From 07e78ba392e60dbc9c143a204a9651c01569ffdb Mon Sep 17 00:00:00 2001 From: SiddharthM29 Date: Thu, 24 Feb 2022 10:42:17 +0530 Subject: [PATCH] Adding a file --- SET_ASSIGN_2.ipynb | 307 +++++++++++++++++++++++++++++++++ Student assignment updates.txt | 2 + 2 files changed, 309 insertions(+) create mode 100644 SET_ASSIGN_2.ipynb diff --git a/SET_ASSIGN_2.ipynb b/SET_ASSIGN_2.ipynb new file mode 100644 index 0000000..c6c906f --- /dev/null +++ b/SET_ASSIGN_2.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hOCqsVcMmwiw" + }, + "outputs": [], + "source": [ + "# PRN : 2019BTECS00102\n", + "# PRN : 2019BTECS00100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 664 + }, + "id": "QzIw6mS8riiQ", + "outputId": "ba651f2b-546a-471e-f5bc-dce35ca1512d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' 60' ' 60' ' 50' ' 50']\n", + "Test\n", + "[['19.890000000000001' '46.299999999999997' '19.199999999999999'\n", + " '44.626666666666701' '19.789999999999999' '44.933333333333302'\n", + " '18.926666666666701' '45.890000000000001' '17.166666666666700'\n", + " '55.090000000000003' ' 6.559999999999999609' '83.1566666666666947'\n", + " '17.199999999999999' '41.433333333333302' '18.199999999999999'\n", + " '48.729999999999997' '17.000000000000000' '45.500000000000000'\n", + " ' 6.3666666666666698e+00' '733.70000000000005' ' 92.000000000000000'\n", + " ' 6.33333333333333037' '55.3333333333333002' ' 5.0999999999999996e+00'\n", + " '28.6426681675948202610' '28.6426681675948202610']\n", + " ['19.890000000000001' '46.693333333333300' '19.199999999999999'\n", + " '44.722499999999997' '19.789999999999999' '44.789999999999999'\n", + " '19.000000000000000' '45.992500000000000' '17.166666666666700'\n", + " '55.200000000000003' ' 6.833333333333330373' '84.0633333333333042'\n", + " '17.199999999999999' '41.560000000000002' '18.199999999999999'\n", + " '48.863333333333301' '17.066666666666698' '45.560000000000002'\n", + " ' 6.4833333333333298e+00' '733.60000000000002' ' 92.000000000000000'\n", + " ' 6.66666666666666963' '59.1666666666666998' ' 5.2000000000000002e+00'\n", + " '18.6061949818395078182' '18.6061949818395078182']]\n", + "[' 50' ' 60']\n", + "Mean squared error is: 14.541278065208676\n", + "(2, 26) (2,)\n" + ] + }, + { + "data": { + "image/png": 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pa3suW8bk/WY0t1+2twfysBHS1cwvc11I++ZieieKm+kdfx6e6y0gXTxAOmb8WTVtj89t3EbaDp9A2g4Pz/0sIh2Dn026SHgS8OlcvgN4Rm7zYcAXgGtzv+voneSfQjreLyVdXZ0BPI10jH846Zi5NM/fglxvKzyYL8+md3y4CHhhXoYnkC6iHpan7SZgPMZYxr2SlD9T25PfGCclcH0l9/08YxeREnwbvSu5TcDfAquBn+aVUK60Sr1b8kJ6ALiMtCEuIF2ZbQVW5TYn8oy9k7Sx3ZNXQDmzKWe2d5HOMj5G70oukgLvXtIGU5+tjeY2Rpl81lVfyZQNurwfdMZdn7H1O0Pud2Y91dn0fLyGnb5hpn+mt3P3xjKaqo1hbkkPan9QPzsH1Fk7w/mrr1LmaplPNz1T9b+/bM9767Wn81PfvZhpu+WKq/u1Sr+vV7qfu32OAv9Lb3sr7Y6SjrmX5/Ltuawc304BvkXvuLqR3nFvrGrnZnq3RD9NCq/r6N312kYKx5Py5815nJeTwmpTbuv0Tr6sBX5Sff4Z8Nu53U2ki5pjcp+vqMZ7J/CpufqLJ39BuhR9Hil8xqphi/OC+B16twIfS0rjrwBvJgXaEXncZ+SFfnge7yzgXHpnsuVM7Pzcz6l5WMxtL8rtHgv8Ob2rEkhXhmP0zsTLhrMwj1fOLMrKLWeZ5X25VVSuDkobVJ/LbZZlneFlw6jPxEu98r4+s+wnTjN8bxibYjpKeXc6JjrDi81T9FPqdP9Rb30lVytnsv2uQAad3Q9aXlMt50HD9uQKottGv/4jk7fJus7KGfRR6gya3kHzXm/XtUHzN902OT6gvFwt9pumbl+lfHPnc3HXgD7K7fh+yj7cNWh6dwwo797NKP3FPsNL2b25fEGnvKiv4Eq75ZmCESbfnSm3NcuxY1fVxgTpwgBSeJRj02j+fBDpOHh9HmcB6W7VQtLx9g25fDG9i4gAfJ50NXV/Hr40l/87KdC20LsLdQ3pWH1EbmNhnsarSFfjq4Ev57IPk75b+xIp+O7K8/yREMKPQginMtippMD+a+DtpIB7UAjhBaSvss6aoo0HF8KMxRh/Qro9uBP4S9JCOTGE8HnS1dPFwItIC3txjHEslz0uxvi7pIW6jd4l+xLSbcwlpFudR9PbodeSknwkxvgY4KuklRtIB4+FpMvze+itaEgb2s2kFXp+VbaA3iX+faSF/iPSFeCP8nirSQf/rwA/r5ZT+a6u3JOPwH+TzjzKTnN3/nkrk3eSshOWWxPl7HqC3nc2ZQMuxjqfS0DWB4FyZge9UO6n7GDle8HS3i8YfMAr5ed1yss83dGZtu9Vn2/s1NnQqVN23q/nn91bbOU2Ufm+tO6n+7m0VeZ9F2k9PVCV12e/tUEH9dJWvzqDxM77chC5vv/o7GTyQbReD90+y+cfVGUPVOVlPsaZXLfcQq/DoewHg0QGh0l9IljrBnmkd5AubV3dqfcf+edtnbr1LeV6/AVMnu56Wy/7Wx1CMHjb7u4npZ+xzuf6JKx7K7S8P6TTZvdEuNxeq5fRMnrLcnuefkjHw3vp3RbemNvdSlqO63L5Fnon+uX5gpjrl31pK/BZ0rF1hHRnDdJV3RrScQt6z1GsyW28kt5zD8tI2+848B1SaJbbo2eQHjK8MNfdDpxOOua+LsZ4TozxJtLV2IeBk0nPc3wBOCfG+Nnc/51l2eSvtpaRLlAuJ2XBl4Fnkm5Nrg8hHAd8Cvj9GOMmpjFUyIUQHh1jPBtYQbrdeC5wWYzxdcAlwAtIK+3ZwM4QQiDdZy0b87+QNoQLSSH2wxjjo4A/Ji2sx9HbMT5MCtSTQwi/QUr1BfQW3C7gn0n3nssTQOQ6byYFwmG5v/tIgVku2W8hbaiHk269PpG0Mn89/zwReDS9jbVsJA+jdyn/ItKKK1ee5T75YbmNg+htfJB24BF6G3j93dNSJu9kJbRKoJad6lfoGWHyAa6EeGmD/HkRux8Qy3cS/Q70W6t2XtUZVtp5ZFVWlmPxhKr+JtKZH6QNuP5u5iX558FMPqA9omq32089/6NVW2U+FubypVV5WX7lhKLMR30QrA+QZX0Fet/hFvUBsT7BqK/W66uuJ3XqFffQO/CVdVeUgC4hW+ahDp8lVR/lzPxKJq/PMdLDAvVJR2lrqiutelrqk4h6WdbzNMHk+a+v9sv6OY7enYMIvCKXH8XkZdPvyrcYGfD+V/uUdduq++h+N1vmZ2mnvDxvUJbJSPW5no5xet8Pl5OPcoJRf79a6m2r2lpAbxmU712PrPrZRtpvRqv5vIN0DFtCCrw19I4hZZwlpNuJT8jtHEI6Hh5D+k7yCHp3Gm4kBddVpNAr0/QdeuvzJNJ3bSOkE/2fk+6efZ10e3EJ8BTS/n58COGYEMJjSPvysdW8Pgv4SbX8vgU8Kr9/BSlT7iCdGL8693tLno8NpAumP40xrmNP7MH3cRfkhsdIV2mnkS4h1+XXB/LCuZR0u/GbeaY3M/lJpTWkHXAdvSfHdtB7Qud/SCvpWna/Z/0t0llHfR96U17I/X59oPS5pRpW3+fuPo3VrVv6aO07Bl++fM3ta6bHjKnG7zdsgnSs7DesPFzSLd9FusM0Su87tnIsvILeiV/dZxmvPK39QPW+3H0qx9Edue8Pkk7cNpCO6TeRwvM00tVYeQ7jzD75Ur5vvJkU0s8lXflvyO3/gnRC/Kk8j9fl17T/icE/6yVJapZ/8USS1CxDTpLULENOktQsQ06S1CxDTpLULENOktQsQ06S1Kz/B455SAdTc1MAAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn import datasets,linear_model\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "\n", + "# importing database\n", + "myDataset = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv\",\n", + "sep= ',', header = None)\n", + "# print(myDataset)\n", + "\n", + "# myDataset_X = myDataset.data[:,np.newaxis,2]\n", + "# print(myDataset_X)\n", + "\n", + "# myDataset.isnull().sum()\n", + "# myDataset.info()\n", + "# myDataset.describe()\n", + "# myDataset.head()\n", + "\n", + "# print(myDataset.values[:,1:3])\n", + "# print(myDataset.values[:,3:29])\n", + "\n", + "# Separating the target variable\n", + "X = myDataset.values[1:5, 3:29]\n", + "Y = myDataset.values[1:5, 1]\n", + "\n", + "# print(X)\n", + "print(Y)\n", + "\n", + "# print(\"X\",Y)\n", + "# Splitting the dataset into train and test\t\n", + "# testSize = 30% train = 70%\n", + "\n", + "X_train, X_test, Y_train, Y_test = train_test_split(\n", + "X, Y, test_size = 0.3, random_state = 100)\n", + "\n", + "print(\"Test\")\n", + "print(X_test)\n", + "print(Y_test)\n", + "\n", + "model = linear_model.LinearRegression()\n", + "model.fit(X_train,Y_train)\n", + "\n", + "Y_predicted = model.predict(X_test)\n", + "\n", + "print(\"Mean squared error is: \", mean_squared_error(Y_test, Y_predicted))\n", + "\n", + "print(X_test.shape, Y_test.shape)\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for i, val in enumerate(Y_test):\n", + " ax.scatter(X_test[i,:],[val]*26)\n", + "plt.show()\n", + "# plt.scatter(X_test[0], Y_test[0])\n", + "# # plt.plot(X_test[0], Y_predicted[0])\n", + "# plt.show()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jl9gnLKDtNJV" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IDJWsRA-4ocG" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "F4VIX3M74oje" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sO2s4p964omK" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a04Gb9un4oou" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Lcp8L7vQeZMc" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 664 + }, + "id": "8JODRDFseZ1y", + "outputId": "d3dc37d6-d439-4db4-e1c0-f4ce481eb0b1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' 60' ' 60' ' 50' ' 50']\n", + "Test\n", + "[['19.890000000000001' '46.299999999999997' '19.199999999999999'\n", + " '44.626666666666701' '19.789999999999999' '44.933333333333302'\n", + " '18.926666666666701' '45.890000000000001' '17.166666666666700'\n", + " '55.090000000000003' ' 6.559999999999999609' '83.1566666666666947'\n", + " '17.199999999999999' '41.433333333333302' '18.199999999999999'\n", + " '48.729999999999997' '17.000000000000000' '45.500000000000000'\n", + " ' 6.3666666666666698e+00' '733.70000000000005' ' 92.000000000000000'\n", + " ' 6.33333333333333037' '55.3333333333333002' ' 5.0999999999999996e+00'\n", + " '28.6426681675948202610' '28.6426681675948202610']\n", + " ['19.890000000000001' '46.693333333333300' '19.199999999999999'\n", + " '44.722499999999997' '19.789999999999999' '44.789999999999999'\n", + " '19.000000000000000' '45.992500000000000' '17.166666666666700'\n", + " '55.200000000000003' ' 6.833333333333330373' '84.0633333333333042'\n", + " '17.199999999999999' '41.560000000000002' '18.199999999999999'\n", + " '48.863333333333301' '17.066666666666698' '45.560000000000002'\n", + " ' 6.4833333333333298e+00' '733.60000000000002' ' 92.000000000000000'\n", + " ' 6.66666666666666963' '59.1666666666666998' ' 5.2000000000000002e+00'\n", + " '18.6061949818395078182' '18.6061949818395078182']]\n", + "[' 50' ' 60']\n", + "Mean squared error is: 14.541278065208676\n", + "(2, 26) (2,)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn import datasets,linear_model\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "\n", + "# importing database\n", + "myDataset = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv\",\n", + "sep= ',', header = None)\n", + "# print(myDataset)\n", + "\n", + "# Separating the target variable\n", + "X = myDataset.values[1:5, 3:29]\n", + "Y = myDataset.values[1:5, 1]\n", + "\n", + "# Splitting the dataset into train and test\t\n", + "# testSize = 30% train = 70%\n", + "X_train, X_test, Y_train, Y_test = train_test_split(\n", + "X, Y, test_size = 0.3, random_state = 100)\n", + "\n", + "print(\"Test\")\n", + "print(X_test)\n", + "print(Y_test)\n", + "\n", + "model = linear_model.LinearRegression()\n", + "model.fit(X_train,Y_train)\n", + "\n", + "Y_predicted = model.predict(X_test)\n", + "\n", + "print(\"Mean squared error is: \", mean_squared_error(Y_test, Y_predicted))\n", + "\n", + "plt.scatter(X_test[0], Y_test[0])\n", + "# plt.plot(X_test[0], Y_predicted[0])\n", + "plt.show()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LC6QOWtU-0wH" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "SET_ASSIGN_2", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Student assignment updates.txt b/Student assignment updates.txt index 9979d7c..d3a9ddc 100644 --- a/Student assignment updates.txt +++ b/Student assignment updates.txt @@ -1,2 +1,4 @@ Write your name and PRN no Hello Updated +Siddharth Mukkanawar 2019BTECS00100 +