analy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "train=pd.read_csv('train2.csv')\n",
    "test=pd.read_csv('test2.csv')\n",
    "sample=pd.read_csv('sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>program_id</th>\n",
       "      <th>program_type</th>\n",
       "      <th>program_duration</th>\n",
       "      <th>test_id</th>\n",
       "      <th>test_type</th>\n",
       "      <th>difficulty_level</th>\n",
       "      <th>trainee_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>city_tier</th>\n",
       "      <th>age</th>\n",
       "      <th>total_programs_enrolled</th>\n",
       "      <th>is_handicapped</th>\n",
       "      <th>trainee_engagement_rating</th>\n",
       "      <th>is_pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9389_150</td>\n",
       "      <td>Y_1</td>\n",
       "      <td>Y</td>\n",
       "      <td>136</td>\n",
       "      <td>150</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>9389</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16523_44</td>\n",
       "      <td>T_1</td>\n",
       "      <td>T</td>\n",
       "      <td>131</td>\n",
       "      <td>44</td>\n",
       "      <td>offline</td>\n",
       "      <td>easy</td>\n",
       "      <td>16523</td>\n",
       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13987_178</td>\n",
       "      <td>Z_2</td>\n",
       "      <td>Z</td>\n",
       "      <td>120</td>\n",
       "      <td>178</td>\n",
       "      <td>online</td>\n",
       "      <td>easy</td>\n",
       "      <td>13987</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13158_32</td>\n",
       "      <td>T_2</td>\n",
       "      <td>T</td>\n",
       "      <td>117</td>\n",
       "      <td>32</td>\n",
       "      <td>offline</td>\n",
       "      <td>easy</td>\n",
       "      <td>13158</td>\n",
       "      <td>F</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10591_84</td>\n",
       "      <td>V_3</td>\n",
       "      <td>V</td>\n",
       "      <td>131</td>\n",
       "      <td>84</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>10591</td>\n",
       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id program_id program_type  program_duration  test_id test_type  \\\n",
       "0   9389_150        Y_1            Y               136      150   offline   \n",
       "1   16523_44        T_1            T               131       44   offline   \n",
       "2  13987_178        Z_2            Z               120      178    online   \n",
       "3   13158_32        T_2            T               117       32   offline   \n",
       "4   10591_84        V_3            V               131       84   offline   \n",
       "\n",
       "  difficulty_level  trainee_id gender            education  city_tier   age  \\\n",
       "0     intermediate        9389      M        Matriculation          3  24.0   \n",
       "1             easy       16523      F  High School Diploma          4  26.0   \n",
       "2             easy       13987      M        Matriculation          1  40.0   \n",
       "3             easy       13158      F        Matriculation          3   NaN   \n",
       "4     intermediate       10591      F  High School Diploma          1  42.0   \n",
       "\n",
       "   total_programs_enrolled is_handicapped  trainee_engagement_rating  is_pass  \n",
       "0                        5              N                        1.0        0  \n",
       "1                        2              N                        3.0        1  \n",
       "2                        1              N                        2.0        1  \n",
       "3                        4              N                        1.0        1  \n",
       "4                        2              N                        4.0        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr=train.drop(['id','trainee_id','test_id','trainee_id','program_id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "te=test.drop(['id','trainee_id','test_id','trainee_id','program_id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Y</td>\n",
       "      <td>136</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>offline</td>\n",
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       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Z</td>\n",
       "      <td>120</td>\n",
       "      <td>online</td>\n",
       "      <td>easy</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T</td>\n",
       "      <td>117</td>\n",
       "      <td>offline</td>\n",
       "      <td>easy</td>\n",
       "      <td>F</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>V</td>\n",
       "      <td>131</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  program_type  program_duration test_type difficulty_level gender  \\\n",
       "0            Y               136   offline     intermediate      M   \n",
       "1            T               131   offline             easy      F   \n",
       "2            Z               120    online             easy      M   \n",
       "3            T               117   offline             easy      F   \n",
       "4            V               131   offline     intermediate      F   \n",
       "\n",
       "             education  city_tier   age  total_programs_enrolled  \\\n",
       "0        Matriculation          3  24.0                        5   \n",
       "1  High School Diploma          4  26.0                        2   \n",
       "2        Matriculation          1  40.0                        1   \n",
       "3        Matriculation          3   NaN                        4   \n",
       "4  High School Diploma          1  42.0                        2   \n",
       "\n",
       "  is_handicapped  trainee_engagement_rating  is_pass  \n",
       "0              N                        1.0        0  \n",
       "1              N                        3.0        1  \n",
       "2              N                        2.0        1  \n",
       "3              N                        1.0        1  \n",
       "4              N                        4.0        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>program_type</th>\n",
       "      <th>program_duration</th>\n",
       "      <th>test_type</th>\n",
       "      <th>difficulty_level</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>city_tier</th>\n",
       "      <th>age</th>\n",
       "      <th>total_programs_enrolled</th>\n",
       "      <th>is_handicapped</th>\n",
       "      <th>trainee_engagement_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T</td>\n",
       "      <td>131</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>F</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y</td>\n",
       "      <td>135</td>\n",
       "      <td>online</td>\n",
       "      <td>easy</td>\n",
       "      <td>M</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "      <td>120</td>\n",
       "      <td>online</td>\n",
       "      <td>easy</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>V</td>\n",
       "      <td>122</td>\n",
       "      <td>offline</td>\n",
       "      <td>vary hard</td>\n",
       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>V</td>\n",
       "      <td>122</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>F</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  program_type  program_duration test_type difficulty_level gender  \\\n",
       "0            T               131   offline     intermediate      F   \n",
       "1            Y               135    online             easy      M   \n",
       "2            Y               120    online             easy      M   \n",
       "3            V               122   offline        vary hard      F   \n",
       "4            V               122   offline     intermediate      F   \n",
       "\n",
       "             education  city_tier   age  total_programs_enrolled  \\\n",
       "0        Matriculation          3  46.0                        2   \n",
       "1            Bachelors          3   NaN                        4   \n",
       "2        Matriculation          3   NaN                        2   \n",
       "3  High School Diploma          1   NaN                        2   \n",
       "4        Matriculation          3   NaN                        2   \n",
       "\n",
       "  is_handicapped  trainee_engagement_rating  \n",
       "0              N                        4.0  \n",
       "1              N                        4.0  \n",
       "2              N                        3.0  \n",
       "3              N                        2.0  \n",
       "4              N                        2.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7effe246b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train[['gender','is_pass']].groupby(['gender']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('pass vs Sex')\n",
    "sns.countplot('gender',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('Sex:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fe2b64240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train[['trainee_engagement_rating','is_pass']].groupby(['trainee_engagement_rating']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('pass vs trainee_engagement_rating')\n",
    "sns.countplot('trainee_engagement_rating',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('trainee_engagement_rating:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fe1b66c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train[['city_tier','is_pass']].groupby(['city_tier']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('pass vs city_tier')\n",
    "sns.countplot('city_tier',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('city_tier:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fe1afc8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('trainee_engagement_rating','is_pass',hue='gender',data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <th class=\"col_heading level0 col0\" >0</th> \n",
       "        <th class=\"col_heading level0 col1\" >1</th> \n",
       "        <th class=\"col_heading level0 col2\" >All</th> \n",
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       "        <th class=\"index_name level0\" >total_programs_enrolled</th> \n",
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       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
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       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row0_col0\" class=\"data row0 col0\" >2127</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row0_col1\" class=\"data row0 col1\" >5395</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row0_col2\" class=\"data row0 col2\" >7522</td> \n",
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       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row1_col0\" class=\"data row1 col0\" >12234</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row1_col1\" class=\"data row1 col1\" >28304</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row1_col2\" class=\"data row1 col2\" >40538</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row2\" class=\"row_heading level0 row2\" >3</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row2_col0\" class=\"data row2 col0\" >2342</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row2_col1\" class=\"data row2 col1\" >5310</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row2_col2\" class=\"data row2 col2\" >7652</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row3\" class=\"row_heading level0 row3\" >4</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row3_col0\" class=\"data row3 col0\" >4122</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row3_col1\" class=\"data row3 col1\" >9399</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row3_col2\" class=\"data row3 col2\" >13521</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row4\" class=\"row_heading level0 row4\" >5</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row4_col0\" class=\"data row4 col0\" >634</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row4_col1\" class=\"data row4 col1\" >1071</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row4_col2\" class=\"data row4 col2\" >1705</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row5\" class=\"row_heading level0 row5\" >6</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row5_col0\" class=\"data row5 col0\" >517</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row5_col1\" class=\"data row5 col1\" >941</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row5_col2\" class=\"data row5 col2\" >1458</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row6\" class=\"row_heading level0 row6\" >7</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row6_col0\" class=\"data row6 col0\" >101</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row6_col1\" class=\"data row6 col1\" >185</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row6_col2\" class=\"data row6 col2\" >286</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row7\" class=\"row_heading level0 row7\" >8</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row7_col0\" class=\"data row7 col0\" >119</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row7_col1\" class=\"data row7 col1\" >181</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row7_col2\" class=\"data row7 col2\" >300</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row8\" class=\"row_heading level0 row8\" >9</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row8_col0\" class=\"data row8 col0\" >46</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row8_col1\" class=\"data row8 col1\" >33</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row8_col2\" class=\"data row8 col2\" >79</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row9\" class=\"row_heading level0 row9\" >10</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row9_col0\" class=\"data row9 col0\" >9</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row9_col1\" class=\"data row9 col1\" >21</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row9_col2\" class=\"data row9 col2\" >30</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row10\" class=\"row_heading level0 row10\" >11</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row10_col0\" class=\"data row10 col0\" >6</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row10_col1\" class=\"data row10 col1\" >9</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row10_col2\" class=\"data row10 col2\" >15</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row11\" class=\"row_heading level0 row11\" >12</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row11_col0\" class=\"data row11 col0\" >19</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row11_col1\" class=\"data row11 col1\" >9</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row11_col2\" class=\"data row11 col2\" >28</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row12\" class=\"row_heading level0 row12\" >14</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row12_col0\" class=\"data row12 col0\" >4</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row12_col1\" class=\"data row12 col1\" >9</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row12_col2\" class=\"data row12 col2\" >13</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106level0_row13\" class=\"row_heading level0 row13\" >All</th> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row13_col0\" class=\"data row13 col0\" >22280</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row13_col1\" class=\"data row13 col1\" >50867</td> \n",
       "        <td id=\"T_5ab807a0_5a8d_11e8_a76a_18cf5e833106row13_col2\" class=\"data row13 col2\" >73147</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f6fe2b64128>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train.total_programs_enrolled,train.is_pass,margins=True).style.background_gradient(cmap='summer_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1we/wg3Tf4R50PTfX+Tx081pc1u7Vi4C3t7ktxnJveza5lm647x9V1ZmtXWt77voruh4hD9D9IPSrFVLW85qkoctjh5lLUv8kuQz4x6r69FS3BSDJmXTzjfz1VLdFkrRxSXIL8NaBeZfWt54n0A1dfUNVXTIRbZOkibLpVDdAktZVkpfSjfn8CV3vj+cC/zaljWrSzfz+GgZmYZckqQ/SLcd5Gd1wxHfTzZPgylaSNjgOLZHUR8+iWwv9XuBdwOuqarTlZydVkvfTdTH9UFXdPNXtkSRpHb2IbijKT4A/BF7l0ENJGyKHlkiSJEmSpN6wR4YkSZIkSeoNgwxJkiRJktQbG91knzvssEPtuuuuU90MSZI2KFdeeeVPqmrGVLdjY+HziCRJv268zyMbXZCx6667snjx4qluhiRJG5Qkt051GzYmPo9IkvTrxvs84tASSZIkSZLUGwYZkiRJkiSpNwwyJEmSJElSb2x0c2RIkiRJkjTd/OIXv2D58uU89NBDU92Utdpiiy2YNWsWm2222Xodb5AhSZIkSVLPLV++nK233ppdd92VJFPdnDFVFStXrmT58uXstttu61WHQ0skSZIkSeq5hx56iO23336DDjEAkrD99ts/rp4jBhmSJEmSJE0DG3qIMeLxttMgQ5IkSZIk9YZBhiRJkiRJ09CLX/ziqW7CUBhkSJIkSZI0DX33u9+d6iYMhUGGJEmSJEnT0FZbbQXAbbfdxv7778+cOXPYa6+9+Pa3v73GY975zney5557cuCBB3LXXXcBcPrpp7P33nvzvOc9j9e+9rX87Gc/A+Dzn/88e+21F8973vPYf//9AViyZAn77LMPc+bM4bnPfS433njjhF6XQYYkSZIkSdPYZz/7WQ466CCuvvpqfvCDHzBnzpwx9/3pT3/K3LlzWbJkCS996Ut53/veB8BrXvMarrjiCn7wgx/w7Gc/mzPOOAOAk046iYsuuogf/OAHLFq0CIB//Md/5O1vfztXX301ixcvZtasWRN6PQYZkiRJkiRNY3vvvTef/vSnOfHEE7n22mvZeuutx9z3CU94An/yJ38CwBvf+Ea+853vAPDDH/6Q3/md3+E5z3kO5557LkuWLAFgv/3244gjjuD000/n0UcfBeBFL3oRH/jABzj11FO59dZb2XLLLSf0egwyJEmSJEmaxvbff3++9a1vMXPmTI444gjOOeeccR87slTqEUccwWmnnca1117LCSecwEMPPQR0vS9OPvlkli1bxgtf+EJWrlzJ61//ehYtWsSWW27JwQcfzNe//vUJvR6DDEmSJEmSprFbb72VHXfckaOOOoq3vvWtXHXVVWPu+8tf/pILLrgA6IakvOQlLwHggQceYKedduIXv/gF55577q/2/9GPfsS8efM46aSTmDFjBsuWLeOmm27iGc94Bn/xF3/BggULuOaaayb0ejad0NokSZIkSdIG5Rvf+AYf+tCH2Gyzzdhqq63W2CPjN3/zN7n88ss5+eSTecpTnsJ5550HwPvf/37mzZvHjBkzmDdvHg888AAA7373u7nxxhupKg488ECe97znceqpp/KZz3yGzTbbjKc+9am8973vndDrSVVNaIUburlz59bixYunuhmSJG1QklxZVXOnuh0bC59HJEkT7frrr+fZz372465nq6224sEHH5yAFq3ZaO0d7/OIQ0skSZIkSVJvOLREkiRJkqSNzLx583j44YcfU/aZz3xmUnpjPF4GGau56sQXTkg9LzjxygmpR5IkaUP3/056znodt8vfXDvBLZEkjddll1021U1Yb0MfWpJkkyTfT/KV9nm3JJclWZrkvCSbt/Ints9L2/ZdB+o4vpXfkOSggfL5rWxpkuOGfS2SJEmSJGlqTcYcGW8Hrh/4fCrwkaraHbgHOLKVHwnc08o/0vYjyWzgEGBPYD7wiRaObAL8A/AKYDZwaNtXkiRJkiRNU0MNMpLMAn4f+Kf2OcABwAVtl7OBV7X3C9pn2vYD2/4LgIVV9XBV3QwsBfZpr6VVdVNV/RxY2PaVJEmSJEnT1LB7ZPw98F+BX7bP2wP3VtUj7fNyYGZ7PxNYBtC239f2/1X5aseMVS5JkiRJkqbIv/3bv/GsZz2L3XffnQ9+8IMTXv/QJvtM8gfAnVV1ZZKXDes842zL0cDRALvssstUNkWSJEmSpEnxwnefM6H1Xfmhw9a6z6OPPsoxxxzDxRdfzKxZs9h777155StfyezZEzcTxDB7ZOwHvDLJLXTDPg4APgpsk2QkQJkFrGjvVwA7A7TtTwZWDpavdsxY5b+mqj5VVXOrau6MGTMe/5VJkiRJkqRfc/nll7P77rvzjGc8g80335xDDjmEL3/5yxN6jqEFGVV1fFXNqqpd6Sbr/HpVvQG4BHhd2+1wYOSKFrXPtO1fr6pq5Ye0VU12A/YALgeuAPZoq6Bs3s6xaFjXI0mSJEmS1mzFihXsvPOqPgezZs1ixYpR+xyst6ENLVmD9wALk5wMfB84o5WfAXwmyVLgbrpggqpakuR84DrgEeCYqnoUIMmxwEXAJsCZVbVkUq9EkiRJkiRNqkkJMqrqG8A32vub6FYcWX2fh4A/GuP4U4BTRim/ELhwApsqSZIkSZLW08yZM1m2bNW6HMuXL2fmzIldl2PYq5ZIkiRJkqSNxN57782NN97IzTffzM9//nMWLlzIK1/5ygk9x1QMLZEkSZIkSdPQpptuymmnncZBBx3Eo48+ylve8hb23HPPiT3HhNYmSZIkSZI2CONZLnUYDj74YA4++OCh1e/QEkmSJEmS1BsGGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJE2It7zlLTzlKU9hr732Gto5Nh1azZIkSZIkacr8v5OeM6H17fI31651nyOOOIJjjz2Www47bELPPcgeGZIkSZIkaULsv//+bLfddkM9h0GGJEmSJEnqDYMMSZIkSZLUGwYZkiRJkiSpNwwyJEmSJElSbxhkSJIkSZKkCXHooYfyohe9iBtuuIFZs2ZxxhlnTPg5XH5VkiRJkqRpaDzLpU60z33uc0M/hz0yJEmSJElSbxhkSJIkSZKk3jDIkCRJkiRJvWGQIUmSJEnSNFBVU92EcXm87TTIkCRJkiSp57bYYgtWrly5wYcZVcXKlSvZYost1rsOVy2RJEmSJKnnZs2axfLly7nrrrumuilrtcUWWzBr1qz1Pt4gQ5IkSZKknttss83YbbfdproZk8KhJZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPWGQYYkSZIkSeoNgwxJkiRJktQbBhmSJEmSJKk3DDIkSdK0lWTnJJckuS7JkiRvb+UnJlmR5Or2OnjgmOOTLE1yQ5KDBsrnt7KlSY4bKN8tyWWt/Lwkm0/uVUqStHExyJAkSdPZI8C7qmo2sC9wTJLZbdtHqmpOe10I0LYdAuwJzAc+kWSTJJsA/wC8ApgNHDpQz6mtrt2Be4AjJ+viJEnaGBlkSJKkaauqbquqq9r7B4DrgZlrOGQBsLCqHq6qm4GlwD7ttbSqbqqqnwMLgQVJAhwAXNCOPxt41XCuRpIkgUGGJEnaSCTZFXg+cFkrOjbJNUnOTLJtK5sJLBs4bHkrG6t8e+DeqnpktXJJkjQkBhmSJGnaS7IV8AXgHVV1P/BJ4JnAHOA24O8moQ1HJ1mcZPFdd9017NNJkjRtGWRIkqRpLclmdCHGuVX1RYCquqOqHq2qXwKn0w0dAVgB7Dxw+KxWNlb5SmCbJJuuVv5rqupTVTW3qubOmDFjYi5OkqSNkEGGJEmattocFmcA11fVhwfKdxrY7dXAD9v7RcAhSZ6YZDdgD+By4Apgj7ZCyeZ0E4IuqqoCLgFe144/HPjyMK9JkqSN3aZr30WSJKm39gPeBFyb5OpW9l66VUfmAAXcAvwpQFUtSXI+cB3diifHVNWjAEmOBS4CNgHOrKolrb73AAuTnAx8ny44kSRJQzK0ICPJFsC3gCe281xQVSckOQt4KXBf2/WIqrq6/WLyUeBg4Get/KpW1+HAX7f9T66qs1v5C4GzgC2BC4G3t19GJEmSqKrvABll04VrOOYU4JRRyi8c7biquolVQ1MkSdKQDbNHxsPAAVX1YBub+p0kX23b3l1VF6y2/yvoum/uAcyjm4RrXpLtgBOAuXS/mlyZZFFV3dP2OYpu9vEL6dZ7/yqSJEmSJGlaGtocGdV5sH3crL3W1FtiAXBOO+5SuomzdgIOAi6uqrtbeHExML9te1JVXdp6YZyD67ZLkiRJkjStDXWyzySbtPGod9KFESPrtp/S1m3/SJIntrJ1Xbd9Znu/erkkSZIkSZqmhhpktGXN5tAtRbZPkr2A44HfBvYGtqObIGuoXLddkiRJkqTpYVKWX62qe+mWJptfVbe14SMPA59m/ddtX9Her14+2vldt12SJEmSpGlgaEFGkhlJtmnvtwR+D/iPkXXb2yolr+Kx67Yfls6+wH1VdRvdMmcvT7Jtkm2BlwMXtW33J9m31XUYrtsuSZIkSdK0NsxVS3YCzk6yCV1gcn5VfSXJ15PMoFsK7Wrgz9r+F9ItvbqUbvnVNwNU1d1J3g9c0fY7qarubu/fxqrlV7+KK5ZIkiRJkjStDS3IqKprgOePUn7AGPsXcMwY284EzhylfDGw1+NrqSRJkiRJ6otJmSNDkiRJkiRpIhhkSJIkSZKk3jDIkCRJkiRJvWGQIUmSJEmSesMgQ5IkSZIk9YZBhiRJkiRJ6g2DDEmSJEmS1BsGGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPWGQYYkSZIkSeoNgwxJkiRJktQbBhmSJEmSJKk3DDIkSZIkSVJvGGRIkiRJkqTeMMiQJEmSJEm9YZAhSZIkSZJ6wyBDkiRJkiT1hkGGJEmSJEnqDYMMSZIkSZLUGwYZkiRJkiSpNwwyJEmSJElSbxhkSJIkSZKk3jDIkCRJkiRJvWGQIUmSJEmSesMgQ5IkSZIk9YZBhiRJkiRJ6g2DDEmSJEmS1BsGGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPWGQYYkSZIkSeoNgwxJkiRJktQbBhmSJEmSJKk3DDIkSZIkSVJvGGRIkiRJkqTeMMiQJEmSJEm9MbQgI8kWSS5P8oMkS5K8r5XvluSyJEuTnJdk81b+xPZ5adu+60Bdx7fyG5IcNFA+v5UtTXLcsK5FkiRJkiRtGIbZI+Nh4ICqeh4wB5ifZF/gVOAjVbU7cA9wZNv/SOCeVv6Rth9JZgOHAHsC84FPJNkkySbAPwCvAGYDh7Z9JUmSJEnSNDW0IKM6D7aPm7VXAQcAF7Tys4FXtfcL2mfa9gOTpJUvrKqHq+pmYCmwT3straqbqurnwMK2ryRJkiRJmqaGOkdG6zlxNXAncDHwI+Deqnqk7bIcmNnezwSWAbTt9wHbD5avdsxY5aO14+gki5Msvuuuuybi0iRJkiRJ0hQYapBRVY9W1RxgFl0Pit8e5vnW0I5PVdXcqpo7Y8aMqWiCJEmSJEmaAJOyaklV3QtcArwI2CbJpm3TLGBFe78C2BmgbX8ysHKwfLVjxiqXJEmSJEnT1DBXLZmRZJv2fkvg94Dr6QKN17XdDge+3N4vap9p279eVdXKD2mrmuwG7AFcDlwB7NFWQdmcbkLQRcO6HkmSJEmSNPU2Xfsu620n4Oy2usgTgPOr6itJrgMWJjkZ+D5wRtv/DOAzSZYCd9MFE1TVkiTnA9cBjwDHVNWjAEmOBS4CNgHOrKolQ7weSZIkSZI0xYYWZFTVNcDzRym/iW6+jNXLHwL+aIy6TgFOGaX8QuDCx91YSZIkSZLUC5MyR4YkSZIkSdJEMMiQJEmSJEm9YZAhSZIkSZJ6wyBDkiRJkiT1hkGGJEmatpLsnOSSJNclWZLk7a18uyQXJ7mx/XfbVp4kH0uyNMk1SV4wUNfhbf8bkxw+UP7CJNe2Yz6WJJN/pZIkbTz7uDe1AAAgAElEQVQMMiRJ0nT2CPCuqpoN7Asck2Q2cBzwtaraA/ha+wzwCmCP9joa+CR0wQdwAjCPbvW1E0bCj7bPUQPHzZ+E65IkaaNlkCFJkqatqrqtqq5q7x8ArgdmAguAs9tuZwOvau8XAOdU51JgmyQ7AQcBF1fV3VV1D3AxML9te1JVXVpVBZwzUJckSRoCgwxJkrRRSLIr8HzgMmDHqrqtbbod2LG9nwksGzhseStbU/nyUcolSdKQGGRIkqRpL8lWwBeAd1TV/YPbWk+KmoQ2HJ1kcZLFd91117BPJ0nStGWQIUmSprUkm9GFGOdW1Rdb8R1tWAjtv3e28hXAzgOHz2playqfNUr5r6mqT1XV3KqaO2PGjMd3UZIkbcQMMiRJ0rTVVhA5A7i+qj48sGkRMLLyyOHAlwfKD2url+wL3NeGoFwEvDzJtm2Sz5cDF7Vt9yfZt53rsIG6JEnSEGw61Q2QJEkaov2ANwHXJrm6lb0X+CBwfpIjgVuBP27bLgQOBpYCPwPeDFBVdyd5P3BF2++kqrq7vX8bcBawJfDV9pIkSUNikCFJkqatqvoOkDE2HzjK/gUcM0ZdZwJnjlK+GNjrcTRTkiStA4eWSJIkSZKk3jDIkCRJkiRJvWGQIUmSJEmSesMgQ5IkSZIk9YZBhiRJkiRJ6g2DDEmSJEmS1BsGGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPWGQYYkSZIkSeoNgwxJkiRJktQbBhmSJEmSJKk3DDIkSZIkSVJvGGRIkiRJkqTeMMiQJEmSJEm9YZAhSZIkSZJ6wyBDkiRJkiT1hkGGJEmSJEnqDYMMSZIkSZLUGwYZkiRJkiSpNwwyJEmSJElSbxhkSJIkSZKk3jDIkCRJkiRJvWGQIUmSJEmSesMgQ5IkSZIk9YZBhiRJkiRJ6o2hBRlJdk5ySZLrkixJ8vZWfmKSFUmubq+DB445PsnSJDckOWigfH4rW5rkuIHy3ZJc1srPS7L5sK5HkiRJkiRNvWH2yHgEeFdVzQb2BY5JMrtt+0hVzWmvCwHatkOAPYH5wCeSbJJkE+AfgFcAs4FDB+o5tdW1O3APcOQQr0eSJEmSJE2xoQUZVXVbVV3V3j8AXA/MXMMhC4CFVfVwVd0MLAX2aa+lVXVTVf0cWAgsSBLgAOCCdvzZwKuGczWSJEmSJGlDMClzZCTZFXg+cFkrOjbJNUnOTLJtK5sJLBs4bHkrG6t8e+DeqnpktfLRzn90ksVJFt91110TcEWSJEmSJGkqDD3ISLIV8AXgHVV1P/BJ4JnAHOA24O+G3Yaq+lRVza2quTNmzBj26SRJkiRJ0pBsOszKk2xGF2KcW1VfBKiqOwa2nw58pX1cAew8cPisVsYY5SuBbZJs2nplDO4vSZIkSZKmoWGuWhLgDOD6qvrwQPlOA7u9Gvhhe78IOCTJE5PsBuwBXA5cAezRVijZnG5C0EVVVcAlwOva8YcDXx7W9UiSJEmSpKk3zB4Z+wFvAq5NcnUrey/dqiNzgAJuAf4UoKqWJDkfuI5uxZNjqupRgCTHAhcBmwBnVtWSVt97gIVJTga+TxecSJIkSZKkaWpoQUZVfQfIKJsuXMMxpwCnjFJ+4WjHVdVNdKuaSJIkSZKkjcCkrFoiSZIkSZI0EQwyJEmSJElSbxhkSJIkSZKk3jDIkCRJkiRJvWGQIUmSJEmSesMgQ5IkSZIk9YZBhiRJkiRJ6g2DDEmSJEmS1BsGGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPWGQYYkSZIkSeoNgwxJkiRJktQbBhmSJEmSJKk3DDIkSZIkSVJvGGRIkiRJkqTeGFeQkWS/8ZRJkiRJkiQN03h7ZHx8nGWSJEmSJElDs+maNiZ5EfBiYEaSvxzY9CRgk2E2TJIkaVCSr1XVgWsrkyRJ09sagwxgc2Crtt/WA+X3A68bVqMkSZJGJNkC+A1ghyTbAmmbngTMnLKGSZKkKbHGIKOqvgl8M8lZVXXrJLVJkiRp0J8C7wCeBlzJqiDjfuC0qWqUJEmaGmvrkTHiiUk+Bew6eExVHTCMRkmSJI2oqo8CH03y51XlHF2SJG3kxhtkfB74R+CfgEeH1xxJkqTRVdXHk7yYX/9h5Zwpa5QkSZp04w0yHqmqTw61JZIkSWuQ5DPAM4GrWfXDSgEGGZIkbUTGu/zqvyR5W5Kdkmw38hpqyyRJkh5rLrBfVb2tqv68vf5iTQckOTPJnUl+OFB2YpIVSa5ur4MHth2fZGmSG5IcNFA+v5UtTXLcQPluSS5r5ecl2XyCr1mSJK1mvEHG4cC7ge/STbJ1JbB4WI2SJEkaxQ+Bp67jMWcB80cp/0hVzWmvCwGSzAYOAfZsx3wiySZJNgH+AXgFMBs4tO0LcGqra3fgHuDIdWyfJElaR+MaWlJVuw27IZIkSWuxA3BdksuBh0cKq+qVYx1QVd9Ksus4618ALKyqh4GbkywF9mnbllbVTQBJFgILklwPHAC8vu1zNnAi4HBcSZKGaFxBRpLDRit3ci1JkjSJTpzAuo5tzzeLgXdV1T3ATODSgX2WtzKAZauVzwO2B+6tqkdG2f/XJDkaOBpgl112mYhrkCRpozTeyT73Hni/BXAgcBVOriVJkiZJVX1zgqr6JPB+uolC3w/8HfCWCap7TFX1KeBTAHPnzq1hn0+SpOlqvENL/nzwc5JtgIVDaZEkSdIokjxAFz4AbA5sBvy0qp60LvVU1R0DdZ4OfKV9XAHsPLDrrFbGGOUrgW2SbNp6ZQzuL0mShmS8k32u7qeA82ZIkqRJU1VbV9WTWnCxJfBa4BPrWk+SnQY+vppuElGARcAhSZ6YZDdgD+By4Apgj7ZCyeZ0E4IuqqoCLgFe144/HPjyelyaJElaB+OdI+NfWPULyCbAs4Hzh9UoSZKkNWkhwv9OcgJw3Fj7Jfkc8DJghyTLgROAlyWZQ/dscwvwp63OJUnOB64DHgGOqapHWz3HAhfRPQedWVVL2ineAyxMcjLwfeCMCb5USZK0mvHOkfE/B94/AtxaVcuH0B5JkqRRJXnNwMcnAHOBh9Z0TFUdOkrxmGFDVZ0CnDJK+YXAhaOU38SqlU0kSdIkGO8cGd9MsiOrJv28cXhNkiRJGtUfDrx/hK43xYKpaYokSZoq4x1a8sfAh4BvAAE+nuTdVXXBENsmSZL0K1X15qlugyRJmnrjHVry34C9q+pOgCQzgH8HDDIkSdKkSDIL+DiwXyv6NvB2h7tKkrRxGe+qJU8YCTGaletwrCRJ0kT4NN3KIk9rr39pZZIkaSMy3h4Z/5bkIuBz7fOfMMqEV5IkSUM0o6oGg4uzkrxjylojSZKmxBqDjCS7AztW1bvbTOEvaZu+B5w77MZJkiQNWJnkjaz6YeVQul6ikiRpI7K24SF/D9wPUFVfrKq/rKq/BL7Uto0pyc5JLklyXZIlSd7eyrdLcnGSG9t/t23lSfKxJEuTXJPkBQN1Hd72vzHJ4QPlL0xybTvmY0myfl+DJEnqgbcAfwzcDtwGvA44YiobJEmSJt/agowdq+ra1Qtb2a5rOfYR4F1VNRvYFzgmyWzgOOBrVbUH8LX2GeAVwB7tdTTwSeiCD+AEYB7dOu0njIQfbZ+jBo6bv5Y2SZKk/joJOLyqZlTVU+iCjfdNcZskSdIkW1uQsc0atm25pgOr6raquqq9fwC4HphJt9772W23s4FXtfcLgHOqcymwTZKdgIOAi6vq7qq6B7gYmN+2PamqLq2qAs4ZqEuSJE0/z23PAgBU1d3A86ewPZIkaQqsLchYnOSo1QuTvBW4crwnSbIr3YPGZXS9PG5rm24HdmzvZwLLBg5b3srWVL58lHJJkjQ9PWGgV+ZIr83xTlwuSZKmibXd/N8BfCnJG1gVXMwFNgdePZ4TJNkK+ALwjqq6f3Aai6qqJLXOrV5HSY6mG67CLrvsMuzTSZKk4fg74HtJPt8+/xFwyhS2R5IkTYE1BhlVdQfw4iS/C+zViv+1qr4+nsqTbEYXYpxbVV9sxXck2amqbmvDQ+5s5SuAnQcOn9XKVgAvW638G6181ij7j3YdnwI+BTB37tyhByeSJGniVdU5SRYDB7Si11TVdVPZJkmSNPnG1R2zqi4BLlmXitsKImcA11fVhwc2LQIOBz7Y/vvlgfJjkyykm9jzvhZ2XAR8YKAr6cuB46vq7iT3J9mXbsjKYcDH16WNkiSpX1pwYXghSdJGbJjjSvcD3gRcm+TqVvZeugDj/CRHArfSLaMGcCFwMLAU+BnwZugm8kryfuCKtt9JbXIvgLcBZ9FNPPrV9pIkSZIkSdPU0IKMqvoOkDE2HzjK/gUcM0ZdZwJnjlK+mFVDXiRJkiRJ0jS3tlVLJEmSJEmSNhgGGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPWGQYYkSZIkSeoNgwxJkiRJktQbBhmSJEmSJKk3DDIkSZIkSVJvGGRIkiRJkqTeMMiQJEmSJEm9YZAhSZIkSZJ6wyBDkiRJkiT1hkGGJEmSJEnqDYMMSZIkSZLUGwYZkiRJkiSpNwwyJEmSJElSbxhkSJIkSZKk3th0qhsgSZKkDcML333Oeh33pa0nuCGSJK2BPTIkSZIkSVJvGGRIkiRJkqTeMMiQJEmSJEm9YZAhSZIkSZJ6wyBDkiRJkiT1hkGGJEmSJEnqDYMMSZIkSZLUGwYZkiRJkiSpNwwyJEmSJElSbxhkSJIkSZKk3jDIkCRJkiRJvWGQIUmSJEmSesMgQ5IkSZIk9YZBhiRJkiRJ6g2DDEmSJEmS1BsGGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPXG0IKMJGcmuTPJDwfKTkyyIsnV7XXwwLbjkyxNckOSgwbK57eypUmOGyjfLcllrfy8JJsP61okSZIkSdKGYZg9Ms4C5o9S/pGqmtNeFwIkmQ0cAuzZjvlEkk2SbAL8A/AKYDZwaNsX4NRW1+7APcCRQ7wWSZIkSZK0ARhakFFV3wLuHufuC4CFVfVwVd0MLAX2aa+lVXVTVf0cWAgsSBLgAOCCdvzZwKsm9AIkSZIkSdIGZyrmyDg2yTVt6Mm2rWwmsGxgn+WtbKzy7YF7q+qR1colSZIkSdI0NtlBxieBZwJzgNuAv5uMkyY5OsniJIvvuuuuyTilJEmSJEkagkkNMqrqjqp6tKp+CZxON3QEYAWw88Cus1rZWOUrgW2SbLpa+Vjn/VRVza2quTNmzJiYi5EkSZIkSZNuUoOMJDsNfHw1MLKiySLgkCRPTLIbsAdwOXAFsEdboWRzuglBF1VVAZcAr2vHHw58eTKuQZIkSZIkTZ1hLr/6OeB7wLOSLE9yJPC3Sa5Ncg3wu8A7AapqCXA+cB3wb8AxrefGI8CxwEXA9cD5bV+A9wB/mWQp3ZwZZwzrWiRJUj+NsRz8dkkuTnJj+++2rTxJPtaWdr8myQsGjjm87X9jksMHyl/Ynm2WtmMzuVcoSdLGZ9O177J+qurQUYrHDBuq6hTglFHKLwQuHKX8JlYNTZEkSRrNWcBpwDkDZccBX6uqDyY5rn1+D91y73u01zy6ub3mJdkOOAGYCxRwZZJFVXVP2+co4DK655X5wFcn4bokSdpoTcWqJZIkSZNijOXgF9At3Q6PXcJ9AXBOdS6lm49rJ+Ag4OKquruFFxcD89u2J1XVpW3Y6zm4HLwkSUNnkCFJkjY2O1bVbe397cCO7f26Lgc/s71fvVySJA2RQYYkSdpotZ4UNRnncjl4SZImhkGGJEna2NwxspJa+++drXxdl4Nf0d6vXj4ql4OXJGliGGRIkqSNzSK6pdvhsUu4LwIOa6uX7Avc14agXAS8PMm2bYWTlwMXtW33J9m3rVZyGC4HL0nS0A1t1RJJkqSp1paDfxmwQ5LldKuPfBA4vy0Nfyvwx233C4GDgaXAz4A3A1TV3UneD1zR9jupqkYmEH0b3cooW9KtVuKKJZIkDZlBhiRJmrbGWA4e4MBR9i3gmDHqORM4c5TyxcBej6eNkiRp3Ti0RJIkSZIk9YZBhiRJkiRJ6g2DDEmSJEmS1BvOkbGhuyWPv45d6/HXIUmSpHXy/056znofu8vfXDuBLZGk6cUeGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPWGQYYkSZIkSeoNgwxJkiRJktQbBhmSJEmSJKk3DDIkSZIkSVJvGGRIkiRJkqTeMMiQJEmSJEm9YZAhSZIkSZJ6wyBDkiRJkiT1hkGGJEmSJEnqDYMMSZIkSZLUGwYZkiRJkiSpNwwyJEmSJElSbxhkSJIkSZKk3jDIkCRJkiRJvWGQIUmSJEmSesMgQ5IkSZIk9YZBhiRJkiRJ6g2DDEmSJEmS1BsGGZIkSZIkqTcMMiRJkiRJUm8YZEiSJEmSpN4wyJAkSZIkSb1hkCFJkiRJknrDIEOSJEmSJPWGQYYkSZIkSeqNoQUZSc5McmeSHw6UbZfk4iQ3tv9u28qT5GNJlia5JskLBo45vO1/Y5LDB8pfmOTadszHkmRY1yJJkiRJkjYMw+yRcRYwf7Wy44CvVdUewNfaZ4BXAHu019HAJ6ELPoATgHnAPsAJI+FH2+eogeNWP5ckSZIkSZpmhhZkVNW3gLtXK14AnN3enw28aqD8nOpcCmyTZCfgIODiqrq7qu4BLgbmt21PqqpLq6qAcwbqkiRJkiRJ09Rkz5GxY1Xd1t7fDvz/7d17vCRlfefxzxdQQUQBGYgC46ASjTvIACOgotEFR1RWMKioG7l44eWKkXgNRlcQNgq6alDjLRGFqKioA5glwoiIrpH7bUYQYRGBCTdFEUWNwC9/1HPW43gOwzBnqrrnfN6vV79O9dPVVd+uM9On+tfP89QWbXlL4PpJ693Q2u6t/YYp2iVJkiRJ0lpssMk+W0+K6mNfSQ5OckGSC2699dY+dilJkiRJktaAvgsZN7dhIbSft7T25cDWk9bbqrXdW/tWU7RPqao+WVULq2rhnDlzVvtFSJIkSZKkYfRdyDgVmLjyyAHAKZPa929XL9kVuL0NQTkdWJRkkzbJ5yLg9PbYL5Ls2q5Wsv+kbUmSJEmSpLXUemtqw0lOBJ4BbJbkBrqrjxwNfCnJK4EfAy9uq58GPBe4GrgTOAigqm5LchRwflvvyKqamED0tXRXRtkA+Nd2kyRJkiRJa7E1VsioqpdO89DuU6xbwCHTbOc44Lgp2i8A5q9ORkmSJEmSNF4Gm+xTkiRJkiRpVVnIkCRJkiRJY8NChiRJkiRJGhsWMiRJkiRJ0tiwkCFJkiRJksaGhQxJkiRJkjQ2LGRIkiRJkqSxYSFDkiRJkiSNDQsZkiRJkiRpbFjIkCRJkiRJY8NChiRJkiRJGhsWMiRJkiRJ0tiwkCFJkiRJksaGhQxJkiRJkjQ2LGRIkiRJkqSxYSFDkiRJkiSNDQsZkiRJkiRpbFjIkCRJkiRJY2O9oQNIkiRp1V135Hb363lz37l0hpNIktQvCxm6z5YtW7ba25g/f/4MJJEkSZqaBR5JWvtZyNBYuuiInVZ7GzseceEMJJEkjask1wJ3AHcDd1XVwiSbAl8E5gHXAi+uqp8lCXAs8FzgTuDAqrqobecA4B1ts/+rqo7v83VIkjTbOEeGJEmazZ5ZVQuqamG7fxhwZlVtC5zZ7gM8B9i23Q4GPgbQCh+HA7sAOwOHJ9mkx/ySJM06FjIkSZJ+b29gokfF8cA+k9pPqM45wMZJHgE8G1hSVbdV1c+AJcCefYeWJGk2sZAhSZJmqwLOSHJhkoNb2xZVdWNbvgnYoi1vCVw/6bk3tLbp2iVJ0hriHBmSJGm22q2qlifZHFiS5AeTH6yqSlIztbNWLDkYYO7cuTO1WUmSZh17ZEiSpFmpqpa3n7cAi+nmuLi5DRmh/bylrb4c2HrS07dqbdO1T7W/T1bVwqpaOGfOnJl8KZIkzSoWMiRJ0qyTZMMkG00sA4uAZcCpwAFttQOAU9ryqcD+6ewK3N6GoJwOLEqySZvkc1FrkyRJa4hDSyRJ0my0BbC4u6oq6wGfr6qvJzkf+FKSVwI/Bl7c1j+N7tKrV9NdfvUggKq6LclRwPltvSOr6rb+XoYkSbOPhQxJkjTrVNU1wPZTtP8U2H2K9gIOmWZbxwHHzXRGSZI0NYeWSJIkSZKksWEhQ5IkSZIkjQ0LGZIkSZIkaWxYyJAkSZIkSWPDQoYkSZIkSRobFjIkSZIkSdLYsJAhSZIkSZLGhoUMSZIkSZI0NtYbOoAkSZI0ynZ6ywn363mLN5rhIJIkwB4ZkiRJkiRpjFjIkCRJkiRJY8NChiRJkiRJGhsWMiRJkiRJ0tgYpJCR5NokS5NckuSC1rZpkiVJrmo/N2ntSfKhJFcnuSzJjpO2c0Bb/6okBwzxWiRJkiRJUn+G7JHxzKpaUFUL2/3DgDOralvgzHYf4DnAtu12MPAx6AofwOHALsDOwOETxQ9JkiRJkrR2GqWhJXsDx7fl44F9JrWfUJ1zgI2TPAJ4NrCkqm6rqp8BS4A9+w4tSZIkSZL6M1Qho4AzklyY5ODWtkVV3diWbwK2aMtbAtdPeu4NrW26dkmSJEmStJZab6D97lZVy5NsDixJ8oPJD1ZVJamZ2lkrlhwMMHfu3JnarCRJkiRJ6tkgPTKqann7eQuwmG6Oi5vbkBHaz1va6suBrSc9favWNl37VPv7ZFUtrKqFc+bMmcmXIkmSJEmSetR7ISPJhkk2mlgGFgHLgFOBiSuPHACc0pZPBfZvVy/ZFbi9DUE5HViUZJM2yeei1iZJkiRJktZSQwwt2QJYnGRi/5+vqq8nOR/4UpJXAj8GXtzWPw14LnA1cCdwEEBV3ZbkKOD8tt6RVXVbfy9DkiRJkiT1rfdCRlVdA2w/RftPgd2naC/gkGm2dRxw3ExnlCRJkiRJo2mULr8qSZIkSZJ0ryxkSJIkSZKksWEhQ5IkSZIkjQ0LGZIkSZIkaWxYyJAkSZIkSWPDQoYkSZIkSRobFjIkSZIkSdLYsJAhSZIkSZLGhoUMSZIkSZI0NixkSJIkSZKksWEhQ5IkSZIkjQ0LGZIkSZIkaWysN3QASZIkaUU7veWE+/W8xRvNcBBJ0sixR4YkSZIkSRobFjIkSZIkSdLYcGiJJEnSgBxCIUnSqrFHhiRJkiRJGhsWMiRJkiRJ0tiwkCFJkiRJksaGhQxJkiRJkjQ2LGRIkiRJkqSxYSFDkiRJkiSNDQsZkiRJkiRpbFjIkCRJkiRJY8NChiRJkiRJGhsWMiRJkiRJ0tiwkCFJkiRJksaGhQxJkiRJkjQ21hs6gDTuLjpipxnZzo5HXDgj25EkSZKktZk9MiRJkiRJ0tiwR4YkSZI0C1x35Hb363lz37l0hpNI0uqxkCGtTa7NzGxnXs3MdiRJkiRphlnIkCRJktQre4dIWh3OkSFJkiRJksaGhQxJkiRJkjQ2LGRIkiRJkqSxYSFDkiRJkiSNDSf7lCRJksbITm854X49b/FGMxxEkgZiIUOSJEnS/WJRRdIQHFoiSZIkSZLGhoUMSZIkSZI0NixkSJIkSZKksTH2hYwkeya5MsnVSQ4bOo8kSZp9PB+RJKk/Yz3ZZ5J1gX8AngXcAJyf5NSqunzYZJKWLVs2I9uZP3/+am/joiN2moEksOMRF87IdiStXTwfkcbbdUdud7+eN/edS2c4yWhlkUbZWBcygJ2Bq6vqGoAkXwD2BjxxkCRJffF8RBoBo3QFlVHKIq2Nxr2QsSVw/aT7NwC7DJRFklbK3iHSWsnzEUkj6/4XVd53v55n7xD1IVU1dIb7LckLgT2r6lXt/suBXarqdSusdzBwcLv7OODK1dz1ZsBPVnMbM8Us0xulPGaZmlmmZpapjVIWGK08M5HlUVU1ZybCzDaejwBmmc4oZYHRymOWqZllamaZ2ihlgR7PR8a9R8ZyYOtJ97dqbX+gqj4JfHKmdprkgqpaOFPbWx1mmd4o5THL1MwyNbNMbZSywGjlGaUss5TnI2aZ0ihlgdHKY5apmWVqZpnaKGWBfvOM+1VLzge2TbJNkgcCLwFOHTiTJEmaXTwfkSSpR2PdI6Oq7kryOuB0YF3guKr6/sCxJEnSLOL5iCRJ/RrrQgZAVZ0GnNbzbmesW+gMMMv0RimPWaZmlqmZZWqjlAVGK88oZZmVPB8xyzRGKQuMVh6zTM0sUzPL1EYpC/SYZ6wn+5QkSZIkSbPLuM+RIUmSJEmSZhELGZIkSWMkyXFJbkmybASybJ3krCSXJ/l+kkMHzLJ+kvOSXNqyvGuoLJMyrZvk4iT/MnCOa5MsTXJJkgsGzrJxki8n+UGSK5I8eaAcj2vHY+L2iyR/PUSWlucN7d/tsiQnJll/qCwtz6Ety/f7Pi5Tvccl2TTJkiRXtZ+bDJjlRe243JOktyuGTJPlfe3/0mVJFifZeKgskx57U5JKstmazGAh4z5I8vgkuyd5yArtew6VaVKGEwbc985JntSWn5DkjUmeO1SeUZVkt3ZsFg2w712SPLQtb5DkXUm+luSYJA/rOcsDk+yfZI92/2VJPpLkkCQP6DnL65NsvfI1+5Hk0UnenOTYJB9I8pqJ35skTeEzwODnIM1dwJuq6gnArsAhSZ4wUJbfAv+1qrYHFgB7Jtl1oCwTDgWuGDjDhGdW1YIRuFTjscDXq+rxwPYMdHyq6sp2PBYAOwF3AouHyJJkS+D1wMKqmk83afBLhsjS8swHXg3sTPc72ivJY3uM8Bn++D3uMODMqtoWOLPdHyrLMuAvgJE2UXUAAA1ZSURBVG/3lOHesiwB5lfVE4EfAm8bMAvt/HoRcN2aDmAhYyWSvB44BfgrYFmSvSc9/O6es5y6wu1rwF9M3O85y+HAh4CPJXkP8BFgQ+CwJG/vM8vKJDmo5/2dN2n51XTHZiPg8CR9velOOI7uDzN0Jw4PA45pbZ/uOcungecBhyb5Z+BFwLnAk4B/6jnLUcC5Sb6T5LVJ5vS8//+vvcd8HFif7lg8CNgaOCfJM4bKJWl0VdW3gduGzgFQVTdW1UVt+Q66D6VbDpSlquqX7e4D2m2wyeCSbEX3d6/vv3Ejq32J8nTgUwBV9R9V9fNhUwGwO/D/qurHA2ZYD9ggyXrAg4F/HzDLnwHnVtWdVXUXcDbdB/deTPMetzdwfFs+HthnqCxVdUVVXdnH/u9DljPa7wjgHGCrobI0HwTeSg/vvWN/1ZIevBrYqap+mWQe8OUk86rqWCA9Z9kKuJzuD2K1/S8E3t9zDoAX0n3b8SDgJmCrqvpFkv9N9+H07wbINJ130e+H9sm9Cw4GnlVVt7Zjcw5wdI9Z1pn05rawqnZsy/83ySU95gDYrqqe2P5ALwceWVV3J/kscGnPWa6h+/ZlD2A/4F1JLgROBL7aTsb78mpgQTsWHwBOq6pnJPkEXRF1hx6zaEwl2byqbhk6h2a3dp60A915wFAZ1gUuBB4L/ENVDZYF+Hu6E/qNBswwoYAzkhTwiaoa6koH2wC3Ap9Osj3d7+rQqvrVQHkmvITuHGAQVbW8nSdeB/waOKOqzhgqD12Pg79L8vCW57nAoEOSgC2q6sa2fBOwxZBhRtQrgC8OtfP2hf/yqro0WfMfk+2RsXLrTFT3q+pa4BnAc9oHjr4LGQvp3vDfDtxeVd8Cfl1VZ1fV2T1nuauq7q6qO+kq2L8AqKpfA/f0nIU2Lmyq21L6f6NbJ8km7c0/VXUrQPsjfde9P3XGLZvUI+XSiXF8Sf4U+F3PWdZJ8kC6E7oH0/UOga4Y1uvQErov7u5pVexXAo8EPkrXRe6anrPA74vKDwIe0gJeR8/HJcnDkhzdxlreluSn6cYvH93XmMsV8jw0yXuS/HOSl63w2Ef7zjOdJP/a8/42XeH2cOC89r6zaZ9ZpAlt+O1XgL+eOCcYQjs3WUD35c/OrYt875LsBdxSVRcOsf8p7Na+zHgO3fCfpw+UYz1gR+BjVbUD8Cv6GyIwpXZu8nzgpAEzbELX42AbunOSDZP85VB5quoKuh68ZwBfBy4B7h4qz4qqu+yml96cpPWIvwv43ED7fzDwt8A7+9qnPTJW7uYkC6rqEoDWM2Mvui772/UZpKruAT6Y5KT282aG+x3+R5IHt0LGThONrctg74UMumLFs4GfrdAe4N96zvIwuoJTgEryiKq6sZ3k9V38ehVwbJJ3AD8BvpfkeuD69lifPgX8gG7c59uBk5JcQzem+gs9Z/mD30NV/Q44FTi1vRH36Z+A85OcCzyN7sSBNtyl767jXwK+CTyjqm5qOf4EOKA91vc8L58GrqL7cPSKJPsCL6uq39L9u+lNkh2ne4iud1qffgKs2P15S+AiuhO7R/ecR7Ncm+foK8DnquqrQ+cBqKqfJzmLrkA9xKSoTwWen27usPWBhyb5bFUN8uG0qpa3n7ckWUw390Hf4/sBbgBumNRT5ssMXMigK+5cVFU3D5hhD+BHE19+Jfkq8BTgs0MFqqpP0YYAJXk33e9uSDdPOqd+BGAvxCbJgcBewO6tyDOEx9AV4iZ6Y2wFXJRk54lzyplmIWPl9meFb9FbV/39W9fv3lXVDcCLkjwPGOpbj6e3DxMTBZYJD6D70NO3fwEeMlFwmizJt/oMUlXzpnnoHuAFPUahqm4HDmwTR25D93/+hiH+WFfVB5N8sS3/e7qJavcA/rGqzrv3Z8+4/aZ7oBXnelNVxyb5Bt141PdX1Q9a+61044j7NK+qjlkh303AMUle0XMWgMdU1b5t+eT2bcM3kzx/gCzn040RnqoY2XdvlbcAzwLeUlVLAZL8qKq26TmHRLoz1k8BV1TVBwbOMgf4XStibED3/+SYlTxtjaiqt9Em3WvzHb15qCJGkg3pehjf0ZYXAUcOkaWqbkpyfZLHtTkGdqcbNj2klzLgsJLmOmDX9mXKr+mOy9BXl9m8Fb7m0s2PMfTEuafSfcY4uv08Zdg4oyHdxSfeCvx53+ewk7Xzkc0n5bqWblj7T9bUPjNc0UaSNEqSnAF8Azh+otiVZAvgQLq5XvboOc8VwH+ZXCxt3zq8ha5w+agesywDXlBVV03x2PVV1etVcNokgh+k6111OHBpVdkTY5ZIciLdUNfNgJuBw9u3p0Nk2Q34DrCU3/fI/NuqOm2ALE+kmwRwXbrh01+qqkE+sE82qZCx10D7fzS/vxrHesDnq2qwucySLKDrjfhAuuGcB1XVij1q+8qyIV0R4dHty5/BpLtc8H50X6BeDLxq4kvDgfJ8B3g43VDkN1bVmT3u+4/e44CT6XqHzqXrlfjiqlrjPVenyXIb8GFgDvBz4JKqevZAWd5GNzT5p221c6rqNUNkmfx3yEKGJKk3bYzuYXTjdCeq6jfTfQtydN8nmkneSzfh2TdWaN8T+HB1l2DrK8sLgaU1xSzlSfapqpP7yrLCvp9PNyZ1XlX9yRAZJEmS+mYhQ5K0UkkOqqq+L9k7rVHKM3SW1oX+MVW1bOgskiRJfbCQIUlaqSTXVdXcoXNMGKU8ZpEkSeqXk31KkoDuMsbTPcQA12sfpTxmkSRJGh0WMiRJE0bpMsYwWnnMIkmSNCIsZEiSJozMZYybUcpjFkmSpBHhHBmSJEmSJGlsrDN0AEmSJEmjIcnGSV67knXmJXnZfdjWvCTLZi7d7JbkW0kWtuVrk2y2Cs89MMlH1lw6qV8WMiRJkiRN2Bi410IGMA9YaSFjJqSzWp9ZkozNcPok6w6dQRoHFjIkSZIkTTgaeEySS5K8r92WJVmaZL9J6zytrfOG1vPiO0kuaren3JcdtV4Cp7SeBlclOby1z0tyZZITgGXA1kle2jIsS3LMpG28MskPk5yX5B8neh0k+UySjyc5F3hvkp2TfC/JxUn+LcnjJmU4OcmS1svhdUne2NY7J8mmbb3XJ7k8yWVJvnAvr2nDJMe1PBcn2XvSfr6a5Ovttb530nN+meT9SS4Fnpxk9/bcpW1bD1rJcfzLtr9LknxiohiS5KCJYwM89b78TqRxMTbVSUmSJElr3GHA/KpakGRf4DXA9sBmwPlJvt3WeXNV7QWQ5MHAs6rqN0m2BU4EFt7H/e0MzAfubNv/P8BPgG2BA6rqnCSPBI4BdqK7YtMZSfYBzgP+J7AjcAfwTeDSSdveCnhKVd2d5KHA06rqriR7AO8G9m3rzQd2ANYHrgb+pqp2SPJBYH/g79tr3qaqfptk43t5PW8HvllVr2jrnZfkG+2xBW0/vwWuTPLhqroe2BA4t6relGR94Cpg96r6YSvm/I+W4Y8k+TNgP+CpVfW7JB8F/nuSJcC72jG7HTgLuPhecktjxUKGJEmSpKnsBpxYVXcDNyc5G3gS8IsV1nsA8JEkC4C7gT9dhX0sqaqfAiT5atvnycCPq+qcts6TgG9V1a1tvc8BT2+PnV1Vt7X2k1bY90ktO8DDgONboaVa5glnVdUdwB1Jbge+1tqXAk9sy5cBn0tycss3nUXA85O8ud1fH5jbls+sqttb1suBRwHX0x2zr7R1Hgf8qKp+2O4fDxzCNIUMYHe6YsX5SQA2AG4BduEPj9kXWbXfizTSLGRIkiRJWh1vAG6m67mxDvCbVXjuipdQnLj/qxnINXkbR9EVLF6QZB7wrUmP/XbS8j2T7t/D7z8vPY+uePLfgLcn2a6q7ppinwH2raor/6Ax2WWF/dw9adu/mVRwWVUBjq+qt62wv33u5/akseAcGZIkSZIm3AFs1Ja/A+yXZN0kc+g+yJ+3wjrQ9Xa4saruAV4OrMqElc9KsmmSDYB9gO9Osc55wJ8n2azN//BS4Gzg/Na+SZvQc98pnjs54/K2fOAq5KNNNrp1VZ0F/E3b1kOmWf104K/Sukck2WFV9gVcCcxL8th2/+V0r3U6ZwIvTLJ529+mSR4FnEt3bB6e5AHAi1YxhzTSLGRIkiRJAqAN8/huusumPpluSMWldPNPvLWqbmptdye5NMkbgI8CB7TJKh/PqvWmOI9uWMVlwFeq6oIpMt1IN0fFWS3LhVV1SlUtp5vr4jy6Asi1dPNBTOW9wHuSXMyq90pfF/hskqV080x8qKp+Ps26R9ENW7ksyffb/fusqn4DHASc1PZ3D/Dxe1n/cuAddPOGXAYsAR7RjtkRwPfojs0Vq5JDGnWpWrE3lyRJkiStWUkOBBZW1etWYxsPqapfth4Zi4HjqmrxTGWUNJrskSFJkiRpXB2R5BK6y7T+iHufiFPSWsIeGZIkSZLWmCTPprt86mQ/qqoXDJFnJiQ5CDh0hebvVtUhQ+SRZhsLGZIkSZIkaWw4tESSJEmSJI0NCxmSJEmSJGlsWMiQJEmSJEljw0KGJEmSJEkaGxYyJEmSJEnS2PhPMSIbYZLzXgsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fdf1e8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train['total_programs_enrolled'].value_counts().plot.bar(color=['#CD7F32','#FFDF00','#D3D3D3'],ax=ax[0])\n",
    "ax[0].set_title('Number Of Passengers By Pclass')\n",
    "ax[0].set_ylabel('Count')\n",
    "sns.countplot('total_programs_enrolled',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('pogram:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fdf6f55f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train[['is_handicapped','is_pass']].groupby(['is_handicapped']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('pass vs is_handicapped')\n",
    "sns.countplot('is_handicapped',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('is_handicapped:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aPPCBD8xJJ52Uv/zLv1yS89pZVNVemQQK7+zuP0mS7v5Cd9/b3d9K8uZMbm9IkluTHDS1+dzQtrX2TUkeUlV7btEOAMvGrjgWESoAsNu45ZZbcuCBB+bFL35xXvSiF+UTn/jEVvt+61vfynvfO7k9/+KLL86P/uiPJknuuuuuPPzhD883v/nNvPOd7/x2/09/+tM59thjc95552XlypVZv359brrppvzgD/5gXvrSl+aUU07JJz/5yaU9wWVsmPPgLUmu7+7fmWp/+FS3n0ryqeH9miSnVtX3DU91OCzJx5JcmeSwqjq0qvbOZDLHNd3dST6U5GeG7U9P8v6lPCcA2F674ljEIyUB2KEW8gjIpfJXf/VX+c3f/M3stdde2Weffe7314EHPehB+djHPpbXvOY1eehDH5p3v/vdSZJXv/rVOfbYY7Ny5coce+yxueuuu5IkL3/5y3PjjTemu/O0pz0tT3jCE/K6170u73jHO7LXXnvlYQ97WF75ylfukPNcpp6S5N8luaaqrhraXpnJ0xuOzOQxkzcn+fkk6e5rq+rSJNdl8uSIl3T3vUlSVWcluTyTR0pe2N3XDvv71SSXVNVrkvxDJiEGACwbu+JYpCbB/o63evXqXrt27UyODcCOc/311+cxj3nMrMvYbvvss0++8pWvLPlx5vt8qurj3b16yQ+O8QgAi26xxj47y1jE7Q8AAADAKG5/AGC3duyxx+Yb3/jGd7W94x3v2CG/DAAA7OxjEaEC7EQOOfsDsy5hu83y/nlYiI9+9KOzLgFYxj573uNmXcJ3Ofica2ZdArDIdvaxiNsfAFhys5q/Z7nzuQAAOzuhAgBLasWKFdm0aZM/oLfQ3dm0aVNWrFgx61IAAEZz+wMAS2pubi4bNmzIxo0bZ13KsrNixYrMzc3NugwAgNGECgAsqb322iuHHnrorMsAANjt/Pmf/3le9rKX5d57782LXvSinH322Yt+DKECAAAALKGjXn7Rou7v47/5vG32uffee/OSl7wkV1xxRebm5nL00Ufn5JNPzuGHH76otZhTAQAAAHYxH/vYx/LoRz86P/iDP5i99947p556at7//vcv+nGECgAAALCLufXWW3PQQQd9e3lubi633nrroh9HqAAAAACMIlQAAACAXcyqVauyfv36by9v2LAhq1atWvTjCBUAAABgF3P00UfnxhtvzGc+85ncfffdueSSS3LyyScv+nE8/QEAAAB2MXvuuWfe+MY35vjjj8+9996bF7zgBXnsYx+7+MdZ9D0CAAAA37aQR0AuhZNOOiknnXTSkh5jQbc/VNUJVXVDVa2rqrPnWX9wVX2oqv6hqj5ZVUtbNQAAADBz2wwVqmqPJOcnOTHJ4UlOq6rDt+j2n5Nc2t1PTHJqkt9b7EIBAACA5WUhVyock2Rdd9/U3XcnuSTJKVv06SQPHt5/f5LPLV6JAAAAwHK0kDkVViVZP7W8IcmxW/R5VZK/qKpfSPKgJD+xKNUBAAAAy9ZiPVLytCRv6+65JCcleUdV3WffVXVmVa2tqrUbN25cpEMDAAAAs7CQUOHWJAdNLc8NbdNemOTSJOnuv0+yIskBW+6ouy/o7tXdvXrlypXjKgYAAACWhYWEClcmOayqDq2qvTOZiHHNFn0+m+RpSVJVj8kkVHApAgAAAMzAC17wgjz0oQ/NEUccsaTH2eacCt19T1WdleTyJHskubC7r62q85Ks7e41SX4lyZur6pcymbTxjO7upSwcAAAAdgafPe9xi7q/g8+5Zpt9zjjjjJx11ll53vOet6jH3tJCJmpMd1+W5LIt2s6Zen9dkqcsbmkAAADAGMcdd1xuvvnmJT/OYk3UCAAAAOxmhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAB2Maeddlqe/OQn54Ybbsjc3Fze8pa3LMlxFvT0BwAAAGCchTwCcrG9613v2iHHcaUCAAAAMIpQAQAAABhFqAAAAACMIlQAAACARdTdsy5hQRajTqECAAAALJIVK1Zk06ZNyz5Y6O5s2rQpK1as+J724+kPAAAAsEjm5uayYcOGbNy4cdalbNOKFSsyNzf3Pe1DqAAAAACLZK+99sqhhx466zJ2GLc/AAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEYRKgAAAACjCBUAAACAUfacdQFL4ZCzPzDrEka5+bXPmHUJAAAAsGCuVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMIlQAAAAARhEqAAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYJQ9Z10AO7dDzv7ArEsY5ebXPmPWJQAAAOz0XKkAAAAAjCJUAAAAAEZZUKhQVSdU1Q1Vta6qzp5n/eur6qrh9b+r6ouLXyoAAACwnGxzToWq2iPJ+Ul+MsmGJFdW1Zruvm5zn+7+pan+v5DkiUtQKwAAALCMLORKhWOSrOvum7r77iSXJDnlfvqfluRdi1EcAAAAsHwtJFRYlWT91PKGoe0+quqRSQ5N8pffe2kAAADAcrbYEzWemuS93X3vfCur6syqWltVazdu3LjIhwYAAAB2pIWECrcmOWhqeW5om8+puZ9bH7r7gu5e3d2rV65cufAqAQAAgGVnIaHClUkOq6pDq2rvTIKDNVt2qqp/kWS/JH+/uCUCAAAAy9E2Q4XuvifJWUkuT3J9kku7+9qqOq+qTp7qemqSS7q7l6ZUAAAAYDnZ5iMlk6S7L0ty2RZt52yx/KrFKwsAAABY7hZ7okYAAABgNyFUAAAAAEYRKgAAS66qDqqqD1XVdVV1bVW9bGj/gaq6oqpuHP7db2ivqnpDVa2rqk9W1Q9P7ev0of+NVXX6VPtRVXXNsM0bqqp2/JkCwO5FqAAA7Aj3JPmV7j48yZOSvKSqDk9ydpIPdvdhST44LCfJiUkOG15nJvn9ZBJCJDk3ybFJjkly7uYgYujz4qntTtgB5wUAuzWhAgCw5Lr7tu7+xPD+rkyeKLUqySlJ3j50e3uSZw7vT0lyUU98JMlDqurhSY5PckV339Hddya5IskJw7oHd/dHhidRXTS1LwBgiQgVAIAdqqoOSfLEJB9NcmB33zas+nySA4f3q5Ksn9psw9B2f+0b5mkHAJaQUAEA2GGqap8kf5zkF7v7y9PrhisMegfUcGZVra2qtRs3blzqwwHALk2oAADsEFW1VyaBwju7+0+G5i8Mty5k+Pf2of3WJAdNbT43tN1f+9w87ffR3Rd09+ruXr1y5crv7aQAYDcnVAAAltzwJIa3JLm+u39natWaJJuf4HB6kvdPtT9veArEk5J8abhN4vIkT6+q/YYJGp+e5PJh3Zer6knDsZ43tS8AYInsOesCAIDdwlOS/Lsk11TVVUPbK5O8NsmlVfXCJLckefaw7rIkJyVZl+RrSZ6fJN19R1W9OsmVQ7/zuvuO4f1/TPK2JA9M8mfDCwBYQkIFAGDJdfffJqmtrH7aPP07yUu2sq8Lk1w4T/vaJEd8D2UCANvJ7Q8AAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEYRKgAAAACjCBUAAACAUYQKAAAAwChCBQAAAGCUPWddAMBydMjZH5h1Cdvt5tc+Y9YlAACwm3GlAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEYRKgAAAACjCBUAAACAUYQKAAAAwChCBQAAAGAUoQIAAAAwilABAAAAGEWoAAAAAIwiVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMsqBQoapOqKobqmpdVZ29lT7Prqrrquraqrp4ccsEAAAAlps9t9WhqvZIcn6Sn0yyIcmVVbWmu6+b6nNYklckeUp331lVD12qggEAAIDlYSFXKhyTZF1339Tddye5JMkpW/R5cZLzu/vOJOnu2xe3TAAAAGC5WUiosCrJ+qnlDUPbtB9K8kNV9b+q6iNVdcJiFQgAAAAsT9u8/WE79nNYkqcmmUvy4ap6XHd/cbpTVZ2Z5MwkOfjggxfp0AAAAMAsLORKhVuTHDS1PDe0TduQZE13f7O7P5Pkf2cSMnyX7r6gu1d39+qVK1eOrRkAAABYBhYSKlyZ5LCqOrSq9k5yapI1W/T5H5lcpZCqOiCT2yFuWsQ6AQAAgGVmm6FCd9+T5Kwklye5Psml3X1tVZ1XVScP3S5PsqmqrkvyoSQv7+5NS1U0AAAAMHsLmlOhuy9LctkWbedMve8kvzy8AAAAgN3AQm5/AAAAALgPoQIAAAAwilABAAAAGGVBcyoAwFI55OwPzLqEUW5+7TNmXQIAwMy5UgEAAAAYRagAAAAAjCJUAAAAAEYxpwIAwE7ms+c9btYlfJeDz7lm1iUAMCOuVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMIlQAAAAARhEqAAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEYRKgAAAACjCBUAAACAUYQKAAAAwChCBQAAAGAUoQIAAAAwilABAAAAGEWoAAAAAIwiVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAYIeoqgur6vaq+tRU26uq6taqump4nTS17hVVta6qbqiq46faTxja1lXV2VPth1bVR4f2d1fV3jvu7ABg9yRUAAB2lLclOWGe9td395HD67IkqarDk5ya5LHDNr9XVXtU1R5Jzk9yYpLDk5w29E2S1w37enSSO5O8cEnPBgAQKgAAO0Z3fzjJHQvsfkqSS7r7G939mSTrkhwzvNZ1903dfXeSS5KcUlWV5F8lee+w/duTPHNRTwAAuA+hAgAwa2dV1SeH2yP2G9pWJVk/1WfD0La19v2TfLG779miHQBYQkIFAGCWfj/Jo5IcmeS2JL+91AesqjOram1Vrd24ceNSHw4AdmkLChW2NiHS1Pozqmrj1CRLL1r8UgGAXU13f6G77+3ubyV5cya3NyTJrUkOmuo6N7RtrX1TkodU1Z5btM93zAu6e3V3r165cuXinQwA7Ia2GSpsY0Kkae+emmTpDxe5TgBgF1RVD59a/Kkkm58MsSbJqVX1fVV1aJLDknwsyZVJDhue9LB3JpM5runuTvKhJD8zbH96kvfviHMAgN3Zntvu8p0JkZKkqi7JZPKk65ayMABg11JV70ry1CQHVNWGJOcmeWpVHZmkk9yc5OeTpLuvrapLMxlv3JPkJd1977Cfs5JcnmSPJBd297XDIX41ySVV9Zok/5DkLTvo1ABgt7WQUGG+CZGOnaffT1fVcUn+d5Jf6u718/QBAHZT3X3aPM1b/cO/u389ya/P035Zksvmab8p37l9AgDYARZrosY/TXJIdz8+yRWZPMbpPkyMBAAAALuOhYQKW5sQ6du6e1N3f2NY/MMkR823IxMjAQAAwK5jIaHCvBMiTXfYYpKlk5Ncv3glAgAAAMvRNudU6O575psQqarOS7K2u9ckeWlVnZzJREp3JDljCWsGAAAAloGFTNQ474RI3X3O1PtXJHnF4pYGAAAALGeLNVEjAAAAsJsRKgAAAACjCBUAAACAUYQKAAAAwChCBQAAAGAUoQIAAAAwilABAAAAGEWoAAAAAIwiVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQDYLlX1wYW0AQC7vj1nXQAAsHOoqhVJ/lmSA6pqvyQ1rHpwklUzKwwAmBmhAgCwUD+f5BeTPCLJx/OdUOHLSd44q6IAgNkRKgAAC9Ldv5vkd6vqF7r7v8+6HgBg9oQKAMB26e7/XlU/kuSQTI0luvuimRUFAMyEUAEA2C5V9Y4kj0pyVZJ7h+ZOIlQAgN2MUAEA2F6rkxze3T3rQgCA2fJISQBge30qycNmXQQAMHuuVAAAttcBSa6rqo8l+cbmxu4+eXYlAQCzIFQAALbXq2ZdAACwPAgVAIDt0t1/PesaAIBxGV/nAAAgAElEQVTlQagAAGyXqrork6c9JMneSfZK8tXufvDsqgIAZkGoAABsl+7ed/P7qqokpyR50uwqAgBmxdMfAIDReuJ/JDl+1rUAADueKxUAgO1SVc+aWnxAktVJvj6jcgCAGRIqAADb699Mvb8nyc2Z3AIBAOxmhAoAwHbp7ufPugYAYHkwpwIAsF2qaq6q3ldVtw+vP66quVnXBQDseEIFAGB7vTXJmiSPGF5/OrQBALsZoQIAsL1Wdvdbu/ue4fW2JCtnXRQAsOMJFQCA7bWpqp5bVXsMr+cm2TTrogCAHU+oAABsrxckeXaSzye5LcnPJDljlgUBALPh6Q8AwPY6L8np3X1nklTVDyT5rUzCBgBgN+JKBQBgez1+c6CQJN19R5InzrAeAGBGhAoAwPZ6QFXtt3lhuFLB1Y8AsBsyAAAAttdvJ/n7qnrPsPxvk/z6DOsBAGZkQVcqVNUJVXVDVa2rqrPvp99PV1VX1erFKxEAWE66+6Ikz0ryheH1rO5+x2yrAgBmYZtXKlTVHknOT/KTSTYkubKq1nT3dVv02zfJy5J8dCkKBQCWj2EccN02OwIAu7SFXKlwTJJ13X1Td9+d5JIkp8zT79VJXpfk64tYHwAAALBMLSRUWJVk/dTyhqHt26rqh5Mc1N0fWMTaAAAAgGXse376Q1U9IMnvJPmVBfQ9s6rWVtXajRs3fq+HBgAAAGZoIaHCrUkOmlqeG9o22zfJEUn+qqpuTvKkJGvmm6yxuy/o7tXdvXrlypXjqwYAAABmbiGhwpVJDquqQ6tq7ySnJlmzeWV3f6m7D+juQ7r7kCQfSXJyd69dkooBAACAZWGboUJ335PkrCSXJ7k+yaXdfW1VnVdVJy91gQAAAMDytM1HSiZJd1+W5LIt2s7ZSt+nfu9lAQAAAMvd9zxRIwAAALB7EioAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMIlQAAAAARhEqAAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEYRKgAAAACjCBUAAACAUYQKAAAAwChCBQAAAGAUoQIAsENU1YVVdXtVfWqq7Qeq6oqqunH4d7+hvarqDVW1rqo+WVU/PLXN6UP/G6vq9Kn2o6rqmmGbN1RV7dgzBIDdj1ABANhR3pbkhC3azk7ywe4+LMkHh+UkOTHJYcPrzCS/n0xCiCTnJjk2yTFJzt0cRAx9Xjy13ZbHAgAWmVABANghuvvDSe7YovmUJG8f3r89yTOn2i/qiY8keUhVPTzJ8Umu6O47uvvOJFckOWFY9+Du/kh3d5KLpvYFACwRoQIAMEsHdvdtw/vPJzlweL8qyfqpfhuGtvtr3zBPOwCwhIQKAMCyMFxh0Et9nKo6s6rWVtXajRs3LvXhAGCXJlQAAGbpC8OtCxn+vX1ovzXJQVP95oa2+2ufm6f9Prr7gu5e3d2rV65cuSgnAQC7K6ECADBLa5JsfoLD6UneP9X+vOEpEE9K8qXhNonLkzy9qvYbJmh8epLLh3VfrqonDU99eN7UvgCAJbLnrAsAAHYPVfWuJE9NckBVbcjkKQ6vTXJpVb0wyS1Jnj10vyzJSUnWJflakucnSXffUVWvTnLl0O+87t48+eN/zOQJEw9M8mfDCwBYQkIFAGCH6O7TtrLqafP07SQv2cp+Lkxy4Tzta5Mc8b3UyGwd9fKLZl3Cfbxv31lXALC8uf0BAAAAGEWoAAAAAIwiVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhlz1kXAACwnB318otmXcJ9vG/fWVcAABOuVAAAAABGWVCoUFUnVNUNVbWuqs6eZ/2/r6prquqqqvrbqjp88UsFAAAAlpNthgpVtUeS85OcmOTwJKfNExpc3N2P6+4jk/zXJL+z6JUCAAAAy8pCrlQ4Jsm67r6pu+9OckmSU6Y7dPeXpxYflKQXr0QAAABgOVrIRI2rkqyfWt6Q5NgtO1XVS5L8cpK9k/yrRakOAAAAWLYWbaLG7j6/ux+V5FeT/Of5+lTVmVW1tqrWbty4cbEODQAAAMzAQkKFW5McNLU8N7RtzSVJnjnfiu6+oLtXd/fqlStXLrxKAAAAYNlZSKhwZZLDqurQqto7yalJ1kx3qKrDphafkeTGxSsRAAAAWI62OadCd99TVWcluTzJHkku7O5rq+q8JGu7e02Ss6rqJ5J8M8mdSU5fyqIBAACA2VvIRI3p7suSXLZF2zlT71+2yHUBAAC7kc+e97hZl3AfB59zzaxLgGVv0SZqBAAAAHYvQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMIlQAAAAARhEqAAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEYRKgAAAACjCBUAAACAUYQKAAAAwChCBQAAAGAUoQIAAAAwilABAAAAGEWoAAAAAIwiVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMIlQAAAAARhEqAAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEYRKgAAAACjCBUAAACAUYQKAAAAwCgLChWq6oSquqGq1lXV2fOs/+Wquq6qPllVH6yqRy5+qQAAAMByss1Qoar2SHJ+khOTHJ7ktKo6fItu/5BkdXc/Psl7k/zXxS4UAAAAWF4WcqXCMUnWdfdN3X13kkuSnDLdobs/1N1fGxY/kmRuccsEAAAAlpuFhAqrkqyfWt4wtG3NC5P82fdSFAAAALD87bmYO6uq5yZZneTHt7L+zCRnJsnBBx+8mIcGAAAAdrCFXKlwa5KDppbnhrbvUlU/keQ/JTm5u78x3466+4LuXt3dq1euXDmmXgAAAGCZWEiocGWSw6rq0KraO8mpSdZMd6iqJyb5g0wChdsXv0wAAABgudlmqNDd9yQ5K8nlSa5Pcml3X1tV51XVyUO330yyT5L3VNVVVbVmK7sDAAAAdhELmlOhuy9LctkWbedMvf+JRa4LAAAAWOYWcvsDAAAAwH0IFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAMxcVd1cVddU1VVVtXZo+4GquqKqbhz+3W9or6p6Q1Wtq6pPVtUPT+3n9KH/jVV1+qzOBwB2F0IFAGC5+JfdfWR3rx6Wz07ywe4+LMkHh+UkOTHJYcPrzCS/n0xCiCTnJjk2yTFJzt0cRAAAS0OoAAAsV6ckefvw/u1JnjnVflFPfCTJQ6rq4UmOT3JFd9/R3XcmuSLJCTu6aADYnQgVAIDloJP8RVV9vKrOHNoO7O7bhvefT3Lg8H5VkvVT224Y2rbWDgAskT1nXQAAQJIf7e5bq+qhSa6oqn+cXtndXVW9GAcaQoszk+Tggw9ejF0CwG7LlQoAwMx1963Dv7cneV8mcyJ8YbitIcO/tw/db01y0NTmc0Pb1tq3PNYF3b26u1evXLlysU8FAHYrQgUAYKaq6kFVte/m90menuRTSdYk2fwEh9OTvH94vybJ84anQDwpyZeG2yQuT/L0qtpvmKDx6UMbALBE3P4AAMzagUneV1XJZGxycXf/eVVdmeTSqnphkluSPHvof1mSk5KsS/K1JM9Pku6+o6peneTKod953X3HjjsNANj9CBUAgJnq7puSPGGe9k1JnjZPeyd5yVb2dWGSCxe7RgBgfm5/AAAAAEYRKgAAAACjCBUAAACAUYQKAAAAwChCBQAAAGAUoQIAAAAwilABAAAAGEWoAAAAAIwiVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMIlQAAAAARhEqAAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEZZUKhQVSdU1Q1Vta6qzp5n/XFV9YmquqeqfmbxywQAAACWm22GClW1R5Lzk5yY5PAkp1XV4Vt0+2ySM5JcvNgFAgAAAMvTngvoc0ySdd19U5JU1SVJTkly3eYO3X3zsO5bS1AjAAAAsAwt5PaHVUnWTy1vGNoAAACA3dhCrlRYNFV1ZpIzk+Tggw/ekYcGAACmHPXyi2Zdwnd5376zrgAYYyFXKtya5KCp5bmhbbt19wXdvbq7V69cuXLMLgAAAIBlYiGhwpVJDquqQ6tq7ySnJlmztGUBAAAAy902Q4XuvifJWUkuT3J9kku7+9qqOq+qTk6Sqjq6qjYk+bdJ/qCqrl3KogEAAIDZW9CcCt19WZLLtmg7Z+r9lZncFgEAAADsJhZy+wMAAADAfQgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMIlQAAAAARhEqAAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYRagAAAAAjCJUAAAAAEYRKgAAAACjCBUAAACAUYQKAAAAwChCBQAAAGAUoQIAAAAwilABAAAAGEWoAAAAAIwiVAAAAABGESoAAAAAowgVAAAAgFGECgAAAMAoQgUAAABgFKECAAAAMIpQAQAAABhFqAAAAACMIlQAAAAARhEqAAAAAKMIFQAAAIBRhAoAAADAKEIFAAAAYBShAgAAADCKUAEAAAAYZc9ZFwAAAMCO9dnzHjfrEr7LwedcM+sSGMmVCgAAAMAorlQAAACAJbKrXxWyoFChqk5I8rtJ9kjyh9392i3Wf1+Si5IclWRTkud0982LWikAwDZsa8wCwK7tqJdfNOsS7uN9+866gqW1zdsfqmqPJOcnOTHJ4UlOq6rDt+j2wiR3dvejk7w+yesWu1AAgPuzwDELALCIFnKlwjFJ1nX3TUlSVZckOSXJdVN9TknyquH9e5O8saqqu3sRawUAuD8LGbMA7HB+PWdXtpCJGlclWT+1vGFom7dPd9+T5EtJ9l+MAgEAFmghYxYAYBHt0Ikaq+rMJGcOi1+pqht25PEXyQFJ/mkpdlxuGtmSz3rH8VnvWEvyefus57Wz/r/9yCXd+25uVxiPPHIJ/98e5dyadQVLxme94yy7zzrxee9IPusdZ2Gf9YLHIgsJFW5NctDU8tz/be/O4+Wo6ryPf75hkTVBRGdQR4KKqCCETUUZBQV9QGRQwA0UcHvcBnRGRH0UARcURB1hHLchAdwRVERkX0RlC5AQQECGxQ2EAWQ3kOT7/HFOJ53LXTo3fW/d1P2+X69+3apT1dW/rttV3XXqnN+pZYOt8ydJKwPTKAkbl2L7m8A3ew1uIpI02/bWTccxGWRfj5/s6/GV/T1+sq8nnV5+s+T3SCyT7Ovxk309vrK/x0/b93Uv3R8uBzaStKGkVYE3AacOWOdUYN86vSdwXvIpRERExDjr5TdLRERE9NGILRVsL5D0AeBMyvBMx9m+VtLhwGzbpwL/DZwo6SbgHsqXeERERMS4Geo3S8NhRUREtFpPORVsnw6cPqDskK7pvwN79Te0CWuFbi65gsm+Hj/Z1+Mr+3v8ZF9PMoP9ZmmpfLbHT/b1+Mm+Hl/Z3+On1fta6aUQEREREREREaPRS06FiIiIiIiIiIjHSaVCRERExDKSZEnf6ZpfWdJdkk4b4XkzJO0yzPKtJX11lDEdKunDI6yzu6Tnd80fLmnH0bzeCK/z4ID5/SQdW6ffI+ltIzx/8fojrLerpKskzZV0naT/O8y60yVd0+t7GOF1Z0nac4jyW2o8N0o6QdLTu5afLmmdEbZ9gaQJmyVe0kJJc+p7vFLSS0a5nUH34QjPeXDktdpttOeeIba1jqT39TfC5tR9c3TX/IclHbqM29hd0tWSrpd0zbJ+Rgdsa/E5p/vcLukJks6px9EbJX27+7y8DNtf6vtE0m6SPjraeJdHTzkVIiIiImIpDwGbSlrd9iPATgwyfOUgZgBbM0jeB0kr254NzO5rpEvbHTgNuA6WzpE1Xmx/vR/bkbQKpZ/yC23/SdITgOn92PZyOsj2jyUJ+CBwnqRNbT9qe8gKpRXII7ZnAEh6NXAE8PJmQ3q8ejwtaDqOMTDac89g1gHeB3yt1yfUz7VsLxrla46l+cDrJR1h+3+X9cmSNge+COxk+xZJGwLnSLrF9hXLE9iAc/sWtWxGnf/hKDe71PdJHUChkRGP0lKhB5L2krR2nf6EpFMkbdl0XG0j6cWSLpf0oKRHa034/U3H1VaSDpQ0VcV/17sNr2o6rojlIen1kn4v6T5J90t6IOeRGEOnA6+p028Gvt9ZIOmFki6ud9F/K2ljlWEuDwfe2HWH6lBJJ0r6DWUkre07dxwlrSVppqR59c7ZHrX8wa7X2VPSrIGBSXpX/U6dK+lkSWvUO8q7AUfV139W991iSa+s8c6TdFy9SEfSrZIOq98T8yQ9d3l2mrpaVEjapr63OZKO0tItCZ4q6Yx6TB85yKbWptwguxvA9nzbN9Tt/oOkn9T3P1dL7qavJOlbkq6VdJak1ev6MyRdUmP5iaQnDlfeCxdfBu4Adq7bu1XSeip3MK+X9F1Jv5P0Y0lrDLKv3lz3+TWSvtBV/mDdX9eq3PF8oUoLh5sl7VbXmS7povp/G3WLghFMBe6tr7eWpHO7Pif/0hXv2+o+nCvpxK7nv6weHzer646wpIPq5/dqSYcNsl/U+bzU13pjLd++vudTgeskrSnpF/V1r+ms1wLLdO6p5ZtIuqwea1dL2gj4PPCszvFX13vcvq+fpRsknQBcA/xTPXd09v+HxvG9D2cBpaLxcfHU93BefV/nSnrGIM//MPA527cA1L+fA/69bmNxK6J6HN/ate1hj7X62TxN0lOA7wDbaMl5uHu7/6duY66kc2tZr98n3S3CBn2/9f/21cGOu+ViO48RHsDV9e92wAWUg/jSpuNq24NSe/ds4CrKUGD7A0c0HVdbH8Dc+vfVwCnAJsCVTcfV1gfwHOBbwFnAeZ1H03G17QHcBDyv6TjyaP8DeBDYDPgxsBowB9geOK0unwqsXKd3BE6u0/sBx3Zt51DgCmD1Ot+9jS8AX+la94md1+4q2xOY1bWtD9fpJ3Wt8xngX+v0LGDPrmWz6jZWA/4IPKeWnwB8sE7f2vX89wHf7mH/LKz7pPP4Q+d9D4jzGmDbOv154Jqu/XQzMK3GdhvwT4O8zreBOykXVXsDU2r5D7viX6luZzrlomNGLf8RsE+dvhp4eZ0+vLPfhylfaj8O3J8Dyr4CHNy1L9ersRh4aS0/rmufXEC5+/jUut+eTKk8OQ/Yva5jYOc6/RPKd8sqwObAnFq+BrBand6IMhR8Pz77nf/t9cB9wFa1fGVgap1ej3I+FuX3xY3AenXZul376iTKTc7nAzfV8ldRLgxVl50GvKz7sw/sAZxd/7f/UPfT+pTj5yFgw671vtUV+7Smzx192P+jPfccA+xdp1cFVq+fw2u6tj3ovq/rLQJeXNfbCji763nrNL1fuvbN1HqcTaNUEhxal/0c2LdOvx346SDPvxLYfEBZ9zF1AbB112f81jo96LHWvX8H/I8WT3dvl3Ks/7Hr89s5Vnr9Plk8P9T7ZYjjbnkf6f7Qm4X172uAb9r+haTPNBlQW9m+SdJKthcCMyVdBXys6bhaSvXvLsCJtq+VpOGeEMvlJODrlIqFhSOsG6P3V9u/azqImBxsXy1pOuVO4cDuDNOA4+vdQFMu+IZyqksz5oF2BN7U9Xr3LkN4m9bfKusAawFnjrD+xsAttm+s88cD76dcEEOpfIZSAfL6Hl5/cRN5KDkSKD+a6SpbB1jb9sW16HvArl2rnGv7vrrudcAGlB/ci9l+p6QXUPbVhylNwfcDXgG8ra6zELhPpZXBLbbndL2X6ZKmUS6KLux67ycNVd7Dex9oqO/WP9r+TZ3+DnAApel1xzbABbbvApD0XcoF3k+BR4Ez6nrzgPm2H5M0jyVdQFYBjpU0g/K985xRxD6Y7u4P2wInSNqU8j4/J+lllAvQp1Eu+F8BnOTaHN32PV3b+qlLM/rrJP1DLXtVfVxV59eiXKj9qut52wHfr//bv0q6kLK/7gcuc73TTNk3R6u08jjN9kV92geNGuW552Lg/6nk+DjF9u8H+dk31L7/A3Cb7Utq+c3AMyUdA/yCUqk1Idi+v7aoOADoPq9uy5Jz14nAYK2fRqtfx9qLgV95SUuJzrGyLN8nHcO938GOu+WSSoXe/FnSNyhfVF9QaQ6YriP993BtyjNHpZnj7WQ/j6UrJJ0FbAh8TKWLz0TsH9cWC2z/V9NBTAKzJf2Q8qN7fqfQ9ilDPyViuZxKuRDcHnhSV/mngfNtv67++L9gmG08tIyv2T0e+GpDrDOLcld7br2g334ZX2OgzvG0kPH7/Ti/a3rI17U9D5in0qz+FkqlQq/bXH05Y+zFFsC5g5QPHNd9WcZ5f8z1tiPlu3s+gO1Fkjr76UPAXyl3WqcAf1+G7ffE9sWS1qPcYd2l/t2qVnDcytCfz47u/4e6/h5h+xujDGvx8WT7RpUuy7sAn5F0ru3DR7ndiWaZzj22vyfpUspN0tNVkprePGCbg+77up3u/XqvSv6BVwPvAd5AuRs+UXyF0upg5jI+7zpKK4y5XWVbsSQXwgKWXJt0f7bH+lhblu+TXgx23C2XXLD15g2UGv5X2/4bsC5wULMhtdJbKc3YPkA5cf0Tpdla9FltkXAI8FFgG9sPU5rC7d9oYO32c0nvk7S+pHU7j6aDaqGpwMOUOy2vrY9dh31GxPI5DjisXth2m8aS5Gn7dZU/QMkF0IuzKa0FANCS/vx/lfQ8SVOA1w3x3LWB21WSGe7dw+vfQLlr/+w6/1bgwkHW65v6m+oBSS+qRW8abv2BVPrwb99VNIPSTQLKRfx763or1VYHQ8VxH3CvpH+uRW8FLhyqfBnik6QDKM3yzxhklWfUO/0AbwF+PWD5ZcDLa9/tlSh3pZflfzINuL3ekez8xuorlfwaK1HyWkwD7qwVCjtQWpZA6baxl6Qn1eeM9N13JvB2SWvV9Z9W+6F3u4jSl3wlSU+mtOC4bJD4ngo8bPs7wFFAm3KiLdO5R9IzgZttfxX4GaULxcDzQS/7nlqRNMX2ycAnmGD7td7h/xHwjq7i37LkHLM35TM00BcpN9qmw+LKlA9SPjtQulVsVae7cxH061i7hJJnZMP6+p1jZTTfJ728375JS4UR1JP4lbYXJyWyfTvlLnr0ke3OD4FHgMcl5Yn+sW1Jp9t+QVfZ3dRkVzEm9q1/uyskDTyzgVhay3YqxmJc2f4TMNgQkEdSmqt+gtI8uON84KOS5lCy5g/nM8B/qiQvXEj5bjyFUiF8GnAX5Q7aWoM895PApXWdS1nyw/MHwLfqxe7iH8W2/y5pf0qz/5WByyldtsbaO2o8iygXzPctw3MFfKS2Jn2EckNiv7rsQOCbkt5B2XfvZfjfbvsCX1dJlngzSyrZhyofzlGSPknpZ30JsIPtRwdZ7wbg/ZKOo9whXao1m+3bVYaHO7++11/Y/lkPr9/xNeBkleE7z2DZW8QMZfX6+aXGta/thbV7xs9rF4zZlJwL1O6VnwUulLSQ0rR+v6E2bvssSc8DLq7N8x8E9qHkzuj4CaV591zKd+lHbN+hxycRfQHl/7EIeIxa0dQGozj3vAF4q6THKMlDP2f7Hkm/qeeYX9o+aIh9P7Db5tMo3ZQ7N6gnYlfloyk3Kjv+lRLzQZTz4uOOZdtzJB1M+Rx3RpPZwTUBLKXS4UeS3s3S+7Yvx5rtu+q2T6n79k5KS/nRfJ+M+H77SUtaTsVQJP2MkqDoD03H0maSdqU079mAUuElyvXv1EYDaylJx1OSuVzedCwR/VL7ih4DvLQWXQQcWH98RcQEI2kt2w/W6Y8C69s+sOGwxly9A3qa7U0bDiUihiDp88CLKK3VB6sYjCqVCj2Q9CtKf7jLWLo/0W6NBdVCkm6iJBSZ53wwx5yk6ynJd26lfK47lTibNRlXW9UmyO+lNNGE0h/uG7YfayyoFpJ0NiXZW2fIsn0o2a53ai6qiBiKyhB/H6PcTLgN2K+TmLDNUqkQEW2SSoUeSHr5YOVd2YCjDySdD7yy9keKMSZpg8HKu7qhRB9J+jYlW+/xteitwELb72wuqvaRNKc74/xQZRERERHRH8mp0APbF6oMt7FNLbrM9p3DPSdG5SOUbLQXsnTW9i81F1J72b5N0nbARrZn1kRHg/XLjf7YxvbmXfPnSZo75NoxWndL2ocyZj2UxGbJFRIRERExRjL6Qw8kvYHS9WEvSpKTSyXtOfyzYhQ+S8navholoVTnEWNA0qeAg1mSXGcVyjjZMTYWSnpWZ6ZmYR6Y+CiW39sp5+k7KEnZ9iSjmkRERESMmXR/6EG9m7hTp3VCvaN7zoC7jrGcJF2TvoXjp2aK3YIyuskWtezq5FQYG5JeSRkv+WZK/ooNgP1tn99oYBERERERyyHdH3ozZUB3h7tJK4+xcLqkV9k+q+lAJolH69CSBpC0ZtMBtZntcyVtBGxci26wPX+450TvJH3E9pGSjqEML7YU2wc0EFZExKQjaT9ga9sfGGndZdjm7sCNtq+r84cDv7J9Tr9eIyJGL5UKvTlD0pks6aP7RuD0BuNpq/cCH5b0KGUsYciQkmPpR3Vs73UkvYvSbPzbDcfUOpJeYfs8Sa8fsOjZkrB9SiOBtc/v6t/ZjUYRERFjYXfgNOA6ANuHNBtORHRL94ceSdqDrnHPbf+kyXgi+kHSTsCrKM3xz7R9dsMhtY6kw2x/StLMQRbb9tvHPagWk7SX7ZNGKouIiNGpyYQ/P4cAABSQSURBVHAPAFYFLgXeB7yNkqPpb8BcYL7tD0iaRRk688f1uQ/aXqtOH0wZ9ncR8EvbH603Od5dt30TZaSkGZQKhfvqYw/gk53t1u6FX6TcLL0ceK/t+ZJupYy49FpK3qi9bF8/lvsmYrJKpUJMKJJ2A15WZy+wfVqT8bSZpC/YPniksugPSRvavmWkslg+kq60veVIZRERsewkPQ84Eni97cckfY1SsfBpYCvKRf/5wFXDVSpI2plSMbCj7YclrWv7HklPsn13XfczwF9tHzPIdmZRKhpOA35PGZL8RkknUHJFfaVWKhxdn/8+YMsM4xwxNtL9YRiSHmCQvrmUu7pplt9nkj5PGbbzu7XoQEkvtf2xYZ4Wo7cTZfSHbjsPUhb9cTIw8ML2x5QfYbGc6g/UXYCnSfpq16KpwIJmooqIaJ1XUr63LpcEsDrwEsqNoLsAJP0QeM4I29kRmGn7YQDb99TyTWtlwjqUYa7PHGE7GwO32L6xzh8PvB/4Sp3vdDG8AhjYDTEi+iSVCsOwneEMx9cuwAzbiwAkHQ9cxZIhD6MPJL2X0lTxmZKu7lq0NvCbZqJqL0nPBTYBpg3IqzCVMnxq9MdfKPkUdqP8eOx4APhQIxFFRLSPgOO7b/jUJIpDXbAvoCY3lzSF0q1hOLOA3W3PrQkft1/OeDsJkReS656IMZODq0eStgM2sj1T0nrA2mm2PCbWATq11dOaDKTFvgf8EjgC+GhX+QNddwqifzYGdqV8tl/bVf4A8K5GImoh23OBuZK+Z/uxEZ8QERGjcS7wM0lftn2npHUpN4D+Q9KTgPuBvSh5FQBupbRs+BGl0neVWn42cIik73Z3f6Dc4Lhd0irA3sCf6/oP1GUD3QBMl/Rs250cDBf29y1HxEhSqdADSZ8CtqZcHMyk1LJ+hyWJG6M/jgCuknQ+pSb8ZSx90Rt9YPs+4D5JnwDuqMmMtgc2k3SC7b81G2G72P4Z5QfYtrYvbjqeSWC6pCOA59PVEsT2M5sLKSKiHWxfV38/nFVbHjxG6W5wKHAxJVHjnK6nfIvyHTgXOAN4qG7nDEkzgNl11K/TgY9T8ixcCtxV/3YqEn4AfEvSAcCeXfH8XdL+wEmSOokavz4W7z0ihpZEjT2QNAfYgpL4ZYtadrXtzZqNrH0krU/JqwBwme07moynzernemtgOuXL/GfAJrZ3aTKutpK0GvAOSleI7ovdjP7QR5J+DXwK+DKlZcj+wJQMPxYRERExNqY0HcAK4lGX2hcDSFqz4XhaRdKWnQewPvCn+nhqLYuxscj2Ako/yGNsH0TZ/zE2TgT+EXg1pWnm0ynNOaO/Vrd9LqXS/DbbhwKvaTimiIiIiNZK94fe/EjSN4B16vi5b6c054r+OHqYZQZeMV6BTDKPSXozZWzpTl//VYZZP5bPs23vJelfbB8v6XvARU0H1ULza5Pc30v6AKU/7loNxxQRERHRWqlU6IHtL0raiZJ8ZmPgENtnNxxWa9jeoekYJqn9gfcAn7V9i6QNKXfTY2x0kgf+TdKmwB3AUxqMp60OBNYADqCMm74DsG+jEUVERES0WHIqxIRR+5y/D9iO0kLhIuDrtv/eaGARfSDpncDJwGaUhK9rUSook1AqIiIiIlZYqVToQR1b/guUu4qqD9ue2mhgLSPpR5Q+5t+pRW8B1rG9V3NRtZekjSgjbiRLfrSGpLOBvTqjmEh6IvAD269uNrKIiIiIdkr3h94cCbzW9u+aDqTlNrX9/K758yVd11g07TeTJVnyd6BmyW80ohaS9G/DLbf9pfGKZZJYr3tYVNv3Sko3k4iIiIgxkguI3vw1FQrj4kpJL+7MSHoRMLvBeNouWfLHx9ojPKK/Fkl6RmdG0gbUkXsiIiIiov/SUmEYtdsDwGxJPwR+CszvLLd9SiOBtddWwG8l/aHOPwO4QdI8SneTzZoLrZWSJX8c2D6s6Rgmmf8H/FrShZSuav8MvLvZkCIiIiLaKzkVhiFp5jCLbfvt4xbMJFDvKA7J9m3jFctkIGkb4HfAOpQs+dOAI21f0mhgLVXPJ4874eY80n+S1gM6rZ4usf2/TcYTERER0WapVIjGSZpq+35J6w623PY94x1TRL9J2qNrdjXgdcBfbB/QUEitIum5tq+XtOVgy21fOd4xRUREREwGqVTogaTjgQMHZBM/OncY+0PSabZ3lXQL5U6uuhY7oxH0l6RTh1tue7fximUyq11Pfm37JU3H0gaSvmn73ZLOH2Sxbb9i3IOKiIiImASSU6E3mw2STXyLJgNqE9u71r8bNh3LJLEt8Efg+8ClLF2JE+NnI8owtdEfZ9e/77B9c6ORREREREwiqVTozRRJT7R9L0Btpp9910eSVgZ2Bp5bi64DzrS9oLmoWusfgZ2ANwNvAX4BfN/2tY1G1XKSHmDpnAp3AAc3FE4bfQw4CfgxMGgXiIiIiIjov3R/6IGktwEfp/xgFbAn8FnbJzYaWEtIehpwHnA7cBVlH29BufjdwfZfGgyv1SQ9gVK5cBRwmO1jGw4pYlQknU2ptNkGuGjg8nTriYiIiBgbqVTokaRNgB3q7Hm2r2synjaRNAuYY/srA8oPALayvW8jgbVYrUx4DaVCYTpwKnCc7T83GVebSXod5dxxX51fB9je9k+bjawdJK1KaaFwIvDOgcttXzjuQUVERERMAqlUWAaSnkLJ2g6A7T80GE5rSLre9nOHWHaD7Y3HO6Y2k3QCsClwOvAD29c0HNKkIGmO7RkDyq6ynfwsfSTpybbvajqOiIiIiMkieQF6IGk34GjgqcCdwAbA74BNmoyrRR4ZZtnD4xbF5LEP8BBwIHCAtDhPoyhZ8qc2FVjLTRmkLOfgPpH0FdsfBI6T9Lja8nR/iIiIiBgb+UHbm08DLwbOsb2FpB0oF2bRH9MkvX6QcgG5wO0z24Nd3MbYmy3pS8B/1vn3A1c0GE/bdHLcfLHRKCIiIiImmXR/6IGk2ba3ljQX2ML2IklzbW/edGxtIGnmcMtt7z9esUSMFUlrAp8EdqxFZwOfsf1Qc1FFRERERCyfVCr0QNI5wO7AEcB6lC4Q29h+SaOBRUQEAJLmsfSQnUuxvdk4hhMRERExaaRSoQf1DuMjlD7RewPTgO/avrvRwCJiwuv09Zf0cwa56E1f//6QtMFwy23fNl6xREREREwmqVRYRpLWA+52dlxE9EDSVravkPTywZZnqMOIiIiIWJGlUmEYkl4MfB64h5Ks8URK94cpwNtsn9FgeBGxgpC0EnCC7b2bjqXt6nn7GOB5wKrASsBDGdUkIiIiYmxk9IfhHQt8nNLd4TxgZ9uXSHou8H0glQp9MMTID4vZPmW8YokYC7YXStpA0qq2H206npY7FngTcBKwNfA24DmNRhQRERHRYqlUGN7Kts8CkHS47UsAbF8vqdnI2uW1wywzkEqFaIObgd9IOhVYPOKD7S81F1I72b5J0kq2FwIzJV0FfKzpuCIiIiLaKJUKw1vUNf3IgGXpN9InGTIyJon/qY8pwNq1LOeR/ntY0qrAHElHArdT9nlEREREjIHkVBiGpIWUO4oCVgce7iwCVrO9SlOxtZGkacCngJfVoguBw23f11xUEf0haS/bJ41UFsunjgJxJ7AK8CFK97Wv2b6p0cAiIiIiWiqVCjFhSDoZuAY4vha9Fdjc9rA5FyJWBJKutL3lSGURERERESuSdH+IieRZtvfomj9M0pzGoonoA0k7A7sAT5P01a5FU4EFzUTVXpJuYZBuJbaf2UA4EREREa2XSoWYSB6RtJ3tXwNIeimPz2URsaL5CzAb2A24oqv8AUrz/OivrbumVwP2AtZtKJaIiIiI1kv3h5gwJM2gdH2YRslbcQ+wr+2rGw0sog8krWL7sabjmIwkXWF7q6bjiIiIiGijtFSICcP2HGBzSVPr/P0NhxTRT9MlHQE8n3IHHUiz/H6T1J2jYgql5UK+6yIiIiLGSH5oxYQxcPQHSRn9IdpkJuXz/WVgB2B/MtThWDi6a3oBcCvwhmZCiYiIiGi/dH+ICSOjP0SbdZrgS5pn+wXdZU3HFhERERExWmmpEBNJRn+INpsvaQrwe0kfAP4MrNVwTK0iaQvg3yldTKAkyDzS9k2SVrad0TYiIiIi+ixNb2MieUTSdp2ZjP4QLXMgsAZwALAVpSXOvo1G1CKS9gBOAs4D9quPS4AfS9oWOLOx4CIiIiJaLN0fYsKQtDlwAkuP/rCf7bmNBhYRE56kq4HdbN86oHw6cD3wJdsfH//IIiIiItotlQox4WT0h2gTSacOt9z2buMVS5tJus7284dYdoPtjcc7poiIiIjJIDkVYsKQ9ARgD2A6sLIkAGwf3mBYEctrW+CPwPeBSymtcKL/HpP0DNt/6C6UtAEwv6GYIiIiIlovlQoxkfwMuA+4glwERHv8I7AT8GbgLcAvgO/bvrbRqNrnU8A5kj5HOYcAbA18FDi4sagiIiIiWi7dH2LCkHSN7U2bjiNirNTWOG8GjgIOs31swyG1Ss3L8u/AJrXoWuDo5GWJiIiIGDupVIgJQ9I3gWNsz2s6loh+qpUJr6FUKEwHTgWOs/3nJuOKiIiIiFheqVSIxkmaB5jSHWcj4GZK9wcBtr1Zg+FFLBdJJwCbAqcDP7B9TcMhRURERET0TSoVonE1kdqQbN82XrFE9JukRcBDdbb7hNupNJs6/lFFRERERPRHKhViwpD0YuBa2w/U+anA82xf2mxkERERERERMZgpTQcQ0eW/gAe75h+sZRERPZH0dEk/kXSXpDslnSzp6U3HFREREdFWqVSIiUTuajpjexEZ9jQils1MSiLM9YGnAj+vZRERERExBlKpEBPJzZIOkLRKfRxISdoYEdGrJ9ueaXtBfcwCntx0UBERERFtlUqFmEjeA7wE+HN9vAh4d6MRRcSK5m5J+0haqT72Ae5uOqiIiIiItkqixoiIaI06mswxwLaU0TZ+Cxxg+w+NBhYRERHRUqlUiMZJehdwge3fSxLw38AewG3AfravbDTAiIiIiIiIGFSS4MVEcCAwq06/GdgceCawBfAfwD83E1ZErCgkHTLMYtv+9LgFExERETGJJKdCTAQLbD9Wp3cFTrB9t+1zgDUbjCsiVhwPDfIAeAdwcFNBRURERLRdWirERLBI0vrAvcArgc92LVu9mZAiYkVi++jOtKS1KS2g9gd+ABw91PMiIiIiYvmkUiEmgkOA2cBKwKm2rwWQ9HIypGRE9EjSusC/AXsDxwNb2r632agiIiIi2i2JGmNCkLQysHb3BYCkNSmf0QebiywiVgSSjgJeD3wT+M+cNyIiIiLGRyoVIiJihSdpETAfWEAZSnLxIkqixqmNBBYRERHRcqlUiIiIiIiIiIhRyegPERERERERETEqSdQYE4qkpwEb0PXZtP2r5iKKiIiIiIiIoaRSISYMSV8A3ghcByysxQZSqRARERERETEBJadCTBiSbgA2sz2/6VgiIiIiIiJiZMmpEBPJzcAqTQcRERERERERvUn3h2icpGMo3RweBuZIOpcyNBwAtg9oKraIiIiIiIgYWioVYiKYXf9eAZzaZCARERERERHRu+RUiIiIiIiIiIhRSUuFmDAkzaN0g+h2H6Ulw2ds3z3+UUVERERERMRQUqkQE8kvKUNJfq/OvwlYA7gDmAW8tpmwIiIiIiIiYjDp/hAThqQrbW85WJmkebZf0FRsERERERER8XgZUjImkpUkvbAzI2kbYKU6u6CZkCIiIiIiImIo6f4QE8k7geMkrQUIuB94p6Q1gSMajSwiIiIiIiIeJ90fYsKRNA3A9n1NxxIRERERERFDS6VCNE7SPra/I+nfBltu+0vjHVNERERERESMLN0fYiJYs/5du9EoIiIiIiIiYpmkpUJEREREREREjEpaKkTjJH11uOW2DxivWCIiIiIiIqJ3qVSIieCKrunDgE81FUhERERERET0Lt0fYkKRdJXtLZqOIyIiIiIiIkY2pekAIgZILVdERERERMQKIpUKERERERERETEq6f4QjZP0AEtaKKwBPNxZBNj21EYCi4iIiIiIiGGlUiEiIiIiIiIiRiXdHyIiIiIiIiJiVFKpEBERERERERGjkkqFiIiIiIiIiBiVVCpEtISk/SQd2+dt7i7p+V3zh0vasZ+vERERERERK65UKkTEcHYHFlcq2D7E9jkNxhMRERERERNIKhUiVhCS9pF0maQ5kr4haSVJ+0u6UdJlwEu71p0lac+u+Qe7pg+WNE/SXEmfr2XvknR5LTtZ0hqSXgLsBhxVX/NZ3duV9EpJV9VtHSfpCbX8VkmHSbqyLnvuOO2iiIiIiIgYZ6lUiFgBSHoe8EbgpbZnAAuBfYDDKJUJ29HVomCY7ewM/AvwItubA0fWRafY3qaW/Q54h+3fAqcCB9meYft/urazGjALeKPtFwArA+/teqn/tb0l8F/Ah0f/ziMiIiIiYiJLpULEiuGVwFbA5ZLm1PkPARfYvsv2o8APe9jOjsBM2w8D2L6nlm8q6SJJ84C9gU1G2M7GwC22b6zzxwMv61p+Sv17BTC9h7giIiIiImIFlEqFiBWDgONri4EZtjcGDh1m/QXU41vSFGDVEbY/C/hAbXVwGLDacsY7v/5dSGnFEBERERERLZRKhYgVw7nAnpKeAiBpXeAq4OWSniRpFWCvrvVvpbRsgJIXYZU6fTawv6Q1urYDsDZwe93O3l3beaAuG+gGYLqkZ9f5twIXjv7tRURERETEiiiVChErANvXAZ8AzpJ0NaVyYH1Ka4WLgd9QciF0fItS4TAX2BZ4qG7nDEqehNm1G0Un38EngUvrdq7v2s4PgINqQsZndcXzd2B/4KTaZWIR8PV+vueIiIiIiJj4ZLvpGCIiIiIiIiJiBZSWChERERERERExKqlUiIiIiIiIiIhRSaVCRERERERERIxKKhUiIiIiIiIiYlRSqRARERERERERo5JKhYiIiIiIiIgYlVQqRERERERERMSopFIhIiIiIiIiIkbl/wM1WJj1ACqMBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fdefebf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train[['education','is_pass']].groupby(['education']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('pass vs edu')\n",
    "sns.countplot('education',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('education:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id</th>\n",
       "      <th>program_id</th>\n",
       "      <th>program_type</th>\n",
       "      <th>program_duration</th>\n",
       "      <th>test_id</th>\n",
       "      <th>test_type</th>\n",
       "      <th>difficulty_level</th>\n",
       "      <th>trainee_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>city_tier</th>\n",
       "      <th>age</th>\n",
       "      <th>total_programs_enrolled</th>\n",
       "      <th>is_handicapped</th>\n",
       "      <th>trainee_engagement_rating</th>\n",
       "      <th>is_pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9389_150</td>\n",
       "      <td>Y_1</td>\n",
       "      <td>Y</td>\n",
       "      <td>136</td>\n",
       "      <td>150</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>9389</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16523_44</td>\n",
       "      <td>T_1</td>\n",
       "      <td>T</td>\n",
       "      <td>131</td>\n",
       "      <td>44</td>\n",
       "      <td>offline</td>\n",
       "      <td>easy</td>\n",
       "      <td>16523</td>\n",
       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13987_178</td>\n",
       "      <td>Z_2</td>\n",
       "      <td>Z</td>\n",
       "      <td>120</td>\n",
       "      <td>178</td>\n",
       "      <td>online</td>\n",
       "      <td>easy</td>\n",
       "      <td>13987</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13158_32</td>\n",
       "      <td>T_2</td>\n",
       "      <td>T</td>\n",
       "      <td>117</td>\n",
       "      <td>32</td>\n",
       "      <td>offline</td>\n",
       "      <td>easy</td>\n",
       "      <td>13158</td>\n",
       "      <td>F</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10591_84</td>\n",
       "      <td>V_3</td>\n",
       "      <td>V</td>\n",
       "      <td>131</td>\n",
       "      <td>84</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>10591</td>\n",
       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id program_id program_type  program_duration  test_id test_type  \\\n",
       "0   9389_150        Y_1            Y               136      150   offline   \n",
       "1   16523_44        T_1            T               131       44   offline   \n",
       "2  13987_178        Z_2            Z               120      178    online   \n",
       "3   13158_32        T_2            T               117       32   offline   \n",
       "4   10591_84        V_3            V               131       84   offline   \n",
       "\n",
       "  difficulty_level  trainee_id gender            education  city_tier   age  \\\n",
       "0     intermediate        9389      M        Matriculation          3  24.0   \n",
       "1             easy       16523      F  High School Diploma          4  26.0   \n",
       "2             easy       13987      M        Matriculation          1  40.0   \n",
       "3             easy       13158      F        Matriculation          3   NaN   \n",
       "4     intermediate       10591      F  High School Diploma          1  42.0   \n",
       "\n",
       "   total_programs_enrolled is_handicapped  trainee_engagement_rating  is_pass  \n",
       "0                        5              N                        1.0        0  \n",
       "1                        2              N                        3.0        1  \n",
       "2                        1              N                        2.0        1  \n",
       "3                        4              N                        1.0        1  \n",
       "4                        2              N                        4.0        1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fdf1f9470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train[['difficulty_level','is_pass']].groupby(['difficulty_level']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('pass vs difficulty_level')\n",
    "sns.countplot('difficulty_level',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('difficulty_level:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fdee11e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train[['test_type','is_pass']].groupby(['test_type']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('pass vs test_type')\n",
    "sns.countplot('test_type',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('test_type:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fdef303c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train[['program_type','is_pass']].groupby(['program_type']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('pass vs program_type')\n",
    "sns.countplot('program_type',hue='is_pass',data=train,ax=ax[1])\n",
    "ax[1].set_title('program_type:pass vs fail')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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InwAAAAAYRvgEG8DxSapqVcv2rVvn3T4AAADHsE3zbgC4f3cl6VWeW0tLa9kKAAAAHBF3PgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAAAAGEb4BACwgKrquKr6i6r6o2n71Kr6cFXtrarfr6oHT/Xjp+290/7tM9d4xVT/VFU9az6/BADY6IRPAACL6ReTfGJm+1eTvLa7vy/J7UnOn+rnJ7l9qr92Oi5VdVqS5yd5fJKzk7y+qo5bp94BgAUifAIAWDBVtS3J30/yu9N2JfmxJO+YDrksyXOn9XOn7Uz7z5yOPzfJ27v7ru7+bJK9SZ66Pr8AAFgkwicAgMXzG0n+RZJvTtvfleRL3X33tL0vycnT+slJPp8k0/47puP/pn6Ic/5GVe2qqj1VtefAgQNr/TsAgAUgfAIAWCBV9RNJbu3ua9fj+7r7ku7e2d07N2/evB5fCQBsMJvm3QAAAGvqR5I8p6rOSfKQJI9M8ptJTqiqTdPdTduS7J+O35/klCT7qmpTkkcl+eJM/aDZcwAAVsydTwAAC6S7X9Hd27p7e5ZfGP7+7n5hkg8k+UfTYeclec+0fsW0nWn/+7u7p/rzp9nwTk2yI8lH1ulnAAALxJ1PAADHhl9O8vaq+r+S/EWSN071Nyb5varam+S2LAdW6e4bquryJB9PcneSC7r7nvVvGwDY6IRPAAALqrv/NMmfTuufySFmq+vuv0ry04c5/zVJXjOuQwDgWOCxOwAAAACGET4BAAAAMIzwCQAAAIBhhoVPVXVpVd1aVR+bqV1UVfur6rppOWdm3yuqam9VfaqqnjVTP3uq7a2ql4/qFwAAAIC1N/LOpzclOfsQ9dd29+nTcmWSVNVpWZ5Z5fHTOa+vquOq6rgk/zHJs5OcluQF07EAAAAAbADDZrvr7g9V1fYVHn5ukrd3911JPjtN9XtwNpa90+wsqaq3T8d+fI3bBQAAAGCAebzz6Req6vrpsbwTp9rJST4/c8y+qXa4+iFV1a6q2lNVew4cOLDWfQMAAABwhNY7fLo4yfcmOT3JzUl+bS0v3t2XdPfO7t65efPmtbw0AAAAAKsw7LG7Q+nupYPrVfWGJH80be5PcsrModumWu6jDgAAAMBRbl3vfKqqk2Y2fyrJwZnwrkjy/Ko6vqpOTbIjyUeSXJNkR1WdWlUPzvJLya9Yz54BAAAAWL1hdz5V1duSPCPJY6pqX5ILkzyjqk5P0kk+l+TnkqS7b6iqy7P8IvG7k1zQ3fdM1/mFJO9NclySS7v7hlE9AwAAALC2Rs5294JDlN94H8e/JslrDlG/MsmVa9gaAAAAAOtkHrPdAQAAAHCMED4BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAICj3tZtW1NVR7wAMH+b5t0AAADA/Vnav5RctIoTV3MOAGvKnU8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAAAAGEb4BAAAAMAwwicAAAAAhhE+AQAAADCM8AkAAACAYYRPAAAAAAwjfAIAAABgGOETAAAAAMMInwAAAAAYRvgEAAAAwDDCJwAAAACGET4BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAAAO5bikqla1bN22dd7dAxw1Ns27AQAAgKPSPUkuWt2pSxctrWUnABuaO58AAAAAGEb4BAAAAMAww8Knqrq0qm6tqo/N1P5dVX2yqq6vqndX1QlTfXtVfb2qrpuW35455ylV9dGq2ltVr6uqGtUzAAAAAGtr5J1Pb0py9r1qu5M8obv/TpL/muQVM/s+3d2nT8vPz9QvTvKzSXZMy72vCQAAAMBRalj41N0fSnLbvWp/0t13T5tXJ9l2X9eoqpOSPLK7r+7uTvLmJM8d0S8AAAAAa2+e73z6X5L855ntU6vqL6rqg1X19Kl2cpJ9M8fsm2qHVFW7qmpPVe05cODA2ncMAAAAwBGZS/hUVf9nkruTvGUq3Zzksd39pCQvS/LWqnrkkV63uy/p7p3dvXPz5s1r1zAAAAAAq7Jpvb+wqv5pkp9Icub0KF26+64kd03r11bVp5N8f5L9+dZH87ZNNQAAAAA2gHW986mqzk7yL5I8p7u/NlPfXFXHTeuPy/KLxT/T3TcnubOqzphmuXtxkvesZ88AAAAArN6wO5+q6m1JnpHkMVW1L8mFWZ7d7vgku5ezpFw9zWz3o0leVVV/neSbSX6+uw++rPylWZ4576FZfkfU7HuiAAAAADiKDQufuvsFhyi/8TDHvjPJOw+zb0+SJ6xhawAAAACsk3nOdgcAAADAghM+AQAAADCM8AkAAACAYYRPAAAAAAwjfAIAAABgGOETAAAAAMMInwAAAAAYRvgEAAAAwDDCJwAAAACGET4BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAWSFU9pKo+UlV/WVU3VNW/meqnVtWHq2pvVf1+VT14qh8/be+d9m+fudYrpvqnqupZ8/lFAMBGJ3wCAFgsdyX5se5+YpLTk5xdVWck+dUkr+3u70tye5Lzp+PPT3L7VH/tdFyq6rQkz0/y+CRnJ3l9VR23rr8EAFgIwicAgAXSy74ybT5oWjrJjyV5x1S/LMlzp/Vzp+1M+8+sqprqb+/uu7r7s0n2JnnqOvwEAGDBCJ8AABZMVR1XVdcluTXJ7iSfTvKl7r57OmRfkpOn9ZOTfD5Jpv13JPmu2fohzpn9rl1Vtaeq9hw4cGDEzwEANjjhEwDAgunue7r79CTbsny30g8O/K5Luntnd+/cvHnzqK8BADawFYVPVfU/jW4EAIBv9UDHYN39pSQfSPLDSU6oqk3Trm1J9k/r+5OcMn3fpiSPSvLF2fohzgEAWLGV3vn0+mnWlJdW1aOGdgQAwEFHPAarqs1VdcK0/tAkP57kE1kOof7RdNh5Sd4zrV8xbWfa//7u7qn+/Gk2vFOT7EjykbX4UQDAsWVF4VN3Pz3JC7P879e1VfXWqvrxoZ0BABzjVjkGOynJB6rq+iTXJNnd3X+U5JeTvKyq9mb5nU5vnI5/Y5LvmuovS/Ly6btvSHJ5ko8n+eMkF3T3PWv6AwGAY8Km+z9kWXffWFX/MsmeJK9L8qRpJpRXdve7RjUIAHAsO9IxWHdfn+RJh6h/JoeYra67/yrJTx/mu1+T5DUP7BcAAMe6lb7z6e9U1WuzfMv2jyX5ye7+H6f11w7sDwDgmGUMBgAsgpXe+fQfkvxulv9h+/rBYnd/YfonDgCAtWcMBgBseCsNn/5+kq8ffM6/qr4jyUO6+2vd/XvDugMAOLYZgwEAG95KZ7t7X5KHzmw/bKoBADCOMRgAsOGtNHx6SHd/5eDGtP6wMS0BADAxBgMANryVhk9fraonH9yoqqck+fp9HA8AwANnDAYAbHgrfefTLyX5g6r6QpJKsjXJPx7WFQAAiTEYALAAVhQ+dfc1VfWDSX5gKn2qu/96XFvAWjk+SVUd8Xnfs2VLPnfLLWvfEAArZgwGACyCld75lCQ/lGT7dM6Tqyrd/eYhXQFr5q4kvYrzamlprVsBYHWMwQCADW1F4VNV/V6S701yXZJ7pnInMfABjgrbt27NTQIzYMEYgwEAi2Cldz7tTHJad6/mBgqA4W5aWlrVHV7J8ktUAI5SxmAAwIa30tnuPpblF1wCALB+jMEAgA1vpXc+PSbJx6vqI1l+hUySpLufM6QrAAASYzAAYAGsNHy6aGQTAAAc0kXzbgAA4IFaUfjU3R+squ9JsqO731dVD0ty3NjWAACObcZgAGvsuKTqyN/4ueXkLbll3y0DGoJjw0pnu/vZJLuSPDrLM66cnOS3k5w5rjUAgGObMRjAGrsnq7qndOkisyrDA7HSF45fkORHktyZJN19Y5LvHtUUAABJjMEAgAWw0vDpru7+xsGNqtqUrHpWcwAAVsYYDADY8FYaPn2wql6Z5KFV9eNJ/iDJfxrXFgAAMQYDABbASsOnlyc5kOSjSX4uyZVJ/uWopgAASGIMBgAsgJXOdvfNJG+YFgAA1oExGACwCFY6291nc4j3C3T349a8I4BjwPFZ3TS/SfI9W7bkc7eY6heOBcZgAMAiWFH4lGTnzPpDkvx0lqf8BWAV7srq3xhcS6b6hWOIMRhwVNu6bWuW9hubAPdtpY/dffFepd+oqmuT/Ou1bwkAgMQYDDj6Le1fSi5a5cmrPQ/YcFb62N2TZza/I8v/wq30rikAAFbBGAwAWAQrHbz82sz63Uk+l+R5a94NAACzjMEAgA1vpY/dPXN0IwAAfCtjMABgEaz0sbuX3df+7v71tWkHAICDjMEAgEVwJLPd/VCSK6btn0zykSQ3jmgKAIAkxmAAwAJYafi0LcmTu/vLSVJVFyX5/7r7n4xqDAAAYzAAYOP7jhUetyXJN2a2vzHVAAAYxxgMANjwVnrn05uTfKSq3j1tPzfJZWNaAgBgYgwGAGx4K7rzqbtfk+QlSW6flpd097+9v/Oq6tKqurWqPjZTe3RV7a6qG6fPE6d6VdXrqmpvVV1fVU+eOee86fgbq+q8I/2RAAAb0WrHYAAAR5OVPnaXJA9Lcmd3/2aSfVV16grOeVOSs+9Ve3mSq7p7R5Krpu0keXaSHdOyK8nFyXJYleTCJE9L8tQkFx4MrAAAjgGrGYMBABw1VhQ+VdWFSX45ySum0oOS/L/3d153fyjJbfcqn5u/vV38sizfPn6w/uZednWSE6rqpCTPSrK7u2/r7tuT7M63B1oAAAtntWMwAICjyUrvfPqpJM9J8tUk6e4vJHnEKr9zS3ffPK3fkr99aebJST4/c9y+qXa4+repql1Vtaeq9hw4cGCV7QEAHDXWcgwGADAXKw2fvtHdnaSTpKoevhZfPnvNNbreJd29s7t3bt68ea0uCwAwL0PGYAAA62ml4dPlVfU7WX4U7meTvC/JG1b5nUvT43SZPm+d6vuTnDJz3Lapdrg6AMCiW8sxGADAXKx0trt/n+QdSd6Z5AeS/Ovu/g+r/M4rkhycse68JO+Zqb94mvXujCR3TI/nvTfJWVV14vSi8bOmGgDAQlvjMRgAwFxsur8Dquq4JO/r7mdm+WXfK1ZVb0vyjCSPqap9WZ617ley/C/e+UluSvK86fArk5yTZG+Sr2V5WuF0921V9eok10zHvaq77/0ScwCAhfJAxmAAAEeT+w2fuvueqvpmVT2qu+84kot39wsOs+vMQxzbSS44zHUuTXLpkXw3AMBG9kDGYAAAR5P7DZ8mX0ny0aranWm2lSTp7v9tSFcAACTGYADAAlhp+PSuaQEAYP0YgwEAG959hk9V9dju/m/dfdl6NQQAcKwzBgMAFsn9zXb3hwdXquqdg3sBAGCZMRgAsDDuL3yqmfXHjWwEAIC/YQwGACyM+wuf+jDrAACMYwwGACyM+3vh+BOr6s4s//v20Gk903Z39yOHdgcAcGwyBgMAFsZ9hk/dfdx6NQIAwDJjMABgkdzfY3cAAAAAsGrCJwAAAACGET4BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAAAAGEb4BAAAAMAwwicAAAAAhhE+AQAAADCM8AkAAACAYYRPAAAAAAwjfAIAAABgGOETAAAAAMMInwAAAAAYRvgEAAAAwDDCJwAAAACGET4BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwBVVeiZAAAX+UlEQVQAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAADW2nFJVR3xsnXb1nl3DrDmNs27AQAAgIVzT5KLjvy0pYuW1roTgLlz5xMAAAAAwwifAAAWSFWdUlUfqKqPV9UNVfWLU/3RVbW7qm6cPk+c6lVVr6uqvVV1fVU9eeZa503H31hV583rNwEAG5vwCQBgsdyd5J9192lJzkhyQVWdluTlSa7q7h1Jrpq2k+TZSXZMy64kFyfLYVWSC5M8LclTk1x4MLACADgSwicAgAXS3Td3959P619O8okkJyc5N8ll02GXJXnutH5ukjf3squTnFBVJyV5VpLd3X1bd9+eZHeSs9fxpwAAC0L4BACwoKpqe5InJflwki3dffO065YkW6b1k5N8fua0fVPtcPV7f8euqtpTVXsOHDiwpv0DAItB+AQAsICq6juTvDPJL3X3nbP7uruT9Fp8T3df0t07u3vn5s2b1+KSAMCCWffwqap+oKqum1nurKpfqqqLqmr/TP2cmXNeMb0E81NV9az17hkAYCOpqgdlOXh6S3e/ayovTY/TZfq8darvT3LKzOnbptrh6gAAR2Tdw6fu/lR3n97dpyd5SpKvJXn3tPu1B/d195VJMr0g8/lJHp/l9wy8vqqOW+++AQA2gqqqJG9M8onu/vWZXVckOThj3XlJ3jNTf/E0690ZSe6YHs97b5KzqurE6UXjZ001AIAjsmnO339mkk93903L46RDOjfJ27v7riSfraq9WZ5x5c/WqUcAgI3kR5K8KMlHq+q6qfbKJL+S5PKqOj/JTUmeN+27Msk5SfZm+U/BlyRJd99WVa9Ocs103Ku6+7b1+QkAwCKZd/j0/CRvm9n+hap6cZI9WZ4i+PYsv9jy6pljDvmyy2T5hZdZniI4j33sY4c0DABwNOvu/5LkcP/qnXmI4zvJBYe51qVJLl277gCAY9HcXjheVQ9O8pwkfzCVLk7yvUlOT3Jzkl870mt64SUAAADA0WWes909O8mfd/dSknT3Unff093fTPKGLD9al3jZJQAAAMCGNc/w6QWZeeTu4Owrk59K8rFp/Yokz6+q46vq1CQ7knxk3boEAAAAYNXm8s6nqnp4kh9P8nMz5f+7qk5P0kk+d3Bfd99QVZcn+XiSu5Nc0N33rG/HAAAAAKzGXMKn7v5qku+6V+1F93H8a5K8ZnRfAAAAAKyteT52BwAAAMCCEz4BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAAAAGEb4BAAAAMAwwicAAAAAhhE+AQAAADCM8AkAAACAYYRPAAAAAAwjfAIAAABgGOETAAAAAMMInwAAAAAYRvgEAAAAwDDCJwAAAACGET4BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAAAAGEb4BAAAAMAwwicAAAAAhhE+AQAAADCM8AkAAACAYYRPAAAAAAwjfAIAAABgGOETAAAAAMMInwAAAAAYRvgEAAAAwDDCJwAAAACGET4BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMHMLn6rqc1X10aq6rqr2TLVHV9Xuqrpx+jxxqldVva6q9lbV9VX15Hn1DQAAAMDKzfvOp2d29+ndvXPafnmSq7p7R5Krpu0keXaSHdOyK8nF694pAAAAAEds3uHTvZ2b5LJp/bIkz52pv7mXXZ3khKo6aR4NAgAAALBy8wyfOsmfVNW1VbVrqm3p7pun9VuSbJnWT07y+Zlz9021b1FVu6pqT1XtOXDgwKi+AQAAAFihTXP87r/b3fur6ruT7K6qT87u7O6uqj6SC3b3JUkuSZKdO3ce0bkAAAAArL253fnU3funz1uTvDvJU5MsHXycbvq8dTp8f5JTZk7fNtUAAAAAOIrNJXyqqodX1SMOric5K8nHklyR5LzpsPOSvGdavyLJi6dZ785IcsfM43kAAAAAHKXm9djdliTvrqqDPby1u/+4qq5JcnlVnZ/kpiTPm46/Msk5SfYm+VqSl6x/ywAAAAAcqbmET939mSRPPET9i0nOPES9k1ywDq0BAAAAsIbmOdsdAAAAAAtO+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAAAAGEb4BAAAAMAwwicAAAAAhhE+AQAAADCM8AkAAACAYYRPAAAAAAwjfOKYtX3r1lTVES8AAADAym2adwMwLzctLaVXcZ74CQAAAFbOnU8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAsECq6tKqurWqPjZTe3RV7a6qG6fPE6d6VdXrqmpvVV1fVU+eOee86fgbq+q8efwWAGAxCJ8AABbLm5Kcfa/ay5Nc1d07klw1bSfJs5PsmJZdSS5OlsOqJBcmeVqSpya58GBgBQBwpIRPAAALpLs/lOS2e5XPTXLZtH5ZkufO1N/cy65OckJVnZTkWUl2d/dt3X17kt359kALAGBFhE8AAItvS3ffPK3fkmTLtH5yks/PHLdvqh2u/m2qaldV7amqPQcOHFjbrgGAhSB8AgA4hnR3J+k1vN4l3b2zu3du3rx5rS4LACwQ4RMAwOJbmh6ny/R561Tfn+SUmeO2TbXD1QEAjpjwCQBg8V2R5OCMdeclec9M/cXTrHdnJLljejzvvUnOqqoTpxeNnzXVAACO2KZ5NwAAwNqpqrcleUaSx1TVvizPWvcrSS6vqvOT3JTkedPhVyY5J8neJF9L8pIk6e7bqurVSa6ZjntVd9/7JeYAACsifAIAWCDd/YLD7DrzEMd2kgsOc51Lk1y6hq0BAMcoj90BAAAAMIzwCQAAAIBhhE8AAAAADCN8AgAAAGAY4RMAAAAAwwifAAAAABhG+AQAAADAMMInAAAAAIYRPgEAAAAwjPAJAAAAgGGETwAAAAAMI3wCAAAAYBjhEwAAAADDCJ8AAAAAGEb4BAAAAMAwwicAAAAAhhE+AQAAADCM8AkAAACAYYRPAAAAAAwjfAIAAABgGOETAAAAAMMInwAAAAAYRvgEAAAAwDDCJwAAAACGET4BAAAAMIzwCQAAAIBh1j18qqpTqv7/9u4u1rKzrAP4/8m0HaE0FKSZXToNUwhiKjGFIEIgxIAfpRoqSS9KlGAiqTE0AT+iRRMzxHCh4UMvDKZ8GkCQL7UhJgrSxHgDtGUo0xZkoC100tmtGgS9KFBeL/aadrc950xnz1nvOufs3y9Zmb3XOes875O91jrPPGetd9WNVXV7Vd1WVW8c1h+uquNVdWRYrlja5s1VdayqvlZVv9R7zAAAwJmbHZylqlZaANi9zpog5g+T/F5r7ZaqOi/JzVX1meFr72ytvW35m6vq0iRXJ/mpJE9P8tmq+onW2oNdRw0AAJyR+fF5cnjFjVfdDoDJdb/yqbV2b2vtluH195LckeSiLTa5MslHW2sPtNbuTHIsyQvHHykAAAAAZ2rSOZ+q6lCS5yX5/LDq2qq6tareV1VPGdZdlOTbS5vdk02aVVV1TVXdVFU33X///SONGgAAAIDHa7LmU1U9Kcknk7yptfbdJO9K8qwklyW5N8nbT/dnttaub629oLX2ggsuuGBbxwsAAADA6Zuk+VRVZ2fRePpwa+1TSdJam7fWHmyt/SjJu/PwrXXHk1y8tPnBYR0AAAAAO9wUT7urJO9Nckdr7R1L6y9c+rZXJzk6vL4hydVVtb+qLkny7CRf6DVeAAAAAFY3xdPuXpLktUm+UlVHhnV/lOQ1VXVZkpbkriS/lSSttduq6mNJbs/iSXlv8KQ7AAAAgN2he/OptfbvSWqDL/3TFtu8NclbRxsUAAAAAKOY9Gl3AAAAAOxtmk/AhvYnqaqVlkOz2dTDBwAAYIeYYs4nYBd4IIsJ2FZR8/l2DgUAAIBdzJVPAAAAAIxG8wkAAACA0Wg+AQAAADAazScAAAAARqP5BAAAAMBoNJ8AAAAAGI3mEwAAAACj0XwCAAAAYDSaTwAAAACMRvMJAAAAgNFoPgEAAAAwGs0nAAAAAEaj+QTsKIdms1TVaS8AAADsTGdNPQCAZXfP52krbKf9BAAAsDO58gkAAACA0Wg+AQAAADAazScAAAAARqP5BAAAAMBoNJ8Adpn9yUpPBDw0m009dAAAYA152h3ALvNAstoTAefz7R4KAADAKbnyCQAAAIDRaD4BAAAAMBrNJ3a1Q7PZSnPfVNXUQwcAAIC1YM4ndrW75/OV5r5JEu0nAAAAGJ8rnwAAAHaKfas91baqMjvoybbAzuTKJwAAgJ3iwSSHV9t0ftiTbUczNAVXceCiAzlxz4ltHhDsLppPAAAAa2x2cJb5cY2rLWkKwhnRfAK23f6s/pchAAD6mh+fr9xYWXk7YK1oPgHb7oHERPAAAAAkMeE4AAAAACPSfAIAAABgNJpPAAAAAIxG8wmAUzo0m6WqTns5NJtNPXQARjA7uNrvBQDWkwnHATilu+fzlSaRr7lHCwPsRSs/HW2VbWBNzQ7OFsfaCg5cdCAn7jmxzSOC1Wk+MblDs1nu9h9UAACAh6zc5E0y/9P5ylcbalwxBs0nJrfqFRVJ4uJtAACAR3kwqzeuDrswgO1nzicAAAAARqP5BAAAAMBo3HYHAACwF+yLpwoCO5LmEwAAwF6w6jw/q2wDcBrcdgcAAADAaDSfAAAAABiN5hMAAAAAo9F8AmA0+7OY+HSV5dBsNvXwAQCAbWDCcQBG80CStuK2NZ9v51AAAICJaD4BrImTVyEBAAD0pPkEsCbO6Cqk7RwIAACwVsz5BAAAAMBoNJ8AAAAAGI3b7gAAYA3NDs4yP+7hDjC6febdBM0nAABYQ/Pj8+Twihuvuh2sowez2jGzyjawQ7ntDgAAAIDR7JrmU1VdXlVfq6pjVXXd1OMBAFgHajAA4EztiuZTVe1L8ldJXpnk0iSvqapLpx0VAMDepgYDWEPDHFWnu8wOzqYeOTvYbpnz6YVJjrXWvpkkVfXRJFcmuX3SUQEwmv1ZbXLOZxw4kLtOnNj+AcF6UoMBrJsV56iaH/YAAzZXrbWpx3BKVXVVkstba68f3r82yc+21q591Pddk+Sa4e1zkxztOtCHPS3Jf04Ue93jr3Pu6x5/nXOfOv465z51/HXOPUme01o7b8L4e97jqcF2UP2VTL9POh+Iv26x1z3+Ouc+dfx1zn0nxD/tGmy3XPn0uLTWrk9yfZJU1U2ttRdMMY4pY697/HXOfd3jr3PuU8df59ynjr/OuZ+MP1VsHrZT6q91j7/Oua97/HXOfer465z71PHXOfedEv90t9kVcz4lOZ7k4qX3B4d1AACMRw0GAJyx3dJ8+mKSZ1fVJVV1TpKrk9ww8ZgAAPY6NRgAcMZ2xW13rbUfVtW1Sf45yb4k72ut3XaKza4ff2Q7Mva6x1/n3Nc9/jrnPnX8dc596vjrnPtOiL/nrVCDTf2ZrHP8dc593eOvc+5Tx1/n3KeOv86578r4u2LCcQAAAAB2p91y2x0AAAAAu5DmEwAAAACj2XPNp6q6vKq+VlXHquq6CeLfVVVfqaojPR4BXVXvq6r7quro0rqnVtVnqurrw79P6Rj7cFUdH/I/UlVXjBF7iHVxVd1YVbdX1W1V9cZh/ej5bxG7S/5V9WNV9YWq+vIQ/y3D+kuq6vPD/v93w+SwPeN/oKruXMr/sjHiD7H2VdWXqurTw/suuW8Rv2fujznP9Drut4jfa98/v6o+UVVfrao7qurFnXPfKH6v3J+zFONIVX23qt7U8Zy/Wfxe+f/OcL45WlUfGc5DXY97tlZqMDWYGkwNpgYb87ifrP4aYqnBJqjBtojd87PfnhqstbZnliwmwvxGkmcmOSfJl5Nc2nkMdyV5Wsd4L0vy/CRHl9b9eZLrhtfXJfmzjrEPJ/n9TrlfmOT5w+vzkvxHkkt75L9F7C75J6kkTxpen53k80lelORjSa4e1v91kt/uHP8DSa7q9Pn/bpK/TfLp4X2X3LeI3zP3x5xneh33W8Tvte//TZLXD6/PSXJ+59w3it/tvLc0jn1JTiR5Rs/8N4k/ev5JLkpyZ5InDO8/luQ3eh/3llPuE2owNZgarKnBxj4XbxC/Z+6POc90PO43it3zuFeDtWlrsHSuv4aY21aD7bUrn16Y5Fhr7Zutte8n+WiSKyce06haa/+W5L8ftfrKLA7ODP/+asfY3bTW7m2t3TK8/l6SO7I4OEbPf4vYXbSF/x3enj0sLcnLk3xiWD/mZ79Z/C6q6mCSX07ynuF9pVPuG8XfIboc91Oqqidn8R+u9yZJa+37rbXvpFPuW8SfwiuSfKO1dnem+eyX4/dyVpInVNVZSZ6Y5N50PO45JTXYghpMDZaowdRge4wa7BGmrMGmqL+SbarB9lrz6aIk3156f086/jIatCT/UlU3V9U1nWOfdKC1du/w+kSSA53jX1tVt9bikvDRLr1cVlWHkjwvi7/+dM3/UbGTTvkPlxwfSXJfks9k8Rfn77TWfjh8y6j7/6Pjt9ZO5v/WIf93VtX+kcL/RZI/SPKj4f2Pp2PuG8Q/qUfuycbnmZ77/WbnubH3/UuS3J/k/cPl9u+pqnPTL/fN4if9z3tXJ/nI8HqKc/5y/GTk/Ftrx5O8Lcm3sih4/ifJzel73LM1NdiCGkwNlqjB1GD9Yid99ns12MOmrMG61l/J9tZge635tBO8tLX2/CSvTPKGqnrZlINpi+vguv01JMm7kjwryWVZ7JxvHztgVT0pySeTvKm19t3lr42d/waxu+XfWnuwtXZZkoNZ/MX5J8eK9XjiV9Vzk7x5GMfPJHlqkj/c7rhV9StJ7mut3bzdP/sM44+e+5ItzzMdjvuN4vfY98/K4jaTd7XWnpfk/7K4xPkhI+e+Wfyu573hnvpXJfn4o7/W45y/QfzR8x8KqiuzKD6fnuTcJJdvdxx2PTWYGkwNpgbbyzXYVPVXogZLMm0NNkX9NcTdthpsrzWfjie5eOn9wWFdN0NnMK21+5L8fRa/kHqbV9WFSTL8e1+vwK21+fAL8UdJ3p2R86+qs7MoPD7cWvvUsLpL/hvF7p3/EPM7SW5M8uIk5w+XQyad9v+l+JcPl8K31toDSd6fcfJ/SZJXVdVdWdzW8fIkf5l+uT8mflV9qFPuSTY9z3Q77jeK32nfvyfJPUt/4f1EFoVIr9w3jD/Bcf/KJLe01ubD+97n/EfE75T/zye5s7V2f2vtB0k+lcWx2P2cx6bUYAtqMDVYogZTg3WK3XG/V4MtTFmDTVF/JdtYg+215tMXkzy7FjOvn5PFZWk39ApeVedW1XknXyf5xSRHt95qFDcked3w+nVJ/rFX4JMH3+DVGTH/qqos7vu9o7X2jqUvjZ7/ZrF75V9VF1TV+cPrJyT5hSzmPLgxyVXDt4322W8S/6tLJ9/K4r7fbc+/tfbm1trB1tqhLI7xz7XWfi2dct8k/q/3yH34+ZudZ7oc95vF77Hvt9ZOJPl2VT1nWPWKJLenU+6bxe953hu8Jo+85Lr3Of8R8Tvl/60kL6qqJw7H2MnPvstxz+OiBltQg6nBEjWYGqxT7F77vRrsIVPWYFPUX8l21mCt48zwPZYkV2Tx1ItvJPnjzrGfmcXTXb6c5LYe8bPYAe9N8oMsOsK/mcW91/+a5OtJPpvkqR1jfzDJV5LcmsXBeOGIub80i0sbb01yZFiu6JH/FrG75J/kp5N8aYhzNMmfLO2DX0hyLItLMvd3jv+5If+jST6U4WksI+4DP5eHn3TSJfct4nfJfbPzTMfjfrP4vfb9y5LcNMT5hyRP6ZX7FvF7nvfOTfJfSZ68tK5n/hvF7/XZvyXJV4dj7INJ9k9x3Fu2/IzUYGowNZgaTA02zn4/af01xFKDTVSDbRK7Z+7bUoPV8MMAAAAAYNvttdvuAAAAANhBNJ8AAAAAGI3mEwAAAACj0XwCAAAAYDSaTwAAAACMRvMJAAAAgNFoPgEAAAAwmv8H2e52CJ2XOhMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fdf7c3f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,10))\n",
    "train[train['is_pass']==0].age.plot.hist(ax=ax[0],bins=20,edgecolor='black',color='red')\n",
    "ax[0].set_title('is_pass= 0')\n",
    "x1=list(range(0,85,5))\n",
    "ax[0].set_xticks(x1)\n",
    "train[train['is_pass']==1].age.plot.hist(ax=ax[1],color='green',bins=20,edgecolor='black')\n",
    "ax[1].set_title('is_pass= 1')\n",
    "x2=list(range(0,85,5))\n",
    "ax[1].set_xticks(x2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "######################################\n",
    "\n",
    "# X=tr.iloc[:,:-1].values\n",
    "Y=train.iloc[:,-1].values\n",
    "\n",
    "X=np.array(train[['test_type','difficulty_level','education', 'trainee_engagement_rating','program_duration' ,'city_tier','total_programs_enrolled','age','program_type','gender']])\n",
    "test_X=np.array(test[['test_type','difficulty_level','education', 'trainee_engagement_rating','program_duration','city_tier','total_programs_enrolled','age','program_type','gender']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>id</th>\n",
       "      <th>program_id</th>\n",
       "      <th>program_type</th>\n",
       "      <th>program_duration</th>\n",
       "      <th>test_id</th>\n",
       "      <th>test_type</th>\n",
       "      <th>difficulty_level</th>\n",
       "      <th>trainee_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>city_tier</th>\n",
       "      <th>age</th>\n",
       "      <th>total_programs_enrolled</th>\n",
       "      <th>is_handicapped</th>\n",
       "      <th>trainee_engagement_rating</th>\n",
       "      <th>is_pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9389_150</td>\n",
       "      <td>Y_1</td>\n",
       "      <td>Y</td>\n",
       "      <td>136</td>\n",
       "      <td>150</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>9389</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16523_44</td>\n",
       "      <td>T_1</td>\n",
       "      <td>T</td>\n",
       "      <td>131</td>\n",
       "      <td>44</td>\n",
       "      <td>offline</td>\n",
       "      <td>easy</td>\n",
       "      <td>16523</td>\n",
       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13987_178</td>\n",
       "      <td>Z_2</td>\n",
       "      <td>Z</td>\n",
       "      <td>120</td>\n",
       "      <td>178</td>\n",
       "      <td>online</td>\n",
       "      <td>easy</td>\n",
       "      <td>13987</td>\n",
       "      <td>M</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13158_32</td>\n",
       "      <td>T_2</td>\n",
       "      <td>T</td>\n",
       "      <td>117</td>\n",
       "      <td>32</td>\n",
       "      <td>offline</td>\n",
       "      <td>easy</td>\n",
       "      <td>13158</td>\n",
       "      <td>F</td>\n",
       "      <td>Matriculation</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10591_84</td>\n",
       "      <td>V_3</td>\n",
       "      <td>V</td>\n",
       "      <td>131</td>\n",
       "      <td>84</td>\n",
       "      <td>offline</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>10591</td>\n",
       "      <td>F</td>\n",
       "      <td>High School Diploma</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id program_id program_type  program_duration  test_id test_type  \\\n",
       "0   9389_150        Y_1            Y               136      150   offline   \n",
       "1   16523_44        T_1            T               131       44   offline   \n",
       "2  13987_178        Z_2            Z               120      178    online   \n",
       "3   13158_32        T_2            T               117       32   offline   \n",
       "4   10591_84        V_3            V               131       84   offline   \n",
       "\n",
       "  difficulty_level  trainee_id gender            education  city_tier   age  \\\n",
       "0     intermediate        9389      M        Matriculation          3  24.0   \n",
       "1             easy       16523      F  High School Diploma          4  26.0   \n",
       "2             easy       13987      M        Matriculation          1  40.0   \n",
       "3             easy       13158      F        Matriculation          3   NaN   \n",
       "4     intermediate       10591      F  High School Diploma          1  42.0   \n",
       "\n",
       "   total_programs_enrolled is_handicapped  trainee_engagement_rating  is_pass  \n",
       "0                        5              N                        1.0        0  \n",
       "1                        2              N                        3.0        1  \n",
       "2                        1              N                        2.0        1  \n",
       "3                        4              N                        1.0        1  \n",
       "4                        2              N                        4.0        1  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_X High School Diploma Matriculation Bachelors Masters No Qualification\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(X[:,1]):\n",
    "#     print(type(i))\n",
    "        if j=='intermediate':\n",
    "            X[:,1][i]=1\n",
    "        elif j=='easy':\n",
    "            X[:,1][i]=0\n",
    "        elif j=='hard':\n",
    "            X[:,1][i]=2\n",
    "        elif j=='vary hard':\n",
    "            X[:,1][i]=3\n",
    "    \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(test_X[:,1]):\n",
    "#     print(type(i))\n",
    "        if j=='intermediate':\n",
    "            test_X[:,1][i]=1\n",
    "        elif j=='easy':\n",
    "            test_X[:,1][i]=0\n",
    "        elif j=='hard':\n",
    "            test_X[:,1][i]=2\n",
    "        elif j=='vary hard':\n",
    "            test_X[:,1][i]=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(X[:,0]):\n",
    "#     print(type(i))\n",
    "        if j=='offline':\n",
    "            X[:,0][i]=1\n",
    "        elif j=='online':\n",
    "            X[:,0][i]=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(test_X[:,0]):\n",
    "#     print(type(i))\n",
    "        if j=='offline':\n",
    "            test_X[:,0][i]=1\n",
    "        elif j=='online':\n",
    "            test_X[:,0][i]=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(X[:,-1]):\n",
    "#     print(type(i))\n",
    "        if j=='M':\n",
    "            X[:,-1][i]=1\n",
    "        elif j=='F':\n",
    "            X[:,-1][i]=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(test_X[:,-1]):\n",
    "#     print(type(i))\n",
    "        if j=='M':\n",
    "            test_X[:,-1][i]=1\n",
    "        elif j=='F':\n",
    "            test_X[:,-1][i]=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(X[:,2]):\n",
    "#     print(type(i))\n",
    "        if j=='No Qualification':\n",
    "            X[:,2][i]=0\n",
    "        elif j=='Matriculation':\n",
    "            X[:,2][i]=1\n",
    "        elif j=='High School Diploma':\n",
    "            X[:,2][i]=2\n",
    "        elif j=='Bachelors':\n",
    "            X[:,2][i]=3\n",
    "        elif j=='Masters':\n",
    "            X[:,2][i]=4    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(X[:,-2]):\n",
    "#     print(type(i))\n",
    "        if j=='S':\n",
    "            X[:,-2][i]=0\n",
    "        elif j=='T':\n",
    "            X[:,-2][i]=1\n",
    "        elif j=='U':\n",
    "            X[:,-2][i]=2\n",
    "        elif j=='V':\n",
    "            X[:,-2][i]=3\n",
    "        elif j=='W':\n",
    "            X[:,-2][i]=4    \n",
    "        elif j=='X':\n",
    "            X[:,-2][i]=5 \n",
    "        elif j=='Y':\n",
    "            X[:,-2][i]=6 \n",
    "        elif j=='Z':\n",
    "            X[:,-2][i]=7         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(test_X[:,-2]):\n",
    "#     print(type(i))\n",
    "        if j=='S':\n",
    "            test_X[:,-2][i]=0\n",
    "        elif j=='T':\n",
    "            test_X[:,-2][i]=1\n",
    "        elif j=='U':\n",
    "            test_X[:,-2][i]=2\n",
    "        elif j=='V':\n",
    "            test_X[:,-2][i]=3\n",
    "        elif j=='W':\n",
    "            test_X[:,-2][i]=4    \n",
    "        elif j=='X':\n",
    "            test_X[:,-2][i]=5 \n",
    "        elif j=='Y':\n",
    "            test_X[:,-2][i]=6 \n",
    "        elif j=='Z':\n",
    "            test_X[:,-2][i]=7         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in enumerate(test_X[:,2]):\n",
    "#     print(type(i))\n",
    "        if j=='No Qualification':\n",
    "            test_X[:,2][i]=0\n",
    "        elif j=='Matriculation':\n",
    "            test_X[:,2][i]=1\n",
    "        elif j=='High School Diploma':\n",
    "            test_X[:,2][i]=2\n",
    "        elif j=='Bachelors':\n",
    "            test_X[:,2][i]=3\n",
    "        elif j=='Masters':\n",
    "            test_X[:,2][i]=4    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31349, 10)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "engage=X[:,3]\n",
    "engage=engage.reshape(-1,1)\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)\n",
    "imputer = imputer.fit(engage)\n",
    "engage = imputer.transform(engage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "engage1=test_X[:,3]\n",
    "engage1=engage1.reshape(-1,1)\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)\n",
    "imputer = imputer.fit(engage1)\n",
    "engage1 = imputer.transform(engage1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X[:,3]=engage1.reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[:,3]=engage.reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.0, 26.0, 40.0, ..., nan, 27.0, 31.0], dtype=object)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "age=X[:,7]\n",
    "age=age.reshape(-1,1)\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)\n",
    "imputer = imputer.fit(age)\n",
    "age = imputer.transform(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "age1=test_X[:,7]\n",
    "age1=age1.reshape(-1,1)\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)\n",
    "imputer = imputer.fit(age1)\n",
    "age1 = imputer.transform(age1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[:,7]=age.reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 24.0, 6, 1],\n",
       "       [1, 0, 2, ..., 26.0, 1, 0],\n",
       "       [0, 0, 1, ..., 40.0, 7, 1],\n",
       "       ...,\n",
       "       [0, 0, 1, ..., 45.0, 6, 1],\n",
       "       [1, 1, 2, ..., 27.0, 3, 1],\n",
       "       [0, 0, 2, ..., 31.0, 6, 0]], dtype=object)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X[:,7]=age1.reshape(1,-1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "# labelencoder_X = LabelEncoder()\n",
    "# #Encodes the Categorical values into numerical values\n",
    "# X[:,0] = labelencoder_X.fit_transform(X[:,0])\n",
    "# X[:,2] = labelencoder_X.fit_transform(X[:,2])\n",
    "# X[:,1] = labelencoder_X.fit_transform(X[:,1])#selecting the column \n",
    "# # X[:,4] = labelencoder_X.fit_transform(X[:,4])#selecting the column \n",
    "# # X[:,5] = labelencoder_X.fit_transform(X[:,5])#selecting the column \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "# labelencoder_X = LabelEncoder()\n",
    "# #Encodes the Categorical values into numerical values\n",
    "# test_X[:,0] = labelencoder_X.fit_transform(test_X[:,0])\n",
    "# test_X[:,2] = labelencoder_X.fit_transform(test_X[:,2])\n",
    "# test_X[:,1] = labelencoder_X.fit_transform(test_X[:,1])#selecting the column \n",
    "# # test_X[:,4] = labelencoder_X.fit_transform(test_X[:,4])#selecting the column \n",
    "# # test_X[:,5] = labelencoder_X.fit_transform(test_X[:,5])#selecting the column \n",
    "# # test_X[:,9] = labelencoder_X.fit_transform(test_X[:,9])#selecting the column \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X[:,9] = labelencoder_X.fit_transform(X[:,9])#selecting the column \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 46.0, 1, 0],\n",
       "       [0, 0, 3, ..., 45.0, 6, 1],\n",
       "       [0, 0, 1, ..., 45.0, 6, 1],\n",
       "       ...,\n",
       "       [0, 0, 3, ..., 45.0, 6, 1],\n",
       "       [1, 0, 1, ..., 45.0, 7, 0],\n",
       "       [1, 2, 1, ..., 26.0, 5, 1]], dtype=object)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 24.0, 6, 1],\n",
       "       [1, 0, 2, ..., 26.0, 1, 0],\n",
       "       [0, 0, 1, ..., 40.0, 7, 1],\n",
       "       ...,\n",
       "       [0, 0, 1, ..., 45.0, 6, 1],\n",
       "       [1, 1, 2, ..., 27.0, 3, 1],\n",
       "       [0, 0, 2, ..., 31.0, 6, 0]], dtype=object)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# onehotencoder = OneHotEncoder(categorical_features= [0,1,2])#onehot encoder\n",
    "# X= onehotencoder.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# onehotencoder = OneHotEncoder(categorical_features= [0,1,2])#onehot encoder\n",
    "# test_X= onehotencoder.fit_transform(test_X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "# sc_X = StandardScaler() \n",
    "# X = sc_X.fit_transform(X)\n",
    "# test_X = sc_X.transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73147, 10)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31349, 10)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_tr,x_te,y_tr,y_te=train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from xgboost import XGBClassifier\n",
    "# clf2=XGBClassifier()\n",
    "clf=RandomForestClassifier(n_estimators=300,max_features='sqrt', min_samples_leaf=1)\n",
    "clf.fit(x_tr,y_tr)\n",
    "# clf2.fit(x_tr,y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pickle\n",
    "# filename = 'finalized_model.sav'\n",
    "# pickle.dump(clf, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loaded_model = pickle.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filename = 'finalized_model.sav'\n",
    "# pickle.dump(loaded_model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loade = pickle.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.716267942583732"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_te,y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>is_pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1626_45</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>11020_130</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12652_146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7038_72</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>888_71</td>\n",
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      "text/plain": [
       "          id  is_pass\n",
       "0    1626_45        0\n",
       "1  11020_130        0\n",
       "2  12652_146        0\n",
       "3    7038_72        0\n",
       "4     888_71        0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=clf.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample['is_pass']=ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample.to_csv('anally.csv', sep=',',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31349, 2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.shape"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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