tushar-space/Testing-Jupyter.ipynb

## Testing-Jupyter.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Used Vehicle Dataset](https://drive.google.com/uc?id=1p8Y7l-auKNo3I6lbQ6486a_lO8vYqTMf \"Used Vehicle Dataset\")\n",
    "Suppose the prediction of Y based on the i<sup>th</sup> observation of X is,\n",
    "$$y_i' = \\beta_0' + \\beta_1'x_i$$\n",
    "\n",
    "than,4\n",
    "\n",
    "$$\n",
    "e_i = y_i - y_i'\n",
    "$$\n",
    "\n",
    "is the  i<sup>th</sup> residue / error. Residue is the difference between the observed response value in our training data and the corresponding predicted response value. It shows how much error there is in our prediction. Here, we define a term- **Residual Sum of Squares**(RSS) as,\n",
    "a\n",
    "$$\n",
    "RSS = e_1^2 + e_2^2 + .... +e_n^2\n",
    "$$\n",
    "Replacing value of $e_i$ and $y_i'$, RSS can be rewritten as:\n",
    "\n",
    "$$\n",
    "RSS = (y_1-\\beta_0'-\\beta_1x_1)^2+(y_2-\\beta_0'-\\beta_1x_2)^2+....+(y_n-\\beta_0'-\\beta_1'x_n)^2\n",
    "\\tag{3}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'John'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cat": "Other",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "colors": "BasicMagics",
        "conda": "PackagingMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "cp": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "lf": "Other",
        "lk": "Other",
        "ll": "Other",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "lx": "Other",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "man": "KernelMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "mv": "Other",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "PackagingMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rm": "Other",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %conda  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = 1 + np.sin(2*np.pi*t)\n",
    "plt.plot(t, s)\n",
    "\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('voltage (mV)')\n",
    "plt.title('About as simple as it gets, folks')\n",
    "plt.grid(True)\n",
    "plt.savefig(\"test.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "  <title>Name of the website</title>\n",
       "</head>\n",
       "\n",
       "<body>\n",
       "  <h1>Heading for the content below</h1>\n",
       "  <p>Text text text</p>\n",
       "</body>\n",
       "\n",
       "</html>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<!DOCTYPE html>\n",
    "<html>\n",
    "<head>\n",
    "  <title>Name of the website</title>\n",
    "</head>\n",
    "\n",
    "<body>\n",
    "  <h1>Heading for the content below</h1>\n",
    "  <p>Text text text</p>\n",
    "</body>\n",
    "\n",
    "</html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.5 Âµs Â± 1.54 Âµs per loop (mean Â± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "square_evens = [n*n for n in range(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.481866</td>\n",
       "      <td>0.001308</td>\n",
       "      <td>-1.094625</td>\n",
       "      <td>-0.916809</td>\n",
       "      <td>0.691133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.462437</td>\n",
       "      <td>0.391881</td>\n",
       "      <td>-0.188657</td>\n",
       "      <td>-0.024955</td>\n",
       "      <td>-0.987060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.163987</td>\n",
       "      <td>0.812832</td>\n",
       "      <td>-0.738107</td>\n",
       "      <td>-0.962870</td>\n",
       "      <td>-1.262917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.092257</td>\n",
       "      <td>1.838061</td>\n",
       "      <td>0.197576</td>\n",
       "      <td>0.944639</td>\n",
       "      <td>1.086074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.172263</td>\n",
       "      <td>-0.393126</td>\n",
       "      <td>-0.606783</td>\n",
       "      <td>-1.175095</td>\n",
       "      <td>-0.030111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  1.481866  0.001308 -1.094625 -0.916809  0.691133\n",
       "1 -0.462437  0.391881 -0.188657 -0.024955 -0.987060\n",
       "2 -0.163987  0.812832 -0.738107 -0.962870 -1.262917\n",
       "3  1.092257  1.838061  0.197576  0.944639  1.086074\n",
       "4  1.172263 -0.393126 -0.606783 -1.175095 -0.030111"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame(np.random.randn(10,5))\n",
    "df.head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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