├── README.md
└── Lassoand Regression.ipynb


/README.md:
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1 | # RegressionandLasso


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/Lassoand Regression.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Ridge and LAsso Regression implementation"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 5,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "from sklearn.datasets import load_boston"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "code",
 21 |    "execution_count": 4,
 22 |    "metadata": {},
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "import numpy as np\n",
 26 |     "import pandas as pd\n",
 27 |     "import matplotlib.pyplot as plt"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 6,
 33 |    "metadata": {},
 34 |    "outputs": [],
 35 |    "source": [
 36 |     "df=load_boston()"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "code",
 41 |    "execution_count": 8,
 42 |    "metadata": {},
 43 |    "outputs": [
 44 |     {
 45 |      "data": {
 46 |       "text/plain": [
 47 |        "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
 48 |        "         4.9800e+00],\n",
 49 |        "        [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
 50 |        "         9.1400e+00],\n",
 51 |        "        [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
 52 |        "         4.0300e+00],\n",
 53 |        "        ...,\n",
 54 |        "        [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
 55 |        "         5.6400e+00],\n",
 56 |        "        [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
 57 |        "         6.4800e+00],\n",
 58 |        "        [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
 59 |        "         7.8800e+00]]),\n",
 60 |        " 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
 61 |        "        18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
 62 |        "        15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
 63 |        "        13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
 64 |        "        21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
 65 |        "        35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
 66 |        "        19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
 67 |        "        20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
 68 |        "        23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
 69 |        "        33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
 70 |        "        21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
 71 |        "        20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
 72 |        "        23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
 73 |        "        15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
 74 |        "        17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
 75 |        "        25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
 76 |        "        23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
 77 |        "        32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
 78 |        "        34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
 79 |        "        20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
 80 |        "        26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
 81 |        "        31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
 82 |        "        22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
 83 |        "        42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
 84 |        "        36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
 85 |        "        32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
 86 |        "        20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
 87 |        "        20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
 88 |        "        22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
 89 |        "        21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
 90 |        "        19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
 91 |        "        32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
 92 |        "        18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
 93 |        "        16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
 94 |        "        13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
 95 |        "         7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
 96 |        "        12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
 97 |        "        27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
 98 |        "         8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
 99 |        "         9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
100 |        "        10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
101 |        "        15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
102 |        "        19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
103 |        "        29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
104 |        "        20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
105 |        "        23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),\n",
106 |        " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
107 |        "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
108 |        " 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n",
109 |        " 'filename': 'C:\\\\Users\\\\krish.naik\\\\AppData\\\\Local\\\\Continuum\\\\anaconda3\\\\envs\\\\myenv\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\boston_house_prices.csv'}"
110 |       ]
111 |      },
112 |      "execution_count": 8,
113 |      "metadata": {},
114 |      "output_type": "execute_result"
115 |     }
116 |    ],
117 |    "source": [
118 |     "df"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": 10,
124 |    "metadata": {},
125 |    "outputs": [
126 |     {
127 |      "name": "stdout",
128 |      "output_type": "stream",
129 |      "text": [
130 |       "        0     1     2    3      4      5     6       7    8      9     10  \\\n",
131 |       "0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
132 |       "1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
133 |       "2  0.02729   0.0  7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
134 |       "3  0.03237   0.0  2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
135 |       "4  0.06905   0.0  2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n",
136 |       "\n",
137 |       "       11    12  \n",
138 |       "0  396.90  4.98  \n",
139 |       "1  396.90  9.14  \n",
140 |       "2  392.83  4.03  \n",
141 |       "3  394.63  2.94  \n",
142 |       "4  396.90  5.33  \n"
143 |      ]
144 |     }
145 |    ],
146 |    "source": [
147 |     "dataset = pd.DataFrame(df.data)\n",
148 |     "print(dataset.head())"
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "code",
153 |    "execution_count": 11,
154 |    "metadata": {},
155 |    "outputs": [],
156 |    "source": [
157 |     "dataset.columns=df.feature_names"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 12,
163 |    "metadata": {},
164 |    "outputs": [
165 |     {
166 |      "data": {
167 |       "text/html": [
168 |        "<div>\n",
169 |        "<style scoped>\n",
170 |        "    .dataframe tbody tr th:only-of-type {\n",
171 |        "        vertical-align: middle;\n",
172 |        "    }\n",
173 |        "\n",
174 |        "    .dataframe tbody tr th {\n",
175 |        "        vertical-align: top;\n",
176 |        "    }\n",
177 |        "\n",
178 |        "    .dataframe thead th {\n",
179 |        "        text-align: right;\n",
180 |        "    }\n",
181 |        "</style>\n",
182 |        "<table border=\"1\" class=\"dataframe\">\n",
183 |        "  <thead>\n",
184 |        "    <tr style=\"text-align: right;\">\n",
185 |        "      <th></th>\n",
186 |        "      <th>CRIM</th>\n",
187 |        "      <th>ZN</th>\n",
188 |        "      <th>INDUS</th>\n",
189 |        "      <th>CHAS</th>\n",
190 |        "      <th>NOX</th>\n",
191 |        "      <th>RM</th>\n",
192 |        "      <th>AGE</th>\n",
193 |        "      <th>DIS</th>\n",
194 |        "      <th>RAD</th>\n",
195 |        "      <th>TAX</th>\n",
196 |        "      <th>PTRATIO</th>\n",
197 |        "      <th>B</th>\n",
198 |        "      <th>LSTAT</th>\n",
199 |        "    </tr>\n",
200 |        "  </thead>\n",
201 |        "  <tbody>\n",
202 |        "    <tr>\n",
203 |        "      <th>0</th>\n",
204 |        "      <td>0.00632</td>\n",
205 |        "      <td>18.0</td>\n",
206 |        "      <td>2.31</td>\n",
207 |        "      <td>0.0</td>\n",
208 |        "      <td>0.538</td>\n",
209 |        "      <td>6.575</td>\n",
210 |        "      <td>65.2</td>\n",
211 |        "      <td>4.0900</td>\n",
212 |        "      <td>1.0</td>\n",
213 |        "      <td>296.0</td>\n",
214 |        "      <td>15.3</td>\n",
215 |        "      <td>396.90</td>\n",
216 |        "      <td>4.98</td>\n",
217 |        "    </tr>\n",
218 |        "    <tr>\n",
219 |        "      <th>1</th>\n",
220 |        "      <td>0.02731</td>\n",
221 |        "      <td>0.0</td>\n",
222 |        "      <td>7.07</td>\n",
223 |        "      <td>0.0</td>\n",
224 |        "      <td>0.469</td>\n",
225 |        "      <td>6.421</td>\n",
226 |        "      <td>78.9</td>\n",
227 |        "      <td>4.9671</td>\n",
228 |        "      <td>2.0</td>\n",
229 |        "      <td>242.0</td>\n",
230 |        "      <td>17.8</td>\n",
231 |        "      <td>396.90</td>\n",
232 |        "      <td>9.14</td>\n",
233 |        "    </tr>\n",
234 |        "    <tr>\n",
235 |        "      <th>2</th>\n",
236 |        "      <td>0.02729</td>\n",
237 |        "      <td>0.0</td>\n",
238 |        "      <td>7.07</td>\n",
239 |        "      <td>0.0</td>\n",
240 |        "      <td>0.469</td>\n",
241 |        "      <td>7.185</td>\n",
242 |        "      <td>61.1</td>\n",
243 |        "      <td>4.9671</td>\n",
244 |        "      <td>2.0</td>\n",
245 |        "      <td>242.0</td>\n",
246 |        "      <td>17.8</td>\n",
247 |        "      <td>392.83</td>\n",
248 |        "      <td>4.03</td>\n",
249 |        "    </tr>\n",
250 |        "    <tr>\n",
251 |        "      <th>3</th>\n",
252 |        "      <td>0.03237</td>\n",
253 |        "      <td>0.0</td>\n",
254 |        "      <td>2.18</td>\n",
255 |        "      <td>0.0</td>\n",
256 |        "      <td>0.458</td>\n",
257 |        "      <td>6.998</td>\n",
258 |        "      <td>45.8</td>\n",
259 |        "      <td>6.0622</td>\n",
260 |        "      <td>3.0</td>\n",
261 |        "      <td>222.0</td>\n",
262 |        "      <td>18.7</td>\n",
263 |        "      <td>394.63</td>\n",
264 |        "      <td>2.94</td>\n",
265 |        "    </tr>\n",
266 |        "    <tr>\n",
267 |        "      <th>4</th>\n",
268 |        "      <td>0.06905</td>\n",
269 |        "      <td>0.0</td>\n",
270 |        "      <td>2.18</td>\n",
271 |        "      <td>0.0</td>\n",
272 |        "      <td>0.458</td>\n",
273 |        "      <td>7.147</td>\n",
274 |        "      <td>54.2</td>\n",
275 |        "      <td>6.0622</td>\n",
276 |        "      <td>3.0</td>\n",
277 |        "      <td>222.0</td>\n",
278 |        "      <td>18.7</td>\n",
279 |        "      <td>396.90</td>\n",
280 |        "      <td>5.33</td>\n",
281 |        "    </tr>\n",
282 |        "  </tbody>\n",
283 |        "</table>\n",
284 |        "</div>"
285 |       ],
286 |       "text/plain": [
287 |        "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
288 |        "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
289 |        "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
290 |        "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
291 |        "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
292 |        "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
293 |        "\n",
294 |        "   PTRATIO       B  LSTAT  \n",
295 |        "0     15.3  396.90   4.98  \n",
296 |        "1     17.8  396.90   9.14  \n",
297 |        "2     17.8  392.83   4.03  \n",
298 |        "3     18.7  394.63   2.94  \n",
299 |        "4     18.7  396.90   5.33  "
300 |       ]
301 |      },
302 |      "execution_count": 12,
303 |      "metadata": {},
304 |      "output_type": "execute_result"
305 |     }
306 |    ],
307 |    "source": [
308 |     "dataset.head()"
309 |    ]
310 |   },
311 |   {
312 |    "cell_type": "code",
313 |    "execution_count": 13,
314 |    "metadata": {},
315 |    "outputs": [
316 |     {
317 |      "data": {
318 |       "text/plain": [
319 |        "(506,)"
320 |       ]
321 |      },
322 |      "execution_count": 13,
323 |      "metadata": {},
324 |      "output_type": "execute_result"
325 |     }
326 |    ],
327 |    "source": [
328 |     "df.target.shape"
329 |    ]
330 |   },
331 |   {
332 |    "cell_type": "code",
333 |    "execution_count": 15,
334 |    "metadata": {},
335 |    "outputs": [],
336 |    "source": [
337 |     "dataset[\"Price\"]=df.target"
338 |    ]
339 |   },
340 |   {
341 |    "cell_type": "code",
342 |    "execution_count": 16,
343 |    "metadata": {},
344 |    "outputs": [
345 |     {
346 |      "data": {
347 |       "text/html": [
348 |        "<div>\n",
349 |        "<style scoped>\n",
350 |        "    .dataframe tbody tr th:only-of-type {\n",
351 |        "        vertical-align: middle;\n",
352 |        "    }\n",
353 |        "\n",
354 |        "    .dataframe tbody tr th {\n",
355 |        "        vertical-align: top;\n",
356 |        "    }\n",
357 |        "\n",
358 |        "    .dataframe thead th {\n",
359 |        "        text-align: right;\n",
360 |        "    }\n",
361 |        "</style>\n",
362 |        "<table border=\"1\" class=\"dataframe\">\n",
363 |        "  <thead>\n",
364 |        "    <tr style=\"text-align: right;\">\n",
365 |        "      <th></th>\n",
366 |        "      <th>CRIM</th>\n",
367 |        "      <th>ZN</th>\n",
368 |        "      <th>INDUS</th>\n",
369 |        "      <th>CHAS</th>\n",
370 |        "      <th>NOX</th>\n",
371 |        "      <th>RM</th>\n",
372 |        "      <th>AGE</th>\n",
373 |        "      <th>DIS</th>\n",
374 |        "      <th>RAD</th>\n",
375 |        "      <th>TAX</th>\n",
376 |        "      <th>PTRATIO</th>\n",
377 |        "      <th>B</th>\n",
378 |        "      <th>LSTAT</th>\n",
379 |        "      <th>Price</th>\n",
380 |        "    </tr>\n",
381 |        "  </thead>\n",
382 |        "  <tbody>\n",
383 |        "    <tr>\n",
384 |        "      <th>0</th>\n",
385 |        "      <td>0.00632</td>\n",
386 |        "      <td>18.0</td>\n",
387 |        "      <td>2.31</td>\n",
388 |        "      <td>0.0</td>\n",
389 |        "      <td>0.538</td>\n",
390 |        "      <td>6.575</td>\n",
391 |        "      <td>65.2</td>\n",
392 |        "      <td>4.0900</td>\n",
393 |        "      <td>1.0</td>\n",
394 |        "      <td>296.0</td>\n",
395 |        "      <td>15.3</td>\n",
396 |        "      <td>396.90</td>\n",
397 |        "      <td>4.98</td>\n",
398 |        "      <td>24.0</td>\n",
399 |        "    </tr>\n",
400 |        "    <tr>\n",
401 |        "      <th>1</th>\n",
402 |        "      <td>0.02731</td>\n",
403 |        "      <td>0.0</td>\n",
404 |        "      <td>7.07</td>\n",
405 |        "      <td>0.0</td>\n",
406 |        "      <td>0.469</td>\n",
407 |        "      <td>6.421</td>\n",
408 |        "      <td>78.9</td>\n",
409 |        "      <td>4.9671</td>\n",
410 |        "      <td>2.0</td>\n",
411 |        "      <td>242.0</td>\n",
412 |        "      <td>17.8</td>\n",
413 |        "      <td>396.90</td>\n",
414 |        "      <td>9.14</td>\n",
415 |        "      <td>21.6</td>\n",
416 |        "    </tr>\n",
417 |        "    <tr>\n",
418 |        "      <th>2</th>\n",
419 |        "      <td>0.02729</td>\n",
420 |        "      <td>0.0</td>\n",
421 |        "      <td>7.07</td>\n",
422 |        "      <td>0.0</td>\n",
423 |        "      <td>0.469</td>\n",
424 |        "      <td>7.185</td>\n",
425 |        "      <td>61.1</td>\n",
426 |        "      <td>4.9671</td>\n",
427 |        "      <td>2.0</td>\n",
428 |        "      <td>242.0</td>\n",
429 |        "      <td>17.8</td>\n",
430 |        "      <td>392.83</td>\n",
431 |        "      <td>4.03</td>\n",
432 |        "      <td>34.7</td>\n",
433 |        "    </tr>\n",
434 |        "    <tr>\n",
435 |        "      <th>3</th>\n",
436 |        "      <td>0.03237</td>\n",
437 |        "      <td>0.0</td>\n",
438 |        "      <td>2.18</td>\n",
439 |        "      <td>0.0</td>\n",
440 |        "      <td>0.458</td>\n",
441 |        "      <td>6.998</td>\n",
442 |        "      <td>45.8</td>\n",
443 |        "      <td>6.0622</td>\n",
444 |        "      <td>3.0</td>\n",
445 |        "      <td>222.0</td>\n",
446 |        "      <td>18.7</td>\n",
447 |        "      <td>394.63</td>\n",
448 |        "      <td>2.94</td>\n",
449 |        "      <td>33.4</td>\n",
450 |        "    </tr>\n",
451 |        "    <tr>\n",
452 |        "      <th>4</th>\n",
453 |        "      <td>0.06905</td>\n",
454 |        "      <td>0.0</td>\n",
455 |        "      <td>2.18</td>\n",
456 |        "      <td>0.0</td>\n",
457 |        "      <td>0.458</td>\n",
458 |        "      <td>7.147</td>\n",
459 |        "      <td>54.2</td>\n",
460 |        "      <td>6.0622</td>\n",
461 |        "      <td>3.0</td>\n",
462 |        "      <td>222.0</td>\n",
463 |        "      <td>18.7</td>\n",
464 |        "      <td>396.90</td>\n",
465 |        "      <td>5.33</td>\n",
466 |        "      <td>36.2</td>\n",
467 |        "    </tr>\n",
468 |        "  </tbody>\n",
469 |        "</table>\n",
470 |        "</div>"
471 |       ],
472 |       "text/plain": [
473 |        "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
474 |        "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
475 |        "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
476 |        "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
477 |        "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
478 |        "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
479 |        "\n",
480 |        "   PTRATIO       B  LSTAT  Price  \n",
481 |        "0     15.3  396.90   4.98   24.0  \n",
482 |        "1     17.8  396.90   9.14   21.6  \n",
483 |        "2     17.8  392.83   4.03   34.7  \n",
484 |        "3     18.7  394.63   2.94   33.4  \n",
485 |        "4     18.7  396.90   5.33   36.2  "
486 |       ]
487 |      },
488 |      "execution_count": 16,
489 |      "metadata": {},
490 |      "output_type": "execute_result"
491 |     }
492 |    ],
493 |    "source": [
494 |     "dataset.head()"
495 |    ]
496 |   },
497 |   {
498 |    "cell_type": "code",
499 |    "execution_count": 18,
500 |    "metadata": {},
501 |    "outputs": [],
502 |    "source": [
503 |     "X=dataset.iloc[:,:-1] ## independent features\n",
504 |     "y=dataset.iloc[:,-1] ## dependent features"
505 |    ]
506 |   },
507 |   {
508 |    "cell_type": "markdown",
509 |    "metadata": {},
510 |    "source": [
511 |     "## Linear Regression\n"
512 |    ]
513 |   },
514 |   {
515 |    "cell_type": "code",
516 |    "execution_count": 19,
517 |    "metadata": {},
518 |    "outputs": [
519 |     {
520 |      "name": "stderr",
521 |      "output_type": "stream",
522 |      "text": [
523 |       "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
524 |       "  return f(*args, **kwds)\n"
525 |      ]
526 |     },
527 |     {
528 |      "name": "stdout",
529 |      "output_type": "stream",
530 |      "text": [
531 |       "-37.131807467699204\n"
532 |      ]
533 |     }
534 |    ],
535 |    "source": [
536 |     "from sklearn.model_selection import cross_val_score\n",
537 |     "from sklearn.linear_model import LinearRegression\n",
538 |     "\n",
539 |     "lin_regressor=LinearRegression()\n",
540 |     "mse=cross_val_score(lin_regressor,X,y,scoring='neg_mean_squared_error',cv=5)\n",
541 |     "mean_mse=np.mean(mse)\n",
542 |     "print(mean_mse)"
543 |    ]
544 |   },
545 |   {
546 |    "cell_type": "markdown",
547 |    "metadata": {},
548 |    "source": [
549 |     "## Ridge Regression"
550 |    ]
551 |   },
552 |   {
553 |    "cell_type": "code",
554 |    "execution_count": 22,
555 |    "metadata": {},
556 |    "outputs": [
557 |     {
558 |      "data": {
559 |       "text/plain": [
560 |        "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
561 |        "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
562 |        "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
563 |        "       fit_params=None, iid='warn', n_jobs=None,\n",
564 |        "       param_grid={'alpha': [1e-15, 1e-10, 1e-08, 0.001, 0.01, 1, 5, 10, 20, 30, 35, 40, 45, 50, 55, 100]},\n",
565 |        "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
566 |        "       scoring='neg_mean_squared_error', verbose=0)"
567 |       ]
568 |      },
569 |      "execution_count": 22,
570 |      "metadata": {},
571 |      "output_type": "execute_result"
572 |     }
573 |    ],
574 |    "source": [
575 |     "from sklearn.linear_model import Ridge\n",
576 |     "from sklearn.model_selection import GridSearchCV\n",
577 |     "\n",
578 |     "ridge=Ridge()\n",
579 |     "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40,45,50,55,100]}\n",
580 |     "ridge_regressor=GridSearchCV(ridge,parameters,scoring='neg_mean_squared_error',cv=5)\n",
581 |     "ridge_regressor.fit(X,y)"
582 |    ]
583 |   },
584 |   {
585 |    "cell_type": "code",
586 |    "execution_count": 23,
587 |    "metadata": {},
588 |    "outputs": [
589 |     {
590 |      "name": "stdout",
591 |      "output_type": "stream",
592 |      "text": [
593 |       "{'alpha': 100}\n",
594 |       "-29.871945115432595\n"
595 |      ]
596 |     }
597 |    ],
598 |    "source": [
599 |     "print(ridge_regressor.best_params_)\n",
600 |     "print(ridge_regressor.best_score_)"
601 |    ]
602 |   },
603 |   {
604 |    "cell_type": "markdown",
605 |    "metadata": {},
606 |    "source": [
607 |     "## Lasso Regression"
608 |    ]
609 |   },
610 |   {
611 |    "cell_type": "code",
612 |    "execution_count": 24,
613 |    "metadata": {},
614 |    "outputs": [
615 |     {
616 |      "name": "stderr",
617 |      "output_type": "stream",
618 |      "text": [
619 |       "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
620 |       "  ConvergenceWarning)\n",
621 |       "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
622 |       "  ConvergenceWarning)\n",
623 |       "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
624 |       "  ConvergenceWarning)\n",
625 |       "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
626 |       "  ConvergenceWarning)\n",
627 |       "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
628 |       "  ConvergenceWarning)\n"
629 |      ]
630 |     },
631 |     {
632 |      "name": "stdout",
633 |      "output_type": "stream",
634 |      "text": [
635 |       "{'alpha': 1}\n",
636 |       "-35.491283263627096\n"
637 |      ]
638 |     }
639 |    ],
640 |    "source": [
641 |     "from sklearn.linear_model import Lasso\n",
642 |     "from sklearn.model_selection import GridSearchCV\n",
643 |     "lasso=Lasso()\n",
644 |     "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40,45,50,55,100]}\n",
645 |     "lasso_regressor=GridSearchCV(lasso,parameters,scoring='neg_mean_squared_error',cv=5)\n",
646 |     "\n",
647 |     "lasso_regressor.fit(X,y)\n",
648 |     "print(lasso_regressor.best_params_)\n",
649 |     "print(lasso_regressor.best_score_)"
650 |    ]
651 |   },
652 |   {
653 |    "cell_type": "code",
654 |    "execution_count": 25,
655 |    "metadata": {},
656 |    "outputs": [],
657 |    "source": [
658 |     "from sklearn.model_selection import train_test_split\n",
659 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
660 |    ]
661 |   },
662 |   {
663 |    "cell_type": "code",
664 |    "execution_count": 26,
665 |    "metadata": {},
666 |    "outputs": [],
667 |    "source": [
668 |     "prediction_lasso=lasso_regressor.predict(X_test)\n",
669 |     "prediction_ridge=ridge_regressor.predict(X_test)"
670 |    ]
671 |   },
672 |   {
673 |    "cell_type": "code",
674 |    "execution_count": 28,
675 |    "metadata": {},
676 |    "outputs": [
677 |     {
678 |      "name": "stderr",
679 |      "output_type": "stream",
680 |      "text": [
681 |       "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
682 |       "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
683 |      ]
684 |     },
685 |     {
686 |      "data": {
687 |       "text/plain": [
688 |        "<matplotlib.axes._subplots.AxesSubplot at 0x1b4bf5736a0>"
689 |       ]
690 |      },
691 |      "execution_count": 28,
692 |      "metadata": {},
693 |      "output_type": "execute_result"
694 |     },
695 |     {
696 |      "data": {
697 |       "image/png": 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\n",
698 |       "text/plain": [
699 |        "<Figure size 432x288 with 1 Axes>"
700 |       ]
701 |      },
702 |      "metadata": {
703 |       "needs_background": "light"
704 |      },
705 |      "output_type": "display_data"
706 |     }
707 |    ],
708 |    "source": [
709 |     "import seaborn as sns\n",
710 |     "\n",
711 |     "sns.distplot(y_test-prediction_lasso)"
712 |    ]
713 |   },
714 |   {
715 |    "cell_type": "code",
716 |    "execution_count": 29,
717 |    "metadata": {},
718 |    "outputs": [
719 |     {
720 |      "data": {
721 |       "text/plain": [
722 |        "<matplotlib.axes._subplots.AxesSubplot at 0x1b4bf777240>"
723 |       ]
724 |      },
725 |      "execution_count": 29,
726 |      "metadata": {},
727 |      "output_type": "execute_result"
728 |     },
729 |     {
730 |      "data": {
731 |       "image/png": 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\n",
732 |       "text/plain": [
733 |        "<Figure size 432x288 with 1 Axes>"
734 |       ]
735 |      },
736 |      "metadata": {
737 |       "needs_background": "light"
738 |      },
739 |      "output_type": "display_data"
740 |     }
741 |    ],
742 |    "source": [
743 |     "import seaborn as sns\n",
744 |     "\n",
745 |     "sns.distplot(y_test-prediction_ridge)"
746 |    ]
747 |   },
748 |   {
749 |    "cell_type": "code",
750 |    "execution_count": null,
751 |    "metadata": {},
752 |    "outputs": [],
753 |    "source": []
754 |   }
755 |  ],
756 |  "metadata": {
757 |   "kernelspec": {
758 |    "display_name": "Python 3",
759 |    "language": "python",
760 |    "name": "python3"
761 |   },
762 |   "language_info": {
763 |    "codemirror_mode": {
764 |     "name": "ipython",
765 |     "version": 3
766 |    },
767 |    "file_extension": ".py",
768 |    "mimetype": "text/x-python",
769 |    "name": "python",
770 |    "nbconvert_exporter": "python",
771 |    "pygments_lexer": "ipython3",
772 |    "version": "3.6.7"
773 |   }
774 |  },
775 |  "nbformat": 4,
776 |  "nbformat_minor": 2
777 | }
778 | 


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