├── README.md
├── Bengali Digit Recognizer
    └── .gitignore
├── .gitignore
├── Image Preprocessing
    └── .gitignore
├── LICENSE
└── Exploratory Data Analysis
    └── Part 2 - Exploring dataset E.ipynb


/README.md:
--------------------------------------------------------------------------------
1 | # Numta
2 | 
3 | This repository contains tutorials based on the [NumtaDB](https://bengali.ai/datasets/) database.
4 | The database is also hosted on [Kaggle](https://www.kaggle.com/BengaliAI/numta) where you will find many useful tutorials/analysis based on this dataset.
5 | 


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/Bengali Digit Recognizer/.gitignore:
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 1 | # Build and Release Folders
 2 | bin-debug/
 3 | bin-release/
 4 | [Oo]bj/
 5 | [Bb]in/
 6 | 
 7 | # Other files and folders
 8 | .settings/
 9 | 
10 | # Executables
11 | *.swf
12 | *.air
13 | *.ipa
14 | *.apk
15 | 
16 | # Project files, i.e. `.project`, `.actionScriptProperties` and `.flexProperties`
17 | # should NOT be excluded as they contain compiler settings and other important
18 | # information for Eclipse / Flash Builder.
19 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
 1 | # Build and Release Folders
 2 | bin/
 3 | bin-debug/
 4 | bin-release/
 5 | [Oo]bj/ # FlashDevelop obj
 6 | [Bb]in/ # FlashDevelop bin
 7 | 
 8 | # Other files and folders
 9 | .settings/
10 | 
11 | # Executables
12 | *.swf
13 | *.air
14 | *.ipa
15 | *.apk
16 | 
17 | # Project files, i.e. `.project`, `.actionScriptProperties` and `.flexProperties`
18 | # should NOT be excluded as they contain compiler settings and other important
19 | # information for Eclipse / Flash Builder.
20 | 


--------------------------------------------------------------------------------
/Image Preprocessing/.gitignore:
--------------------------------------------------------------------------------
 1 | # Build and Release Folders
 2 | bin/
 3 | bin-debug/
 4 | bin-release/
 5 | [Oo]bj/ # FlashDevelop obj
 6 | [Bb]in/ # FlashDevelop bin
 7 | 
 8 | # Other files and folders
 9 | .settings/
10 | 
11 | # Executables
12 | *.swf
13 | *.air
14 | *.ipa
15 | *.apk
16 | 
17 | # Project files, i.e. `.project`, `.actionScriptProperties` and `.flexProperties`
18 | # should NOT be excluded as they contain compiler settings and other important
19 | # information for Eclipse / Flash Builder.
20 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 BengaliAI
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/Exploratory Data Analysis/Part 2 - Exploring dataset E.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Exploring dataset E"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "As we have seen in the previuos notebook, there are some additional information in the label file of dataset **E**. We are going to explore that in this notebook.\n",
 15 |     "<br>\n",
 16 |     "<br>\n",
 17 |     "The digits from dataset **E** are extracted from the `BanglaLekha-Isolated` dataset which contains Bangla handwritten numerals, basic characters and compound characters. \n",
 18 |     "<br>\n",
 19 |     "The `BanglaLekha-Isolated` dataset was collected from Dhaka and Comilla. The age group of the subjects ranged from 6 years to 28 years with a high density between the ages of 16–20. Among the subjects, 59.4% were males while the remaining 40.6% were females.\n",
 20 |     "More details can be found in the dataset description [here.](https://www.sciencedirect.com/science/article/pii/S2352340917301117)"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 2,
 26 |    "metadata": {
 27 |     "collapsed": true
 28 |    },
 29 |    "outputs": [],
 30 |    "source": [
 31 |     "# Importing necessary libraries\n",
 32 |     "import numpy as np\n",
 33 |     "import os\n",
 34 |     "import glob\n",
 35 |     "import cv2\n",
 36 |     "import matplotlib.pyplot as plt\n",
 37 |     "import pandas as pd"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 3,
 43 |    "metadata": {
 44 |     "collapsed": true
 45 |    },
 46 |    "outputs": [],
 47 |    "source": [
 48 |     "# Declare fontsize variables which will be used while plotting the data\n",
 49 |     "FS_AXIS_LABEL=14\n",
 50 |     "FS_TITLE=17\n",
 51 |     "FS_TICKS=12\n",
 52 |     "FIG_WIDTH=16"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": 50,
 58 |    "metadata": {
 59 |     "collapsed": true
 60 |    },
 61 |    "outputs": [],
 62 |    "source": [
 63 |     "# Setup paths\n",
 64 |     "project_dir='..'\n",
 65 |     "path_label_train_e=os.path.join(project_dir,'Final_DB','training-e.csv')\n",
 66 |     "path_label_test_e=os.path.join(project_dir,'Final_DB','testing-e.csv')"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 57,
 72 |    "metadata": {
 73 |     "scrolled": true
 74 |    },
 75 |    "outputs": [
 76 |     {
 77 |      "data": {
 78 |       "text/html": [
 79 |        "<div>\n",
 80 |        "<style>\n",
 81 |        "    .dataframe thead tr:only-child th {\n",
 82 |        "        text-align: right;\n",
 83 |        "    }\n",
 84 |        "\n",
 85 |        "    .dataframe thead th {\n",
 86 |        "        text-align: left;\n",
 87 |        "    }\n",
 88 |        "\n",
 89 |        "    .dataframe tbody tr th {\n",
 90 |        "        vertical-align: top;\n",
 91 |        "    }\n",
 92 |        "</style>\n",
 93 |        "<table border=\"1\" class=\"dataframe\">\n",
 94 |        "  <thead>\n",
 95 |        "    <tr style=\"text-align: right;\">\n",
 96 |        "      <th></th>\n",
 97 |        "      <th>original filename</th>\n",
 98 |        "      <th>districtid</th>\n",
 99 |        "      <th>institutionid</th>\n",
100 |        "      <th>gender</th>\n",
101 |        "      <th>age</th>\n",
102 |        "      <th>datestamp</th>\n",
103 |        "      <th>scanid</th>\n",
104 |        "      <th>digit</th>\n",
105 |        "      <th>database name original</th>\n",
106 |        "      <th>database name</th>\n",
107 |        "      <th>filename</th>\n",
108 |        "    </tr>\n",
109 |        "  </thead>\n",
110 |        "  <tbody>\n",
111 |        "    <tr>\n",
112 |        "      <th>0</th>\n",
113 |        "      <td>01_0001_0_17_0916_0193_55.png</td>\n",
114 |        "      <td>1</td>\n",
115 |        "      <td>1</td>\n",
116 |        "      <td>0</td>\n",
117 |        "      <td>17</td>\n",
118 |        "      <td>916</td>\n",
119 |        "      <td>193</td>\n",
120 |        "      <td>4</td>\n",
121 |        "      <td>BanglaLekha-Isolated</td>\n",
122 |        "      <td>training-e</td>\n",
123 |        "      <td>e00000.png</td>\n",
124 |        "    </tr>\n",
125 |        "    <tr>\n",
126 |        "      <th>1</th>\n",
127 |        "      <td>02_0002_0_23_1016_0858_57.png</td>\n",
128 |        "      <td>2</td>\n",
129 |        "      <td>2</td>\n",
130 |        "      <td>0</td>\n",
131 |        "      <td>23</td>\n",
132 |        "      <td>1016</td>\n",
133 |        "      <td>858</td>\n",
134 |        "      <td>6</td>\n",
135 |        "      <td>BanglaLekha-Isolated</td>\n",
136 |        "      <td>training-e</td>\n",
137 |        "      <td>e00001.png</td>\n",
138 |        "    </tr>\n",
139 |        "    <tr>\n",
140 |        "      <th>2</th>\n",
141 |        "      <td>02_0002_0_14_1016_1673_51.png</td>\n",
142 |        "      <td>2</td>\n",
143 |        "      <td>2</td>\n",
144 |        "      <td>0</td>\n",
145 |        "      <td>14</td>\n",
146 |        "      <td>1016</td>\n",
147 |        "      <td>1673</td>\n",
148 |        "      <td>0</td>\n",
149 |        "      <td>BanglaLekha-Isolated</td>\n",
150 |        "      <td>training-e</td>\n",
151 |        "      <td>e00002.png</td>\n",
152 |        "    </tr>\n",
153 |        "    <tr>\n",
154 |        "      <th>3</th>\n",
155 |        "      <td>02_0002_0_26_1016_1828_56.png</td>\n",
156 |        "      <td>2</td>\n",
157 |        "      <td>2</td>\n",
158 |        "      <td>0</td>\n",
159 |        "      <td>26</td>\n",
160 |        "      <td>1016</td>\n",
161 |        "      <td>1828</td>\n",
162 |        "      <td>5</td>\n",
163 |        "      <td>BanglaLekha-Isolated</td>\n",
164 |        "      <td>training-e</td>\n",
165 |        "      <td>e00003.png</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>4</th>\n",
169 |        "      <td>02_0002_1_19_1016_1336_59.png</td>\n",
170 |        "      <td>2</td>\n",
171 |        "      <td>2</td>\n",
172 |        "      <td>1</td>\n",
173 |        "      <td>19</td>\n",
174 |        "      <td>1016</td>\n",
175 |        "      <td>1336</td>\n",
176 |        "      <td>8</td>\n",
177 |        "      <td>BanglaLekha-Isolated</td>\n",
178 |        "      <td>training-e</td>\n",
179 |        "      <td>e00004.png</td>\n",
180 |        "    </tr>\n",
181 |        "  </tbody>\n",
182 |        "</table>\n",
183 |        "</div>"
184 |       ],
185 |       "text/plain": [
186 |        "               original filename  districtid  institutionid  gender  age  \\\n",
187 |        "0  01_0001_0_17_0916_0193_55.png           1              1       0   17   \n",
188 |        "1  02_0002_0_23_1016_0858_57.png           2              2       0   23   \n",
189 |        "2  02_0002_0_14_1016_1673_51.png           2              2       0   14   \n",
190 |        "3  02_0002_0_26_1016_1828_56.png           2              2       0   26   \n",
191 |        "4  02_0002_1_19_1016_1336_59.png           2              2       1   19   \n",
192 |        "\n",
193 |        "   datestamp  scanid  digit database name original database name    filename  \n",
194 |        "0        916     193      4   BanglaLekha-Isolated    training-e  e00000.png  \n",
195 |        "1       1016     858      6   BanglaLekha-Isolated    training-e  e00001.png  \n",
196 |        "2       1016    1673      0   BanglaLekha-Isolated    training-e  e00002.png  \n",
197 |        "3       1016    1828      5   BanglaLekha-Isolated    training-e  e00003.png  \n",
198 |        "4       1016    1336      8   BanglaLekha-Isolated    training-e  e00004.png  "
199 |       ]
200 |      },
201 |      "execution_count": 57,
202 |      "metadata": {},
203 |      "output_type": "execute_result"
204 |     }
205 |    ],
206 |    "source": [
207 |     "# Let's get the label files in a dataframe \n",
208 |     "df_train_e=pd.read_csv(path_label_train_e)\n",
209 |     "df_test_e=pd.read_csv(path_label_test_e)\n",
210 |     "df_train_e.head()"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "markdown",
215 |    "metadata": {},
216 |    "source": [
217 |     "The information in the districtid and institutionid column is similar. Here, `1` stands for `Dhaka` and `2` stands for `Comilla`. For the `gender` column `0` denotes `Male` and `1` denotes `Female`."
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "markdown",
222 |    "metadata": {},
223 |    "source": [
224 |     "### Distribution of digits by age"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "markdown",
229 |    "metadata": {},
230 |    "source": [
231 |     "The `age` columns contains the age information of the participants. We are going to use the `.value_counts()` method to get the number of occurrence of each age. The `.sort_index()` method sorts the index (age) of the resulting Series so that we can plot the age values in ascending order. This will give us a better understanding of the distribution of the participant's ages."
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "code",
236 |    "execution_count": 64,
237 |    "metadata": {
238 |     "collapsed": true
239 |    },
240 |    "outputs": [],
241 |    "source": [
242 |     "train_age_vc=df_train_e['age'].value_counts().sort_index()"
243 |    ]
244 |   },
245 |   {
246 |    "cell_type": "code",
247 |    "execution_count": 173,
248 |    "metadata": {},
249 |    "outputs": [
250 |     {
251 |      "data": {
252 |       "image/png": 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JsPM0wpMkSZIkDamB3a5JkiRJkqTZwMRYkiRJkjTUTIwlSZIkSUPNxFiSJEmSNNRMjCVJ\nkiRJQ83EWJIkSZI01EyMJUmSJElDzcRYkiRJkjTUTIwlSZIkSUPNxFiSJEmSNNRMjCVJkiRJQ83E\nWJIkSZI01EyMJUmSJElDzcRYkiRJkjTUTIwlSZIkSUPNxFiSJEmSNNRMjCVJkiRJQ83EWJIkSZI0\n1EyMJUmSJElDzcRYkiRJkjTUTIwlSZIkSUPNxFiSJEmSNNTmDToASTNnwdHnTVi/5Pg9+hSJJEmS\nNHc4YixJkiRJGmomxpIkSZKkoWZiLEmSJEkaaibGkiRpWpI8KslFSX6W5Iokh7flxyW5LslP2uOF\ng45VkqSxuPmWJEmarnuBI6vqx0nWAi5JcmFb976qes8AY5MkaVImxpIkaVqq6nrg+vb7ZUl+Dmw8\n2KgkSeqeU6klSdKMSbIAeBLww7botUkuS3JqknUHFpgkSRMwMZYkSTMiyZrA2cDrqupW4GPA5sB2\nNCPKJ45z3SFJFiVZtHTp0r7FK0nSCBNjSZI0bUlWo0mKP1NV5wBU1e+r6r6quh/4d+CpY11bVSdX\n1Q5VtcP8+fP7F7QkSS0TY0mSNC1JApwC/Lyq3ttRvlHHaXsDP+13bJIkdcPNtyRJ0nQ9C9gfuDzJ\nT9qyfwX2S7IdUMAS4NDBhCdJ0sRMjCVJ0rRU1XeBjFF1fr9jkSRpKpxKLUmSJEkaaibGkiRJkqSh\nZmIsSZIkSRpqJsaSJEmSpKFmYixJkiRJGmomxpIkSZKkoWZiLEmSJEkaan1LjJOsnuSUJNckWZbk\nJ0le0FG/S5Irk9ye5KIkm3XUJckJSW5ujxOSpKN+QXvN7W0bu/brdUmSJEmS5rZ+jhjPA64FngOs\nA7wJ+I82qV0fOAc4BlgPWAR8vuPaQ4C9gG2BJwJ7Aod21J8JXAo8Angj8IUk81foq5EkSZIkrRT6\nlhhX1W1VdVxVLamq+6vqq8BvgO2BfYArquqsqroTOA7YNsmW7eUHACdW1W+r6jrgPcCBAEkeCzwZ\nOLaq7qiqs4HLgIX9em2SJEmSpLlrYGuMk2wIPBa4AtgaWDxSV1W3Ab9qyxld337fWXd1VS0bp16S\nJEmSpHENJDFOshrwGeD0qroSWBP406jTbgXWar8fXX8rsGa7zniya0c/9yFJFiVZtHTp0um9EEmS\nJEnSnNf3xDjJKsAZwN3AYW3xcmDtUaeuAywbp34dYHlVVRfXPkhVnVxVO1TVDvPnuwxZkiRJkoZd\nXxPjdoT3FGBDYGFV3dNWXUGzsdbIeQ8DtmjL/6K+/b6zbvMka41TL0mSJEnSuPo9YvwxYCtgz6q6\no6P8i8A2SRYmWQM4FljcTrMG+BRwRJKNk2wMHAmcBlBVVwE/AY5NskaSfYAnAGf35RVJkiRJkua0\nef16ova+xIcCdwE3dNyG+NCq+kyShcCHgU8DPwT27bj8JGBz4PL28SfashH70iTKtwD/A7ykqlxA\nLEmSJEmaVN8S46q6BsgE9V8HthynroCj2mOs+iXAztMOUpIkSZI0dAZ2uyZJkiRJkmYDE2NJkiRJ\n0lAzMZYkSZIkDTUTY0mSJEnSUOvb5luSNCgLjj5v0nOWHL9HHyKRJEnSbOSIsSRJkiRpqJkYS5Ik\nSZKGmomxJEmSJGmomRhLkiRJkoaaibEkSZIkaaiZGEuSJEmShpqJsSRJkiRpqJkYS5IkSZKGmomx\nJEmSJGmomRhLkiRJkoaaibEkSZIkaaiZGEuSJEmShpqJsSRJkiRpqJkYS5IkSZKGmomxJEmSJGmo\nzRt0AJI0ngVHnzfpOUuO36MPkTRmWzySJEmaGY4YS5IkSZKGmomxJEmSJGmomRhLkiRJwLXXXstz\nn/tcHv/4x7P11lvzgQ98AICzzjqLrbfemlVWWYVFixY9cP56661HkoEcwECed7311hvIz0Za0Vxj\nLEmSJAHz5s3jxBNP5MlPfjLLli1j++23Z7fddmObbbbhnHPO4dBDD33Q+bfccgtVNaBoB2MkKZdW\nNibGkiRJErDRRhux0UYbAbDWWmux1VZbcd1117HbbrsNODJJK5pTqSVJkqRRlixZwqWXXsrTnva0\nQYciqQ9MjCVJkqQOy5cvZ+HChbz//e9n7bXXHnQ4kvrAxFiSJElq3XPPPSxcuJCXv/zl7LPPPoMO\nR1KfmBhLkiRJQFVx8MEHs9VWW3HEEUcMOhxJfWRiLEmSpiXJo5JclORnSa5Icnhbvl6SC5P8sv26\n7qBjlSbyve99jzPOOINvfvObbLfddmy33Xacf/75fPGLX2STTTbh+9//PnvssQfPe97zBh2qpBnm\nrtSSJGm67gWOrKofJ1kLuCTJhcCBwDeq6vgkRwNHA28YYJzShHbcccdxb7+099579zkaSf3kiLEk\nSZqWqrq+qn7cfr8M+DmwMfBi4PT2tNOBvQYToSRJEzMxliRJMybJAuBJwA+BDavq+rbqBmDDAYUl\nSdKEekqMk6y3ogKRJElzW5I1gbOB11XVrZ111cxPHXOOapJDkixKsmjp0qV9iFSSpAfrdcT4d0k+\nl2S3FRKNJEmak5KsRpMUf6aqzmmLf59ko7Z+I+DGsa6tqpOraoeq2mH+/Pn9CViSpA69JsZ7t9d8\nJcmSJMcm2WwFxCVJkuaIJAFOAX5eVe/tqPoycED7/QHAuf2OTZKkbvSUGFfVf1bVS4G/Bt5Hs4nG\nr5NckORlSR6yIoKUJEmz2rOA/YG/TfKT9nghcDywW5JfAru2jyVJmnWmtPlWVf2hqj5QVU8CXgfs\nBJxJM9X67Un+aiaDlCRJs1dVfbeqUlVPrKrt2uP8qrq5qnapqsdU1a5V9YdBxypp+g466CA22GAD\nttlmmwfKFi9ezDOe8Qye8IQnsOeee3LrrbdO0II0+0wpMU6yQZJ/SnIF8C7gC8AuwOE0t2b40syF\nKEmSJGm2OPDAA/na1772oLJXv/rVHH/88Vx++eXsvffevPvd7x5QdNLU9Lor9YuSfAm4FnglcBKw\ncVW9oqouqqrP0Eyv3nnGI5UkSZI0cDvttBPrrffgm9VcddVV7LTTTgDstttunH322YMITZqyXkeM\nPwPcBOzUTpf6YFXdMuqc3wEnzEh0kiRJkma9rbfemnPPbfbXO+uss7j22msHHJHUm14T442q6tVV\n9cPxTqiqO6rqmGnGJUmSJGmOOPXUU/noRz/K9ttvz7Jly3jIQ9yTV3NLr4nx7kn+bnRhkj2T7D3Z\nxUkOS7IoyV1JTusoX5CkkizvOI7pqE+SE5Lc3B4ntLeG6Lz+oiS3J7kyya49vi5JkiRJU7Tlllty\nwQUXcMkll7DffvuxxRZbDDokqSe9JsZvBe4ao/zOtm4yvwPeDpw6Tv3Dq2rN9nhbR/khNGuXtwWe\nCOwJHNpRfyZwKfAI4I3AF5LM7yIeSZIkSdN04403AnD//ffz9re/nde85jUDjkjqzbwez98CuGqM\n8l+2dROqqnMAkuwAbNLD8x4AnFhVv22vfw9NsvzxJI8FngzsXlV3AGcnORxYCHy8h+eQJEmSulbH\nrg3HrTPoMPqqjl2b/fbbj4svvpibbrqJTTbZhLe85S0sX76cj3zkIwDss88+vOpVrxpwpFJvek2M\n/wg8GrhmVPljgGUzEM81SQq4EPjnqrqpLd8aWNxx3uK2bKTu6qpaNk79gyQ5hCapZtNNN52BkCVJ\nkjSM8pZbqapBh9FXSag6c8y6ww8/vM/RSDOn16nUXwbel+SB0eEkjwZObOum6ibgKcBmwPbAWjQ7\nYI9YE/hTx+NbgTXbdcaj60bq1xrriarq5Kraoap2mD/f2daSJEmSNOx6TYyPAm4HrkzymyS/AX4O\n3AH881SDqKrlVbWoqu6tqt8Dh9Fs9DWS3C4H1u64ZB1geTUf0Y2uG6mfiRFsSZIkSdJKrqep1FX1\npyTPAJ4PbNcWXwr8V83sPJKRtkYS9ytoNt76f+3jbduykbrNk6zVMZ16Wx484ixJkiRJ0ph6XWNM\nmwD/Z3v0JMm89jlXBVZNsgZwL8306T/SbOK1LvBB4OKqGpki/SngiCTnt4+PbM+hqq5K8hPg2CRv\nAl4IPAE4u9f4JEmSJEnDp+fEOMn2wC7ABoyail1VR0xy+ZuAYzsevwJ4C/AL4B1tm7fSbL61X8d5\nJwGbA5e3jz/Rlo3YFzgNuAX4H+AlVbW029ckSZIkSRpePSXGSV5Ps9HWEpp7EndOn550KnVVHQcc\nN0712Nvb8cAo9VHtMVb9EmDnyZ5fkiRJkqTReh0xfj1wRFW9f0UEI0mSJElSv/W6K/U6TO+2TJIk\nSZIkzSq9Jsb/Aey+IgKRJEmSJGkQep1K/WvgbUmeTrMR1j2dlVX1wZkKTJIkSZKkfug1Mf5H4E6a\nXal3GVVXtLdQkiRJc0uSHYAtgK9W1W1JHgbcVVX3Djg0SZJWuJ4S46p61IoKRJIk9V+SDYFzgafS\nfMj9GOBq4L00H4YfPrjoJEnqj17XGD8gySOSZCaDkSRJffc+4PfAI4DbO8rPwn1FJElDoqfEOMlq\nSd6R5I80v0T/pi1/Z5LXrIgAJUnSCrUL8MaqumVU+a+BTQcQjyRJfdfriPExwELgYOCujvJLgFfN\nVFCSJKlvHgrcPUb5fJqp1JIkrfR6TYxfDhxaVWcD93eUXw48bsaikiRJ/fJt4MCOx5VkVeANwDcG\nEpEkSX3W667Ufw0sGaN81Sm0JUmSBu8o4FtJngKsDpwIbA2sAzxrkIFJktQvvY4Y/wx49hjlfw9c\nOv1wJElSP1XVz4AnAP8NXACsQbPx1pOq6teDjE2SpH7pdZT3rcBpSf6aJqneJ8njgFcCe850cJIk\nacWrqhuAYwcdhyRJg9LrfYzPTfJy4I0006f/jWakeK+qumAFxCdJK5UFR5836TlLjt+jD5FIjSQ7\njVNVNJtv/bqq/tDHkCRJ6rue1wVX1fnA+SsgFkmS1H8X0yTBAGm/dj6+P8mXgf2r6rY+xyZJUl/0\nusZYkiStXF5As4fIK4BHt8crgCtobtG4ENgOOH5QAUqStKL1NGKc5Bb+/CnyX6iq9aYdkSRJ6qe3\nA6+rqs5bM12dZClwQlVtn+Q+4EPAawcSoSRJK1ivU6n/adTj1YAnAXsB75yRiCRJUj9tA1w3Rvl1\nwOPb7y8HHtm3iCRJ6rNeN986ZazyJIuA58xIRJIkqZ9+Brwxyaur6i6AJKsD/9rWATwKuGFA8UmS\ntML1vPnWOL4BvHeG2pIkSf3zf4CvANcl+Wlbtg1wP/B37ePNgY8OIDZJkvpiphLjvwdunqG2JElS\nn1TVD5P8Dc2GW49riz/bHk9vz/nUgMKTJKkvet1861IevPlWaNYczQcOm8G4JM1x3q9Xmjva2zCd\nBJBkY+BVwGJgAbDq4CKTJKk/eh0x/uqox/cDS4GLquqKmQlJkiT1U5JVgRcDBwO7A5fRJMpnDTIu\nSZL6pdfNt45ZUYFIkqT+SvI44NXAK4HbaKZPPw/Yv6p+NtG1kiStTFYZdACSJKn/knwH+AGwLvDS\nqtq8qt7Eg5dMSZI0FHpdY3wPXf7CrKqHTCkiSZLUD88APgKc7HIoSdKw63XE+EjgVuAzNJttHdZ+\nfytwBLB/xyFJkmavp9B8QP7dJJcmeX2SRw46KGnQDjroIDbYYAO22WabB5V/6EMfYsstt2Trrbfm\nqKOOeqA8yVAd6667br9/JFJf9Lr51t8Cb6yqkzrKTk7yGuCFVfWimQtNkiStKFV1KfCPSY6kue3i\nQcC7aD403yPJ9VV1yyBjlAbhwAMP5LDDDuOVr3zlA2UXXXQR5557LosXL2b11VfnxhtvBKBqcCsP\nkgz0+aWVTa8jxrsC3xij/OvALtMPR5Ik9VNV3VlVZ1TVc4GtgHcDrwduSPKfg41O6r+ddtqJ9dZb\n70FlH/vYxzj66KNZffXVAdhggw0GEZqkFajXxPhmYJ8xyvcGbpp+OJIkaVCq6ldVdTTwKOClwN0D\nDkmaFa666iq+853v8LSnPY3nPOc5/OhHPxp0SJJmWK9TqY8DPpHkOcD327KnA88HDpnBuCRJ0oBU\n1X3Aue0hDb17772XP/zhD/zgBz/gRz/6ES996Uu5+uqrSTLo0CTNkJ5GjKvqk8CzgeU0nyS/lOa+\nh8+pqlNyClWWAAAYqElEQVRnPjxJkiRpsDbZZBP22WcfkvDUpz6VVVZZhZtucrKktDLpdcSYqvpv\n4L9XQCySJEnSrLPXXntx0UUX8dznPperrrqKu+++m/XXX3/QYUmaQb2uMSbJ/CSvS/LBJI9oy56e\nZLOZD0+SJEnqn/32249nPOMZ/OIXv2CTTTbhlFNO4aCDDuLqq69mm222Yd999+X00093GrW0kulp\nxDjJk2h2pb4OeBzwfpoNuV4APBp4+UwHKEmSZrckpwJ/B9xYVdu0ZccB/wtY2p72r1V1/mAilLp3\n5plnjln+6U9/us+RSOqnXkeMTwQ+WlVPAO7qKP8asOOMRSVJkuaS02g24hztfVW1XXuYFEuSZq1e\nE+PtgU+OUf47YMPphyNJkuaaqvo28IdBxyFJ0lT1mhjfCaw9Rvnj+PNUKUmSJIDXJrksyalJ1h3v\npCSHJFmUZNHSpf45IUnqv14T468Ab06yWvu4kmwKHA+cM6ORSZKkuexjwObAdsD1NMuxxlRVJ1fV\nDlW1w/z58/sVnyRJD+g1MT4SeCRwI/BQ4FvAr4DbgTfObGiSJGmuqqrfV9V9VXU/8O/AUwcdkyRJ\n4+lpV+qq+lOSZwK7AU+mSax/DPxXVdUKiE+SJM1BSTaqquvbh3sDPx1kPJIkTaTrxLidPn0xcFBV\nXQBcsKKCkiRJc0eSM4GdgfWT/BY4Ftg5yXZAAUuAQwcWoCRJk+h6KnVV3QM8Brh/qk+W5LB2c427\nkpw2qm6XJFcmuT3JRUk266hLkhOS3NweJ6TjrupJFrTX3N62setUY5QkSb2pqv2qaqOqWq2qNqmq\nU6pq/6p6QlU9sape1DF6LEnSrNPrGuMzgIOn8Xy/A94OnNpZmGR9ms27jgHWAxYBn+845RBgL2Bb\n4InAnjz4k+czgUuBR9Csdf5CEnfvkCRJkiRNqqc1xsBDgFe3I7KXALd1VlbVERNdXFXnACTZAdik\no2of4IqqOqutPw64KcmWVXUlcABwYlX9tq1/D02y/PEkj6VZ77x7Vd0BnJ3kcGAh8PEeX58kSZIk\nacj0mhhvB1zWfv/4UXXT2Xxra2DxAw1V3ZbkV235laPr2++37rj26qpaNk69JEmSJEnj6ioxTvJE\n4KdV9ewVFMeawNJRZbcCa3XU/2lU3ZrtOuPRdSP1G4/1REkOoRltZtNNN51e1JIkSZKkOa/bNcaX\nAuuPPEhyXpKNZjCO5cDao8rWAZaNU78OsLy9RdRk1z5IVZ1cVTtU1Q7z57sMWZIkSZKGXbeJcUY9\n3gl46AzGcQXNxlrNkyUPA7Zoy/+ivv2+s27zJGuNUy9JkiRJ0rh63ZV6WpLMS7IGsCqwapI1kswD\nvghsk2RhW38ssLjdeAvgU8ARSTZOsjFwJHAaQFVdBfwEOLZtbx/gCcDZ/XxtkiRJkqS5qdvEuPjL\nzbWmstnWm4A7gKOBV7Tfv6mqltLsIv1vwC3AU4F9O647CfgKcHl7fLUtG7EvsEN77TuBl7RtSpIk\nSZI0oW53pQ7w6SR3tY/XAP49ye2dJ1XViyZqpKqOA44bp+7rwJbj1BVwVHuMVb8E2Hmi55YkSZIk\naSzdJsanj3r86ZkORJIkSZKkQegqMa6qV63oQCRJkiRJGoS+br4lSZIkSdJsY2IsSZIkSRpqJsaS\nJEmSpKFmYixJkiRJGmomxpIkSZKkoWZiLEmSJEkaaibGkiRJkqSh1tV9jCVJs8uCo8+b9Jwlx+/R\nh0gkSZLmPkeMJUmSJElDzcRYkiRJkjTUTIwlSZIkSUPNxFiSJEmSNNRMjCVJkiRJQ83EWJIkSZI0\n1EyMJUmSJElDzcRYkiRJkjTUTIwlSZIkSUPNxFiSJEmSNNRMjCVJkiRJQ83EWJIkSZI01EyMJUmS\nJElDzcRYkiRJkjTUTIwlSZIkSUPNxFiSJEmSNNTmDToASdLgLDj6vEnPWXL8Hn2IRJIkaXAcMZYk\nSZIkDTUTY0mSJEnSUDMxliRJkiQNNdcYS7OA6zwlSZKkwXHEWJIkSZI01EyMJUmSJElDzcRYkiRJ\nkjTUXGMsTYNrgyVJkqS5zxFjSZIkSdJQMzGWJEmSJA01E2NJkiRJ0lAzMZYkSdOS5NQkNyb5aUfZ\nekkuTPLL9uu6g4xRkqSJmBhLkqTpOg14/qiyo4FvVNVjgG+0jyVJmpVMjCVJ0rRU1beBP4wqfjFw\nevv96cBefQ1KkqQemBhLkqQVYcOqur79/gZgw0EGI0nSREyMJUnSClVVBdR49UkOSbIoyaKlS5f2\nMTJJkhqzKjFOcnGSO5Msb49fdNTtkuTKJLcnuSjJZh11SXJCkpvb44QkGcyrkCRJwO+TbATQfr1x\nvBOr6uSq2qGqdpg/f37fApQkacSsSoxbh1XVmu3xOIAk6wPnAMcA6wGLgM93XHMIzdqlbYEnAnsC\nh/Y1akmS1OnLwAHt9wcA5w4wFkmSJjQbE+Ox7ANcUVVnVdWdwHHAtkm2bOsPAE6sqt9W1XXAe4AD\nBxKpJElDJsmZwPeBxyX5bZKDgeOB3ZL8Eti1fSxJ0qw0b9ABjOGdSY4HfgG8saouBrYGFo+cUFW3\nJflVW37l6Pr2+63HajzJITQjzGy66aYrIn5JkoZKVe03TtUufQ1EkqQpmm0jxm8ANgc2Bk4GvpJk\nC2BN4E+jzr0VWKv9fnT9rcCaY60zdh2TJEmSJKnTrEqMq+qHVbWsqu6qqtOB7wEvBJYDa486fR1g\nWfv96Pp1gOXtLpiSJEmSJI1rViXGYyggwBU0G2sBkORhwBZtOaPr2++vQJIkSZKkScyaxDjJw5M8\nL8kaSeYleTmwE/A14IvANkkWJlkDOBZYXFVXtpd/CjgiycZJNgaOBE4bwMuQJEmSJM0xs2nzrdWA\ntwNbAvfRbKq1V1VdBZBkIfBh4NPAD4F9O649iWZt8uXt40+0ZZIkSZIkTWjWJMZVtRR4ygT1X6dJ\nmseqK+Co9pAkSZIkqWuzJjGWJEmShsUYN0/paxvuUSs9mImxJEmS1GcmptLsMms235IkSZIkaRBM\njCVJkiRJQ83EWJIkSZI01EyMJUmSJElDzcRYkiRJkjTUTIwlSZIkSUPN2zVJkiRJc8RY9y721k/S\n9DliLEmSJM0BnUnx5z73uTHLJU2NibEkSZI0h1QVL3vZyxwplmaQibEkSZI0R3SOFI/1WNLUmBhL\nkiRJc8S+++474WNJU2NiLEmSJM0hSfj85z/v2mJpBpkYS5IkSXNA55rizpFi1xpL0+ftmiRJkqQ5\nwiRYWjEcMZYkSZIkDTUTY0mSJEnSUDMxliRJkiQNNdcYS5IkSXPEWDtRu+5Ymj5HjCVJkqQ5oDMp\nPuaYY8YslzQ1jhhraC04+rwJ65ccv0efIpEkSereyAjxW9/6VpNiaYY4YixJkiTNEZ0jxWM9ljQ1\nJsaSJEnSHPG2t71twseSpsbEWJIkSZpDkvDmN7/ZadTSDHKNsfpmsjW94LpeaS7y37Yk9UdVPZAM\nd44Uuyu1NH0mxpIkSdIcYRIsrRhOpZYkSZIkDTVHjDXnOG1TkiRJ0kxyxFiSJEmSNNQcMVZXHKWV\nJEkavLF2onbdsTR9jhhLkiRJc0BnUnzCCSeMWS5pakyMJUmSpDmkqjjqqKMcKZZmkFOpJUmzgks2\nJGlynSPFI4/f8IY3DCgaaeXhiLEkSZI0R4xOgk2KpZlhYixJkiTNIUl417ve5dpiaQaZGEuSJElz\nQOea4s6RYtcaS9PnGmNJkiRpjjAJllYME2NJ0kplsk283MBLkiSN5lRqSZIkSdJQMzGWJEmSJA01\np1Kv5LwvqCRpkJIsAZYB9wH3VtUOg41IkqS/tNIkxknWA04BdgduAv6lqj47iFhMRiVJepDnVtVN\ngw5CkqTxrDSJMfAR4G5gQ2A74Lwki6vqim4bMKGVJEmSpOGzUiTGSR4GLAS2qarlwHeTnAvsDxw9\n0OCmyCRdkgbH/4NnVAFfT3IfcFJVnTzogCRJGm2lSIyBx9KsW7qqo2wxsPNgwpEkSa0dq+q6JBsA\nFya5sqq+3XlCkkOAQwA23XTTQcQoSRpyWRluEp7k2cBZVfXIjrL/Bby8qnYede4Dv3yBxwG/mKT5\n9WnWLE/HTLSxMsYyU+3Mplhmqp3ZFMtMtTObYpmpdoxlxbYzm2Lptp3Nqmr+DDzXSinJccDyqnrP\nBOcsBa7pW1CSpJVdV7+bV5YR4+XA2qPK1qHZBfNB2ilcXU/jSrJoujtozkQbK2MsM9XObIplptqZ\nTbHMVDuzKZaZasdYVmw7symWmWxnmLRLnVapqmXt97sDb53oGj9YkCQNwsqSGF8FzEvymKr6ZVu2\nLdD1xluSJGnGbQh8MQk0f3N8tqq+NtiQJEn6SytFYlxVtyU5B3hrklcDTwJeBDxzsJFJkjS8qupq\nmg+qJUma1VYZdAAz6P8ADwVuBD4L/O9ebtU0gZnYPXOmduBc2WKZqXZmUywz1c5simWm2plNscxU\nO8ayYtuZTbHMZDuSJGmWWSk235IkSZIkaapWphFjSZIkSZJ6ZmIsSZIkSRpqJsYTSLJvkp8nuS3J\nr9v7JXd77fJRx31JPjTFOBYkOT/JLUluSPLhJD1tnJZkqyTfTPKnJL9KsneX1x2WZFGSu5KcNqpu\nlyRXJrk9yUVJNuuljSQPSfKFJEuSVJKdpxJLkqcnuTDJH5IsTXJWko2m0M7j2/Jb2uPrSR7f6/vS\ncc6b29e16xRiWdBe29mHjum1nbbur5J8NMlN7c//2z3G8vJRcdzexrb9FGJ5aftvalmSnyXZa4qv\n6dVtP16e5GtJ/nqcNlZPckqSa9rn/EmSF3TUT9qHJ2qjlz48STtd9+FJ2umqD0/2vnScN2EfniSW\nrvtwFz+nSfvwJLF03Ye7iKXrPixJkuYOE+NxJNkNOAF4FbAWsBNwdbfXV9WaIwfwSOAO4KwphvNR\nYCmwEbAd8Byazca6kiaJPhf4KrAecAjw6SSP7eLy3wFvB04d1eb6wDnAMW2bi4DP99JG67vAK4Ab\nphoLsC7NpjgLgM1o7l/9ySm08zvgZcD67fFl4HM9tgFAki2AvweunyCOSdsBHt7Rl942xXZOpvkZ\nbdV+fX0vbVTVZ0b15/9D82/hx720k2Rj4NPAETT3Hf9n4LNJNuixnZ2BdwAvbl/Pb4Azx2ljHnAt\nzb+ZdYA3Af/RJm3d9uFx22jru+3DE7XTSx+eqJ1u+/Bkr6nbPjxpO3TXhydrp5s+PG4bPfbhifpM\nr31YkiTNFVXlMcYB/Ddw8Ay1dQDNH2GZ4vU/B17Y8fjdwEk9XL8NsLzz+YELgLf10MbbgdM6Hh8C\n/HfH44fRJP9bdtvGqLrfAjtPJZYx6p8MLJtOOzR/HP8jcPtU2gC+BrwQWALsOoX3dwFQwLwe+8ro\ndrYEbgXWnmobY9RfBBw7hVieBtw46pylwDN6bOc9wEc7Hv91+15t0eXruwxYOJU+PLqNqfbhidrp\npQ9PEk9XfXi8Nnrtw2O8v1Pqw2O003Mf7uL97aoPjxHLlPqwh4eHh4eHx+w/HDEeQ5JVgR2A+Wmm\na/42zfTlh06xyQOAT1XVVLcAfz/wsnY64cbAC2j+aJ2O0CTMU7U1sHjkQVXdBvyqLR+0nYAp36or\nyR+BO4EP0YxM9nr93wN3VdX5U42hwzVt//tkO8LZq6cC1wBvaaehXp5k4VSDSTPVeCfgU1O4fBHw\n8yR7Jlm1nYJ6F03SMR1pv07an5NsCDyWpn9MqQ+PamPKJmmn6z48Vju99uHRbUy1D4/zmnruw6Pa\nmVIfHu/97bUPj2pnRfVhSZI0YCbGY9sQWA14CfBsmunLT6KZUteT9o+w5wCnTyOeb9P80X8rzajU\nIuBLPVz/C5r7O/9zktWS7N7G9FfTiGlN4E+jym6lmXY+MEmeCLyZZorjlFTVw2mmUB4GXNrj869F\nk4gcPtXnb90EPIVmWu32NO/rZ6bQziY0fedPNCOrhwGnJ9lqinG9EvhOVf2m1wur6j6aZORMmmTi\ns8ChbULai68Bf5/kie2HVW+mGZmcsD8nWY3mPTy9qq5kCn14jDamZKJ2eunD47XTSx8e3cZU+/AY\nsUypD4/RTs99eJKfU9d9eHQ7M9iHJUnSLGNiPLY72q8fqqrrq+om4L000wp7tT/w3akkEgBJVqFJ\nBM6hmeq5Ps16xBO6baOq7gH2AvagWQd5JPAfNEn2VC2nWWPXaR2atZEDkeTRwH8Ch1fVd6bTVvuH\n7seBT/W4fvA44IyqWjLN519eVYuq6t6q+j1NMrB7m7T04g7gHuDtVXV3VX2LZhrp7lMM7ZVM8UOe\nNBs4vQvYGXgIzYczn0iyXS/tVNXXad7ns2mm+S6h6Xfj9uf239EZwN007yX02IfHaaNnE7XTSx+e\nLJ5u+vA4bRxHj314rHam0ofHiaenPtzFz6mrPjxWOzPVhyVJ0uxjYjyGqrqF5o/szqnPU50GPeVE\norUesCnw4aq6q6puptmUp6ckvaouq6rnVNUjqup5wObA/5tGXFcA2448SPIwYAumOb10qtqR+a/T\nrJs+Y4aaXYVmFHLjHq7ZBfi/aXYPvwF4FM3GPW+YZiwj/a/Xf7NjTfGcUl9O8iyaEbsvTOV6mpkX\n326Tpfur6kfAD4Fxd+0eT1V9pKoeU1Ub0iTI84CfjhN3gFNoZoIsbD8ogh768ARt9GSidnrpwz3E\nM24fnqCNnvpwD7FM2IcnaKfrPjxZLN324QnambE+LEmSZhcT4/F9Enhtkg2SrEuzC+pXe2kgyTNp\n/iCd6m7UtKPVvwFek2RekofTrFnuaU1bO+10jXad8j/R7HB9WhfXzUuyBrAqsGrbxjzgi8A2SRa2\n9ccCi8eaXjpBGyO3RlmjPfUhbV1GtzFRO2nWXX+T5sODj0/1NSXZLcmT2rWDa9PMEriFZvOzbl/T\nLjTTPrdrj98BhwIf6TGWpyV5XJJVkjwC+CBwcVWNnvo7WTzfBv4H+Jf2nGcBzwX+q4c2RhwAnF1V\nE84KmKCdHwE7joyuJXkSzVKFMfvyBO/NGkm2SWNTmh2LP9B+oDWWj9HsZrxnVd3RUd51H56gjZ76\n8Hjt9NqHJ2in6z48wWvqqQ9PEEtPfXiCeLruwxO0MaKrPjxBOz31YUmSNIcMevev2XrQrDH+KPBH\nmunHHwTW6LGNk2imJE43lu2Ai2n+wL2JZhr0hj228e72+uU0UzUf3eV1x9GMznQex7V1uwJX0kx1\nvBhYMIU2loxR11M7NAlNta/tgaPX10Rza5or2+uXAucBT+z1NY06bwkT7Og7QSz70XwgchvN7XI+\nBTxyij+nrYHvt239DNh7Cm2sQfNvYZdp9pnDaDa4WkazU/uRU3hvHk6TiNxG82/zncCq47SxWXvd\nnaP6x8u77cNdtLFkjDh7aoce+vAk7XTVhyd7Td324Uli6boPd/EeT9qHu2ijqz7cRTtd92EPDw8P\nDw+PuXOkaqozhCVJkiRJmvucSi1JkiRJGmomxpIkSZKkoWZiLEmSJEkaaibGkiRJkqShZmIsSZIk\nSRpqJsaSJEmSpKFmYixJkiRJGmomxtKQSvLkJPcl+d6gY5EkSZIGycRYGl6vBj4KbJNkq0EHI0mS\nJA2KibE0hJI8FPgH4GTgC8DBo+qfluTHSe5MckmS5yepJDt3nPP4JOclWZbkxiRnJnlkX1+IJEmS\nNANMjKXh9BLgmqq6HDgDeGWS1QCSrAl8FbgS2B44GnhP58VJNgK+DfwUeCqwK7AmcG4S/1+RJEnS\nnOIfsNJwOpgmIQb4FnA78OL28cuBVYGDq+qKqroQeMeo6/83sLiq3lBVP6+qy4BX0iTJO6zw6CVJ\nkqQZZGIsDZkkjwZ2BD4LUFUFfIY/T6fekv/fzh27aHHEcRz+TLBJEdKIkkIIWsZOEERIZSdil/YQ\n/RMEiZXWadLYeKUQDClSBMHO2kabs7VQkAS0E6JI3BT7Hhx6QuDy3pHs88CwsLM7/Lb8Mju/2pqm\n6c8drz38YJlT1bdjjNfbo3q+mjuxtuIBAGANDh10AcC+u9K8I/xsjLF9b1SNMY79wzU+q+5VV3eZ\n+2OvBQIAwH4SjGFBxhiHqo3q++ZzxDvdqS41ny3eGGN8vmPX+PQHzz6qvms+p/xujSUDAMDa+ZUa\nluV8dbjanKZpa+eo7jYH45+qv6rNVefpc9X11fvT6nqr+rL6edXB+vgY49wY4/YY44v9/SQAANgb\nwRiW5XL1YJqmV7vM/VJ9XZ2pLlTfVI+rH6obq2feVE3T9KI6W72v7ldPmsPy29UAAID/jDH33QH4\ntDHGxerX6sg0TS8Puh4AAPg3OWMMfGSMsVE9be40fbL6sfpNKAYA4P9IMAZ2c7S6WX1V/d7cgfra\ngVYEAABr4ldqAAAAFk3zLQAAABZNMAYAAGDRBGMAAAAWTTAGAABg0QRjAAAAFk0wBgAAYNH+BtSt\nBopG0xC5AAAAAElFTkSuQmCC\n",
253 |       "text/plain": [
254 |        "<matplotlib.figure.Figure at 0x1489e73ac50>"
255 |       ]
256 |      },
257 |      "metadata": {},
258 |      "output_type": "display_data"
259 |     }
260 |    ],
261 |    "source": [
262 |     "#barplot\n",
263 |     "plt.figure(figsize=(FIG_WIDTH,5))\n",
264 |     "plt.suptitle('Age Distribution (Train)',fontsize=FS_TITLE)\n",
265 |     "plt.subplot(1,2,1)\n",
266 |     "train_age_vc.plot(kind='bar')\n",
267 |     "plt.xticks(rotation='horizontal',fontsize=FS_TICKS)\n",
268 |     "plt.yticks(fontsize=FS_TICKS)\n",
269 |     "plt.xlabel('Age', fontsize=FS_AXIS_LABEL)\n",
270 |     "plt.ylabel('Frequency', fontsize=FS_AXIS_LABEL)\n",
271 |     "#boxplot\n",
272 |     "plt.subplot(1,2,2)\n",
273 |     "bp_dict=plt.boxplot(df_train_e['age'])\n",
274 |     "plt.xticks([0],[''],fontsize=FS_TICKS)\n",
275 |     "plt.ylabel('Age', fontsize=FS_AXIS_LABEL)\n",
276 |     "for line in bp_dict['medians']:\n",
277 |     "    # get position data for median line (2nd quartile line)\n",
278 |     "    x, y = line.get_xydata()[1] # terminal point of median line\n",
279 |     "    plt.text(x, y, '{:.0f}'.format(y), horizontalalignment='left') \n",
280 |     "\n",
281 |     "for line in bp_dict['boxes']:\n",
282 |     "    # get position data for 1st quartile line\n",
283 |     "    x, y = line.get_xydata()[0] \n",
284 |     "    plt.text(x,y, '{:.0f}'.format(y), horizontalalignment='right', verticalalignment='top')     \n",
285 |     "    # get position data for 3rd quartile line\n",
286 |     "    x, y = line.get_xydata()[3] \n",
287 |     "    plt.text(x,y, '{:.0f}'.format(y), horizontalalignment='right', verticalalignment='top')\n",
288 |     "plt.show()"
289 |    ]
290 |   },
291 |   {
292 |    "cell_type": "code",
293 |    "execution_count": 66,
294 |    "metadata": {
295 |     "collapsed": true
296 |    },
297 |    "outputs": [],
298 |    "source": [
299 |     "test_age_vc=df_test_e['age'].value_counts().sort_index()"
300 |    ]
301 |   },
302 |   {
303 |    "cell_type": "code",
304 |    "execution_count": 174,
305 |    "metadata": {},
306 |    "outputs": [
307 |     {
308 |      "data": {
309 |       "image/png": 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lvOtd7+Loo4+epOik3vWa+L4ceG1VfRl4qKP8cmCbkXRQVf8LWAd4Fs305vuB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\n",
310 |       "text/plain": [
311 |        "<matplotlib.figure.Figure at 0x1489e70aa90>"
312 |       ]
313 |      },
314 |      "metadata": {},
315 |      "output_type": "display_data"
316 |     }
317 |    ],
318 |    "source": [
319 |     "#barplot\n",
320 |     "plt.figure(figsize=(FIG_WIDTH,5))\n",
321 |     "plt.suptitle('Age Distribution (Test)',fontsize=FS_TITLE)\n",
322 |     "plt.subplot(1,2,1)\n",
323 |     "test_age_vc.plot(kind='bar')\n",
324 |     "plt.xticks(rotation='horizontal',fontsize=FS_TICKS)\n",
325 |     "plt.yticks(fontsize=FS_TICKS)\n",
326 |     "plt.xlabel('Age', fontsize=FS_AXIS_LABEL)\n",
327 |     "plt.ylabel('Frequency', fontsize=FS_AXIS_LABEL)\n",
328 |     "#boxplot\n",
329 |     "plt.subplot(1,2,2)\n",
330 |     "bp_dict=plt.boxplot(df_test_e['age'])\n",
331 |     "plt.xticks([0],[''],fontsize=FS_TICKS)\n",
332 |     "plt.ylabel('Age', fontsize=FS_AXIS_LABEL)\n",
333 |     "for line in bp_dict['medians']:\n",
334 |     "    # get position data for median line (2nd quartile line)\n",
335 |     "    x, y = line.get_xydata()[1] # terminal point of median line\n",
336 |     "    plt.text(x, y, '{:.0f}'.format(y), horizontalalignment='left') \n",
337 |     "\n",
338 |     "for line in bp_dict['boxes']:\n",
339 |     "    # get position data for 1st quartile line\n",
340 |     "    x, y = line.get_xydata()[0] \n",
341 |     "    plt.text(x,y, '{:.0f}'.format(y), horizontalalignment='right', verticalalignment='top')     \n",
342 |     "    # get position data for 3rd quartile line\n",
343 |     "    x, y = line.get_xydata()[3] \n",
344 |     "    plt.text(x,y, '{:.0f}'.format(y), horizontalalignment='right', verticalalignment='top')\n",
345 |     "plt.show()"
346 |    ]
347 |   },
348 |   {
349 |    "cell_type": "markdown",
350 |    "metadata": {},
351 |    "source": [
352 |     "There are some differences between the distribution in the test and train set (e.g., 17, 19, 22 year old participants). The interquartile range (the range within which the innermost 50% of the data resides) for both train and test set is between 16 to 21 years. "
353 |    ]
354 |   },
355 |   {
356 |    "cell_type": "markdown",
357 |    "metadata": {},
358 |    "source": [
359 |     "### Distribution of digits by district"
360 |    ]
361 |   },
362 |   {
363 |    "cell_type": "code",
364 |    "execution_count": 111,
365 |    "metadata": {},
366 |    "outputs": [],
367 |    "source": [
368 |     "train_district_vc=df_train_e['districtid'].value_counts()"
369 |    ]
370 |   },
371 |   {
372 |    "cell_type": "code",
373 |    "execution_count": 112,
374 |    "metadata": {
375 |     "scrolled": true
376 |    },
377 |    "outputs": [
378 |     {
379 |      "data": {
380 |       "image/png": 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lYwrNVfWaiWxEkh2A3YEdRyleBKzft20DYGFH+QbAoqqqJMurex9VdTxwPMDs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\nRK5CYII=\n",
381 |       "text/plain": [
382 |        "<matplotlib.figure.Figure at 0x1489e7b3da0>"
383 |       ]
384 |      },
385 |      "metadata": {},
386 |      "output_type": "display_data"
387 |     }
388 |    ],
389 |    "source": [
390 |     "plt.figure(figsize=(FIG_WIDTH,5))\n",
391 |     "train_district_vc.plot(kind='bar')\n",
392 |     "plt.yticks(fontsize=FS_TICKS)\n",
393 |     "xlabels=['Dhaka ({:.2f}%)'.format(train_district_vc.loc[1]/train_district_vc.sum()*100),\n",
394 |     "        'Comilla ({:.2f})%'.format(train_district_vc.loc[2]/train_district_vc.sum()*100)]\n",
395 |     "plt.xticks([0,1],xlabels,rotation='horizontal',fontsize=FS_TICKS)\n",
396 |     "plt.xlabel('District', fontsize=FS_AXIS_LABEL)\n",
397 |     "plt.ylabel('Frequency', fontsize=FS_AXIS_LABEL)\n",
398 |     "plt.title('District Distribution (Train)',fontsize=FS_TITLE)\n",
399 |     "plt.show()"
400 |    ]
401 |   },
402 |   {
403 |    "cell_type": "code",
404 |    "execution_count": 113,
405 |    "metadata": {
406 |     "collapsed": true
407 |    },
408 |    "outputs": [],
409 |    "source": [
410 |     "test_district_vc=df_test_e['districtid'].value_counts()"
411 |    ]
412 |   },
413 |   {
414 |    "cell_type": "code",
415 |    "execution_count": 114,
416 |    "metadata": {
417 |     "scrolled": true
418 |    },
419 |    "outputs": [
420 |     {
421 |      "data": {
422 |       "image/png": 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7fsd2mSRJkiRJqzTurtRVdTPwYOC2ySqmvRu9BQOzUSf56yTbJblbknsDHwLO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\nGYwlSZIkSZ32/wHqvrmdmkqIhwAAAABJRU5ErkJggg==\n",
423 |       "text/plain": [
424 |        "<matplotlib.figure.Figure at 0x1489c517f28>"
425 |       ]
426 |      },
427 |      "metadata": {},
428 |      "output_type": "display_data"
429 |     }
430 |    ],
431 |    "source": [
432 |     "plt.figure(figsize=(FIG_WIDTH,5))\n",
433 |     "test_district_vc.plot(kind='bar')\n",
434 |     "plt.yticks(fontsize=FS_TICKS)\n",
435 |     "xlabels=['Dhaka ({:.2f}%)'.format(test_district_vc.loc[1]/test_district_vc.sum()*100),\n",
436 |     "        'Comilla ({:.2f})%'.format(test_district_vc.loc[2]/test_district_vc.sum()*100)]\n",
437 |     "plt.xticks([0,1],xlabels,rotation='horizontal',fontsize=FS_TICKS)\n",
438 |     "plt.xlabel('District', fontsize=FS_AXIS_LABEL)\n",
439 |     "plt.ylabel('Frequency', fontsize=FS_AXIS_LABEL)\n",
440 |     "plt.title('District Distribution (Test)',fontsize=FS_TITLE)\n",
441 |     "plt.show()"
442 |    ]
443 |   },
444 |   {
445 |    "cell_type": "markdown",
446 |    "metadata": {},
447 |    "source": [
448 |     "There are less digits from Dhaka than Comilla. The ratio is slightly higher in the test dataset.  "
449 |    ]
450 |   },
451 |   {
452 |    "cell_type": "markdown",
453 |    "metadata": {},
454 |    "source": [
455 |     "### Distribution of images by gender"
456 |    ]
457 |   },
458 |   {
459 |    "cell_type": "code",
460 |    "execution_count": 97,
461 |    "metadata": {},
462 |    "outputs": [],
463 |    "source": [
464 |     "train_gender_vc=df_train_e['gender'].value_counts()"
465 |    ]
466 |   },
467 |   {
468 |    "cell_type": "code",
469 |    "execution_count": 39,
470 |    "metadata": {},
471 |    "outputs": [
472 |     {
473 |      "data": {
474 |       "image/png": 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3jtG+JEmSJEkaCoMuBHYF910ILMAjgGnAq8ewLkmSJEmSxt2gC4F9vu/zPcBi4NKqWjg2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\nkiRJUgdDsyRJkiRJHf4/UAOK0AB8m4QAAAAASUVORK5CYII=\n",
475 |       "text/plain": [
476 |        "<matplotlib.figure.Figure at 0x1489c4fd908>"
477 |       ]
478 |      },
479 |      "metadata": {},
480 |      "output_type": "display_data"
481 |     }
482 |    ],
483 |    "source": [
484 |     "plt.figure(figsize=(FIG_WIDTH,5))\n",
485 |     "train_gender_vc.plot(kind='bar')\n",
486 |     "plt.yticks(fontsize=FS_TICKS)\n",
487 |     "xlabels=['Male ({:.2f}%)'.format(train_gender_vc.loc[0]/train_gender_vc.sum()*100),\n",
488 |     "        'Female ({:.2f})%'.format(train_gender_vc.loc[1]/train_gender_vc.sum()*100)]\n",
489 |     "plt.xticks([0,1],xlabels,rotation='horizontal',fontsize=FS_TICKS)\n",
490 |     "plt.xlabel('Gender', fontsize=FS_AXIS_LABEL)\n",
491 |     "plt.ylabel('Frequency', fontsize=FS_AXIS_LABEL)\n",
492 |     "plt.title('Gender Distribution (Train)',fontsize=FS_TITLE)\n",
493 |     "plt.show()"
494 |    ]
495 |   },
496 |   {
497 |    "cell_type": "code",
498 |    "execution_count": 106,
499 |    "metadata": {
500 |     "collapsed": true
501 |    },
502 |    "outputs": [],
503 |    "source": [
504 |     "test_gender_vc=df_test_e['gender'].value_counts()"
505 |    ]
506 |   },
507 |   {
508 |    "cell_type": "code",
509 |    "execution_count": 107,
510 |    "metadata": {},
511 |    "outputs": [
512 |     {
513 |      "data": {
514 |       "image/png": 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JkkY06BxjksxO8rokH0qyRVu2S/fjk8aiqq6jWUn6VUlmJNmMJhn+BfAF4LFJ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\nAElFTkSuQmCC\n",
515 |       "text/plain": [
516 |        "<matplotlib.figure.Figure at 0x1489e2717b8>"
517 |       ]
518 |      },
519 |      "metadata": {},
520 |      "output_type": "display_data"
521 |     }
522 |    ],
523 |    "source": [
524 |     "plt.figure(figsize=(FIG_WIDTH,5))\n",
525 |     "test_gender_vc.plot(kind='bar')\n",
526 |     "plt.yticks(fontsize=FS_TICKS)\n",
527 |     "xlabels=['Male ({:.2f}%)'.format(test_gender_vc.loc[0]/test_gender_vc.sum()*100),\n",
528 |     "        'Female ({:.2f})%'.format(test_gender_vc.loc[1]/test_gender_vc.sum()*100)]\n",
529 |     "plt.xticks([0,1],xlabels,rotation='horizontal',fontsize=FS_TICKS)\n",
530 |     "plt.xlabel('Gender', fontsize=FS_AXIS_LABEL)\n",
531 |     "plt.ylabel('Frequency', fontsize=FS_AXIS_LABEL)\n",
532 |     "plt.title('Gender Distribution (Test)',fontsize=FS_TITLE)\n",
533 |     "plt.show()"
534 |    ]
535 |   },
536 |   {
537 |    "cell_type": "markdown",
538 |    "metadata": {},
539 |    "source": [
540 |     "There are more digits from male participants. The male to female ratio is similar in both test and train set."
541 |    ]
542 |   },
543 |   {
544 |    "cell_type": "markdown",
545 |    "metadata": {},
546 |    "source": [
547 |     "Since we have age, district and gender information of the participants, the digits could be differenciated based on them. We have to bear in mind that the dataset is imbalanced (with respect to age/distric/gender) which would have significant effect on the classifier model."
548 |    ]
549 |   }
550 |  ],
551 |  "metadata": {
552 |   "kernelspec": {
553 |    "display_name": "Python 3",
554 |    "language": "python",
555 |    "name": "python3"
556 |   },
557 |   "language_info": {
558 |    "codemirror_mode": {
559 |     "name": "ipython",
560 |     "version": 3
561 |    },
562 |    "file_extension": ".py",
563 |    "mimetype": "text/x-python",
564 |    "name": "python",
565 |    "nbconvert_exporter": "python",
566 |    "pygments_lexer": "ipython3",
567 |    "version": "3.5.3"
568 |   }
569 |  },
570 |  "nbformat": 4,
571 |  "nbformat_minor": 2
572 | }
573 | 


--------------------------------------------------------------------------------