MSME-AI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mobile :- +91-7418490015\n",
    "\n",
    "LinkedIn :- https://www.linkedin.com/in/sharmasaravanan/\n",
    "\n",
    "Notebook :- https://github.com/sharmasaravanan/Machine_learning \n",
    "               (Search MSME-AI)\n",
    "\n",
    "Gdrive :- https://drive.google.com/drive/folders/17dMizJX_2XY845HuCl3Gy54OMBrBJpv7?usp=sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "a = 5\n",
    "b = 10\n",
    "print(a+b)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 5 # int\n",
    "s = \"apple\" # string\n",
    "f = 3.14 # float\n",
    "b = True # bool\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1234\n"
     ]
    }
   ],
   "source": [
    "print(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world!!\n"
     ]
    }
   ],
   "source": [
    "print(\"hello world!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the numerical value : \n"
     ]
    }
   ],
   "source": [
    "num = input(\"Enter the numerical value : \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your name : \n"
     ]
    }
   ],
   "source": [
    "name = input(\"Enter your name : \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: ''",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-969c5441fc01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: ''"
     ]
    }
   ],
   "source": [
    "num = int(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'56'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int('56')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-16-a6b31d007d18>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-16-a6b31d007d18>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    + - * / % //\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "+ - * / % // "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.142857142857143"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "22/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "22//7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-19-1f72d009c07b>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-19-1f72d009c07b>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    < > <= >= == !=\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "< > <= >= == !="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-20-b2df2b9bb465>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-20-b2df2b9bb465>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    and or not\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "and or not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the numerical value : 74\n",
      "even\n"
     ]
    }
   ],
   "source": [
    "num = int(input(\"Enter the numerical value : \"))\n",
    "if num % 2 == 0:\n",
    "    print('even')\n",
    "else:\n",
    "    print('odd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the numerical value : 5\n",
      "odd\n"
     ]
    }
   ],
   "source": [
    "num = int(input(\"Enter the numerical value : \"))\n",
    "if num % 4 == 0:\n",
    "    print(\"Divisible by 4 and even\")\n",
    "elif num % 2 == 0:\n",
    "    print('even')\n",
    "else:\n",
    "    print('odd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the numerical value : 63\n",
      "odd\n",
      "Enter the numerical value : 45\n",
      "odd\n",
      "Enter the numerical value : 96\n",
      "Divisible by 4 and even\n",
      "3 iterations are over\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "while i <= 3:\n",
    "    num = int(input(\"Enter the numerical value : \"))\n",
    "    if num % 4 == 0:\n",
    "        print(\"Divisible by 4 and even\")\n",
    "    elif num % 2 == 0:\n",
    "        print('even')\n",
    "    else:\n",
    "        print('odd')\n",
    "    i += 1\n",
    "else:\n",
    "    print(\"3 iterations are over\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the numerical value : 96\n",
      "Divisible by 4 and even\n",
      "Enter the numerical value : 45\n",
      "odd\n",
      "Enter the numerical value : 78\n",
      "even\n",
      "Enter the numerical value : 36\n",
      "Divisible by 4 and even\n",
      "Enter the numerical value : 25\n",
      "Skipping!!!\n",
      "Enter the numerical value : 12\n",
      "Divisible by 4 and even\n",
      "Enter the numerical value : 15\n",
      "Stopping!!!\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    num = int(input(\"Enter the numerical value : \"))\n",
    "    \n",
    "    if num == 15:\n",
    "        print(\"Stopping!!!\")\n",
    "        break\n",
    "    elif num == 25:\n",
    "        print(\"Skipping!!!\")\n",
    "        continue\n",
    "        \n",
    "    if num % 4 == 0:\n",
    "        print(\"Divisible by 4 and even\")\n",
    "    elif num % 2 == 0:\n",
    "        print('even')\n",
    "    else:\n",
    "        print('odd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"elephant\"\n",
    "sent = \"elephant is an animal\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'p'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'p'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elepha'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ephant'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'eleph'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'eeh'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[0:6:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ELEPHANT'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elephant'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elephant'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.casefold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Elephant Is An Animal'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Elephant'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elephant is an animal'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.encode('utf-16').decode('utf-16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elephant'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" elephant \".strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elepha'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.strip('nt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.isidentifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" elephant \".isidentifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.isprintable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\\t\\n\".isprintable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "rock beats scissor\n",
    "scissor beats paper\n",
    "paper beats rock\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = ['a','b','c',2,3,4,5,'apple','mango']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 2, 3, 4, 5, 'apple', 'mango']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "l[4] = 45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 2, 45, 4, 5, 'apple', 'mango']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "del l[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 2, 4, 5, 'apple', 'mango']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.append('kiwi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 2, 4, 5, 'apple', 'mango', 'kiwi']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.insert(4,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 2, 8, 4, 5, 'apple', 'mango', 'kiwi']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.pop(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 8, 4, 5, 'apple', 'mango', 'kiwi']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.remove(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 8, 4, 'apple', 'mango', 'kiwi']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.append('b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.count('kiwi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.index('apple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.extend(['rock','paper'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 8, 4, 'apple', 'mango', 'kiwi', 'rock', 'paper']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = ('apple', 'mango', 'kiwi', 'rock',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('apple', 'mango', 'kiwi', 'rock')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 8, 4, 'apple', 'mango', 'kiwi', 'rock', 'paper']"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object doesn't support item deletion",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-92-fac3d15da1b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object doesn't support item deletion"
     ]
    }
   ],
   "source": [
    "del t[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {} dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'a':'apple','b':'ball',23:\"cat\",\"d\":35,3.14:name}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'apple', 'b': 'ball', 23: 'cat', 'd': 35, 3.14: 'elephant'}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cat'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['a', 'b', 23, 'd', 3.14])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['apple', 'ball', 'cat', 35, 'elephant'])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('a', 'apple'), ('b', 'ball'), (23, 'cat'), ('d', 35), (3.14, 'elephant')])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['f'] = 'fish'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'apple', 'b': 'ball', 23: 'cat', 'd': 35, 3.14: 'elephant', 'f': 'fish'}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del d['f']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.update({'g':'goat','k':'kiwi'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'apple',\n",
       " 'b': 'ball',\n",
       " 23: 'cat',\n",
       " 'd': 35,\n",
       " 3.14: 'elephant',\n",
       " 'f': 'fish',\n",
       " 'g': 'goat',\n",
       " 'k': 'kiwi'}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "for i in range(21,26):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "23\n",
      "25\n",
      "27\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "for i in range(21,31,2):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c\n",
      "8\n",
      "4\n",
      "apple\n"
     ]
    }
   ],
   "source": [
    "for i in l[2:6]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "mango\n",
      "kiwi\n",
      "rock\n"
     ]
    }
   ],
   "source": [
    "for i in t:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a apple\n",
      "b ball\n",
      "23 cat\n",
      "d 35\n",
      "3.14 elephant\n",
      "f fish\n",
      "g goat\n",
      "k kiwi\n"
     ]
    }
   ],
   "source": [
    "for k,v in d.items():\n",
    "    print(k,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "in, is , not in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1,2,3,1,2,1,2,2,3,4,5]\n",
    "b = [2,4,5,2,1,3,3,2,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "2\n",
      "2\n",
      "2\n",
      "3\n",
      "3\n",
      "1\n",
      "1\n",
      "2\n",
      "2\n",
      "2\n",
      "1\n",
      "1\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "3\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(a)):\n",
    "    for j in range(len(b)):\n",
    "        if a[i]==b[j]:\n",
    "            print(a[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "2\n",
      "2\n",
      "2\n",
      "3\n",
      "3\n",
      "1\n",
      "1\n",
      "2\n",
      "2\n",
      "2\n",
      "1\n",
      "1\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "2\n",
      "3\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for i in a:\n",
    "    for j in b:\n",
    "        if i==j:\n",
    "            print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = []\n",
    "for i in a:\n",
    "    if i in b and i not in c:\n",
    "        c.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4, 5}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(a)&set(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "447"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random as r\n",
    "\n",
    "r.randint(100,999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['4', '4', '7']"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list('447')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is an error : name 'n' is not defined (Take an action)\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(n)\n",
    "except Exception as e:\n",
    "    print(\"There is an error : %s (%s)\"%(str(e),\"Take an action\"))\n",
    "\n",
    "print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a type error : 'n' is an invalid keyword argument for this function\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(n=6)\n",
    "except NameError as e:\n",
    "    print(\"There is an name error : %s\"%(str(e)))\n",
    "except TypeError as e:\n",
    "    print(\"There is a type error : %s\"%str(e))\n",
    "print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is an error : 'n' is an invalid keyword argument for this function (Take an action)\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(n=8)\n",
    "except (NameError,TypeError) as e:\n",
    "    print(\"There is an error : %s (%s)\"%(str(e),\"Take an action\"))\n",
    "\n",
    "print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addition():\n",
    "    a = int(input(\"A :\"))\n",
    "    b = int(input(\"B :\"))\n",
    "    print(a+b,a*b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A :5\n",
      "B :3\n",
      "8 15\n"
     ]
    }
   ],
   "source": [
    "addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addition():\n",
    "    a = int(input(\"A :\"))\n",
    "    b = int(input(\"B :\"))\n",
    "    return a+b,a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A :8\n",
      "B :4\n"
     ]
    }
   ],
   "source": [
    "s,m = addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A :6\n",
      "B :2\n"
     ]
    }
   ],
   "source": [
    "a = addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 12)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addition(a,b):\n",
    "    return a+b,a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A :4\n",
      "B :5\n"
     ]
    }
   ],
   "source": [
    "a = int(input(\"A :\"))\n",
    "b = int(input(\"B :\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "s,m = addition(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 10\n",
    "b = 20\n",
    "flag = 0\n",
    "c = []\n",
    "for i in range(a,b):\n",
    "    for j in range(2,int(i/2)+1):\n",
    "        if i%j == 0:\n",
    "            flag = 0\n",
    "            break\n",
    "        else:\n",
    "            flag = 1\n",
    "    if flag:\n",
    "        c.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[11, 13, 17, 19]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 40\n",
    "b = 50\n",
    "c = []\n",
    "for i in range(a,b):\n",
    "    for j in range(2,int(i/2)+1):\n",
    "        if i%j == 0:\n",
    "            break\n",
    "    else:\n",
    "        c.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[41, 43, 47]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1,2,3,4,5,6]\n",
    "y1 = [i**2 for i in x]\n",
    "y2 = [i**3 for i in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 8, 27, 64, 125, 216]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fwGOPwddfRx3JtmX1YH2NG8OKFVV7zWTDfT/99NN8/PHHHH744WWO3dpw3/fffz9169YlJyeHZ599tsw58eG+v479y0oc7vvOO+/cbnzDhw/n/fffp27dujRt2pSRI0du9xwRqVonnADffQd7p/mTXhruW5LSn6NI1fvuO/jhB+jdO9o4Kjrcd1ZXMYmIVKdBg0IV9rp1UUdSMVldxSQiUp1GjICpU8NcDzVBRpYganK1WTrQn59I1Zo9O4zS0Lgx9OkTdTQVl3EJol69ehQUFOgmV0nuTkFBAfXq1Ys6FJGMsGoVHHYYDBgQdSQ7LuOqmFq1asX8+fNJx4H8aop69erRqlWrqMMQyQiNGsFdd4VhfWqajEsQdevWpV27dlGHISJCYWGY3+GSS6KOpHIyropJRCQdfPUVtG8PX3wRdSSVpwQhIpICu+0GXbrA//1f1JFUXsZVMYmIpIP994e33446ip2jEoSISBUaPRquvz60P9R0ShAiIlVo4kQYOxZqZcDdVVVMIiJV6N57YcOGLUeKrokyIMeJiETviSdKpyvOlOdMlSBERHbSihVw220wbFjUkVStDCgEiYhEq2nT8NxDs2ZRR1K1VIIQEakkd3j//fC+dWuoXz/aeKqaEoSISCWNHg3HHAPvvRd1JKmhKiYRkUo666xQijj66KgjSQ0lCBGRHbRuXUgMOTlw3nlRR5M6qmISEdlBV18NBx0UnnfIZCpBiIjsoIsugs6dM+d5h61RghARqaCSkjCExuGHhyXTqYpJRKQCiorgiCNgxIioI6k+ShAiIhXw44/hgbjdd486kuqjKiYRkQpo3Bj+/ncwizqS6qMShIjINixZAr/8JSxdml3JAZQgRES26YsvQsnhhx+ijqT6qYpJRGQbTjkF5s4NVUzZRiUIEZEkvvoKPvoovM/G5AAqQYiIJHXHHTBpEsyYAbvuGnU00VCCEBFJ4sUXYc6c7E0OoComEZEyJk6ETZvCQHydO0cdTbSUIEREYpYsgb594frro44kPaiKSUQkZo894M9/hj59oo4kPaSsBGFmT5nZEjObkrBtiJktMLOJseXEhH23mNm3ZvY/MzsuVXGJiCSzZk14PfdcaNs22ljSRSqrmJ4Bjk+y/SF37xZb3gYws47AOUCn2DmPmVntFMYmIrLZ++9Dbi7k50cdSXpJWYJw90+A5RU8/DRgtLtvdPfvgG+Bg1MVm4hIonbt4KSToGPHqCNJL1E0Ul9lZpNiVVBNY9taAvMSjpkf27YFM+tvZvlmlr906dJUxyoiGcw9vLZvD88+C/XrRxtPuqnuBPE40B7oBiwCHtjRC7j7CHfPc/e8Fi1aVHV8IpJFHnoIrrwyzPUgW6rWBOHui9292N1LgCcprUZaALROOLRVbJuISMosXw6LF0Md9edMqlr/WMxsb3dfFFs9A4j3cHoTeMHMHgR+AnQAxlVnbCKSfe6+G4qLs28Y74pKWYIwsxeBI4DmZjYfuBM4wsy6AQ7MAS4HcPepZvYyMA3YBAxw9+JUxSYi2e23v4WzzgqN0rXVX3KrUpYg3P3cJJv/uo3j7wHuSVU8IiIQ5nV47LHw/s47o40l3anmTUSyyl57weTJ2TW3dGVpLCYRyQqFhfDqq6Fr6x57qGG6IpQgRCQrPPNMaHcYOzbqSGoO5VARyQqXXhrGWOrdO+pIag6VIEQkoy1bBitXQq1acJyGAd0hShAiktEuvjiUGjZtijqSmkdVTCKS0QYPhlmz1ChdGfojE5GMVFgIu+wChxwSFtlxqmISkYyzZg106waPPx51JDWbEoSIZBz3kCA6dYo6kppNVUwiknEaNYIXXog6ippPJQgRyRizZsHpp8OiRds/VrZPCUJEMsaUKWFeaU0AVDWUIEQkY5x2Gnz7LbRpE3UkmUEJQkRqrOJiGDoUGjcOQ2kUF0O9elFHlTmUIESkRpo5E/LyYMgQWL0annoqrM+cGXVkmWO7CcLM9jWzD8xsSmz9QDO7LfWhiYhs3aGHwqRJsG5dWHcP64ceGm1cmaQiJYgngVuAIgB3nwSck8qgRES2Z999oaSk7LaSEujcOZp4MlFFEkR9dx9XbpuGvRKRSHXrtuW2nJwwOJ9UjYokiGVm1h5wADM7E1AvYxGJRLzUcM894YG4RHXqwCmnVH9MmaoiCWIA8GdgfzNbAAwErkhpVCIiSXz9NXTpAtOnh55Lq1aFtof4smJF2C5VY7tDbbj7bOBoM2sA1HL3NakPS0RkSw0bhqWW+l9Wi+0mCDO7o9w6AO7+uxTFJCJSxvTpcMABsM8+8N//Quw2JClWkTy8LmEpBk4AclMYk4jIZv/5T6hWeu65sK7kUH0qUsX0QOK6mQ0F3k1ZRCIiCQ45BO66C844I+pIsk9lavLqA62qOhARkTh3GDYsNDrXrg233BK6sEr1qkgbxGRiXVyB2kALQO0PIpIy06bBDTfApk1w3XVRR5O9KjJh0MkJ7zcBi91dD8qJSJVzD20MnTrB+PF6KjpqW61iMrPdzWx3YE3Csh5oFNsuIlJl1qyBk0+G994L6126qEE6atsqQYwnVC0l+ytyYJ+URCQiWam4GH74ARYvjjoSidtqgnD3dtUZiIhkp8WLoXlzaNIEvvgiDJch6aFCvZjMrKmZHWxmfeNLqgMTkcy3YgUcdBDceGNYV3JILxXpxXQpcA2ha+tEoBfwX+Co1IYmIpmuaVMYMACOOy7qSCSZipQgrgEOAua6+5FAd2BlSqMSkYz20Ucwa1Z4f/PNyYfuluhVJEFscPcNAGa2q7t/A+yX2rBEJFNt2AAXXKDnG2qCitT4zTezJsDfgffMbAUwN7VhiUimqlcP3n4b2rSJOhLZnoqMxRQfAWWImX0ENAbeSWlUIpJR3OGPfwxtDv37Q9euUUckFbHdKiYzG2ZmfQDc/WN3f9PdCytw3lNmtsTMpiRs293M3jOzmbHXprHtFvucb81skpn12JkvJSLppaQEPvkEPv00JAupGSrSBjEeuM3MZpnZUDPLq+C1nwGOL7dtEPCBu3cAPoitQxhCvENs6Q88XsHPEJE0tmkTrFsXBtx77TUYOVJPR9ck200Q7j7S3U8k9GT6H3Cfmc2swHmfAMvLbT4NGBl7PxI4PWH7sx6MBZqY2d4V/A4ikobc4eyz4fTTw1PS9eppJriaZkceS/k/YH+gLTC9kp+3p7svir3/Adgz9r4lMC/huPmxbYsox8z6E0oZtFErl0jaMoNTTw1jLNWuHXU0UhkVeVDuj8AZwCxgNHCXu+/0cxDu7ma2w7WR7j4CGAGQl5en2kyRNLNkCcydG56Q/sUvoo5GdkZFShCzgN7uvqwKPm+xme3t7otiVUhLYtsXAK0TjmsV2yYiNcwll8CECeFBuHr1oo5GdkZF2iD+XEXJAeBNIP6b4hfAGwnbL4r1ZuoFrEqoihKRGuTRR0ODtJJDzZeyJiMze5EwZtN+ZjbfzC4B7gWOiTVyHx1bB3gbmA18CzwJXJmquESk6v33v3DHHaFhuk0b6NUr6oikKmy1isnM3gaudPc5lbmwu5+7lV0/S3KsAwMq8zkiEr2//Q1efz0Mn9GkSdTRSFXZVgniaWCMmQ02s7rVFZCI1ByFsUdm77sPxo1Tcsg0W00Q7v4K0ANoBOSb2Q1mdl18qbYIRSQtPfkk9OgBBQXh+YbdNRFxxtleL6ZCYB2wK9AQKEl5RCJSI+y3H3TqBPXrRx2JpMq22iCOBx4k9DDq4e4/VltUIpKWSkogPx8OPhj69g2LZK5ttUEMBs5y90FKDiICMHQoHHooTJsWdSRSHbZagnD3n1ZnICKS/n79a2jWDA44IOpIpDpo6CwR2aYVK+D228PIrI0ahSelNSJrdlCCEJFt+te/wmQ/48dHHYlUtx0ZzVVEskhJSei+et550KcP5OZGHZFUN5UgRGQLkyZBt27wv/+FdSWH7KQEISJb2G032GWXUIqQ7KUqJhHZbOZM6NAhLF9+qcbobKcShIgA8PnnofvqCy+EdSUHUYIQEQAOOQSGDIFTTok6EkkXShAiWcw9TPCzahXUqQO33QYNG0YdlaQLJQiRLDZtGlx7Lfz1r1FHIulIjdQiWaxTpzCPQ9euUUci6UglCJEss3YtnHYafPRRWO/WTQ3SkpwShEiWKSyEuXNh/vyoI5F0pyomkSyxZEkYiXX33cOcDnX0v1+2QyUIkSywfDkcdBDcfHNYV3KQitA/E5EssPvu0L8/nHBC1JFITaIEIZLBPv4Y2rYNg+0NHhx1NFLTqIpJJEOtXw/nngsDB0YdidRUShAiGaK4OMwZ3bw5PPBAGI31n/+Ep56KOjKpqZQgRDLAzJmQlxfGUioogFtvDY3SDRuG9geRylAbhEgGOPTQkBji8zcUFsLXX4ftS5ZEG5vUXCpBiNRwGzdCo0ZbTu5TUgKdO0cTk2QGJQiRGm7BApgzB3bdtez2nBy4+OJIQpIMoQQhUgONHQt//GN4v88+Yfa33XYre0ydOprbQXaOEoRIDfTaazBsGKxZE9a7d4cVK8L8DvFlxQpo3DjaOKVmU4IQqQHWrIGbboIJE8L6HXfAN99och9JLfViEqkBSkpg5EjYay/o0UOJQaqHEoRImnr3XXjlFXjyyVBVNGOGqoykeqmKSSRNzZoFn30GS5eGdSUHqW5KECJpYtUq+MUv4I03wnr//jB5MuyxR7RxSfZSghCJmHt4bdAgJIQ5c8J6nTpQt25kYYlE0wZhZnOANUAxsMnd88xsd+AlIBeYA/Rz9xVRxCdSXV5/HR58ED74IAyuN26cJvOR9BFlCeJId+/m7nmx9UHAB+7eAfggti6SkeLDYtSrF97H2xmUHCSdpFMV02nAyNj7kcDpEcYikhLr1oVZ3R55JKwff3xoiG7ZMtq4RJKJKkE4MMbMxptZ/9i2Pd19Uez9D8CeyU40s/5mlm9m+UvjP7tE0lxRUXht0ACaNIH69cO6WVhE0lFUCeIwd+8BnAAMMLO+iTvd3QlJZAvuPsLd89w9r0WLFtUQqsjOefllaN8+DMcN8OKLcPnl0cYkUhGRJAh3XxB7XQK8DhwMLDazvQFirxrFXmqskpIw5SdAx45h8p4NG6KNSWRHVXuCMLMGZtYw/h44FpgCvAn8InbYL4A3qjs2kapQVAR9+sDNN4f1zp3D4HpqZ5CaJoo+E3sCr1uoeK0DvODu75jZl8DLZnYJMBfoF0FsIpVWUADNmoVnF44/HvbbL+qIRHaOuSet6q8R8vLyPD8/P+owRHjllfAU9IQJsP/+UUcjsm1mNj7hEYOtSqduriI1ysaNpfM9H344XHIJ7L57tDGJVCU9liNSCSUl0Ls3tGoFb74Zxkv605+ijkqkailBiOyA6dPhgAOgVi0YODDMzyCSqVTFJFJBb7wRuqx++GFYv+giOPbYaGMSSSUlCJFtWLkylBoAjjsO7rsPDj442pgkyz3/POTmhmJsbm5YTxFVMYlshXsoIRQVhd5J9eqFeaFFIvP882GikB9/DOtz54Z1gPPPr/KPUwlCJIE7jBkDxcVhjKT77oOnntJ4SRKxyZPhnnvKJoe4H3+EwYNT8rEqQYgk+PDDUJU0alT4QXbkkVFHJFlh06Ywx+y0aTB1aunrc8/BgQfCl1/Cbbdt/fzvv09JWEoQkvXmz4dvv4UjjoCjjoLRo+H//b+oo5KMtGlT+McWTwAnnQQ9eoQZo44/vvS43Fzo1CkUZQHOPhvOOgu6dAnVSuW1aZOScJUgJOv96lcwc2b4AVe7dvi/KLJTiorCP6hdd4V27cKvkBNOgP/9r3Tsdwhjs/ToAXl58MwzISnsvz/k5JS9XoMG4TVZNVP9+mF7CihBSNYpLoZnn4Wf/xwaNYJhw2C33UJyENkh7qGBqqQE7r67tHoongiuuQYefhhatAiJ4sQTQxLo2DE8UBO/8TdrFsZq2Z54Q/TgwaFaqU2bkBxS0EANGotJstBXX4UfbY8+CldeGXU0UmP8738waVJp9dDUqaF94MUXw/7WrcPE4vEE0KlT6BOdhqM2VnQsJpUgJCMVF8NDD8G998Itt8DRR4f/2xdeCN27w9ixep5BkigsDPWN8SRQVFRafXPhhaGx2Az22SfqfVzEAAASRklEQVQkgV69Ss+dPTsM5ZtBVIKQjDNzJvTrF17XrQul+F12gTp1Qqm8Xr2oI5TIFRbCjBmhwfj008O23/wGnngiNCRDSATdu8P48WH9P/8JdZH77Vc6Z2wNpRKEZK1DDw1zM5SUhPV168LsbrvvruSQcZ5/ftv18Rs3hl/1tWrBW2/B00+HksHMmaU9hFatCo1RvXuH13j10H77hYQQ16dP9X63NKAEIRlh48bw469Ll/B/+9//Lru/pCTskwyS7Kniiy+GV18NCWHatJAIJk8ODcILFoR6xk6dQj/mTp3CEi8NnHdedN8lTSlBSI01ZkyoIj7ppPAj8fe/Dz8eL7kE8vNh7drSY3Nywr1DarDCwtBQPHt26EJ6551bPlVcWAh//zvsu28oCfz856VdRvv3Lx2WQipEbRBSY3z3HXzzTehODnDYYaGX4eefh/Xly0M10qpV4TmjlStLz23SBObMgcaNqztqqTB3WLo03PwTl7POglNOgYkTQ5vA9sS7ncpWqQ1CarwNG0KnkZ/+NKzfdRf87W+wbFlocH7uOdh779Lj47O5NW4MK1ZUf7xSAUVFob0gXgqYNSs8JHb22aHhaM89S481g5YtQ6MShFLB6NGhB1H79qGvcjU+VZyV3L3GLj179nTZcT/++KP37dvX8/PzvVevXt6xY0fv0qWLjx49epvnDR061AFfunSpu7v/4x//8Ntvv71KY5s9272wMLy/7z53cJ8/P6zPmOE+c2aVfpykwurV7hMnur/2mvv997uPHBm2l5S4N2oU/lLjy667ut9wQ+n+4cPd//lP9+nT3dev3/bnjBrlXr9+2evVrx+2yzYB+V6Be6yqmLLQo48+yqZNmzjhhBMwMzp06MDChQvp2bMn06dPp0mTJlucM2/ePC699FK++eYbxo8fT/PmzXF3evToweeff079Snb727gxdCapXx/efTcMR/PRR2FcpO+/Dx1OjjoqjFggacIdfvgh/PqfPTusx58C7tMH/vvfssefeGLoQQTwyCOhp1D79qEk8JOfhAblytpeLyZJqqJVTJGXAnZmUQmicnr37u3ffffdFtsPPPBAnzFjRtJzfv7zn/vEiRO9bdu2m0sQ7u4DBw70l156aYc+v6govM6f796ggfsTT4T11avdH37YfeHCHbqcpMLGjaHI9q9/uT/3XOn2Sy7Z8ld7+/al+x9+2P0Pf3B/+WX3/Hz3FSuqP3bZLipYglAbRJYpLCxk9uzZ5Obmltk+btw4CgsLad++/RbnvPHGG7Rs2ZKuXbtusS8vL49PP/2Ufv36bfezS0pCdfMRR8CDD4Yfj1dfHaqSARo2DEPXSCXt6K/pVatKSwE//3mo8//jH+Gxx2DevNKG3l12gXPPDYNV9egR/qLaty9dEv8t6S8woyhBZJlly5ZtUYW0aNEiLrzwQkaOHEmtcsX9H3/8kd///veMGTMm6fX22GMPFi5cuNXPu/pqWLIktC3WqhV6IB1wQNhnFrqmShXY2kxjK1aE8YLy8kI93iuvwNChITEUFJSev2gR7LUX7LFHaBSO3/zjDcLxfxcavCqrKEFkmd12240NGzZsXl+9ejUnnXQS99xzD70Sx5WJmTVrFt99993m0sP8+fPp0aMH48aNY6+99mLDhg3slvC06ciR4Tmlf/wjrO+5Z+hxFJeiUYmz09q14eGvBQtg0KDkM4395jfh/bhxcNBB4S+jUaNQYkgsBTRrFo775S/DIoISRNZp2rQpxcXFbNiwgVq1anHGGWdw0UUXceaZZ5Y57pZbbuHggw/mjDPOYMmSJZu35+bmkp+fT/PmzQF4770ZfP11581jHhUWhvvSmjWhJiJFMyFmtuLiUOyK3/wXLIC+faFz5zA59gUXhG2rV5ees605Ud95p3RE0TPOCItIBShBZKFjjz2Wzz77jB9++IFPPvmEgoICnnnmGQCeeeYZunXrxuTJkzn11FO3OLe4ODyPcNVV0KEDjBv3EUuX/oHZs8NQFpddFhbZiuLiUOefePNfsCDMc3riiWHwuP33Lx0nKO6RR0KCaNo01NEdfXR4RiC+XHRRmJSmvLZtw7VFKkEJIgsNGDCAhx56iOeee44LLrgg6TFFRUX07t2bDRvCcPcdO8Ihh8Dnn8+hffswynGjRotp0GA9ixZ12eYP2KziDv/8Z/IEcN11YdTAffcte07DhuEmf+KJ4cm/m28ue/Nv2TK0DUCYdOa117b83HvvrdaZxiQ7KEFkoR49enDkkUdSXFxM7STTqH3+OQwe/C4Q2iavuQYuvTQkiDZtQrtnTg58+eX3PPDAA5mfHDZsCNU58Zv044+HoaLLVwE9+2yo6rnoojDOR61aoeG3ZcuQOCD8wY0aVbq9ZcuQIOIaNKjcTb2aZxqT7KAH5bJA+clzBg4sO73m4sWhZiM+okG3bmHYig8/DOtz54b7TY1KBBXp8ukeevIsWBDGBI8P53zXXWHs//jNf/nysC8+6NOBB4ZqosRf+IcdBpdfHvZPmhQafcu30IukiYo+KKcEkeGSTZ7ToUO4B558cjjm7LPh00/DvdAMpkyBVq3CAHc10qhRobpl/frSbbvsAk89FZLEzTeH7p4LF4ZHuSEkkfi4PhddFB7hTkwABxwQev5AuG69ejUsY4qUUoIQINSKJE6eA+G+5h5+GDdtGn7wFheHkkPa3fOKikKgBQWh7r5OHfjiizDhQ0FBGLmvoCAsH30Uxv1u2LDsWN9x8STw4INhlrDEBNC6dahDE8kCGs01C7iHG3pBQZgb4fDDw9PJ+fmh3eCxx5JPnuMetsfHNzrwwGoOeOnSUExJvLkvWwY33RQaaUeNCmP9FxSEp33jvv8+3Mjffx9uuy18gebNQ3VO8+bhl33duqGolMy8eeH1uutS/11FMoASRBqKV43XrRuGrl6+HIYNC0Pi9+wJ06eHKu+//CV0aZ87N0yG9be/hfXddgv3zqKiMHnOl/8tYt3G0snUc+oVMWhQ3cpPq+sebsLlb/CHHRZ+pX/9dWjwSNxXUBAGbDv8cHjvvS3bA3JywqTwe+8dij29eoUbf/zm36xZaZ3XwIFhqV8/eZEnsbqo/HYRqTAliAiUlIQBL1u0CLUm69fDr34FZ54ZlmXLwj3ykUfCUBXFxfC734XSQc+eoQPM2WeHdgIIXVCnTg09ICGUDuINzB2+epnfbDwWKG1QqLNhHaesHwP0C8GsWhVu4I0ahQ9esSLM3Vu+Cufaa+HUU0MVT+/eW36xF14IN+F160IxplmzcMPv3Dm832uvcNxRR4UA4zf+Zs3KDtd67LFh2ZoGDbb9B3zPPeryKVIF1AZRRdxDe2e9emF91KjQieWYY8J6nz5hKOs77gjH7rZbGAXh/vvDeufO4eGzK64I68OGwZFHhuofd9i0KZQoNtu0KdSzr1tX+tq4cRg2oaQkjHmxbh3cfnvZqdXi6tQJDRDLl5c+lHX33aU9f9q2DV2dEn/FX389nHZaqCJ65pktf+G3br39m3d10TDQIlulRuqtKH7uBR66+jvuXXk5tzR5goHD9qH2hdufrHzhwnAfjj/jNHRo6Bhz9dVh/cADwy/50aPDeocO4df+6OeKYO1aLr1yF3p138ilN4Vpzz5++Cva7bKANvWWlN7kW7cOwyhAGBRt3ryySeBnP4M//Snsb9QojGeR6JJLQr2Te8gm5Z/GLe/yy8ve4PPywpcoKQn9/hs3TsNWaxHZWTU2QZjZ8cAjQG3gL+5+79aO3dEEMfOBN+l3Y1tmenvWkUMD1rKvfctL93/P6kNPYNGcjZx8Tpjg/PYBy1nw3UaeGjAB1q7lqCE/ZeOm2nw+M0yJeEKn72m4fjEvH/wArF3LYzOOpkUL56zPrwXghyPOoenn/2TXTQkNpoccAmPHhvddu4buQ4mOOgo++CC8P+aYUK2TkxN+lefkhAcVBg4M+x98MNy84/tycsLIm507h/1z54ZqlZ49SxtnE7VtGyZpFpGsUyMThJnVBmYAxwDzgS+Bc919WrLjdzRB7FF7GQUlTShJaHqpxSaasZzjeYeP7QjmloSGzNs6/52FU5fzFJcA8BFHYI0bc8TKv4cTL7ggdA+K36AbNAjVO08/HfYPHx7Gxkm8wceHUwCYNi380k/cv+uuVf+Lvfww0BASx4gRqnIRyVI1tZvrwcC37j4bwMxGA6cBSRPEjupUMpl/c2SZbSXUoTNT+N31q/H6HwC/AuDuF/aBgsaQMw4aNODI+K/0uFGjtv1hV1217f0dO1biG1SChmAQkUpKtwTREkisD5kPlHl6ycz6A/0B2uxgt8VLmr1BfkFP1tJo87YcVnNxszfJHfpw2YOr7eGAanD++UoIIrLDdmK28Gi4+wh3z3P3vBYtWuzQuaf8oQ91KCmzrQ4lnPKHPlUZoohIRki3BLEAaJ2w3iq2rUo0vqwfK0a9hbfNxa0W3jaXFaPeovFl259PWUQk26RbFdOXQAcza0dIDOcA2++DuiNU3SIiUiFplSDcfZOZXQW8S+jm+pS7T404LBGRrJRWCQLA3d8G3o46DhGRbJdubRAiIpImlCBERCQpJQgREUkqrYba2FFmthRIMvB/hTQHllVhODWBvnN20HfODjvzndu6+3YfJKvRCWJnmFl+RcYiyST6ztlB3zk7VMd3VhWTiIgkpQQhIiJJZXOCGBF1ABHQd84O+s7ZIeXfOWvbIEREZNuyuQQhIiLboAQhIiJJZV2CMLOnzGyJmU2JOpbqYmatzewjM5tmZlPN7JqoY0o1M6tnZuPM7OvYd/5t1DFVBzOrbWZfmdk/o46lupjZHDObbGYTzazicxDXUGbWxMxeNbNvzGy6mfVO2WdlWxuEmfUF1gLPunvnqOOpDma2N7C3u08ws4bAeOD0rc31nQnMzIAG7r7WzOoCnwHXuPvYiENLKTO7DsgDGrn7yVHHUx3MbA6Q5+5Z8aCcmY0EPnX3v5jZLkB9d1+Zis/KuhKEu38CLI86jurk7ovcfULs/RpgOmF614zlwdrYat3YktG/hsysFXAS8JeoY5HUMLPGQF/grwDuXpiq5ABZmCCynZnlAt2BL6KNJPVi1S0TgSXAe+6e6d/5YeAmKDevbuZzYIyZjY/NWZ/J2gFLgadjVYl/MbMGqfowJYgsYmY5wGvAQHdfHXU8qebuxe7ejTB17cFmlrFVimZ2MrDE3cdHHUsEDnP3HsAJwIBYNXKmqgP0AB539+7AOmBQqj5MCSJLxOrhXwOed/e/RR1PdYoVwT8Cjo86lhQ6FDg1Vh8/GjjKzEZFG1L1cPcFsdclwOvAwdFGlFLzgfkJpeFXCQkjJZQgskCswfavwHR3fzDqeKqDmbUwsyax97sBxwDfRBtV6rj7Le7eyt1zCXO5f+juF0QcVsqZWYNYxwtiVS3HAhnbQ9HdfwDmmdl+sU0/A1LW2STtphxNNTN7ETgCaG5m84E73f2v0UaVcocCFwKTY3XyALfGpnfNVHsDI82sNuGH0MvunjVdP7PInsDr4TcQdYAX3P2daENKud8Az8d6MM0GfpWqD8q6bq4iIlIxqmISEZGklCBERCQpJQgREUlKCUJERJJSghARkaSUIEQSxEa+/c7Mdo+tN42t51bBtddu/yiR9KEEIZLA3ecBjwP3xjbdC4xw9zmRBSUSESUIkS09BPQys4HAYcDQ8geY2b1mNiBhfYiZ3WBmOWb2gZlNiM1RcFqSc49InK/BzIab2S9j73ua2cexgefejQ3VLhIJJQiRcty9CLiRkCgGxtbLewnol7DeL7ZtA3BGbPC4I4EHYkOdbFdsvKw/AWe6e0/gKeCeSn8RkZ2UdUNtiFTQCcAioDPwXvmd7v6Vme1hZj8BWgAr3H1e7Cb/+9iIoiWEeTf2BH6owGfuF/+8WE6pHYtBJBJKECLlmFk3wuB+vYDPzGy0uye7Ub8CnAnsRSg9AJxPSBg93b0oNrpqvXLnbaJs6T2+34Cp7p6yKSRFdoSqmEQSxKqDHidULX0P3E+SNoiYlwgjp55JSBYAjQnzMhSZ2ZFA2yTnzQU6mtmusRFnfxbb/j+gRXyOYTOra2adquJ7iVSGEoRIWZcB37t7vFrpMeAAMzu8/IHuPhVoCCxIKGE8D+SZ2WTgIpIMMR7rKfUyYVjql4GvYtsLCcnmPjP7GpgI9KnC7yayQzSaq4iIJKUShIiIJKUEISIiSSlBiIhIUkoQIiKSlBKEiIgkpQQhIiJJKUGIiEhS/x/SGTOnCqX5bwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1,'ro--',x,y2,'bp:')\n",
    "plt.xlabel(\"X value\")\n",
    "plt.ylabel(\"Y value\")\n",
    "plt.title(\"Simple plot chart\")\n",
    "plt.legend([\"X vs X^2\", \"X vs X^3\"])\n",
    "plt.text(2,15,\"(2,4)\")\n",
    "plt.savefig(\"MSME-AI.jpeg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\"a\",'b','c','a','a','c','c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['apple','mango','orange'],[23,35,29])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(['apple','mango','orange'],[23,35,29])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie([23,35,29],labels=['apple','mango','orange'],labeldistance=0.5,explode=[0.1,0,0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = open(\"MSME-AI.txt\",'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infile.write(\"\\ni am good\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile.writelines(\"hey how do you do?\")\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "outfile = open(\"MSME-AI.txt\",'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hey how do you do?hi how are you?\\ni am good'"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outfile.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outfile.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hey how do you do?hi how are you?\\n', 'i am good']"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outfile.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "csvData = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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>class</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            class  petal_length  petal_width  sepal_length  sepal_width\n",
       "0  Iris-virginica           5.5          1.8           6.4          3.1\n",
       "1  Iris-virginica           5.9          2.3           6.8          3.2\n",
       "2  Iris-virginica           5.4          2.3           6.2          3.4\n",
       "3  Iris-virginica           4.8          1.8           6.0          3.0\n",
       "4  Iris-virginica           5.1          2.3           6.9          3.1"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csvData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "excelData = pd.read_excel(\"./Course Syllabus.xlsx\",sheet_name=\"Machine Learning\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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>Syllabus</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.Introduction to Machine learning</td>\n",
       "      <td>60 mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.a  Introduction to Data Operation</td>\n",
       "      <td>150 mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Operations</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Moving Data Around</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Computing on Data</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Syllabus  Duration\n",
       "0   1.Introduction to Machine learning   60 mins\n",
       "1  1.a  Introduction to Data Operation  150 mins\n",
       "2                     Basic Operations       NaN\n",
       "3                   Moving Data Around       NaN\n",
       "4                    Computing on Data       NaN"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excelData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsvData = pd.read_csv(\"fruits_vege.tsv\",sep=\"\\t\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "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>fruit</th>\n",
       "      <th>vegetable</th>\n",
       "      <th>fruits 2</th>\n",
       "      <th>vegetable 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>apple</td>\n",
       "      <td>onion</td>\n",
       "      <td>papaya</td>\n",
       "      <td>onion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>mango</td>\n",
       "      <td>potato</td>\n",
       "      <td>cherry</td>\n",
       "      <td>potato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>orange</td>\n",
       "      <td>beans</td>\n",
       "      <td>pine apple</td>\n",
       "      <td>carrot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit vegetable    fruits 2 vegetable 2\n",
       "a   apple     onion      papaya       onion\n",
       "b   mango    potato      cherry      potato\n",
       "c  orange     beans  pine apple      carrot"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsvData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import MySQLdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = MySQLdb.connect(host=\"localhost\", user=\"admin\", passwd=\"test@123\", db=\"testDB\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "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>Person_Id</th>\n",
       "      <th>Person_Name</th>\n",
       "      <th>EmailId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Niraj</td>\n",
       "      <td>v.a@emails.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Vishwanath</td>\n",
       "      <td>v.v@emails.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Chetan</td>\n",
       "      <td>c.v@emails.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>sharma</td>\n",
       "      <td>ss@test.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Person_Id Person_Name         EmailId\n",
       "0          1       Niraj  v.a@emails.com\n",
       "1          2  Vishwanath  v.v@emails.com\n",
       "2          3      Chetan  c.v@emails.com\n",
       "3          4      sharma     ss@test.com"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(\"SELECT * FROM Person\",db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur = db.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"SELECT * FROM testDB.Person\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 'Niraj', 'v.a@emails.com'),\n",
       " (2, 'Vishwanath', 'v.v@emails.com'),\n",
       " (3, 'Chetan', 'c.v@emails.com'),\n",
       " (4, 'sharma', 'ss@test.com'))"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('Person_Id', 3, 1, 11, 11, 0, 0),\n",
       " ('Person_Name', 253, 10, 255, 255, 0, 0),\n",
       " ('EmailId', 253, 14, 255, 255, 0, 0))"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"Insert into Person values (5,'John','john@test.com')\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fruit'] = ['apple','orange','kiwi','pineapple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Vegetable'] = [\"carrot\",'potato','brinjal','cabbage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "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>Fruit</th>\n",
       "      <th>Vegetable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>carrot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>orange</td>\n",
       "      <td>potato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kiwi</td>\n",
       "      <td>brinjal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pineapple</td>\n",
       "      <td>cabbage</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fruit Vegetable\n",
       "0      apple    carrot\n",
       "1     orange    potato\n",
       "2       kiwi   brinjal\n",
       "3  pineapple   cabbage"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"\")\n",
    "df.to_excel(\"\")\n",
    "df.to_csv(\"\",sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing lib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "dataset = pd.read_csv(\"./Advertising.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "1  230.1   37.8       69.2   22.1\n",
       "2   44.5   39.3       45.1   10.4\n",
       "3   17.2   45.9       69.3    9.3\n",
       "4  151.5   41.3       58.5   18.5\n",
       "5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset[[\"TV\",\"radio\",\"newspaper\"]]\n",
    "y = dataset['sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper\n",
       "1  230.1   37.8       69.2\n",
       "2   44.5   39.3       45.1\n",
       "3   17.2   45.9       69.3\n",
       "4  151.5   41.3       58.5\n",
       "5  180.8   10.8       58.4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    22.1\n",
       "2    10.4\n",
       "3     9.3\n",
       "4    18.5\n",
       "5    12.9\n",
       "Name: sales, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((140, 3), (60, 3))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape,xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining a model\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# testing\n",
    "ypred = model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4869629749520692"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# metrics\n",
    "np.sqrt(mean_squared_error(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.14518995])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = [50,70,0]\n",
    "model.predict([test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.04716414,  0.18583963, -0.00200634])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.778209131519807"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.145190231519805"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.04716414*50)+(0.18583963*70)+(-0.00200634*0)+2.778209131519807"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "testDf = xtest\n",
    "testDf.loc[:,'Actual Output'] = ytest\n",
    "testDf.loc[:,'Predicted output'] = ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>Actual Output</th>\n",
       "      <th>Predicted output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>262.7</td>\n",
       "      <td>28.8</td>\n",
       "      <td>15.9</td>\n",
       "      <td>20.2</td>\n",
       "      <td>20.488509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>188.4</td>\n",
       "      <td>18.1</td>\n",
       "      <td>25.6</td>\n",
       "      <td>14.9</td>\n",
       "      <td>14.976268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>237.4</td>\n",
       "      <td>27.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>19.063496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>166.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>19.6</td>\n",
       "      <td>18.443229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>239.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>36.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>14.776079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  radio  newspaper  Actual Output  Predicted output\n",
       "55   262.7   28.8       15.9           20.2         20.488509\n",
       "163  188.4   18.1       25.6           14.9         14.976268\n",
       "69   237.4   27.5       11.0           18.9         19.063496\n",
       "194  166.8   42.0        3.6           19.6         18.443229\n",
       "82   239.8    4.1       36.9           12.3         14.776079"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>199.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>66.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>24.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>214.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.8</td>\n",
       "      <td>35.1</td>\n",
       "      <td>65.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>97.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>204.1</td>\n",
       "      <td>32.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>195.4</td>\n",
       "      <td>47.7</td>\n",
       "      <td>52.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67.8</td>\n",
       "      <td>36.6</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>281.4</td>\n",
       "      <td>39.6</td>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>147.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>218.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>53.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>237.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>49.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>228.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>62.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>262.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>142.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>240.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>248.8</td>\n",
       "      <td>27.1</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>70.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>50.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>164.5</td>\n",
       "      <td>20.9</td>\n",
       "      <td>47.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>19.6</td>\n",
       "      <td>20.1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>168.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>222.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>276.9</td>\n",
       "      <td>48.9</td>\n",
       "      <td>41.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>248.4</td>\n",
       "      <td>30.2</td>\n",
       "      <td>20.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>170.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>276.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>165.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>156.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>218.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>56.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>287.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>253.8</td>\n",
       "      <td>21.3</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>205.0</td>\n",
       "      <td>45.1</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>139.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>191.1</td>\n",
       "      <td>28.7</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>286.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>18.7</td>\n",
       "      <td>12.1</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>39.5</td>\n",
       "      <td>41.1</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>75.5</td>\n",
       "      <td>10.8</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>17.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>166.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>149.7</td>\n",
       "      <td>35.6</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  radio  newspaper\n",
       "1    230.1   37.8       69.2\n",
       "2     44.5   39.3       45.1\n",
       "3     17.2   45.9       69.3\n",
       "4    151.5   41.3       58.5\n",
       "5    180.8   10.8       58.4\n",
       "6      8.7   48.9       75.0\n",
       "7     57.5   32.8       23.5\n",
       "8    120.2   19.6       11.6\n",
       "9      8.6    2.1        1.0\n",
       "10   199.8    2.6       21.2\n",
       "11    66.1    5.8       24.2\n",
       "12   214.7   24.0        4.0\n",
       "13    23.8   35.1       65.9\n",
       "14    97.5    7.6        7.2\n",
       "15   204.1   32.9       46.0\n",
       "16   195.4   47.7       52.9\n",
       "17    67.8   36.6      114.0\n",
       "18   281.4   39.6       55.8\n",
       "19    69.2   20.5       18.3\n",
       "20   147.3   23.9       19.1\n",
       "21   218.4   27.7       53.4\n",
       "22   237.4    5.1       23.5\n",
       "23    13.2   15.9       49.6\n",
       "24   228.3   16.9       26.2\n",
       "25    62.3   12.6       18.3\n",
       "26   262.9    3.5       19.5\n",
       "27   142.9   29.3       12.6\n",
       "28   240.1   16.7       22.9\n",
       "29   248.8   27.1       22.9\n",
       "30    70.6   16.0       40.8\n",
       "..     ...    ...        ...\n",
       "171   50.0   11.6       18.4\n",
       "172  164.5   20.9       47.4\n",
       "173   19.6   20.1       17.0\n",
       "174  168.4    7.1       12.8\n",
       "175  222.4    3.4       13.1\n",
       "176  276.9   48.9       41.8\n",
       "177  248.4   30.2       20.3\n",
       "178  170.2    7.8       35.2\n",
       "179  276.7    2.3       23.7\n",
       "180  165.6   10.0       17.6\n",
       "181  156.6    2.6        8.3\n",
       "182  218.5    5.4       27.4\n",
       "183   56.2    5.7       29.7\n",
       "184  287.6   43.0       71.8\n",
       "185  253.8   21.3       30.0\n",
       "186  205.0   45.1       19.6\n",
       "187  139.5    2.1       26.6\n",
       "188  191.1   28.7       18.2\n",
       "189  286.0   13.9        3.7\n",
       "190   18.7   12.1       23.4\n",
       "191   39.5   41.1        5.8\n",
       "192   75.5   10.8        6.0\n",
       "193   17.2    4.1       31.6\n",
       "194  166.8   42.0        3.6\n",
       "195  149.7   35.6        6.0\n",
       "196   38.2    3.7       13.8\n",
       "197   94.2    4.9        8.1\n",
       "198  177.0    9.3        6.4\n",
       "199  283.6   42.0       66.2\n",
       "200  232.1    8.6        8.7\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.loc[:,\"TV\":\"newspaper\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      22.1\n",
       "2      10.4\n",
       "3       9.3\n",
       "4      18.5\n",
       "5      12.9\n",
       "6       7.2\n",
       "7      11.8\n",
       "8      13.2\n",
       "9       4.8\n",
       "10     10.6\n",
       "11      8.6\n",
       "12     17.4\n",
       "13      9.2\n",
       "14      9.7\n",
       "15     19.0\n",
       "16     22.4\n",
       "17     12.5\n",
       "18     24.4\n",
       "19     11.3\n",
       "20     14.6\n",
       "21     18.0\n",
       "22     12.5\n",
       "23      5.6\n",
       "24     15.5\n",
       "25      9.7\n",
       "26     12.0\n",
       "27     15.0\n",
       "28     15.9\n",
       "29     18.9\n",
       "30     10.5\n",
       "       ... \n",
       "171     8.4\n",
       "172    14.5\n",
       "173     7.6\n",
       "174    11.7\n",
       "175    11.5\n",
       "176    27.0\n",
       "177    20.2\n",
       "178    11.7\n",
       "179    11.8\n",
       "180    12.6\n",
       "181    10.5\n",
       "182    12.2\n",
       "183     8.7\n",
       "184    26.2\n",
       "185    17.6\n",
       "186    22.6\n",
       "187    10.3\n",
       "188    17.3\n",
       "189    15.9\n",
       "190     6.7\n",
       "191    10.8\n",
       "192     9.9\n",
       "193     5.9\n",
       "194    19.6\n",
       "195    17.3\n",
       "196     7.6\n",
       "197     9.7\n",
       "198    12.8\n",
       "199    25.5\n",
       "200    13.4\n",
       "Name: sales, Length: 200, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>1.6</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>3.2</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4.8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>5.7</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5.9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6.7</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6.9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>7.0</th>\n",
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       "      <th>7.2</th>\n",
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       "      <th>7.3</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>7.6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.7</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>9.5</th>\n",
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       "      <td>3</td>\n",
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       "      <th>9.6</th>\n",
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       "      <th>9.7</th>\n",
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       "      <td>5</td>\n",
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       "      <th>9.9</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>10.1</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.7</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TV  radio  newspaper\n",
       "sales                      \n",
       "1.6     1      1          1\n",
       "3.2     1      1          1\n",
       "4.8     1      1          1\n",
       "5.3     2      2          2\n",
       "5.5     1      1          1\n",
       "5.6     1      1          1\n",
       "5.7     1      1          1\n",
       "5.9     1      1          1\n",
       "6.6     2      2          2\n",
       "6.7     1      1          1\n",
       "6.9     1      1          1\n",
       "7.0     1      1          1\n",
       "7.2     2      2          2\n",
       "7.3     2      2          2\n",
       "7.6     2      2          2\n",
       "8.0     1      1          1\n",
       "8.1     1      1          1\n",
       "8.4     1      1          1\n",
       "8.5     1      1          1\n",
       "8.6     1      1          1\n",
       "8.7     3      3          3\n",
       "8.8     2      2          2\n",
       "9.2     1      1          1\n",
       "9.3     2      2          2\n",
       "9.4     1      1          1\n",
       "9.5     3      3          3\n",
       "9.6     2      2          2\n",
       "9.7     5      5          5\n",
       "9.9     1      1          1\n",
       "10.1    3      3          3\n",
       "...    ..    ...        ...\n",
       "19.0    2      2          2\n",
       "19.2    2      2          2\n",
       "19.4    1      1          1\n",
       "19.6    2      2          2\n",
       "19.7    1      1          1\n",
       "19.8    1      1          1\n",
       "20.1    1      1          1\n",
       "20.2    2      2          2\n",
       "20.7    3      3          3\n",
       "20.8    1      1          1\n",
       "21.2    1      1          1\n",
       "21.4    1      1          1\n",
       "21.5    1      1          1\n",
       "21.7    1      1          1\n",
       "21.8    1      1          1\n",
       "22.1    1      1          1\n",
       "22.2    1      1          1\n",
       "22.3    1      1          1\n",
       "22.4    1      1          1\n",
       "22.6    2      2          2\n",
       "23.2    1      1          1\n",
       "23.7    1      1          1\n",
       "23.8    2      2          2\n",
       "24.2    1      1          1\n",
       "24.4    1      1          1\n",
       "24.7    1      1          1\n",
       "25.4    3      3          3\n",
       "25.5    1      1          1\n",
       "26.2    1      1          1\n",
       "27.0    1      1          1\n",
       "\n",
       "[121 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby('sales').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>class</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            class  petal_length  petal_width  sepal_length  sepal_width\n",
       "0  Iris-virginica           5.5          1.8           6.4          3.1\n",
       "1  Iris-virginica           5.9          2.3           6.8          3.2\n",
       "2  Iris-virginica           5.4          2.3           6.2          3.4\n",
       "3  Iris-virginica           4.8          1.8           6.0          3.0\n",
       "4  Iris-virginica           5.1          2.3           6.9          3.1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 petal_length  petal_width  sepal_length  sepal_width\n",
       "class                                                                \n",
       "Iris-setosa                35           35            35           35\n",
       "Iris-versicolor            35           35            35           35\n",
       "Iris-virginica             35           35            35           35"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(\"class\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.loc[:,\"petal_length\":\"sepal_width\"]\n",
    "\n",
    "y = dataset[\"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petal_length  petal_width  sepal_length  sepal_width\n",
       "0           5.5          1.8           6.4          3.1\n",
       "1           5.9          2.3           6.8          3.2\n",
       "2           5.4          2.3           6.2          3.4\n",
       "3           4.8          1.8           6.0          3.0\n",
       "4           5.1          2.3           6.9          3.1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 4)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89, 4)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[2.3,1.4,5.6,3.4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.84763101, 0.1483504 , 0.00401859]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba([[2.3,1.4,5.6,3.4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>class</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            class  petal_length  petal_width  sepal_length  sepal_width\n",
       "0  Iris-virginica           5.5          1.8           6.4          3.1\n",
       "1  Iris-virginica           5.9          2.3           6.8          3.2\n",
       "2  Iris-virginica           5.4          2.3           6.2          3.4\n",
       "3  Iris-virginica           4.8          1.8           6.0          3.0\n",
       "4  Iris-virginica           5.1          2.3           6.9          3.1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.loc[:,\"petal_length\":\"sepal_width\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterModel = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterModel.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2,\n",
       "       0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterModel.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.29736842, 1.39210526, 5.8       , 2.71842105],\n",
       "       [1.47714286, 0.24571429, 5.02857143, 3.46571429],\n",
       "       [5.58125   , 2.003125  , 6.74375   , 3.0125    ]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterModel.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = list(clusterModel.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 38, 1: 35, 2: 32}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(Counter(output).most_common())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int32)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterModel.predict([[2.3,1.4,5.6,3.4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = [['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],\n",
    "           ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],\n",
    "           ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],\n",
    "           ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],\n",
    "           ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],\n",
       " ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],\n",
       " ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],\n",
       " ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],\n",
       " ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "te = TransactionEncoder()\n",
    "te_ary = te.fit_transform(dataset)\n",
    "df = pd.DataFrame(te_ary, columns=te.columns_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Apple</th>\n",
       "      <th>Corn</th>\n",
       "      <th>Dill</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Ice cream</th>\n",
       "      <th>Kidney Beans</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Nutmeg</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Unicorn</th>\n",
       "      <th>Yogurt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Apple   Corn   Dill   Eggs  Ice cream  Kidney Beans   Milk  Nutmeg  Onion  \\\n",
       "0  False  False  False   True      False          True   True    True   True   \n",
       "1  False  False   True   True      False          True  False    True   True   \n",
       "2   True  False  False   True      False          True   True   False  False   \n",
       "3  False   True  False  False      False          True   True   False  False   \n",
       "4  False   True  False   True       True          True  False   False   True   \n",
       "\n",
       "   Unicorn  Yogurt  \n",
       "0    False    True  \n",
       "1    False    True  \n",
       "2    False   False  \n",
       "3     True    True  \n",
       "4    False   False  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequent_itemsets = apriori(df, min_support=0.6, use_colnames=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(Kidney Beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs, Kidney Beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Milk, Kidney Beans)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Eggs, Onion)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                     itemsets\n",
       "0       0.8                       (Eggs)\n",
       "1       1.0               (Kidney Beans)\n",
       "2       0.6                       (Milk)\n",
       "3       0.6                      (Onion)\n",
       "4       0.6                     (Yogurt)\n",
       "5       0.8         (Eggs, Kidney Beans)\n",
       "6       0.6                (Eggs, Onion)\n",
       "7       0.6         (Milk, Kidney Beans)\n",
       "8       0.6        (Kidney Beans, Onion)\n",
       "9       0.6       (Kidney Beans, Yogurt)\n",
       "10      0.6  (Kidney Beans, Eggs, Onion)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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