Merge branch 'IT17176798_Dilhara_N_B' of...

Merge branch 'IT17176798_Dilhara_N_B' of http://gitlab.sliit.lk/20_21-J09/20_21-j09 into IT17176798_Dilhara_N_B

marks.prediction 2.ipynb

-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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nraTzJfWTdJekZeljuxOtm5lZ98os8UfEnyNiTESMAf4BeA2YC0wH5kfECGB++trMzKqkWqWeScBfIuJp4DhgVrp8FnB8lWIwMzOql/g/AcxJnw+MiFUA6eNuVYrBzGpcI7Q8rgeZJ35J2wLHAr/u5HbTJC2StKi5uTmb4MyspjRCy+N6kHnLBknHAWdHxOT09Z+BiRGxStIgYEFEjGxvH27ZYNbYylset6jHlse1JM+WDSfzdpkH4FbgtPT5acAtVYjBzGpYUWfzykumiV9SX+AI4OaSxTOAIyQtS9fNyDIGM6t9jdTyuB5kmvgj4rWI6B8Rr5QsWxMRkyJiRPr4YpYxmBVFvZ8YbWl5PPesg5m6/zCa12/MO6SG5bbMZg3i63OXMPvBJqZOGMolJ4zOOxyrAW3V+D0Ri1mda6S5YK063KvHrM75xKh1lhO/WZ3ziVHrLJd6zBpAo8wFa9Xhk7tmZg3Kc+6amRngxG9mVjhO/GZmBePEb2ZWME78ZmYF48RvZlYwTvxmdaTeG7FZbXDiN6sjnqHKuoPv3DWrA27EZt3JI36zOuBGbNadnPjN6oAbsVl3ynrqxZ0l3SjpSUlLJR0oqZ+kuyQtSx93yTIGs1rT1RO0nqHKuktFTdokbQ+8HhFvStoT2Av4bURs6mC7WcDCiLhS0rZAX+Bi4MWImCFpOrBLRFzY3n7cpM0aiWfKsmppq0lbpYl/MfBBYBfgfmAR8FpETG1nm52APwJ7RMlBJP0ZmBgRqyQNAhZExMj2ju/Eb42g/ARtC5+gtaxsbXdORcRrwInAjyPiBGCfDrbZA2gGfiHpEUlXpt8cBkbEKoD0cbc2Ap4maZGkRc3NzRWGaVa7fILWakXFiV/SgcBU4PZ0WUeXgvYE3g9cHhFjgVeB6ZUGFhEzI2JcRIwbMGBApZuZ1SyfoLVaUWniPw+4CJgbEU9I2gO4p4NtVgArIuKB9PWNJB8Ez6clHtLH1Z0P26w++QSt1YJKa/zDI2J52bLxEfFQB9stBD4bEX+W9C1g+3TVmpKTu/0i4oL29uMav5lZ57VV46/0zt2bJB0bEc+mO/sQcBnQ0SUJ5wCz0yt6/gqcTvIt4wZJZwBNwJQKYzAzs25QaeI/E/iNpI+SlGu+CxzV0UYR8SiwxacNMKniCM3MrFtVlPgj4iFJ5wLzgA3AERHhS23MzOpQu4lf0n8ApScB+gKvAFdJIiKOzTI4MzPrfh2N+H9QlSjMzKxq2k38EfF7SdsA/xkRh1cpJjMzy1CH1/FHxBvAa5LeVYV4zMwsY5Ve1bMBWCLpLpI7cAGIiHMzicrMzDJTaeK/nbdbNZg1nNVrN/CFOY9w2Slj3ULBGl6ll3POyjoQszyVzmXrVsnW6CpK/JJGAN8j6cj51nAoIvbIKC6zqvBctlZElTZp+wVwObAZOBS4BvhlVkGZVYtbJVsRVZr4+0TEfJKmbk9HxLeAw7ILy6w63CrZiqjiq3ok9QCWSfoC8CxtTKBiVm9aWiWfMmEo1z3YRHMn58I1qzeVtmUeDywFdgb+L/Au4F8i4v5sw0u4LbOZWedtVVvmkr7760laK5uZWZ3qqEnbre2td5M2M7P609GI/0DgGWAO8ACgzCMyM7NMdZT4dweOAE4GTiG5e3dORDxRyc4lLQfWAW8AmyNinKR+wPXAcGA5cFJEvNSV4M3MrPPavZwzIt6IiDsj4jTgAOD/AwskndOJYxwaEWNKTjBMB+ZHxAhgfvrazMyqpMOTu5K2A44mGfUPB34E3LwVxzwOmJg+nwUsAC7civ2ZmVkndHRydxYwCvgt8O2IeLyT+w9gnqQAroiImcDAiFgFEBGrJLV6P4CkacA0gKFDh3bysGZm1pZ2r+OX9CZvt2EufaOAiIid2t25NDgiVqbJ/S7gHODWiNi55D0vRcQu7e3H1/GbmXVel67jj4hKWzq0tf3K9HG1pLnABOB5SYPS0f4gYPXWHMPMzDpnqxJ7eyRtL2nHlufAZOBx4FbgtPRtpwG3ZBWDmZltqdJePV0xEJgrqeU410XEnZIeAm6QdAbQBEzJMAYzMyuTWeKPiL8C+7WyfA0wKavjmmXNs3VZvcus1GPWqEpn6zKrR1mWeswaimfrskbhEb9ZhTxblzUKJ36zCnm2LmsULvWYdYJn67JGUNEMXHnznbtmZp3X1p27LvWYmRWME7+ZWcE48ZuZFYwTv5lZwTjxm5kVjBO/mVnBOPFbRVav3cBJV9zHal+3blb3nPitIm5MZtY4fOeutaueG5O5fbJZ6zzit3bVc2Myf0sxa13mI35J2wCLgGcj4hhJ/YDrgeHAcuCkiHgp6zisa+qxMVk9f0sxq4ZqjPjPA5aWvJ4OzI+IEcD89LXVsJbGZHPPOpip+w+jef3GvENqVz1/SzGrhkxH/JKGAEcD3wG+lC4+DpiYPp8FLAAuzDIO2zpXnPp2j6dLjh+VYySVqcdvKWbVlHWp54fABcCOJcsGRsQqgIhYJWm3jGOwAnL7ZLO2ZZb4JR0DrI6IxZImdmH7acA0gKFDh3ZzdNbo6u1bilk1ZVnjPxg4VtJy4FfAYZKuBZ6XNAggfVzd2sYRMTMixkXEuAEDBmQYpplZsWSW+CPioogYEhHDgU8Av4uITwK3AqelbzsNuCWrGMzMbEt5XMc/AzhC0jLgiPS1mZlVSVXu3I2IBSRX7xARa4BJ1TiumZltyXfumpkVjBO/mVnBOPGbmRWME7+ZWcE48VvVeVIXs3w58VvVuV2yWb48EYtVjdslm9UGj/itatwu2aw2OPFb1bhdslltcKnHqsrtks3yp4jIO4YOjRs3LhYtWpR3GGZmdUXS4ogYV77cpR4zs4Jx4jczKxgnfjOzgnHiNzMrGCd+M7OCceI3MyuYzBK/pN6SHpT0R0lPSPp2uryfpLskLUsfd8kqhkbUXoOzvJqfuemaWX3JcsS/ETgsIvYDxgAflnQAMB2YHxEjgPnpa6tQew3O8mp+5qZrZvWlKjdwSeoL3At8HrgGmBgRqyQNAhZExMj2tvcNXFs2OGuxXc/ks7utdVk2P2svJjddM8tfLjdwSdpG0qPAauCuiHgAGBgRqwDSx93a2HaapEWSFjU3N2cZZl1or8FZXs3P3HTNrD5lmvgj4o2IGAMMASZIGtWJbWdGxLiIGDdgwIDsgqwT7TU4y6v5mZuumdWnqjRpi4iXJS0APgw8L2lQSalndTViaATtNTjLq/mZm66Z1Z/MavySBgCb0qTfB5gHfB/4ELAmImZImg70i4gL2tuXa/xmZp3XVo0/yxH/IGCWpG1ISko3RMRtku4DbpB0BtAETMkwBjMzK5NZ4o+Ix4CxrSxfA0zK6rjWPVav3cAX5jzCZaeMdc3erMH4zl1rla/NN2tcnoHL3sETops1Po/47R18bb5Z43Pit3fwtflmjc+Jv4F0V7O0lmvz5551MFP3H0bz+o3dFKGZ1QJPtt5Avj53CbMfbGLqhKFccsLovMMxs5zlcR1/YVX7UkifkDWzznCpJwPVvhTSJ2TNrDM84u9GeY28fULWzDrDI/5ulOfI2ydkzaxSHvF3ozxH3lec+vb5m0uOr7j7tZkVkBN/N3ObYjOrdb6c08ysQeUy9aKZmdUeJ/46011355pZcTnx1xm3SzazrZXZyV1J7wGuAXYH3gRmRsSlkvoB1wPDgeXASRHxUlZxdFWtTUTiu3PNrLtkOeLfDHw5IvYGDgDOlrQPMB2YHxEjgPnp65pTayNr351rZt0ly6kXVwGr0ufrJC0F3g0cB0xM3zYLWABcmFUcnVWrI2vfnWtm3aUqNX5Jw0nm330AGJh+KLR8OOzWxjbTJC2StKi5ubkaYQK1PbL23blm1h0yv4FL0g7ATcD5EbFWUkXbRcRMYCYk1/FnF+E71fLI2nfnmll3yDTxS+pFkvRnR8TN6eLnJQ2KiFWSBgGrs4yhK3z3rZk1sszu3FUytJ8FvBgR55cs/1dgTUTMkDQd6BcRF7S3L9+5a2bWeXlMxHIwcCqwRNKj6bKLgRnADZLOAJqAKRnGYGZmZbK8qudeoK2C/qSsjluq1q7FNzOrBQ19526tXYtvZlYLGrItc61ei29mVgsacsRfy9fim5nlrSETfy1fi29mlreGLPWAr8U3M2uLZ+AyM2tQnoHLzMwAJ34zs8Jx4jczKxgnfjOzgnHiNzMrGCd+M7OCqYvLOSU1A09X+PZdgRcyDKerHFflajEmqM24ajEmqM24ajEmyDauYRExoHxhXST+zpC0qLXrVvPmuCpXizFBbcZVizFBbcZVizFBPnG51GNmVjBO/GZmBdOIiX9m3gG0wXFVrhZjgtqMqxZjgtqMqxZjghziargav5mZta8RR/xmZtYOJ34zs4JpmMQv6eeSVkt6PO9YSkl6j6R7JC2V9ISk82ogpt6SHpT0xzSmb+cdUwtJ20h6RNJtecfSQtJySUskPSqpZvqDS9pZ0o2Snkz/fR2Yczwj099Ry5+1ks7PM6YWkr6Y/lt/XNIcSbnPyiTpvDSeJ6r9e2qYGr+kQ4D1wDURMSrveFpIGgQMioiHJe0ILAaOj4g/5RiTgO0jYr2kXsC9wHkRcX9eMbWQ9CVgHLBTRByTdzyQJH5gXETU1M0/kmYBCyPiSknbAn0j4uW844LkAxx4Ftg/Iiq9+TKrWN5N8m98n4h4XdINwB0RcXWOMY0CfgVMAP4O3Al8PiKWVeP4DTPij4j/Al7MO45yEbEqIh5On68DlgLvzjmmiIj16cte6Z/cRwCShgBHA1fmHUutk7QTcAhwFUBE/L1Wkn5qEvCXvJN+iZ5AH0k9gb7Aypzj2Ru4PyJei4jNwO+BE6p18IZJ/PVA0nBgLPBAvpG8VVJ5FFgN3BURuccE/BC4AHgz70DKBDBP0mJJ0/IOJrUH0Az8Ii2NXSlp+7yDKvEJYE7eQQBExLPAD4AmYBXwSkTMyzcqHgcOkdRfUl/gKOA91Tq4E3+VSNoBuAk4PyLW5h1PRLwREWOAIcCE9KtnbiQdA6yOiMV5xtGGgyPi/cBHgLPTsmLeegLvBy6PiLHAq8D0fENKpGWnY4Ff5x0LgKRdgOOA9wKDge0lfTLPmCJiKfB94C6SMs8fgc3VOr4TfxWkdfSbgNkRcXPe8ZRKywMLgA/nHMrBwLFpPf1XwGGSrs03pERErEwfVwNzSeqyeVsBrCj5pnYjyQdBLfgI8HBEPJ93IKnDgb9FRHNEbAJuBg7KOSYi4qqIeH9EHEJSpq5KfR+c+DOXnki9ClgaEf+edzwAkgZI2jl93ofkP8aTecYUERdFxJCIGE5SJvhdROQ6KgOQtH16Up60lDKZ5Gt6riLiOeAZSSPTRZOA3C4YKHMyNVLmSTUBB0jqm/5/nERyri1XknZLH4cCJ1LF31nPah0oa5LmABOBXSWtAL4ZEVflGxWQjGRPBZakNXWAiyPijhxjGgTMSq+86AHcEBE1c/lkjRkIzE3yBT2B6yLiznxDess5wOy0tPJX4PSc4yGtVx8BfC7vWFpExAOSbgQeJimnPEJttG+4SVJ/YBNwdkS8VK0DN8zlnGZmVhmXeszMCsaJ38ysYJz4zcwKxonfzKxgnPjNzArGid8sJWl92etPS7osr3jMsuLEb5ax9H4Js5rhxG9WAUnDJM2X9Fj6ODRdfrWkj5W8b336ODGdh+E6kpv3tpd0ezoHwuOSPp7Tj2LWOHfumnWDPiV3VwP0A25Nn19GMtfDLEmfAX4EHN/B/iYAoyLib5L+EVgZEUcDSHpXN8duVjGP+M3e9npEjGn5A/xTyboDgevS578EPlDB/h6MiL+lz5cAh0v6vqQPRsQr3Re2Wec48Zt1TUuvk82k/4/SBmDblrzn1bfeHPEU8A8kHwDfk1T6oWJWVU78ZpX5A0nXUICpJFP5ASwnSeiQ9Hzv1drGkgYDr0XEtSSTgtRKC2UrINf4zSpzLvBzSV8lmfmqpRPmz4BbJD0IzKdklF9mNPCvkt4k6cb4+YzjNWuTu3OamRWMSz1mZgXjxG9mVjBO/GZmBePEb2ZWME78ZmYF48RvZlYwTvxmZgXzP4odCjJW+7qnAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.0181600414346974\n",
-      "[9.91065648]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import mysql.connector\n",
-    "mydb = mysql.connector.connect(\n",
-    "  host=\"localhost\",\n",
-    "  user=\"root\",\n",
-    "  password=\"root\",\n",
-    "  database=\"inteljr\"\n",
-    ")\n",
-    "\n",
-    "mycursor = mydb.cursor()\n",
-    "\n",
-    "dataset = pd.read_csv(\"D:/rashmika/projects/freelance/eLearning-analysis/dataset.csv\") \n",
-    "dataset.plot(x='hours',y='score',style='*')\n",
-    "plt.title('Student Mark Prediction')\n",
-    "plt.xlabel('Hours')\n",
-    "plt.ylabel('Marks')\n",
-    "plt.show()\n",
-    "\n",
-    "X=dataset.iloc[:,:-1].values\n",
-    "Y=dataset.iloc[:,1].values\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=0)\n",
-    "\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "regressor=LinearRegression()\n",
-    "regressor.fit(X_train,Y_train)\n",
-    "print(regressor.intercept_)\n",
-    "print(regressor.coef_)\n",
-    "Y_pred=regressor.predict(X_test)\n",
-    "df=pd.DataFrame({'Actual':Y_test, 'Predicted':Y_pred})\n",
-    "\n",
-    "for index, row in df.iterrows():\n",
-    "    sql = '''INSERT INTO student_prediction (student_id,actual,predicted) VALUES (\"%s\", \"%s\", \"%s\")'''\n",
-    "    val = (1,float(row['Actual']),float(row['Predicted']))\n",
-    "    mycursor.execute(sql, val)\n",
-    "\n",
-    "mydb.commit()"
-   ]
-  },
-  {
-   "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": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}