\n",
    "algorithms.append(\"Logistic Regression\")\n",
    "print(\"test accuracy {}\".format(lr.score(x_test,y_test)))\n",
    "#Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred=lr.predict(x_test)\n",
    "y_true=y_test\n",
    "cm=confusion_matrix(y_true,y_pred)\n",
    "#Confusion Matrix on Heatmap\n",
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidths=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.title(\"Logistic Regression Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace1 = {\n",
    "  'x': algorithms,\n",
    "  'y': score,\n",
    "  'name': 'score',\n",
    "  'type': 'bar'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "name": "score",
         "type": "bar",
         "uid": "6d5a7a5c-3056-471d-9ba0-69312ee34ecf",
         "x": [
          "KNN",
          "Navie-Bayes",
          "Support Vector Machine",
          "Decision Tree",
          "Random Forest",
          "Logistic Regression"
         ],
         "y": [
          96.58536585365853,
          97.07317073170731,
          97.07317073170731,
          95.60975609756098,
          97.5609756097561,
          96.09756097560975
         ]
        }
       ],
       "layout": {
        "title": "Comparison of the accuracy of classification algorithms",
        "xaxis": {
         "title": "Classification Algorithms"
        }
       }
      },
      "text/html": [
       "<div id=\"b1593e0a-3c29-40ae-94f7-53e48ec9894a\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"b1593e0a-3c29-40ae-94f7-53e48ec9894a\", [{\"name\": \"score\", \"x\": [\"KNN\", \"Navie-Bayes\", \"Support Vector Machine\", \"Decision Tree\", \"Random Forest\", \"Logistic Regression\"], \"y\": [96.58536585365853, 97.07317073170731, 97.07317073170731, 95.60975609756098, 97.5609756097561, 96.09756097560975], \"type\": \"bar\", \"uid\": \"6d5a7a5c-3056-471d-9ba0-69312ee34ecf\"}], {\"title\": \"Comparison of the accuracy of classification algorithms\", \"xaxis\": {\"title\": \"Classification Algorithms\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"b1593e0a-3c29-40ae-94f7-53e48ec9894a\"));});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"b1593e0a-3c29-40ae-94f7-53e48ec9894a\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"b1593e0a-3c29-40ae-94f7-53e48ec9894a\", [{\"name\": \"score\", \"x\": [\"KNN\", \"Navie-Bayes\", \"Support Vector Machine\", \"Decision Tree\", \"Random Forest\", \"Logistic Regression\"], \"y\": [96.58536585365853, 97.07317073170731, 97.07317073170731, 95.60975609756098, 97.5609756097561, 96.09756097560975], \"type\": \"bar\", \"uid\": \"6d5a7a5c-3056-471d-9ba0-69312ee34ecf\"}], {\"title\": \"Comparison of the accuracy of classification algorithms\", \"xaxis\": {\"title\": \"Classification Algorithms\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"b1593e0a-3c29-40ae-94f7-53e48ec9894a\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [trace1];\n",
    "layout = {\n",
    "  'xaxis': {'title': 'Classification Algorithms'},\n",
    "  'title': 'Comparison of the accuracy of classification algorithms'\n",
    "};\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}