7_Predict Diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import the necessary library and the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "diabetes=pd.read_csv('diabetes.csv')\n",
    "print(diabetes.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dimension  of the data: (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"dimension  of the data: {}\".format(diabetes.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outcome\n",
      "0    500\n",
      "1    268\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#grouping data based on outcome\n",
    "print(diabetes.groupby('Outcome').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raj Krishna Mondal\\anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "C:\\Users\\Raj Krishna Mondal\\anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "C:\\Users\\Raj Krishna Mondal\\anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "C:\\Users\\Raj Krishna Mondal\\anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14107b34988>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(diabetes['Outcome'], label=\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "# some information of our data\n",
    "diabetes.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Nearest Neighbours to Predict Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14111920288>"
      ]
     },
     "execution_count": 82,
     "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": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test=train_test_split(diabetes.loc[:,diabetes.columns!='Outcome'], diabetes['Outcome'], random_state=80, train_size=.6)\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "train_accuracy=[]\n",
    "test_accuracy=[]\n",
    "\n",
    "nbd=range(1,15)\n",
    "\n",
    "for n_nbd in nbd:\n",
    "    #build the model\n",
    "    knn=KNeighborsClassifier(n_neighbors=n_nbd)\n",
    "    knn.fit(x_train, y_train)\n",
    "    \n",
    "    #record the accuracy \n",
    "    train_accuracy.append(knn.score(x_train, y_train))\n",
    "    test_accuracy.append(knn.score(x_test, y_test))\n",
    "    \n",
    "plt.plot(nbd, train_accuracy, label=\"train accuracy\")\n",
    "plt.plot(nbd, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"nbd\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7866449511400652\n",
      "0.7272727272727273\n"
     ]
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=11)\n",
    "knn.fit(x_train, y_train)\n",
    "print(knn.score(x_train, y_train))\n",
    "print(knn.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier to Predict Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.7077922077922078\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree=DecisionTreeClassifier(random_state=0)\n",
    "\n",
    "tree.fit(x_train, y_train)\n",
    "print(tree.score(x_train, y_train))\n",
    "print(tree.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7964169381107492\n",
      "0.6883116883116883\n"
     ]
    }
   ],
   "source": [
    "tree=DecisionTreeClassifier(max_depth=3, random_state=0)\n",
    "tree.fit(x_train, y_train)\n",
    "\n",
    "print(tree.score(x_train, y_train))\n",
    "print(tree.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance in Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.74355317 0.         0.         0.         0.25644683\n",
      " 0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(tree.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkqS5ZtB7yj7Qt/0g8P2qunUa6pEkSZqTBr18+dKqurT9XF5VtyZ537RWJkmSNIcMGspeOE7bSzZkIZIkSXPZpJcvk5wA/AGwS5IVfV2PBy6fzsIkSZLmkqnuKfsc8M/Ae4GT+9rvrKqfTFtVkiRJc8ykoayq1gBrgNcAJHkyvQ+P3TrJ1lX1b9NfoiRJ0uw30D1lSX49yXeAm4FLgVvoraBJkiRpAxj0Rv//Cfwy8O325eQvwHvKJEmSNphBQ9kDVfVjYJMkm1TVxcDiaaxLkiRpThn0w2P/M8nWwNeBzya5jd6HyEqSJGkDGHSl7BX0vu/yrcB5wPeAX5+uoiRJkuaagVbKquruJE8Fdq+qTyXZEpg3vaVJkiTNHYO++/L3gLOAj7WmHYGvTFdRkiRJc82gly9PBA4G7gCoqu8AT56uoiRJkuaaQUPZfVV1/7oHSeYDNT0lSZIkzT2DhrJLk/wpsEWSFwJfBP5p+sraeCX5sySrkqxIsizJc5LckmS7cfb95hTn+nI7x3eTrGnby5IcNMk5X57k5PHO1/oXJrnu0T07SZI0XQb9SIyTgeOAlcDvA18DPj5dRW2skhwIvAzYr6rua6HpcRPtX1UHTXa+qnplO+8hwElV9bK+sSY65qvAV9e7eEmSNFSTrpQl2Rmgqh6qqr+tqqOq6si27eXLR9oBWF1V9wFU1eqq+uG6ziRbJDmvvXGCJHe134ckuSTJWUluSPLZTJS6Hu5NSa5NsjLJonauY5N8uG1v31bblrefh4XAJLsk+VaSA9pxX2r1fSfJX/Tt96IkV7Sxvtg+s44kpyW5vq0KfqC1HZXkujbeZY9lMiVJmkumunz5X++wTHL2NNcyG1wA7JTk20nOSPKrfX1b07vk+7mq+ttxjn0Wvc+BeyawC703VkxldVXtB3wEOGmc/tOBS6tqX2A/YNW6jiRPB84GfreqrmnNi4Gjgb2Bo5Ps1Fb7TgEOa2ONAn+Y5InAK4E9q2ofel/FBXAq8OI25ssHeA6SJImpQ1n/as0u01nIbFBVdwH7A8cDtwNfSHJs6/5H4JNV9ekJDr+6qm6tqoeAZcDCAYb8Uvu9dIL9D6UX2KiqtVW1prUvaPX8dlUt69v/oqpaU1X3AtcDT6X3nafPBC5Psgx4XWu/A7gX+HiS36D34cLQ+07UM9tq4LifZZfk+CSjSUZvv/32AZ6mJEmz31ShrCbY1gRa+Lmkqt4BvBF4Veu6HHjJJJcl7+vbXstg9/utO2bQ/ddZA/w7j1yNG6+GABdW1eL288yqOq6qHgSeTW+17Qh63/RAVb2B3sraTsCyJE8aO3hVLamqkaoaWbBgwXqULUnS7DVVKNs3yR1J7gT2adt3JLkzyR0zUeDGJMnTk+ze17QY+H7bPhX4MXDGDJZ0EXBCq21ekm1a+/30gtRrk/zWFOe4Ejg4yW7tPFsm2aPdV7ZtVX2N3mXXxa1/16q6qqpOBVbTC2eSJGkKk66uVJVfpbR+tgY+lOQJ9L6w/bv0LmWue9fkW4FPJPmLqvqjGajnLcCSJMfRW/k6AfgR/NdXZ70MuDDJ3ROdoKpub5dg/z7JZq35FOBO4B+TbE5vNe1tre/9LZiGXihcvuGfliRJs098E6WGaWRkpEZHR4ddhiRJMyLJ0qoaGa9v0A+PlSRJ0jQylEmSJHWAoUySJKkDDGWSJEkdYCiTJEnqAEOZJElSBxjKJEmSOsBQJkmS1AGGMkmSpA4wlEmSJHWAoUySJKkDDGWSJEkdYCiTJEnqAEOZJElSBxjKJEmSOsBQJkmS1AGGMkmSpA4wlEmSJHWAoUySJKkDDGWSJEkdYCiTJEnqAEOZJElSBxjKJEmSOsBQJkmS1AGGMkmSpA4wlEmSJHWAoUySJKkDDGWSJEkdYCjbQJKsTbIsyfIk1yY5qLUvTHLdBhrjkiQjbfuWJCvbeBck+cUNMYYkSRoOQ9mG87OqWlxV+wJ/Arx3BsZ8fhtvFPjTsZ1J5s1ADTM+liRJs5GhbHpsA/x0bGOSzZN8sq1wfSvJ86do3yLJ55OsSPIFYIsJxrsM2K0dc1eSP09yFXBgkv2TXJpkaZLzk+zQ9ntzkuvbuT/f2n61rfYta3U8PskhSc7pew4fTnJs274lyalJvgEclWTXJOe1sb6eZNEGmk9Jkma9+cMuYBbZIskyYHNgB+DQcfY5EaCq9m6B5YIke0zSfgJwT1Xtk2Qf4NoJxn4ZsLJtbwVcV1WnJtkUuBR4RVXdnuRo4D3A64GTgadV1X1JntCOPQk4saouT7I1cO8Az/veqnouQJKLgDdU1XeSPAc4Y7x5SHI8cDzAzjvvPMAQkiTNfoayDednVbUYIMmBwKeT7DVmn+cCHwKoqhuSfB/YY5L25wGnt/YVSVaMOd/FSdYCK4BTWtta4Oy2/XRgL+DCJADzgB+1vhXAZ5N8BfhKa7sc+MsknwW+VFW3tuMm84X2nLcGDgK+2HfMZuMdUFVLgCUAIyMjNdUAkiTNBYayaVBVVyTZDlgwpmuihDNZ8pkstDy/qlaPabu3qtb2nXdVVR04zrGH0wt9Lwf+R5I9q+q0JOcCLwWuTHIY8CAPv8y9+Zjz3N1+bwL857pgKkmS1o/3lE2DdglyHvDjMV2XAce0ffYAdgZuHLB9L2Cf9SzlRmBBW7kjyaZJ9kyyCbBTVV0M/BHwBGDrJLtW1cqqeh+9Nw8sAr4PPDPJZkm2BV4w3kBVdQdwc5Kj2lhJsu961itJ0pzlStmGs+6eMuitUL2uqtaOufx3BvDRJCvprUAd2+7pmqj9I8An22XLZcDV61NQVd2f5Ejg9Bao5gMfBL4N/F1rC/BXVfWfSd7d3mSwFrge+OdWxz/Qu9z5HeBbkwx5DPCRJKcAmwKfB5avT82SJM1VqfKWHg3PyMhIjY6ODrsMSZJmRJKlVTUyXp+XLyVJkjrAUCZJktQBhjJJkqQOMJRJkiR1gKFMkiSpAwxlkiRJHWAokyRJ6gBDmSRJUgcYyiRJkjrAUCZJktQBhjJJkqQOMJRJkiR1gKFMkiSpAwxlkiRJHWAokyRJ6gBDmSRJUgfMH3YBmttW/mANC08+d9hlSJL0CLecdviMjudKmSRJUgcYyiRJkjrAUCZJktQBhjJJkqQOMJRJkiR1gKFMkiSpAwxlkiRJHWAokyRJ6gBDmSRJUgcYyiRJkjrAUDZLJNk+yeeS3JRkaZIrkrwyySFJzhl2fZIkaXKGslkgSYCvAJdV1S5VtT/wauCXhluZJEkalKFsdjgUuL+qPrquoaq+X1Uf6t8pyTuTnNT3+LokC9v2a5OsSLI8yWda21OTXNTaL0qyc2s/qh27PMllrW1ekvcnuabt//vT/qwlSZpF5g+7AG0QewLXPtqDk+wJ/BlwcFWtTvLE1vVh4NNV9akkrwdOB44ATgVeXFU/SPKEtu9xwJqqOiDJZsDlSS6oqpvHGe944HiAedsseLRlS5I0q7hSNgsl+Zu2inXNgIccCpxVVasBquonrf1A4HNt+zPAc9v25cCZSX4PmNfaXgS8Nsky4CrgScDu4w1WVUuqaqSqRuZtue36PDVJkmYtV8pmh1XAq9Y9qKoTk2wHjI7Z70EeHsQ3b78D1ADjVDv/G5I8BzgcWJZkcTvHm6rq/Ef3FCRJmttcKZsd/gXYPMkJfW1bjrPfLcB+AEn2A57W2i8CfjPJk1rfusuX36T3hgGAY4BvtP5dq+qqqjoVWA3sBJwPnJBk07bPHkm22jBPT5Kk2c+VslmgqirJEcBfJfkj4HbgbuCPx+x6Nj+/xHgN8O12/Kok7wEuTbIW+BZwLPBm4BNJ3t7O+bvtPO9Psju91bGLgOXACmAhcG17N+jt9O4/kyRJA0jVIFetpOmx2Q671w6v++Cwy5Ak6RFuOe3wDX7OJEuramS8Pi9fSpIkdYChTJIkqQMMZZIkSR1gKJMkSeoAQ5kkSVIHGMokSZI6wFAmSZLUAYYySZKkDjCUSZIkdYBfs6Sh2nvHbRmdhk9MliRpY+NKmSRJUgcYyiRJkjrAUCZJktQBhjJJkqQOMJRJkiR1gKFMkiSpA/xIDA3Vyh+sYeHJ5874uLf4MRySpI5xpUySJKkDDGWSJEkdYCiTJEnqAEOZJElSBxjKJEmSOsBQJkmS1AGGMkmSpA4wlEmSJHWAoUySJKkDDGWSJEkdYCgbgiRrkyxLcl2SLybZctg1DSrJN4ddgyRJs5GhbDh+VlWLq2ov4H7gDf2d6enkf5uqOmjYNUiSNBt18i/+OebrwG5JFib51yRnANcCOyV5UZIrklzbVtS2Bkjy0iQ3JPlGktOTnNPa35nkE0kuSXJTkjevGyTJV5IsTbIqyfF97XcleU+S5UmuTLJ9a98+yZdb+/IkB63bv+/Ytye5JsmKJO9qbVslObcdc12So2dgDiVJ2ugZyoYoyXzgJcDK1vR04NNV9SzgbuAU4LCq2g8YBf4wyebAx4CXVNVzgQVjTrsIeDHwbOAdSTZt7a+vqv2BEeDNSZ7U2rcCrqyqfYHLgN9r7acDl7b2/YBVY2p/EbB7G2cxsH+S5wG/BvywqvZtK4HnPfoZkiRp7jCUDccWSZbRC1r/Bvyf1v79qrqybf8y8Ezg8rbv64Cn0gtdN1XVzW2/vx9z7nOr6r6qWg3cBmzf2t+cZDlwJbATvUAFvcun57TtpcDCtn0o8BGAqlpbVWvGjPOi9vMteit7i9o5VwKHJXlfkl8Z5ziSHJ9kNMno2nse0S1J0pw0f9gFzFE/q6rF/Q1JoLc69l9NwIVV9Zox+z1rinPf17e9Fpif5BDgMODAqronySXA5m2fB6qq+vcf8DkEeG9VfewRHcn+wEuB9ya5oKr+vL+/qpYASwA222H3Gnu8JElzkStl3XUlcHCS3QCSbJlkD+AGYJckC9t+g9yztS3w0xbIFtFbhZvKRcAJbex5SbYZ038+8Pq++9x2TPLkJE8B7qmqvwM+QO/SpyRJmoIrZR1VVbcnORb4+ySbteZTqurbSf4AOC/JauDqAU53HvCGJCuAG+kFvqm8BViS5Dh6K2gnAFf01XdBkmcAV7RVvruA3wZ2A96f5CHggXacJEmaQn5+5UobiyRbV9Vd6aWhvwG+U1V/Ney6Ho3Ndti9dnjdB2d83FtOO3zGx5QkKcnSqhoZr8/Llxun32s3/6+id2nyEfd1SZKkjYuXLzdCbVVso1wZkyRJ43OlTJIkqQMMZZIkSR1gKJMkSeoAQ5kkSVIHGMokSZI6wFAmSZLUAYYySZKkDvBzyjRUe++4LaN+ur4kSa6USZIkdYGhTJIkqQMMZZIkSR1gKJMkSeoAQ5kkSVIHGMokSZI6wFAmSZLUAYYySZKkDjCUSZIkdYChTJIkqQMMZZIkSR1gKJMkSeoAQ5kkSVIHGMokSZI6wFAmSZLUAYYySZKkDjCUSZIkdYChTJIkqQMMZZIkSR1gKJMkSeoAQ5kkSVIHpKqGXYPmsCR3AjcOu44h2w5YPewiOsB56HEenIN1nIee2TYPT62qBeN1zJ/pSqQxbqyqkWEXMUxJRuf6HIDzsI7z4Bys4zz0zKV58PKlJElSBxjKJEmSOsBQpmFbMuwCOsA56HEeepwH52Ad56FnzsyDN/pLkiR1gCtlkiRJHWAo07RL8mtJbkzy3SQnj9OfJKe3/hVJ9htGndNtgHlYlOSKJPclOWkYNc6EAebhmPY6WJHkm0n2HUad02mAOXhFe/7Lkowmee4w6pxuU81D334HJFmb5MiZrG+mDPB6OCTJmvZ6WJbk1GHUOZ0GeS20eViWZFWSS2e6xhlRVf74M20/wDzge8AuwOOA5cAzx+zzUuCfgQC/DFw17LqHNA9PBg4A3gOcNOyahzgPBwG/0LZfMtteDwPOwdb8/PaSfYAbhl33MOahb79/Ab4GHDnsuof0ejgEOGfYtQ55Dp4AXA/s3B4/edh1T8ePK2Wabs8GvltVN1XV/cDngVeM2ecVwKer50rgCUl2mOlCp9mU81BVt1XVNcADwyhwhgwyD9+sqp+2h1cCvzTDNU63Qebgrmp/8wBbAbPx5t9B/mwAeBNwNnDbTBY3gwadh9lskDn4LeBLVfVv0PvzcoZrnBGGMk23HezSzZ8AAAUJSURBVIF/73t8a2tb3302dnPhOQ5ifefhOHqrqLPJQHOQ5JVJbgDOBV4/Q7XNpCnnIcmOwCuBj85gXTNt0P8nDkyyPMk/J9lzZkqbMYPMwR7ALyS5JMnSJK+dsepmkJ/or+mWcdrG/qt/kH02dnPhOQ5i4HlI8nx6oWy23U810BxU1ZeBLyd5HvBu4LDpLmyGDTIPHwT+uKrWJuPtPisMMg/X0vtqnruSvBT4CrD7tFc2cwaZg/nA/sALgC2AK5JcWVXfnu7iZpKhTNPtVmCnvse/BPzwUeyzsZsLz3EQA81Dkn2AjwMvqaofz1BtM2W9XgtVdVmSXZNsV1Wz6fv/BpmHEeDzLZBtB7w0yYNV9ZWZKXFGTDkPVXVH3/bXkpwxy14Pg/49sbqq7gbuTnIZsC8wq0KZly813a4Bdk/ytCSPA14NfHXMPl8FXtvehfnLwJqq+tFMFzrNBpmHuWDKeUiyM/Al4Hdm27+Cm0HmYLe0JNLejfw4YLaF0ynnoaqeVlULq2ohcBbwB7MskMFgr4df7Hs9PJve392z6fUwyJ+P/wj8SpL5SbYEngP86wzXOe1cKdO0qqoHk7wROJ/eO2w+UVWrkryh9X+U3ruqXgp8F7gH+N1h1TtdBpmHJL8IjALbAA8leSu9dyDdMeGJNzIDvh5OBZ4EnNH+HnqwZtGXEQ84B6+i9w+VB4CfAUf33fg/Kww4D7PegPNwJHBCkgfpvR5ePZteD4PMQVX9a5LzgBXAQ8DHq+q64VU9PfxEf0mSpA7w8qUkSVIHGMokSZI6wFAmSZLUAYYySZKkDjCUSZIkdYChTJI6IsldMzzewiS/NZNjSpqYoUyS5qAk84GF9L7oWVIH+OGxktQxSQ4B3gX8P2AxvW84WAm8hd73/h1RVd9LciZwL7AnsD3wh1V1TpLNgY/Q+5qiB1v7xUmOBQ4HNge2ArYEnpFkGfAp4MvAZ1ofwBur6putnncCq4G9gKXAb1dVJTkA+Ot2zH30vpvwHuA04BBgM+BvqupjG3qepNnGUCZJ3bQv8AzgJ8BN9D7B/NlJ3gK8CXhr228h8KvArsDFSXYDTgSoqr2TLAIuSLJH2/9AYJ+q+kkLWydV1csA2tfXvLCq7k2yO/D39IIdwLPohb8fApcDBye5GvgCvW8cuCbJNvQ+cf44el+XdkCSzYDLk1xQVTdPwzxJs4ahTJK66Zp13wGb5HvABa19JfD8vv3+oaoeAr6T5CZgEfBc4EMAVXVDku8D60LZhVX1kwnG3BT4cJLFwNq+YwCurqpbWz3L6IXBNcCPquqaNtYdrf9FwD5JjmzHbgvsDhjKpEkYyiSpm+7r236o7/FDPPzP7rHflVdAJjnv3ZP0vY3eJdN96d1zfO8E9axtNWSc8Wntb6qq8ycZS9IY3ugvSRu3o5JskmRXYBfgRuAy4BiAdtly59Y+1p3A4/seb0tv5esh4HfofTn0ZG4AntLuKyPJ49sbCM6n9wXam66rIclWk5xHEq6USdLG7kbgUno3+r+h3Q92BvDRJCvp3eh/bFXdlzxiAW0F8GCS5cCZwBnA2UmOAi5m8lU1qur+JEcDH0qyBb37yQ4DPk7v8ua16Q16O3DEhniy0myWqvFWniVJXdfefXlOVZ017FokPXZevpQkSeoAV8okSZI6wJUySZKkDjCUSZIkdYChTJIkqQMMZZIkSR1gKJMkSeoAQ5kkSVIH/H/WGsuOxcdPuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diabetes_features=diabetes.loc[:,diabetes.columns!='Outcome']\n",
    "def plot_FI(model):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    features=8\n",
    "    plt.barh(range(features),model.feature_importances_)\n",
    "    plt.ylabel(\"Feature\")\n",
    "    plt.xlabel(\"Importance\")\n",
    "    plt.yticks(np.arange(features), diabetes_features)\n",
    "    \n",
    "    plt.ylim(-1,features)\n",
    "\n",
    "plot_FI(tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning to Predict Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7717391304347826\n",
      "0.7045454545454546\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp=MLPClassifier(random_state=45)\n",
    "\n",
    "mlp.fit(x_train, y_train)\n",
    "\n",
    "print(mlp.score(x_train, y_train))\n",
    "print(mlp.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8152173913043478\n",
      "0.775974025974026\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raj Krishna Mondal\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scale=StandardScaler()\n",
    "x_train_scale=scale.fit_transform(x_train)\n",
    "x_test_scale=scale.fit_transform(x_test)\n",
    "\n",
    "mlp=MLPClassifier(random_state=0)\n",
    "\n",
    "mlp.fit(x_train_scale, y_train)\n",
    "\n",
    "print(mlp.score(x_train_scale, y_train))\n",
    "print(mlp.score(x_test_scale, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1410b0d7d88>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a+8x0a7a7rP/WYedz++N5wr/7Qrfmwxcd1a259fT+ebruxin+RPrE/nPiOQee3a3Zfd3CvR7f+0eP727jEa85tVvzgfc+vFtz06/3f54HffCWbs1Fz+4fv3f+/Lu6Nb9y6L/v1sxu039e7fqN/uvFVQ/tP8/XbNM/PrPX98/l7R56Tbdm7ZqFH9eNN/f3c7c/375bs+71l3drLr26/5xZf1h/PNdd0H/92ntj/9x53n9/abfmHicv/FqaJE9942e6Ne/+4C93a27drX8Obvvb/d9rl597twXX73ladxNpa6b43fisC7s1a6r/c9jpXw7p1rz+cR/s1rzq68/p1uy59w+7NTs+vf+776oL+u+V3/3F/o3Vt929W5K7XND/WcyuX/g432+773S38W9eeGK35h8//gvdmt3P6M9V+e5j+o/pwI/2XycveOHabk1O6pfAtIRYK6C1ttMWll2U5KjNlv1rkgdsYROfba0dXnPp0RuTnDq5q+BhSR5cVc/IXK+r12aux9UbWmvH9cbSWvunJP80+fwHSZ7cqf+bJH+zWcn/ydwsLQAAAICtxuWEP5t+Z9Ls/ZzMXSr4liQfTHJia+2QyV0Ifz3J/is4RgAAALhza3fSj0EJsX4Gtdb+urV2dGvtiNbaM5Mcm+TW1tqb59Vc3Fr72/nfV1X/sapeMe/rszf11qqq36yqM6vqjKr6n5Nld6+qz0yWf6aqDpwsf/rke8+oqhMny9ZW1V9W1SmT+v41gAAAAABTcjnhncORSb621G+uqiOT/GGSh7bWrqyqTVeGvyHJO1pr/1BVz01yfJKnJHldkse11r5XVbtOap+X5LrW2gMmPbxOmtyZsH+hPgAAAECHmVh3QlX1xsksqVOm/JZHJfmn1tqVSdJau3qy/Ngk7558/j+TbOpufVKSt1fV7yTZ1MnvsUl+c3KZ41eS7JHknrczvhdU1alVdep1V/ebBQIAAAAIse4czklyv01ftNZ+N8kvJbnrZnUb8+M/8+0m/1amu+q1Tbb/wiSvSXJAktOrao/JNl46uczx6Nbawa21T21xI62d0Fo7prV2zC67T3E3CwAAABhNS9LqzvnRUVWPr6rzquqCqnr1FtZXVXwDMuYAACAASURBVB0/WX9mVd1vS9tZLCHWncO/Jtmuql40b9kOW6i7KJOwa3ICHTxZ/pkkvzoJozLvcsIvZa5BfJI8M8kXJ+vv0Vr7SmvtdUmuzFyY9ckkL6qq9ZOaw6pqx63z8AAAAIARVNXaJG9MclySI5I8o6qO2KzsuMxdnXXPJC9I8qatse9F98Sqqt2SHNBaO3NrDIA7rrXWquopSf66qv4gyRVJNiR51Wal/5wfXfJ3SpJvTb7/nKr6kySfr6qZJF9P8pwkL0vy1qp65WSbvz3Zzl9W1T0zN/vqM0nOSHJmkoOSfK2qalL/lJ/OIwYAAABWyAOTXNBa+3aSVNV7kjw5ybnzap6cuR7bLcnJVbVrVe3TWrvsjuy45rbXKar6XJInZS70Oj1zAcXnW2svvyM7h512O6Dd55d+b8Gaa599fXc7N150l/6+LupPPNztW7d1a2a272/ngX+4cDuyf/nsA7vbqH1v7tbc425XdGvOv3Svbs1eH9u2W3PVk27s1ux44k7dmj3Puqlbc8FvrO/WrLm5/3OY3W62W7PdZf0s/xXPeH+35vUnPbFb8+T7nr7g+s+9vX9eXHf0rd2ae751Y7fm8L85p1vz4bPu3a3Z6Rv9cyfHXtvfzna3dGs2fHrvbs0uF/b77F36tP4x3O99/XPwiqPv+L1RbtmrP97tLu1fdt36w82tu/afD4fe65JuzXVv3b9bc8UD+uO566n9mse98gvdmnd//Be6NTsc3j8H89nduiXrbuq/Z/rhPRZeP7NdfxuH3eu73ZoDduw/plO/f0C35rrrtjRx+8fd/d39c/C7j+nXHHrf/uO6bba/nas/2D8Hb+2/LchdLuo/J7a5vl/z+bec0K05+GPPX3D9vvtfveD6JLnxw3fr1mw4oH9+bXd5/xKRm/bub2fdITf0a9b1X+NuvLD/w3rbU97crfm9//Libs2uT/let+ZVB3+iW/Nn3z6uW3Pxd/dccP3PH3JpdxuXv/Pu3ZqrHtJ/73qvQ/uv7Rtf1P85XHdk/3Xy+8f2z68D79X//+v3T9qvWzPN77XZbfs1O17U/32+tv+2PNs85soF1+/+x/33TN993M7dmtkp3n60Nf3n8J4P+EG35rh9z+3W/POFR3drznzS609rrR3TLfwZtO2BB7R9XvX7Kz2Mn4qLX/KKizN35dUmJ7TWTkiSqvqVJI9vrT1/8vWzkzyotfaSTcVV9ZEkf9Za23RF12eSvKq1NsW7wNs37eWEu7TWfpjk3yZ5W2vt/kkefUd2fGdTVTNVdfqkofrXquohk+UHVdXZW2kfn6uqYyafX1RVZ03296mq6r+zAQAAAOi7clMv68nH/L/abCmx3jw9naZm0aYNsdZV1T5JfjXJR+7oTu+kbpo0NL9Pkv8vyZ8uwz4fOdnfqUn+/81XTq5TXRbLuS8AAAAYQbU750fHJZnrjb3J/kk2n2I6Tc2iTRti/XHmGnf/n9baKVV1SJLz7+jO78TukuSazRdW1XZV9bbJDKqvV9UjO8u3r6r3TDr5vzfJ9rezvxOTHDr5nhuq6o+r6itJjq2q+1fV56vqtKr65CSMTFW9rKrOnWz7PZNlvziZTXb6ZBw7V9UjJtMANz2GN1TVcyafX1RVr6uqLyZ5elXdo6o+MdnXF6rq8K10PAEAAIAxnJLknlV1cFVtk7kbwn1os5oPZa4nd1XVg5Ncd0f7YSVTNnZvrb0vyfvmff3tJE+7ozu/k9l+0jB9uyT7JHnUFmp+N0laa/eaBDyfqqrDFlj+oiQ3ttbuXVX3TvK129n3E5OcNfl8xyRnt9ZeN7lT4OeTPLm1dkVV/VqSP0ny3CSvTnJwa+2Wqtp18r2vSPK7rbWTqmqnJFNc/Z2bW2sPS/7fNa4vbK2dX1UPSvJ3t3McAAAAgJ9BrbWNVfWSzE12WpvkrZMbxr1wsv7NST6W5AlJLkhyY350o7g7ZKoQaxKovCnJ3q21oyaBypNaa6/fGoO4k7iptXZ0klTVsUneUVVHbVbzsCR/mySttW9W1cVJDltg+S8kOX6y/Myq2vyOkJ+d3E3wzCSvmSybydxdCJPk55IcleTTczcMzNokm5LPM5O8q6o+mOSDk2UnJfmvVfWuJO9vrV0y+b6FvHfymHdK8pAk75v3PVvsXlhVL8jcLTazzfa7bqkEAAAAGFRr7WOZC6rmL3vzvM9bJhN2tqZpb63035O8MslbJoM5s6renUSItQWttS9X1Z5J7rrZqttLhBZKiha6GvWRrbXNb4Nxc2tt061gKsk5rbVjt/C9v5y5kOxJSV5bVUe21v6sqj6aubT05Kp6dJKN+fHLTrfbbDsbJv+uSXLtpiBvIZOGcCckc3cn7NUDAADAkPyPdllN2xNrh9baVzdb1r+H+yo1uSRwbZKrNlt1YpJnTmoOS3JgkvOmXH5Ukv797n/ceUnuOpkZlqpaX1VHVtWaJAe01j6b5A+S7Jpkp6q6R2vtrNban2euWfzhSS5OckRVbVtVuyT5pS3taHL3ygur6umTfVVV3WeR4wUAAADYomlnYl1ZVffIJGOsql/Jjy5LY86mnljJ3Ayo32qtzWx2Od7fJXlzVZ2VuRDwOZOeVLe3/E1J3ja5jPD0JJsHiQtqrd06+VkdPwmg1iX5b0m+leSdk2WV5K9ba9dW1X+eNJWfSXJuko9PxvG/Mnf54flJvr7ALp+Z5E1V9Zok65O8J8kZixkzAAAAwJbU3GWKnaK5uxGekLmeR9ckuTDJM1trF/90h8ed3V5H7NF+9Z2PW7Dmog17dLdz3okHd2vW39Dt75UNd+9PMNzr5LXdmhvvtvC+tr+8/7y7+SnXdmt2+/uduzX1ssu7NT/8wD7dmm1+2B/zupv7NZc9vP9zOOxt13Vrvvninbo121zZz+lnDr6pW7P99rf29/XxXbo113dO0/0+3z//vv+g9f2x9A9fbtqr/7N6+KPO6tZ89bIDuzW3nt0/NuuP/GG3Zo+37tit+c4TuiXZ/3/3z8FrnnVDt+aovft/y/nBjQs/Ry8+t//c2+Gy/qTpm47qn8c7f+X2bnD7I+s39M+Lu37l6m7Nt39t927Nvif2n1ezf7D5hOafdP0/94/hsc+/vfui/Mi/fuj+3ZqjH/+Nbs2lGxY+32uKe1av/ZP+773vP3jzq/t/0iHHfbtb863PHdKt2Xho//za9bP98cz8m5+4cfNPuOG83bo1v/xLp3RrvvimB3RrDvqt/o22T//u/t2a/itK8qCDLlpw/ZdO/bnuNvY4vf9asPbWKc6vZ/XfF9w203+vc9vGfs3GL/ZfCw5+Yv88veLG/uv/NV/du1tzwKdv7Nbc8tr+L9Gj97ikW/Phr953wfVrN/SP355HXtGtWfv2Pbs1B/zet7o1Z3yqf1Pxjdv3z6/jHn1qt+aLl/Zfd3b6H/33Dpc8pv/s2+6y/nHe74v917iLXzTTrdn14wufp1cc29/Gnl/pj/dBL+7/Tvvoaf0LYvb5bP81Zf2Ns92am3ftj/nUd/yH01prx3QLfwZte+ABbd9X/v5KD+On4qKXvWLIn1v3zJ1cenZMa+3RmevxdHhr7WECrC2rqj+sqnOq6syqOr2qHlRVF016ZG1e+6XOtj4w2cYFVXXd5PPTq+ohC2zzSVX16gW2eVBVnb20RwcAAACwMrrTFFprs5NbJ/6v1tqGXv1qNuk99cQk95tchrdnkm1ur7619pCFttdae+pku49I8orW2hPn7ev2vudDST606MEDAAAADGzaxu6frqpXVNUBVbX7po+f6sh+Nu2T5MrW2i1J0lq7srV26aaVVbV9VX2iqn5n8vUNk38fUVWfq6p/qqpvVtW76vZSqh/30qr6WlWdNWkmn6p6TlW9YfL53pPZXGdMPn4sNKuqQ6rq61X1gMn3vX8yvvOr6i/m1T22qr482df7qmqnyfI/q6pzJ7PO/mqy7OlVdfZkfyfekYMJAAAAsMm0jd2fO/n3d+cta0n6FzSvLp9K8rqq+laS/53kva21z0/W7ZS5RufvaK29Ywvfe98kRya5NMlJSR6a5Iud/V3ZWrtfVb04ySuSPH+z9ccn+Xxr7alVtXYyht2SpKp+bjKe326tnV5VRyY5ejKOW5KcV1V/m+SmJK9J8ujW2oaqelWSl0+Csqdm7vLSVlW7Tvb5uiSPa619b94yAAAAgDtkqhCrtdbvmk1aazdU1f2TPDzJI5O8d15/qn9J8hettXfdzrd/tbV2SZJM7nJ4UPoh1vsn/56W5N9uYf2jkvzmZGwzSa6rqt0y19vsX5I8rbV2zrz6z7TWrpuM4dwkd0+ya5Ijkpw0mRy2TZIvJ/lhkpuT/H1VfTTJRybbOCnJ2yd3NHx/tqCqXpDkBUmy093+L3t3HmZpWd6J/3uzdjcIsgtNSyOyCUYU4oYaRoIal2gUE4kxZMaME6PGNY5JHDXjzC/GGDWjoxGNkSQmxqBxjxujRhFRQGQRFQWiyI7sQtPL8/vjnNayqa6nquw+9XbX53Nd56qz3PW8T516z6nT337e+13W+REBAABgmGZxzhY2oVmFWFX129Pdv5EVRYvaOCz6fJLPV9UFSU4eP3RGkl+pqn9s058SctWU62szu9/N+u+Zbf16Nyf5QUarvaaGWNPNoZJ8prV20oaDVNWDkxyf5BlJnp/k0a2136uqhyR5QpLzquqo1trPnF6qtXZKRme7zN7328NLHgAAAOiabU+sX5xyeWSS1yT51c00py1WVR1aVQdPueuoJOvP4viqJDckedsEp3R6kueO57ZtVe0yvv+uJE9J8ttV9ZudMb6S5Niquu94nGVVdci4L9aurbVPJHlRRj9rquqg1tpZrbVXJbk+yYpN/lMBAAAAi85sDyd8wdTbVbVrkr/fLDPasu2c5C3jXlBrknw3o8Pm1p9V8EVJ3l1Vr2+tvXwC83lhklOq6tkZrax6bpKrkmTc3+qJGTXt3+hZJ1tr11XV7yT5p6racXz3K5PcmuTDVbUko9VaLx4/9hfjIK8yCtG+sel/LAAAAGCxqemPbOt8U9X2Sc5vrR2+6afEYrLjgfu3e73m+TPWPOn+53fH+eqbj+7WPOuPP96t+fg19+/WXPaFld2aNUtmfl2t2WNNd4xtl/VrDjylW5Lb77Vjt+a6o/snw9z1O/1t3TqL7nmr91/VrTnw7/rzuemgHbo1u198Z7fmxsOW9Gse1R/nN448p1vzoj3PnPHxR576su4Y26zuPzerD7mjW7Puhv7z95uP+nK35tNvekS3Ztkzr+rW/OiT+3Vr9rzwrm7NDb+/0Uz+J269ZWm35kEHfr9bc95XDu7W1PKZfxdrVm3bHWPpd/uv4fs+5tJuzQ//sf8CvX15tyS/97R/69asbv2f63O/dEC35tpfO7Rbc/Mh3ZKc/LjPdWtuXN3v0Xifpdd1a364arcZH/+nrz+4O8ZjjryoW/O9l/c/gt2x1/bdmoe+4mvdmi+87SHdmm2een235q5P7dWtufnI1d2aow+/rFvzzU/1d4w7Duy/pyy9vP9euWq3dd2afb888+eC1cv6B0csvb7/ueCOPfr/P730Wf335Hpj/3f1g9/u/67+19Ef7tb8xXdO6NbssN3abs3Re17RrTnz6v77zrp1/d/Fgbvd0K/ZaeaaD3++/16wV/+jRVbvNIvPTIf1/72348pbuzXbfnnXbk17xE3dmlXf7o9z2MP6r/Mjd7myW/OvH+p/TrlzRf+94L4HXtOtecSe35vx8ff/83HdMdY9sP972Obce3Rrfryy//pc+v3+34g7lvffd7a5q/+aufyFLzuntXZMt3ALtOO9V7TlL3vRQk9js7hsoL+32fbE+mhGZyNMRocg3i/Jv2yuSbFpVdVtrbWdN+F4K5N8rLV2ZFUdk+S3W2t/sKnGBwAAgC1C64fJbDqzbQb+hinX1yT5j/Vn0mNxa62dneTshZ4HAAAAsHWbbWP3x7fWvjC+nNFau6Kq/nyzzoxNrqqOq6rPV9VpVfWtqnpvVdX4sddV1Ter6vyqesP4vvdU1YlTvv+2jYz5sfH111TVu8fbuLSqrM4CAAAANonZhljTHaz+K5tyIkzMAzNqMH+/JPfJ6MyDuyf5tSRHtNZ+Icn/+jnGPyzJY5M8OMmrx/3TAAAAAH4uM4ZYVfXcqrogyaHjFTrrL5cl6XfbZoi+2lq7orW2Lsl5SVYmuSXJnUneVVVPTfLjn2P8j7fWVrXWrk9ybZJ9NiyoqudU1dlVdfbaW/tNmAEAAGBw2lZ8GaheT6x/TPJvSf4sySum3H9ra+1Hm21WbE5TTw23Nsl2rbU1VfXgJMcneUaS5yd5dEb9z7ZJkvFhh/1T9Ewz/oYFrbVTkpySjM5OOI+fAQAAAFhkZgyxWms3J7k5yUlJUlV7J1mSZOeq2rm11j8HOYNXVTsnWdZa+0RVfSXJd8cPXZ7k6CTvT/LkJA4NBAAAABbErM5OWFVPSvLGJPtldIjYAUkuTnLE5psaE3SPJB+uqiVJKsmLx/e/c3z/V5OcnsSxfwAAAMCCqNb6R3NV1TcyOrzss621B1bVf0pyUmvtOZt7gmzdlu29oh36tBfPWHPXLtUdZ/U9+vvxukP6Gdy6Hy7r1jzoIZd0a751/d4zPr77O3bujnHb8n7GfPMh3ZLs9IP+81ezOKjziN/8ZrfmjPNmMaHt+xv7laMu6Nac/dYHdmvu2rX/s9/7aZd2a/ZdenO35osf7c9nh84we5/Tb0d316tv6tasOeVureju5uqH9p+bbVb3ax78Sxd3a771t4d3a1bt1t/WjjfM4u/VLPbl649d3a3Z75PbdmuW/GhNt+b2fWdewHrNseu6Yyzbu//etWpVf6Hsy476TLfmA899TLfm0t/tliQ37Ngt2ePgG7o1p93/b7s1/+2J/Qnd+vq7ujU337GkW3PAbjd2a65798qZH390fy67fbl/9P5sXjNLr+u/IG6axdv2QR+428mJ7+bqh96jW7Pkxv7+fs2j1nZrDlh5XbfmynP37dbscFP/OTz5tz7Vrfmlnb7VrXnmv8x8wuaDjukf3PDdc1d0a+73i5d3a669vf8Z5Nh9+n8bd9u+/zfrU6/+pW7NLffuv9/+eL/+vvzap7yvW/NnbzupW7Pjzf1t7fqsK7o1V/z7zL+vA4+7vDvGf3x6Zbdmx4f130vv/Noe3Zp9z1jVrbnyuf33r33f2X//v+Hw/nvcsuv67xerZvFvhGf/wce6NW/44uO6NcsP6D/PV31r5s//O63sf55cdeE9uzUnPPbcbs1X3vGgbs1Nh3ZLssv3+jVHnnxRt+a9D333Oa21Y/qjbXl2XLGiLX/JzP+e3VJd9pKXDvL3NtuzE65urd2QZJuq2qa19rkkR23GeTEAVbW2qs6rqm9U1blV9fDx/SurqlXVa6fU7llVq6vqrePbr6mqly3U3AEAAICty2xDrJvGfZO+mOS9VfVXGTX9Zut2R2vtqNbaA5L8UUYN/te7NMkTp9x+epJ+DA8AAAAwD7MNsZ6c5MdJXpTkk0m+l+RJm2tSDNIuSaYeR3FHkourav3ywt/IqAE8AAAAwCY3q8burbXbq+qAJAe31k6tqmVJ+gews6VbWlXnZXRGyn0z6os21fuSPKOqrk6yNsmVGTX/BwAAgK3ebHqysunMaiVWVf3XJKclecf4ruVJPrS5JsVgrD+c8LAkj0vyd1U1tYPiJ5OckOSkJP8820Gr6jlVdXZVnb3mDic8BAAAAPpmezjh85Icm+SWJGmtXZJk5tMvsFVprZ2ZZM8ke025764k5yR5aZIPzGGsU1prx7TWjtlu6U6bfK4AAADA1mdWhxMmWdVau2v9Ipyq2i6JRXOLSFUdltEhpDckWTblob9M8oXW2g0/u0gLAAAAYNOZbYj1har644x6JJ2Q5PeTfHTzTYuBWN8TK0kqycmttbVTw6rW2kVxVkIAAABgM5ttiPWKJM9OckGS/5bkE0netbkmxTC01qZt3t9auzzJkdPc/54k7xlff81stlFrkx1unXlR31336K/wWrV8dbdm+WlLuzXXPPXObs1lN+3Rrdnp/bvO+PiVj+z/TLNpELhm57Xdml2eeF235uZP7tutOWin/jhXfPzgbs0NR2zfrTnjwgd1a+68b/8JWr1b//m54NLl3Zrr7tU/7HX6V8vPWrtk5scvf37/Zzp2lxu6Nec9q7OhJAfs9ONuzZ1r+n8izjj/kG5N/xWT3OvMO7o1Nx7W/7naLF432/6ovw9eeXx/39l5n/6cD97j+zM+vu5v7tsd4849Z34/SZLau/+DX3XEPbs1P3jemm7Ncw7/SrfmP+7s/9b/7YK7/Rm5m8d+9eXdmmUP6//sS3NNt+b2W/r71zXb36Nbs8Md62Z8fPsrduiOceMDZh4jSbb5cb8jxO7f6u/HBz/ykm7NJd8+rFtzyyH9bW13Xn/O2yzr74NPW/71bs2bLtmnW3PiY8/s1vzj2x/brTn1Hv2aZatmfvyKTx/QHSN79Pf1b35tZbem7dOZTJJPr+r/zu/48Y7dmh0P7/9xPPAxl3Vr1vzhnt2a1x/+mG7N7Q/p/+271z7Xd2su+Wb/s8PSzkfT757Z/52vPviubk2d0X+/3f17/ddn/rj/OW/VRf3zN91weP91vvSx13Zrrrux/377mEMu7ta89X1P6tY88lcu7NZ8/7WHdmvaE2Z+jd52xS7dMWpZ/3X+hfcf3a3ZcRYfiPb6er/m9l+/uVtz2+r+e8FWzzFqEzXju0xV3TtJWmvrWmvvbK09vbV24vj6jL+qqlpbVedV1UVV9Y2qeklVbTN+7Jiq+j+d7/+dqnrrXH6Y8Wqxeamq91TVZeM5n1tVD5vj9982/rpfVZ0233nMYXuvqaofjud7XlW9bhOP/5Squt+U2/+zqn55U24DAAAAYLZ6UflPzkBYVbNu3D22/sx2R2R0BrvHJ3l1krTWzm6t/cEcx5uNeYdYY3/YWjsqo5Vn7+gVT6e1dmVr7cS5fE9VzWINx7TeNH6Oj2qtvWKeY2zMU5L8JMRqrb2qtfbZTbwNAAAAgFnphVhTj3m6z3w30lq7Nslzkjy/Ro6rqo8lSVU9uKq+XFVfH3+dulZzRVV9sqq+XVWv/smkqn6rqr46XoH0jqradrwSaen4vvfOULfteNXVhVV1QVW9eJop/3uS+47HOGg8h3Oq6ovjBuepqgOr6syq+lpVvXbK3FZW1YXj68uq6v1VdX5V/XNVnVVVx4wfu228uumsJA+rqqOr6gvj7XyqqvadafsbU1WXV9We4+vHVNXnx9dfU1XvrqrPV9WlVfUHU77nt8dz/EZV/X1VPTzJryb5i/Fzd9D4OTtxXH/8+Pd1wXjMHads+0/HK9ku6M0VAAAAYLZ6IVbbyPU5a61dOt7e3hs89K0kj2qtPTDJq5L8f1Mee3CSZyY5KsnTx6HM4Ul+I8mx41VTa5M8c7wSaf3qr2durG481vLW2pGttfsn+dtppvukjPp/JckpSV7QWjs6ycuSvG18/18leXtr7ReTXL2RH/v3k9zYWvuFJK9NMvUA5p2SXNhae0iSs5K8JcmJ4+28O8n/7mw/SV485XDCfkOG5LAkj83oeX11VW1fVUck+ZMkj26tPSDJC1trX07ykYxXprXWvrd+gKpaklHfq98YP3/bJXnulG1c31p7UJK3j+cLAAAAW6e2lV4Gqte19wFVdUtGK7KWjq9nfLu11vrd6X7WdN2sd01yalUdnNFTNbXj7mdaazckSVV9MMkjkqzJKAz6Wo3Okrc0yXTdAY/fSN1Hk9ynqt6S5ONJPj3le/6iql6Z5Lokz66qnZM8PMm/1E/PyLe+c92xSZ42vv73Sf58mjk8IqOwK621C6vq/CmPrU2y/hDNQzNqlP6Z8Xa2TXJVZ/vJ6HDCN0yz3Y35eGttVZJVVXVtkn2SPDrJaa2168fz/FFnjEOTXNZa+8749qlJnpfkzePbHxx/PSfJU6cboKqek9HKvOywbLc5TB8AAABYrGYMsTZ2drr5qKr7ZBTcXJvk8CkPvTbJ51prv1ZVK5N8fuoUNpxSRkHYqa21P+ptcmN1VfWAjFYkPS/Jryf5L+OH/rC1dtqUul2S3DReyTWdXj450yno7mytrZ1Sd1Fr7Weayc9i+9NZk5+usNvwVEtTT0ezNqPff2VuOWvvtHrrt7F+/LtprZ2S0Qqz7LTHigFnvAAAAMBQ9M+BuglU1V5J/jrJW6c5q+GuSX44vv47Gzx2QlXtXlVLM2o0fkaS05OcWFV7j8fevarWn6N2dVWtX8k1bd24X9Q2rbUPJPkfSR60sXm31m5JcllVPX08Ro0DsIzn8ozx9WduZIgvZRSSpUZn+rv/Ruq+nWSvGp8Rcf1hfp3tb8zl+elhi0+boW6905P8elXtMd7G7uP7b00y3bltv5VkZVWtPy/8s5J8YRbbAQAAAJi3zRlirW+yflGSz2Z02N6fTlP3+iR/VlVnZHQY3VRfyuhQvfOSfGB8VsNvJnllkk+PD8/7TJJ9x/WnJDm/qt47Q93yJJ+vqvMy6u3UW9H1zIwOLfxGkouSPHl8/wuTPK+qvpZREDedt2UUTp2f5L8nOT/JzRsWtdbuSnJikj8fb+e8jA4jnGn7G/OnSf6qqr6Y0WqoGbXWLsqo/9YXxtt44/ih9yX5w3ED94Om1N+Z5D9ndIjjBUnWZRRQAgAAwKJRbeu9DFXdfWEUm0pVbZtk+9baneMg6PQkh4xDK5Is2X9F2//5050g8qfu86+3dce5fcWybs32t3UzvVz5iO27NXuf0x9n7Y4z58O7XnBDd4w1u/V/pmPf/rVuzfs+cFy35s5913Rrtru1f3Txmt364+z55V4rvmSb/lOc2/frHdma3H6f1d2a3c7tz2fXp13ZrfnR7f3f1+MOuHjGx8977i90x7h85pdLkmT11f25HHTkJMGFTwAAHjNJREFUD7s119+2U7fmuYf8e7fmQ1f3j4j+8RuWd2uuemj/d3WP/+iWZO2S/r6z/W39v423Le+Pc8d9V834+IoP9V9XO7/oim7Nupf1+wvedPh0i2s32NYV/T9Pr/yb93RrXv/Y3v+3JDc+eJ9uzU337f9f2y6X939XJ7zkS92a91+80YXZP7HXbrd2a7bfZt2Mj69+Z//nvvrh/X3r2cd/rlvz/ncd361ps/jvzFUP7//cdVF//7r3n365W/OD//Hwbs2aI/qfC+6zd//v7Lcv3bdbs+v5O/Tn03/LzV7Hz/yeu3pt/73gN1ac06356394QrfmgU/6Zrfmezfv0a255tI9uzWHvfm6bs23f3/Dcz5NM85f/qBbc/H/7r+2Dn3jHd2aK07ov5+u6+8WuePQO2d8/HH36/8eznxP/31pNk1BDjrpO92a/Zbe7f/Z7+YrbzmmW7PnmdO1Kv5Z337uXt2aXS7pvznd9Av9z533+E7/s8Muj9nYObp+avs3918T199/5n9H7H5xf77tBf3XzOp1/efmrg/1X1c33q+/8+x+Qf/v0Q0P6f9c3//d/35Oa62/A22Bluy/ou3/wll8QN8Cfe/lLx3k720ihxMuYsuSfGm8wulfkzx3SwuwqurXqqpV1WELPRcAAABg8epH08xba+3WJINLLufopIwO63xGktcs7FQAAACAxcpKLDaqqnZOcmySZ2fcxL6qtqmqt1XVRVX1sar6RFWdOH7s6Kr6QlWdU1Wfqqr+On0AAADYUrXaOi8DJcRiJk9J8snW2neS/KiqHpTkqUlWZnSmxd9N8pMzKiZ5S5ITW2tHJ3l3Rg3j76aqnlNVZ1fV2Wtvv33z/xQAAADAFs/hhMzkpCRvHl9/3/j29kn+pbW2LsnVVbW+s+yhSY5M8pmqSkZnmrxqukFba6dkdCbJLNl/hTMLAAAAAF1CLKZVVXskeXSSI6uqZRRKtYwa1E/7LUkuaq09bEJTBAAAABYRhxOyMScm+bvW2gGttZWttRVJLktyfZKnjXtj7ZPkuHH9t5PsVVU/Obywqo5YiIkDAAAAWx8rsdiYk5K8boP7PpDk8CRXJLkwyXeSnJXk5tbaXeMG7/+nqnbNaN96c5KLJjdlAAAAmCANciaqWvOMMzdVtXNr7bbxIYdfTXJsa+3qeY51XZL/mHLXnhmt9oItnX2ZrYH9mK2FfZmthX2ZLdEBrbW9FnoSm8OS/Ve0/V/wkoWexmbxvVe85JzW2jELPY8NWYnFfHysqu6ZZIckr51vgJUkG76ZVdXZQ3yhwFzZl9ka2I/ZWtiX2VrYl4HFTojFnLXWjlvoOQAAAACLixALAAAAYB5Kh6aJcnZChuaUhZ4AbCL2ZbYG9mO2FvZlthb2ZWBRE2IxKK01f5jZKtiX2RrYj9la2JfZWtiXgcVOiAUAAADA4OmJBQAAADAfemJNlJVYDEZVPa6qvl1V362qVyz0fGA2qmpFVX2uqi6uqouq6oXj+3evqs9U1SXjr7st9Fyhp6q2raqvV9XHxrftx2xxquqeVXVaVX1r/N78MPsyW6KqevH4s8WFVfVPVbXEvgwsdkIsBqGqtk3yf5P8SpL7JTmpqu63sLOCWVmT5KWttcOTPDTJ88b77iuSnN5aOzjJ6ePbMHQvTHLxlNv2Y7ZEf5Xkk621w5I8IKN92r7MFqWqlif5gyTHtNaOTLJtkmfEvgwsckIshuLBSb7bWru0tXZXkvclefICzwm6WmtXtdbOHV+/NaN/LC3PaP89dVx2apKnLMwMYXaqav8kT0jyril324/ZolTVLkkeleRvkqS1dldr7abYl9kybZdkaVVtl2RZkitjXwYWOSEWQ7E8yQ+m3L5ifB9sMapqZZIHJjkryT6ttauSUdCVZO+FmxnMypuTvDzJuin32Y/Z0twnyXVJ/nZ8aOy7qmqn2JfZwrTWfpjkDUm+n+SqJDe31j4d+zIMS0tqK70MlRCLoahp7hvwSwd+VlXtnOQDSV7UWrtloecDc1FVT0xybWvtnIWeC/yctkvyoCRvb609MMntcbgVW6Bxr6snJzkwyX5Jdqqq31rYWQEsPCEWQ3FFkhVTbu+f0ZJpGLyq2j6jAOu9rbUPju++pqr2HT++b5JrF2p+MAvHJvnVqro8o8O5H11V/xD7MVueK5Jc0Vo7a3z7tIxCLfsyW5pfTnJZa+261trqJB9M8vDYl4FFTojFUHwtycFVdWBV7ZBR48qPLPCcoKuqKqPeKxe31t445aGPJDl5fP3kJB+e9Nxgtlprf9Ra27+1tjKj99//11r7rdiP2cK01q5O8oOqOnR81/FJvhn7Mlue7yd5aFUtG3/WOD6jvpv2ZWBR226hJwBJ0lpbU1XPT/KpjM6+8u7W2kULPC2YjWOTPCvJBVV13vi+P07yuiTvr6pnZ/RB9OkLND/4ediP2RK9IMl7x/8pdmmS/5zRf9zal9litNbOqqrTkpyb0ZmQv57klCQ7x74MLGLVmrZDAAAAAHOxZPmKdu/ff8lCT2OzuOSVLzmntXbMQs9jQw4nBAAAAGDwhFgAAAAADJ4QCwAAAIDB09gdAAAAYD60GZ8oK7EAAAAAGDwhFgCwVauqN1XVi6bc/lRVvWvK7b+sqhlPLVRVX57Fdi6vqj2nuf+4qnr4XOc9G1X1O1W13wyP/8+q+uXNsW0AgEkTYgEAW7svJ3l4klTVNkn2THLElMcfnuSMmQZorf08IdRx67e/GfxOkmlDrKratrX2qtbaZzfTtgEAJkqIBQBs7c7IT0OkI5JcmOTWqtqtqnZMcniSrydJVf1hVX2tqs6vqj9dP0BV3Tb+uk1Vva2qLqqqj1XVJ6rqxCnbekFVnVtVF1TVYVW1MsnvJXlxVZ1XVY+cOrGqek1VnVpVnx6v5HpqVb1+/P2frKrtx3WvGs/rwqo6pUZOTHJMkveOx146HuNVVfWlJE+vqvdU1YlVtWtVfbuqDh2P909V9V839RMNAItNta3zMlRCLABgq9ZauzLJmqq6d0Zh1plJzkrysIxCoPNba3dV1WOSHJzkwUmOSnJ0VT1qg+GemmRlkvsn+d3xGFNd31p7UJK3J3lZa+3yJH+d5E2ttaNaa1+cZooHJXlCkicn+Yckn2ut3T/JHeP7k+StrbVfbK0dmWRpkie21k5LcnaSZ47HvmNce2dr7RGttfdNeQ5uTvL8JO+pqmck2a219s5ZPYEAAAMhxAIAFoP1q7HWh1hnTrm9vt/VY8aXryc5N8lhGYVaUz0iyb+01ta11q5O8rkNHv/g+Os5GYVds/FvrbXVSS5Ism2ST47vv2DKGP+pqs6qqguSPDo/ezjkhv55ujtba58Zj/l/MwrgAAC2KNst9AQAACZgfV+s+2d0OOEPkrw0yS1J3j2uqSR/1lp7xwzjVGc7q8Zf12b2n7NWJUlrbV1VrW6trV/Evy7JdlW1JMnbkhzTWvtBVb0myZIZxrt92omP+oEdntEKr92TXDHL+QEADIKVWADAYnBGkicm+VFrbW1r7UdJ7pnR4YBnjms+leS/VNXOSVJVy6tq7w3G+VKSp417Y+2TUdP2nluT3OPnmPv6wOr68dym9uCay9gvTnJxkpOSvHt9vy0AgC2FEAsAWAwuyOishF/Z4L6bW2vXJ0lr7dNJ/jHJmePD9k7L3QOiD2S0gunCJO/IqLfWzZ1tfzTJr03X2H02Wms3JXnneL4fSvK1KQ+/J8lfr2/svrExquqQjA4hfOm4L9e/J3nlXOcCALCQ6qcr1gEA6KmqnVtrt1XVHkm+muTYcX8sAGARWbJ8RTvg916y0NPYLL7zqpec01o7ZqHnsSE9sQAA5uZjVXXPJDskea0ACwBgMoRYAABz0Fo7bqHnAACwGAmxAAAAAOZDh6aJ0tgdAAAAgMETYgEAAAAweEIsAAAAAAZPTywAAACAuWpJ6Yk1UVZiAQAAADB4QiwAAAAABk+IBQAAAMDg6YkFAAAAMB96Yk2UlVgAAAAADJ4QCwAAAIDBE2IBAAAAMHh6YgEAAADMh55YE2UlFgAAAACDJ8QCAAAAYPCEWAAAAAAMnhALAAAAgMHT2B0AAABgjipJaew+UVZiAQAAADB4QiwAAAAABk+IBQAAAMDg6YkFAAAAMB96Yk2UlVgAAAAADJ4QCwAAAIDBE2IBAAAAMHh6YgEAAADMVUtKT6yJshILAAAAgMETYgEAAAAweEIsAAAAAAZPTywAAACA+dATa6KsxAIAAABg8IRYAAAAAAyeEAsAAACAwRNiAQAAADB4GrsDAAAAzIfG7hNlJRYAAAAAgyfEAgAAAGDwhFgAAAAADJ6eWAAAAADzUHpiTZSVWAAAAAAMnhALAAAAgMETYgEAAAAweHpiAQAAAMyHnlgTZSUWAAAAAIMnxAIAAABg8IRYAAAAAAyenlgAAAAAc9WiJ9aEWYkFAAAAwOAJsQAAAAAYPCEWAAAAAIMnxAIAAABg8DR2BwAAAJiH0th9oqzEAgAAAGDwhFgAAAAADJ4QCwAAAIDB0xMLAAAAYD70xJooK7EAAAAAGDwhFgAAAACDJ8QCAAAAYPD0xAIAAACYh9ITa6KsxAIAAABg8IRYAAAAAAyeEAsAAACAwdMTCwAAAGA+9MSaKCuxAAAAABg8IRYAAAAAgyfEAgAAAGDwhFgAAAAADJ7G7gAAAABz1aKx+4RZiQUAAADA4AmxAAAAABg8IRYAAAAAg6cnFgAAAMAc1fjC5FiJBQAAAMDgCbEAAAAAGDwhFgAAAACDpycWAAAAwHy0hZ7A4mIlFgAAAACDJ8QCAAAAYPCEWAAAAAAMnp5YAAAAAPNQemJNlJVYAAAAAAyeEAsAAACAwRNiAQAAADB4QiwAAAAABk9jdwAAAID50Nh9oqzEAgAAAGDwhFgAAAAADJ4QCwAAAIDB0xMLAAAAYD70xJooK7EAAAAAGDwhFgAAAACDJ8QCAAAAYPD0xAIAAACYq5aUnlgTZSUWAAAAAIMnxAIAAABg8IRYAAAAAAyenlgAAAAA86En1kRZiQUAAADA4AmxAAAAABg8IRYAAAAAgyfEAgAAAGDwNHYHAAAAmIfS2H2irMQCAAAAYPCEWAAAAAAMnhALAAAAgMHTEwsAAABgPvTEmigrsQAAAAAYPCEWAAAAAIMnxAIAAABg8PTEAgAAAJiH0hNroqzEAgAAAGDwhFgAAAAADJ4QCwAAAIDB0xMLAAAAYK7a+MLEWIkFAAAAwOAJsQAAAAAYPCEWAAAAAIMnxAIAAABg8DR2BwAAAJgPjd0nykosAAAAAAZPiAUAAADA4AmxAAAAABg8PbEAAAAA5qiSlJ5YE2UlFgAAAACDJ8QCAAAAYPCEWAAAAAAMnp5YAAAAAPOhJ9ZEWYkFAAAAwOAJsQAAAAAYPCEWAAAAAIOnJxYAAADAPFTTFGuSrMQCAAAAYPCEWAAAAAAMnhALAAAAgMETYgEAAAAweBq7AwAAAMxVG1+YGCuxAAAAABg8IRYAAAAAgyfEAgAAAGDw9MQCAAAAmIfSE2uirMQCAAAAYJOoqt2r6jNVdcn4627T1Kyoqs9V1cVVdVFVvXA2YwuxAAAAANhUXpHk9NbawUlOH9/e0JokL22tHZ7koUmeV1X36w0sxAIAAABgU3lyklPH109N8pQNC1prV7XWzh1fvzXJxUmW9wbWEwsAAABgPvTEms4+rbWrklFYVVV7z1RcVSuTPDDJWb2BhVgAAAAATLVnVZ095fYprbVT1t+oqs8mudc03/cnc9lIVe2c5ANJXtRau6VXL8QCAAAAYKrrW2vHbOzB1tovb+yxqrqmqvYdr8LaN8m1G6nbPqMA672ttQ/OZlJ6YgEAAACwqXwkycnj6ycn+fCGBVVVSf4mycWttTfOdmAhFgAAAMA8VNs6Lz+n1yU5oaouSXLC+Haqar+q+sS45tgkz0ry6Ko6b3x5fG9ghxMCAAAAsEm01m5Icvw091+Z5PHj619KUnMd20osAAAAAAZPiAUAAADA4AmxAAAAABg8PbEAAAAA5uPnb4LOHFiJBQAAAMDgCbEAAAAAGDwhFgAAAACDpycWAAAAwFy1pPTEmigrsQAAAAAYPCEWAAAAAIMnxAIAAABg8PTEAgAAAJgPPbEmykosAAAAAAZPiAUAAADA4AmxAAAAABg8PbEAAAAA5qiSlJ5YE2UlFgAAAACDJ8QCAAAAYPCEWAAAAAAMnhALAAAAgMHT2B0AAABgPprO7pNkJRYAAAAAgyfEAgAAAGDwhFgAAAAADJ6eWAAAAADzUFpiTZSVWAAAAAAMnhALAAAAgMETYgEAAAAweHpiAQAAAMxVG1+YGCuxAAAAABg8IRYAAAAAgyfEAgAAAGDw9MQCAAAAmIdat9AzWFysxAIAAABg8IRYAAAAAAyeEAsAAACAwRNiAQAAADB4GrsDAAAAzEdb6AksLlZiAQAAADB4QiwAAAAABk+IBQAAAMDg6YkFAAAAMA+lJ9ZEWYkFAAAAwOAJsQAAAAAYPCEWAAAAAIOnJxYAAADAXLUkTVOsSbISCwAAAIDBE2IBAAAAMHhCLAAAAAAGT08sAAAAgHkoLbEmykosAAAAAAZPiAUAAADA4AmxAAAAABg8PbEAAAAA5kNPrImyEgsAAACAwRNiAQAAADB4QiwAAAAABk+IBQAAAMDgaewOAAAAMEeVpDR2nygrsQAAAAAYPCEWAAAAAIMnxAIAAABg8PTEAgAAAJir1kYXJsZKLAAAAAAGT4gFAAAAwOAJsQAAAAAYPD2xAAAAAOahtMSaKCuxAAAAABg8IRYAAAAAgyfEAgAAAGDw9MQCAAAAmA89sSbKSiwAAAAABk+IBQAAAMDgCbEAAAAAGDwhFgAAAACDp7E7AAAAwDyUxu4TZSUWAAAAAIMnxAIAAABg8IRYAAAAAAyenlgAAAAAc9WSrNMUa5KsxAIAAABg8IRYAAAAAAyeEAsAAACAwdMTCwAAAGA+tMSaKCuxAAAAABg8IRYAAAAAgyfEAgAAAGDw9MQCAAAAmIfSE2uirMQCAAAAYPCEWAAAAAAMnhALAAAAgMETYgEAAAAweBq7AwAAAMxH09l9kqzEAgAAAGDwhFgAAAAADJ4QCwAAAIDB0xMLAAAAYB5KS6yJshILAAAAgMETYgEAAAAweEIsAAAAAAZPTywAAACAuWrjCxNjJRYAAAAAgyfEAgAAAGDwhFgAAAAADJ6eWAAAAABzVEmqaYo1SVZiAQAAADB4QiwAAAAABk+IBQAAAMDgCbEAAAAAGDyN3QEAAADmY91CT2BxsRILAAAAgMETYgEAAAAweEIsAAAAAAZPTywAAACAeajWFnoKi4qVWAAAAAAMnhALAAAAgMETYgEAAAAweHpiAQAAAMxVG1+YGCuxAAAAABg8IRYAAAAAgyfEAgAAAGDw9MQCAAAAmLOWNE2xJslKLAAAAAAGT4gFAAAAwOAJsQAAAAAYPCEWAAAAAIOnsTsAAADAPJS+7hNlJRYAAAAAgyfEAgAAAGDwhFgAAAAADJ6eWAAAAADz0TTFmiQrsQAAAAAYPCEWAAAAAIMnxAIAAABg8PTEAgAAAJirltS6hZ7E4mIlFgAAAACDJ8QCAAAAYPCEWAAAAAAMnp5YAAAAAPPR2kLPYFGxEgsAAACAwRNiAQAAADB4QiwAAAAABk+IBQAAAMDgaewOAAAAMB/6uk+UlVgAAAAADJ4QCwAAAIDBE2IBAAAAMHh6YgEAAADMQzVNsSbJSiwAAAAABk+IBQAAAMDgCbEAAAAAGDw9sQAAAADmQ0+sibISCwAAAIDBE2IBAAAAMHhCLAAAAAAGT08sAAAAgLlqSdYt9CQWFyuxAAAAABg8IRYAAAAAgyfEAgAAAGDwhFgAAAAADJ7G7gAAAABzVGmp1hZ6GouKlVgAAAAADJ4QCwAAAIDBE2IBAAAAMHh6YgEAAADMh55YE2UlFgAAAACDJ8QCAAAAYPCEWAAAAAAMnp5YAAAAAPOhJ9ZEWYkFAAAAwOAJsQAAAAAYPCEWAAAAAIOnJxYAAADAXLUk6xZ6EouLlVgAAAAADJ4QCwAAAIDBE2IBAAAAMHhCLAAAAAAGT2N3AAAAgHmo1hZ6CouKlVgAAAAADJ4QCwAAAIDBE2IBAAAAMHh6YgEAAADMh55YE2UlFgAAAACDJ8QCAAAAYPCEWAAAAAAMnp5YAAAAAHPW9MSaMCuxAAAAABg8IRYAAAAAgyfEAgAAAGDw9MQCAAAAmKsWPbEmzEosAAAAAAZPiAUAAADA4AmxAAAAABg8IRYAAAAAg6exOwAAAMB8rFvoCSwuVmIBAAAAMHhCLAAAAAAGT4gFAAAAwODpiQUAAAAwD9XaQk9hUbESCwAAAIDBE2IBAAAAMHhCLAAAAAAGT08sAAAAgPnQE2uirMQCAAAAYPCEWAAAAAAMnhALAAAAgMHTEwsAAABgrlqSdXpiTZKVWAAAAAAMnhALAAAAgMETYgEAAAAweEIsAAAAAAZPY3cAAACAOWtJ09h9kqzEAgAAAGDwhFgAAAAADJ4QCwAAAIDB0xMLAAAAYD70xJooK7EAAAAAGDwhFgAAAACDJ8QCAAAAYPD0xAIAAACYDz2xJspKLAAAAAAGT4gFAAAAwOAJsQAAAAAYPD2xAAAAAOaqJVmnJ9aGqmr3JP+cZGWSy5P8emvtxo3Ubpvk7CQ/bK09sTe2lVgAAAAAbCqvSHJ6a+3gJKePb2/MC5NcPNuBhVgAAAAAbCpPTnLq+PqpSZ4yXVFV7Z/kCUneNduBhVgAAAAATLVnVZ095fKcOXzvPq21q5Jk/HXvjdS9OcnLk6yb7cB6YgEAAAAw1fWttWM29mBVfTbJvaZ56E9mM3hVPTHJta21c6rquNlOSogFAAAAMGctabNeRLRVaa398sYeq6prqmrf1tpVVbVvkmunKTs2ya9W1eOTLEmyS1X9Q2vtt2barsMJAQAAANhUPpLk5PH1k5N8eMOC1toftdb2b62tTPKMJP+vF2AlQiwAAAAANp3XJTmhqi5JcsL4dqpqv6r6xM8zsMMJAQAAANgkWms3JDl+mvuvTPL4ae7/fJLPz2ZsIRYAAADAfLS20DNYVBxOCAAAAMDgCbEAAAAAGDwhFgAAAACDpycWAAAAwFy1JOv0xJokK7EAAAAAGDwhFgAAAACDJ8QCAAAAYPD0xAIAAACYj6Yn1iRZiQUAAADA4AmxAAAAABg8IRYAAAAAgyfEAgAAAGDwNHYHAAAAmA+N3SfKSiwAAAAABk+IBQAAAMDgCbEAAAAAGDw9sQAAAADmrOmJNWFWYgEAAAAweEIsAAAAAAZPiAUAAADA4OmJBQAAADBXLcm6dQs9i0XFSiwAAAAABk+IBQAAAMDgCbEAAAAAGDw9sQAAAADmo7WFnsGiYiUWAAAAAIMnxAIAAABg8IRYAAAAAAyeEAsAAACAwdPYHQAAAGA+NHafKCuxAAAAABg8IRYAAAAAgyfEAgAAAGDw9MQCAAAAmLOWrNMTa5KsxAIAAABg8IRYAAAAAAyeEAsA/v927uAmohgIouBsDgRFyARFDG7u3L4l1o1cFcGcn+wGAADq2cQCAAAAeCozyTp9xVW8xAIAAACgnogFAAAAQD0RCwAAAIB6NrEAAAAAdqycvuAqXmIBAAAAUE/EAgAAAKCeiAUAAABAPRELAAAAgHqG3QEAAAB2xLD7O3mJBQAAAEA9EQsAAACAeiIWAAAAAPVsYgEAAAA8lcysdfqKq3iJBQAAAEA9EQsAAACAeiIWAAAAAPVsYgEAAADsSE5fcBUvsQAAAACoJ2IBAAAAUE/EAgAAAKCeTSwAAACADVnr9AlX8RILAAAAgHoiFgAAAAD1RCwAAAAA6olYAAAAANQz7A4AAADwWGaS00dcxUssAAAAAOqJWAAAAADUE7EAAAAAqGcTCwAAAOCpzMyyifVOXmIBAAAAUE/EAgAAAKCeiAUAAABAPZtYAAAAADuyTl9wFS+xAAAAAKgnYgEAAABQT8QCAAAAoJ5NLAAAAICHMjNZOX3GVbzEAgAAAKCeiAUAAABAPRELAAAAgHoiFgAAAAD1DLsDAAAAPJXMZJ2+4ipeYgEAAABQT8QCAAAAoJ6IBQAAAEA9m1gAAAAAG7Jy+oSreIkFAAAAQD0RCwAAAIB6IhYAAAAA9WxiAQAAAOzIOn3BVbzEAgAAAKCeiAUAAABAPRELAAAAgHqvJKdvAAAAAPhXXq/X18x8nL7jj3wn+Tx9xG8iFgAAAAD1fCcEAAAAoJ6IBQAAAEA9EQsAAACAeiIWAAAAAPVELAAAAADq/QBIonc17bchvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "plt.imshow(mlp.coefs_[0], interpolation='none', cmap='viridis')\n",
    "\n",
    "plt.yticks(range(8),diabetes_features)\n",
    "plt.xlabel(\"Weight matrix\")\n",
    "plt.ylabel(\"Features\")\n",
    "\n",
    "plt.colorbar()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}