├── .gitignore
├── Linear_Regression.ipynb
├── decisiontree_kararagaci.py
├── evrişimli_sinir_agları_metin_sınıflandırma.ipynb
├── gdp_per_capita_2017.xlsx
├── iris.pdf
├── kmeans.ipynb
├── logistic_regression.ipynb
├── ml_notebook.ipynb
├── oecd_bli_2017.csv
└── titanic.xls


/.gitignore:
--------------------------------------------------------------------------------
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  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | env/
 12 | build/
 13 | develop-eggs/
 14 | dist/
 15 | downloads/
 16 | eggs/
 17 | .eggs/
 18 | lib/
 19 | lib64/
 20 | parts/
 21 | sdist/
 22 | var/
 23 | wheels/
 24 | *.egg-info/
 25 | .installed.cfg
 26 | *.egg
 27 | 
 28 | # PyInstaller
 29 | #  Usually these files are written by a python script from a template
 30 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 31 | *.manifest
 32 | *.spec
 33 | 
 34 | # Installer logs
 35 | pip-log.txt
 36 | pip-delete-this-directory.txt
 37 | 
 38 | # Unit test / coverage reports
 39 | htmlcov/
 40 | .tox/
 41 | .coverage
 42 | .coverage.*
 43 | .cache
 44 | nosetests.xml
 45 | coverage.xml
 46 | *.cover
 47 | .hypothesis/
 48 | 
 49 | # Translations
 50 | *.mo
 51 | *.pot
 52 | 
 53 | # Django stuff:
 54 | *.log
 55 | local_settings.py
 56 | 
 57 | # Flask stuff:
 58 | instance/
 59 | .webassets-cache
 60 | 
 61 | # Scrapy stuff:
 62 | .scrapy
 63 | 
 64 | # Sphinx documentation
 65 | docs/_build/
 66 | 
 67 | # PyBuilder
 68 | target/
 69 | 
 70 | # Jupyter Notebook
 71 | .ipynb_checkpoints
 72 | 
 73 | # pyenv
 74 | .python-version
 75 | 
 76 | # celery beat schedule file
 77 | celerybeat-schedule
 78 | 
 79 | # SageMath parsed files
 80 | *.sage.py
 81 | 
 82 | # dotenv
 83 | .env
 84 | 
 85 | # virtualenv
 86 | .venv
 87 | venv/
 88 | ENV/
 89 | 
 90 | # Spyder project settings
 91 | .spyderproject
 92 | .spyproject
 93 | 
 94 | # Rope project settings
 95 | .ropeproject
 96 | 
 97 | # mkdocs documentation
 98 | /site
 99 | 
100 | # mypy
101 | .mypy_cache/
102 | 


--------------------------------------------------------------------------------
/Linear_Regression.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {
   7 |     "collapsed": true
   8 |    },
   9 |    "outputs": [],
  10 |    "source": [
  11 |     "# Kullanılacak Modüller\n",
  12 |     "import matplotlib\n",
  13 |     "import matplotlib.pyplot as plt\n",
  14 |     "import numpy as np\n",
  15 |     "import pandas as pd\n",
  16 |     "import sklearn.linear_model"
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "code",
  21 |    "execution_count": 2,
  22 |    "metadata": {},
  23 |    "outputs": [
  24 |     {
  25 |      "data": {
  26 |       "text/html": [
  27 |        "<div>\n",
  28 |        "<table border=\"1\" class=\"dataframe\">\n",
  29 |        "  <thead>\n",
  30 |        "    <tr style=\"text-align: right;\">\n",
  31 |        "      <th>Indicator</th>\n",
  32 |        "      <th>Air pollution</th>\n",
  33 |        "      <th>Dwellings without basic facilities</th>\n",
  34 |        "      <th>Educational attainment</th>\n",
  35 |        "      <th>Employees working very long hours</th>\n",
  36 |        "      <th>Employment rate</th>\n",
  37 |        "      <th>Feeling safe walking alone at night</th>\n",
  38 |        "      <th>Homicide rate</th>\n",
  39 |        "      <th>Household net adjusted disposable income</th>\n",
  40 |        "      <th>Household net financial wealth</th>\n",
  41 |        "      <th>Housing expenditure</th>\n",
  42 |        "      <th>...</th>\n",
  43 |        "      <th>Time devoted to leisure and personal care</th>\n",
  44 |        "      <th>Voter turnout</th>\n",
  45 |        "      <th>Water quality</th>\n",
  46 |        "      <th>Years in education</th>\n",
  47 |        "      <th>Subject Descriptor</th>\n",
  48 |        "      <th>Units</th>\n",
  49 |        "      <th>Scale</th>\n",
  50 |        "      <th>Country/Series-specific Notes</th>\n",
  51 |        "      <th>GDP per capita</th>\n",
  52 |        "      <th>Estimates Start After</th>\n",
  53 |        "    </tr>\n",
  54 |        "    <tr>\n",
  55 |        "      <th>Country</th>\n",
  56 |        "      <th></th>\n",
  57 |        "      <th></th>\n",
  58 |        "      <th></th>\n",
  59 |        "      <th></th>\n",
  60 |        "      <th></th>\n",
  61 |        "      <th></th>\n",
  62 |        "      <th></th>\n",
  63 |        "      <th></th>\n",
  64 |        "      <th></th>\n",
  65 |        "      <th></th>\n",
  66 |        "      <th></th>\n",
  67 |        "      <th></th>\n",
  68 |        "      <th></th>\n",
  69 |        "      <th></th>\n",
  70 |        "      <th></th>\n",
  71 |        "      <th></th>\n",
  72 |        "      <th></th>\n",
  73 |        "      <th></th>\n",
  74 |        "      <th></th>\n",
  75 |        "      <th></th>\n",
  76 |        "      <th></th>\n",
  77 |        "    </tr>\n",
  78 |        "  </thead>\n",
  79 |        "  <tbody>\n",
  80 |        "    <tr>\n",
  81 |        "      <th>South Africa</th>\n",
  82 |        "      <td>22.0</td>\n",
  83 |        "      <td>37.0</td>\n",
  84 |        "      <td>43.0</td>\n",
  85 |        "      <td>18.68</td>\n",
  86 |        "      <td>43.0</td>\n",
  87 |        "      <td>36.1</td>\n",
  88 |        "      <td>10.0</td>\n",
  89 |        "      <td>10872.0</td>\n",
  90 |        "      <td>17042.0</td>\n",
  91 |        "      <td>18.0</td>\n",
  92 |        "      <td>...</td>\n",
  93 |        "      <td>14.73</td>\n",
  94 |        "      <td>73.0</td>\n",
  95 |        "      <td>69.0</td>\n",
  96 |        "      <td>15.3</td>\n",
  97 |        "      <td>Gross domestic product per capita, current prices</td>\n",
  98 |        "      <td>U.S. dollars</td>\n",
  99 |        "      <td>Units</td>\n",
 100 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 101 |        "      <td>5588.959</td>\n",
 102 |        "      <td>2016.0</td>\n",
 103 |        "    </tr>\n",
 104 |        "    <tr>\n",
 105 |        "      <th>Mexico</th>\n",
 106 |        "      <td>16.0</td>\n",
 107 |        "      <td>4.2</td>\n",
 108 |        "      <td>37.0</td>\n",
 109 |        "      <td>29.48</td>\n",
 110 |        "      <td>61.0</td>\n",
 111 |        "      <td>45.9</td>\n",
 112 |        "      <td>17.9</td>\n",
 113 |        "      <td>13891.0</td>\n",
 114 |        "      <td>4750.0</td>\n",
 115 |        "      <td>21.0</td>\n",
 116 |        "      <td>...</td>\n",
 117 |        "      <td>12.74</td>\n",
 118 |        "      <td>63.0</td>\n",
 119 |        "      <td>67.0</td>\n",
 120 |        "      <td>14.8</td>\n",
 121 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 122 |        "      <td>U.S. dollars</td>\n",
 123 |        "      <td>Units</td>\n",
 124 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 125 |        "      <td>7993.174</td>\n",
 126 |        "      <td>2015.0</td>\n",
 127 |        "    </tr>\n",
 128 |        "    <tr>\n",
 129 |        "      <th>Turkey</th>\n",
 130 |        "      <td>20.0</td>\n",
 131 |        "      <td>6.5</td>\n",
 132 |        "      <td>39.0</td>\n",
 133 |        "      <td>33.77</td>\n",
 134 |        "      <td>51.0</td>\n",
 135 |        "      <td>60.6</td>\n",
 136 |        "      <td>1.7</td>\n",
 137 |        "      <td>17067.0</td>\n",
 138 |        "      <td>4429.0</td>\n",
 139 |        "      <td>20.0</td>\n",
 140 |        "      <td>...</td>\n",
 141 |        "      <td>12.59</td>\n",
 142 |        "      <td>85.0</td>\n",
 143 |        "      <td>63.0</td>\n",
 144 |        "      <td>17.9</td>\n",
 145 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 146 |        "      <td>U.S. dollars</td>\n",
 147 |        "      <td>Units</td>\n",
 148 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 149 |        "      <td>9826.248</td>\n",
 150 |        "      <td>2016.0</td>\n",
 151 |        "    </tr>\n",
 152 |        "    <tr>\n",
 153 |        "      <th>Brazil</th>\n",
 154 |        "      <td>10.0</td>\n",
 155 |        "      <td>6.7</td>\n",
 156 |        "      <td>49.0</td>\n",
 157 |        "      <td>7.15</td>\n",
 158 |        "      <td>64.0</td>\n",
 159 |        "      <td>37.3</td>\n",
 160 |        "      <td>27.6</td>\n",
 161 |        "      <td>12227.0</td>\n",
 162 |        "      <td>7102.0</td>\n",
 163 |        "      <td>20.0</td>\n",
 164 |        "      <td>...</td>\n",
 165 |        "      <td>14.45</td>\n",
 166 |        "      <td>79.0</td>\n",
 167 |        "      <td>72.0</td>\n",
 168 |        "      <td>15.9</td>\n",
 169 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 170 |        "      <td>U.S. dollars</td>\n",
 171 |        "      <td>Units</td>\n",
 172 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 173 |        "      <td>10308.811</td>\n",
 174 |        "      <td>2016.0</td>\n",
 175 |        "    </tr>\n",
 176 |        "    <tr>\n",
 177 |        "      <th>Russia</th>\n",
 178 |        "      <td>15.0</td>\n",
 179 |        "      <td>13.8</td>\n",
 180 |        "      <td>95.0</td>\n",
 181 |        "      <td>0.16</td>\n",
 182 |        "      <td>70.0</td>\n",
 183 |        "      <td>52.2</td>\n",
 184 |        "      <td>11.3</td>\n",
 185 |        "      <td>16657.0</td>\n",
 186 |        "      <td>2260.0</td>\n",
 187 |        "      <td>19.0</td>\n",
 188 |        "      <td>...</td>\n",
 189 |        "      <td>14.90</td>\n",
 190 |        "      <td>65.0</td>\n",
 191 |        "      <td>54.0</td>\n",
 192 |        "      <td>16.1</td>\n",
 193 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 194 |        "      <td>U.S. dollars</td>\n",
 195 |        "      <td>Units</td>\n",
 196 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 197 |        "      <td>10885.484</td>\n",
 198 |        "      <td>2016.0</td>\n",
 199 |        "    </tr>\n",
 200 |        "    <tr>\n",
 201 |        "      <th>Poland</th>\n",
 202 |        "      <td>22.0</td>\n",
 203 |        "      <td>2.7</td>\n",
 204 |        "      <td>91.0</td>\n",
 205 |        "      <td>6.68</td>\n",
 206 |        "      <td>65.0</td>\n",
 207 |        "      <td>66.3</td>\n",
 208 |        "      <td>0.8</td>\n",
 209 |        "      <td>18906.0</td>\n",
 210 |        "      <td>14997.0</td>\n",
 211 |        "      <td>23.0</td>\n",
 212 |        "      <td>...</td>\n",
 213 |        "      <td>14.42</td>\n",
 214 |        "      <td>55.0</td>\n",
 215 |        "      <td>80.0</td>\n",
 216 |        "      <td>17.7</td>\n",
 217 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 218 |        "      <td>U.S. dollars</td>\n",
 219 |        "      <td>Units</td>\n",
 220 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 221 |        "      <td>12721.758</td>\n",
 222 |        "      <td>2015.0</td>\n",
 223 |        "    </tr>\n",
 224 |        "    <tr>\n",
 225 |        "      <th>Hungary</th>\n",
 226 |        "      <td>19.0</td>\n",
 227 |        "      <td>4.3</td>\n",
 228 |        "      <td>83.0</td>\n",
 229 |        "      <td>3.05</td>\n",
 230 |        "      <td>67.0</td>\n",
 231 |        "      <td>50.7</td>\n",
 232 |        "      <td>1.2</td>\n",
 233 |        "      <td>16821.0</td>\n",
 234 |        "      <td>23289.0</td>\n",
 235 |        "      <td>18.0</td>\n",
 236 |        "      <td>...</td>\n",
 237 |        "      <td>15.06</td>\n",
 238 |        "      <td>62.0</td>\n",
 239 |        "      <td>76.0</td>\n",
 240 |        "      <td>16.6</td>\n",
 241 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 242 |        "      <td>U.S. dollars</td>\n",
 243 |        "      <td>Units</td>\n",
 244 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 245 |        "      <td>12767.023</td>\n",
 246 |        "      <td>2015.0</td>\n",
 247 |        "    </tr>\n",
 248 |        "    <tr>\n",
 249 |        "      <th>Chile</th>\n",
 250 |        "      <td>16.0</td>\n",
 251 |        "      <td>9.4</td>\n",
 252 |        "      <td>65.0</td>\n",
 253 |        "      <td>10.06</td>\n",
 254 |        "      <td>62.0</td>\n",
 255 |        "      <td>51.1</td>\n",
 256 |        "      <td>4.5</td>\n",
 257 |        "      <td>16588.0</td>\n",
 258 |        "      <td>21409.0</td>\n",
 259 |        "      <td>18.0</td>\n",
 260 |        "      <td>...</td>\n",
 261 |        "      <td>14.90</td>\n",
 262 |        "      <td>49.0</td>\n",
 263 |        "      <td>69.0</td>\n",
 264 |        "      <td>17.3</td>\n",
 265 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 266 |        "      <td>U.S. dollars</td>\n",
 267 |        "      <td>Units</td>\n",
 268 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 269 |        "      <td>13662.906</td>\n",
 270 |        "      <td>2015.0</td>\n",
 271 |        "    </tr>\n",
 272 |        "    <tr>\n",
 273 |        "      <th>Latvia</th>\n",
 274 |        "      <td>11.0</td>\n",
 275 |        "      <td>12.9</td>\n",
 276 |        "      <td>89.0</td>\n",
 277 |        "      <td>2.09</td>\n",
 278 |        "      <td>69.0</td>\n",
 279 |        "      <td>60.7</td>\n",
 280 |        "      <td>6.6</td>\n",
 281 |        "      <td>15269.0</td>\n",
 282 |        "      <td>17105.0</td>\n",
 283 |        "      <td>23.0</td>\n",
 284 |        "      <td>...</td>\n",
 285 |        "      <td>13.83</td>\n",
 286 |        "      <td>59.0</td>\n",
 287 |        "      <td>77.0</td>\n",
 288 |        "      <td>17.9</td>\n",
 289 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 290 |        "      <td>U.S. dollars</td>\n",
 291 |        "      <td>Units</td>\n",
 292 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 293 |        "      <td>14187.602</td>\n",
 294 |        "      <td>2016.0</td>\n",
 295 |        "    </tr>\n",
 296 |        "    <tr>\n",
 297 |        "      <th>Slovak Republic</th>\n",
 298 |        "      <td>21.0</td>\n",
 299 |        "      <td>1.4</td>\n",
 300 |        "      <td>92.0</td>\n",
 301 |        "      <td>5.04</td>\n",
 302 |        "      <td>65.0</td>\n",
 303 |        "      <td>60.1</td>\n",
 304 |        "      <td>0.8</td>\n",
 305 |        "      <td>20265.0</td>\n",
 306 |        "      <td>10846.0</td>\n",
 307 |        "      <td>24.0</td>\n",
 308 |        "      <td>...</td>\n",
 309 |        "      <td>15.01</td>\n",
 310 |        "      <td>60.0</td>\n",
 311 |        "      <td>82.0</td>\n",
 312 |        "      <td>15.9</td>\n",
 313 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 314 |        "      <td>U.S. dollars</td>\n",
 315 |        "      <td>Units</td>\n",
 316 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 317 |        "      <td>16411.686</td>\n",
 318 |        "      <td>2015.0</td>\n",
 319 |        "    </tr>\n",
 320 |        "    <tr>\n",
 321 |        "      <th>Greece</th>\n",
 322 |        "      <td>18.0</td>\n",
 323 |        "      <td>0.5</td>\n",
 324 |        "      <td>72.0</td>\n",
 325 |        "      <td>7.30</td>\n",
 326 |        "      <td>52.0</td>\n",
 327 |        "      <td>61.8</td>\n",
 328 |        "      <td>1.0</td>\n",
 329 |        "      <td>17002.0</td>\n",
 330 |        "      <td>18117.0</td>\n",
 331 |        "      <td>24.0</td>\n",
 332 |        "      <td>...</td>\n",
 333 |        "      <td>14.67</td>\n",
 334 |        "      <td>64.0</td>\n",
 335 |        "      <td>69.0</td>\n",
 336 |        "      <td>16.9</td>\n",
 337 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 338 |        "      <td>U.S. dollars</td>\n",
 339 |        "      <td>Units</td>\n",
 340 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 341 |        "      <td>17805.686</td>\n",
 342 |        "      <td>2016.0</td>\n",
 343 |        "    </tr>\n",
 344 |        "    <tr>\n",
 345 |        "      <th>Estonia</th>\n",
 346 |        "      <td>8.0</td>\n",
 347 |        "      <td>6.9</td>\n",
 348 |        "      <td>89.0</td>\n",
 349 |        "      <td>2.69</td>\n",
 350 |        "      <td>72.0</td>\n",
 351 |        "      <td>67.2</td>\n",
 352 |        "      <td>3.1</td>\n",
 353 |        "      <td>18665.0</td>\n",
 354 |        "      <td>16967.0</td>\n",
 355 |        "      <td>18.0</td>\n",
 356 |        "      <td>...</td>\n",
 357 |        "      <td>14.90</td>\n",
 358 |        "      <td>64.0</td>\n",
 359 |        "      <td>82.0</td>\n",
 360 |        "      <td>15.8</td>\n",
 361 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 362 |        "      <td>U.S. dollars</td>\n",
 363 |        "      <td>Units</td>\n",
 364 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 365 |        "      <td>17891.084</td>\n",
 366 |        "      <td>2016.0</td>\n",
 367 |        "    </tr>\n",
 368 |        "    <tr>\n",
 369 |        "      <th>Czech Republic</th>\n",
 370 |        "      <td>20.0</td>\n",
 371 |        "      <td>0.6</td>\n",
 372 |        "      <td>93.0</td>\n",
 373 |        "      <td>5.77</td>\n",
 374 |        "      <td>72.0</td>\n",
 375 |        "      <td>68.3</td>\n",
 376 |        "      <td>0.8</td>\n",
 377 |        "      <td>21103.0</td>\n",
 378 |        "      <td>24258.0</td>\n",
 379 |        "      <td>24.0</td>\n",
 380 |        "      <td>...</td>\n",
 381 |        "      <td>15.06</td>\n",
 382 |        "      <td>59.0</td>\n",
 383 |        "      <td>87.0</td>\n",
 384 |        "      <td>17.3</td>\n",
 385 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 386 |        "      <td>U.S. dollars</td>\n",
 387 |        "      <td>Units</td>\n",
 388 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 389 |        "      <td>18534.333</td>\n",
 390 |        "      <td>2015.0</td>\n",
 391 |        "    </tr>\n",
 392 |        "    <tr>\n",
 393 |        "      <th>Portugal</th>\n",
 394 |        "      <td>10.0</td>\n",
 395 |        "      <td>1.0</td>\n",
 396 |        "      <td>47.0</td>\n",
 397 |        "      <td>8.20</td>\n",
 398 |        "      <td>65.0</td>\n",
 399 |        "      <td>72.1</td>\n",
 400 |        "      <td>1.0</td>\n",
 401 |        "      <td>20519.0</td>\n",
 402 |        "      <td>31877.0</td>\n",
 403 |        "      <td>21.0</td>\n",
 404 |        "      <td>...</td>\n",
 405 |        "      <td>14.89</td>\n",
 406 |        "      <td>56.0</td>\n",
 407 |        "      <td>87.0</td>\n",
 408 |        "      <td>17.1</td>\n",
 409 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 410 |        "      <td>U.S. dollars</td>\n",
 411 |        "      <td>Units</td>\n",
 412 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 413 |        "      <td>19707.489</td>\n",
 414 |        "      <td>2015.0</td>\n",
 415 |        "    </tr>\n",
 416 |        "    <tr>\n",
 417 |        "      <th>Slovenia</th>\n",
 418 |        "      <td>16.0</td>\n",
 419 |        "      <td>0.3</td>\n",
 420 |        "      <td>87.0</td>\n",
 421 |        "      <td>4.46</td>\n",
 422 |        "      <td>66.0</td>\n",
 423 |        "      <td>84.7</td>\n",
 424 |        "      <td>0.6</td>\n",
 425 |        "      <td>20505.0</td>\n",
 426 |        "      <td>20048.0</td>\n",
 427 |        "      <td>18.0</td>\n",
 428 |        "      <td>...</td>\n",
 429 |        "      <td>14.75</td>\n",
 430 |        "      <td>52.0</td>\n",
 431 |        "      <td>89.0</td>\n",
 432 |        "      <td>18.1</td>\n",
 433 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 434 |        "      <td>U.S. dollars</td>\n",
 435 |        "      <td>Units</td>\n",
 436 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 437 |        "      <td>21061.552</td>\n",
 438 |        "      <td>2015.0</td>\n",
 439 |        "    </tr>\n",
 440 |        "    <tr>\n",
 441 |        "      <th>Spain</th>\n",
 442 |        "      <td>11.0</td>\n",
 443 |        "      <td>0.1</td>\n",
 444 |        "      <td>58.0</td>\n",
 445 |        "      <td>4.55</td>\n",
 446 |        "      <td>60.0</td>\n",
 447 |        "      <td>83.1</td>\n",
 448 |        "      <td>0.6</td>\n",
 449 |        "      <td>23129.0</td>\n",
 450 |        "      <td>35443.0</td>\n",
 451 |        "      <td>22.0</td>\n",
 452 |        "      <td>...</td>\n",
 453 |        "      <td>15.93</td>\n",
 454 |        "      <td>70.0</td>\n",
 455 |        "      <td>73.0</td>\n",
 456 |        "      <td>17.9</td>\n",
 457 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 458 |        "      <td>U.S. dollars</td>\n",
 459 |        "      <td>Units</td>\n",
 460 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 461 |        "      <td>26643.351</td>\n",
 462 |        "      <td>2016.0</td>\n",
 463 |        "    </tr>\n",
 464 |        "    <tr>\n",
 465 |        "      <th>Korea</th>\n",
 466 |        "      <td>28.0</td>\n",
 467 |        "      <td>4.2</td>\n",
 468 |        "      <td>87.0</td>\n",
 469 |        "      <td>20.84</td>\n",
 470 |        "      <td>66.0</td>\n",
 471 |        "      <td>63.9</td>\n",
 472 |        "      <td>1.1</td>\n",
 473 |        "      <td>21723.0</td>\n",
 474 |        "      <td>33495.0</td>\n",
 475 |        "      <td>15.0</td>\n",
 476 |        "      <td>...</td>\n",
 477 |        "      <td>14.70</td>\n",
 478 |        "      <td>77.0</td>\n",
 479 |        "      <td>78.0</td>\n",
 480 |        "      <td>17.4</td>\n",
 481 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 482 |        "      <td>U.S. dollars</td>\n",
 483 |        "      <td>Units</td>\n",
 484 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 485 |        "      <td>29114.719</td>\n",
 486 |        "      <td>2015.0</td>\n",
 487 |        "    </tr>\n",
 488 |        "    <tr>\n",
 489 |        "      <th>Italy</th>\n",
 490 |        "      <td>18.0</td>\n",
 491 |        "      <td>0.6</td>\n",
 492 |        "      <td>60.0</td>\n",
 493 |        "      <td>3.90</td>\n",
 494 |        "      <td>57.0</td>\n",
 495 |        "      <td>58.3</td>\n",
 496 |        "      <td>0.8</td>\n",
 497 |        "      <td>26063.0</td>\n",
 498 |        "      <td>64019.0</td>\n",
 499 |        "      <td>23.0</td>\n",
 500 |        "      <td>...</td>\n",
 501 |        "      <td>14.89</td>\n",
 502 |        "      <td>75.0</td>\n",
 503 |        "      <td>71.0</td>\n",
 504 |        "      <td>16.4</td>\n",
 505 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 506 |        "      <td>U.S. dollars</td>\n",
 507 |        "      <td>Units</td>\n",
 508 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 509 |        "      <td>29747.133</td>\n",
 510 |        "      <td>2016.0</td>\n",
 511 |        "    </tr>\n",
 512 |        "    <tr>\n",
 513 |        "      <th>France</th>\n",
 514 |        "      <td>13.0</td>\n",
 515 |        "      <td>0.5</td>\n",
 516 |        "      <td>78.0</td>\n",
 517 |        "      <td>7.76</td>\n",
 518 |        "      <td>65.0</td>\n",
 519 |        "      <td>69.6</td>\n",
 520 |        "      <td>0.6</td>\n",
 521 |        "      <td>31137.0</td>\n",
 522 |        "      <td>59479.0</td>\n",
 523 |        "      <td>21.0</td>\n",
 524 |        "      <td>...</td>\n",
 525 |        "      <td>16.36</td>\n",
 526 |        "      <td>75.0</td>\n",
 527 |        "      <td>82.0</td>\n",
 528 |        "      <td>16.5</td>\n",
 529 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 530 |        "      <td>U.S. dollars</td>\n",
 531 |        "      <td>Units</td>\n",
 532 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 533 |        "      <td>37294.720</td>\n",
 534 |        "      <td>2016.0</td>\n",
 535 |        "    </tr>\n",
 536 |        "    <tr>\n",
 537 |        "      <th>United Kingdom</th>\n",
 538 |        "      <td>11.0</td>\n",
 539 |        "      <td>0.4</td>\n",
 540 |        "      <td>81.0</td>\n",
 541 |        "      <td>12.68</td>\n",
 542 |        "      <td>74.0</td>\n",
 543 |        "      <td>77.4</td>\n",
 544 |        "      <td>0.2</td>\n",
 545 |        "      <td>28408.0</td>\n",
 546 |        "      <td>83405.0</td>\n",
 547 |        "      <td>24.0</td>\n",
 548 |        "      <td>...</td>\n",
 549 |        "      <td>14.92</td>\n",
 550 |        "      <td>69.0</td>\n",
 551 |        "      <td>85.0</td>\n",
 552 |        "      <td>16.8</td>\n",
 553 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 554 |        "      <td>U.S. dollars</td>\n",
 555 |        "      <td>Units</td>\n",
 556 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 557 |        "      <td>37812.509</td>\n",
 558 |        "      <td>2015.0</td>\n",
 559 |        "    </tr>\n",
 560 |        "    <tr>\n",
 561 |        "      <th>Japan</th>\n",
 562 |        "      <td>14.0</td>\n",
 563 |        "      <td>6.4</td>\n",
 564 |        "      <td>94.0</td>\n",
 565 |        "      <td>21.81</td>\n",
 566 |        "      <td>74.0</td>\n",
 567 |        "      <td>70.6</td>\n",
 568 |        "      <td>0.3</td>\n",
 569 |        "      <td>28641.0</td>\n",
 570 |        "      <td>97595.0</td>\n",
 571 |        "      <td>22.0</td>\n",
 572 |        "      <td>...</td>\n",
 573 |        "      <td>14.85</td>\n",
 574 |        "      <td>53.0</td>\n",
 575 |        "      <td>86.0</td>\n",
 576 |        "      <td>16.4</td>\n",
 577 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 578 |        "      <td>U.S. dollars</td>\n",
 579 |        "      <td>Units</td>\n",
 580 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 581 |        "      <td>38281.579</td>\n",
 582 |        "      <td>2016.0</td>\n",
 583 |        "    </tr>\n",
 584 |        "    <tr>\n",
 585 |        "      <th>Israel</th>\n",
 586 |        "      <td>21.0</td>\n",
 587 |        "      <td>4.4</td>\n",
 588 |        "      <td>87.0</td>\n",
 589 |        "      <td>15.04</td>\n",
 590 |        "      <td>69.0</td>\n",
 591 |        "      <td>70.2</td>\n",
 592 |        "      <td>1.7</td>\n",
 593 |        "      <td>24036.0</td>\n",
 594 |        "      <td>61805.0</td>\n",
 595 |        "      <td>20.0</td>\n",
 596 |        "      <td>...</td>\n",
 597 |        "      <td>13.93</td>\n",
 598 |        "      <td>72.0</td>\n",
 599 |        "      <td>67.0</td>\n",
 600 |        "      <td>15.8</td>\n",
 601 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 602 |        "      <td>U.S. dollars</td>\n",
 603 |        "      <td>Units</td>\n",
 604 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 605 |        "      <td>39125.718</td>\n",
 606 |        "      <td>2016.0</td>\n",
 607 |        "    </tr>\n",
 608 |        "    <tr>\n",
 609 |        "      <th>Belgium</th>\n",
 610 |        "      <td>15.0</td>\n",
 611 |        "      <td>2.3</td>\n",
 612 |        "      <td>75.0</td>\n",
 613 |        "      <td>4.31</td>\n",
 614 |        "      <td>62.0</td>\n",
 615 |        "      <td>70.7</td>\n",
 616 |        "      <td>1.0</td>\n",
 617 |        "      <td>29968.0</td>\n",
 618 |        "      <td>104084.0</td>\n",
 619 |        "      <td>21.0</td>\n",
 620 |        "      <td>...</td>\n",
 621 |        "      <td>15.77</td>\n",
 622 |        "      <td>89.0</td>\n",
 623 |        "      <td>84.0</td>\n",
 624 |        "      <td>18.2</td>\n",
 625 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 626 |        "      <td>U.S. dollars</td>\n",
 627 |        "      <td>Units</td>\n",
 628 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 629 |        "      <td>40696.472</td>\n",
 630 |        "      <td>2015.0</td>\n",
 631 |        "    </tr>\n",
 632 |        "    <tr>\n",
 633 |        "      <th>New Zealand</th>\n",
 634 |        "      <td>5.0</td>\n",
 635 |        "      <td>0.3</td>\n",
 636 |        "      <td>77.0</td>\n",
 637 |        "      <td>15.02</td>\n",
 638 |        "      <td>76.0</td>\n",
 639 |        "      <td>64.8</td>\n",
 640 |        "      <td>1.3</td>\n",
 641 |        "      <td>24366.0</td>\n",
 642 |        "      <td>52718.0</td>\n",
 643 |        "      <td>26.0</td>\n",
 644 |        "      <td>...</td>\n",
 645 |        "      <td>14.87</td>\n",
 646 |        "      <td>77.0</td>\n",
 647 |        "      <td>90.0</td>\n",
 648 |        "      <td>17.8</td>\n",
 649 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 650 |        "      <td>U.S. dollars</td>\n",
 651 |        "      <td>Units</td>\n",
 652 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 653 |        "      <td>41107.509</td>\n",
 654 |        "      <td>2016.0</td>\n",
 655 |        "    </tr>\n",
 656 |        "    <tr>\n",
 657 |        "      <th>Germany</th>\n",
 658 |        "      <td>14.0</td>\n",
 659 |        "      <td>0.1</td>\n",
 660 |        "      <td>86.0</td>\n",
 661 |        "      <td>4.60</td>\n",
 662 |        "      <td>75.0</td>\n",
 663 |        "      <td>75.9</td>\n",
 664 |        "      <td>0.4</td>\n",
 665 |        "      <td>33652.0</td>\n",
 666 |        "      <td>57358.0</td>\n",
 667 |        "      <td>20.0</td>\n",
 668 |        "      <td>...</td>\n",
 669 |        "      <td>15.55</td>\n",
 670 |        "      <td>72.0</td>\n",
 671 |        "      <td>93.0</td>\n",
 672 |        "      <td>18.3</td>\n",
 673 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 674 |        "      <td>U.S. dollars</td>\n",
 675 |        "      <td>Units</td>\n",
 676 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 677 |        "      <td>41243.875</td>\n",
 678 |        "      <td>2015.0</td>\n",
 679 |        "    </tr>\n",
 680 |        "    <tr>\n",
 681 |        "      <th>Finland</th>\n",
 682 |        "      <td>6.0</td>\n",
 683 |        "      <td>0.5</td>\n",
 684 |        "      <td>88.0</td>\n",
 685 |        "      <td>3.91</td>\n",
 686 |        "      <td>69.0</td>\n",
 687 |        "      <td>82.9</td>\n",
 688 |        "      <td>1.4</td>\n",
 689 |        "      <td>29374.0</td>\n",
 690 |        "      <td>27972.0</td>\n",
 691 |        "      <td>23.0</td>\n",
 692 |        "      <td>...</td>\n",
 693 |        "      <td>15.17</td>\n",
 694 |        "      <td>69.0</td>\n",
 695 |        "      <td>94.0</td>\n",
 696 |        "      <td>19.8</td>\n",
 697 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 698 |        "      <td>U.S. dollars</td>\n",
 699 |        "      <td>Units</td>\n",
 700 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 701 |        "      <td>42611.825</td>\n",
 702 |        "      <td>2014.0</td>\n",
 703 |        "    </tr>\n",
 704 |        "    <tr>\n",
 705 |        "      <th>Canada</th>\n",
 706 |        "      <td>7.0</td>\n",
 707 |        "      <td>0.2</td>\n",
 708 |        "      <td>91.0</td>\n",
 709 |        "      <td>3.73</td>\n",
 710 |        "      <td>73.0</td>\n",
 711 |        "      <td>80.9</td>\n",
 712 |        "      <td>1.4</td>\n",
 713 |        "      <td>29850.0</td>\n",
 714 |        "      <td>85758.0</td>\n",
 715 |        "      <td>22.0</td>\n",
 716 |        "      <td>...</td>\n",
 717 |        "      <td>14.41</td>\n",
 718 |        "      <td>68.0</td>\n",
 719 |        "      <td>91.0</td>\n",
 720 |        "      <td>16.7</td>\n",
 721 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 722 |        "      <td>U.S. dollars</td>\n",
 723 |        "      <td>Units</td>\n",
 724 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 725 |        "      <td>43611.264</td>\n",
 726 |        "      <td>2016.0</td>\n",
 727 |        "    </tr>\n",
 728 |        "    <tr>\n",
 729 |        "      <th>Austria</th>\n",
 730 |        "      <td>16.0</td>\n",
 731 |        "      <td>1.0</td>\n",
 732 |        "      <td>85.0</td>\n",
 733 |        "      <td>6.78</td>\n",
 734 |        "      <td>72.0</td>\n",
 735 |        "      <td>80.7</td>\n",
 736 |        "      <td>0.4</td>\n",
 737 |        "      <td>32544.0</td>\n",
 738 |        "      <td>59574.0</td>\n",
 739 |        "      <td>21.0</td>\n",
 740 |        "      <td>...</td>\n",
 741 |        "      <td>14.55</td>\n",
 742 |        "      <td>75.0</td>\n",
 743 |        "      <td>93.0</td>\n",
 744 |        "      <td>17.1</td>\n",
 745 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 746 |        "      <td>U.S. dollars</td>\n",
 747 |        "      <td>Units</td>\n",
 748 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 749 |        "      <td>43786.110</td>\n",
 750 |        "      <td>2016.0</td>\n",
 751 |        "    </tr>\n",
 752 |        "    <tr>\n",
 753 |        "      <th>Netherlands</th>\n",
 754 |        "      <td>14.0</td>\n",
 755 |        "      <td>0.0</td>\n",
 756 |        "      <td>77.0</td>\n",
 757 |        "      <td>0.45</td>\n",
 758 |        "      <td>75.0</td>\n",
 759 |        "      <td>81.2</td>\n",
 760 |        "      <td>0.6</td>\n",
 761 |        "      <td>28783.0</td>\n",
 762 |        "      <td>90002.0</td>\n",
 763 |        "      <td>20.0</td>\n",
 764 |        "      <td>...</td>\n",
 765 |        "      <td>15.90</td>\n",
 766 |        "      <td>82.0</td>\n",
 767 |        "      <td>93.0</td>\n",
 768 |        "      <td>18.7</td>\n",
 769 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 770 |        "      <td>U.S. dollars</td>\n",
 771 |        "      <td>Units</td>\n",
 772 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 773 |        "      <td>44654.229</td>\n",
 774 |        "      <td>2016.0</td>\n",
 775 |        "    </tr>\n",
 776 |        "    <tr>\n",
 777 |        "      <th>Sweden</th>\n",
 778 |        "      <td>6.0</td>\n",
 779 |        "      <td>0.0</td>\n",
 780 |        "      <td>83.0</td>\n",
 781 |        "      <td>1.11</td>\n",
 782 |        "      <td>76.0</td>\n",
 783 |        "      <td>75.9</td>\n",
 784 |        "      <td>1.0</td>\n",
 785 |        "      <td>30553.0</td>\n",
 786 |        "      <td>90708.0</td>\n",
 787 |        "      <td>20.0</td>\n",
 788 |        "      <td>...</td>\n",
 789 |        "      <td>15.18</td>\n",
 790 |        "      <td>86.0</td>\n",
 791 |        "      <td>95.0</td>\n",
 792 |        "      <td>19.2</td>\n",
 793 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 794 |        "      <td>U.S. dollars</td>\n",
 795 |        "      <td>Units</td>\n",
 796 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 797 |        "      <td>49824.319</td>\n",
 798 |        "      <td>2014.0</td>\n",
 799 |        "    </tr>\n",
 800 |        "    <tr>\n",
 801 |        "      <th>Denmark</th>\n",
 802 |        "      <td>9.0</td>\n",
 803 |        "      <td>0.6</td>\n",
 804 |        "      <td>81.0</td>\n",
 805 |        "      <td>2.20</td>\n",
 806 |        "      <td>75.0</td>\n",
 807 |        "      <td>83.0</td>\n",
 808 |        "      <td>0.7</td>\n",
 809 |        "      <td>28950.0</td>\n",
 810 |        "      <td>73543.0</td>\n",
 811 |        "      <td>24.0</td>\n",
 812 |        "      <td>...</td>\n",
 813 |        "      <td>15.87</td>\n",
 814 |        "      <td>86.0</td>\n",
 815 |        "      <td>94.0</td>\n",
 816 |        "      <td>19.7</td>\n",
 817 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 818 |        "      <td>U.S. dollars</td>\n",
 819 |        "      <td>Units</td>\n",
 820 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 821 |        "      <td>52870.790</td>\n",
 822 |        "      <td>2015.0</td>\n",
 823 |        "    </tr>\n",
 824 |        "    <tr>\n",
 825 |        "      <th>Australia</th>\n",
 826 |        "      <td>5.0</td>\n",
 827 |        "      <td>1.1</td>\n",
 828 |        "      <td>80.0</td>\n",
 829 |        "      <td>13.20</td>\n",
 830 |        "      <td>72.0</td>\n",
 831 |        "      <td>63.6</td>\n",
 832 |        "      <td>1.0</td>\n",
 833 |        "      <td>33417.0</td>\n",
 834 |        "      <td>57462.0</td>\n",
 835 |        "      <td>20.0</td>\n",
 836 |        "      <td>...</td>\n",
 837 |        "      <td>14.35</td>\n",
 838 |        "      <td>91.0</td>\n",
 839 |        "      <td>92.0</td>\n",
 840 |        "      <td>21.2</td>\n",
 841 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 842 |        "      <td>U.S. dollars</td>\n",
 843 |        "      <td>Units</td>\n",
 844 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 845 |        "      <td>55215.275</td>\n",
 846 |        "      <td>2015.0</td>\n",
 847 |        "    </tr>\n",
 848 |        "    <tr>\n",
 849 |        "      <th>United States</th>\n",
 850 |        "      <td>10.0</td>\n",
 851 |        "      <td>0.1</td>\n",
 852 |        "      <td>90.0</td>\n",
 853 |        "      <td>11.45</td>\n",
 854 |        "      <td>69.0</td>\n",
 855 |        "      <td>74.1</td>\n",
 856 |        "      <td>4.9</td>\n",
 857 |        "      <td>44049.0</td>\n",
 858 |        "      <td>176076.0</td>\n",
 859 |        "      <td>18.0</td>\n",
 860 |        "      <td>...</td>\n",
 861 |        "      <td>14.44</td>\n",
 862 |        "      <td>68.0</td>\n",
 863 |        "      <td>84.0</td>\n",
 864 |        "      <td>17.1</td>\n",
 865 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 866 |        "      <td>U.S. dollars</td>\n",
 867 |        "      <td>Units</td>\n",
 868 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 869 |        "      <td>59609.070</td>\n",
 870 |        "      <td>2016.0</td>\n",
 871 |        "    </tr>\n",
 872 |        "    <tr>\n",
 873 |        "      <th>Ireland</th>\n",
 874 |        "      <td>7.0</td>\n",
 875 |        "      <td>0.1</td>\n",
 876 |        "      <td>80.0</td>\n",
 877 |        "      <td>4.66</td>\n",
 878 |        "      <td>65.0</td>\n",
 879 |        "      <td>75.5</td>\n",
 880 |        "      <td>0.6</td>\n",
 881 |        "      <td>25439.0</td>\n",
 882 |        "      <td>43493.0</td>\n",
 883 |        "      <td>21.0</td>\n",
 884 |        "      <td>...</td>\n",
 885 |        "      <td>15.28</td>\n",
 886 |        "      <td>65.0</td>\n",
 887 |        "      <td>82.0</td>\n",
 888 |        "      <td>18.7</td>\n",
 889 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 890 |        "      <td>U.S. dollars</td>\n",
 891 |        "      <td>Units</td>\n",
 892 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 893 |        "      <td>62085.417</td>\n",
 894 |        "      <td>2016.0</td>\n",
 895 |        "    </tr>\n",
 896 |        "    <tr>\n",
 897 |        "      <th>Iceland</th>\n",
 898 |        "      <td>3.0</td>\n",
 899 |        "      <td>0.0</td>\n",
 900 |        "      <td>78.0</td>\n",
 901 |        "      <td>15.06</td>\n",
 902 |        "      <td>86.0</td>\n",
 903 |        "      <td>87.0</td>\n",
 904 |        "      <td>0.9</td>\n",
 905 |        "      <td>30453.0</td>\n",
 906 |        "      <td>64398.0</td>\n",
 907 |        "      <td>24.0</td>\n",
 908 |        "      <td>...</td>\n",
 909 |        "      <td>14.15</td>\n",
 910 |        "      <td>79.0</td>\n",
 911 |        "      <td>99.0</td>\n",
 912 |        "      <td>19.3</td>\n",
 913 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 914 |        "      <td>U.S. dollars</td>\n",
 915 |        "      <td>Units</td>\n",
 916 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 917 |        "      <td>67570.058</td>\n",
 918 |        "      <td>2016.0</td>\n",
 919 |        "    </tr>\n",
 920 |        "    <tr>\n",
 921 |        "      <th>Norway</th>\n",
 922 |        "      <td>5.0</td>\n",
 923 |        "      <td>0.0</td>\n",
 924 |        "      <td>82.0</td>\n",
 925 |        "      <td>3.17</td>\n",
 926 |        "      <td>74.0</td>\n",
 927 |        "      <td>87.7</td>\n",
 928 |        "      <td>0.6</td>\n",
 929 |        "      <td>35739.0</td>\n",
 930 |        "      <td>20347.0</td>\n",
 931 |        "      <td>17.0</td>\n",
 932 |        "      <td>...</td>\n",
 933 |        "      <td>15.56</td>\n",
 934 |        "      <td>78.0</td>\n",
 935 |        "      <td>96.0</td>\n",
 936 |        "      <td>18.1</td>\n",
 937 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 938 |        "      <td>U.S. dollars</td>\n",
 939 |        "      <td>Units</td>\n",
 940 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 941 |        "      <td>73450.396</td>\n",
 942 |        "      <td>2015.0</td>\n",
 943 |        "    </tr>\n",
 944 |        "    <tr>\n",
 945 |        "      <th>Switzerland</th>\n",
 946 |        "      <td>15.0</td>\n",
 947 |        "      <td>0.0</td>\n",
 948 |        "      <td>87.0</td>\n",
 949 |        "      <td>6.91</td>\n",
 950 |        "      <td>80.0</td>\n",
 951 |        "      <td>84.0</td>\n",
 952 |        "      <td>0.5</td>\n",
 953 |        "      <td>36378.0</td>\n",
 954 |        "      <td>128415.0</td>\n",
 955 |        "      <td>21.0</td>\n",
 956 |        "      <td>...</td>\n",
 957 |        "      <td>15.02</td>\n",
 958 |        "      <td>49.0</td>\n",
 959 |        "      <td>96.0</td>\n",
 960 |        "      <td>17.5</td>\n",
 961 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 962 |        "      <td>U.S. dollars</td>\n",
 963 |        "      <td>Units</td>\n",
 964 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 965 |        "      <td>78245.451</td>\n",
 966 |        "      <td>2016.0</td>\n",
 967 |        "    </tr>\n",
 968 |        "    <tr>\n",
 969 |        "      <th>Luxembourg</th>\n",
 970 |        "      <td>12.0</td>\n",
 971 |        "      <td>0.0</td>\n",
 972 |        "      <td>79.0</td>\n",
 973 |        "      <td>3.76</td>\n",
 974 |        "      <td>66.0</td>\n",
 975 |        "      <td>72.0</td>\n",
 976 |        "      <td>0.6</td>\n",
 977 |        "      <td>41317.0</td>\n",
 978 |        "      <td>74141.0</td>\n",
 979 |        "      <td>20.0</td>\n",
 980 |        "      <td>...</td>\n",
 981 |        "      <td>15.15</td>\n",
 982 |        "      <td>91.0</td>\n",
 983 |        "      <td>85.0</td>\n",
 984 |        "      <td>15.1</td>\n",
 985 |        "      <td>Gross domestic product per capita, current prices</td>\n",
 986 |        "      <td>U.S. dollars</td>\n",
 987 |        "      <td>Units</td>\n",
 988 |        "      <td>See notes for:  Gross domestic product, curren...</td>\n",
 989 |        "      <td>101715.451</td>\n",
 990 |        "      <td>2015.0</td>\n",
 991 |        "    </tr>\n",
 992 |        "  </tbody>\n",
 993 |        "</table>\n",
 994 |        "<p>38 rows × 30 columns</p>\n",
 995 |        "</div>"
 996 |       ],
 997 |       "text/plain": [
 998 |        "Indicator        Air pollution  Dwellings without basic facilities  \\\n",
 999 |        "Country                                                              \n",
1000 |        "South Africa              22.0                                37.0   \n",
1001 |        "Mexico                    16.0                                 4.2   \n",
1002 |        "Turkey                    20.0                                 6.5   \n",
1003 |        "Brazil                    10.0                                 6.7   \n",
1004 |        "Russia                    15.0                                13.8   \n",
1005 |        "Poland                    22.0                                 2.7   \n",
1006 |        "Hungary                   19.0                                 4.3   \n",
1007 |        "Chile                     16.0                                 9.4   \n",
1008 |        "Latvia                    11.0                                12.9   \n",
1009 |        "Slovak Republic           21.0                                 1.4   \n",
1010 |        "Greece                    18.0                                 0.5   \n",
1011 |        "Estonia                    8.0                                 6.9   \n",
1012 |        "Czech Republic            20.0                                 0.6   \n",
1013 |        "Portugal                  10.0                                 1.0   \n",
1014 |        "Slovenia                  16.0                                 0.3   \n",
1015 |        "Spain                     11.0                                 0.1   \n",
1016 |        "Korea                     28.0                                 4.2   \n",
1017 |        "Italy                     18.0                                 0.6   \n",
1018 |        "France                    13.0                                 0.5   \n",
1019 |        "United Kingdom            11.0                                 0.4   \n",
1020 |        "Japan                     14.0                                 6.4   \n",
1021 |        "Israel                    21.0                                 4.4   \n",
1022 |        "Belgium                   15.0                                 2.3   \n",
1023 |        "New Zealand                5.0                                 0.3   \n",
1024 |        "Germany                   14.0                                 0.1   \n",
1025 |        "Finland                    6.0                                 0.5   \n",
1026 |        "Canada                     7.0                                 0.2   \n",
1027 |        "Austria                   16.0                                 1.0   \n",
1028 |        "Netherlands               14.0                                 0.0   \n",
1029 |        "Sweden                     6.0                                 0.0   \n",
1030 |        "Denmark                    9.0                                 0.6   \n",
1031 |        "Australia                  5.0                                 1.1   \n",
1032 |        "United States             10.0                                 0.1   \n",
1033 |        "Ireland                    7.0                                 0.1   \n",
1034 |        "Iceland                    3.0                                 0.0   \n",
1035 |        "Norway                     5.0                                 0.0   \n",
1036 |        "Switzerland               15.0                                 0.0   \n",
1037 |        "Luxembourg                12.0                                 0.0   \n",
1038 |        "\n",
1039 |        "Indicator        Educational attainment  Employees working very long hours  \\\n",
1040 |        "Country                                                                      \n",
1041 |        "South Africa                       43.0                              18.68   \n",
1042 |        "Mexico                             37.0                              29.48   \n",
1043 |        "Turkey                             39.0                              33.77   \n",
1044 |        "Brazil                             49.0                               7.15   \n",
1045 |        "Russia                             95.0                               0.16   \n",
1046 |        "Poland                             91.0                               6.68   \n",
1047 |        "Hungary                            83.0                               3.05   \n",
1048 |        "Chile                              65.0                              10.06   \n",
1049 |        "Latvia                             89.0                               2.09   \n",
1050 |        "Slovak Republic                    92.0                               5.04   \n",
1051 |        "Greece                             72.0                               7.30   \n",
1052 |        "Estonia                            89.0                               2.69   \n",
1053 |        "Czech Republic                     93.0                               5.77   \n",
1054 |        "Portugal                           47.0                               8.20   \n",
1055 |        "Slovenia                           87.0                               4.46   \n",
1056 |        "Spain                              58.0                               4.55   \n",
1057 |        "Korea                              87.0                              20.84   \n",
1058 |        "Italy                              60.0                               3.90   \n",
1059 |        "France                             78.0                               7.76   \n",
1060 |        "United Kingdom                     81.0                              12.68   \n",
1061 |        "Japan                              94.0                              21.81   \n",
1062 |        "Israel                             87.0                              15.04   \n",
1063 |        "Belgium                            75.0                               4.31   \n",
1064 |        "New Zealand                        77.0                              15.02   \n",
1065 |        "Germany                            86.0                               4.60   \n",
1066 |        "Finland                            88.0                               3.91   \n",
1067 |        "Canada                             91.0                               3.73   \n",
1068 |        "Austria                            85.0                               6.78   \n",
1069 |        "Netherlands                        77.0                               0.45   \n",
1070 |        "Sweden                             83.0                               1.11   \n",
1071 |        "Denmark                            81.0                               2.20   \n",
1072 |        "Australia                          80.0                              13.20   \n",
1073 |        "United States                      90.0                              11.45   \n",
1074 |        "Ireland                            80.0                               4.66   \n",
1075 |        "Iceland                            78.0                              15.06   \n",
1076 |        "Norway                             82.0                               3.17   \n",
1077 |        "Switzerland                        87.0                               6.91   \n",
1078 |        "Luxembourg                         79.0                               3.76   \n",
1079 |        "\n",
1080 |        "Indicator        Employment rate  Feeling safe walking alone at night  \\\n",
1081 |        "Country                                                                 \n",
1082 |        "South Africa                43.0                                 36.1   \n",
1083 |        "Mexico                      61.0                                 45.9   \n",
1084 |        "Turkey                      51.0                                 60.6   \n",
1085 |        "Brazil                      64.0                                 37.3   \n",
1086 |        "Russia                      70.0                                 52.2   \n",
1087 |        "Poland                      65.0                                 66.3   \n",
1088 |        "Hungary                     67.0                                 50.7   \n",
1089 |        "Chile                       62.0                                 51.1   \n",
1090 |        "Latvia                      69.0                                 60.7   \n",
1091 |        "Slovak Republic             65.0                                 60.1   \n",
1092 |        "Greece                      52.0                                 61.8   \n",
1093 |        "Estonia                     72.0                                 67.2   \n",
1094 |        "Czech Republic              72.0                                 68.3   \n",
1095 |        "Portugal                    65.0                                 72.1   \n",
1096 |        "Slovenia                    66.0                                 84.7   \n",
1097 |        "Spain                       60.0                                 83.1   \n",
1098 |        "Korea                       66.0                                 63.9   \n",
1099 |        "Italy                       57.0                                 58.3   \n",
1100 |        "France                      65.0                                 69.6   \n",
1101 |        "United Kingdom              74.0                                 77.4   \n",
1102 |        "Japan                       74.0                                 70.6   \n",
1103 |        "Israel                      69.0                                 70.2   \n",
1104 |        "Belgium                     62.0                                 70.7   \n",
1105 |        "New Zealand                 76.0                                 64.8   \n",
1106 |        "Germany                     75.0                                 75.9   \n",
1107 |        "Finland                     69.0                                 82.9   \n",
1108 |        "Canada                      73.0                                 80.9   \n",
1109 |        "Austria                     72.0                                 80.7   \n",
1110 |        "Netherlands                 75.0                                 81.2   \n",
1111 |        "Sweden                      76.0                                 75.9   \n",
1112 |        "Denmark                     75.0                                 83.0   \n",
1113 |        "Australia                   72.0                                 63.6   \n",
1114 |        "United States               69.0                                 74.1   \n",
1115 |        "Ireland                     65.0                                 75.5   \n",
1116 |        "Iceland                     86.0                                 87.0   \n",
1117 |        "Norway                      74.0                                 87.7   \n",
1118 |        "Switzerland                 80.0                                 84.0   \n",
1119 |        "Luxembourg                  66.0                                 72.0   \n",
1120 |        "\n",
1121 |        "Indicator        Homicide rate  Household net adjusted disposable income  \\\n",
1122 |        "Country                                                                    \n",
1123 |        "South Africa              10.0                                   10872.0   \n",
1124 |        "Mexico                    17.9                                   13891.0   \n",
1125 |        "Turkey                     1.7                                   17067.0   \n",
1126 |        "Brazil                    27.6                                   12227.0   \n",
1127 |        "Russia                    11.3                                   16657.0   \n",
1128 |        "Poland                     0.8                                   18906.0   \n",
1129 |        "Hungary                    1.2                                   16821.0   \n",
1130 |        "Chile                      4.5                                   16588.0   \n",
1131 |        "Latvia                     6.6                                   15269.0   \n",
1132 |        "Slovak Republic            0.8                                   20265.0   \n",
1133 |        "Greece                     1.0                                   17002.0   \n",
1134 |        "Estonia                    3.1                                   18665.0   \n",
1135 |        "Czech Republic             0.8                                   21103.0   \n",
1136 |        "Portugal                   1.0                                   20519.0   \n",
1137 |        "Slovenia                   0.6                                   20505.0   \n",
1138 |        "Spain                      0.6                                   23129.0   \n",
1139 |        "Korea                      1.1                                   21723.0   \n",
1140 |        "Italy                      0.8                                   26063.0   \n",
1141 |        "France                     0.6                                   31137.0   \n",
1142 |        "United Kingdom             0.2                                   28408.0   \n",
1143 |        "Japan                      0.3                                   28641.0   \n",
1144 |        "Israel                     1.7                                   24036.0   \n",
1145 |        "Belgium                    1.0                                   29968.0   \n",
1146 |        "New Zealand                1.3                                   24366.0   \n",
1147 |        "Germany                    0.4                                   33652.0   \n",
1148 |        "Finland                    1.4                                   29374.0   \n",
1149 |        "Canada                     1.4                                   29850.0   \n",
1150 |        "Austria                    0.4                                   32544.0   \n",
1151 |        "Netherlands                0.6                                   28783.0   \n",
1152 |        "Sweden                     1.0                                   30553.0   \n",
1153 |        "Denmark                    0.7                                   28950.0   \n",
1154 |        "Australia                  1.0                                   33417.0   \n",
1155 |        "United States              4.9                                   44049.0   \n",
1156 |        "Ireland                    0.6                                   25439.0   \n",
1157 |        "Iceland                    0.9                                   30453.0   \n",
1158 |        "Norway                     0.6                                   35739.0   \n",
1159 |        "Switzerland                0.5                                   36378.0   \n",
1160 |        "Luxembourg                 0.6                                   41317.0   \n",
1161 |        "\n",
1162 |        "Indicator        Household net financial wealth  Housing expenditure  \\\n",
1163 |        "Country                                                                \n",
1164 |        "South Africa                            17042.0                 18.0   \n",
1165 |        "Mexico                                   4750.0                 21.0   \n",
1166 |        "Turkey                                   4429.0                 20.0   \n",
1167 |        "Brazil                                   7102.0                 20.0   \n",
1168 |        "Russia                                   2260.0                 19.0   \n",
1169 |        "Poland                                  14997.0                 23.0   \n",
1170 |        "Hungary                                 23289.0                 18.0   \n",
1171 |        "Chile                                   21409.0                 18.0   \n",
1172 |        "Latvia                                  17105.0                 23.0   \n",
1173 |        "Slovak Republic                         10846.0                 24.0   \n",
1174 |        "Greece                                  18117.0                 24.0   \n",
1175 |        "Estonia                                 16967.0                 18.0   \n",
1176 |        "Czech Republic                          24258.0                 24.0   \n",
1177 |        "Portugal                                31877.0                 21.0   \n",
1178 |        "Slovenia                                20048.0                 18.0   \n",
1179 |        "Spain                                   35443.0                 22.0   \n",
1180 |        "Korea                                   33495.0                 15.0   \n",
1181 |        "Italy                                   64019.0                 23.0   \n",
1182 |        "France                                  59479.0                 21.0   \n",
1183 |        "United Kingdom                          83405.0                 24.0   \n",
1184 |        "Japan                                   97595.0                 22.0   \n",
1185 |        "Israel                                  61805.0                 20.0   \n",
1186 |        "Belgium                                104084.0                 21.0   \n",
1187 |        "New Zealand                             52718.0                 26.0   \n",
1188 |        "Germany                                 57358.0                 20.0   \n",
1189 |        "Finland                                 27972.0                 23.0   \n",
1190 |        "Canada                                  85758.0                 22.0   \n",
1191 |        "Austria                                 59574.0                 21.0   \n",
1192 |        "Netherlands                             90002.0                 20.0   \n",
1193 |        "Sweden                                  90708.0                 20.0   \n",
1194 |        "Denmark                                 73543.0                 24.0   \n",
1195 |        "Australia                               57462.0                 20.0   \n",
1196 |        "United States                          176076.0                 18.0   \n",
1197 |        "Ireland                                 43493.0                 21.0   \n",
1198 |        "Iceland                                 64398.0                 24.0   \n",
1199 |        "Norway                                  20347.0                 17.0   \n",
1200 |        "Switzerland                            128415.0                 21.0   \n",
1201 |        "Luxembourg                              74141.0                 20.0   \n",
1202 |        "\n",
1203 |        "Indicator                ...            \\\n",
1204 |        "Country                  ...             \n",
1205 |        "South Africa             ...             \n",
1206 |        "Mexico                   ...             \n",
1207 |        "Turkey                   ...             \n",
1208 |        "Brazil                   ...             \n",
1209 |        "Russia                   ...             \n",
1210 |        "Poland                   ...             \n",
1211 |        "Hungary                  ...             \n",
1212 |        "Chile                    ...             \n",
1213 |        "Latvia                   ...             \n",
1214 |        "Slovak Republic          ...             \n",
1215 |        "Greece                   ...             \n",
1216 |        "Estonia                  ...             \n",
1217 |        "Czech Republic           ...             \n",
1218 |        "Portugal                 ...             \n",
1219 |        "Slovenia                 ...             \n",
1220 |        "Spain                    ...             \n",
1221 |        "Korea                    ...             \n",
1222 |        "Italy                    ...             \n",
1223 |        "France                   ...             \n",
1224 |        "United Kingdom           ...             \n",
1225 |        "Japan                    ...             \n",
1226 |        "Israel                   ...             \n",
1227 |        "Belgium                  ...             \n",
1228 |        "New Zealand              ...             \n",
1229 |        "Germany                  ...             \n",
1230 |        "Finland                  ...             \n",
1231 |        "Canada                   ...             \n",
1232 |        "Austria                  ...             \n",
1233 |        "Netherlands              ...             \n",
1234 |        "Sweden                   ...             \n",
1235 |        "Denmark                  ...             \n",
1236 |        "Australia                ...             \n",
1237 |        "United States            ...             \n",
1238 |        "Ireland                  ...             \n",
1239 |        "Iceland                  ...             \n",
1240 |        "Norway                   ...             \n",
1241 |        "Switzerland              ...             \n",
1242 |        "Luxembourg               ...             \n",
1243 |        "\n",
1244 |        "Indicator        Time devoted to leisure and personal care  Voter turnout  \\\n",
1245 |        "Country                                                                     \n",
1246 |        "South Africa                                         14.73           73.0   \n",
1247 |        "Mexico                                               12.74           63.0   \n",
1248 |        "Turkey                                               12.59           85.0   \n",
1249 |        "Brazil                                               14.45           79.0   \n",
1250 |        "Russia                                               14.90           65.0   \n",
1251 |        "Poland                                               14.42           55.0   \n",
1252 |        "Hungary                                              15.06           62.0   \n",
1253 |        "Chile                                                14.90           49.0   \n",
1254 |        "Latvia                                               13.83           59.0   \n",
1255 |        "Slovak Republic                                      15.01           60.0   \n",
1256 |        "Greece                                               14.67           64.0   \n",
1257 |        "Estonia                                              14.90           64.0   \n",
1258 |        "Czech Republic                                       15.06           59.0   \n",
1259 |        "Portugal                                             14.89           56.0   \n",
1260 |        "Slovenia                                             14.75           52.0   \n",
1261 |        "Spain                                                15.93           70.0   \n",
1262 |        "Korea                                                14.70           77.0   \n",
1263 |        "Italy                                                14.89           75.0   \n",
1264 |        "France                                               16.36           75.0   \n",
1265 |        "United Kingdom                                       14.92           69.0   \n",
1266 |        "Japan                                                14.85           53.0   \n",
1267 |        "Israel                                               13.93           72.0   \n",
1268 |        "Belgium                                              15.77           89.0   \n",
1269 |        "New Zealand                                          14.87           77.0   \n",
1270 |        "Germany                                              15.55           72.0   \n",
1271 |        "Finland                                              15.17           69.0   \n",
1272 |        "Canada                                               14.41           68.0   \n",
1273 |        "Austria                                              14.55           75.0   \n",
1274 |        "Netherlands                                          15.90           82.0   \n",
1275 |        "Sweden                                               15.18           86.0   \n",
1276 |        "Denmark                                              15.87           86.0   \n",
1277 |        "Australia                                            14.35           91.0   \n",
1278 |        "United States                                        14.44           68.0   \n",
1279 |        "Ireland                                              15.28           65.0   \n",
1280 |        "Iceland                                              14.15           79.0   \n",
1281 |        "Norway                                               15.56           78.0   \n",
1282 |        "Switzerland                                          15.02           49.0   \n",
1283 |        "Luxembourg                                           15.15           91.0   \n",
1284 |        "\n",
1285 |        "Indicator        Water quality  Years in education  \\\n",
1286 |        "Country                                              \n",
1287 |        "South Africa              69.0                15.3   \n",
1288 |        "Mexico                    67.0                14.8   \n",
1289 |        "Turkey                    63.0                17.9   \n",
1290 |        "Brazil                    72.0                15.9   \n",
1291 |        "Russia                    54.0                16.1   \n",
1292 |        "Poland                    80.0                17.7   \n",
1293 |        "Hungary                   76.0                16.6   \n",
1294 |        "Chile                     69.0                17.3   \n",
1295 |        "Latvia                    77.0                17.9   \n",
1296 |        "Slovak Republic           82.0                15.9   \n",
1297 |        "Greece                    69.0                16.9   \n",
1298 |        "Estonia                   82.0                15.8   \n",
1299 |        "Czech Republic            87.0                17.3   \n",
1300 |        "Portugal                  87.0                17.1   \n",
1301 |        "Slovenia                  89.0                18.1   \n",
1302 |        "Spain                     73.0                17.9   \n",
1303 |        "Korea                     78.0                17.4   \n",
1304 |        "Italy                     71.0                16.4   \n",
1305 |        "France                    82.0                16.5   \n",
1306 |        "United Kingdom            85.0                16.8   \n",
1307 |        "Japan                     86.0                16.4   \n",
1308 |        "Israel                    67.0                15.8   \n",
1309 |        "Belgium                   84.0                18.2   \n",
1310 |        "New Zealand               90.0                17.8   \n",
1311 |        "Germany                   93.0                18.3   \n",
1312 |        "Finland                   94.0                19.8   \n",
1313 |        "Canada                    91.0                16.7   \n",
1314 |        "Austria                   93.0                17.1   \n",
1315 |        "Netherlands               93.0                18.7   \n",
1316 |        "Sweden                    95.0                19.2   \n",
1317 |        "Denmark                   94.0                19.7   \n",
1318 |        "Australia                 92.0                21.2   \n",
1319 |        "United States             84.0                17.1   \n",
1320 |        "Ireland                   82.0                18.7   \n",
1321 |        "Iceland                   99.0                19.3   \n",
1322 |        "Norway                    96.0                18.1   \n",
1323 |        "Switzerland               96.0                17.5   \n",
1324 |        "Luxembourg                85.0                15.1   \n",
1325 |        "\n",
1326 |        "Indicator                                       Subject Descriptor  \\\n",
1327 |        "Country                                                              \n",
1328 |        "South Africa     Gross domestic product per capita, current prices   \n",
1329 |        "Mexico           Gross domestic product per capita, current prices   \n",
1330 |        "Turkey           Gross domestic product per capita, current prices   \n",
1331 |        "Brazil           Gross domestic product per capita, current prices   \n",
1332 |        "Russia           Gross domestic product per capita, current prices   \n",
1333 |        "Poland           Gross domestic product per capita, current prices   \n",
1334 |        "Hungary          Gross domestic product per capita, current prices   \n",
1335 |        "Chile            Gross domestic product per capita, current prices   \n",
1336 |        "Latvia           Gross domestic product per capita, current prices   \n",
1337 |        "Slovak Republic  Gross domestic product per capita, current prices   \n",
1338 |        "Greece           Gross domestic product per capita, current prices   \n",
1339 |        "Estonia          Gross domestic product per capita, current prices   \n",
1340 |        "Czech Republic   Gross domestic product per capita, current prices   \n",
1341 |        "Portugal         Gross domestic product per capita, current prices   \n",
1342 |        "Slovenia         Gross domestic product per capita, current prices   \n",
1343 |        "Spain            Gross domestic product per capita, current prices   \n",
1344 |        "Korea            Gross domestic product per capita, current prices   \n",
1345 |        "Italy            Gross domestic product per capita, current prices   \n",
1346 |        "France           Gross domestic product per capita, current prices   \n",
1347 |        "United Kingdom   Gross domestic product per capita, current prices   \n",
1348 |        "Japan            Gross domestic product per capita, current prices   \n",
1349 |        "Israel           Gross domestic product per capita, current prices   \n",
1350 |        "Belgium          Gross domestic product per capita, current prices   \n",
1351 |        "New Zealand      Gross domestic product per capita, current prices   \n",
1352 |        "Germany          Gross domestic product per capita, current prices   \n",
1353 |        "Finland          Gross domestic product per capita, current prices   \n",
1354 |        "Canada           Gross domestic product per capita, current prices   \n",
1355 |        "Austria          Gross domestic product per capita, current prices   \n",
1356 |        "Netherlands      Gross domestic product per capita, current prices   \n",
1357 |        "Sweden           Gross domestic product per capita, current prices   \n",
1358 |        "Denmark          Gross domestic product per capita, current prices   \n",
1359 |        "Australia        Gross domestic product per capita, current prices   \n",
1360 |        "United States    Gross domestic product per capita, current prices   \n",
1361 |        "Ireland          Gross domestic product per capita, current prices   \n",
1362 |        "Iceland          Gross domestic product per capita, current prices   \n",
1363 |        "Norway           Gross domestic product per capita, current prices   \n",
1364 |        "Switzerland      Gross domestic product per capita, current prices   \n",
1365 |        "Luxembourg       Gross domestic product per capita, current prices   \n",
1366 |        "\n",
1367 |        "Indicator               Units  Scale  \\\n",
1368 |        "Country                                \n",
1369 |        "South Africa     U.S. dollars  Units   \n",
1370 |        "Mexico           U.S. dollars  Units   \n",
1371 |        "Turkey           U.S. dollars  Units   \n",
1372 |        "Brazil           U.S. dollars  Units   \n",
1373 |        "Russia           U.S. dollars  Units   \n",
1374 |        "Poland           U.S. dollars  Units   \n",
1375 |        "Hungary          U.S. dollars  Units   \n",
1376 |        "Chile            U.S. dollars  Units   \n",
1377 |        "Latvia           U.S. dollars  Units   \n",
1378 |        "Slovak Republic  U.S. dollars  Units   \n",
1379 |        "Greece           U.S. dollars  Units   \n",
1380 |        "Estonia          U.S. dollars  Units   \n",
1381 |        "Czech Republic   U.S. dollars  Units   \n",
1382 |        "Portugal         U.S. dollars  Units   \n",
1383 |        "Slovenia         U.S. dollars  Units   \n",
1384 |        "Spain            U.S. dollars  Units   \n",
1385 |        "Korea            U.S. dollars  Units   \n",
1386 |        "Italy            U.S. dollars  Units   \n",
1387 |        "France           U.S. dollars  Units   \n",
1388 |        "United Kingdom   U.S. dollars  Units   \n",
1389 |        "Japan            U.S. dollars  Units   \n",
1390 |        "Israel           U.S. dollars  Units   \n",
1391 |        "Belgium          U.S. dollars  Units   \n",
1392 |        "New Zealand      U.S. dollars  Units   \n",
1393 |        "Germany          U.S. dollars  Units   \n",
1394 |        "Finland          U.S. dollars  Units   \n",
1395 |        "Canada           U.S. dollars  Units   \n",
1396 |        "Austria          U.S. dollars  Units   \n",
1397 |        "Netherlands      U.S. dollars  Units   \n",
1398 |        "Sweden           U.S. dollars  Units   \n",
1399 |        "Denmark          U.S. dollars  Units   \n",
1400 |        "Australia        U.S. dollars  Units   \n",
1401 |        "United States    U.S. dollars  Units   \n",
1402 |        "Ireland          U.S. dollars  Units   \n",
1403 |        "Iceland          U.S. dollars  Units   \n",
1404 |        "Norway           U.S. dollars  Units   \n",
1405 |        "Switzerland      U.S. dollars  Units   \n",
1406 |        "Luxembourg       U.S. dollars  Units   \n",
1407 |        "\n",
1408 |        "Indicator                            Country/Series-specific Notes  \\\n",
1409 |        "Country                                                              \n",
1410 |        "South Africa     See notes for:  Gross domestic product, curren...   \n",
1411 |        "Mexico           See notes for:  Gross domestic product, curren...   \n",
1412 |        "Turkey           See notes for:  Gross domestic product, curren...   \n",
1413 |        "Brazil           See notes for:  Gross domestic product, curren...   \n",
1414 |        "Russia           See notes for:  Gross domestic product, curren...   \n",
1415 |        "Poland           See notes for:  Gross domestic product, curren...   \n",
1416 |        "Hungary          See notes for:  Gross domestic product, curren...   \n",
1417 |        "Chile            See notes for:  Gross domestic product, curren...   \n",
1418 |        "Latvia           See notes for:  Gross domestic product, curren...   \n",
1419 |        "Slovak Republic  See notes for:  Gross domestic product, curren...   \n",
1420 |        "Greece           See notes for:  Gross domestic product, curren...   \n",
1421 |        "Estonia          See notes for:  Gross domestic product, curren...   \n",
1422 |        "Czech Republic   See notes for:  Gross domestic product, curren...   \n",
1423 |        "Portugal         See notes for:  Gross domestic product, curren...   \n",
1424 |        "Slovenia         See notes for:  Gross domestic product, curren...   \n",
1425 |        "Spain            See notes for:  Gross domestic product, curren...   \n",
1426 |        "Korea            See notes for:  Gross domestic product, curren...   \n",
1427 |        "Italy            See notes for:  Gross domestic product, curren...   \n",
1428 |        "France           See notes for:  Gross domestic product, curren...   \n",
1429 |        "United Kingdom   See notes for:  Gross domestic product, curren...   \n",
1430 |        "Japan            See notes for:  Gross domestic product, curren...   \n",
1431 |        "Israel           See notes for:  Gross domestic product, curren...   \n",
1432 |        "Belgium          See notes for:  Gross domestic product, curren...   \n",
1433 |        "New Zealand      See notes for:  Gross domestic product, curren...   \n",
1434 |        "Germany          See notes for:  Gross domestic product, curren...   \n",
1435 |        "Finland          See notes for:  Gross domestic product, curren...   \n",
1436 |        "Canada           See notes for:  Gross domestic product, curren...   \n",
1437 |        "Austria          See notes for:  Gross domestic product, curren...   \n",
1438 |        "Netherlands      See notes for:  Gross domestic product, curren...   \n",
1439 |        "Sweden           See notes for:  Gross domestic product, curren...   \n",
1440 |        "Denmark          See notes for:  Gross domestic product, curren...   \n",
1441 |        "Australia        See notes for:  Gross domestic product, curren...   \n",
1442 |        "United States    See notes for:  Gross domestic product, curren...   \n",
1443 |        "Ireland          See notes for:  Gross domestic product, curren...   \n",
1444 |        "Iceland          See notes for:  Gross domestic product, curren...   \n",
1445 |        "Norway           See notes for:  Gross domestic product, curren...   \n",
1446 |        "Switzerland      See notes for:  Gross domestic product, curren...   \n",
1447 |        "Luxembourg       See notes for:  Gross domestic product, curren...   \n",
1448 |        "\n",
1449 |        "Indicator        GDP per capita  Estimates Start After  \n",
1450 |        "Country                                                 \n",
1451 |        "South Africa           5588.959                 2016.0  \n",
1452 |        "Mexico                 7993.174                 2015.0  \n",
1453 |        "Turkey                 9826.248                 2016.0  \n",
1454 |        "Brazil                10308.811                 2016.0  \n",
1455 |        "Russia                10885.484                 2016.0  \n",
1456 |        "Poland                12721.758                 2015.0  \n",
1457 |        "Hungary               12767.023                 2015.0  \n",
1458 |        "Chile                 13662.906                 2015.0  \n",
1459 |        "Latvia                14187.602                 2016.0  \n",
1460 |        "Slovak Republic       16411.686                 2015.0  \n",
1461 |        "Greece                17805.686                 2016.0  \n",
1462 |        "Estonia               17891.084                 2016.0  \n",
1463 |        "Czech Republic        18534.333                 2015.0  \n",
1464 |        "Portugal              19707.489                 2015.0  \n",
1465 |        "Slovenia              21061.552                 2015.0  \n",
1466 |        "Spain                 26643.351                 2016.0  \n",
1467 |        "Korea                 29114.719                 2015.0  \n",
1468 |        "Italy                 29747.133                 2016.0  \n",
1469 |        "France                37294.720                 2016.0  \n",
1470 |        "United Kingdom        37812.509                 2015.0  \n",
1471 |        "Japan                 38281.579                 2016.0  \n",
1472 |        "Israel                39125.718                 2016.0  \n",
1473 |        "Belgium               40696.472                 2015.0  \n",
1474 |        "New Zealand           41107.509                 2016.0  \n",
1475 |        "Germany               41243.875                 2015.0  \n",
1476 |        "Finland               42611.825                 2014.0  \n",
1477 |        "Canada                43611.264                 2016.0  \n",
1478 |        "Austria               43786.110                 2016.0  \n",
1479 |        "Netherlands           44654.229                 2016.0  \n",
1480 |        "Sweden                49824.319                 2014.0  \n",
1481 |        "Denmark               52870.790                 2015.0  \n",
1482 |        "Australia             55215.275                 2015.0  \n",
1483 |        "United States         59609.070                 2016.0  \n",
1484 |        "Ireland               62085.417                 2016.0  \n",
1485 |        "Iceland               67570.058                 2016.0  \n",
1486 |        "Norway                73450.396                 2015.0  \n",
1487 |        "Switzerland           78245.451                 2016.0  \n",
1488 |        "Luxembourg           101715.451                 2015.0  \n",
1489 |        "\n",
1490 |        "[38 rows x 30 columns]"
1491 |       ]
1492 |      },
1493 |      "execution_count": 2,
1494 |      "metadata": {},
1495 |      "output_type": "execute_result"
1496 |     }
1497 |    ],
1498 |    "source": [
1499 |     "# Kullanılacak Data \n",
1500 |     "\n",
1501 |     "oecd_bli_2017 = pd.read_csv(\"oecd_bli_2017.csv\", thousands=',')\n",
1502 |     "oecd_bli_2017 = oecd_bli_2017[oecd_bli_2017[\"INEQUALITY\"] == \"TOT\"]\n",
1503 |     "oecd_bli_2017 = oecd_bli_2017.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
1504 |     "\n",
1505 |     "\n",
1506 |     "gdp_per_capita2017 = pd.read_excel(\"gdp_per_capita_2017.xlsx\", thousands=',', delimiter='\\t',encoding='latin1', na_values=\"n/a\")\n",
1507 |     "gdp_per_capita2017.rename(columns={2017:\"GDP per capita\"}, inplace=True)\n",
1508 |     "gdp_per_capita2017.set_index(\"Country\", inplace=True)\n",
1509 |     "\n",
1510 |     "\n",
1511 |     "country_stats_2017 = pd.merge(left=oecd_bli_2017, right=gdp_per_capita2017, left_index=True, right_index=True)\n",
1512 |     "country_stats_2017.sort_values(by=\"GDP per capita\", inplace=True)\n",
1513 |     "country_stats_2017"
1514 |    ]
1515 |   },
1516 |   {
1517 |    "cell_type": "code",
1518 |    "execution_count": 3,
1519 |    "metadata": {},
1520 |    "outputs": [
1521 |     {
1522 |      "data": {
1523 |       "text/plain": [
1524 |        "Indicator\n",
1525 |        "GDP per capita       9826.248\n",
1526 |        "Life satisfaction       5.500\n",
1527 |        "Name: Turkey, dtype: float64"
1528 |       ]
1529 |      },
1530 |      "execution_count": 3,
1531 |      "metadata": {},
1532 |      "output_type": "execute_result"
1533 |     }
1534 |    ],
1535 |    "source": [
1536 |     "# Türkiyenin kişi başına düşen Gayrisafi yurt içi hasılası (GSYİH) ve Yaşam Memnuniyeti\n",
1537 |     "country_stats_2017[[\"GDP per capita\", \"Life satisfaction\"]].loc[\"Turkey\"]"
1538 |    ]
1539 |   },
1540 |   {
1541 |    "cell_type": "code",
1542 |    "execution_count": 4,
1543 |    "metadata": {},
1544 |    "outputs": [
1545 |     {
1546 |      "name": "stderr",
1547 |      "output_type": "stream",
1548 |      "text": [
1549 |       "/Users/euclid/anaconda3/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
1550 |       "  warnings.warn(mesg, RuntimeWarning)\n"
1551 |      ]
1552 |     },
1553 |     {
1554 |      "data": {
1555 |       "text/plain": [
1556 |        "(5.660176251069037, 2.453296652801671e-05)"
1557 |       ]
1558 |      },
1559 |      "execution_count": 4,
1560 |      "metadata": {},
1561 |      "output_type": "execute_result"
1562 |     }
1563 |    ],
1564 |    "source": [
1565 |     "# Doğrusal Regresyon\n",
1566 |     "\n",
1567 |     "from sklearn import linear_model\n",
1568 |     "lin1 = linear_model.LinearRegression()\n",
1569 |     "Xsample = np.c_[country_stats_2017[\"GDP per capita\"]]\n",
1570 |     "ysample = np.c_[country_stats_2017[\"Life satisfaction\"]]\n",
1571 |     "lin1.fit(Xsample, ysample)\n",
1572 |     "t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
1573 |     "t0, t1"
1574 |    ]
1575 |   },
1576 |   {
1577 |    "cell_type": "code",
1578 |    "execution_count": 5,
1579 |    "metadata": {},
1580 |    "outputs": [
1581 |     {
1582 |      "data": {
1583 |       "text/plain": [
1584 |        "5.901243264349029"
1585 |       ]
1586 |      },
1587 |      "execution_count": 5,
1588 |      "metadata": {},
1589 |      "output_type": "execute_result"
1590 |     }
1591 |    ],
1592 |    "source": [
1593 |     "# Turkiye için Tahmini Yaşam Memnuniyeti\n",
1594 |     "\n",
1595 |     "turkey_gdp_per_capita_2017 = gdp_per_capita2017.loc[\"Turkey\"][\"GDP per capita\"]\n",
1596 |     "turkey_predicted_life_satisfaction_2017 = lin1.predict(turkey_gdp_per_capita_2017)[0][0]\n",
1597 |     "turkey_predicted_life_satisfaction_2017"
1598 |    ]
1599 |   },
1600 |   {
1601 |    "cell_type": "code",
1602 |    "execution_count": 6,
1603 |    "metadata": {},
1604 |    "outputs": [
1605 |     {
1606 |      "data": {
1607 |       "image/png": 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VIMADwAc1enqXZ5+1xTlX68RRYh0InAq8JyJzgnWjVHViDLFE55Zb7PGoo+KN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\nYII=\n",
1608 |       "text/plain": [
1609 |        "<matplotlib.figure.Figure at 0x10c8edeb8>"
1610 |       ]
1611 |      },
1612 |      "metadata": {},
1613 |      "output_type": "display_data"
1614 |     }
1615 |    ],
1616 |    "source": [
1617 |     "# Grafiğin Üretilmesi\n",
1618 |     "\n",
1619 |     "country_stats_2017.plot(kind=\"scatter\", x=\"GDP per capita\", y=\"Life satisfaction\", figsize=(5,3), s=2)\n",
1620 |     "X=np.linspace(0,60000,1000)\n",
1621 |     "plt.plot(X, t0 + t1*X, \"b\")\n",
1622 |     "plt.axis([0, 60000, 0, 10])\n",
1623 |     "plt.text(5000, 9.0, r\"$\\beta_0 = 5.66$\", fontsize=14, color=\"b\")\n",
1624 |     "plt.text(5000, 7.6, r\"$\\beta_1 = 2.45 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
1625 |     "plt.plot([turkey_gdp_per_capita_2017, turkey_gdp_per_capita_2017], [0, turkey_predicted_life_satisfaction_2017], \"r--\")\n",
1626 |     "plt.text(12000, 4.0, r\"Tahmin = 5.901\", fontsize=14, color=\"b\")\n",
1627 |     "plt.plot(turkey_gdp_per_capita_2017, turkey_predicted_life_satisfaction_2017, \"ro\")\n",
1628 |     "plt.show()"
1629 |    ]
1630 |   },
1631 |   {
1632 |    "cell_type": "code",
1633 |    "execution_count": null,
1634 |    "metadata": {
1635 |     "collapsed": true
1636 |    },
1637 |    "outputs": [],
1638 |    "source": []
1639 |   }
1640 |  ],
1641 |  "metadata": {
1642 |   "kernelspec": {
1643 |    "display_name": "Python 3",
1644 |    "language": "python",
1645 |    "name": "python3"
1646 |   },
1647 |   "language_info": {
1648 |    "codemirror_mode": {
1649 |     "name": "ipython",
1650 |     "version": 3
1651 |    },
1652 |    "file_extension": ".py",
1653 |    "mimetype": "text/x-python",
1654 |    "name": "python",
1655 |    "nbconvert_exporter": "python",
1656 |    "pygments_lexer": "ipython3",
1657 |    "version": "3.6.3"
1658 |   }
1659 |  },
1660 |  "nbformat": 4,
1661 |  "nbformat_minor": 2
1662 | }
1663 | 


--------------------------------------------------------------------------------
/decisiontree_kararagaci.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import pandas as pd
 3 | from sklearn import tree
 4 | from sklearn import metrics
 5 | from sklearn import datasets
 6 | 
 7 | from matplotlib.colors import ListedColormap
 8 | import matplotlib.pyplot as plt
 9 | 
10 | import warnings
11 | warnings.filterwarnings("ignore")
12 | 
13 | # Bu çalışma kapsamında "iris" veri seti kullanılacak !
14 | 
15 | iris = datasets.load_iris()
16 | 
17 | X = iris.data
18 | y = iris.target
19 | 
20 | print('Class labels:', np.unique(y))
21 | 
22 | # Verimizi normalize ediyoruz !
23 | 
24 | from sklearn.preprocessing import StandardScaler
25 | 
26 | ss = StandardScaler()
27 | ss.fit(X)
28 | X = ss.transform(X)
29 | 
30 | # Veri setimizi train ve test diye ikiye ayırıyoruz !
31 | 
32 | from sklearn.cross_validation import train_test_split
33 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
34 | 
35 | # Karar Ağacı algoritması ve elde edilen sonuçlar !
36 | 
37 | dtree = tree.DecisionTreeClassifier(criterion = 'entropy', random_state=0)
38 | dtree.fit(X_train, y_train)
39 | 
40 | # generate evaluation metrics
41 | print("Train - Accuracy :", metrics.accuracy_score(y_train, dtree.predict(X_train)))
42 | print("Train - Confusion matrix :",metrics.confusion_matrix(y_train, dtree.predict(X_train)))
43 | print("Train - classification report :", metrics.classification_report(y_train, dtree.predict(X_train)))
44 | print("\n")
45 | print("Test - Accuracy :", metrics.accuracy_score(y_test, dtree.predict(X_test)))
46 | print("Test - Confusion matrix :",metrics.confusion_matrix(y_test, dtree.predict(X_test)))
47 | print("Test - classification report :", metrics.classification_report(y_test, dtree.predict(X_test)))
48 | 
49 | # Karar ağacının oluşturulması !
50 | 
51 | import graphviz
52 | dot_data = tree.export_graphviz(dtree, out_file=None,feature_names=iris.feature_names,  
53 |                          class_names=iris.target_names, filled=True, rounded=True,  
54 |                          special_characters=True) 
55 | graph = graphviz.Source(dot_data) 
56 | graph.render("iris") 
57 | 
58 | dot_data = tree.export_graphviz(dtree, out_file=None, 
59 |                          feature_names=iris.feature_names,  
60 |                          class_names=iris.target_names,  
61 |                          filled=True, rounded=True,  
62 |                          special_characters=True)  
63 | graph = graphviz.Source(dot_data)  
64 | graph 
65 | 
66 | 
67 | 
68 | 


--------------------------------------------------------------------------------
/gdp_per_capita_2017.xlsx:
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https://raw.githubusercontent.com/kaanulgen/Data-science-with-python/da5553317a1e71634a934d107ddd850392ae5e25/gdp_per_capita_2017.xlsx


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/iris.pdf:
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https://raw.githubusercontent.com/kaanulgen/Data-science-with-python/da5553317a1e71634a934d107ddd850392ae5e25/iris.pdf


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/kmeans.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import matplotlib.pyplot as plt\n",
 10 |     "from sklearn import datasets\n",
 11 |     "from sklearn.cluster import KMeans\n",
 12 |     "import sklearn.metrics as sm\n",
 13 |     " \n",
 14 |     "import pandas as pd\n",
 15 |     "import numpy as np"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 2,
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "iris = datasets.load_iris()"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 3,
 30 |    "metadata": {},
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "x = pd.DataFrame(iris.data)\n",
 34 |     "x.columns = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width']\n",
 35 |     " \n",
 36 |     "y = pd.DataFrame(iris.target)\n",
 37 |     "y.columns = ['Targets']"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 4,
 43 |    "metadata": {},
 44 |    "outputs": [
 45 |     {
 46 |      "data": {
 47 |       "image/png": 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HH01ssHXUzp07w5IagMLCQubNm8euXbsO2caDDz4Y9u8Azu935syZCYv1cCmx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\nzMwseV7YmJmZmZlZ8rywMTMzMzOz5P0flvGydSXsF0AAAAAASUVORK5CYII=\n",
 48 |       "text/plain": [
 49 |        "<matplotlib.figure.Figure at 0x11791db70>"
 50 |       ]
 51 |      },
 52 |      "metadata": {},
 53 |      "output_type": "display_data"
 54 |     }
 55 |    ],
 56 |    "source": [
 57 |     "plt.figure(figsize=(14,7))\n",
 58 |     " \n",
 59 |     "colormap = np.array(['red', 'blue', 'black'])\n",
 60 |     " \n",
 61 |     "plt.subplot(1, 2, 1)\n",
 62 |     "plt.scatter(x.Sepal_Length, x.Sepal_Width, c=colormap[y.Targets], s=40)\n",
 63 |     "plt.title('Sepal')\n",
 64 |     " \n",
 65 |     "plt.subplot(1, 2, 2)\n",
 66 |     "plt.scatter(x.Petal_Length, x.Petal_Width, c=colormap[y.Targets], s=40)\n",
 67 |     "plt.title('Petal')\n",
 68 |     "plt.show()\n",
 69 |     "plt.close()"
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "code",
 74 |    "execution_count": 5,
 75 |    "metadata": {},
 76 |    "outputs": [
 77 |     {
 78 |      "data": {
 79 |       "text/plain": [
 80 |        "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
 81 |        "    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n",
 82 |        "    random_state=None, tol=0.0001, verbose=0)"
 83 |       ]
 84 |      },
 85 |      "execution_count": 5,
 86 |      "metadata": {},
 87 |      "output_type": "execute_result"
 88 |     }
 89 |    ],
 90 |    "source": [
 91 |     "model = KMeans(n_clusters=3)\n",
 92 |     "model.fit(x)"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "code",
 97 |    "execution_count": 6,
 98 |    "metadata": {},
 99 |    "outputs": [
100 |     {
101 |      "data": {
102 |       "text/plain": [
103 |        "Text(0.5,1,'K Mean Classification')"
104 |       ]
105 |      },
106 |      "execution_count": 6,
107 |      "metadata": {},
108 |      "output_type": "execute_result"
109 |     }
110 |    ],
111 |    "source": [
112 |     "plt.figure(figsize=(14,7))\n",
113 |     " \n",
114 |     "colormap = np.array(['red', 'blue', 'black'])\n",
115 |     " \n",
116 |     "plt.subplot(1, 2, 1)\n",
117 |     "plt.scatter(x.Petal_Length, x.Petal_Width, c=colormap[y.Targets], s=40)\n",
118 |     "plt.title('Real Classification')\n",
119 |     " \n",
120 |     "plt.subplot(1, 2, 2)\n",
121 |     "plt.scatter(x.Petal_Length, x.Petal_Width, c=colormap[model.labels_], s=40)\n",
122 |     "plt.title('K Mean Classification')"
123 |    ]
124 |   },
125 |   {
126 |    "cell_type": "code",
127 |    "execution_count": 7,
128 |    "metadata": {},
129 |    "outputs": [
130 |     {
131 |      "name": "stdout",
132 |      "output_type": "stream",
133 |      "text": [
134 |       "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
135 |       " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
136 |       " 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2\n",
137 |       " 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 2 2 2 2 0 2 2 2 0 2 2 2 0 2\n",
138 |       " 2 0]\n",
139 |       "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
140 |       " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
141 |       " 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2 2 1 2 2 2 2\n",
142 |       " 2 2 1 1 2 2 2 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 2\n",
143 |       " 2 1]\n"
144 |      ]
145 |     }
146 |    ],
147 |    "source": [
148 |     "predY = np.choose(model.labels_, [1, 0, 2]).astype(np.int64)\n",
149 |     "print (model.labels_)\n",
150 |     "print (predY)"
151 |    ]
152 |   },
153 |   {
154 |    "cell_type": "code",
155 |    "execution_count": 8,
156 |    "metadata": {},
157 |    "outputs": [
158 |     {
159 |      "data": {
160 |       "text/plain": [
161 |        "Text(0.5,1,'K Mean Classification')"
162 |       ]
163 |      },
164 |      "execution_count": 8,
165 |      "metadata": {},
166 |      "output_type": "execute_result"
167 |     }
168 |    ],
169 |    "source": [
170 |     "plt.figure(figsize=(14,7))\n",
171 |     " \n",
172 |     "colormap = np.array(['red', 'blue', 'black'])\n",
173 |     " \n",
174 |     "plt.subplot(1, 2, 1)\n",
175 |     "plt.scatter(x.Petal_Length, x.Petal_Width, c=colormap[y.Targets], s=40)\n",
176 |     "plt.title('Real Classification')\n",
177 |     " \n",
178 |     "plt.subplot(1, 2, 2)\n",
179 |     "plt.scatter(x.Petal_Length, x.Petal_Width, c=colormap[predY], s=40)\n",
180 |     "plt.title('K Mean Classification')"
181 |    ]
182 |   },
183 |   {
184 |    "cell_type": "code",
185 |    "execution_count": 9,
186 |    "metadata": {},
187 |    "outputs": [
188 |     {
189 |      "data": {
190 |       "text/plain": [
191 |        "0.89333333333333331"
192 |       ]
193 |      },
194 |      "execution_count": 9,
195 |      "metadata": {},
196 |      "output_type": "execute_result"
197 |     }
198 |    ],
199 |    "source": [
200 |     "sm.accuracy_score(y, predY)"
201 |    ]
202 |   },
203 |   {
204 |    "cell_type": "code",
205 |    "execution_count": null,
206 |    "metadata": {},
207 |    "outputs": [],
208 |    "source": []
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": null,
213 |    "metadata": {},
214 |    "outputs": [],
215 |    "source": []
216 |   }
217 |  ],
218 |  "metadata": {
219 |   "kernelspec": {
220 |    "display_name": "Python 3",
221 |    "language": "python",
222 |    "name": "python3"
223 |   },
224 |   "language_info": {
225 |    "codemirror_mode": {
226 |     "name": "ipython",
227 |     "version": 3
228 |    },
229 |    "file_extension": ".py",
230 |    "mimetype": "text/x-python",
231 |    "name": "python",
232 |    "nbconvert_exporter": "python",
233 |    "pygments_lexer": "ipython3",
234 |    "version": "3.6.3"
235 |   }
236 |  },
237 |  "nbformat": 4,
238 |  "nbformat_minor": 2
239 | }
240 | 


--------------------------------------------------------------------------------
/logistic_regression.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {
   7 |     "collapsed": true
   8 |    },
   9 |    "outputs": [],
  10 |    "source": [
  11 |     "import pandas as pd\n",
  12 |     "import numpy as np"
  13 |    ]
  14 |   },
  15 |   {
  16 |    "cell_type": "code",
  17 |    "execution_count": 2,
  18 |    "metadata": {},
  19 |    "outputs": [
  20 |     {
  21 |      "data": {
  22 |       "text/html": [
  23 |        "<div>\n",
  24 |        "<style>\n",
  25 |        "    .dataframe thead tr:only-child th {\n",
  26 |        "        text-align: right;\n",
  27 |        "    }\n",
  28 |        "\n",
  29 |        "    .dataframe thead th {\n",
  30 |        "        text-align: left;\n",
  31 |        "    }\n",
  32 |        "\n",
  33 |        "    .dataframe tbody tr th {\n",
  34 |        "        vertical-align: top;\n",
  35 |        "    }\n",
  36 |        "</style>\n",
  37 |        "<table border=\"1\" class=\"dataframe\">\n",
  38 |        "  <thead>\n",
  39 |        "    <tr style=\"text-align: right;\">\n",
  40 |        "      <th></th>\n",
  41 |        "      <th>pclass</th>\n",
  42 |        "      <th>survived</th>\n",
  43 |        "      <th>name</th>\n",
  44 |        "      <th>sex</th>\n",
  45 |        "      <th>age</th>\n",
  46 |        "      <th>sibsp</th>\n",
  47 |        "      <th>parch</th>\n",
  48 |        "      <th>ticket</th>\n",
  49 |        "      <th>fare</th>\n",
  50 |        "      <th>cabin</th>\n",
  51 |        "      <th>embarked</th>\n",
  52 |        "      <th>boat</th>\n",
  53 |        "      <th>body</th>\n",
  54 |        "      <th>home.dest</th>\n",
  55 |        "    </tr>\n",
  56 |        "  </thead>\n",
  57 |        "  <tbody>\n",
  58 |        "    <tr>\n",
  59 |        "      <th>0</th>\n",
  60 |        "      <td>1</td>\n",
  61 |        "      <td>1</td>\n",
  62 |        "      <td>Allen, Miss. Elisabeth Walton</td>\n",
  63 |        "      <td>female</td>\n",
  64 |        "      <td>29.0000</td>\n",
  65 |        "      <td>0</td>\n",
  66 |        "      <td>0</td>\n",
  67 |        "      <td>24160</td>\n",
  68 |        "      <td>211.3375</td>\n",
  69 |        "      <td>B5</td>\n",
  70 |        "      <td>S</td>\n",
  71 |        "      <td>2</td>\n",
  72 |        "      <td>NaN</td>\n",
  73 |        "      <td>St Louis, MO</td>\n",
  74 |        "    </tr>\n",
  75 |        "    <tr>\n",
  76 |        "      <th>1</th>\n",
  77 |        "      <td>1</td>\n",
  78 |        "      <td>1</td>\n",
  79 |        "      <td>Allison, Master. Hudson Trevor</td>\n",
  80 |        "      <td>male</td>\n",
  81 |        "      <td>0.9167</td>\n",
  82 |        "      <td>1</td>\n",
  83 |        "      <td>2</td>\n",
  84 |        "      <td>113781</td>\n",
  85 |        "      <td>151.5500</td>\n",
  86 |        "      <td>C22 C26</td>\n",
  87 |        "      <td>S</td>\n",
  88 |        "      <td>11</td>\n",
  89 |        "      <td>NaN</td>\n",
  90 |        "      <td>Montreal, PQ / Chesterville, ON</td>\n",
  91 |        "    </tr>\n",
  92 |        "    <tr>\n",
  93 |        "      <th>2</th>\n",
  94 |        "      <td>1</td>\n",
  95 |        "      <td>0</td>\n",
  96 |        "      <td>Allison, Miss. Helen Loraine</td>\n",
  97 |        "      <td>female</td>\n",
  98 |        "      <td>2.0000</td>\n",
  99 |        "      <td>1</td>\n",
 100 |        "      <td>2</td>\n",
 101 |        "      <td>113781</td>\n",
 102 |        "      <td>151.5500</td>\n",
 103 |        "      <td>C22 C26</td>\n",
 104 |        "      <td>S</td>\n",
 105 |        "      <td>NaN</td>\n",
 106 |        "      <td>NaN</td>\n",
 107 |        "      <td>Montreal, PQ / Chesterville, ON</td>\n",
 108 |        "    </tr>\n",
 109 |        "    <tr>\n",
 110 |        "      <th>3</th>\n",
 111 |        "      <td>1</td>\n",
 112 |        "      <td>0</td>\n",
 113 |        "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
 114 |        "      <td>male</td>\n",
 115 |        "      <td>30.0000</td>\n",
 116 |        "      <td>1</td>\n",
 117 |        "      <td>2</td>\n",
 118 |        "      <td>113781</td>\n",
 119 |        "      <td>151.5500</td>\n",
 120 |        "      <td>C22 C26</td>\n",
 121 |        "      <td>S</td>\n",
 122 |        "      <td>NaN</td>\n",
 123 |        "      <td>135.0</td>\n",
 124 |        "      <td>Montreal, PQ / Chesterville, ON</td>\n",
 125 |        "    </tr>\n",
 126 |        "    <tr>\n",
 127 |        "      <th>4</th>\n",
 128 |        "      <td>1</td>\n",
 129 |        "      <td>0</td>\n",
 130 |        "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
 131 |        "      <td>female</td>\n",
 132 |        "      <td>25.0000</td>\n",
 133 |        "      <td>1</td>\n",
 134 |        "      <td>2</td>\n",
 135 |        "      <td>113781</td>\n",
 136 |        "      <td>151.5500</td>\n",
 137 |        "      <td>C22 C26</td>\n",
 138 |        "      <td>S</td>\n",
 139 |        "      <td>NaN</td>\n",
 140 |        "      <td>NaN</td>\n",
 141 |        "      <td>Montreal, PQ / Chesterville, ON</td>\n",
 142 |        "    </tr>\n",
 143 |        "  </tbody>\n",
 144 |        "</table>\n",
 145 |        "</div>"
 146 |       ],
 147 |       "text/plain": [
 148 |        "   pclass  survived                                             name     sex  \\\n",
 149 |        "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
 150 |        "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
 151 |        "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
 152 |        "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
 153 |        "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
 154 |        "\n",
 155 |        "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
 156 |        "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
 157 |        "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
 158 |        "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
 159 |        "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
 160 |        "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
 161 |        "\n",
 162 |        "                         home.dest  \n",
 163 |        "0                     St Louis, MO  \n",
 164 |        "1  Montreal, PQ / Chesterville, ON  \n",
 165 |        "2  Montreal, PQ / Chesterville, ON  \n",
 166 |        "3  Montreal, PQ / Chesterville, ON  \n",
 167 |        "4  Montreal, PQ / Chesterville, ON  "
 168 |       ]
 169 |      },
 170 |      "execution_count": 2,
 171 |      "metadata": {},
 172 |      "output_type": "execute_result"
 173 |     }
 174 |    ],
 175 |    "source": [
 176 |     "data = pd.read_excel(\"titanic.xls\")\n",
 177 |     "data.head()"
 178 |    ]
 179 |   },
 180 |   {
 181 |    "cell_type": "code",
 182 |    "execution_count": 3,
 183 |    "metadata": {},
 184 |    "outputs": [
 185 |     {
 186 |      "name": "stdout",
 187 |      "output_type": "stream",
 188 |      "text": [
 189 |       "<class 'pandas.core.frame.DataFrame'>\n",
 190 |       "RangeIndex: 1309 entries, 0 to 1308\n",
 191 |       "Data columns (total 14 columns):\n",
 192 |       "pclass       1309 non-null int64\n",
 193 |       "survived     1309 non-null int64\n",
 194 |       "name         1309 non-null object\n",
 195 |       "sex          1309 non-null object\n",
 196 |       "age          1046 non-null float64\n",
 197 |       "sibsp        1309 non-null int64\n",
 198 |       "parch        1309 non-null int64\n",
 199 |       "ticket       1309 non-null object\n",
 200 |       "fare         1308 non-null float64\n",
 201 |       "cabin        295 non-null object\n",
 202 |       "embarked     1307 non-null object\n",
 203 |       "boat         486 non-null object\n",
 204 |       "body         121 non-null float64\n",
 205 |       "home.dest    745 non-null object\n",
 206 |       "dtypes: float64(3), int64(4), object(7)\n",
 207 |       "memory usage: 143.2+ KB\n"
 208 |      ]
 209 |     }
 210 |    ],
 211 |    "source": [
 212 |     "data.info()"
 213 |    ]
 214 |   },
 215 |   {
 216 |    "cell_type": "code",
 217 |    "execution_count": 4,
 218 |    "metadata": {},
 219 |    "outputs": [
 220 |     {
 221 |      "data": {
 222 |       "text/html": [
 223 |        "<div>\n",
 224 |        "<style>\n",
 225 |        "    .dataframe thead tr:only-child th {\n",
 226 |        "        text-align: right;\n",
 227 |        "    }\n",
 228 |        "\n",
 229 |        "    .dataframe thead th {\n",
 230 |        "        text-align: left;\n",
 231 |        "    }\n",
 232 |        "\n",
 233 |        "    .dataframe tbody tr th {\n",
 234 |        "        vertical-align: top;\n",
 235 |        "    }\n",
 236 |        "</style>\n",
 237 |        "<table border=\"1\" class=\"dataframe\">\n",
 238 |        "  <thead>\n",
 239 |        "    <tr style=\"text-align: right;\">\n",
 240 |        "      <th></th>\n",
 241 |        "      <th>pclass</th>\n",
 242 |        "      <th>survived</th>\n",
 243 |        "      <th>sex</th>\n",
 244 |        "      <th>age</th>\n",
 245 |        "      <th>sibsp</th>\n",
 246 |        "      <th>parch</th>\n",
 247 |        "      <th>ticket</th>\n",
 248 |        "      <th>fare</th>\n",
 249 |        "      <th>cabin</th>\n",
 250 |        "      <th>embarked</th>\n",
 251 |        "      <th>boat</th>\n",
 252 |        "    </tr>\n",
 253 |        "  </thead>\n",
 254 |        "  <tbody>\n",
 255 |        "    <tr>\n",
 256 |        "      <th>0</th>\n",
 257 |        "      <td>1</td>\n",
 258 |        "      <td>1</td>\n",
 259 |        "      <td>female</td>\n",
 260 |        "      <td>29.0000</td>\n",
 261 |        "      <td>0</td>\n",
 262 |        "      <td>0</td>\n",
 263 |        "      <td>24160</td>\n",
 264 |        "      <td>211.3375</td>\n",
 265 |        "      <td>B5</td>\n",
 266 |        "      <td>S</td>\n",
 267 |        "      <td>2</td>\n",
 268 |        "    </tr>\n",
 269 |        "    <tr>\n",
 270 |        "      <th>1</th>\n",
 271 |        "      <td>1</td>\n",
 272 |        "      <td>1</td>\n",
 273 |        "      <td>male</td>\n",
 274 |        "      <td>0.9167</td>\n",
 275 |        "      <td>1</td>\n",
 276 |        "      <td>2</td>\n",
 277 |        "      <td>113781</td>\n",
 278 |        "      <td>151.5500</td>\n",
 279 |        "      <td>C22 C26</td>\n",
 280 |        "      <td>S</td>\n",
 281 |        "      <td>11</td>\n",
 282 |        "    </tr>\n",
 283 |        "    <tr>\n",
 284 |        "      <th>2</th>\n",
 285 |        "      <td>1</td>\n",
 286 |        "      <td>0</td>\n",
 287 |        "      <td>female</td>\n",
 288 |        "      <td>2.0000</td>\n",
 289 |        "      <td>1</td>\n",
 290 |        "      <td>2</td>\n",
 291 |        "      <td>113781</td>\n",
 292 |        "      <td>151.5500</td>\n",
 293 |        "      <td>C22 C26</td>\n",
 294 |        "      <td>S</td>\n",
 295 |        "      <td>NaN</td>\n",
 296 |        "    </tr>\n",
 297 |        "    <tr>\n",
 298 |        "      <th>3</th>\n",
 299 |        "      <td>1</td>\n",
 300 |        "      <td>0</td>\n",
 301 |        "      <td>male</td>\n",
 302 |        "      <td>30.0000</td>\n",
 303 |        "      <td>1</td>\n",
 304 |        "      <td>2</td>\n",
 305 |        "      <td>113781</td>\n",
 306 |        "      <td>151.5500</td>\n",
 307 |        "      <td>C22 C26</td>\n",
 308 |        "      <td>S</td>\n",
 309 |        "      <td>NaN</td>\n",
 310 |        "    </tr>\n",
 311 |        "    <tr>\n",
 312 |        "      <th>4</th>\n",
 313 |        "      <td>1</td>\n",
 314 |        "      <td>0</td>\n",
 315 |        "      <td>female</td>\n",
 316 |        "      <td>25.0000</td>\n",
 317 |        "      <td>1</td>\n",
 318 |        "      <td>2</td>\n",
 319 |        "      <td>113781</td>\n",
 320 |        "      <td>151.5500</td>\n",
 321 |        "      <td>C22 C26</td>\n",
 322 |        "      <td>S</td>\n",
 323 |        "      <td>NaN</td>\n",
 324 |        "    </tr>\n",
 325 |        "  </tbody>\n",
 326 |        "</table>\n",
 327 |        "</div>"
 328 |       ],
 329 |       "text/plain": [
 330 |        "   pclass  survived     sex      age  sibsp  parch  ticket      fare    cabin  \\\n",
 331 |        "0       1         1  female  29.0000      0      0   24160  211.3375       B5   \n",
 332 |        "1       1         1    male   0.9167      1      2  113781  151.5500  C22 C26   \n",
 333 |        "2       1         0  female   2.0000      1      2  113781  151.5500  C22 C26   \n",
 334 |        "3       1         0    male  30.0000      1      2  113781  151.5500  C22 C26   \n",
 335 |        "4       1         0  female  25.0000      1      2  113781  151.5500  C22 C26   \n",
 336 |        "\n",
 337 |        "  embarked boat  \n",
 338 |        "0        S    2  \n",
 339 |        "1        S   11  \n",
 340 |        "2        S  NaN  \n",
 341 |        "3        S  NaN  \n",
 342 |        "4        S  NaN  "
 343 |       ]
 344 |      },
 345 |      "execution_count": 4,
 346 |      "metadata": {},
 347 |      "output_type": "execute_result"
 348 |     }
 349 |    ],
 350 |    "source": [
 351 |     "data.drop(['body','name','home.dest'], 1, inplace=True)\n",
 352 |     "data.head()"
 353 |    ]
 354 |   },
 355 |   {
 356 |    "cell_type": "code",
 357 |    "execution_count": 5,
 358 |    "metadata": {},
 359 |    "outputs": [
 360 |     {
 361 |      "data": {
 362 |       "text/html": [
 363 |        "<div>\n",
 364 |        "<style>\n",
 365 |        "    .dataframe thead tr:only-child th {\n",
 366 |        "        text-align: right;\n",
 367 |        "    }\n",
 368 |        "\n",
 369 |        "    .dataframe thead th {\n",
 370 |        "        text-align: left;\n",
 371 |        "    }\n",
 372 |        "\n",
 373 |        "    .dataframe tbody tr th {\n",
 374 |        "        vertical-align: top;\n",
 375 |        "    }\n",
 376 |        "</style>\n",
 377 |        "<table border=\"1\" class=\"dataframe\">\n",
 378 |        "  <thead>\n",
 379 |        "    <tr style=\"text-align: right;\">\n",
 380 |        "      <th></th>\n",
 381 |        "      <th>pclass</th>\n",
 382 |        "      <th>survived</th>\n",
 383 |        "      <th>age</th>\n",
 384 |        "      <th>sibsp</th>\n",
 385 |        "      <th>parch</th>\n",
 386 |        "      <th>fare</th>\n",
 387 |        "    </tr>\n",
 388 |        "  </thead>\n",
 389 |        "  <tbody>\n",
 390 |        "    <tr>\n",
 391 |        "      <th>count</th>\n",
 392 |        "      <td>1309.000000</td>\n",
 393 |        "      <td>1309.000000</td>\n",
 394 |        "      <td>1046.000000</td>\n",
 395 |        "      <td>1309.000000</td>\n",
 396 |        "      <td>1309.000000</td>\n",
 397 |        "      <td>1308.000000</td>\n",
 398 |        "    </tr>\n",
 399 |        "    <tr>\n",
 400 |        "      <th>mean</th>\n",
 401 |        "      <td>2.294882</td>\n",
 402 |        "      <td>0.381971</td>\n",
 403 |        "      <td>29.881135</td>\n",
 404 |        "      <td>0.498854</td>\n",
 405 |        "      <td>0.385027</td>\n",
 406 |        "      <td>33.295479</td>\n",
 407 |        "    </tr>\n",
 408 |        "    <tr>\n",
 409 |        "      <th>std</th>\n",
 410 |        "      <td>0.837836</td>\n",
 411 |        "      <td>0.486055</td>\n",
 412 |        "      <td>14.413500</td>\n",
 413 |        "      <td>1.041658</td>\n",
 414 |        "      <td>0.865560</td>\n",
 415 |        "      <td>51.758668</td>\n",
 416 |        "    </tr>\n",
 417 |        "    <tr>\n",
 418 |        "      <th>min</th>\n",
 419 |        "      <td>1.000000</td>\n",
 420 |        "      <td>0.000000</td>\n",
 421 |        "      <td>0.166700</td>\n",
 422 |        "      <td>0.000000</td>\n",
 423 |        "      <td>0.000000</td>\n",
 424 |        "      <td>0.000000</td>\n",
 425 |        "    </tr>\n",
 426 |        "    <tr>\n",
 427 |        "      <th>25%</th>\n",
 428 |        "      <td>2.000000</td>\n",
 429 |        "      <td>0.000000</td>\n",
 430 |        "      <td>21.000000</td>\n",
 431 |        "      <td>0.000000</td>\n",
 432 |        "      <td>0.000000</td>\n",
 433 |        "      <td>7.895800</td>\n",
 434 |        "    </tr>\n",
 435 |        "    <tr>\n",
 436 |        "      <th>50%</th>\n",
 437 |        "      <td>3.000000</td>\n",
 438 |        "      <td>0.000000</td>\n",
 439 |        "      <td>28.000000</td>\n",
 440 |        "      <td>0.000000</td>\n",
 441 |        "      <td>0.000000</td>\n",
 442 |        "      <td>14.454200</td>\n",
 443 |        "    </tr>\n",
 444 |        "    <tr>\n",
 445 |        "      <th>75%</th>\n",
 446 |        "      <td>3.000000</td>\n",
 447 |        "      <td>1.000000</td>\n",
 448 |        "      <td>39.000000</td>\n",
 449 |        "      <td>1.000000</td>\n",
 450 |        "      <td>0.000000</td>\n",
 451 |        "      <td>31.275000</td>\n",
 452 |        "    </tr>\n",
 453 |        "    <tr>\n",
 454 |        "      <th>max</th>\n",
 455 |        "      <td>3.000000</td>\n",
 456 |        "      <td>1.000000</td>\n",
 457 |        "      <td>80.000000</td>\n",
 458 |        "      <td>8.000000</td>\n",
 459 |        "      <td>9.000000</td>\n",
 460 |        "      <td>512.329200</td>\n",
 461 |        "    </tr>\n",
 462 |        "  </tbody>\n",
 463 |        "</table>\n",
 464 |        "</div>"
 465 |       ],
 466 |       "text/plain": [
 467 |        "            pclass     survived          age        sibsp        parch  \\\n",
 468 |        "count  1309.000000  1309.000000  1046.000000  1309.000000  1309.000000   \n",
 469 |        "mean      2.294882     0.381971    29.881135     0.498854     0.385027   \n",
 470 |        "std       0.837836     0.486055    14.413500     1.041658     0.865560   \n",
 471 |        "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
 472 |        "25%       2.000000     0.000000    21.000000     0.000000     0.000000   \n",
 473 |        "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
 474 |        "75%       3.000000     1.000000    39.000000     1.000000     0.000000   \n",
 475 |        "max       3.000000     1.000000    80.000000     8.000000     9.000000   \n",
 476 |        "\n",
 477 |        "              fare  \n",
 478 |        "count  1308.000000  \n",
 479 |        "mean     33.295479  \n",
 480 |        "std      51.758668  \n",
 481 |        "min       0.000000  \n",
 482 |        "25%       7.895800  \n",
 483 |        "50%      14.454200  \n",
 484 |        "75%      31.275000  \n",
 485 |        "max     512.329200  "
 486 |       ]
 487 |      },
 488 |      "execution_count": 5,
 489 |      "metadata": {},
 490 |      "output_type": "execute_result"
 491 |     }
 492 |    ],
 493 |    "source": [
 494 |     "data.describe()"
 495 |    ]
 496 |   },
 497 |   {
 498 |    "cell_type": "code",
 499 |    "execution_count": 6,
 500 |    "metadata": {},
 501 |    "outputs": [
 502 |     {
 503 |      "data": {
 504 |       "text/html": [
 505 |        "<div>\n",
 506 |        "<style>\n",
 507 |        "    .dataframe thead tr:only-child th {\n",
 508 |        "        text-align: right;\n",
 509 |        "    }\n",
 510 |        "\n",
 511 |        "    .dataframe thead th {\n",
 512 |        "        text-align: left;\n",
 513 |        "    }\n",
 514 |        "\n",
 515 |        "    .dataframe tbody tr th {\n",
 516 |        "        vertical-align: top;\n",
 517 |        "    }\n",
 518 |        "</style>\n",
 519 |        "<table border=\"1\" class=\"dataframe\">\n",
 520 |        "  <thead>\n",
 521 |        "    <tr style=\"text-align: right;\">\n",
 522 |        "      <th></th>\n",
 523 |        "      <th>pclass</th>\n",
 524 |        "      <th>survived</th>\n",
 525 |        "      <th>age</th>\n",
 526 |        "      <th>sibsp</th>\n",
 527 |        "      <th>parch</th>\n",
 528 |        "      <th>fare</th>\n",
 529 |        "    </tr>\n",
 530 |        "  </thead>\n",
 531 |        "  <tbody>\n",
 532 |        "    <tr>\n",
 533 |        "      <th>count</th>\n",
 534 |        "      <td>1309.000000</td>\n",
 535 |        "      <td>1309.000000</td>\n",
 536 |        "      <td>1309.000000</td>\n",
 537 |        "      <td>1309.000000</td>\n",
 538 |        "      <td>1309.000000</td>\n",
 539 |        "      <td>1309.000000</td>\n",
 540 |        "    </tr>\n",
 541 |        "    <tr>\n",
 542 |        "      <th>mean</th>\n",
 543 |        "      <td>2.294882</td>\n",
 544 |        "      <td>0.381971</td>\n",
 545 |        "      <td>29.503183</td>\n",
 546 |        "      <td>0.498854</td>\n",
 547 |        "      <td>0.385027</td>\n",
 548 |        "      <td>33.281086</td>\n",
 549 |        "    </tr>\n",
 550 |        "    <tr>\n",
 551 |        "      <th>std</th>\n",
 552 |        "      <td>0.837836</td>\n",
 553 |        "      <td>0.486055</td>\n",
 554 |        "      <td>12.905246</td>\n",
 555 |        "      <td>1.041658</td>\n",
 556 |        "      <td>0.865560</td>\n",
 557 |        "      <td>51.741500</td>\n",
 558 |        "    </tr>\n",
 559 |        "    <tr>\n",
 560 |        "      <th>min</th>\n",
 561 |        "      <td>1.000000</td>\n",
 562 |        "      <td>0.000000</td>\n",
 563 |        "      <td>0.166700</td>\n",
 564 |        "      <td>0.000000</td>\n",
 565 |        "      <td>0.000000</td>\n",
 566 |        "      <td>0.000000</td>\n",
 567 |        "    </tr>\n",
 568 |        "    <tr>\n",
 569 |        "      <th>25%</th>\n",
 570 |        "      <td>2.000000</td>\n",
 571 |        "      <td>0.000000</td>\n",
 572 |        "      <td>22.000000</td>\n",
 573 |        "      <td>0.000000</td>\n",
 574 |        "      <td>0.000000</td>\n",
 575 |        "      <td>7.895800</td>\n",
 576 |        "    </tr>\n",
 577 |        "    <tr>\n",
 578 |        "      <th>50%</th>\n",
 579 |        "      <td>3.000000</td>\n",
 580 |        "      <td>0.000000</td>\n",
 581 |        "      <td>28.000000</td>\n",
 582 |        "      <td>0.000000</td>\n",
 583 |        "      <td>0.000000</td>\n",
 584 |        "      <td>14.454200</td>\n",
 585 |        "    </tr>\n",
 586 |        "    <tr>\n",
 587 |        "      <th>75%</th>\n",
 588 |        "      <td>3.000000</td>\n",
 589 |        "      <td>1.000000</td>\n",
 590 |        "      <td>35.000000</td>\n",
 591 |        "      <td>1.000000</td>\n",
 592 |        "      <td>0.000000</td>\n",
 593 |        "      <td>31.275000</td>\n",
 594 |        "    </tr>\n",
 595 |        "    <tr>\n",
 596 |        "      <th>max</th>\n",
 597 |        "      <td>3.000000</td>\n",
 598 |        "      <td>1.000000</td>\n",
 599 |        "      <td>80.000000</td>\n",
 600 |        "      <td>8.000000</td>\n",
 601 |        "      <td>9.000000</td>\n",
 602 |        "      <td>512.329200</td>\n",
 603 |        "    </tr>\n",
 604 |        "  </tbody>\n",
 605 |        "</table>\n",
 606 |        "</div>"
 607 |       ],
 608 |       "text/plain": [
 609 |        "            pclass     survived          age        sibsp        parch  \\\n",
 610 |        "count  1309.000000  1309.000000  1309.000000  1309.000000  1309.000000   \n",
 611 |        "mean      2.294882     0.381971    29.503183     0.498854     0.385027   \n",
 612 |        "std       0.837836     0.486055    12.905246     1.041658     0.865560   \n",
 613 |        "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
 614 |        "25%       2.000000     0.000000    22.000000     0.000000     0.000000   \n",
 615 |        "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
 616 |        "75%       3.000000     1.000000    35.000000     1.000000     0.000000   \n",
 617 |        "max       3.000000     1.000000    80.000000     8.000000     9.000000   \n",
 618 |        "\n",
 619 |        "              fare  \n",
 620 |        "count  1309.000000  \n",
 621 |        "mean     33.281086  \n",
 622 |        "std      51.741500  \n",
 623 |        "min       0.000000  \n",
 624 |        "25%       7.895800  \n",
 625 |        "50%      14.454200  \n",
 626 |        "75%      31.275000  \n",
 627 |        "max     512.329200  "
 628 |       ]
 629 |      },
 630 |      "execution_count": 6,
 631 |      "metadata": {},
 632 |      "output_type": "execute_result"
 633 |     }
 634 |    ],
 635 |    "source": [
 636 |     "data[\"age\"].fillna(data[\"age\"].median(), inplace=True)\n",
 637 |     "data[\"fare\"].fillna(data[\"fare\"].median(), inplace=True)\n",
 638 |     "data.describe()"
 639 |    ]
 640 |   },
 641 |   {
 642 |    "cell_type": "code",
 643 |    "execution_count": 7,
 644 |    "metadata": {},
 645 |    "outputs": [
 646 |     {
 647 |      "data": {
 648 |       "image/png": 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YHREPRMTPIuLTpb0jIu6NiI0R8Y2IOLS0H1a2N5X9U0b2I0iSXkozR/S/Bs7JzBnAacB5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\nAElFTkSuQmCC\n",
 649 |       "text/plain": [
 650 |        "<matplotlib.figure.Figure at 0x112f42f60>"
 651 |       ]
 652 |      },
 653 |      "metadata": {},
 654 |      "output_type": "display_data"
 655 |     }
 656 |    ],
 657 |    "source": [
 658 |     "import matplotlib.pyplot as plt\n",
 659 |     "colors = [\"r\", \"b\",\"k\"]\n",
 660 |     "survived_ = data[data[\"survived\"]== 1][\"sex\"].value_counts()\n",
 661 |     "dead_ = data[data[\"survived\"]==0][\"sex\"].value_counts()\n",
 662 |     "data_ = pd.DataFrame([survived_, dead_])\n",
 663 |     "data_.index = [\"survived\",\"dead\"]\n",
 664 |     "data_.plot.bar(stacked=True,color=colors)\n",
 665 |     "plt.show()\n",
 666 |     "plt.close()"
 667 |    ]
 668 |   },
 669 |   {
 670 |    "cell_type": "code",
 671 |    "execution_count": 8,
 672 |    "metadata": {},
 673 |    "outputs": [
 674 |     {
 675 |      "name": "stderr",
 676 |      "output_type": "stream",
 677 |      "text": [
 678 |       "/Users/euclid/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
 679 |       "  return getattr(obj, method)(*args, **kwds)\n"
 680 |      ]
 681 |     },
 682 |     {
 683 |      "data": {
 684 |       "image/png": 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3QJLjWmuHJ1mZ5KSqOibJHyY5p7V2UJLvJXnLsP5bknyvtfavkpwzrAcAAMAOGln4tWn3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\nQOeEHwAAQOeEHwAAQOeEHwAAQOf+f39NA+wg1MaiAAAAAElFTkSuQmCC\n",
 685 |       "text/plain": [
 686 |        "<matplotlib.figure.Figure at 0x113032828>"
 687 |       ]
 688 |      },
 689 |      "metadata": {},
 690 |      "output_type": "display_data"
 691 |     }
 692 |    ],
 693 |    "source": [
 694 |     "fig = plt.figure(figsize=(15,10))\n",
 695 |     "plt.hist([data[data[\"survived\"]==1][\"age\"], data[data[\"survived\"]==0][\"age\"]],histtype='bar',stacked=True,bins=20,color=[\"r\",\"b\"],width=3, label=[\"Survived\",\"Dead\"])\n",
 696 |     "plt.xlabel(\"Yaş\")\n",
 697 |     "plt.ylabel(\"N\")\n",
 698 |     "plt.legend()\n",
 699 |     "plt.show()\n",
 700 |     "plt.close()"
 701 |    ]
 702 |   },
 703 |   {
 704 |    "cell_type": "code",
 705 |    "execution_count": 9,
 706 |    "metadata": {},
 707 |    "outputs": [
 708 |     {
 709 |      "data": {
 710 |       "image/png": 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YHhH/M6O6AABAE8XOiPYzSVWS/oekRyUNUG61LwAA0EGKDe3BEfF/JG2KiFskfVrSIdmV\nBQAAmio2tLfmv79pe4SkPpL2z6QiAADQrGJDu8Z2X0mXKDcr2guSZmRWFQAAHezyyy/f64ADDjh4\n0qRJg7I4/gUXXLDP9OnTP/RBjlHsjGg/i4jNkn4n6QBJsr3HB/lgAACaM+aiW8e05/EWXXXmomL6\n3Xjjjf1/9atfvXjQQQdtac/Pb0/FhvY9tj8bEVslyXaVpPsltesvFgB21JiLbs3s2JXrN6pc0svr\nN2b6OYuuOjOzY6M4n/vc5wauWbNmp0mTJg0+4YQTNqxatarXsmXLdt62bZunTZu27owzznhz5syZ\ne86dO3f3hoYGr1ixYudzzz23dsuWLWV33nnnnj179mz4zW9+8+KHPvShbVdffXW/n/70p/23bt3q\n/ffff/Ps2bP/UllZ+b4VMpcuXbrTV77ylYEbNmyo6NWrV8MNN9zw0qhRo95rq85iT4/fK+lu2+W2\n95f0G0nf3NFfCgAAndGsWbNe3muvvbY++uijf9q0aVP5hAkT6pYsWbLsscceW3HJJZcMqKurK5Ok\nP/3pTzvPmTNn1YIFC5ZdccUV1bvsskvDsmXLXhg7duym6667bk9J+vznP//GkiVLlq1YseKFoUOH\nvjtz5sx+TT9v8uTJ+1177bUvL126dNlVV1215pxzzhlYTJ3Fzj1+ve2eyoX3/pL+V0T8odhfBgAA\nqXjkkUd6z5s3b/eZM2dWSdLmzZu9cuXKnpL0sY99bGPfvn0b+vbt27DbbrttO/nkk9+UpEMOOeSd\n5557bhcpN7f59OnTqzdu3Fi+adOm8iOOOOKtwuO/9dZbZc8888xuJ5988ocb27Zs2eJiatuRucct\naV9Jz0r6qO2PMvc4AKCriQjNnj175aGHHrq5sP33v//9rj179vzbetplZWVqXF+7rKxM9fX1lqQp\nU6YMmj179srDDz/83ZkzZ+756KOPvm9K8G3btqmysrJ++fLl281/3pa2To9XFnztJukXklYWtAEA\n0KVMmDCh7uqrr/5QQ0PuMvTjjz++8468/5133ikbOHDg1s2bN/uOO+7Y7qbtPfbYo2HAgAFbbrrp\npr6S1NDQoCeeeKKoz2DucQAACnz/+99fN2XKlIEHHXTQ8IjwgAEDNs+fP39lse+/+OKL140bN25Y\ndXX1lmHDhr3z9ttvlzftc/vtt686++yz97vyyiv3rq+v9wknnLDh8MMPf7etYzsi2urTGe1Q0Vne\n9dlRuLu0ePy9u5dM7x5f+guVb67Ttp16a+PBJ2T2Od34721JWrx48epDDz10famL6YwWL17c79BD\nD92/cbvYu8cBAECJEdoAACSCpTkBAEgES3MCAJAIluYEACARLM0JAEAiPsjSnFdmVhUAAIm47777\nKidMmDC4Iz6r2FW+fhsRb+j9S3Nmst4oAKB7e/myQ9p1BcmB058vamnOFBQ70p7TTNvs9iwEAIBS\nWbFiRc9BgwYdfOqpp+43ZMiQgydNmjTo3nvvrRw9evRB++2334j58+fvMn/+/F1GjRp10LBhw4aP\nGjXqoMWLF+/U9Dh1dXVlJ5988v4jRowYNmzYsOE///nPd2/POttaMOQg5R7z6mP7Xwt29VbBo18A\nAKTulVde6XXnnXeuGjNmzEsjR44cdtttt+25cOHC5bNmzdr9u9/97t533XXXX5566qnlPXr00L33\n3ls5derUAfPmzftz4TG+9a1v7T1hwoS6u+++e/X69evLx44dO2zSpEl1vXv3bmjpc3dEW6fHh0o6\nTtLukj5T0L5R0tntUQAAAJ1BdXX15nHjxr0rSQceeOC7EydOrCsrK9Po0aPfufzyy/fZsGFD+amn\nnjpo9erVvWzH1q1bt1tOs6VlPUePHv1ee9TY1oIhv5T0S9ufjIjfFe6z/fH2KAAAgM6gpWU3y8vL\ntW3bNn/jG9+oPuKIIzY++OCDf16xYkXPiRMnDm16jJaW9WwvxV7T/u9m2n7UnoUAANCZ1dXVlQ8Y\nMGCLJF133XX9muvzQZf1bEtb17QPl/QxSf1tX1Cwq7ek7ZYaAwCgq/rGN75RO3ny5EEzZ86s+ud/\n/ue65vp80GU929LWNe2eknbL96ssaK+TdFJ7FQEAQKNSPKI1dOjQLS+++OLSxu05c+asbm7f6tWr\nlzS2//CHP1wnSccdd9zG4447bqMk7bbbbjFr1qyXsqqzrWvaj0p61PbNEZFZEQAAoG3FTq7yju2r\ntP0qXxMzqQoAAGyn2BvRbpO0XNIgSd+WtFrSgoxqAoBOoaHnrtq2U2819Ny11KUAkoofae8ZETfa\nPr/glPmjWRYGAKW2achRpS6hu2hoaGhwWVlZtN21+2hoaLCk903KsqOrfL1q+9O2Rym3pjYAAB/U\nktdff71PPqSgXGC//vrrfSQtKWwvdqR9ue0+kv5dueeze0v63+1bIgCgO6qvr59cW1t7Q21t7QgV\nP5js6hokLamvr59c2FhsaP8hIt6S9JakCe1dGQCg+xozZsxrkiaVuo4UFPsfzR9t3237WNtFn76w\nfbTtFbZX2r64lX4n2Q7bY4s9NtrX1KlTdeaZZ2rq1KmlLgUA0IJiQ/tASTWSviBppe3v2T6wtTfY\nLpd0jaRjJA2XdLrt4c30q5T0dUl/3JHC0b5qa2u1du1a1dbWlroUAEALigrtyHkwIk6XNFnSFyU9\nZfvR/FSnzRknaWVErIqILZLukHR8M/2+I2mGpHZZAQUAgK6qqNC2vaft820vlHShpK9J6qfcjWmz\nWnhbtaRXCrbX5NsKjztK0r4Rcd+OFg4AQHdT7OnxJ5S7Y/yzEfHpiLgnIuojYqGkn7Twnuauff/t\nGTzbZZL+S7ngb5PtKbYX2l5YU1NTZNkAAHQdxd49PjQimn3oPSKubOE9ayTtW7A9QNK6gu1KSSMk\nPZK/t61K0lzbk/L/DDT9nBrlrqtLBeEPAEB3UWxo97M9VTs29/gCSUNsD5K0VtJpkj5X8N63lDvF\nLkmy/YikC5sLbAAAkOHc4xFRL+k8SfMkLZN0V0QstX2ZbZ7HAwBgB2U693hEPCDpgSZt01voO77I\nWgAA6JaKDe33zT2u3LVp5h4HAKADMfc4AACJKCq0C56jZu5xAABKpNXQtv0jtfJ4VUR8vd0rAgAA\nzWprpF34+NW3JV2aYS0AAKAVrYZ2RNzS+Nr2vxVuAwCAjrUji40zCxkAACW0I6ENAABKqK0b0Tbq\n7yPsXWzXNe5SbsXO3lkWBwAA/q6ta9qVHVUIAABoHafHAQBIBKENAEAiCG0AABJBaAMAkAhCGwCA\nRBDaAAAkotilOVFiL192SKbHr9+wh6QK1W94KdPPGjj9+cyODQBdHSNtAAASQWgDAJAIQhsAgEQQ\n2gAAJILQBgAgEYQ2AACJILQBAEgEoQ0AQCIIbQAAEkFoAwCQCEIbAIBEENoAACSC0AYAIBGENgAA\niSC0AQBIBKENAEAiCG0AABJBaAMAkAhCGwCARBDaAAAkgtAGACARhDYAAIkgtAEASAShDQBAIght\nAAASQWgDAJCIilIXgM6hX68GSfX57wCAzojQhiTpwpFvlroEAEAbOD0OAEAiCG0AABJBaAMAkAhC\nGwCARBDaAAAkgtAGACARhDYAAIkgtAEASAShDQBAIghtAAASQWgDAJAIQhsAgEQQ2gAAJILQBgAg\nEYQ2AACJILQBAEgEoQ0AQCIIbQAAEpFpaNs+2vYK2yttX9zM/gtsv2D7Odu/tb1flvUAAJCyzELb\ndrmkayQdI2m4pNNtD2/S7RlJYyNipKTZkmZkVQ8AAKnLcqQ9TtLKiFgVEVsk3SHp+MIOETE/It7J\nbz4paUCG9QAAkLSKDI9dLemVgu01kj7SSv+zJP0qw3oA5E2dOlW1tbWqqqrSjBmc4AJSkeVI2820\nRbMd7TMkjZV0VYsHs6fYXmh7YU1NTTuVCHRPtbW1Wrt2rWpra0tdCoAdkOVIe42kfQu2B0ha17ST\n7SMlTZN0RERsbulgEVEjqTGtmw1/AAC6sixH2gskDbE9yHZPSadJmlvYwfYoSddJmhQRr2VYCwAA\nycsstCOiXtJ5kuZJWibprohYavsy25Py3a6StJuku20/a3tuC4cDAKDby/L0uCLiAUkPNGmbXvD6\nyCw/HwDAjYddSaahDQAovcYbD5E+pjEFACARhDYAAIkgtAEASAShDQBAIghtAAASQWgDAJAIQhsA\ngEQQ2gAAJILQBgAgEYQ2AACJILQBAEgEc48DQIm9fNkhmR6/fsMekipUv+GlTD9r4PTnMzs2chhp\nAwCQCEbaQCfEyAtAcxhpAwCQCEIbAIBEENoAACSC0AYAIBGENgAAiSC0AQBIBKENAEAiCG0AABJB\naAMAkAhmRAOALq5frwZJ9fnvSBmhDQBd3IUj3yx1CWgnnB4HACARhDYAAIkgtAEASAShDQBAIght\nAAASQWgDAJAIQhsAgEQQ2gAAJILJVYBuiBmygDQR2kA3xAxZQJo4PQ4AQCIIbQAAEkFoAwCQCEIb\nAIBEENoAACSC0AYAIBGENgAAiSC0AQBIBKENAEAiCG0AABJBaAMAkAhCGwCARBDaAAAkgtAGACAR\nhDYAAIkgtAEASAShDQBAIghtAAASQWgDAJAIQhsAgEQQ2gAAJILQBgAgEYQ2AACJILQBAEgEoQ0A\nQCIIbQAAEkFoAwCQCEIbAIBEZBrato+2vcL2StsXN7N/J9t35vf/0fb+WdYDAEDKMgtt2+WSrpF0\njKThkk63PbxJt7MkvRERgyX9l6Qrs6oHAIDUZTnSHidpZUSsiogtku6QdHyTPsdLuiX/erakT9l2\nhjUBAJAsR0Q2B7ZPknR0REzOb39B0kci4ryCPkvyfdbkt/+c77O+meNNkTQlv9lL0nuZFN559ZO0\n3e8FXRZ/7+6lu/6910fE0aUuIiUVGR67uRFz0/8QiumTa4yokVTzQYtKle2FETG21HWgY/D37l74\ne6NYWZ4eXyNp34LtAZLWtdTHdoWkPpI2ZFgTAADJyjK0F0gaYnuQ7Z6STpM0t0mfuZK+mH99kqSH\nI6vz9QAAJC6z0+MRUW/7PEnzJJVLuikiltq+TNLCiJgr6UZJP7O9UrkR9mlZ1dMFdNtLA90Uf+/u\nhb83ipLZjWgAAKB9MSMaAACJILQBAEgEod3J2b7J9mv5Z9rRxdne1/Z828tsL7V9fqlrQnZs97L9\nlO3F+b/3t0tdEzo3rml3crY/KeltSbdGxIhS14Ns2d5b0t4R8bTtSkmLJH02Il4ocWnIQH4GyF0j\n4m3bPST9XtL5EfFkiUtDJ8VIu5OLiN+JZ9e7jYh4NSKezr/eKGmZpOrSVoWsRM7b+c0e+S9GUmgR\noQ10UvlV70ZJ+mNpK0GWbJfbflbSa5IejAj+3mgRoQ10QrZ3kzRH0r9FRF2p60F2ImJbRPyTcrNG\njrPNZTC0iNAGOpn8tc05km6LiHtKXQ86RkS8KekRSSyggRYR2kAnkr8x6UZJyyLiB6WuB9my3d/2\n7vnXO0s6UtLy0laFzozQ7uRs3y7pCUlDba+xfVapa0KmPi7pC5Im2n42/3VsqYtCZvaWNN/2c8qt\n1/BgRNxX4prQifHIFwAAiWCkDQBAIghtAAASQWgDAJAIQhsAgEQQ2gAAJILQBjoJ2+Nt87gPgBYR\n2gAAJILQBjJke3/by23fYvs527Nt72L7MNt/yK+j/FR+Gc7C943L738m/31ovv3gfP9n88cbYntX\n2/fnj7XE9qml+WkBZK2i1AUA3cBQSWdFxOO2b5J0nqSvSDo1IhbY7i3p3SbvWS7pkxFRb/tISd+T\ndGL+fT+MiNts95RULulYSesi4tOSZLtPx/xYADoaoQ1k75WIeDz/+ueSpkl6NSIWSFLjKl65acf/\npo+kW2wPUW595R759ickTbM9QNI9EfGi7ecl/aftKyXdFxGPZf4TASgJTo8D2Ws6V3BdM21NfUfS\n/IgYIekzknpJUkTMkjRJuZH5PNsTI+JPksZIel7SFbant2fxADoPQhvI3kDbh+dfny7pSUn72D5M\nkmxX2m561quPpLX5119qbLR9gKRVETFT0lxJI23vI+mdiPi5pP+UNDqznwRASRHaQPaWSfpifiWn\nPST9SNKpkn5ke7GkB5UfSReYodyo+XHlrls3OlXSEtvPSjpI0q2SDpH0VL5tmqTLs/xhAJQOq3wB\nGbK9v3LXmUeUuBQAXQAjbQDBQY2TAAAAKklEQVQAEsFIGwCARDDSBgAgEYQ2AACJILQBAEgEoQ0A\nQCIIbQAAEvH/AfOCdPguWo+xAAAAAElFTkSuQmCC\n",
 711 |       "text/plain": [
 712 |        "<matplotlib.figure.Figure at 0x1126c5128>"
 713 |       ]
 714 |      },
 715 |      "metadata": {},
 716 |      "output_type": "display_data"
 717 |     }
 718 |    ],
 719 |    "source": [
 720 |     "import seaborn as sns\n",
 721 |     "g = sns.factorplot(x=\"pclass\", y=\"survived\", hue=\"sex\", data=data, size=6, kind=\"bar\")\n",
 722 |     "g.despine(left=True)\n",
 723 |     "g.set_ylabels(\"Hayatta kalma olasılığı\")\n",
 724 |     "plt.show()\n",
 725 |     "plt.close()"
 726 |    ]
 727 |   },
 728 |   {
 729 |    "cell_type": "code",
 730 |    "execution_count": 10,
 731 |    "metadata": {},
 732 |    "outputs": [],
 733 |    "source": [
 734 |     "sex = pd.get_dummies(data[\"sex\"],drop_first=True)\n",
 735 |     "embark = pd.get_dummies(data[\"embarked\"],drop_first=True)"
 736 |    ]
 737 |   },
 738 |   {
 739 |    "cell_type": "code",
 740 |    "execution_count": 11,
 741 |    "metadata": {},
 742 |    "outputs": [
 743 |     {
 744 |      "data": {
 745 |       "text/html": [
 746 |        "<div>\n",
 747 |        "<style>\n",
 748 |        "    .dataframe thead tr:only-child th {\n",
 749 |        "        text-align: right;\n",
 750 |        "    }\n",
 751 |        "\n",
 752 |        "    .dataframe thead th {\n",
 753 |        "        text-align: left;\n",
 754 |        "    }\n",
 755 |        "\n",
 756 |        "    .dataframe tbody tr th {\n",
 757 |        "        vertical-align: top;\n",
 758 |        "    }\n",
 759 |        "</style>\n",
 760 |        "<table border=\"1\" class=\"dataframe\">\n",
 761 |        "  <thead>\n",
 762 |        "    <tr style=\"text-align: right;\">\n",
 763 |        "      <th></th>\n",
 764 |        "      <th>pclass</th>\n",
 765 |        "      <th>survived</th>\n",
 766 |        "      <th>sex</th>\n",
 767 |        "      <th>age</th>\n",
 768 |        "      <th>sibsp</th>\n",
 769 |        "      <th>parch</th>\n",
 770 |        "      <th>ticket</th>\n",
 771 |        "      <th>fare</th>\n",
 772 |        "      <th>cabin</th>\n",
 773 |        "      <th>embarked</th>\n",
 774 |        "      <th>boat</th>\n",
 775 |        "      <th>male</th>\n",
 776 |        "      <th>Q</th>\n",
 777 |        "      <th>S</th>\n",
 778 |        "    </tr>\n",
 779 |        "  </thead>\n",
 780 |        "  <tbody>\n",
 781 |        "    <tr>\n",
 782 |        "      <th>0</th>\n",
 783 |        "      <td>1</td>\n",
 784 |        "      <td>1</td>\n",
 785 |        "      <td>female</td>\n",
 786 |        "      <td>29.0000</td>\n",
 787 |        "      <td>0</td>\n",
 788 |        "      <td>0</td>\n",
 789 |        "      <td>24160</td>\n",
 790 |        "      <td>211.3375</td>\n",
 791 |        "      <td>B5</td>\n",
 792 |        "      <td>S</td>\n",
 793 |        "      <td>2</td>\n",
 794 |        "      <td>0</td>\n",
 795 |        "      <td>0</td>\n",
 796 |        "      <td>1</td>\n",
 797 |        "    </tr>\n",
 798 |        "    <tr>\n",
 799 |        "      <th>1</th>\n",
 800 |        "      <td>1</td>\n",
 801 |        "      <td>1</td>\n",
 802 |        "      <td>male</td>\n",
 803 |        "      <td>0.9167</td>\n",
 804 |        "      <td>1</td>\n",
 805 |        "      <td>2</td>\n",
 806 |        "      <td>113781</td>\n",
 807 |        "      <td>151.5500</td>\n",
 808 |        "      <td>C22 C26</td>\n",
 809 |        "      <td>S</td>\n",
 810 |        "      <td>11</td>\n",
 811 |        "      <td>1</td>\n",
 812 |        "      <td>0</td>\n",
 813 |        "      <td>1</td>\n",
 814 |        "    </tr>\n",
 815 |        "    <tr>\n",
 816 |        "      <th>2</th>\n",
 817 |        "      <td>1</td>\n",
 818 |        "      <td>0</td>\n",
 819 |        "      <td>female</td>\n",
 820 |        "      <td>2.0000</td>\n",
 821 |        "      <td>1</td>\n",
 822 |        "      <td>2</td>\n",
 823 |        "      <td>113781</td>\n",
 824 |        "      <td>151.5500</td>\n",
 825 |        "      <td>C22 C26</td>\n",
 826 |        "      <td>S</td>\n",
 827 |        "      <td>NaN</td>\n",
 828 |        "      <td>0</td>\n",
 829 |        "      <td>0</td>\n",
 830 |        "      <td>1</td>\n",
 831 |        "    </tr>\n",
 832 |        "    <tr>\n",
 833 |        "      <th>3</th>\n",
 834 |        "      <td>1</td>\n",
 835 |        "      <td>0</td>\n",
 836 |        "      <td>male</td>\n",
 837 |        "      <td>30.0000</td>\n",
 838 |        "      <td>1</td>\n",
 839 |        "      <td>2</td>\n",
 840 |        "      <td>113781</td>\n",
 841 |        "      <td>151.5500</td>\n",
 842 |        "      <td>C22 C26</td>\n",
 843 |        "      <td>S</td>\n",
 844 |        "      <td>NaN</td>\n",
 845 |        "      <td>1</td>\n",
 846 |        "      <td>0</td>\n",
 847 |        "      <td>1</td>\n",
 848 |        "    </tr>\n",
 849 |        "    <tr>\n",
 850 |        "      <th>4</th>\n",
 851 |        "      <td>1</td>\n",
 852 |        "      <td>0</td>\n",
 853 |        "      <td>female</td>\n",
 854 |        "      <td>25.0000</td>\n",
 855 |        "      <td>1</td>\n",
 856 |        "      <td>2</td>\n",
 857 |        "      <td>113781</td>\n",
 858 |        "      <td>151.5500</td>\n",
 859 |        "      <td>C22 C26</td>\n",
 860 |        "      <td>S</td>\n",
 861 |        "      <td>NaN</td>\n",
 862 |        "      <td>0</td>\n",
 863 |        "      <td>0</td>\n",
 864 |        "      <td>1</td>\n",
 865 |        "    </tr>\n",
 866 |        "  </tbody>\n",
 867 |        "</table>\n",
 868 |        "</div>"
 869 |       ],
 870 |       "text/plain": [
 871 |        "   pclass  survived     sex      age  sibsp  parch  ticket      fare    cabin  \\\n",
 872 |        "0       1         1  female  29.0000      0      0   24160  211.3375       B5   \n",
 873 |        "1       1         1    male   0.9167      1      2  113781  151.5500  C22 C26   \n",
 874 |        "2       1         0  female   2.0000      1      2  113781  151.5500  C22 C26   \n",
 875 |        "3       1         0    male  30.0000      1      2  113781  151.5500  C22 C26   \n",
 876 |        "4       1         0  female  25.0000      1      2  113781  151.5500  C22 C26   \n",
 877 |        "\n",
 878 |        "  embarked boat  male  Q  S  \n",
 879 |        "0        S    2     0  0  1  \n",
 880 |        "1        S   11     1  0  1  \n",
 881 |        "2        S  NaN     0  0  1  \n",
 882 |        "3        S  NaN     1  0  1  \n",
 883 |        "4        S  NaN     0  0  1  "
 884 |       ]
 885 |      },
 886 |      "execution_count": 11,
 887 |      "metadata": {},
 888 |      "output_type": "execute_result"
 889 |     }
 890 |    ],
 891 |    "source": [
 892 |     "data_ = pd.concat([data, sex, embark], axis=1)\n",
 893 |     "data_.head()"
 894 |    ]
 895 |   },
 896 |   {
 897 |    "cell_type": "code",
 898 |    "execution_count": 12,
 899 |    "metadata": {},
 900 |    "outputs": [
 901 |     {
 902 |      "data": {
 903 |       "text/html": [
 904 |        "<div>\n",
 905 |        "<style>\n",
 906 |        "    .dataframe thead tr:only-child th {\n",
 907 |        "        text-align: right;\n",
 908 |        "    }\n",
 909 |        "\n",
 910 |        "    .dataframe thead th {\n",
 911 |        "        text-align: left;\n",
 912 |        "    }\n",
 913 |        "\n",
 914 |        "    .dataframe tbody tr th {\n",
 915 |        "        vertical-align: top;\n",
 916 |        "    }\n",
 917 |        "</style>\n",
 918 |        "<table border=\"1\" class=\"dataframe\">\n",
 919 |        "  <thead>\n",
 920 |        "    <tr style=\"text-align: right;\">\n",
 921 |        "      <th></th>\n",
 922 |        "      <th>pclass</th>\n",
 923 |        "      <th>survived</th>\n",
 924 |        "      <th>age</th>\n",
 925 |        "      <th>sibsp</th>\n",
 926 |        "      <th>parch</th>\n",
 927 |        "      <th>fare</th>\n",
 928 |        "      <th>male</th>\n",
 929 |        "      <th>Q</th>\n",
 930 |        "      <th>S</th>\n",
 931 |        "    </tr>\n",
 932 |        "  </thead>\n",
 933 |        "  <tbody>\n",
 934 |        "    <tr>\n",
 935 |        "      <th>0</th>\n",
 936 |        "      <td>1</td>\n",
 937 |        "      <td>1</td>\n",
 938 |        "      <td>29.0000</td>\n",
 939 |        "      <td>0</td>\n",
 940 |        "      <td>0</td>\n",
 941 |        "      <td>211.3375</td>\n",
 942 |        "      <td>0</td>\n",
 943 |        "      <td>0</td>\n",
 944 |        "      <td>1</td>\n",
 945 |        "    </tr>\n",
 946 |        "    <tr>\n",
 947 |        "      <th>1</th>\n",
 948 |        "      <td>1</td>\n",
 949 |        "      <td>1</td>\n",
 950 |        "      <td>0.9167</td>\n",
 951 |        "      <td>1</td>\n",
 952 |        "      <td>2</td>\n",
 953 |        "      <td>151.5500</td>\n",
 954 |        "      <td>1</td>\n",
 955 |        "      <td>0</td>\n",
 956 |        "      <td>1</td>\n",
 957 |        "    </tr>\n",
 958 |        "    <tr>\n",
 959 |        "      <th>2</th>\n",
 960 |        "      <td>1</td>\n",
 961 |        "      <td>0</td>\n",
 962 |        "      <td>2.0000</td>\n",
 963 |        "      <td>1</td>\n",
 964 |        "      <td>2</td>\n",
 965 |        "      <td>151.5500</td>\n",
 966 |        "      <td>0</td>\n",
 967 |        "      <td>0</td>\n",
 968 |        "      <td>1</td>\n",
 969 |        "    </tr>\n",
 970 |        "    <tr>\n",
 971 |        "      <th>3</th>\n",
 972 |        "      <td>1</td>\n",
 973 |        "      <td>0</td>\n",
 974 |        "      <td>30.0000</td>\n",
 975 |        "      <td>1</td>\n",
 976 |        "      <td>2</td>\n",
 977 |        "      <td>151.5500</td>\n",
 978 |        "      <td>1</td>\n",
 979 |        "      <td>0</td>\n",
 980 |        "      <td>1</td>\n",
 981 |        "    </tr>\n",
 982 |        "    <tr>\n",
 983 |        "      <th>4</th>\n",
 984 |        "      <td>1</td>\n",
 985 |        "      <td>0</td>\n",
 986 |        "      <td>25.0000</td>\n",
 987 |        "      <td>1</td>\n",
 988 |        "      <td>2</td>\n",
 989 |        "      <td>151.5500</td>\n",
 990 |        "      <td>0</td>\n",
 991 |        "      <td>0</td>\n",
 992 |        "      <td>1</td>\n",
 993 |        "    </tr>\n",
 994 |        "  </tbody>\n",
 995 |        "</table>\n",
 996 |        "</div>"
 997 |       ],
 998 |       "text/plain": [
 999 |        "   pclass  survived      age  sibsp  parch      fare  male  Q  S\n",
1000 |        "0       1         1  29.0000      0      0  211.3375     0  0  1\n",
1001 |        "1       1         1   0.9167      1      2  151.5500     1  0  1\n",
1002 |        "2       1         0   2.0000      1      2  151.5500     0  0  1\n",
1003 |        "3       1         0  30.0000      1      2  151.5500     1  0  1\n",
1004 |        "4       1         0  25.0000      1      2  151.5500     0  0  1"
1005 |       ]
1006 |      },
1007 |      "execution_count": 12,
1008 |      "metadata": {},
1009 |      "output_type": "execute_result"
1010 |     }
1011 |    ],
1012 |    "source": [
1013 |     "data_.drop([\"sex\",\"embarked\",\"ticket\",\"cabin\",\"boat\"], axis=1, inplace=True)\n",
1014 |     "data_.head()"
1015 |    ]
1016 |   },
1017 |   {
1018 |    "cell_type": "code",
1019 |    "execution_count": 13,
1020 |    "metadata": {},
1021 |    "outputs": [
1022 |     {
1023 |      "name": "stderr",
1024 |      "output_type": "stream",
1025 |      "text": [
1026 |       "/Users/euclid/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
1027 |       "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
1028 |      ]
1029 |     }
1030 |    ],
1031 |    "source": [
1032 |     "X = data_.drop(\"survived\", axis=1)\n",
1033 |     "y = data_[\"survived\"]\n",
1034 |     "\n",
1035 |     "from sklearn.cross_validation import train_test_split\n",
1036 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
1037 |     "\n",
1038 |     "from sklearn.preprocessing import StandardScaler\n",
1039 |     "sc = StandardScaler()\n",
1040 |     "sc.fit(X_train)\n",
1041 |     "X_train_std = sc.transform(X_train)\n",
1042 |     "X_test_std = sc.transform(X_test)"
1043 |    ]
1044 |   },
1045 |   {
1046 |    "cell_type": "code",
1047 |    "execution_count": 14,
1048 |    "metadata": {},
1049 |    "outputs": [
1050 |     {
1051 |      "data": {
1052 |       "text/plain": [
1053 |        "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
1054 |        "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
1055 |        "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
1056 |        "          verbose=0, warm_start=False)"
1057 |       ]
1058 |      },
1059 |      "execution_count": 14,
1060 |      "metadata": {},
1061 |      "output_type": "execute_result"
1062 |     }
1063 |    ],
1064 |    "source": [
1065 |     "from sklearn.linear_model import LogisticRegression\n",
1066 |     "lr = LogisticRegression()\n",
1067 |     "lr.fit(X_train_std, y_train)"
1068 |    ]
1069 |   },
1070 |   {
1071 |    "cell_type": "code",
1072 |    "execution_count": 15,
1073 |    "metadata": {},
1074 |    "outputs": [
1075 |     {
1076 |      "name": "stdout",
1077 |      "output_type": "stream",
1078 |      "text": [
1079 |       "             precision    recall  f1-score   support\n",
1080 |       "\n",
1081 |       "          0       0.82      0.87      0.84       246\n",
1082 |       "          1       0.76      0.67      0.71       147\n",
1083 |       "\n",
1084 |       "avg / total       0.79      0.80      0.79       393\n",
1085 |       "\n"
1086 |      ]
1087 |     }
1088 |    ],
1089 |    "source": [
1090 |     "prediction = lr.predict(X_test_std)\n",
1091 |     "from sklearn.metrics import classification_report\n",
1092 |     "print(classification_report(y_test, prediction))"
1093 |    ]
1094 |   },
1095 |   {
1096 |    "cell_type": "code",
1097 |    "execution_count": 16,
1098 |    "metadata": {},
1099 |    "outputs": [
1100 |     {
1101 |      "data": {
1102 |       "text/plain": [
1103 |        "array([[214,  32],\n",
1104 |        "       [ 48,  99]])"
1105 |       ]
1106 |      },
1107 |      "execution_count": 16,
1108 |      "metadata": {},
1109 |      "output_type": "execute_result"
1110 |     }
1111 |    ],
1112 |    "source": [
1113 |     "from sklearn.metrics import confusion_matrix\n",
1114 |     "confusion_matrix(y_test, prediction)"
1115 |    ]
1116 |   }
1117 |  ],
1118 |  "metadata": {
1119 |   "kernelspec": {
1120 |    "display_name": "Python 3",
1121 |    "language": "python",
1122 |    "name": "python3"
1123 |   },
1124 |   "language_info": {
1125 |    "codemirror_mode": {
1126 |     "name": "ipython",
1127 |     "version": 3
1128 |    },
1129 |    "file_extension": ".py",
1130 |    "mimetype": "text/x-python",
1131 |    "name": "python",
1132 |    "nbconvert_exporter": "python",
1133 |    "pygments_lexer": "ipython3",
1134 |    "version": "3.6.3"
1135 |   }
1136 |  },
1137 |  "nbformat": 4,
1138 |  "nbformat_minor": 2
1139 | }
1140 | 


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