boston_sgd (1).ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "fMsWJ6B7AkW2"
   },
   "source": [
    " IMPLEMENTATION OF SGD FOR BOSTON DATASET \n",
    " ================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "si7xvcgJ75Re"
   },
   "outputs": [],
   "source": [
    "!pip install -U -q Pydrive\n",
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plot\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model, datasets\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import linear_model\n",
    "import sklearn\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TvJ9yQR8U_Cb"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
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    "colab_type": "code",
    "id": "eEtfywG8VQd_",
    "outputId": "b966f65b-5c14-40a9-f500-4215bb805732"
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   "outputs": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.9</td>\n",
       "      <td>4.98</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.9</td>\n",
       "      <td>9.14</td>\n",
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       "        0     1     2    3      4      5     6       7    8      9     10  \\\n",
       "0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
       "1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
       "\n",
       "      11    12  \n",
       "0  396.9  4.98  \n",
       "1  396.9  9.14  "
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "bos = pd.DataFrame(boston.data)\n",
    "bos.head(2)"
   ]
  },
  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.593761</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.596783</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.647423</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.593761   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.596783   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.647423   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT       PRICE  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 154,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1009
    },
    "colab_type": "code",
    "id": "b49V-gK2GsoM",
    "outputId": "5329ec01-816e-49f9-9f72-7a28033c5658"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston House Prices dataset\n",
      "===========================\n",
      "\n",
      "Notes\n",
      "------\n",
      "Data Set Characteristics:  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive\n",
      "    \n",
      "    :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",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "http://archive.ics.uci.edu/ml/datasets/Housing\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      "**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",
      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "id": "KDNaezVJBI3F",
    "outputId": "53d4b89d-098c-4101-c5e7-f7fe5022b0cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null float64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null float64\n",
      "TAX        506 non-null float64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "PRICE      506 non-null float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "bos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "id": "MgH6shkSVV7J",
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    {
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
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       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.9</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.9</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "\n",
       "   PTRATIO      B  LSTAT  \n",
       "0     15.3  396.9   4.98  \n",
       "1     17.8  396.9   9.14  "
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos.columns = boston.feature_names\n",
    "bos.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "U3azm-mtVbQ9",
    "outputId": "91b99be9-ec47-48e8-d9ef-b33bd6b3fd21"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2])"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.target[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xI-fL1rS53Ns"
   },
   "outputs": [],
   "source": [
    "\n",
    "bos['PRICE'] = boston.target\n",
    "X = bos.drop('PRICE', axis = 1)\n",
    "Y = bos['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9WEwFTf06D6V"
   },
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(X, Y, test_size = 0.33, random_state = 5)\n",
    "#splitting data into train and test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_iSJSEQQAQ6B"
   },
   "source": [
    "SKLEARN'S SGD REGRESSOR IMPLEMENTATION\n",
    "============"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "colab_type": "code",
    "id": "eKR-25GfMwRX",
    "outputId": "d0c7ad73-0ab0-4181-a540-380a9930c041"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(alpha=0.005, average=False, epsilon=0.1, eta0=0.01,\n",
       "       fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',\n",
       "       loss='squared_loss', max_iter=100000, n_iter=None, penalty='l2',\n",
       "       power_t=0.25, random_state=2, shuffle=True, tol=0.005, verbose=0,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "clf = SGDRegressor(alpha=0.005,max_iter=100000,eta0=0.01,tol=0.005,random_state=2)\n",
    "clf.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387
    },
    "colab_type": "code",
    "id": "4cN1Pyk_7h-D",
    "outputId": "945ce2ee-9b00-4d84-d51b-e5512863c89c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fhskk/BREwcMnTwiuls8QsbxGVKQOep3wz91XNytn4/VthJ70PVk2OtipcT6H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\nEHGEAQAAIu4XJ8DKfs7+s9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc593da02b0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_pred = clf.predict(X_test)\n",
    "plt.scatter(Y_test, Y_pred)\n",
    "plt.xlabel(\"Prices: $Y_i$\")\n",
    "plt.ylabel(\"Predicted prices: $\\hat{Y}_i$\")\n",
    "plt.title(\"Prices vs Predicted prices: $Y_i$ vs $\\hat{Y}_i$\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "_zm9PDZ20Zgs",
    "outputId": "5193347e-e6cd-49ff-ed63-6ea0af5c597d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for SKLEARN'S SGD MODEL : -6.241464326312736e+25\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"R^2 score for SKLEARN'S SGD MODEL :\", r2_score(Y_test, Y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "agTTdGRFAIKN"
   },
   "source": [
    "SKLEARN'S LINEAR REGRESSION  MODEL\n",
    "===="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "4hm3krnct53j",
    "outputId": "0694036f-5382-4c5b-9935-29ba3b8b34f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 =linear_model.LinearRegression()\n",
    "model1.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387
    },
    "colab_type": "code",
    "id": "NkBI8lV6t_pq",
    "outputId": "fe8a749e-0921-4567-8f5c-62efbe889c95"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VmjGobzJD5eIakTyF7C+scR94DOpEFDIC/aHvSRlWqWliFQCdVg2zxdbtPk4hu8ca94HH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\n0LYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc593cc5cf8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = model1.predict(X_test)\n",
    "plt.scatter(Y_test, pred)\n",
    "plt.xlabel(\"Prices: $Y_i$\")\n",
    "plt.ylabel(\"Predicted prices: $\\hat{Y}_i$\")\n",
    "plt.title(\"Prices vs Predicted prices: $Y_i$ vs $\\hat{Y}_i$\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "frhXK0vo1guZ",
    "outputId": "e493559b-0c6c-4a17-fcaf-a9ed96dfc13f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for SKLEARN'S LINEAR MODEL : 0.6955388005506419\n"
     ]
    }
   ],
   "source": [
    "print(\"R^2 score for SKLEARN'S LINEAR MODEL :\", r2_score(Y_test,pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZRRtzMnUAC34"
   },
   "source": [
    "OUR IMPLEMENTATION OF SGD\n",
    "======="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fC5XC2NIo7px"
   },
   "outputs": [],
   "source": [
    "class SGD(BaseEstimator):\n",
    "   \n",
    "    def __init__(self,n_iter=100000,learning_rate=0.0001, tolerence=0.001, batch_size=20):\n",
    "        self.n_iter = n_iter\n",
    "        self.m = None \n",
    "        self.c = None\n",
    "        self.learning_rate = learning_rate\n",
    "        self.tolerence = tolerence\n",
    "        self.batch_size = batch_size\n",
    "        self.error_tot = []\n",
    "        self.m_new = []\n",
    "        self.c_new = []\n",
    "      \n",
    "    def predict(self, X):\n",
    "        return X.dot(self.m) + self.c  \n",
    "      #predicting optimal line\n",
    "    def fit(self, X, Y):\n",
    "        X = np.array(X)\n",
    "        Y = np.array(Y)        \n",
    "        rows, columns = X.shape\n",
    "        self.m = np.zeros(columns)\n",
    "        self.c= 0.0        \n",
    "        self.optimize(X, Y)\n",
    "        return self   \n",
    "    def batch(self, X, Y):\n",
    "      #sampling into batches\n",
    "        val= np.random.randint(X.shape[0], size=self.batch_size)\n",
    "        return X[val,:], Y[val]                             \n",
    "    def compute_error(self, X, Y):\n",
    "      #computing error\n",
    "        return  np.sum((Y - self.predict(X))**2) /len(X)\n",
    "    def optimize(self, X, Y):\n",
    "      #optimising parameters\n",
    "        for i in range(1, self.n_iter + 1):            \n",
    "            self.error_tot.append(self.compute_error(X, Y))           \n",
    "            prev_m = self.m\n",
    "            prev_c = self.c\n",
    "            X_mini, Y_mini = self.batch(X, Y)\n",
    "            pred = self.predict(X_mini)\n",
    "            error = Y_mini- pred                    \n",
    "            m_gradient = -2 * np.dot(X_mini.T, error)\n",
    "            c_gradient = -2 * np.sum(error)            \n",
    "            self.m = self.m - (self.learning_rate/i ) * m_gradient\n",
    "            self.c= self.c- (self.learning_rate/i) * c_gradient            \n",
    "            coef_m = np.linalg.norm(self.m - prev_m)\n",
    "            coef_c = self.c - prev_c            \n",
    "            self.m_new.append(coef_m)\n",
    "            self.c_new.append(coef_c)\n",
    "            \n",
    "            if coef_m < self.tolerence:\n",
    "               if coef_c < self.tolerence:\n",
    "                    self.error_tot.append(self.compute_error(X, Y))\n",
    "                    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4ov2QrhmDK3c"
   },
   "source": [
    "Hyper-parameter tuning for best learningrate and batchsize\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pgsG8uALBrLa"
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "id": "jOf1cvpKBFoI",
    "outputId": "1855a8b4-3361-4347-8219-90f88113eeac"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:32: RuntimeWarning: overflow encountered in reduce\n",
      "  return umr_sum(a, axis, dtype, out, keepdims)\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: RuntimeWarning: overflow encountered in square\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:676: RuntimeWarning: overflow encountered in square\n",
      "  array_means[:, np.newaxis]) ** 2,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise',\n",
       "       estimator=SGD(batch_size=20, learning_rate=0.0001, n_iter=100000, tolerence=0.001),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'learning_rate': (0.1, 0.05, 0.001, 0.005, 0.04, 0.3, 1), 'batch_size': (10, 20, 30, 40)},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='r2', verbose=0)"
      ]
     },
     "execution_count": 207,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr= (0.1,0.05,0.001,0.005,0.04,0.3,1)\n",
    "b_size=(10,20,30,40)\n",
    "hyperparameters = dict(learning_rate=lr,batch_size=b_size)\n",
    "model = GridSearchCV(SGD(),hyperparameters, scoring = 'r2', cv=3)# using grid search cv with 3 fold cross validation\n",
    "model.fit(X_scaled, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "McSMSaV-JkmT",
    "outputId": "ab513097-44e6-4cb2-c137-9ca450155d4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator: SGD(batch_size=10, learning_rate=0.05, n_iter=100000, tolerence=0.001)\n"
     ]
    }
   ],
   "source": [
    "print('Best estimator:',model.best_estimator_)#obtaining best estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I49OI_Bd-AZ6"
   },
   "source": [
    "Observation:\n",
    "--------\n",
    "1.  Stochastic Gradient Descent is sensitive to feature scaling, so it is highly recommended to scale your data.\n",
    " \n",
    "2.   For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize it to have mean 0 and variance 1.\n",
    "\n",
    "3.  Note that the same scaling must be applied to the test vector to obtain meaningful results. This can be easily done using StandardScaler:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "3PvBhQ07Y-h-",
    "outputId": "fbc94943-cda4-4dca-f4b0-1b1f27e87746"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGD(batch_size=10, learning_rate=0.05, n_iter=100000, tolerence=0.001)"
      ]
     },
     "execution_count": 208,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl= SGD(batch_size=10, learning_rate=0.05, n_iter=100000, tolerence=0.001)\n",
    "cl.fit(X_scaled,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387
    },
    "colab_type": "code",
    "id": "ntdQN_cyvf8o",
    "outputId": "4c479e4e-dee9-4db5-ff3e-82371a6eef3f"
   },
   "outputs": [
    {
     "data": {
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2Dp+URi7zxvvH/2VEKmOSE/3gsHH4mMkvPAzqRBowP28I8sdmIzPNggQDkJlmQf7YbCY5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc592ef1208>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_scaled = scaler.transform(X_test)\n",
    "y_pred = cl.predict(X_test_scaled)\n",
    "\n",
    "plt.scatter(Y_test, y_pred)\n",
    "plt.xlabel(\"Prices: $Y_i$\")\n",
    "plt.ylabel(\"Predicted prices: $\\hat{Y}_i$\")\n",
    "plt.title(\"Prices vs Predicted prices: $Y_i$ vs $\\hat{Y}_i$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361
    },
    "colab_type": "code",
    "id": "gwFFhn4cofTS",
    "outputId": "b430967a-1890-4419-95a7-6615a85beccc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rr7/+2he1iYiIiAe5DX2z2YzJZOKvf/2r6w55VVVVXi+soTM0vy8iIn7G7fR+eHg4Tz75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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5932404a8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q=plt.plot(cl.error_tot)\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Total Error  (vs) iterations on test data');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387
    },
    "colab_type": "code",
    "id": "pulFFkwehF6s",
    "outputId": "1d13b708-1d27-4339-a627-602a15bd8679"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ycyxoa7umuu1ixxS7eAi05Y5pQ+FwevpkHO6YNlTVuVNyrtWsO6+ecROWTB2C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\nC4x969m+SRVlaBfpFYeqqugPAJI11t3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc592a07390>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl1= SGD(batch_size=10, learning_rate=0.05, n_iter=100000, tolerence=0.001)\n",
    "cl1.fit(X_scaled,Y_train)\n",
    "predict = cl1.predict(X_scaled)\n",
    "\n",
    "plt.scatter(Y_train,predict)\n",
    "plt.xlabel(\"Prices: $Y_i$\")\n",
    "plt.ylabel(\"Predicted prices: $\\hat{Y}_i$\")\n",
    "plt.title(\"Prices vs Predicted prices: $Y_i$ vs $\\hat{Y}_i$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361
    },
    "colab_type": "code",
    "id": "G9G_eYA-h2Ev",
    "outputId": "722d9739-5125-416c-a4d3-0db46cd57e7c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kACAvLw8333wzQkJCUFhYiMrKSlRVVSE/Px/h4eGIiopCVlYWACAnJwcRERF2fdmO0mK+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\nkQdiwBMREXmg/wfcw4TyBQI2aQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc593710860>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q=plt.plot(cl1.error_tot)\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Total Error  (vs) iterations on train data');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "aNUtuE90hUzt",
    "outputId": "51668eda-6ca6-4b8c-c1be-d115d2103fbd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMS ERROR ON OUR MODEL on traindata: 4.70508377823611\n"
     ]
    }
   ],
   "source": [
    "rms_error=np.sqrt(np.sum((Y_train-cl1.predict(X_scaled)) ** 2) / len(X_scaled))\n",
    "print('RMS ERROR ON OUR MODEL on traindata:',rms_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "oJNo8QxLCdre",
    "outputId": "34022dcb-6889-4831-f800-b6b8679f9cdb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMS ERROR ON OUR MODEL: 5.196652645602167\n"
     ]
    }
   ],
   "source": [
    "rms_error=np.sqrt(np.sum((Y_test-cl.predict(X_test_scaled)) ** 2) / len(X_test_scaled))\n",
    "print('RMS ERROR ON OUR MODEL:',rms_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "fU4KRp0_EMQh",
    "outputId": "8328af7c-4704-4ce6-9b19-985e26ac956f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMS ERROR ON SKLEARN's LINEAR MODEL: 5.342412121468939\n"
     ]
    }
   ],
   "source": [
    "rms_error1=np.sqrt(np.sum((Y_test-model1.predict(X_test)) ** 2) / len(X_test))\n",
    "print(\"RMS ERROR ON SKLEARN's LINEAR MODEL:\",rms_error1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "thuPCGn_FNzD"
   },
   "source": [
    "The RMS ERROR OF OUR MODEL AND SKLEARN'S MODEL ARE ALMOST CLOSE\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "gfbZfpwStdb0",
    "outputId": "4ba357cf-69c2-44fd-9427-77d80b9d4887"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for our model : 0.7119256721666534\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"R^2 score for our model :\", r2_score(Y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "vFHvkrAzmRC8",
    "outputId": "6967064f-9af8-4cba-f3f7-b486086f1ab6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for our model on train data : 0.7226745354368981\n"
     ]
    }
   ],
   "source": [
    "print(\"R^2 score for our model on train data :\", r2_score(Y_train,predict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Fi7jQFu0mjUW",
    "outputId": "b9c4c548-7ae3-4960-d892-cb34fc718418"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for sklearns linearmodel: 0.6955388005506419\n"
     ]
    }
   ],
   "source": [
    "print(\"R^2 score for sklearns linearmodel:\", r2_score(Y_test,pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "w_V0N0MeoYRC",
    "outputId": "cf2b791d-0846-4566-f48c-3b3ee2191ea0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8270"
      ]
     },
     "execution_count": 181,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.best_estimator_.error_tot)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "A6JNHseD_fpV"
   },
   "source": [
    "\n",
    "we clearly observe that as the number of iterations increases the error is decreased\n",
    "---------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "colab_type": "code",
    "id": "QmY64zE66m6o",
    "outputId": "fd3ab080-c016-4cdf-e4e5-5357c03a4dc7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KA3h9fT327NmD9vb2PkVX7rvvvpAMjIgC0z8RzVPJDACKi3L9Pj8QsyZZ8MMl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\nUiEGcCIiIhX6/3mukr45NZLzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc59311eac8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r=plt.scatter(pred, y_pred)\n",
    "plt.xlabel('sklearns model $Y_i$')\n",
    "plt.ylabel('our model $Y_i$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S3kjRghV-xOq"
   },
   "source": [
    "Here we can clearly observe that our model's  prediction of $Y_i$ and sklearn's model when plotted forms straightline.\n",
    "--------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 469
    },
    "colab_type": "code",
    "id": "9icUT7AVp-XF",
    "outputId": "fdcee4a2-9efb-438c-9fd7-f463cda0c520"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>coefficients_ourmodel_traindata</th>\n",
       "      <th>coefficients_ourmodel_testdata</th>\n",
       "      <th>Coefficients_sklearn sgd</th>\n",
       "      <th>coefficients of linear regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>22.454088</td>\n",
       "      <td>44.950560</td>\n",
       "      <td>-4.777185e+11</td>\n",
       "      <td>-0.156381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>41.063019</td>\n",
       "      <td>156.537699</td>\n",
       "      <td>-2.601539e+11</td>\n",
       "      <td>0.038549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>47.912267</td>\n",
       "      <td>160.880940</td>\n",
       "      <td>6.833706e+10</td>\n",
       "      <td>-0.025063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>34.724931</td>\n",
       "      <td>121.365365</td>\n",
       "      <td>-1.454218e+10</td>\n",
       "      <td>0.786440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>7.880395</td>\n",
       "      <td>48.092421</td>\n",
       "      <td>-2.196768e+09</td>\n",
       "      <td>-12.946912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>2.192674</td>\n",
       "      <td>25.096707</td>\n",
       "      <td>-1.532334e+11</td>\n",
       "      <td>4.002689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>1.465921</td>\n",
       "      <td>12.724155</td>\n",
       "      <td>9.027845e+11</td>\n",
       "      <td>-0.011602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>8.338608</td>\n",
       "      <td>2.576553</td>\n",
       "      <td>-5.093802e+10</td>\n",
       "      <td>-1.368288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RAD</td>\n",
       "      <td>3.746239</td>\n",
       "      <td>5.389031</td>\n",
       "      <td>9.944776e+09</td>\n",
       "      <td>0.341757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TAX</td>\n",
       "      <td>1.903799</td>\n",
       "      <td>33.048603</td>\n",
       "      <td>-8.306910e+10</td>\n",
       "      <td>-0.013515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>0.803595</td>\n",
       "      <td>6.291253</td>\n",
       "      <td>-2.138951e+11</td>\n",
       "      <td>-0.988866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B</td>\n",
       "      <td>0.579367</td>\n",
       "      <td>6.029334</td>\n",
       "      <td>-2.591820e+11</td>\n",
       "      <td>0.012059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>0.874652</td>\n",
       "      <td>4.489651</td>\n",
       "      <td>3.087241e+11</td>\n",
       "      <td>-0.472644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   features  coefficients_ourmodel_traindata  coefficients_ourmodel_testdata  \\\n",
       "0      CRIM                        22.454088                       44.950560   \n",
       "1        ZN                        41.063019                      156.537699   \n",
       "2     INDUS                        47.912267                      160.880940   \n",
       "3      CHAS                        34.724931                      121.365365   \n",
       "4       NOX                         7.880395                       48.092421   \n",
       "5        RM                         2.192674                       25.096707   \n",
       "6       AGE                         1.465921                       12.724155   \n",
       "7       DIS                         8.338608                        2.576553   \n",
       "8       RAD                         3.746239                        5.389031   \n",
       "9       TAX                         1.903799                       33.048603   \n",
       "10  PTRATIO                         0.803595                        6.291253   \n",
       "11        B                         0.579367                        6.029334   \n",
       "12    LSTAT                         0.874652                        4.489651   \n",
       "\n",
       "    Coefficients_sklearn sgd  coefficients of linear regression  \n",
       "0              -4.777185e+11                          -0.156381  \n",
       "1              -2.601539e+11                           0.038549  \n",
       "2               6.833706e+10                          -0.025063  \n",
       "3              -1.454218e+10                           0.786440  \n",
       "4              -2.196768e+09                         -12.946912  \n",
       "5              -1.532334e+11                           4.002689  \n",
       "6               9.027845e+11                          -0.011602  \n",
       "7              -5.093802e+10                          -1.368288  \n",
       "8               9.944776e+09                           0.341757  \n",
       "9              -8.306910e+10                          -0.013515  \n",
       "10             -2.138951e+11                          -0.988866  \n",
       "11             -2.591820e+11                           0.012059  \n",
       "12              3.087241e+11                          -0.472644  "
      ]
     },
     "execution_count": 218,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(X.columns,cl1.m_new,cl.m_new,clf.coef_,model1.coef_)),columns=['features',\n",
    " 'coefficients_ourmodel_traindata','coefficients_ourmodel_testdata','Coefficients_sklearn sgd',\n",
    "                                                                                     'coefficients of linear regression'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aY_vS9z_csUn"
   },
   "source": [
    "\n",
    "observation\n",
    "--------\n",
    "\n",
    "1. RMS ERROR ON OUR MODEL TESTDATA= 5.19\n",
    "\n",
    "2.  RMS ERROR ON SKLEARN's LINEAR MODEL: 5.34\n",
    "\n",
    "3.   RMS ERROR ON OUR MODEL ON TRAINDATA: 4.70508377823611\n",
    "\n",
    "-------------------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "1.   R^2 score for our model on train data : 0.7226745354368981\n",
    "2.  R^2 score for our model on test data =0.71\n",
    "1.    R^2 score for our model on sklearn's linearmodel=0.69\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3Qgw1_fzeozY"
   },
   "source": [
    "By looking into cost plot and R^2 score  of test data  and traindata it seems my model is overfitting.\n",
    "--------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YGnA3Yp8dkN5"
   },
   "outputs": [],
   "source": []
  }
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