2-Regression/4-Logistic/notebook.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Pumpkin Varieties and Color\n",
    "\n",
    "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data: \n",
    "\n",
    "Let's look at the relationship between color and variety"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "\r\n",
    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\r\n",
    "\r\n",
    "pumpkins.head()\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
       "\n",
       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
       "\n",
       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ],
      "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>City Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Package</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Sub Variety</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Mostly Low</th>\n",
       "      <th>...</th>\n",
       "      <th>Unit of Sale</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Appearance</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Repack</th>\n",
       "      <th>Trans Mode</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <th>Unnamed: 25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/29/17</td>\n",
       "      <td>270.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5/6/17</td>\n",
       "      <td>270.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/24/16</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/24/16</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11/5/16</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "from sklearn.preprocessing import LabelEncoder\r\n",
    "#give colums\r\n",
    "new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']\r\n",
    "# drop not needed Colums\r\n",
    "new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\r\n",
    "#drop no data lines\r\n",
    "new_pumpkins.dropna(inplace=True)\r\n",
    "#transform same strings to same int\r\n",
    "new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)\r\n",
    "\r\n",
    "new_pumpkins.info"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       City Name  Package  Variety  Origin  Item Size  Color\n",
       "2             1        3        4       3          3      0\n",
       "3             1        3        4      17          3      0\n",
       "4             1        3        4       5          2      0\n",
       "5             1        3        4       5          2      0\n",
       "6             1        4        4       5          3      0\n",
       "...         ...      ...      ...     ...        ...    ...\n",
       "1694         12        3        5       4          6      1\n",
       "1695         12        3        5       4          6      1\n",
       "1696         12        3        5       4          6      1\n",
       "1697         12        3        5       4          6      1\n",
       "1698         12        3        5       4          6      1\n",
       "\n",
       "[991 rows x 6 columns]>"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "import seaborn as sns\r\n",
    "\r\n",
    "g = sns.PairGrid(new_pumpkins)\r\n",
    "g.map(sns.scatterplot)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ff86d28970>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 36 Axes>"
      ],
      "image/png": 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6f0rHDju4t5go6JVzO+N+5VxJkxOjsdr1eT+UpbmSpViQPS7mgbHW+orPi+npaTszM5N2GOhAYE/LdfOI5W16JVdZ7SJzyNUU9FJOJbTaBXmaAFa7cIJcbSGJ1S5uL0U6MuJntYtKNVJxNKfJidFeWe0ikVwNZN4iJR3Og5a5SvEBuI+bD4SCXEUIyFOEglxFKMhVhKJlrvK1CwAAAAAA4BXFBwAAAAAA4BXFBwAAAAAA4BXFBwAAAAAA4NWBtAMwxvxlSd8lyUqalfQd1too3ajQytYThOerkSZG85qcKDh5gjDgEnkK18gpZBF5CemDlSMWapHGC+5WjvDVLrKN8wp8SrX4YIx5TNJ3S/qItbZujHlN0p+U9JNpxoXm1tc3dPlL7+rly2VFaxuNtZPPPf0YJyVkBnkK18gpZBF5Cel+geDNuYpeeu3tRh5cOn9KpyeLsQoFvtpFtnFegW9dZ5Ex5g8ZY75j889jxpjHY8ZwQFLeGHNA0sOSbsVsD57MzVcbJyNJitY29PLlsubmqylHBnyAPIVr5BSyiLyEJN28s9woEEj38+Cl197WzTvLmWwX2cZ5Bb51VXwwxnxC0l+V9IObvxqU9A/3u3Nr7buS/rak/yhpXlLVWvtWk/2+aIyZMcbMLC4u7nd3iGm+GjVORluitQ1VqnxLZgu5mj7ytDPkaufIqfSQp62Rl9mSVq4u1Jrnwe2leHngq12kb69c5bwC37r95MMfk3RG0rIkWWtvSRrZ786NMV8j6aykxyUdlTRsjPnTD25nrX3VWjttrZ0eGxvb7+4Q08RoXrnBnSmTGxxQcTSXUkTZQ66mjzztDLnaOXIqPeRpa+RltqSVq+OFXNM8ODISLw98tYv07ZWrnFfgW7fFh1VrrdX9h0PKGDMcc/9/VNJ/sNYuWmvXJP2CpP8yZpvwZHKioFfOlRonpa3vgU1OjKYcGfAB8hSukVPIIvISknTs8LAunT+1Iw8unT+lY4fj3aL7ahfZxnkFvnX7wMnXjDH/u6RHjDF/TtJ3SvqxGPv/j5L+gDHmYUl1Sd8saSZGe/DowIEBnXv6Mf2+I4dUqUYqjuY0OTHKA2iQKeQpXCOnkEXkJSRpYMDo9GRRT373H9btpUhHRtysSuGrXWQb5xX41lXxwVr7t40x3yKpJumEpAvW2l/c786ttb9ujPk5Sb8paV3Sb0l6db/twb8DBwb09Ie/Rk9/OO1IgNbIU7hGTiGLyEtI9wsFT4wd0hNjh4JoF9nGeQU+db3UprX2F40xv771XmPMh6y1v7vfAKy1n5D0if2+HwAAAAAAZFtXxQdjzJ+X9NckRZI2JBndf/7DE+5DAwAAAAAAvaDbTz58n6SStfarPoIBAAAAAAC9p9unh/z/JL3nIxAAAAAAANCbuv3kww9K+jebz3xY2fqltfa7nUaFxNytR7pRWdZCbUXjhSEdLw7rkTxr+SIZvvKvWo90fVu7J4rDGnWU1xsbVjfvLGuhFmm8wNO/+4WLXK3X1zRbqTXamCoWlM8PeooYEtc4II5aPdI72+bPk8VhFRzNH59z09d1ul+u/5w3sZe49zLdFh/+d0n/XNKs7j/zAQG7W4/0VnlRF66UFa1tKDc4oItnSnq2NMZJBt75yr9qPdIXmrT7sdJY7ALExobVm3MVvfTa2422L50/pdOTxZ68AcF9LnK1Xl/TZ8qVXW28UCpSgPCEaxywf7V6pDebzJ/TpbHYBQifc9PXdbpfrv+cN7EXF/cy3X7tYtBa+5K19iestT+19dN96MiCG5XlRvJIUrS2oQtXyrpRWU45MvQDX/l3vUW71x3k9c07y40bj622X3rtbd28w5zpZS5ydbZSa9rGbKXmJWZwjQPieKfF/HnHwfzxOTd9Xaf75frPeRN7cXEv023x4fPGmBeNMRPGmA9t/XTZBjJiobbSSJ4t0dqGFmorLd4BuOMr/3zm9UItatr27aUodtvILhc5xfk2efQ5sH9+r6XhXaf75frPeRN7cZEf3RYfvl2bz32Q9MXNn5ku20BGjBeGlBvcmQK5wQGNF4ZSigj9xFf++czr8UKuadtHRvgoYi9zkVOcb5NHnwP75/daGt51ul+u/5w3sRcX+dFV8cFa+3iTnye6aQPZcbw4rItnSo0k2vrezvHicMqRoR/4yr8TLdo94SCvjx0e1qXzp3a0fen8KR07zJzpZS5ydapYaNrGVLHgJWZwjQPieLLF/HnSwfzxOTd9Xaf75frPeRN7cXEvY6y1Xe3UGFOS9BFJjVKftfbTXTUSw/T0tJ2Z4cMWrvBE2wbnTwsiV9sLebWL20uRjoyk8rRrcjUFrHbRtUzkKdc4dCATuZpFoa924fo63S/Xf86b2EuH9zItc7Wr1S6MMZ+Q9FHdLz58TtLHJf1rSYkVH+DWI/mcnnmcEwrS4Sv/Rj3m9cCA0RNjh/TE2CEv7SObXORqPj+oZx4/7CgidIJrHLB/BY/zx+fc9HWd7pfrP+dN7CXuvUy3z3z4NknfLKlirf0OSU9LGt333gEAAAAAQM/rtvhQt9ZuSFo3xhQk3Zb0YfdhAQAAAACAXtHV1y4kzRhjHpH0Y7q/0sU9Sb/mOigAAAAAANA7uio+WGv/+80//m/GmDclFay1V+MEsFnM+KSkkiQr6TuttRQ0AAAAAADoER0VH4wxv6fJrzck3TXG/B5r7X+MEcOPSHrTWvttxpiDkh6O0RbEU2qzbHX1fV29VVWlFmmikNPU0VEdPPhQ2mGlhlxFP1lf39DcfFXz1UgTo3lNThR04EC3337MBuZuNjAO8MnnCj0+74d8nWv7ZcUizivYS9z51eknH97Q/U8lbF82w0oak3RE0r7OFsaYUUl/RNKfkSRr7aqk1f20hfvu1iO9VV7UhStlRWsbjfVXny2NceJI2erq+7p89ZYuvL5tbM6WdO7k0b4sQJCr6Cfr6xu6/KV39fLlD/L9lXMlnXv6seAKEMzdbGAc4FO9vqbPlCu78uuFUjH2P7h93g/5Otf67I8s4byCvbiYXx1tZa2dstae3PzvlKQXJP2q7j/z4Xv3ewCSHpe0KOknjDG/ZYz5pDFmOEZ7fe9GZblxwpCkaG1DF66UdaOynHJkuHqr2rjQSptj83pZV29VU44sHeQq+sncfLVxsZbu5/vLl8uamw9v/jN3s4FxgE+zlVrT/Jqt1GK37fN+yNe51md/ZAnnFezFxfzqqgRojPl9xpiflPR53X/g5EestX+vmzYecEDS10v6B9bar5O0LOkHmuz3RWPMjDFmZnFxMcbuet9CbaWREFuitQ0t1FZSiqi/7JWrlVrUYmyiJEPMDHI1XZxXkzVfbT7/K9Xw5n+Sc5c8bY1zaLb0Wq76zC+f90O+zrW9NN/2ytVeOk6452J+dVR8MMaUjDE/I+nnJf2SpJK19pPW2rUu4m3mK5K+Yq399c2//5zuFyN2sNa+aq2dttZOj42NxdxlbxsvDCk3uHNYc4MDGi8MpRRRf9krVycKuRZj058fYyNX08V5NVkTo/mm+V4cDW/+Jzl3ydPWOIdmS6/lqs/88nk/5Otc20vzba9c7aXjhHsu5lenn3z4kqQ/KOlfSXpG0v9qjPm7Wz8d7+0B1tqKpP9kjDmx+atvlvTl/bYH6XhxWBfPlBqJsfVdreNFvs2Stqmjo7p49oGxOVvSyaOjKUeWDnIV/WRyoqBXzu3M91fOlTQ5Ed78Z+5mA+MAn6aKhab5NVUsxG/b4/2Qr3Otz/7IEs4r2IuL+dXpAye/cx/xdeovSfrpzZUuflvSd3jcV897JJ/Ts6UxHXv0GZ5SmzEHDz6kcyeP6olHh7VQizReyOlkH692Qa6inxw4MKBzTz+m33fkkCrVSMXRnCYnRoN72KTE3M0KxgE+5fODeqFU1LFHH3a+uoPP+yFf51qf/ZElnFewFxfzq6Pig7X2p/YdZfu235Y07av9fvRIPqdnHuckkUUHDz6k6WMfSjuMzCBX0U8OHBjQ0x/+Gj394bQjiY+5mw2MA3zK5wf1zOOHvbTt837I17nWZ39kCecV7CXu/Arvf7kAAAAAAICgUHwAAAAAAABedbvUZu9/1ggAAAAAADjV6QMnt/xbY8zbkn5C0uettdZ9SGjmbj3SjcoyD39BT/GV18wXuOYip8hLdOu9+qrKlaVGzpSKI3o4fzDtsBLDnOmMr37ymX+1eqR3tsX8ZHFYBUdj6yvu5fqK5ir3Gu1OFg9pOO9mCcqNDaubd5YbD+A8dnhYAwPGSdvdYt5hL3Hzo9viw3FJf1T3V7/4u8aY1yT9pLX2RpftoAt365HeKi/qwpWyorWNxrI3z5bGOBkgWL7ymvkC11zkFHmJbr1XX9Vnywu7cub50nhfFCCYM53x1U8+869Wj/Rmk5hPl8ZiFyB8xb1cX9Eb5du72n2udCR2AWJjw+rNuYpeeu3tRtuXzp/S6cli4gUI5h324iI/uvrahb3vF6213y7pz0n6f0r6d8aYf2mM+YPdHwI6caOy3BhkSYrWNnThSlk3KsspRwbsn6+8Zr7ANRc5RV6iW+XKUtOcKVeWUo4sGcyZzvjqJ5/5906LmN9xMLa+4p6r3Gva7lzlXuyYb95ZbhQettp+6bW3dfNO8rnOvMNeXORH1898MMZ8jzFmRtL3SfpLkh6V9Fck/aNu2kLnFmorjUHeEq1taKG2klJEQHy+8pr5Atdc5BR5iW71e870+/F3KsRraYht+405atr27aUodtvdx8K8Q2su8qPb1S5+TVJB0jlr7XPW2l+w1q5ba2ck/W9dtoUOjReGlBvcOVS5wQGNF9x8zwxIg6+8Zr7ANRc5RV6iW/2eM/1+/J0K8VoaYtt+Y841bfvISPJfc2DeYS8u8qPb4sPL1tr/t7X2K1u/MMb83yXJWvs3u2wLHTpeHNbFM6XGYG99v+Z4cTjlyID985XXzBe45iKnyEt0q1QcaZozpeJIypElgznTGV/95DP/nmwR85MOxtZX3JPFQ03bnSweih3zscPDunT+1I62L50/pWOHk8915h324iI/TDcLVhhjftNa+/XtfufT9PS0nZmZSWp3mcGTZ71z/kSffs3VbrDaxb6QqylgtYuukacOsNpFInMm+FxltYudQl7t4vZSpCMjLVe7SCRX++xahS51mB8tc7Wj1S6MMR+X9K2SHjPG/N1tLxUkrXcbNLr3SD6nZx5n4qO3+Mpr5gtcc5FT5CW69XD+oJ55/HDaYaSGOdMZX/3kM/8KHsfWV9zD+SE987ifrx8MDBg9MXZIT4zF/yRFXMw77CVufnS61OYtSTOSzkj64rbfL0n6y/veOwAAAAAA6HkdFR+stV+S9CVjzE9ba/mkAwAAAAAA6FinX7t4zVp7XtJvGWO2PyTCSLLW2pNeogMAAAAAAMHr9GsX37P53+d9BQIAAAAAAHpTp8WHYWPMN1hrf3X7L40x3yCpEjcIY8xDuv9MiXettX1R4OBJsgiFz1xltYudQo27H7gYm3v1SF/e1sZHisM6lML4snJHctbXNzQ3X9V8NdLEaF6TEwUdONDtKuet+R6HuO2nnSdp7387nytHhHgtDbFtnzEv1SNd29b2U8VhjaSUq1maN8ieuPnRafHhhyX9YJPf1zZfe6HjPTb3PZKu6f7qGT3vbj3SW+VFXbhSVrS20Vgj9dnSGJMbmeIzV321Her8CjXufuBibO7VI32uSRvfWhpLtADh4ljI1c6sr2/o8pfe1cuXP+inV86VdO7px5wUIHyPQ9z2086TtPe/3Xv1VX22vLArludL47ELECFeS0Ns22fMS/VIn2/S9sdLY4kXILI0b5A9LvKj06vfuLV29sFfbv7uWOch72aM+VpJz0n6ZJx2QnKjstwYNEmK1jZ04UpZNyrLKUcG7OQzV321Her8CjXufuBibL7coo0vJzy+Lo6FXO3M3Hy1UXiQ7vfTy5fLmpuvOmnf9zjEbT/tPEl7/9uVK0tNYylXlmK3HeK1NMS2fcZ8rUXb11LI1SzNG2SPi/zotPjwyB6v5TveW3M/LOn7JW202sAY86IxZsYYM7O4uBhzd+lbqK00Bm1LtLahhdpKShHBFXI1/bZDnV9Jx91rueqTi7HJSl6Gdiwh5+l8NWraT5Vq5KR93+MQt/20cz5L51SupeG3HWLMraSVqwifi/zotPgwY4z5cw/+0hjzXZK+2PHedr//eUm3rbV7tmGtfdVaO22tnR4bG9vv7jJjvDCk3ODOrs8NDmi8MJRSRHCFXE2/7VDnV9Jx91qu+uRibLKSl6EdS8h5OjGab9pPxVE3H132PQ5x208757N0TuVaGn7bIcbcSlq5ivC5yI9Oiw/fK+k7jDG/bIz5O5s//1LSn9UHK2HsxzdIOmOMuSnpZyV9kzHmH8ZoLwjHi8O6eKbUGLyt78scLw6nHBmwk89c9dV2qPMr1Lj7gYux+UiLNj6S8Pi6OBZytTOTEwW9cm5nP71yrqTJiVEn7fseh7jtp50nae9/u1JxpGkspeJI7LZDvJaG2LbPmJ9q0fZTKeRqluYNssdFfhhrbecbG/ONkkqbf52z1v7zLuJt1/ZHJX1fu9Uupqen7czMjKvdpoYnyWaOcd0guZpe26HOrw7jJldTwGoXXbdBnuqD1S4q1UjF0ZwmJ0ZZ7SJ7q10kkqusdhF+2xlY7SKRXE173iLb4p5Xuyo++NRvxQdkDjfKCAW5ihCQpwgFuYpQkKsIRctc7XSpTe+stb8s6ZdTDgMAAAAAADjm7rN/AAAAAAAATVB8AAAAAAAAXlF8AAAAAAAAXmXmmQ+9gKfDohf5zOtqPdL1bW2fKA5rNONPpEZ/SmiFiERkJY4sWF19X1dvVVWpRZoo5DR1dFQHDz7U8fvT7st+3z/8CnV86/U1zVZqjbinigXl84Ox2/XZH1lZDUkKd9yRjLj5QfHBkbv1SG+VF3XhSlnR2kZj3dNnS2NMWATLZ15X65G+0KTtj5XGYhUgmItwzUVOZSUvsxJHFqyuvq/LV2/pwuvb+uJsSedOHu2oAJF2X/b7/uFXqONbr6/pM+XKrrhfKBVjFSB89se9eqTPNWn7W0tjiRcgQh13JMNFfvC1C0duVJYbAyFJ0dqGLlwp60ZlOeXIgP3zmdfXW7R9PWbbzEW45iKnspKXWYkjC67eqjYKD9JmX7xe1tVb1Y7en3Zf9vv+4Veo4ztbqTWNe7ZSi9Wuz/74cou2v5xCX4c67kiGi/yg+ODIQm2lMRBborUNLdRWUooIiM9nXvtqm7kI11zkVFbyMitxZEGlFrXoi6ij96fdl/2+f/gV6viGeG+Rpb7OUizIHhf5QfHBkfHCkHKDO7szNzig8cJQShEB8fnMa19tMxfhmoucykpeZiWOLJgo5Fr0RWcfHU27L/t9//Ar1PEN8d4iS32dpViQPS7yg+KDI8eLw7p4ptQYkK3vwBwvDqccGbB/PvP6RIu2T8Rsm7kI11zkVFbyMitxZMHU0VFdPPtAX5wt6eTR0Y7en3Zf9vv+4Veo4ztVLDSNe6pYiNWuz/74SIu2P5JCX4c67kiGi/ww1lpf8XkxPT1tZ2Zm0g6jKZ4OGzTjusEs52o3WO0ic8jVFLDaRdeCyNOt1S4WapHGCzmdZLWLoPbvSBC5moZQx7eHV7tIJFdDHXcko8P8aJmrFB+A+7j5QCjIVYSAPEUoyFWEglxFKFrmKl+7AAAAAAAAXlF8AAAAAAAAXlF8AAAAAAAAXlF8AAAAAAAAXh1Ic+fGmA9L+rSkcUlW0qvW2h9JM6YH8cRXuOQrn3ytGiH5nQO+2g513q6vb2huvqr5aqSJ0bwmJwo6cCD5GrGL/luur2iucq/RxmTxkIbz3a0TnqUVJrKSU73Wr2lL+zji7j/u+6NoXbPzVVVqKyoWhjQ1Mapczt2toe/+fa++qnJlqdF+qTiih/MHnbXvks9YQ7yWhth2iDGHHguyJ25+pFp8kLQu6a9Ya3/TGDMi6YvGmF+01n455bgk3e/ct8qLunClrGhto7GW6bOlMSYhuuYrn6r1SF9o0u7HSmOxCxA+54CvtkOdt+vrG7r8pXf18uUP4n7lXEnnnn4s0QKEi/5brq/ojfLtXW08VzrS8T+UXcThKheyklO91q9pS/s44u4/7vujaF1XZud3vf/M1ISTAoTv/n2vvqrPlhd2tf98aTxzBQifsYZ4LQ2x7RBjDj0WZI+L/Ej1axfW2nlr7W9u/nlJ0jVJj6UZ03Y3KsuNzpWkaG1DF66UdaOynHJkCJGvfLreot3rDvLU5xzw1Xao83ZuvtooPEj34375cllz89VE43DRf3OVe03bmKvcSzQOV7mQlZzqtX5NW9rHEXf/cd8/O19t+v5ZR+cc3/1briw1bb9cWXLSvks+Yw3xWhpi2yHGHHosyB4X+ZGZZz4YY45J+jpJv97ktReNMTPGmJnFxcXEYlqorTQ6d0u0tqGF2kpiMSAse+Wqr3zymachth3qvJ2vRk3jrlQjL/trlasu+q+X2nDZTlxZ6ZMk+8Pn9T/tcY27/7jvr3g+ft/9m/b4PSiN67/PtkOM2WfbIcbcSlq5ivC5yI9MFB+MMYck/byk77XW1h583Vr7qrV22lo7PTY2llhc44Uh5QZ3dlFucEDjhe6+X4v+sVeu+sonn3kaYtuhztuJ0XzTuIujfj7m2CpXXfRfL7Xhsp24stInSfaHz+t/2uMad/9x31/0fPy++zft8XtQGtd/n22HGLPPtkOMuZW0chXhc5EfqRcfjDGDul94+Glr7S+kHc92x4vDunim1Ojkre+1HC8OpxwZQuQrn060aPeEgzz1OQd8tR3qvJ2cKOiVczvjfuVcSZMTo4nG4aL/JouHmrYxWTyUaByuciErOdVr/Zq2tI8j7v7jvn9qYrTp+6ccnXN892+pONK0/VJxxEn7LvmMNcRraYhthxhz6LEge1zkh7HW+oqv/c6NMZJ+StLvWmu/t5P3TE9P25mZGa9xbccTX/uGcd1gs1xltYtk2g513m6tdlGpRiqO5jQ5MdrsYZPec5XVLvy1E1dA/ZrIOTWutMc1K6tdbL2/T1e7SCRXWe0i/LYzEHPQ96roDXFzNe3iwx+S9K8kzUra+gLJD1lrP9fqPUkXH9A3grhRBkSuIgzkKUJBriIU5CpC0TJXU11q01r7r+VhIgEAAAAAgOxI/ZkPAAAAAACgt1F8AAAAAAAAXlF8AAAAAAAAXqX6zIc08ARXpCnE/MvA050z026/oP9266U+ydIqIlkXd3WRXu+ndist1etrmq3UPlhNo1hQPj+YWHy1eqR3tsX3ZHFYhR7q/y2+jjPE678kra6+r6u3qqrUIk0Ucpo6OqqDBx+K3a7PmDc2rG7eWdZCLdJ4Iadjh4c1MJDOY/F6/byFeOLmR18VH+7WI71VXtSFK2VFaxuNtUmfLY0xqeBdiPnnM2ZfbYfYz1lC/+3WS33i4lh6qT/2slxf0Rvl27uO87nSkY4KEL3eT9V6pC80Ob6PlcY0ms+pXl/TZ8qVXa+/UComUoCo1SO92SS+06WxnipA+DrOEK//0v3Cw+Wrt3Th9W1tny3p3MmjsQoQPmPe2LB6c66il157u9H2pfOndHqymHgBotfPW4jHRX701dcublSWG50lSdHahi5cKetGZTnlyNAPQsw/nzH7ajvEfs4S+m+3XuoTF8fSS/2xl7nKvabHOVe519H7e72frrc4vuubxzdbqTV9fbZSSyS+d1rE906P9P8WX8cZ4vVfkq7eqjYKD422Xy/r6q1qZmO+eWe5UXjYavul197WzTvJ52qvn7cQj4v86Kviw0JtpdFZW6K1DS3UVlKKCP0kxPzzGbOvtkPs5yyh/3brpT5xcSy91B97iXucvd5P7Y4v7eNPe/9JCfFa6rPtSi1q0XYUq12//dE85ttL8WLeXyz9MW+wPy7yo6+KD+OFIeUGdx5ybnBA44XOv78J7FeI+eczZl9th9jPWUL/7dZLfeLiWHqpP/YS9zh7vZ/aHV/ax5/2/pMS4rXUZ9sThVyLtuN9ZcBvfzSP+chI8l9z6Jd5g/1xkR99VXw4XhzWxTOlRqdtfU/leHE45cjQD0LMP58x+2o7xH7OEvpvt17qExfH0kv9sZfJ4qGmxzlZPNTR+3u9n060OL4Tm8c3VSw0fX2qWEgkvidbxPdkj/T/Fl/HGeL1X5Kmjo7q4tkH2j5b0smjo5mN+djhYV06f2pH25fOn9Kxw8nnaq+ftxCPi/ww1lpf8XkxPT1tZ2Zm9v1+nuCKFpw/0adZroaYfyE+7TrEfu6C91zt8f7bl17qk4RWu0jknOobq13srUdWuwg+V1ntYqet1S62Vo44GdBqF7eXIh0ZabnaBfeqSF2H+dEyV/uu+AC0EPzNB/oGuYoQkKcIBbmKUJCrCEXLXO2rr10AAAAAAIDkUXwAAAAAAABeUXwAAAAAAABepV58MMacNsZcN8b8e2PMD6QdDwAAAAAAcOtAmjs3xjwk6e9L+hZJX5H0G8aYK9baL3fTDk9lBcLka+5uPem6Uos0UchpytGTrtG/uM5kUxSta3a+qkptRcXCkKYmRpXLdX5rs/WE+a2n4rd4wnxmhZ6Xofd/VsRdlaWV9fUNzc1XNV+NNDGa1+REQQcOuPn/lj5XQvHVH77a9d12t0I/ryDbUi0+SHpG0r+31v62JBljflbSWUkdFx/u1iO9VV7UhStlRWsbjfVGny2NMVGADPM1d1dX39flq7d04fVt7Z4t6dzJoxQgsC9cZ7IpitZ1ZXZ+17icmZroqACxsWH15lxFL732duP9l86f0unJYhD/AA49L0Pv/6xYrq/ojfLtXXnwXOlIrH+8rq9v6PKX3tXLlz9o95VzJZ17+rHYBYh6fU2fKVd2xfxCqRi7AOGrP3y167vtboV+XkH2pf21i8ck/adtf//K5u86dqOy3JggkhStbejClbJuVJbdRQnAOV9z9+qtaqPw0Gj39bKu3qrGjhn9ietMNs3OV5uOy+x8Z3P95p3lxj98t97/0mtv6+adMMY19LwMvf+zYq5yr2kezFXuxWt3vtooPGy1+/LlsuY6nF97ma3Ums/dSi122976w1O7vtvuVujnFWRf2sWHjhhjXjTGzBhjZhYXF3e8tlBbaUyQLdHahhZqK0mGCEjaO1exk6+5W6lFLdqNYrXba8jVznGdSc9eeVqJOS4LLc4Vt5fCOFeEnpeh9/+D0jqn+sqD+Wrz8alU44+Pz9z11XaIMbfCv6uQprSLD+9K+vC2v3/t5u92sNa+aq2dttZOj42N7XhtvDCk3ODOw8gNDmi8kM73pNDf9spV7ORr7k4Uci3a5eOC25GrneM6k5698rQYc1zGW5wrjoyEca4IPS9D7/8HpXVO9XYtHc03bbc4Gn98fOaur7ZDjLkV/l2FNKVdfPgNSb/PGPO4MeagpD8p6Uo3DRwvDuvimVJjomx9N+l4cdh9tACc8TV3p46O6uLZB9o9W9LJo6OxY0Z/4jqTTVMTo03HZWqis7l+7PCwLp0/teP9l86f0rHDYYxr6HkZev9nxWTxUNM8mCweitfuREGvnNvZ7ivnSprscH7tZapYaD53i4XYbXvrD0/t+m67W6GfV5B9xlqbbgDGfKukH5b0kKQft9b+9b22n56etjMzMzt+x1NZ4YDzp1s1y1Xs5Hu1i60nqJ/srdUuyNUUcJ3pWiJ5urXaReOJ+ftc7eL2UqQjI+GtthB6Xmak/4M/p/pe7aJSjVQczWlyYpTVLtJd7SKRXA39vIJMaJmrqRcfusVNMjwJ/uYDfYNcRQjIU4SCXEUoyFWEomWupv21CwAAAAAA0OMoPgAAAAAAAK+C+9qFMWZR0u+0ePlRSV9NMJw09PoxpnV8X7XWnnbZYA/mKjEnZ6+4k8zVkPqPWN2LEyfn1J2ILx6f8ZGr7YUYsxRm3Fm5/reLpR/RHzvtK1eDKz7sxRgzY62dTjsOn3r9GHv9+LaEeJzEnJysxJ2VODpBrO6FEqeU/ViJL56sx9eNEI8lxJilMOPOUsxZiiUL6I+d9tsffO0CAAAAAAB4RfEBAAAAAAB41WvFh1fTDiABvX6MvX58W0I8TmJOTlbizkocnSBW90KJU8p+rMQXT9bj60aIxxJizFKYcWcp5izFkgX0x0776o+eeuYDAAAAAADInl775AMAAAAAAMgYig8AAAAAAMArig8AAAAAAMArig8AAAAAAMArig8AAAAAAMArig8AAAAAAMArig8AAAAAAMArig8AAAAAAMArig8AAAAAAMCr4IoPp0+ftpL44cf1j3PkKj+efpwjV/nx8OMcecqPpx/nyFV+PP04R67y4+mnpeCKD1/96lfTDgHoCLmKUJCrCAF5ilCQqwgFuYqkBVd8AAAAAAAAYaH4AAAAAAAAvDqQdgDGmEckfVJSSfe/I/Kd1tpfSzWobdbXNzQ3X9V8NdLEaF6TEwUdOBC/ZnO3HulGZVkLtRWNF4Z0vDisR/I5BxEDCBXnBSTBRZ4t1SNd29bGU8VhjZCrXYt7j7FcX9Fc5V5jHCaLhzScH/IYcXc4p4WtXl/TbKXWGL+pYkH5/KCTtjc2rG7eWdZCLdJ4Iadjh4c1MGCctH2vHunL2/LuI8VhHXKQd/2Sz/1ynNif9+qrKleWGvlRKo7o4fzBjt+fevFB0o9IetNa+23GmIOSHk47oC3r6xu6/KV39fLlsqK1DeUGB/TKuZLOPf1YrALE3Xqkt8qLunDlg3Yvninp2dIYkxvoU5wXkAQXebZUj/T5Jm18vDRGAaILce8xlusreqN8e9c4PFc6kokCBOe0sNXra/pMubJr/F4oFWMXIDY2rN6cq+il195utH3p/CmdnizGLkDcq0f6XJO8+9bSWKwCRL/kc78cJ/bnvfqqPlte2JUfz5fGOy5ApPq1C2PMqKQ/IulTkmStXbXW3k0zpu3m5quNmwJJitY29PLlsubmq7HavVFZbgzaVrsXrpR1o7IcO2YAYeK8gCS4yLNrLdq4Rq52Je49xlzlXtNxmKvc8xZzNzinhW22Ums6frOVWuy2b95ZbhQettp+6bW3dfNO/Nz4cou8+3LMvOuXfO6X48T+lCtLTfOjXFnquI20n/nwuKRFST9hjPktY8wnjTHDD25kjHnRGDNjjJlZXFxMLLj5atTo3C3R2oYq1ShWuwu1labtLtRWYrWL9KWVqwhf0ucFcrU/ucizJHO1l/M07j1G1u8lsh6fa72Wqz7Hb6HWPPdvL8W7v77ftp+4eymf98rVXjpOuOciP9IuPhyQ9PWS/oG19uskLUv6gQc3sta+aq2dttZOj42NJRbcxGheucGdXZQbHFBxNN7HjsYLQ03bHS+k/zFJxJNWriJ8SZ8XyNX+5CLPkszVXs7TuPcYWb+XyHp8rvVarvocv/FCrmnbR0bif6zfV9y9lM975WovHSfcc5EfaRcfviLpK9baX9/8+8/pfjEiEyYnCnrlXKnRyVvfx5ycGI3V7vHisC6e2dnuxTMlHS/u+tAHgD7BeQFJcJFnT7Vo4ylytStx7zEmi4eajsNk8ZC3mLvBOS1sU8VC0/GbKhZit33s8LAunT+1o+1L50/p2OH4ufGRFnn3kZh51y/53C/Hif0pFUea5kepONJxG8Za6yu+zgIw5l9J+i5r7XVjzP8sadha+z+02n56etrOzMwkFt/Wk6gr1UjF0ZwmJ0ZZ7aI3uXnE8jZJ5yrC1+F5gVxFLAmtdkGediDuPQarXThBrraQxGoXt5ciHRlhtYsOJZKrGThOZFiHq120zNUsFB9O6f5Smwcl/bak77DW/udW2/fKCR2Zw80HQkGuIgTkKUJBriIU5CpC0TJXU19q01r7tqTptOMAAAAAAAB+pP3MBwAAAAAA0OMoPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8oPgAAAAAAAK8OpB2AJBljbkpakvS+pHVr7XS6EQEAAAAAAFcyUXzY9I3W2q+mHUS/2diwunlnWQu1SOOFnI4dHtbAgEk7LCCzmDP9iXEPE+MGZBNzM7sYG/iUpeIDEraxYfXmXEUvvfa2orUN5QYHdOn8KZ2eLHKSAZpgzvQnxj1MjBuQTczN7GJs4FtWnvlgJb1ljPmiMebFtIPpFzfvLDdOLpIUrW3opdfe1s07yylHBmQTc6Y/Me5hYtyAbGJuZhdjA9+yUnz4Q9bar5f0cUl/wRjzR7a/aIx50RgzY4yZWVxcTCfCHrRQixonly3R2oZuL0UpRRQ+crW39dKcIVc710vjHpo4ecq4IUmcUzvH3EzXXrnK2MC3TBQfrLXvbv73tqR/KumZB15/1Vo7ba2dHhsbSyPEnjReyCk3uDMFcoMDOjKSSymi8JGrva2X5gy52rleGvfQxMlTxg1J4pzaOeZmuvbKVcYGvqVefDDGDBtjRrb+LOlZSeV0o+oPxw4P69L5U42TzNb3uo4dHk45MiCbmDP9iXEPE+MGZBNzM7sYG/iWhQdOjkv6p8YY6X48/8ha+2a6IfWHgQGj05NFPfndf1i3lyIdGeGJtsBemDP9iXEPE+MGZBNzM7sYG/iWevHBWvvbkp5OO45+NTBg9MTYIT0xdijtUIAgMGf6E+MeJsYNyCbmZnYxNvAp9a9dAAAAAACA3kbxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeEXxAQAAAAAAeHUg7QAkyRjzkKQZSe9aa5/v9v2rq+/r6q2qKrVIE4Wcpo6O6uDBh5pue7ce6UZlWQu1FY0XhnS8OKxH8rmWbXezva9t97N9p6r1SNe3tXuiOKzRPdpdX9/Q3HxV89VIE6N5TU4UdOBA8xpWVvrDV98huxjzeOi/3Vz0SS/1ayjH0i7Odq9H0bpm56uq1FZULAxpamJUudwBZ+23e/29+qrKlaXG66XiiB7OH0xs/+1eX66vaK5yr/H6ZPGQhvNDHb9/Y8Pq5p1lLdQijRdyOnZ4WAMDpuP3t7v/y1Ke+ozFV9shxuyz7RBjDj0WZE/c/MhE8UHS90i6JqnQ7RtXV9/X5au3dOH1sqK1DeUGB3TxbEnnTh7dVYC4W4/0VnlRF65s2/ZMSc+Wxpp2Wjfb+9p2P9t3qlqP9IUm7X6sNNa0ALG+vqHLX3pXL1/+YPtXzpV07unHdhUgstIfvvoO2cWYx0P/7eaiT3qpX0M5lnZxtns9itZ1ZXZ+1+tnpiaUyx2I3X6719+rr+qz5YVdrz9fGtfD+YPe99/u9eX6it4o3971+nOlIxrOD7V9/8aG1ZtzFb302tuN1y+dP6XTk0UNDJi27293/5elPPUZi6+2Q4zZZ9shxhx6LMgeF/mR+tcujDFfK+k5SZ/cz/uv3qo2LjySFK1t6MLrZV29Vd217Y3KcqOzGtteKetGZblp291s72vb/Wzfqest2r3eot25+Wqj8LC1/cuXy5qbj9fXWRkX9AbGPB76bzcXfdJL/RrKsbSLs93rs/PVpq/Pbl7z4rbf7vVyZanp6+XKUiL7b/f6XOVe09fnKvc6ev/NO8uNwsPW6y+99rZu3uls/+3u/7KUpz5j8dV2iDH7bDvEmEOPBdnjIj9SLz5I+mFJ3y9po9UGxpgXjTEzxpiZxcXFHa9ValGjA7ZEaxtaqEW72lmorbTYdqXpfrvZ3te2+9m+U922O19t3teVary+zsq4uLBXriIZSY95qFrlKv23m4s+6aV+TfJY4pxT28XZ7vVKzPfzervXm99T3F6KOnp/u/u/LF3/fcaSlXvEXm87xJhbSStXET4X+ZFq8cEY87yk29baL+61nbX2VWvttLV2emxsbMdrE4WccoM7DyM3OKDxwu6PfowXhlpsO7Rr226397XtfrbvVLftTozmm25fHI3X11kZFxf2ylUkI+kxD1WrXKX/dnPRJ73Ur0keS5xzars4271ejPl+Xm/3evP7tyMjuY7e3+7+L0vXf5+xZOUesdfbDjHmVtLKVYTPRX6k/cmHb5B0xhhzU9LPSvomY8w/7KaBqaOjuni21OiIre/8nTw6umvb48VhXTzzwLZnSjpeHG7adjfb+9p2P9t36kSLdk+0aHdyoqBXzu3c/pVzJU1OxOvrrIwLegNjHg/9t5uLPumlfg3lWNrF2e71qYnRpq9PbV7z4rbf7vVScaTp66XiSCL7b/f6ZPFQ09cni4c6ev+xw8O6dP7UjtcvnT+lY4c7HJ82939ZylOfsfhqO8SYfbYdYsyhx4LscZEfxlrrK76uGGM+Kun72q12MT09bWdmZnb8butpx1tPSz7Jahcd2+9qF5VqpOJoTpMTo72y2oVp9t44muUqktHjT2r2nqs93n/7wmoXO3VwLJk4p7pa7WLrdVa78LPaxe2lSEdG9r/aRav7vyxd/0NcKSHEmH22nYGYg89VhC9urvZE8QFwIBM3ykAHyFWEgDxFKMhVhIJcRSha5mpWltqUtfaXJf1yymEAAAAAAADHnD7zwRgzboz5lDHm85t//4gx5s+63AcAAAAAAAiL6wdO/qSkL0g6uvn3G5K+1/E+AAAAAABAQFwXHx611r4maUOSrLXrkt53vA8AAAAAABAQ18WHZWPMYUlWkowxf0BS1fE+AAAAAABAQFw/cPIlSVck/V5jzK9KGpP0bY73AQAAAAAAAuK0+GCt/U1jzH8l6YTuL7Fx3Vq75nIfAAAAAAAgLE6LD8aYP/7Ar44bY6qSZq21t13uCwAAAAAAhMH11y7+rKQ/KOlfbP79o5K+KOlxY8xFa+3/4Xh/AAAAAAAg41wXHw5IespauyBJxphxSZ+W9Psl/Yokig8AAAAAAPQZ16tdfHir8LDp9ubvflcSz34AAAAAAKAPuf7kwy8bYz4r6Z9s/v2/3vzdsKS7jvcFAAAAAAAC4Lr48Bd0v+DwDZt//7Skn7fWWknf6HhfAAAAAAAgAK6X2rSSfm7zBwAAAAAAwO0zH4wxf8AY8xvGmHvGmFVjzPvGmJrLfQAAAAAAgLC4fuDkj0r6dkn/p6S8pO+S9Pcd7wMAAAAAAATE9TMfZK3998aYh6y170v6CWPMb0n6wVbbG2Nyur8M59BmPD9nrf1EN/u8W490o7KshdqKxgtDOl4c1iP5XOxtu92+Wo90fdu2J4rDGk0hDp/90Y3l+ormKvcabU8WD2k4P9R0240Nq5t3lrVQizReyOnY4WENDJiWbWflGBEPY5NNLsalm/nvMw5XOZaVWFz0qwtZmbtRtK7Z+aoqtRUVC0OamhhVLvfBrU27OJfqka5te/2p4rBGtr3e7trUrv24r7fT7v2rq+/r6q2qKrVIE4Wcpo6O6uDBhzo+vnb9Ezc+38eXlTz1zddx+uy/ENsOMebQY0H2xM0P18WH94wxByW9bYz5/0iaV/tPV6xI+iZr7T1jzKCkf22M+by19t92ssO79UhvlRd14UpZ0dqGcoMDunimpGdLY7s6opttu92+Wo/0hSbbfqw0tqsA4TMOn/3RjeX6it4o397V9nOlI7tulDc2rN6cq+il195ubHvp/Cmdniw2LUBk5RgRD2OTTS7GpZv57zMOVzmWlVhc9KsLWZm7UbSuK7Pzu+I4MzWhXO5A2ziX6pE+3+T1j5fGNJLPtb02tWs/7uvttHv/6ur7unz1li68vu31syWdO3lUBw8+1Pb42vVP3Ph8H19W8tQ3X8fps/9CbDvEmEOPBdnjIj9cf+3iv91s8y9KWpb0YUl/fK832Pvubf51cPPHdrrDG5XlRgdIUrS2oQtXyrpRWY61bbfbX2+x7fWE4/DZH92Yq9xr2vZc5d6ubW/eWW7c/Gxt+9Jrb+vmnWT7A8libLLJxbh0M/99xuEqx7ISi4t+dSErc3d2vto0jtn5akdxXmvx+rXN19tdm9q1H/f1dtq9/+qtauMf5o3XXy/r6q1qR8fXrn/ixuf7+LKSp775Ok6f/Rdi2yHGHHosyB4X+eG6+PARa21kra1Za/+atfYlSd/S7k3GmIeMMW9Lui3pF621v/7A6y8aY2aMMTOLi4s73rtQW2l0wJZobUMLtZVd++lmW59thxpHN7qLI2q67e2lyEHb/o6xmb1yFTslPTbYqVWuuhiXXmojS7FkZc4kGcde59RKmzjaxdn+9b2vTfHbj9eP7d5faRH/Qm0r/njHFze+uO9vf3z9cf33dZxZuUfMStshxtyKq39Xof+4yA/XxYf/yRjzTVt/Mcb8D5LOtnuTtfZ9a+0pSV8r6RljTOmB11+11k5ba6fHxsZ2vHe8MKTc4M7DyA0OaLyw+yOo3Wzrs+1Q4+hGd3Hkmm57ZKT5x3eycozN7JWr2CnpscFOrXLVxbj0UhtZiiUrcybJOPY6pxbbxNEuzvav731tit9+vH5s9/6JFvGPF7bij3d8ceOL+/72x9cf139fx5mVe8SstB1izK24+ncV+o+L/HBdfDgj6X8xxvxhY8xfl/QH1EHxYYu19q6kfyHpdKfvOV4c1sUzpUZHbH335HhxONa23W5/osW2JxKOw2d/dGOyeKhp25PFQ7u2PXZ4WJfOn9qx7aXzp3TscLL9gWQxNtnkYly6mf8+43CVY1mJxUW/upCVuTs1Mdo0jqmJ0Y7ifKrF609tvt7u2tSu/bivt9Pu/VNHR3Xx7AOvny3p5NHRjo6vXf/Ejc/38WUlT33zdZw++y/EtkOMOfRYkD0u8sNY2/HjFTpr0Jgjkn5J0hclfadtswNjzJikNWvtXWNMXtJbkv6mtfazzbafnp62MzMzO37HahfJbNut/ax2cXsp0pGRVFa7aL2zfWqWq9iJJyrvi/dczcqqDFlZYSJLsQS02kUi59St1S624vC12kWra1Moq11srWZxssVqF62OL5TVLlodX79c/1ndIZm2MxBzIrnKvRn2EjdXnRQfjDFLuv+QSLP534OS1jf/bK21hT3ee1LST0l6SPc/ifGatfZiq+35Bx08Cf7mA32DXEUIyFOEglxFKMhVhKJlrjpZatNaOxLjvVclfZ2LOAAAAAAAQPY4feaDMeaPGWNGt/39EWPMOZf7AAAAAAAAYXH9wMlPWGurW3/ZfIDkJxzvAwAAAAAABMR18aFZe06+2gEAAAAAAMLkuvgwY4y5ZIz5vZs/l3R/1QsAAAAAANCnXBcf/pKkVUn/ePNnRdJfcLwPAAAAAAAQEKdfibDWLkv6AZdtAgAAAACAsDktPhhjxiR9v6RJSbmt31trv8nlfgAAAAAAQDhcf+3ipyW9I+lxSX9N0k1Jv+F4HwAAAAAAICCuiw+HrbWfkrRmrf2X1trvlMSnHgAAAAAA6GOul8Fc2/zvvDHmOUm3JH3I8T4AAAAAAEBAXBcfXjHGjEr6K5L+nqSCpL/seB8AAAAAACAgTooPxpicpP9O0v9V0mOSPmWt/UYXbQMAAAAAgLC5eubDT0maljQr6eOS/o6jdgEAAAAAQOBcfe3iI9baKUkyxnxK0r9z1C4AAAAAAAicq08+bD1oUtba9U7fZIz5sDHmXxhjvmyMmTPGfI+jeAAAAAAAQEa4+uTD08aY2uafjaT85t+NJGutLbR437qkv2Kt/U1jzIikLxpjftFa+2VHce1ytx7pRmVZC7UVjReGdLw4rEfyOSfb+9o2VEv1SNe2HeNTxWGN9Ngx9oN+yFX0ruX6iuYq9xr5O1k8pOH8UNphpYo5nZx2fR13LNIeS9/HF1cUrWt2vqpKbUXFwpCmJkaVy7l+1jr6Rdr5nJR+OU7sT9z8cHIGttY+tM/3zUua3/zzkjHmmu4/sNJL8eFuPdJb5UVduFJWtLah3OCALp4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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "corr = new_pumpkins.corr()\r\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ff892de520>"
      ],
      "text/html": [
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            color:  #000000;\n",
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       "            background-color:  #3b4cc0;\n",
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       "            color:  #000000;\n",
       "        }#T_244cb_row2_col3{\n",
       "            background-color:  #7ea1fa;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row2_col4{\n",
       "            background-color:  #b1cbfc;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row2_col5{\n",
       "            background-color:  #8fb1fe;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row3_col0{\n",
       "            background-color:  #4e68d8;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row3_col1{\n",
       "            background-color:  #98b9ff;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row3_col2{\n",
       "            background-color:  #b6cefa;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row3_col4{\n",
       "            background-color:  #6788ee;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row3_col5{\n",
       "            background-color:  #8db0fe;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row4_col0{\n",
       "            background-color:  #3c4ec2;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_244cb_row4_col1{\n",
       "            background-color:  #4f69d9;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row4_col2{\n",
       "            background-color:  #bfd3f6;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row4_col5{\n",
       "            background-color:  #96b7ff;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row5_col0{\n",
       "            background-color:  #3f53c6;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_244cb_row5_col1{\n",
       "            background-color:  #4c66d6;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row5_col2{\n",
       "            background-color:  #9ebeff;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row5_col3{\n",
       "            background-color:  #5f7fe8;\n",
       "            color:  #000000;\n",
       "        }#T_244cb_row5_col4{\n",
       "            background-color:  #94b6ff;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_244cb_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >City Name</th>        <th class=\"col_heading level0 col1\" >Package</th>        <th class=\"col_heading level0 col2\" >Variety</th>        <th class=\"col_heading level0 col3\" >Origin</th>        <th class=\"col_heading level0 col4\" >Item Size</th>        <th class=\"col_heading level0 col5\" >Color</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_244cb_level0_row0\" class=\"row_heading level0 row0\" >City Name</th>\n",
       "                        <td id=\"T_244cb_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_244cb_row0_col1\" class=\"data row0 col1\" >0.172795</td>\n",
       "                        <td id=\"T_244cb_row0_col2\" class=\"data row0 col2\" >-0.096936</td>\n",
       "                        <td id=\"T_244cb_row0_col3\" class=\"data row0 col3\" >-0.028212</td>\n",
       "                        <td id=\"T_244cb_row0_col4\" class=\"data row0 col4\" >-0.091324</td>\n",
       "                        <td id=\"T_244cb_row0_col5\" class=\"data row0 col5\" >-0.078687</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_244cb_level0_row1\" class=\"row_heading level0 row1\" >Package</th>\n",
       "                        <td id=\"T_244cb_row1_col0\" class=\"data row1 col0\" >0.172795</td>\n",
       "                        <td id=\"T_244cb_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_244cb_row1_col2\" class=\"data row1 col2\" >-0.318438</td>\n",
       "                        <td id=\"T_244cb_row1_col3\" class=\"data row1 col3\" >0.053098</td>\n",
       "                        <td id=\"T_244cb_row1_col4\" class=\"data row1 col4\" >-0.230375</td>\n",
       "                        <td id=\"T_244cb_row1_col5\" class=\"data row1 col5\" >-0.238770</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_244cb_level0_row2\" class=\"row_heading level0 row2\" >Variety</th>\n",
       "                        <td id=\"T_244cb_row2_col0\" class=\"data row2 col0\" >-0.096936</td>\n",
       "                        <td id=\"T_244cb_row2_col1\" class=\"data row2 col1\" >-0.318438</td>\n",
       "                        <td id=\"T_244cb_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_244cb_row2_col3\" class=\"data row2 col3\" >0.167156</td>\n",
       "                        <td id=\"T_244cb_row2_col4\" class=\"data row2 col4\" >0.202606</td>\n",
       "                        <td id=\"T_244cb_row2_col5\" class=\"data row2 col5\" >0.078082</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_244cb_level0_row3\" class=\"row_heading level0 row3\" >Origin</th>\n",
       "                        <td id=\"T_244cb_row3_col0\" class=\"data row3 col0\" >-0.028212</td>\n",
       "                        <td id=\"T_244cb_row3_col1\" class=\"data row3 col1\" >0.053098</td>\n",
       "                        <td id=\"T_244cb_row3_col2\" class=\"data row3 col2\" >0.167156</td>\n",
       "                        <td id=\"T_244cb_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_244cb_row3_col4\" class=\"data row3 col4\" >-0.054065</td>\n",
       "                        <td id=\"T_244cb_row3_col5\" class=\"data row3 col5\" >0.073002</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_244cb_level0_row4\" class=\"row_heading level0 row4\" >Item Size</th>\n",
       "                        <td id=\"T_244cb_row4_col0\" class=\"data row4 col0\" >-0.091324</td>\n",
       "                        <td id=\"T_244cb_row4_col1\" class=\"data row4 col1\" >-0.230375</td>\n",
       "                        <td id=\"T_244cb_row4_col2\" class=\"data row4 col2\" >0.202606</td>\n",
       "                        <td id=\"T_244cb_row4_col3\" class=\"data row4 col3\" >-0.054065</td>\n",
       "                        <td id=\"T_244cb_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_244cb_row4_col5\" class=\"data row4 col5\" >0.102963</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_244cb_level0_row5\" class=\"row_heading level0 row5\" >Color</th>\n",
       "                        <td id=\"T_244cb_row5_col0\" class=\"data row5 col0\" >-0.078687</td>\n",
       "                        <td id=\"T_244cb_row5_col1\" class=\"data row5 col1\" >-0.238770</td>\n",
       "                        <td id=\"T_244cb_row5_col2\" class=\"data row5 col2\" >0.078082</td>\n",
       "                        <td id=\"T_244cb_row5_col3\" class=\"data row5 col3\" >0.073002</td>\n",
       "                        <td id=\"T_244cb_row5_col4\" class=\"data row5 col4\" >0.102963</td>\n",
       "                        <td id=\"T_244cb_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "sns.swarmplot(x=\"Color\", y=\"Item Size\", data=new_pumpkins)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\Leo\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 80.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\Leo\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 37.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Color', ylabel='Item Size'>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "sns.catplot(x=\"Color\", y=\"Item Size\",\r\n",
    "            kind=\"violin\", data=new_pumpkins)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ff89aeb7c0>"
      ]
     },
     "metadata": {},
     "execution_count": 18
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Build your model"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "from sklearn.model_selection import train_test_split\r\n",
    "\r\n",
    "Selected_features = ['Origin','Item Size','Variety','City Name','Package']\r\n",
    "\r\n",
    "X = new_pumpkins[Selected_features]\r\n",
    "y = new_pumpkins['Color']\r\n",
    "\r\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "from sklearn.model_selection import train_test_split\r\n",
    "from sklearn.metrics import accuracy_score, classification_report \r\n",
    "from sklearn.linear_model import LogisticRegression\r\n",
    "\r\n",
    "model = LogisticRegression()\r\n",
    "model.fit(X_train, y_train)\r\n",
    "predictions = model.predict(X_test)\r\n",
    "\r\n",
    "print(classification_report(y_test, predictions))\r\n",
    "print('Predicted labels: ', predictions)\r\n",
    "print('Accuracy: ', accuracy_score(y_test, predictions))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.98      0.90       166\n",
      "           1       0.00      0.00      0.00        33\n",
      "\n",
      "    accuracy                           0.81       199\n",
      "   macro avg       0.42      0.49      0.45       199\n",
      "weighted avg       0.69      0.81      0.75       199\n",
      "\n",
      "Predicted labels:  [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 1 0 0 0 0 0 0 0 0]\n",
      "Accuracy:  0.8140703517587939\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "from sklearn.metrics import confusion_matrix\r\n",
    "confusion_matrix(y_test, predictions)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[162,   4],\n",
       "       [ 33,   0]], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\r\n",
    "\r\n",
    "y_scores = model.predict_proba(X_test)\r\n",
    "# calculate ROC curve\r\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\r\n",
    "sns.lineplot([0, 1], [0, 1])\r\n",
    "sns.lineplot(fpr, tpr)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\Leo\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Leo\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 25
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "auc = roc_auc_score(y_test,y_scores[:,1])\r\n",
    "print(auc)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.6976998904709748\n"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}