diff --git a/Simple Linear Regression.ipynb b/Simple Linear Regression.ipynb new file mode 100644 index 0000000..5691f43 --- /dev/null +++ b/Simple Linear Regression.ipynb @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Import the libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the dataset\n", + "dataset = pd.read_csv('C:/Users/Susan/Desktop/Salary_Data.csv')\n", + "X = dataset.iloc[:, :-1].values\n", + "y = dataset.iloc[:, 1].values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", + " \"This module will be removed in 0.20.\", DeprecationWarning)\n" + ] + } + ], + "source": [ + "# split data into training set and test set\n", + "from sklearn.cross_validation import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fitting simple linear regression to training set \n", + "from sklearn.linear_model import LinearRegression\n", + "regressor = LinearRegression()\n", + "regressor.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Predicting the test set results\n", + "y_pred = regressor.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/zmzMzIbgvvtg+vTe8Z13hoceKq4+rcIx2MysTttuWxloFixwoKmXMxszs0HcdRfstVdl\nmd87OTTObMzMBrD11pWB5tRTHWiGw5mNmVkNt94Kr399ZZmDzPA5szEzq7LZZpWB5itfcaDZWM5s\nzMySP/8Z9t+/ssxBpjGc2ZiZkb1rpjzQnHWWA00jObMxs7b2xz/Cm95UWeYg03jObMysbXV2Vgaa\nH/zAgSYvDjZm1nauvjp7c3T5q24i4CMfqWPhEfzq6ZHMwcbM2sqYMXDEEb3j8+YNIZspvXq6FKVK\nr552wBmUg42ZtYVf/zpLRsoDSwQcd9wQVjICXz3dKhxszGzUk+Ad7+gdv/jiYV6bGUGvnm41DjZm\nNmrNn58FmnIR8N73DnOFI+TV063IwcbMRiUJ3vOe3vFf/aoBPc36e8V0k1493cocbMxsVPnRj2pn\nM0cf3YCVF/jq6VbnmzrNbNSoDjJXXAFHHtngjTT51dOjhTMbM2t5n/507Wym4YHGhs2ZjZm1tOog\nc/758P73F1MX658zGzNrSR/9aO1sxoFmZHJmY2YtpzrIXHIJvOtdxdTF6pNbZiPph5JWSrq9rGyS\npKsl3Zt+TiybdoqkJZLukXRkWfl+khanad+Rsj8zSWMlXZzKb5Q0rWyZmWkb90qamdc+mllzHXts\n7WzGgWbky7MZ7TzgqKqyk4FrImJ34Jo0jqS9gBnA3mmZbkmlu6TmAB8Gdk+f0jqPB1ZHxHTgW8BX\n07omAacCfwvsD5xaHtTMrDVJcMEFveNXXOEnNLeS3IJNRFwHPFFVfDQwLw3PA44pK78oIl6IiAeA\nJcD+knYEtoqIhRERwI+rlimtaz5waMp6jgSujognImI1cDV9g56ZtYh/+Af3NBsNmn3NZvuIWJGG\nHwG2T8NTgIVl8y1LZS+l4ery0jJLASJinaQngW3Ky2ssU0HSLGAWwC677DK8PTKz3FQHmd//Hg4+\nuJi62MYprDdaylQKTYIjYm5EdEVE1+TJk4usilnryuH9LkceWTubcaBpXc0ONo+mpjHSz5WpfDmw\nc9l8U1PZ8jRcXV6xjKROYGvg8QHWZWaNlsP7XSS46qre8Ztu8rWZ0aDZweZSoNQ7bCawoKx8Ruph\nthtZR4CbUpPbWkkHpOsxx1UtU1rXu4FrU7Z0JXCEpImpY8ARqczMGq2B73d505tqZzNveMMw6mUj\nTm7XbCRdCBwCbCtpGVkPsTOAn0k6HngQeC9ARNwh6WfAncA64GMRUXpBxGyynm3jgcvTB+Bc4CeS\nlpB1RJiR1vWEpC8Bf07zfTEiqjsqmFkjNOD9Lhs29H1C/+LFsM8+G1EvG3EUzk8B6Orqip6enqKr\nYdZaOjtrB5aODli3btDFu7pg0aLKMp+SWoukRRHRNdh8flyNmQ3fMN/vsmFD1mRWHmj+8hcHmtHM\nj6sxs+ErPWp/7twsw+noyALNAI/g33tvuPPO3nEpCz42ujnYmNnGqfP9LuvWwSabVJb99a+w6675\nVMtGFjejmVnuXvGKykDT0ZE1mTnQtA9nNmaWmxdfhLFjK8tWrIAddiimPlYcZzZmrSiHu/Ybbaed\nKgPN2LFZNuNA056c2Zi1mtJd+yWlu/ahrmsneXv2Wdh888qyxx+HSZOKqY+NDM5szFpNA+/ab7Rt\ntqkMNFtskWUzDjTmzMas1TTgrv1GW7sWtt66suzJJ2GrrYqpj408DjZmraajo/+79gtQ/TyzSZOy\nZjOzcm5GM2s1w7xrf1gG6IjwyCN9A80zzzjQWG3ObMxazTDu2h+WAToiaE7ltsaMKbQVz1qAH8SZ\n+EGcZlVqPGTzQXZhGg9WlD33HIwb18yK2UhS74M4ndmYWW1VgUZVL9bddFN44YVmVshama/ZmFlt\nqcPBXby6T6B56SUHGhsaBxszq23WLESwF3e/XLQ5TxMnzKbTbSI2RA42ZtbHzTf37QSwXp08fcJn\nRsRTCqz1+PuJmVXo/76Zwd+8adYfZzZmBsCf/tQ30Kxf7/tmrDGc2ZhZnyCz006wfHkxdbHRyZmN\nWRu78sq+gSbCgcYaz5mNWZuqDjKvfCUsWVJMXWz0c2Zj1mZ++cva2YwDjeXJmY1ZG6kOMq95Ddx2\nWzF1sfZSV2YjqZhnl5tZQ5x/fu1sxoHGmqXezOZeSb8AfhQRd+ZZITNrrOogc+CBcP31xdTF2le9\n12z2Bf4CnCNpoaRZkvwOPrMR7Oyza2czDjRWhLqCTUQ8FRH/ExEHAZ8FTgVWSJonaXquNTSzIZPg\nox/tHT/ssCzQmBWl7ms2kt4p6ZfAWcCZwCuA/wUuy7F+ZjYEZ55ZO5u5+upi6mNWUvc1G+C3wNcj\nojwJny/p4MZXy8yGqjrIvOtdcMklxdTFrNqgmU3qiXZeRBxfFWgAiIgTc6mZmdXlC1+onc040NhI\nMmiwiYj1wNubUBczGyIJTjutd/wDH/C1GRuZ6m1G+5Ok7wEXA8+UCiPi5lxqZWYDet/74KKLKssc\nZGwkqzfYvC79/GJZWQBvbmx1zKxfs2fD3LlofeV7ZT76UZgzp6A6mdWprmATEf9f3hUxswHMns3b\n5ryVy6h8S2acMNtvzrSWoKgz95b0NmBvYFypLCK+2P8SraWrqyt6enqKroZZTdUdAN7PTzif46Cj\nA9b5DZpWHEmLIqJrsPnqvc/mB8A/Av8KCHgPsOtGVO6Tku6QdLukCyWNkzRJ0tWS7k0/J5bNf4qk\nJZLukXRkWfl+khanad+Rsn9JSWMlXZzKb5Q0bbh1NSvSwQfX6GmGskAD2as0zVpAvY+rOSgijgNW\nR8RpwIHAq4azQUlTgBOBrojYB+gAZgAnA9dExO7ANWkcSXul6XsDRwHdZQ8GnQN8GNg9fY5K5cen\nuk4HvgV8dTh1NSuSBH/4Q+/4bL5HUBV5OvyMXGsN9Qab59LPZyXtBLwE7LgR2+0ExkvqBDYDHgaO\nBual6fOAY9Lw0cBFEfFCRDwALAH2l7QjsFVELIysLfDHVcuU1jUfOLSU9ZiNdK9/fY1s5oTZfJ9/\n7TvzrFnNqZTZRqo32Pxa0gTg68DNwF+BC4ezwYhYDnwDeAhYATwZEVcB20fEijTbI8D2aXgKsLRs\nFctS2ZQ0XF1esUxErAOeBLaprkt6oGiPpJ5Vq1YNZ3fMGkqCW2/tHT/55NSlubsbTjihN5Pp6MjG\n3TnAWkS9vdG+lAZ/IenXwLiIeHI4G0zXYo4GdgPWAD+XdGzV9kJS7ncNRMRcYC5kHQTy3p5Zf171\nKrj33sqyPn13ursdXKxlDRhsJP3DANOIiOE8EOMw4IGIWJXWcwlwEPCopB0jYkVqIluZ5l8O7Fy2\n/NRUtjwNV5eXL7MsNdVtDTw+jLqa5a66yez00+GUU4qpi1leBsts3jHAtACGE2weAg6QtBnZtaBD\ngR6yJxPMBM5IPxek+S8Ffirpm8BOZB0BboqI9ZLWSjoAuBE4Dvhu2TIzgRuAdwPXRr19vM2aZMoU\nePjhyjL/ldpoNWCwiYh/bvQGI+JGSfPJrv2sA24ha8raAviZpOOBB4H3pvnvkPQz4M40/8fS89oA\nZgPnAeOBy9MH4FzgJ5KWAE+Q9WYzGzGqs5nvfhc+/vFi6mLWDL6pM/FNndYMkybB6tWVZc5mrJWN\n6Js6zdqRVBlo5s1zoLH2Ue+DOA+KiNdKui0iTpN0Jr1NVmY2gM03h2efrSxzkLF2M9ybOtexcTd1\nmrUFqTLQXHKJA421p3ozm9JNnV8DFqWyc/Kpklnr22STvs/HdJCxdjZgZiPpDZJ2iIgvRcQash5j\ni4Gfkz1zzMzKbNiQZTPlgeaqqxxozAZrRjsbeBFA0sFk98CcTfb4l7n5Vs2stYwZ0/e5mBFw+OHF\n1MdsJBks2HRExBNp+B+BuRHxi4j4PDA936qZtYZ167Jspjx7uf56ZzNm5Qa7ZtMhqTM9zPJQoPwR\ns/Ve7zEbtWo9S9xBxqyvwTKbC4HfS1pA1iPtDwCSppM1pZm1peef7xtoFi50oDHrz2CPq/lvSdeQ\ndXO+quz5YmOg1ss1zEY/ZzNmQzdoU1hELKxR9pd8qmM2cq1dC1tvXVm2eDHss08x9TFrJb7uYlYH\nZzNmG6feJwiYtaXHHusbaO6/34HGbKic2Zj1w9mMWeM4szGr8tBDfQPNihUONGYbw5mNWRlnM2b5\ncGZjBtxzT99As3q1A41ZozizsbbnbMYsf85srG3dfHPfQPPMMw40ZnlwsLG2JMF++1WWRcBmmw2w\n0OzZ0NmZLdzZmY2bWV0cbKyt/PGPfbOZF16oI5uZPRvmzIH167Px9euzcQccs7oo3GYAQFdXV/T0\n9BRdDcvRRl2b6ezsDTTlOjr6vpLTrI1IWhQRXYPN58zGRr3LLusbaNavH+K1mVqBZqByM6vgYGOj\nmgRve1tlWUT2Vs0hqX4FZ3W5r+eYDcjBxkalCy9sQDZTbtas/st9PcdsUL7PxkadXO6b6e7Ofs6d\nmwWTjo4s0HR3Z5lMLXPn9i5n1uac2VjjFdSkdPbZfQNNRAPvm+nuzjoDRGQ/S4HE13PMBuXMxhqr\n1KRUUmpSgly/5VcHGQk2bMhtc5U6OvrvqWZmgDMba7S5c4dWvpG+9rXa2UzTAg0MfD3HzABnNtZo\nTWxSqg4ym26a3aDZdANdzzEzwJmNNdpgXYQb4Mtfrp3NFBJoSvq7nmNmgIONNVrOTUoSfP7zvePb\nbecHZ5q1Agcba6zubjjhhN5MpqMjG9/Ib/onnVQ7m3n00Y1arZk1ia/ZWON1dze0Gak6yOy2G9x/\nf8NWb2ZN4MzGRqxZs2pnMw0LNH7EjFnTOLOxEak6yOyzDyxe3MANFHQ/kFm7KiSzkTRB0nxJd0u6\nS9KBkiZJulrSvennxLL5T5G0RNI9ko4sK99P0uI07TtSdoqSNFbSxan8RknTmr+XbSCHzGDGjNrZ\nTEMDDTT9fiCzdldUM9q3gSsiYg9gX+Au4GTgmojYHbgmjSNpL2AGsDdwFNAtqdSPdg7wYWD39Dkq\nlR8PrI6I6cC3gK82Y6faSg4Pn5Tg4ot7xw88MMeeZn7EjFlTNT3YSNoaOBg4FyAiXoyINcDRwLw0\n2zzgmDR8NHBRRLwQEQ8AS4D9Je0IbBURCyN7A9yPq5YprWs+cGgp67EGaWBmcNRRtbOZ668fRr3q\n1YT7gcysVxGZzW7AKuBHkm6RdI6kzYHtI2JFmucRYPs0PAVYWrb8slQ2JQ1Xl1csExHrgCeBbaor\nImmWpB5JPatWrWrIzrWNBmUGElx5Ze/4EUc06b4ZP2LGrKmKCDadwN8AcyLi9cAzpCazkpSp5H7K\niYi5EdEVEV2TJ0/Oe3Ojy0ZmBgcdVDubKQ88ucrpfiAzq62IYLMMWBYRN6bx+WTB59HUNEb6uTJN\nXw7sXLb81FS2PA1Xl1csI6kT2Bp4vOF70s42IjOQ4IYbesff/e6CngLgR8yYNU3Tg01EPAIslfTq\nVHQocCdwKTAzlc0EFqThS4EZqYfZbmQdAW5KTW5rJR2QrsccV7VMaV3vBq5N2ZI1yjAyg9e+tnY2\n8/Of51hPMxsRiuqN9q/ABZJuA14HnA6cARwu6V7gsDRORNwB/IwsIF0BfCwiShcGZgPnkHUauA+4\nPJWfC2wjaQnwKaqa6axBhpAZSJXdlz/0oQGymXq7VPumTLPWERH+RLDffvuF1eGEEyI6OrIXYHZ0\nZOMD2G230rsyez+Drr96Aei7nXrnM7NcAT1RxzlW4dYlALq6uqKnp6foaoxs1Xfdl/TTfFbdZPap\nT8GZZw6yjc7O/t96uW7d0Oczs1xJWhQRXYPO52CTcbCpQ50n+GOOgQULKmep+89soNuhyldS73xm\nlqt6g40fxGn1q+PeGqky0HzhC0M899fbpdo3ZZq1FAcbq98AJ/jDD6/d0+zUU4e4jXq7VPumTLOW\n4mBj9evnRK716/jNb3rH//ct3UTHMHuJ1dul2jdlmrUUX7NJfM2mTrNnZ88/W7+eA7mehRxYMTlO\nGFonAjNrbe4gMEQONkNT3WR2zTXw5jfjXmJmbabeYOOXp9mQdHXBokWVZRXfV/zofjOrwddsrC4b\nNmTZTHmguf76Gj3N3EvMzGpwsLFBHXhg31gRkZX34V5iZlaDm9GsXxs29A0yd94Je+45wEKlTgCp\nEwEdHVkz+rgIAAAMEElEQVSgcecAs7bmYGM17bsv3HZbZVndfUm6ux1czKyCg41VWLcONtmksuz+\n+2G33Yqpj5mNDr5mYy971asqA82YMVk240BjZhvLmY3x4oswdmxl2dKlMHVq7fnNzIbKwabN7bJL\nFlhKNtkkCz5mZo3kYNOmnn8exo+vLHv0Udhuu2LqY2ajm6/ZtKHtt68MNJttll2bcaAxs7w4s2kj\nTz8NW25ZWbZ6NUyYUEx9zKx9OLNpExMmVAaaCROybMaBxsyawZnNKLdmDUycWFn21FOwxRbF1MfM\n2pMzm1Fss80qA81222XZjAONmTWbg00zzJ6dvedlOG+uHIY1a7JNPfdcb9lzz2W9zczMiuBgk7fZ\n6c2Vpfe5rF+fjecUcPbdtzKb2XnnLJsZNy6XzZmZ1cVv6kxye1Nnk95c+dhjMHlyZdlLL2WbNzPL\nS71v6nRmk7cmvLlyjz0qA8373pdlMw40ZjZS+HSUt46O/jObjfTII7DjjpVl69dnD9A0MxtJfFrK\nW05vrtxtt8pAc/zxWTbjQGNmI5Ezm7w1+M2VDz0Eu+5aWbYeMea8DtjUb8Q0s5HJ34Obobs76wwQ\nkf0cZkCYMqUy0Hyc7xIo+yXm3MvNzGxjONi0gAceyO6befjh3rL16uS7nNh35rlzm1cxM7M6OdiM\ncJMnwyte0Tv+7/+ers1E/r3czMwaxddsRqi77oK99qosq7glKsdebmZmjebMZgSaMKEy0Hz+81WB\nBnLr5WZmlgdnNiPIbbdlj5sp1+8DHhrcy83MLE/ObEaILbaoDDSnnz5AoClpUC83M7O8FRZsJHVI\nukXSr9P4JElXS7o3/ZxYNu8pkpZIukfSkWXl+0lanKZ9R5JS+VhJF6fyGyVNa/b+1aunJ+tp9swz\nvWURcMopxdXJzKzRisxsPgHcVTZ+MnBNROwOXJPGkbQXMAPYGzgK6JZUugo+B/gwsHv6HJXKjwdW\nR8R04FvAV/PdleEZNw7e8Ibe8TPPrCObqVeTX2tgZjaQQoKNpKnA24BzyoqPBual4XnAMWXlF0XE\nCxHxALAE2F/SjsBWEbEwskdX/7hqmdK65gOHlrKeXNV5gv/Tn7JZXnihtywCPvWpBtajia81MDMb\nTFGZzVnAZ4ANZWXbR8SKNPwIsH0angIsLZtvWSqbkoaryyuWiYh1wJPANtWVkDRLUo+knlWrVm3U\nDtV7gu/shDe+sXe8u7uB2UxJfzd2+oZPMytI04ONpLcDKyNiUX/zpEwl9xftRMTciOiKiK7J1S+D\nGapBTvDXXptlM+W3xkTACSds3GZrasJrDczMhqKIzObvgHdK+itwEfBmSecDj6amMdLPlWn+5cDO\nZctPTWXL03B1ecUykjqBrYHH89iZlw1wgn/b2+DQQ3uLzjsvh2ymXH83dvqGTzMrSNODTUScEhFT\nI2Ia2YX/ayPiWOBSYGaabSawIA1fCsxIPcx2I+sIcFNqclsr6YB0Pea4qmVK63p32ka+mVKNE/k9\n7I4ILrustywCZs7sM2tj+YZPMxthRtJ9NmcAh0u6FzgsjRMRdwA/A+4ErgA+FvHyg8Fmk3UyWALc\nB1yeys8FtpG0BPgUqWdbrqpO5IdzFXvwl5fHb7kl52ymXHd31j5XCoAdHdm478Mxs4Io7y/8raKr\nqyt6eno2biWzZ3PHD65jn7j95aK//3v43e82brVmZiOVpEUR0TXYfCMps2l5T3+tuyLQ3H67A42Z\nGfjZaA216aaw554wdSpcdVXRtTEzGzkcbBpo003hzjuLroWZ2cjjZjQzM8udg42ZmeXOwcbMzHLn\nYGNmZrlzsDEzs9w52JiZWe4cbMzMLHcONmZmljs/Gy2RtAp4sOh6DNO2wGNFV6JA7bz/7bzv0N77\nP1L2fdeIGPSFYA42o4CknnoehDdatfP+t/O+Q3vvf6vtu5vRzMwsdw42ZmaWOweb0WFu0RUoWDvv\nfzvvO7T3/rfUvvuajZmZ5c6ZjZmZ5c7BxszMcudg08Ik7Szpt5LulHSHpE8UXadmk9Qh6RZJvy66\nLs0maYKk+ZLulnSXpAOLrlOzSPpk+pu/XdKFksYVXac8SfqhpJWSbi8rmyTpakn3pp8Ti6zjYBxs\nWts64NMRsRdwAPAxSXsVXKdm+wRwV9GVKMi3gSsiYg9gX9rkOEiaApwIdEXEPkAHMKPYWuXuPOCo\nqrKTgWsiYnfgmjQ+YjnYtLCIWBERN6fhp8hONlOKrVXzSJoKvA04p+i6NJukrYGDgXMBIuLFiFhT\nbK2aqhMYL6kT2Ax4uOD65CoirgOeqCo+GpiXhucBxzS1UkPkYDNKSJoGvB64sdiaNNVZwGeADUVX\npAC7AauAH6VmxHMkbV50pZohIpYD3wAeAlYAT0bEVcXWqhDbR8SKNPwIsH2RlRmMg80oIGkL4BfA\nv0XE2qLr0wyS3g6sjIhFRdelIJ3A3wBzIuL1wDOM8GaURknXJo4mC7g7AZtLOrbYWhUrsntYRvR9\nLA42LU7SJmSB5oKIuKTo+jTR3wHvlPRX4CLgzZLOL7ZKTbUMWBYRpUx2PlnwaQeHAQ9ExKqIeAm4\nBDio4DoV4VFJOwKknysLrs+AHGxamCSRtdnfFRHfLLo+zRQRp0TE1IiYRnZx+NqIaJtvtxHxCLBU\n0qtT0aHAnQVWqZkeAg6QtFn6HziUNukcUeVSYGYangksKLAug3KwaW1/B3yA7Fv9renz1qIrZU3z\nr8AFkm4DXgecXnB9miJlc/OBm4HFZOexlnp0y1BJuhC4AXi1pGWSjgfOAA6XdC9ZtndGkXUcjB9X\nY2ZmuXNmY2ZmuXOwMTOz3DnYmJlZ7hxszMwsdw42ZmaWOwcbG9WU+aOkt5SVvUfSFQXX6WeSbpN0\nYtW0L0taXtaV/VZJW+Zcnyvz3oaZuz7bqCdpH+DnZM+O6wRuAY6KiPs2Yp2dEbFumMtOBX6TntZc\nPe3LwGMRcdZw6zaEeojsHNCOz5azJnNmY6NeRNwO/C/wWeC/gB9HxH2SZkq6KWUP3ZLGAEiaK6kn\nvS/lv0rrSTfTnSHpFuBd6Z0qd6YMpc+jciSNlzRP0mJJN0s6OE26Ctg1bbeux6xIOknS3DT8urTN\n8SkTmidpYXqvyb+ULXNy2r/bSvshaXqq8wXAHcCOab8mpOl9jomkTklr0r7/n6QbJG2X5t9B0oK0\njf+T9Lf9rWdIvzQbfSLCH39G/QfYHLiH7I7zscA+wK+AzjR9LvBPaXhS+tkJ/AHYK40vAz5Vts4V\nwKZpeEKNbX4WmJuG9wYeBDYFpgO39lPPLwPLgVvT5zepfAzwJ+CdZJnZAWXz3wyMA7ZLddweeCvQ\nDSgtewXZ88Omkz0lu6tsm8uACf0dk3QcAnhLKv8mcHIa/gXw8bLjtdVAx9af9v101h2VzFpYRDwj\n6WLg6Yh4QdJhwBuAnqw1ifHA0jT7+9LjQDrJniq8F73PHbu4bLV3AOdLWkB2cq32RuDraft3SHqY\n7GT/4iDV/XpUNaNFxAZJHyQLQN+LiIVlk38VEc8Dz0u6Lu3XYcBbyAITwBbAq8ge1nhfRPTU2O5A\nx+S5iLg8DS8C3pSGDyG9uCyyZsW1gxxba1MONtZONtD77hsBP4yIz5fPIGl3srd/7h8Ra1LzWPkr\nh58pGz4S+HuybONzkl4bEetzq30WLJ4mC4Dlqi+8Btn+fTkizi2fIGk6lftQMZnax6STygC5nspz\nR/X2a67H2pvbUa1d/QZ4r6RtASRtI2kXsmagp8i+oe9IFlD6kNQBTI2Ia8le4LYt2Rsjy/0BeH+a\nf09gR2DJcCqr7B0u3yRrCpsiqfytjMdIGitpMlnG0QNcCRyv9EI1SVNL+zqA/o7JQH4LfDTN3yFp\nq2Gux0Y5ZzbWliJisaTTgN+ki9cvkZ00e8iazO4mu8byp35W0Qn8NHUZHgN8I7JXc5f7LnC2pMVp\n/cdFxIupaWkgJ6Ums5J3AP8NfDuyjg3/nOr9xzT9duD3wDbAqRHxKHCZpD2AhWl7T5Fdf+nXAMdk\noFcufxz4H0kfAdYBH4mIm/pZz0OD7biNXu76bNbCmtlV2mxjuBnNzMxy58zGzMxy58zGzMxy52Bj\nZma5c7AxM7PcOdiYmVnuHGzMzCx3/z9B3wQZ18AWtAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizing the training set\n", + "plt.scatter(X_train, y_train, color = 'red')\n", + "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n", + "plt.title('Salary vs Experience (training set)')\n", + "plt.xlabel('Years of Experience')\n", + "plt.ylabel('Salary')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Bk/JrSi4/BnglIiYCPwR+0BcnZT0zb177bObNN92l2ayR9HmwkbQ1cABwIUBE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\n/j/YUWjEH8OHGAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizing the test set \n", + "plt.scatter(X_test, y_test, color = 'red')\n", + "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n", + "plt.title('Salary vs Experience (test set)')\n", + "plt.xlabel('Years of Experience')\n", + "plt.ylabel('Salary')\n", + "plt.show()" + ] + } + ], + "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": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}