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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "code",
  5. "execution_count": 4,
  6. "metadata": {},
  7. "outputs": [
  8. {
  9. "data": {
  10. "text/plain": [
  11. "'0.20.3'"
  12. ]
  13. },
  14. "execution_count": 4,
  15. "metadata": {},
  16. "output_type": "execute_result"
  17. }
  18. ],
  19. "source": [
  20. "import pandas as pd\n",
  21. "import numpy as np\n",
  22. "import sklearn\n",
  23. "from sklearn import linear_model, ensemble\n",
  24. "from sklearn.model_selection import train_test_split\n",
  25. "from sklearn.utils import shuffle\n",
  26. "import copy\n",
  27. "sklearn.__version__"
  28. ]
  29. },
  30. {
  31. "cell_type": "code",
  32. "execution_count": 5,
  33. "metadata": {},
  34. "outputs": [
  35. {
  36. "data": {
  37. "text/html": [
  38. "<div>\n",
  39. "<style scoped>\n",
  40. " .dataframe tbody tr th:only-of-type {\n",
  41. " vertical-align: middle;\n",
  42. " }\n",
  43. "\n",
  44. " .dataframe tbody tr th {\n",
  45. " vertical-align: top;\n",
  46. " }\n",
  47. "\n",
  48. " .dataframe thead th {\n",
  49. " text-align: right;\n",
  50. " }\n",
  51. "</style>\n",
  52. "<table border=\"1\" class=\"dataframe\">\n",
  53. " <thead>\n",
  54. " <tr style=\"text-align: right;\">\n",
  55. " <th></th>\n",
  56. " <th>age</th>\n",
  57. " <th>workclass</th>\n",
  58. " <th>fnlwgt</th>\n",
  59. " <th>education</th>\n",
  60. " <th>education-num</th>\n",
  61. " <th>marital-status</th>\n",
  62. " <th>occupation</th>\n",
  63. " <th>relationship</th>\n",
  64. " <th>race</th>\n",
  65. " <th>sex</th>\n",
  66. " <th>capital-gain</th>\n",
  67. " <th>capital-loss</th>\n",
  68. " <th>hours-per-week</th>\n",
  69. " <th>native-country</th>\n",
  70. " <th>yearly-income</th>\n",
  71. " </tr>\n",
  72. " </thead>\n",
  73. " <tbody>\n",
  74. " <tr>\n",
  75. " <th>0</th>\n",
  76. " <td>39</td>\n",
  77. " <td>State-gov</td>\n",
  78. " <td>77516</td>\n",
  79. " <td>Bachelors</td>\n",
  80. " <td>13</td>\n",
  81. " <td>Never-married</td>\n",
  82. " <td>Adm-clerical</td>\n",
  83. " <td>Not-in-family</td>\n",
  84. " <td>White</td>\n",
  85. " <td>Male</td>\n",
  86. " <td>2174</td>\n",
  87. " <td>0</td>\n",
  88. " <td>40</td>\n",
  89. " <td>United-States</td>\n",
  90. " <td><=50K</td>\n",
  91. " </tr>\n",
  92. " <tr>\n",
  93. " <th>1</th>\n",
  94. " <td>50</td>\n",
  95. " <td>Self-emp-not-inc</td>\n",
  96. " <td>83311</td>\n",
  97. " <td>Bachelors</td>\n",
  98. " <td>13</td>\n",
  99. " <td>Married-civ-spouse</td>\n",
  100. " <td>Exec-managerial</td>\n",
  101. " <td>Husband</td>\n",
  102. " <td>White</td>\n",
  103. " <td>Male</td>\n",
  104. " <td>0</td>\n",
  105. " <td>0</td>\n",
  106. " <td>13</td>\n",
  107. " <td>United-States</td>\n",
  108. " <td><=50K</td>\n",
  109. " </tr>\n",
  110. " <tr>\n",
  111. " <th>2</th>\n",
  112. " <td>38</td>\n",
  113. " <td>Private</td>\n",
  114. " <td>215646</td>\n",
  115. " <td>HS-grad</td>\n",
  116. " <td>9</td>\n",
  117. " <td>Divorced</td>\n",
  118. " <td>Handlers-cleaners</td>\n",
  119. " <td>Not-in-family</td>\n",
  120. " <td>White</td>\n",
  121. " <td>Male</td>\n",
  122. " <td>0</td>\n",
  123. " <td>0</td>\n",
  124. " <td>40</td>\n",
  125. " <td>United-States</td>\n",
  126. " <td><=50K</td>\n",
  127. " </tr>\n",
  128. " <tr>\n",
  129. " <th>3</th>\n",
  130. " <td>53</td>\n",
  131. " <td>Private</td>\n",
  132. " <td>234721</td>\n",
  133. " <td>11th</td>\n",
  134. " <td>7</td>\n",
  135. " <td>Married-civ-spouse</td>\n",
  136. " <td>Handlers-cleaners</td>\n",
  137. " <td>Husband</td>\n",
  138. " <td>Black</td>\n",
  139. " <td>Male</td>\n",
  140. " <td>0</td>\n",
  141. " <td>0</td>\n",
  142. " <td>40</td>\n",
  143. " <td>United-States</td>\n",
  144. " <td><=50K</td>\n",
  145. " </tr>\n",
  146. " <tr>\n",
  147. " <th>4</th>\n",
  148. " <td>28</td>\n",
  149. " <td>Private</td>\n",
  150. " <td>338409</td>\n",
  151. " <td>Bachelors</td>\n",
  152. " <td>13</td>\n",
  153. " <td>Married-civ-spouse</td>\n",
  154. " <td>Prof-specialty</td>\n",
  155. " <td>Wife</td>\n",
  156. " <td>Black</td>\n",
  157. " <td>Female</td>\n",
  158. " <td>0</td>\n",
  159. " <td>0</td>\n",
  160. " <td>40</td>\n",
  161. " <td>Cuba</td>\n",
  162. " <td><=50K</td>\n",
  163. " </tr>\n",
  164. " </tbody>\n",
  165. "</table>\n",
  166. "</div>"
  167. ],
  168. "text/plain": [
  169. " age workclass fnlwgt education education-num \\\n",
  170. "0 39 State-gov 77516 Bachelors 13 \n",
  171. "1 50 Self-emp-not-inc 83311 Bachelors 13 \n",
  172. "2 38 Private 215646 HS-grad 9 \n",
  173. "3 53 Private 234721 11th 7 \n",
  174. "4 28 Private 338409 Bachelors 13 \n",
  175. "\n",
  176. " marital-status occupation relationship race sex \\\n",
  177. "0 Never-married Adm-clerical Not-in-family White Male \n",
  178. "1 Married-civ-spouse Exec-managerial Husband White Male \n",
  179. "2 Divorced Handlers-cleaners Not-in-family White Male \n",
  180. "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n",
  181. "4 Married-civ-spouse Prof-specialty Wife Black Female \n",
  182. "\n",
  183. " capital-gain capital-loss hours-per-week native-country yearly-income \n",
  184. "0 2174 0 40 United-States <=50K \n",
  185. "1 0 0 13 United-States <=50K \n",
  186. "2 0 0 40 United-States <=50K \n",
  187. "3 0 0 40 United-States <=50K \n",
  188. "4 0 0 40 Cuba <=50K "
  189. ]
  190. },
  191. "execution_count": 5,
  192. "metadata": {},
  193. "output_type": "execute_result"
  194. }
  195. ],
  196. "source": [
  197. "df = pd.read_csv(\"income.data\", header = 0)\n",
  198. "df.columns = [col.strip() for col in df.columns]\n",
  199. "df.head()"
  200. ]
  201. }
  202. ],
  203. "metadata": {
  204. "kernelspec": {
  205. "display_name": "Python 3",
  206. "language": "python",
  207. "name": "python3"
  208. },
  209. "language_info": {
  210. "codemirror_mode": {
  211. "name": "ipython",
  212. "version": 3
  213. },
  214. "file_extension": ".py",
  215. "mimetype": "text/x-python",
  216. "name": "python",
  217. "nbconvert_exporter": "python",
  218. "pygments_lexer": "ipython3",
  219. "version": "3.6.5"
  220. }
  221. },
  222. "nbformat": 4,
  223. "nbformat_minor": 2
  224. }
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