SimpleLinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model with [0.00027217] % accuracy\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "x=np.array([2,5,9,3,5,7])\n",
    "x=x.reshape(-1,1)\n",
    "y=np.array([20,45,93,33,50,72])\n",
    "mind=LinearRegression()\n",
    "mind.fit(x,y)\n",
    "mind.coef_\n",
    "r=mind.predict([[8]])\n",
    "sqd=((80-r)/80)**2\n",
    "print(\"Model with\",sqd,\"% accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Training!\n",
      "Enter the number of dataset you want to enter10\n",
      "Enter key value pair:\n",
      ">>>3\n",
      ">>>32\n",
      "Enter key value pair:\n",
      ">>>4\n",
      ">>>38\n",
      "Enter key value pair:\n",
      ">>>5\n",
      ">>>50\n",
      "Enter key value pair:\n",
      ">>>6\n",
      ">>>55\n",
      "Enter key value pair:\n",
      ">>>7\n",
      ">>>78\n",
      "Enter key value pair:\n",
      ">>>8\n",
      ">>>79\n",
      "Enter key value pair:\n",
      ">>>9\n",
      ">>>90\n",
      "Enter key value pair:\n",
      ">>>1\n",
      ">>>20\n",
      "Enter key value pair:\n",
      ">>>2\n",
      ">>>19\n",
      "Enter key value pair:\n",
      ">>>3\n",
      ">>>23\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data\n",
      "Enter the number of dataset you want to enter2\n",
      "Enter key value pair:\n",
      ">>>3\n",
      ">>>30\n",
      "Predicted value is  [30.85849057] . Actual value is  30\n",
      "Model with [99.91811044] % accuracy\n",
      "Enter key value pair:\n",
      ">>>5\n",
      ">>>50\n",
      "Predicted value is  [50.3490566] . Actual value is  50\n",
      "Model with [99.99512638] % accuracy\n",
      "Model works with approximate  [99.91323682] % accuracy!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "key=[]\n",
    "value=[]\n",
    "final=0\n",
    "print(\"Model Training!\")\n",
    "num=int(input(\"Enter the number of dataset you want to enter\"))\n",
    "for i in range(num):\n",
    "    print(\"Enter key value pair:\")\n",
    "    k=int(input(\">>>\"))\n",
    "    v=int(input(\">>>\"))\n",
    "    key.append(k)\n",
    "    value.append(v)\n",
    "    \n",
    "fig=plt.figure(figsize=(10,5))\n",
    "plt.bar(key, value, color='green', width=0.4)\n",
    "plt.xlabel(\"Key\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.title(\"Linear Regression\")\n",
    "plt.show()\n",
    "    \n",
    "key=np.array(key)\n",
    "value=np.array(value)\n",
    "key=key.reshape(-1,1)\n",
    "mind=LinearRegression()\n",
    "mind.fit(key,value)\n",
    "mind.coef_\n",
    "\n",
    "print(\"Test Data\")\n",
    "tnum=int(input(\"Enter the number of dataset you want to enter\"))\n",
    "for i in range(tnum):\n",
    "    print(\"Enter key value pair:\")\n",
    "    tk=int(input(\">>>\"))\n",
    "    tv=int(input(\">>>\"))\n",
    "    r=mind.predict([[tk]])\n",
    "    print(\"Predicted value is \",r,\". Actual value is \",tv)\n",
    "    sqd=(((tv-r)/tv)**2)*100\n",
    "    print(\"Model with\",100-sqd,\"% accuracy\")\n",
    "    final+=sqd\n",
    "print(\"Model works with approximate \",100-(final/i),\"% accuracy!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oQFVMbxIWqjR9ofWflzguLfAHpt7qOEOXVf0WV7wxfjSwkDCM2SqGasu9a9YtPWFX89Guo1S7jMraz7R8+72FelhQxtopcOryWTNCOzmlbXHBbUSttABEul8+fFi/phppch2lmrVo3fIzWr7XGqDSAn/CgO+6DtEVVV1c8cb4mcDfuc5RLm0iB543ckTadY5qtV17piY1/7Dv2zo8iFclnzR65pyPug7RWVVbXLnLi33PdY5yW9WtbuJNgwfa8a4K26D9X5vQfPcBG6kf4jrLftzoOkBnVW1xAefjXxU48h7s1/fkF3v2WOQ6R7VYlm14flLzXeO20yvoH/icPHrmnOmuQ3RGVRZXvDEuwEzXOSpGpNsVw4cN2FJTE+qPwMPgleyRT53R8r2JrdR1d52lQKHc6qrK4gLOJmTnI3ZVRmTUuaOGL1X/EyVTBo9lTpp7bktyslITpvfVKaNnzpnmOkSxwvQDLqXq2drKs7au7sQbhwyy410lpkr2J21nP/XV1qtdjIYvhdBtdVVdccUb46cBJ7vO4cojffuc+nSvngtd54gKVVpubEu8OKvtgsmus3TBqaNnzjmzsy8Wkc+LyEIRWSAis0sZrD1VV1zAN10HcEqk7qsNQ4durKnZ4DpK2Kmy9crWaxfdl/lYFE6v6tRWl4iMBW4AzlDVY4FrSpqqHVVVXPHG+DHAWa5zuJYVGXHuqBHL7XhX52VV1n+u5caV/5udcJzrLCVy2uiZc07sxOvOAH6rqhsAVHVTaWPtW1UVF/A11wGCYkNd7Qkzhw5+ynWOMGrV2nemt/zHjld0TMx1lhK7shOvERz8AayacxXjjfEBwCqgl+ssgaGauXPdBu+MHTvHu44SFju1+xunN3+/fg2DnF7VvEx2ACOXz5pR8GliuV3FR4BJqrpRRAZVYqurmra4LsJK68NEar82bMjI9bU1611HCYPN2nfBhOa7GyJaWgC9gc8X8wJVXYR/pfe5IrIA+H45gu2pmorrctcBgigrMuycUSNWZv2rH5t2rNQhL57UfNeYrfSpd52lzK4o9gWq2qiq41T1WFW9pAyZ9lIVxRVvjB8PHOM6R1Btrq097uvDhtjxrnZ42dFPT26+44Rmuvd0naUCxo6eOSfwMwFXRXEBl7gOEHRP9O41+Y99er/qOkfQPJE57slPtHzntCw10Zv6qH0J1wE6EvmD87nrJK4CBrvOEnSiuv5P76zKDM9khrvO4poqel/mo0/d2HZJWEfDd8UG/IP0ra6DtKcatrg+ipVWQVRk6Lmjhq/NQMZ1FpdUab2l7YLnqrS0AIYAM1yH2J9qKK5Puw4QJk21tcde0zDkadc5XFFl+9WtX13ws8zZp7jO4tgFrgPsT6R3FXOTBa7Gv0CAKZSq3rJ+46tnb98R+IO0pZRV2XRh6/Wrn8+OHes6SwA0AUOWz5rR5jrIvkR9i+skrLSKJyLXDx186Lt1tatcR6mUNq1Z9Xctt2yx0npfPRDYczCjXly2m9hJKjLocyNHbGyDQP7FLaVd2u2tKc23S1oPOtR1loAJ7Hm9US+uT7kOEGZba2viX24Y+qzrHOX0nvb2Tmq+a9C7DB3hOksAWXFVWrwxfhRwpOscYfd8715Tfte3z0uuc5TDGh348sTmuw/bQr+BrrME1PjRM+cEstAjW1wE/OPcMEkOGXTkirq6la5zlNKS7AHPntL8g+N20qO36ywB9zHXAfYlysUV5hkpA0VFBpw3anhTC7S4zlIKz2TGzf1Yy60nZ6itc50lBAK5uxjJ4spdxafax+GU1PaamrFXDB/2vOscXfVg25QnL2q9fgqIuM4SEtNHz5wTuNOdIllcQAwbLV9yr/TqOeWBfn1fcJ2jM1TJ3N56zjPXtV0x1XWWkBmIP6woUKJaXKe6DhBVNw8eGFteV7fCdY5iqLLzG21fevXOzDn2e9E5p7kOsCcrLlMckfrzRg3f3gLNrqMUQpWmS1u/8ebDmakTXGcJscDNkGvFZYq2s6Ym9oURDS+6ztGRjMqaT7bctO7J7Hibi61rjnUdYE+RK654Y3wEcIjrHFG3oGePyff17/ec6xztadG6Zae3fL/N00OPcJ0lAo4cPXNOoIaNRK64COBfh6i6bdCA+NJu3Za5zrGnbdpz8aTmH/ZboQ0HuM4SETVA3HWIfFEsrqNdB6gaIv3OH9nQuktkp+sou23Q/q9NbL77oI3UD3GdJWICdZwrisUVtWvdBdqumpojEyOGveI6B8DfsiOem9R817jt9OrrOksEWXGVmW1xVdjiHj1Ou6e+v9OTsV/Kjpl7ZsttJ7VS191ljggL1CGYKBaXbXE58IOB9ePT3bu95WLd/5OZ9OR5LTdOUWqi+PscFPHRM+cE5ucbmCClEG+MD8cf6WsqTaTPRSMadKfIjkqtUpXsj9s+8dTVrVdNrdQ6q1hf4DDXIXaLVHFhu4lONdfUHH7hyIbXKrEuVZr/te2Sl25tO99Opq+cwAwzilpx2QyWjr3Zvfupdw+oL+vFNlR574rWry2enflo4M6hi7jAXLYuasUVyEnPqs1PBvQ/4fXu3d8sx7IzKuvPaUmu+lP2xOPKsXyzX1ZcZRKYH2xVE+n1+ZENddtFtpVysa1au2J6y207XtMjjyrlck3BAvP+suIyZdEqcsj5I4cvLNXydmiPJac239nzbzry4FIt0xQtMO+vqBVXg+sA5gPLunc7+faB9U91dTmbte/8Cc13j1jLILvUnFuBeX9FrbgC8xfB+H5Z33/i/B7d0519/Yrs0BcmNt8d20bv/qXMZTolMO8vKy5TXiI9Lh3R0GuryHvFvnRh9pCnp7bcfmIL3XqUI5opWmDeX5EprnhjvDfQx3UOs7c2kYP/ftTwRcW85k+Z45/8ZMvNp2WpCdx851Vs4OiZcwJxSlVkigsI1HxB5sPe6dZt0q2DBnR4vEsV/WXbx+d+qfXrUysQyxRHgEAcZ4xScQXiL4Fp36/69zvp5Z49Frf3uCqt32m78Plvt31+SiVzmaIEYuYNKy5TOSLdLx8+rH9TjTTt+ZAq265qvWrhPZkZJ7uIZgoWiGtRWnGZisqIHHDeyBEf+pQxq7Lx/NYb3n4sO+l4V7lMway4SsyKKyRWdaub+O+DB84FaNOad89queW9F7Jjx7rOZQoSiOIKRIgSseIKkYf69T15wubsX3+08R+HbaT/0CFsWes6k+lYm9QGYmPHisu4IdKt++N9es3a8tNxrqOYYv2D6wCR2lXMug5gCleT1baGLTZ/Wgi1ug4A0Squ7a4DmMId+zddLFDvOocpmhVXiZV0ChVTXtPm6ybXGUynWHGVmBVXiMSXa2DOezNFseIqMdtVDImBW3Vdj1bGuM5hOsWKq5S8hLcLaHOdw3TsjAX6hvjnvZnwCcSeTWSKK8e2ukLgtNezNuNDOG2LpVObXYeA6BVX0XM+mcqqyWpmxGa7aG9IveM6wG5RK67VrgOY/Ru3XBcLDHCdw3TKCtcBdotacQXmB2v27aPzdKPrDKbTAvP+ilpxBWZT1uxbfJkG5oILpmiBeX9FrbgC8xfB7G3ANl3fsxW7JmJ4Beb9FbXiCsxfBLO30xfoEhsGEWpWXGUSmB+s2dvk17NR+32rNoF5f0XtFykwP1jzYaKaHbHJdhNDTIGVrkPsFrXiWgdsdR3C7G3ccl1cA4Nc5zCdti6WTjW7DrFbyYtLRK7P+3q0iLxe6nW0x0t4CrR7FRnjzvR5usF1BtMlgdqbKccW1/UdP6WsPMfrN/tw7DINxPX4TKctdx0gX4fFldtqSotIo4gsFJHfisgMEXkk7znTReR3IjIL6CUi80Xk/tzDtSJyj4gsEpE/iUiv3GvGi8gLuWU+IiIDc/c/KSK3ishLIvKGiJxW5PdkxRUw9dt1Q88WO80n5F52HSBfoVtcY4Cfqeox+OcDHg3ERGRo7vFLgXtVdSawU1XHq+qFuceOAO5W1bHAFuCc3P33AdfllukBN+atr05VJwDX7nF/IeYV+XxTZlMX2mwQEfCM6wD5Ci2ud1T12dzXvwJOAWYDF4nIAGAS8Id2XrtMVefnvn4VGC0i9cAAVZ2bu78RmJz3mt/lP7/AjLvNw/8ExATE5Nez9v8j3HbivxcDo9Di2vMXT4F7gYuA84GHVbW9ubDyP4nIUNiVhXa/ptDnv89LeNuAN4p5jSkfUc2O2mDDIELupVg61eI6RL5Ci+sgEZmU+/p84BlVXQWsAv4F+M+857aKSLf9LUxVm4DNecevLgbm7uclxQrU/ng1O3qFpmpgsOscpksCtZsIhRdXCkiIyEL8sTg/zt1/P/5uZP4QhJ8BC/MOzrcnAdyWW+Z44NuFx+5QKUvQdMH013S96wymywJXXIXuhmVV9cp93H8qcE/+Hap6HXBd3l3j8h77bt7X84GT9lygqk7N+3oDxR/jAvhLJ15jymD833Rox88yAZYFnnMdYk+dHsclIq8Cx+AfrA8UL+EtA5a5zlHt+u3QTb1sGETYLYylU4GbWbjD4lLV5aq612XSVfV4VZ2sqoE5DWAPf3UdoNpN8TQt0TutrNoEbjcRov1LZbuLjk31bBhEBFhxVZhtcbmkqgds4EjXMUyXWXFVkpfw1gIVO8HbfFjsHVI1ih2YD7dlsXTqXdch9iWyxZXzqOsA1Wr6vOw61xlMl/236wDtiXpxPeQ6QLU67i0d4jqD6bLZrgO0J9LF5SW8+djpPxXXd6du6d1swyBCblEsnQrshAWRLq6ch10HqDaTPV0sUOs6h+mSjs58caoaist2FyvsdC+bdZ3BdIlixeWWl/AWAmnXOaqGqh64njGuY5gueSqWTgVqquY9Rb64cmx3sULGrGSJDYMIvcCdxrenaimu+7DJBSti+rzsGtcZTJfsIgR/6KuiuLyEtxT4s+sc1eAjb6ldgizcHoulU02uQ3SkKoor50euA0Rdn53a1GcXY13nMF0S+N1EqK7ieoyAXRsuak5bZMMgQm4j8LjrEIWomuLyEl4Gf3ZWUyanL8xmXGcwXfJALJ1qdR2iEFVTXDk/BwI16X+UHLyOI1xnMJ2WAe5wHaJQVVVcuRkj/st1jig6/F1dUqM0uM5hOu23sXRqqesQhaqq4sr5bsdPMcX66LzsatcZTJfc4jpAMaquuLyE9xo23U3JHf+mDnSdwXTa47F0aoHrEMWouuLK+TfXAaKk9y5t6mvDIMLsO64DFKsqi8tLeK/iD48wJXDqIk1JkVccN4ExN5ZOPes6RLGqsrhykq4DRMXpC7NtrjOYTrvBdYDOqNriym11zXGdIwpGr+Vw1xlMpzwexq0tqOLiykliJ193yWGr9c1aZbjrHKZoSki3tqDKi8tLeK8Q8AnTgm76a9lAXgXGdOi3sXRqvusQnVXVxZXzDWCr6xBhdcKbOsB1BlO0DPAt1yG6ouqLy0t4q4GbXOcIo17NurXfThsGEUJ3xNKpJa5DdEXVF1fOHdjVgIp2ymJdLNDNdQ5TlKWEfGsLrLgA8BJeC3Ct6xxhc8aCrJ2wHi4KfDGWTu10HaSrrLhyvIT3B2xQalEOWcNhrjOYovw0lk7NdR2iFKy4PuxqYJvrEGFwyBpdWquMdJ3DFOwd/A+iIsGKK4+X8JYB/+g6RxjYMIjQuSKWTkXm03Mrrj14Ce8eQjJ9rUsnvqn9XWcwBZsdS6f+4DpEKVlx7dsXgU2uQwRVzxbd1n+HDYMIibVE8IMnK659yI3t+orrHEE1KaWLBbq7zmEKclUsnYrcH2ErrnZ4Ce8B4CHXOYLozPnZXa4zmII8EkunAn9x186w4tq//4dd0mwvh63hUNcZTIfWAF92HaJcrLj2w0t4m4Bz8C9LboCD1+pbtVkOcJ3D7Fcz8JlYOrXGdZByseLqQG4Gicj+5SrW9HnZla4zmA59MZZOveA6RDlZcRXAS3j3Aj9xnSMIJizRfq4zmP2aFUunfuU6RLlZcRXuGuB51yFc6tGi2+ttGESQ/Q9wvesQlWDFVaDcidjn4h/0rEqT0rpIoIfrHGafFgIXxtKpqpjR14qrCF7CWwV8lio9WG/DIAJrPfDJWDpVNefZWnEVyUt4zwMXAFnXWSrtsNUc4jqD2UsL8NlYOvW26yCVZPJO16YAAAbMSURBVMXVCV7CewS4ynWOSjpwnS6ry3Kg6xxmL1fG0qlnXIeoNCuuTvIS3o+Af3edo1Kmz8u+4zqD2cv3YunUva5DuGDF1QVewvtX4Ieuc1TCxCXa23UG8yH3EqH5tYplxdV11wCzXYcop+6tunPAdsa5zmHedxdwWSydqrrjrLtZcXWRl/AUuBRodJ2lXE5K6+sCPV3nMADcGkunrqqWYQ/tseIqAS/hZfDL68eus5TDmfOzO1xnMAB8K5ZOzXQdIgisuErES3jqJbwvA991naXUjljFaNcZDP8YS6fs+p85Vlwl5iW8fwaSrnOUygEb9O26LAe7zlHFsvjzxd/uOkiQWHGVgZfw/g34J9c5SmHavOxy1xmqWAZIxNKpn7kOEjRWXGXiJbzvAQn8uZFC66S09nGdoUq1AOdVw0wPnWHFVUZewrsPOAP/ggWh061Ndw3cZrNBOLAD+HQsnfqd6yBBZcVVZl7Cew6YACxwnaVYE5fo6wK9XOeoMm8BJ0XtcmKlZsVVAV7CWwGcAjziOksxzpyX3e46Q5WZA5wQS6c810GCzoqrQryEtx1//vqbXWcp1JGr7NPECskCNwKfiKVTW1yHCQNRreoBuE7EG+Mz8M81G+o6S3tGbNQVd/4sc5DrHFVgE3BxLJ2yq6cXwba4HPAS3hzgGOBPrrO0Z7oNg6iEucCxVlrFs+JyxEt4a4CP44/3anEcZy+T0moH5cunDfgWcEYsnbKrJnWC7SoGQLwxfhzwG2CM6ywAdW3afP9tmYyATWVTem8DF8TSqedcBwkz2+IKAC/hzQM+AtyJP1raqQlv6OtWWiWXBX6Ov2topdVFVlwB4SW8HV7CuxaYCLziMsu0+Vo1F12okOeBCbF06vJYOtVU7ItFxIpuD1ZcAeMlvFfxy+sq4D0XGcasVJtbvjTWAJ8HTomlU692diGqenLpIkWDFVcAeQkv6yW8u4AY8HAl192wSVd2y3BoJdcZQa340xsdGUunZnd10j8R2SYifUXkLyLymoh4IvKp3GO3isiX856bFJGvt/f8qLCD8yEQb4xPB24Fjiv3ui76a+apT76ok8u9ngj7I3BtLJ1aUqoFisg2YADQW1XfE5EhwAvAEcB44A5VnZJ77mL8T6tX7ev5GpE3vG1xhYCX8P4MHA/8PfBGOdd1ckptiubOeQv/oqxnlbK08gjwHRFZCDwBjAIaVHUeMExERorIscBmVV3R3vPLkMsJK66QyM2w+hAwFrgcKPn4n7o2bR78ns0GUaQtwA3A2Fg69WgZ13Mh/pkWx6vqePwZR3b/kfktcC7+H7YHCnh+6NW5DmCK4yW8NuDn8cb4bODLwExgWCmWfcJSXSwV2B2NiDfxh6/8ZyydqsTJ6PXAOlVtFZHT4UPnkT4A3AMMAaYU8PzQs2NcIRdvjPfA/+v6NejaJcRu+E1m7rHLdUrHz6xq/wfcDsyp1OXBRGQrcAjwKNANmI8/28hZqro89xwP2KCqp+f+PWR/zw87K64IiTfGp+EX2Fn4xziK8qv/aHure4bDSh4s/Frwz2y4I5ZOza/kikVkMPCaqkZqi6mrrLgiKN4YPwr/QrUXAwVNvTxsi757148zo8oaLHw2AD8B7o6lU2sqvXIRGQk8CfxQVaviiumFsuKKsHhjvA/wafwCmwbUtvfcC/4v89SnX7BhEDnzgR8Bs2Pp1C7XYczerLiqRLwxPhw4H7gI/7zID7n77rYXh77HxIoHC4YM8DTwe+D3sXRqmeM8pgNWXFUo3hg/GjgPOBv4SG1G2379H5lmgb6Oo1XSduB/8cvqsVg6tclxHlMEK64qF2+Mjzj6bT0z+evMJ/GvSDTYdaYyWov/SdvvgSdsNzC8rLjM+1JHxQT/FJJpuduphHt6m834M228BDwOvFCpIQymvKy4TLtSR8W645/oHQOOzt1i+OfIdXMYbV82A6/jF9XLwMuxdGqp20imXKy4TNFSR8Xq8Mtrz0IbQ/muw5jFP71mE7AaSAGLcrfFsXRqdZnWawLIisuUTOqoWA0wHP8g/75ufdq5bwd+IW3O/XfTPv7d1NXpYUx0WHEZY0LHZocwxoSOFZcxJnSsuIwxoWPFZYwJHSsuY0zoWHEZY0LHissYEzpWXMaY0LHiMsaEjhWXMSZ0rLiMMaFjxWWMCR0rLmNM6FhxGWNCx4rLGBM6VlzGmNCx4jLGhI4VlzEmdKy4jDGhY8VljAkdKy5jTOhYcRljQseKyxgTOlZcxpjQseIyxoSOFZcxJnSsuIwxoWPFZYwJHSsuY0zoWHEZY0LHissYEzpWXMaY0LHiMsaEzv8HLbCTP6GUPHMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data={'c':20, 'c++':40, \"python\":60, \"java\":50}\n",
    "language=list(data.keys())\n",
    "number=list(data.values())\n",
    "fig=plt.figure(figsize=(10,5))\n",
    "plt.pie(number, labels=language)\n",
    "#plt.xlabel(\"Key\")\n",
    "#plt.ylabel(\"Value\")\n",
    "plt.title(\"Linear Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}