temp-feed-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "fd88dd9b-d242-4e5b-bfbc-fdd4c602d032"
    }
   },
   "source": [
    "# Sample Application for understanding.\n",
    "\n",
    "Demo for applying linear regression on two variables and plotting the same using matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "nbpresent": {
     "id": "33f9b397-6c5f-4fbc-b433-ab141f6a21f9"
    }
   },
   "outputs": [],
   "source": [
    "# Setting up Python ecosystem environment for data analysis.\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "nbpresent": {
     "id": "91a60416-7a18-46eb-a8c1-24ca13262db3"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    c1         c2\n",
      "0  0.0   0.968012\n",
      "1  1.0   2.712641\n",
      "2  2.0  11.958873\n",
      "3  3.0  10.889784\n",
      "4  4.0   3.968012\n"
     ]
    }
   ],
   "source": [
    "# Loading the data into pandas dataframe.\n",
    "\n",
    "data = pd.read_csv('/home/gopinathankm/work/workspace/machine-learning/linear-regression/test.csv', index_col=False, header=0)\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "nbpresent": {
     "id": "b44db11a-92ab-4fed-988c-1068bc29382d"
    }
   },
   "outputs": [],
   "source": [
    "# Making a numpy array of length 10.\n",
    "length = 10\n",
    "x = np.arange(length, dtype=float).reshape((length, 1))\n",
    "y = x + (np.random.rand(length)*10).reshape((length, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "nbpresent": {
     "id": "9da833ab-4385-49ea-9c3e-b9c186620cb4"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]\n"
     ]
    }
   ],
   "source": [
    "# fitting dataframe data into numpy array\n",
    "x = data.c1.values\n",
    "y = data.c2.values\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "nbpresent": {
     "id": "c5f39f88-a82c-4357-afa6-075d7a8d33b4"
    }
   },
   "outputs": [],
   "source": [
    "# Reshaping the numpy arrays\n",
    "x = x.reshape(length, 1)\n",
    "y = y.reshape(length, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "nbpresent": {
     "id": "6445e4c0-4aad-4720-8c13-1546679c9c95"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression model is created\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Fitting the numpy array into model\n",
    "regr.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "nbpresent": {
     "id": "424bbecb-dcd8-49a8-81ac-0f64e3902989"
    }
   },
   "outputs": [
    {
     "data": {
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WNq8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4087730e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot it as in the example at http://scikit-learn.org/\n",
    "plt.scatter(x, y,  color='black')\n",
    "plt.plot(x, regr.predict(x), color='blue', linewidth=3)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a359a0f9-363b-48de-80d9-bb9c73737527"
    }
   },
   "source": [
    "# Study of Feed Temperature and Column Feed(Taken from the SampleData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3fe55246-bfe8-409a-b406-33cc303f10b5"
    }
   },
   "source": [
    "# Solution 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "nbpresent": {
     "id": "b4c43696-e62f-4022-99cb-0799a3161c30"
    }
   },
   "outputs": [],
   "source": [
    "# Setting up Python ecosystem environment for data analysis.\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "nbpresent": {
     "id": "b65b09ec-8935-44ee-be19-c51adfb9bb71"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Timestamp  feed-temp  Feed-column  Column-overhead  \\\n",
      "0      21/09/2016     179.52         6.19           117.75   \n",
      "1      21/09/2016     178.90         6.20           117.74   \n",
      "2  21/09/16 01:00     178.32         6.21           117.54   \n",
      "3  21/09/16 01:30     179.54         6.20           117.68   \n",
      "4  21/09/16 02:00     180.74         6.19           117.90   \n",
      "\n",
      "   Column-top-pressure  Column-reflux  Column-performance  \n",
      "0                66.49          68.99                0.29  \n",
      "1                66.68          68.20                0.28  \n",
      "2                66.44          67.55                0.25  \n",
      "3                66.50          65.96                0.30  \n",
      "4                66.66          64.77                0.30  \n"
     ]
    }
   ],
   "source": [
    "# Loading the SampleDate provided by George Mani after deleting all other column except feed temperature and column feed.\n",
    "# To the study the relationship among temperature and column feed.\n",
    "# Loading the SampleDate provided by George Mani after deleting many columns.\n",
    "# Loading the data inot pandas dataframe for review.\n",
    "\n",
    "feeddata0 = pd.read_csv('/home/gopinathankm/work/workspace/machine-learning/linear-regression/feed-data.csv', index_col=False, header=0)\n",
    "print(feeddata0.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "nbpresent": {
     "id": "bef5628d-2379-4374-beea-0e9de77e5907"
    }
   },
   "outputs": [],
   "source": [
    "# Drop other columns except feed-temp and Feed-column.\n",
    "# df.columns is zero-based pd.Index\n",
    "feeddata1 = feeddata0.drop(feeddata0.columns[[0, 3, 4, 5, 6]], axis=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "nbpresent": {
     "id": "992ea744-0e97-46ff-9c2a-f70741a46722"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(961, 2)\n"
     ]
    }
   ],
   "source": [
    "# Show the number of rows and columns in dataframe.\n",
    "print(feeddata1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "nbpresent": {
     "id": "570f215f-9033-4b2b-8c64-2e17a528375b"
    }
   },
   "outputs": [],
   "source": [
    "# Changing the column names of dataframe.\n",
    "feeddata1.columns = ['temp','feed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "nbpresent": {
     "id": "ecd19cbf-db05-4ace-b935-6d651322eb36"
    }
   },
   "outputs": [],
   "source": [
    "# Making a numpy array of length 10.\n",
    "length = 961\n",
    "x = np.arange(length, dtype=float).reshape((length, 1))\n",
    "y = x + (np.random.rand(length)*100).reshape((length, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "nbpresent": {
     "id": "6b112053-8ff9-4759-a7de-e9c7d3bf96c7"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 179.52  178.9   178.32  179.54  180.74  179.93  179.72  178.61  179.81\n",
      "  179.3   179.89  179.7   180.28  179.52  178.64  178.33  178.07  178.88\n",
      "  178.62  179.55  178.95  179.3   179.24  179.76  179.43  180.73  180.07\n",
      "  179.64  180.22  179.6   180.11  179.5   179.27  179.67  179.99  180.12\n",
      "  181.06  179.83  179.59  178.6   177.39  178.72  179.42  178.51  178.06\n",
      "  179.64  179.34  180.27  180.09  179.9   181.07  179.93  180.74  180.29\n",
      "  179.98  181.59  180.68  180.95  180.73  180.13  179.87  179.51  179.\n",
      "  178.55  179.    179.43  180.03  179.44  179.96  179.64  180.05  179.77\n",
      "  180.54  180.26  180.4   180.47  179.77  180.98  180.58  181.33  180.18\n",
      "  180.71  181.1   179.73  179.59  178.75  179.69  178.45  178.81  179.59\n",
      "  179.24  180.05  179.25  179.22  179.32  179.68  179.65  179.08  179.85\n",
      "  179.85  179.15  179.33  179.19  179.73  181.07  180.28  180.14  179.74\n",
      "  180.31  179.56  178.89  179.64  178.61  179.03  178.66  178.89  178.95\n",
      "  179.32  179.44  179.05  179.76  179.43  179.54  179.13  179.81  178.71\n",
      "  179.67  179.08  179.19  179.33  178.58  179.07  178.86  179.23  178.72\n",
      "  179.32  178.55  179.33  178.21  178.66  179.82  179.38  180.04  179.47\n",
      "  179.86  179.87  179.44  180.18  179.42  179.93  178.86  178.54  178.85\n",
      "  178.77  178.88  179.66  179.16  178.71  178.81  178.99  177.86  176.75\n",
      "  177.19  176.43  177.04  176.26  177.    176.76  176.62  177.12  176.75\n",
      "  177.29  177.19  177.12  177.04  176.73  177.33  177.59  177.15  177.2\n",
      "  177.31  176.69  177.07  176.56  176.89  176.64  176.65  176.91  176.52\n",
      "  176.8   176.82  176.68  176.87  176.95  176.99  177.12  177.14  176.37\n",
      "  177.2   176.42  176.78  176.35  176.37  176.2   176.78  176.33  176.18\n",
      "  175.93  177.4   176.85  177.5   177.9   177.    177.16  177.6   177.33\n",
      "  177.75  177.42  176.75  177.32  176.39  177.28  177.29  176.79  177.42\n",
      "  177.27  177.2   177.76  176.93  176.98  176.52  176.03  175.85  173.44\n",
      "  174.    172.7   173.25  172.41  172.3   173.94  174.32  173.39  172.81\n",
      "  166.56  164.93  173.29  178.55  174.71  171.64  172.4   171.97  170.98\n",
      "  171.36  171.73  171.61  170.02  170.1   171.31  171.21  171.29  171.16\n",
      "  170.38  171.08  170.47  170.29  170.58  170.44  170.98  170.05  169.85\n",
      "  171.39  171.36  171.84  171.81  171.47  172.45  172.3   171.75  171.84\n",
      "  170.87  170.41  170.55  170.28  171.02  171.32  170.21  170.57  170.03\n",
      "  169.17  170.36  169.63  170.49  170.62  170.22  171.1   170.7   170.54\n",
      "  170.71  169.74  170.35  169.91  170.03  170.47  170.34  171.1   170.79\n",
      "  170.52  170.54  170.54  171.08  171.63  170.37  170.64  170.65  170.64\n",
      "  170.69  170.89  171.34  170.58  170.    170.84  170.85  170.82  171.67\n",
      "  171.45  170.8   170.56  170.23  170.23  170.51  169.44  169.63  170.1\n",
      "  169.58  169.78  169.94  170.13  170.76  170.72  170.04  170.75  170.05\n",
      "  169.94  170.44  170.04  169.97  169.81  169.68  170.08  170.13  169.49\n",
      "  170.18  169.47  168.83  170.17  170.56  170.88  171.05  170.53  170.15\n",
      "  169.81  170.42  168.81  167.84  169.3   169.64  168.08  168.18  168.5\n",
      "  169.97  168.44  167.74  169.07  168.75  168.95  169.03  167.53  168.54\n",
      "  168.3   165.87  168.66  168.77  168.83  169.09  168.55  168.4   168.63\n",
      "  167.25  168.38  167.86  167.07  168.21  168.8   168.71  168.74  169.29\n",
      "  170.12  168.79  169.55  170.01  169.46  169.44  169.75  169.96  169.5\n",
      "  169.35  168.97  169.08  170.63  171.54  170.49  166.15  162.69  162.22\n",
      "  165.69  172.19  175.71  171.13  166.37  163.47  171.71  170.97  170.51\n",
      "  169.98  169.84  170.7   170.38  169.26  169.54  168.92  168.9   170.4\n",
      "  170.33  169.78  169.41  170.12  169.23  170.09  170.06  170.1   170.16\n",
      "  170.81  170.96  170.97  171.19  171.12  171.02  170.99  170.77  171.22\n",
      "  170.87  170.96  170.78  171.09  170.74  170.98  171.28  170.56  172.17\n",
      "  171.81  171.44  170.41  170.29  170.98  170.23  171.4   172.68  172.31\n",
      "  172.02  173.01  171.77  171.37  170.87  171.05  171.15  170.74  169.74\n",
      "  171.46  170.93  171.33  170.66  171.45  171.77  171.19  171.94  171.02\n",
      "  171.5   171.54  170.9   171.21  170.66  170.99  170.73  170.97  171.53\n",
      "  170.76  171.13  170.87  170.93  171.54  171.14  171.25  170.94  171.25\n",
      "  171.31  170.9   171.49  171.01  171.76  171.43  171.22  172.21  171.51\n",
      "  172.53  171.81  171.29  172.4   172.25  171.33  171.68  171.49  172.53\n",
      "  171.18  171.71  171.36  171.87  172.2   172.25  172.06  171.77  171.4\n",
      "  171.66  172.31  171.47  171.17  171.42  170.7   171.33  171.08  170.8\n",
      "  170.93  171.16  170.09  172.18  171.07  172.09  170.69  172.6   172.01\n",
      "  171.47  172.22  173.1   172.55  172.44  174.05  172.46  171.83  173.45\n",
      "  171.62  172.86  173.47  172.43  171.89  172.2   171.69  172.43  170.77\n",
      "  170.66  170.12  170.59  172.07  170.39  171.75  171.86  171.37  172.38\n",
      "  170.69  171.56  171.47  171.07  172.41  171.12  170.95  172.34  171.42\n",
      "  171.54  171.46  171.06  171.26  171.98  171.23  171.29  171.41  171.34\n",
      "  172.1   171.25  171.71  172.28  171.07  172.24  171.59  171.03  172.43\n",
      "  171.48  171.95  171.4   171.52  171.77  172.86  171.45  172.3   171.25\n",
      "  172.37  171.93  171.35  171.87  171.34  170.88  171.15  170.64  171.78\n",
      "  171.54  170.74  172.12  170.5   171.09  170.83  170.67  171.57  172.\n",
      "  170.92  172.02  170.88  172.83  172.04  171.06  172.04  171.08  170.04\n",
      "  171.5   168.56  155.7   146.08  151.1   167.56  172.69  172.56  171.56\n",
      "  176.2   175.23  176.31  174.66  175.01  176.25  175.53  176.09  175.09\n",
      "  177.7   178.15  171.88  174.81  175.41  178.59  179.1   178.29  177.62\n",
      "  177.1   177.26  177.35  177.68  177.54  177.55  177.12  176.99  176.57\n",
      "  175.72  175.29  175.13  175.43  175.39  175.06  175.26  175.04  175.16\n",
      "  175.48  174.82  174.39  175.35  175.41  174.6   174.55  174.16  174.77\n",
      "  176.7   176.89  177.06  176.31  176.32  177.59  178.58  178.61  178.45\n",
      "  177.98  177.71  178.18  179.52  178.51  177.54  178.62  179.13  178.54\n",
      "  177.66  175.74  176.51  177.55  177.55  177.99  178.43  177.67  177.12\n",
      "  176.02  175.8   176.96  177.68  177.51  177.52  177.42  177.16  176.92\n",
      "  176.93  177.    176.52  175.81  175.75  176.57  177.22  177.16  177.31\n",
      "  177.51  177.45  177.55  177.88  178.09  178.22  178.47  179.15  179.41\n",
      "  180.22  179.01  178.89  177.36  178.79  179.88  180.    179.    179.41\n",
      "  179.23  178.85  178.82  177.48  176.12  177.74  176.64  177.17  177.71\n",
      "  177.29  176.75  177.1   177.46  177.78  177.87  178.03  178.03  177.87\n",
      "  177.6   177.67  177.63  177.66  177.72  177.67  177.86  177.72  177.62\n",
      "  177.55  177.35  177.29  177.3   177.74  178.08  178.13  178.28  178.3\n",
      "  178.25  178.65  178.87  178.17  178.05  180.23  178.63  177.36  177.96\n",
      "  178.88  178.98  178.78  178.81  178.6   177.18  177.41  177.59  177.95\n",
      "  178.27  178.3   177.49  176.55  175.61  176.18  175.88  175.46  175.43\n",
      "  174.2   174.2   174.69  175.41  176.23  175.98  175.6   175.49  174.47\n",
      "  174.47  175.55  176.23  176.29  176.42  176.25  176.06  175.63  175.61\n",
      "  175.68  175.93  176.44  176.96  176.2   176.25  176.4   175.89  176.24\n",
      "  175.77  175.41  175.33  174.56  174.93  175.45  175.37  175.35  175.34\n",
      "  175.37  175.08  175.21  175.09  175.01  174.41  175.06  175.34  174.89\n",
      "  175.02  174.76  174.77  174.72  175.2   175.22  175.4   174.87  175.\n",
      "  174.59  174.99  175.61  175.53  175.59  175.63  175.67  175.65  175.67\n",
      "  175.02  175.37  176.09  176.14  176.21  175.77  175.4   175.21  172.93\n",
      "  172.59  174.88  175.49  175.4   175.51  175.58  175.75  175.43  175.8\n",
      "  175.94  175.26  175.4   175.22  174.65  181.69  172.61  171.19  176.96\n",
      "  174.63  175.83  176.1   176.18  174.42  174.87  175.7   175.29  175.95\n",
      "  174.94  175.48  174.9   174.11  174.15  174.67  174.63  174.74  175.29\n",
      "  174.45  175.26  173.83  173.61  175.7   174.88  175.17  173.43  173.\n",
      "  175.3   174.6   173.76  173.87  174.7   175.09  175.31  175.52  175.02\n",
      "  175.41  175.58  172.46  174.22  173.79  173.78  169.4   169.83  172.52\n",
      "  175.66  176.86  177.99  175.3   173.87  171.26  173.38]\n"
     ]
    }
   ],
   "source": [
    "# fitting dataframe data into numpy array\n",
    "x = feeddata1.temp.values\n",
    "y = feeddata1.feed.values\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "nbpresent": {
     "id": "2030e28d-41a3-4bbb-adee-0257d0058022"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6.19  6.2   6.21  6.2   6.19  6.2   6.2   6.21  6.2   6.2   6.2   6.2\n",
      "  6.2   6.2   6.2   6.19  6.2   6.2   6.2   6.19  6.2   6.2   6.2   6.19\n",
      "  6.21  6.2   6.2   6.2   6.2   6.2   6.2   6.2   6.2   6.2   6.2   6.2\n",
      "  6.2   6.2   6.2   6.2   6.21  6.2   6.2   6.2   6.2   6.2   6.2   6.21\n",
      "  6.2   6.21  6.19  6.2   6.2   6.19  6.2   6.19  6.2   6.21  6.2   6.2\n",
      "  6.2   6.2   6.2   6.19  6.2   6.2   6.2   6.2   6.2   6.21  6.19  6.21\n",
      "  6.2   6.2   6.21  6.2   6.2   6.2   6.2   6.2   6.21  6.05  6.    6.    6.\n",
      "  6.    6.    6.    6.    5.99  6.    6.    5.99  6.    6.01  6.    6.\n",
      "  6.01  6.    5.99  6.    5.99  6.    5.99  6.    6.    5.99  5.99  6.01\n",
      "  6.    6.    5.99  6.01  6.01  5.99  6.    6.    6.    6.    6.    6.01\n",
      "  6.    6.    6.    6.    6.01  5.99  6.    6.    6.01  6.    6.    6.    6.\n",
      "  6.    6.    6.01  6.    6.    6.    5.99  6.    6.    6.    6.    6.01\n",
      "  6.    6.    6.    6.01  5.99  6.01  6.01  6.    6.01  6.    6.01  6.    6.\n",
      "  6.01  6.    6.    6.01  6.    6.    6.01  6.    6.    6.    6.    6.    6.\n",
      "  6.    6.    5.99  6.    6.    5.99  6.    6.    6.    6.    6.01  6.\n",
      "  5.99  6.    6.    6.    6.    6.    6.    6.    6.    6.    6.01  6.    6.\n",
      "  5.99  6.    6.    6.    6.01  6.01  6.    6.    6.    6.    6.01  6.    6.\n",
      "  6.01  6.    6.01  6.    6.    6.01  6.01  6.01  6.    6.    6.    6.    6.\n",
      "  6.    6.    6.    6.    6.    6.01  6.    6.    6.    6.    6.    5.99\n",
      "  6.    6.    5.99  6.    6.    6.    5.99  5.99  6.    6.    6.03  6.    6.\n",
      "  6.    6.    6.    6.01  6.    6.12  6.26  6.1   6.1   6.1   6.11  6.1\n",
      "  6.09  6.11  6.1   6.11  6.1   6.1   6.1   6.1   6.11  6.1   6.1   6.1\n",
      "  6.09  6.1   6.1   6.09  6.1   6.1   6.1   6.11  6.1   6.1   6.1   6.1\n",
      "  6.1   6.1   6.1   6.1   6.1   6.09  6.11  6.09  6.11  6.11  6.1   6.11\n",
      "  6.1   6.1   6.09  6.1   6.1   6.1   6.09  6.11  6.09  6.11  6.11  6.09\n",
      "  6.1   6.09  6.11  6.11  6.1   6.1   6.11  6.1   6.1   6.1   6.09  6.1\n",
      "  6.11  6.11  6.11  6.1   6.33  6.5   6.49  6.5   6.51  6.5   6.51  6.5\n",
      "  6.5   6.5   6.5   6.5   6.51  6.49  6.49  6.5   6.5   6.5   6.49  6.5\n",
      "  6.5   6.5   6.5   6.5   6.51  6.5   6.51  6.5   6.49  6.5   6.51  6.5\n",
      "  6.5   6.5   6.51  6.49  6.5   6.5   6.5   6.5   6.51  6.49  6.5   6.5\n",
      "  6.5   6.5   6.5   6.51  6.5   6.5   6.5   6.5   6.51  6.5   6.49  6.51\n",
      "  6.5   6.49  6.5   6.49  6.51  6.5   6.49  6.49  6.5   6.5   6.51  6.5\n",
      "  6.5   6.5   6.51  6.5   6.5   6.5   6.5   6.49  6.5   6.5   6.5   6.5\n",
      "  6.51  6.5   6.5   6.51  6.51  6.49  6.49  6.49  6.49  6.5   6.5   6.51\n",
      "  6.49  6.5   6.49  6.52  6.5   6.49  6.5   6.5   6.51  6.5   6.49  6.5\n",
      "  6.49  6.5   6.51  6.5   6.5   6.51  6.5   6.5   6.5   6.5   6.5   6.5\n",
      "  6.5   6.5   6.5   6.5   6.5   6.5   6.49  6.5   6.51  6.49  6.5   6.5\n",
      "  6.49  6.5   6.49  6.5   6.51  6.5   6.5   6.51  6.5   6.49  6.49  6.5\n",
      "  6.51  6.5   6.5   6.5   6.5   6.5   6.51  6.5   6.5   6.51  6.5   6.5\n",
      "  6.5   6.51  6.5   6.5   6.49  6.51  6.51  6.5   6.5   6.5   6.5   6.5\n",
      "  6.5   6.51  6.49  6.5   6.51  6.49  6.32  6.33  6.3   6.3   6.3   6.19\n",
      "  6.21  6.19  6.19  6.2   6.19  6.2   6.21  6.2   6.2   6.19  6.21  6.2\n",
      "  6.2   6.2   6.19  6.06  6.    5.99  6.    6.01  6.    5.99  5.99  6.    6.\n",
      "  5.99  6.01  6.    6.    6.    6.    6.    6.    6.    6.    5.99  6.\n",
      "  5.99  6.    6.01  6.    5.9   5.67  5.51  5.3   5.2   5.2   5.19  5.19\n",
      "  5.21  5.2   5.09  4.99  5.    5.    5.    5.01  5.    5.    4.99  5.\n",
      "  5.25  5.48  5.5   5.49  5.51  5.51  5.5   5.5   5.5   5.5   5.51  5.51\n",
      "  5.5   5.5   5.5   5.5   5.5   5.51  5.51  5.5   5.51  5.5   5.51  5.5\n",
      "  5.5   5.5   5.5   5.51  5.5   5.5   5.5   5.49  5.5   5.5   5.51  5.5\n",
      "  5.51  5.5   5.5   5.5   5.5   5.5   5.5   5.5   5.5   5.5   5.51  5.5\n",
      "  5.5   5.5   5.5   5.51  5.5   5.5   5.5   5.51  5.5   5.5   5.5   5.49\n",
      "  5.5   5.49  5.5   5.51  5.5   5.5   5.5   5.5   5.49  5.5   5.51  5.5\n",
      "  5.5   5.5   5.51  5.5   5.51  4.73  2.5   1.18  1.18  1.16  1.14  1.19\n",
      "  1.23  1.22  1.2   1.22  1.18  1.18  1.18  1.18  1.22  1.21  1.18  1.18\n",
      "  1.19  1.22  1.22  1.22  1.22  1.22  1.22  1.24  1.26  1.23  1.14  1.24\n",
      "  1.26  1.18  1.14  1.14  1.14  1.14  1.14  1.14  1.14  1.14  1.14  1.14\n",
      "  1.14  1.14  1.14  1.14  1.14  1.14  1.14  1.14  1.14  1.16  1.22  1.26\n",
      "  1.23  1.22  1.22  1.19  1.17  1.18  1.24  1.17  1.18  1.21  1.2   1.18\n",
      "  1.18  1.18  1.18  1.18  1.21  1.22  1.22  1.22  1.22  1.22  1.22  1.22\n",
      "  1.22  1.22  1.22  1.22  1.22  1.22  1.22  1.22  1.22  1.24  1.26  1.26\n",
      "  1.26  1.19  1.14  1.19  1.26  1.14  1.14  1.14  1.14  1.23  1.22  1.24\n",
      "  1.22  1.22  1.21  1.27  1.26  1.26  1.25  1.27  1.3   1.29  1.26  1.28\n",
      "  1.3   1.3   1.3   1.3   1.3   1.3   1.24  1.22  1.22  1.22  1.22  1.23\n",
      "  1.26  1.26  1.26  1.26  1.26  1.26  1.26  1.26  1.26  1.26  1.26  1.26\n",
      "  1.19  1.14  1.14  1.23  1.26  1.23  1.14  1.14  1.14  1.23  1.21  1.25\n",
      "  1.22  1.22  1.22  1.2   1.18  1.17  1.25  1.18  1.28  1.28  1.21  1.3\n",
      "  1.3   1.3   1.3   1.28  1.22  1.2   1.22  1.22  1.22  1.22  1.22  1.22\n",
      "  1.22  1.23  1.24  1.25  1.26  1.2   1.16  1.17  1.18  1.18  1.18  1.18\n",
      "  1.18  1.18  1.18  1.15  1.1   1.1   1.09  1.09  1.09  1.09  1.1   1.17\n",
      "  1.18  1.18  1.18  1.16  1.22  1.21  1.24  1.17  1.18  1.17  1.14  1.14\n",
      "  1.17  1.18  1.18  1.18  1.18  1.18  1.18  1.18  1.15  1.08  1.05  1.07\n",
      "  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09\n",
      "  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09\n",
      "  1.16  1.18  1.17  1.17  1.14  1.14  1.14  1.14  1.17  1.17  1.18  1.18\n",
      "  1.18  1.18  1.18  1.18  1.18  1.17  1.1   1.1   1.14  1.05  1.05  1.05\n",
      "  1.05  1.08  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09  1.09\n",
      "  1.09  1.1   1.11  1.1   1.02  1.    1.    1.    1.    1.11  1.1   1.09\n",
      "  1.13  1.05  1.05  1.09  1.18  1.17  1.14  1.14  1.61  1.66  1.23  1.23\n",
      "  1.24  1.14  1.14  1.14  1.14  2.12  3.5   4.    4.01  4.    4.    4.    4.\n",
      "  4.    4.    4.01]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "nbpresent": {
     "id": "3c0dcfb9-c858-48ef-b321-f5a78e589ba3"
    }
   },
   "outputs": [],
   "source": [
    "x = x.reshape(length, 1)\n",
    "y = y.reshape(length, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "nbpresent": {
     "id": "8b0a575f-c25d-4f9a-b33f-2bdbcbd1c635"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6ZIcmvaSELyLdGl2UQ7bf0+n8tl6PcduHT6Qg28+19y6nvjmUggilp5TwRaRb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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40877ae5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "\n",
    "plt.title('Feed Info')\n",
    "plt.ylabel('feed')\n",
    "plt.xlabel('temp')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "nbpresent": {
     "id": "477cf9d4-6fc2-4af3-9328-b0acf5c91d96"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression model is created\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "nbpresent": {
     "id": "13643680-314c-49b8-9186-cea685bdc098"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PS0VFhSfhzeAuYVZ/Ialw7HwmXnxuqWMAUCLSI5wikYiKx+OmYVteXm5ZTyKR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\nRAHD4CYiCpj/D2+Z3j/HXzN7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40862c1eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot data.\n",
    "\n",
    "plt.title('Feed Info')\n",
    "plt.ylabel('feed')\n",
    "plt.xlabel('temp')\n",
    "plt.scatter(x, y,  color='black')\n",
    "plt.plot(x, regr.predict(x), color='blue', linewidth=3)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bc31ba70-9535-4be1-ad1f-28304e5cd182"
    }
   },
   "source": [
    "# Solution 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "nbpresent": {
     "id": "daa08dd6-88bf-43f9-8441-899fd4b4835b"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(961, 2)\n"
     ]
    }
   ],
   "source": [
    "print(feeddata1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "nbpresent": {
     "id": "026781b8-cbc3-43f7-8e0a-8422cc3ae924"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "null_columns=feeddata1.columns[df1.isnull().any()]\n",
    "\n",
    "print(df1[null_columns].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "nbpresent": {
     "id": "1fc67ecc-7226-409d-a619-1ff433222c3c"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['temp' 'feed']\n"
     ]
    }
   ],
   "source": [
    "column_names = feeddata1.columns.values\n",
    "print(column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "nbpresent": {
     "id": "d0f5e6e5-083d-4768-949f-ce9acbf591de"
    }
   },
   "outputs": [],
   "source": [
    "feeddata1.columns = ['temp','feed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "nbpresent": {
     "id": "c4c8ff46-f530-4f9c-9303-d36a4b206dd3"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f40875b90f0>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j1p3szZYlmoNetpy3kje/oatiZQlp8i5qgARwtbNtTaQGO7JZGZt/f3Y/tz/+KrGUe2UJ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\nIYRL/j+KUtxZ6dXEegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40875b9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recommended way\n",
    "sns.lmplot(x='temp', y='feed', data=feeddata1)\n",
    " \n",
    "# Alternative waysns.lmplot(x='temp', y='feed', data=feeddata1)\n",
    "\n",
    "# sns.lmplot(x=feeddata1.feed, y=feeddata1.temp, data=feeddata1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "nbpresent": {
     "id": "a36678bc-8ed3-45a0-ae00-f030f1c225ab"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      179.52\n",
      "1      178.90\n",
      "2      178.32\n",
      "3      179.54\n",
      "4      180.74\n",
      "5      179.93\n",
      "6      179.72\n",
      "7      178.61\n",
      "8      179.81\n",
      "9      179.30\n",
      "10     179.89\n",
      "11     179.70\n",
      "12     180.28\n",
      "13     179.52\n",
      "14     178.64\n",
      "15     178.33\n",
      "16     178.07\n",
      "17     178.88\n",
      "18     178.62\n",
      "19     179.55\n",
      "20     178.95\n",
      "21     179.30\n",
      "22     179.24\n",
      "23     179.76\n",
      "24     179.43\n",
      "25     180.73\n",
      "26     180.07\n",
      "27     179.64\n",
      "28     180.22\n",
      "29     179.60\n",
      "        ...  \n",
      "931    175.70\n",
      "932    174.88\n",
      "933    175.17\n",
      "934    173.43\n",
      "935    173.00\n",
      "936    175.30\n",
      "937    174.60\n",
      "938    173.76\n",
      "939    173.87\n",
      "940    174.70\n",
      "941    175.09\n",
      "942    175.31\n",
      "943    175.52\n",
      "944    175.02\n",
      "945    175.41\n",
      "946    175.58\n",
      "947    172.46\n",
      "948    174.22\n",
      "949    173.79\n",
      "950    173.78\n",
      "951    169.40\n",
      "952    169.83\n",
      "953    172.52\n",
      "954    175.66\n",
      "955    176.86\n",
      "956    177.99\n",
      "957    175.30\n",
      "958    173.87\n",
      "959    171.26\n",
      "960    173.38\n",
      "Name: temp, Length: 961, dtype: float64 0      6.19\n",
      "1      6.20\n",
      "2      6.21\n",
      "3      6.20\n",
      "4      6.19\n",
      "5      6.20\n",
      "6      6.20\n",
      "7      6.21\n",
      "8      6.20\n",
      "9      6.20\n",
      "10     6.20\n",
      "11     6.20\n",
      "12     6.20\n",
      "13     6.20\n",
      "14     6.20\n",
      "15     6.19\n",
      "16     6.20\n",
      "17     6.20\n",
      "18     6.20\n",
      "19     6.19\n",
      "20     6.20\n",
      "21     6.20\n",
      "22     6.20\n",
      "23     6.19\n",
      "24     6.21\n",
      "25     6.20\n",
      "26     6.20\n",
      "27     6.20\n",
      "28     6.20\n",
      "29     6.20\n",
      "       ... \n",
      "931    1.10\n",
      "932    1.09\n",
      "933    1.13\n",
      "934    1.05\n",
      "935    1.05\n",
      "936    1.09\n",
      "937    1.18\n",
      "938    1.17\n",
      "939    1.14\n",
      "940    1.14\n",
      "941    1.61\n",
      "942    1.66\n",
      "943    1.23\n",
      "944    1.23\n",
      "945    1.24\n",
      "946    1.14\n",
      "947    1.14\n",
      "948    1.14\n",
      "949    1.14\n",
      "950    2.12\n",
      "951    3.50\n",
      "952    4.00\n",
      "953    4.01\n",
      "954    4.00\n",
      "955    4.00\n",
      "956    4.00\n",
      "957    4.00\n",
      "958    4.00\n",
      "959    4.00\n",
      "960    4.01\n",
      "Name: feed, Length: 961, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(feeddata1.temp, feeddata1.feed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "nbpresent": {
     "id": "68de0296-cf2f-4c06-8f60-06b99f35b4ba"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "feeddata1 = feeddata1.replace('?', np.nan)\n",
    "df1 = df1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "nbpresent": {
     "id": "18781cad-e3ea-4116-a9e1-60b77cb99355"
    }
   },
   "outputs": [],
   "source": [
    "# Getting X and y from columns of dataframe)\n",
    "X = df1[['temp']]\n",
    "y = df1[['feed']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "nbpresent": {
     "id": "41a52a5c-8973-455c-a4ed-1f833cab687a"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6ZIcmvaSELyLdGl2UQ7bf0+n8tl6PcduHT6Qg28+19y6nvjmUggilp5TwRaRb\nHo8xqaTr6Q6HF2Tzqw/PYuveBm5+eHXbzV1JP0r4ItKthuYQlQea8MZGzuzMKeXD+K/zpvDvlbu4\nd8m2JEYnvaGELyLd+tNLb7G3voVr5k3u9rhrziznzGNL+f6/32DNztokRSe9oYQvIl2qbmjhd89t\n4dypI5hTNqTbYz0e4xcfOpFh+Vlce+9yahs1F266UcIXkS799rnN1LeEuOn8KT06fmheFr/+6Cx2\n1TTy1QdXqj0/zSjhi0indtc28ueXt3LprLEcO6Kgx5+bUzaUr194HAvW7uHul7YmLkDpNSV8EenU\nbU9tBAdfPveYXn/206dP5NypI/jRY2+yfFt1AqKTvlDCF5EjbKqs5+/LtnPFyWWMHZLb68+bGbd+\nYCajirO5/t7lVLcblkFSRwlfRI7wsyfXk+P3ct1Z5X0+R1Gunzs+Opu99S185YEVRCJqz081JXwR\n6WDl9hoeX1PBZ8+YxLD8QL/ONWNsMd+efzzPrq/izuc3xylC6SslfBHp4CcL1jE0L4vPvGtSXM53\nxcllzJ8xilsXrGfxln1xOaf0jRK+iLR5ceNeXtq0j+vPmkx+wBeXc5oZt1w2gwnD8vjC/a9TdaA5\nLueV3lPCFxEAnHP8+Il1jCnO4WMnj4/rufMDPu742GxqG4N86f9eJ6z2/JRQwhcRAB5fU8HqnbV8\n+dxjCfi8cT//8aMK+f77pvHSpn386pmNcT+/HJ0SvogQCke4dcF6jhmez/tnjUlYOZfPHcels8fw\nq4UbNX5+CijhiwgPLtvBlr0N3HT+lG5HxewvM+PKUyfgHOzY35iwcqRzCUv4ZjbOzJ41szfNbK2Z\nfTFRZYlI3zUFw9z29EZmjS/m3KkjEl6eEf2Dolb85EtkDT8E/Jdz7njgZOA6M5uawPJEpA/+8vJW\nKuqa+NoFx2GWuNp9qyQUIV1IWMJ3zu12zi2PLR8A3gQS1zgoIr1W2xjkN4s2M29KKSdPGpbUsjWS\nZvIlpQ3fzCYAs4Alney72syWmtnSqqqqZIQjIjF3Pb+Z2sZgj4c/jiel++RLeMI3s3zgIeBLzrkj\nbss75+5yzs11zs0tLS1NdDgiElNZ18TdL27lvTNHM210UdLKVZNO6iQ04ZuZn2iyv9c593AiyxKR\n3rl94SaC4QhfOffYlJSvFp3kS2QvHQP+CLzpnPt5osoRkd57e18D97+6jQ+fNI4JJXlJLbu1l44k\nXyJr+KcBHwfONrMVsddFCSxPRHro509twOc1vnB27yc3iR9V8ZMtPqMjdcI59yLoT7lIulm7q5Z/\nrtjFtfPKGV6YnfTyW9vw1aSCWOQ2AAAOG0lEQVSTfHrSViTD/HTBeopy/HzuzL5PbtIfummbOkr4\nIhlk8ZZ9LFpfxTXzyinK8ac0FlXwk08JXyRDOOf4yRPrGFEY4JOnTEhZHLppmzpK+CIZ4uk3K1m+\nrYYvvvtYcrLiP/xxb6kNP/mU8EUyQDji+OmCdUwqyePyuWNTGkvbTVs16iSdEr5IBnjk9Z1s2FPP\nf503BZ83tf/t+9KgEwpHeHj5DuqagnGPJ5Mo4YsMcs2hMD9/agMnjCniwukjUx1Om5426bSEItxw\n/+t85YGVPLpyd2KDGuSU8EUGufuWbGNnTSNfvWAKngRObtJTvemW6ZzjuvuW8/iaCgB21hxMUFSZ\nQQlfZBCrbw7x64WbOLV8GKdPLkl1ODE9nwBlT10zT72xhxvOnszYITnsqmlKbGiDnBK+yCD2xxfe\nYl9DC19N0uQmvdGT8fBX76wFYN6UUkYX5bCzRtMi9ocSvsggta++mbue38wF00Zy4rjiVIfTpjd/\nd9bsrMVjMHVUEaOLs9mlhN8vSvgig9Qdz26mMRjmxvNTM/xxPKzZWcvk4fnkZHkZXZxDRW0T4Yi6\nc/aVEr7IILSj+iB/W/w2H5gzlsnDC1IdTge9aVhavbOW6bHJWUYX5xCKOKoONCcmsAyghC8yCN32\n9EYw+NI56Ve7b72XcLQm/Mq6JioPNDN9TDThjynOAWBXrZp1+koJX2SQ2bDnAA8v38EnTyljdCxJ\npqOjPWm7Zlf0hu0JYw/V8AG14/eDEr7IIHPrgvXkZfm4dt7kVIfSqZ426azeUYcZTB1VCMCo4ujY\n/Ur4faeELzKILN9WzZNv7OHqMyYxJC8r1eF062hNOqt31jKpJI+8QHSepsJsPwUBn/ri94MSvsgg\n4Zzjx4+voyQ/i0+dPjHV4XSpp90y1+6q5YRY+32r0cXqi98fCZviUESS6/mNe1ny1n6+995pbbXi\ndNQ6Hn53Nfy99c3srm1qu2HbanRxNrv7cdM2GI7wzxW78BjkB3xEHLyyeS8Pv76TpmCYYLj7rx2f\nOKWs0+0NzWHqmoJ8Z/5Uxg3N7XN8iZa+vxUi0mORSHRyk7FDcvjISeNTHU6PdJdaF2/ZB8DEkjyc\nc209e4bkZfHs+ioiEUd9S4jC7K5n7aprCrJsazUb9hzA7/UQ8Hv414pdLHlrf59jvueVt7vd/9Qb\ne5g8PJ9fXH5i283mdKKELzIIPLp6N2t31fGLD80ky5ealtpQOEJTKELA58HfzRDMnTXphCOO1Ttr\neWFDFc9vrOK1rdUAfPovSzs9x6RvPHbEts+fWU5jS4jZZUOYObaYebcu6tPP0V+bKutpDIZTUvbR\nWE/Gs0iWuXPnuqVLO/8HFpHOBcMRzvn5c+T4vTx6w+mEIo6mYJjmUITmYISmULjDe3MoTFPsvTkU\n6fGxjS1hdtU0sqtWN02PZsk33s2IwuyklGVmy5xzc3tyrGr4acQ5h3MQcY5wu+WIi9aAXGw5us0R\niRxadrFjWo93sXO0HuMc0XV36Dytx7cvJ50qAP3Vek2i18ERiv284Ui7V2xfuMP+6PUIhdvtP+xz\nERc7vt152u/rWAax8iNEInR/ri72tSbho/3zTP7m4/26Zn6vEfB5yfZ7CPi8BGLvoXCEjZX1/Tr3\nYDFzXDHHjyxg3NBcrjmzPC2GnO6pQZHw73h2Ez9dsL7f5/EYeMzwmGFtyxxa91jbNmu3r/3xjo6J\nuC35Rg4la9dJsm1N8CJ99bkzJ5Ht85Lt9xLwRduss9sl7ezD3gM+T4djAz4v3i6SVzji+PY/17Bg\nTQUjCrMZURhgRGE2wwsClBYECPi9+L2G1+PB7zF8Xg8+j+HzGl6P4W9d93h46o0KfrVwU5KvTvys\n3F7Dyu01APg8xufOLE9xRD03KJp0Tv/xQnZUq6vWQOb1RBOD16LvHgOf14PHDK+H6HZvdL+n3XGt\nL48ZPk/X+7we8Hk8sf3RP96+Hn/WOsTX+tlDx4PX48Hroe14X7vPtpXVGrvnUCUhXqaNLiTgS93E\n5L9/fgv/89ibKSu/P647q5yDLWGWbInezG0Mhnlrb8MRx508aSgtoQgt4Qj1TSH8Xg/zppRS2xik\ntjFIXWOo3XKQA82hHsdw/rQR/O7jPWqVOULGNem8+LWzO6y31qhDrV/Vw9Gv063berMeDLeeK9Lu\nXI5wJNLu2B6ux5Y9sYTjtWgNqC0xWLskdNSk1pokOyaa7hPTkYmsfWJqS1xmeDydJOHDj4+VITKp\nNC/VIfTZHc9u7tFxi7cc2bsnXs1cxyRpgLtBkfAPZ7FEmsIKj0hGeffxI9h6y8VHPe63izbz4yfW\nJSGi+PB5jKIcP4WxV1GOn8JsH8W5rcv+Q/uz/eQGvOT4veRmecnJii7n+L0pnzi+VcISvpndDcwH\nKp1z0xNVjogkxqbKes75+XOpDiMuPnlKGedPGxlNwllecv2+tuUcf9f3LgabRNbw/wz8GrgngWWI\nSIIMpkHKRhblcGrazOmbOglL+M65581sQqLOLyKJdcaxpT1qpumtqgPNnP7jhRTm+CnOOfJJ2XDE\nsaWTm6YAq757XrdP10r3Ut6Gb2ZXA1cDjB8/MB4JF5G+Ky0I8IP3TWfRhsoujzluVAHVDUHe2ttA\nRV30Qa/jRhaQlSZt4QNVQrtlxmr4j/a0DV9P2oqI9E5vumXqz6WISIZQwhcRyRAJS/hmdj/wCjDF\nzHaY2acTVZaIiBxdInvpfCRR5xYRkd5Tk46ISIZQwhcRyRBK+CIiGUIJX0QkQ6TVePhmVgV0Nktw\nCbA3yeH0xUCJExRrogyUWAdKnKBYj6bMOVfakwPTKuF3xcyW9vRJslQaKHGCYk2UgRLrQIkTFGs8\nqUlHRCRDKOGLiGSIgZLw70p1AD00UOIExZooAyXWgRInKNa4GRBt+CIi0n8DpYYvIiL9pIQvIpIh\nUp7wzexuM6s0szWd7LvRzJyZlcTW55lZrZmtiL2+k+pYzey7ZrazXUwXtdt3s5ltMrP1ZnZ+usZq\nZhPMrLHd9jtTGWds+w2x67bWzH7SbntaXdOuYk3lNe0qVjP7v3bxbDWzFe32pdV17SrWdPtdNbMT\nzWxxLJalZnZSbLuZ2a9i13SVmc1OVpzdcs6l9AWcAcwG1hy2fRywgOiDWCWxbfOIzqCVNrEC3wVu\n7OTYqcBKIABMBDYD3jSNdcLh1z/FcZ4FPA0EYuvD0/iadhVryq5pV7Eetv9nwHfS9bp2E2u6/a4+\nCVwYW74IWNRu+XHAgJOBJan6XWj/SnkN3zn3PLC/k12/AL4KpM1d5W5i7cz7gP/nnGt2zr0FbAJO\nSlhwh+llrCnTRZzXALc455pjx7ROfpqO17SrWFOqu39/MzPgcuD+2KZ0vK5Ap7GmTBdxOqAwtlwE\n7Iotvw+4x0UtBorNbFRyIu1ayhN+Z8zsvcBO59zKTnafYmYrzexxM5uW7Ni6cH3sa9vdZjYktm0M\nsL3dMTti21Kts1gBJprZ62b2nJm9K2XRRR0LvMvMlsTieUdsezpe065ihfS6pu29C9jjnNsYW0/H\n69rq8Fghva7rl4Cfmtl24Fbg5tj2tLymaZfwzSwX+CbQWfv8cqLjRswEbgceSWZsXfgtUA6cCOwm\n+vUTol/lDpfqbytdxbobGO+cmwV8BbjPzAo7P0VS+IAhRL8K3wQ8EKvppeM17SrWdLum7X2EjjXm\ndLyurQ6PNd2u6zXAl51z44AvA3+MbU/La5p2CZ9oQpoIrDSzrcBYYLmZjXTO1Tnn6gGcc48Bfovd\n0E0V59we51zYORcBfs+hr8I7iN6HaDWWQ1/3UqKrWGNf5ffFlpcRbcM9NnWRsgN4OPZ1+FUgQnRQ\nqrS7pnQRaxpeUwDMzAdcCvxfu83peF07jTUNr+sngYdjy38njf//QxomfOfcaufccOfcBOfcBKIX\nbrZzrsLMRsZqT8TuhnuAfSkMl8Pa5d4PtN7B/xfwYTMLmNlE4Bjg1WTH115XsZpZqZl5Y8uTiMa6\nJfkRtnkEODsWz7FAFtERCNPumtJFrGl4TVudA6xzzu1oty0dryt0EmsaXtddwJmx5bOB1qanfwGf\niPXWORmodc7tTkWAHaT6rjHRr2u7gSDR5P7pw/Zv5VAvneuBtUR7FCwGTk11rMBfgdXAKqL/yKPa\nHf9NojWQ9cTu5KdjrMBl7a7rcuA9KY4zC/gb0T9Iy4Gz0/iadhprKq9pV7HGtv8Z+Hwnx6fVde0q\n1jT8XT0dWBaLZwkwJ3asAXfErulqYG4yr2lXLw2tICKSIdKuSUdERBJDCV9EJEMo4YuIZAglfBGR\nDKGELyKSIZTwJeOYWbGZXZvqOESSTQlfMlExoIQvGUcJXzLRLUB5bAzzn5rZTWb2WmxQue9B27jr\n68zsD2a2xszuNbNzzOwlM9vYbtzz75rZX81sYWz7Z1P6k4l0QwlfMtHXgc3OuROBp4g+nn8S0UHl\n5pjZGbHjJgO/BGYAxwEfJfpk5Y3AN9qdbwZwMXAK8B0zG52MH0Kkt5TwJdOdF3u9TvRR/eOI/gEA\neMtFx3aKEH2c/xkXfTR9NdGJOFr90znX6JzbCzxLEseSF+kNX6oDEEkxA37knPtdh41mE4Dmdpsi\n7dYjdPy/c/j4JBqvRNKSaviSiQ4ABbHlBcCnzCwfwMzGmNnwXp7vfWaWbWbDiE7D+VrcIhWJI9Xw\nJeM45/bFbr6uITrv6H3AK7GRt+uBK4BwL075KvAfYDzwA+dcysc9F+mMRssU6Qcz+y5Q75y7NdWx\niByNmnRERDKEavgiIhlCNXwRkQyhhC8ikiGU8EVEMoQSvohIhlDCFxHJEP8fdE+hdjmRhLwAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4087680748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y)\n",
    "\n",
    "plt.title('Feed Info')\n",
    "plt.ylabel('feed')\n",
    "plt.xlabel('temp')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "nbpresent": {
     "id": "d38326fc-86c2-44ca-8243-042afb3866e5"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split X and y into X_\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "nbpresent": {
     "id": "900a5484-5c74-47bd-a1dd-e8bdbb84bcd2"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model.\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "regression_model = LinearRegression()\n",
    "regression_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "nbpresent": {
     "id": "fd444d12-70a6-4cca-8c1d-ae2040f1d474"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient for temp is -0.19092713661678018\n"
     ]
    }
   ],
   "source": [
    "for idx, col_name in enumerate(X_train.columns):\n",
    "    print(\"The coefficient for {} is {}\".format(col_name, regression_model.coef_[0][idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "nbpresent": {
     "id": "dcacff6e-1b00-4a7f-80d6-f77ca3e5e449"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept for our model is 37.80186670867235\n"
     ]
    }
   ],
   "source": [
    "intercept = regression_model.intercept_[0]\n",
    "print(\"The intercept for our model is {}\".format(intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "nbpresent": {
     "id": "ec6613a5-dd51-4963-9089-035e6f5ff16a"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024021467204045632"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "nbpresent": {
     "id": "897c5793-bc30-462c-9e70-ef93f6ae4541"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.2497195317169556"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_predict = regression_model.predict(X_test)\n",
    "\n",
    "regression_model_mse = mean_squared_error(y_predict, y_test)\n",
    "\n",
    "regression_model_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "nbpresent": {
     "id": "c35098a1-3f20-47b8-be17-7b090556397f"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2912266434634865"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "math.sqrt(regression_model_mse)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:my_env36]",
   "language": "python",
   "name": "conda-env-my_env36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}