lin_reg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression\n",
    "This notebooks uses code from this [guide](https://towardsdatascience.com/a-beginners-guide-to-linear-regression-in-python-with-scikit-learn-83a8f7ae2b4f) and should save some copy and paste work. The code was slightly adapted and augmented with some inline comments.\n",
    "#### Preparation\n",
    "* Clone the repository to $HOME/gitwork/github or adjust the cell which loads the dataset.\n",
    "* Download the [Weather.csv](https://drive.google.com/open?id=1fiHg5DyvQeRC4SyhsVnje5dhJNyVWpO1) file to the folder ./data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as seabornInstance \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change dir to jupyter project folder and load dataset\n",
    "import os\n",
    "from os.path import expandvars\n",
    "os.chdir(expandvars('$HOME/gitwork/github/jupyter-notebooks'))\n",
    "\n",
    "dataset = pd.read_csv('./data/Weather.csv', usecols=['MinTemp', 'MaxTemp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k4BRgubufBqwAPhzc7QzgtxGFKCIZvtG+nqtXbhxoHfS5EkM1jBudGLTHfa62Wb0vn/E14FozuxjoAK6MOB4RCSxZ9WzUITSkru5eOi54/8D1XPtol3P5jDgMZcXd73b344PLf3X3g9z9Le5+sru/EXV8IpLsW1B/QjQy54uE7Yan5TNEpKraOzpZeMsGzVmI0Lbtg5cgSfUrhC1oWC5KDiKSk1ZOjYfukI6dE2e0VXQJjViUlUQknnItnCf1Ty0HkQaVa5+FdJrl3LjUchBpQGFbcn5p6VomzV/G7MXLByZTNVvYvFSptij+G9RyEGlAYeWi1Dikzq4evnzdWkCzneMiiv8GtRxEGlChyVL9Dl+/6YEqRSOFtJVx/kKx1HKQsiqmji3R27u1pWB/QtgIGams1pYEb2zvH9SqK/f8hWKp5SBl097Rybwb1g2qY8+7YV3F151vdKm9EyZn9Bfkc/hULVYZR1t6ell00rRBS2UsOmlaJF+wzOugpjhz5kxfvXp11GE0vBkX/Z5XurMnSo0bnRg09V/KJ2weQmpRtrbWFg6fOp4Vj7zIc109vKklgVlyKQYsmjq25NfW2sK984+o2uuZ2Rp3nxl2m8pKUjZhiSHfcRm+Qh3LV6/cOHB80AxnJYbYiap8lIuSg0gNK+cqnBKdthj2zyk5SNm0tiRC199p1SbzFdM6OqGWWR2oZimpWOqQlrJZeMIBJJoGz9ZJNBkLTzggoojqn/oNal8Uw1SLoZaDlE01VoqUwbZopdSaFrd+hnRKDlJWlV4pUgbLN1+hrbWF0SObeHzz61WOSooRx36GdEoOUlaZ+wufevA+XHzitKjDqkvtHZ28/sb2rOOJJuOSk9/J6mdeHjRaSeLj6cXHRR1CQUoOUjap/YVT+twHritBlFd7Ryfzrl9Hb392p0NqXvM1SgwyDOqQlrLJ9S1V317Lb+EtG0ITA0Bfv3PhrRs0lSGmTp81MeoQiqKWg0iM5VqrqtCWnRreGj+1VmZVchCJqcylMTq7ejjvpvURRyVDMWWPMdz15cOiDqMkKiuJxFTY0hg9vX1aSrvG1GJiACUHkdjKtTSGltKuHW2tLTWZGEBlJZHIpPoT0ucppOrSM/fdlSYz7cRW42p57SslB5EIhC21DTuG/2qEV33YO6ZLYxRDZSWRCIT1J0h9ifPSGMVQchCJQC2XGyTb7P125fI502Oxg1u5qKwkEoFi9nCW+Lt8zvRBCaCWk0EmJQeRKsiczHb41PHcuKZTpaUaNmWPMXWVDDKprCRlk2td+riuV18tqXWQOrt6cHZs37m9T4mhVtXq3IVSqOUgZXP41PGho2wOnzo+gmjiI9c6SJquUHsyy0j1TC0HKZvb1j1f0vFGUWgdJIm/ttaWhkoMoJaDlFGuk6BOjlKrWlsSrF3w/qjDiISSg0iZ5FpBddzohFZJrUGNvv+5koOUTZNB2BYDTVb9WKot1wqqi25/SImhRrQkmhiVaKaru1f7n6PkIGWUY++ZnMfrSa4VVDVUNf5aEk0sOunAhk4EYZQcpGzackzsaoShrJrxXHtqbfOdalNykLKZtFt4cpi0W/0nB814rh1PLz4u6hBqQmRDWc1sHzNbYWYPm9kGMzsnOL6rmd1lZo8H/46LKkYpzX1/fbmk4/Wk0edy1IrL50yPOoSaEeU8h+3Aue7+NmAW8DkzezswH/iju08B/hhclxrQyH0OKx55MeoQpIBGm6cwXJGVldz9eeD54PKrZvYw0AZ8EDgsuNsvgbuBr0UQokiWXMNV1ecQb22tLUoMJYpFn4OZTQJmAKuAPYPEgbs/b2Z75HjMp4BPAUycOLE6gUpeLYkmekLWhGhJ1NZE/FwJINdw1dXPvAwGNEALqRbV+r4KUYn8r9bMxgI3AnPd/R/FPs7df+LuM9195vjxqvfGwaKTDsz6hWoKjteKVAJIXyTvvJvWDySMsOGq16zciHbzjI/RiSZaWxJ1s69CVCJtOZhZgmRiuMbdbwoOv2BmewWthr2AzdFFKKVI/QGGfeuuFbkSQOo9hVFeiIdxoxMs+MABNfX7FmeRJQczM+BK4GF3vzTtpluAM4DFwb+/jSA8aVC5EkBnVw/NZvSpiRAbiSbY3k9NfgmpBVG2HGYDHwPWm9na4NjXSSaF68zsbGAjcHJE8UmJctXkoXZ2yMo1X8EgNDGoqyEap8+aqMlrFRblaKU/kfzbCvPeasYi5ZGvJBPn5JDeAd06OkGiyQbtv5ArAaRm2C69/9nQ/RqkMpQYqqOkDmkzG21m9T/dVYYkV0kmzsM8MzugX+nuBWNQh2au036/OzP33TX3VxwpqyZLzlVQYqiOopKDmb3LzDqAx4AnzGyNmc2obGhSa/bOsYZSruNxENba6e1zxuw0gqcWH8e984/IuTZUkxlzl66lt0+thkppNgaS9KUf0SS2aiq2rPQLkkNNVwCY2WHAVcA7KxOW1KJ5R+0/qM8B4j/GPF9rJ1Vu6uzqCS0tqXO68p5cpHWQolJscng9lRgA3P1uM3utQjFJjYrbUNZck9nS5eqAbh2dGJTonB19D2ZoXkMVnD5Lk1ujZF7Eb7mZXQaMBJaQ/PuYA/wDuA7A3R+oYIwFzZw501evXh1lCBIzmSOnINmKyZwQlet+O41oCt3eNNey5FJe6nSuDjNb4+4zw24rtuWQenDmVNd/I5ks/nWIsYlURLEjp3K1dr60dC1hlBgqR/srxEtRycHd/6XSgYiUUykjp06c0ZZVbkr1NUjlhbXoJHpFJQcz2wU4HZiU/hh3/3JlwhIZnlx9CcWOnArrXJfya9Ps5tgqtqx0O/AXYD2QveymSMwMd+RUerkp12glKZ1KR7Wj2OQw2t2/WNFIpC4UM0KoWjH09PYNrIeU+Q31G+3rWbLqWfrcaTLYaUQTW3v7B8Wc+pm9eLlKTGXQ1trCvfOPiDoMKVKxM6R/Y2Znmtl4M9sl9VPRyKTm5FvuOooYYMdchO5t2wfu84329Vy9cuPAbf0OPb39AzHPXbqW6Rf+fiDuOM/wrhVxn+8i2YodyvoZ4DvAq+xoXbu7x2IgsoayxkOub9jV/MaY71t+quPzS0vXFl0iGjc6AQTLasiQGHCZtuiMpXIMZZ0HTHF37a0gOUW9tlJ7R2fe8k9qKGspfQdKCsPTZGjZixpVbFnpIZKT3kRyinJtpVQ5qRCViKpH6yHVtmJbDtuADjNbDryROqihrJIuyrWVwia9hdm7tYWu7m28vk1DVCtBcxbqRylDWW+vZCBSmzJHJ/37u9tY8ciLJY9WGuoop/TF8QppSTRz+NTx3LbueUDJodw0Z6G+FDtD+kozGwlMdPcnKhyT1Iiwnd9uXNNZ8jfHQjvI5UocYesiZWoKFsnbu7WFw6eO58Y1nZrYVkZKCPWr2BnSxwGXklx8b7KZTQcWuPuHKhmcxFu5dn7L9zxAzsRRTClpl1EJ1i54P5AcyaTEMHyJJuOSk9+phFDnii0rXQQcDKwAcPe1ZvaWikUlsVaolFNqp2+u+3d29XDudeuy9k1IJY5iXqerp5dJ85cNTIaT4VFLoXEUmxx63b3LbNB+iPpLa0DFlHJKGZ3U3tFJU54Td67jz3X18KaWROiy2qU8jxSvtSWhGc4NpNjk8LCZfQRoMrPJwDnAysqFJdVWbIdwoVJO2OikQn0GQzlx793aMmjWs1RWoslYeMIBUYchVVTsPIfPA+8muejeTcBWYG6lgpLqKmXZi3ylnLbWlpyb6YQ9d7HDTzOlElDXECeojWy2wneSAW2tLepjaEB5Ww5m9m13/7q7vw58LfiROlNKx3KupbBTS2S0d3Qye/HygVZC97btOZ+7lL6JZjP63QdaHkDeclQ+2/pUYiqGFsprbIVaDkdXJQqpqNQJe/L8ZcxevDyrRVDKshfzjtqflkTzoGOpb/JhrYRcy0+kkkexdmkZwWVzpg+crIZajpLiaKE8KdTn0Gxm40iunZXF3V8uf0hSToXmEEBpG+Pk2lYztbR1sWWi1OOK3VDnle7ekoawSmnGjGwm0dzElp7eyJZal3gplBymAmsITw4OvLnsEUlZFVMyKnXZi7BtNaH4Iayp5049x4W3bihqgbuhlKMkvzEjm/nWh7TchWQrlBwecvcZVYlEKqKYklG+1kApcrVAWlsSjNlpROhzpxJN+uY7+XR29TBudEKrpQ6TdmSTQoodyio1qtiSUa7WQClytUAWnnBA3udu7+hk6f2FE0PKa1u3k2g2etM6lhPNBg69/eqHyOfpxcdFHYLUiELJ4XsAZjbK3bem32Bmu7v73ysWmZRFsSWjfPMcip0DMZQWSHtHZ0mb78COBJA+67lXI5AKunzO9KhDkBpS7E5w64H/cPeVwfV/Bxa5+1srHF9RtBNcfoVO7mGznlNLLwM5bxtuS6OY2dYyfONGJ1jwgfytN2lM+XaCKzY5TAN+DtwN7A3sBnzS3TeVMc4hU3IYnnzbewIV2/oz35aeMnxaB0kKGfY2oe6+3sy+Bfya5D7S/xqXxCDDN5TtPTu7egbmS+RqlaR3Mod1gCoxVM7psyaqs1mGpdglu68E9gMOBN4K3GpmV7j7DyoZnFRHoU7rXCfxedevA9tR70+fQ7H6mZe5euXGgfv2uXP1yo3ctGYT3z7pwHK/BQloFJKUS7GjlR4kWUZy4Ckzm0VyfwepA4U6rXP1C4SNDErNRfjblq1ZtwF09/YPJBUpD23NKZVQbFnpsozrW4CzKxKRVF3YKKPDp44fuP6mlkRJncbPBctn5KLhpsPXRHIWqmYzS6UUW1aaAiwC3g6MSh13d82QrhPp8xwyRxF19fRiFL+Bx96tLfxty1atfVRBf9V8BamwYstKvwAWAJcBhwNnosJArBQzF2E4ezY4ZCWIRJPRD/RltARef2M7s948jnuf1NJblXD6rIlRhyANoNj9HFrc/Y8kh74+4+4LAa3lGxPF7MdQjj0bnOTwSAv+nXPQPqG/QF09vfxl4xZm77crFvIVItFkyRnNUpJmM41CkqoptuWw1cyagMfN7PNAJ7BH5cKSUhSzuF4592xImb14ec7+g57ePp5+qYenFh0X2mK5fvVGtSwKmL3frlzzH++JOgxpUMUmh7nAaOCLwDdJthrOqFRQAGZ2NMnlO5qBn7n74kq+3nAUW66plFzf9Du7epg8f1nOk336Y9Pfw5taEllrF7Ukmjl86vhBG/kUmqeQeu7MdZu+0b5eiaEISgwSpWJHK/05uPgayf6GijKzZuAHwJHAJuDPZnaLuz9U6dcuVTH7JVRavhN1qoSU77FhHdCJJmPc6ARd3b0Do5duXNM56H0W6qQePbJ5YBZ0ah2kttYWnt+iyW+FjE4UW/EVqYxC24Teku92dz+hvOEMOAh4wt3/GsRxLfBBIHbJoZRyTaWUsmlOutRchrD30NvvjB45go4L3g8QupFPobFIr2/r4/VtyUSQGrmkWdGFNRmaKCiRK9RyeA/wLLAEWEX1Rii1Ba+bsgk4OP0OZvYp4FMAEydGN3pjKEtPlFvmPIViBpA2GQMTp760dG3ofdLfgzbYqQ6thyRxUSg5/BPJ0s6pwEeBZcASd99Q4bhy7Ty344r7T4CfQHLhvQrHk1MpW2xWUnpdv5gF7fq9tG1Ci+ljkNJpdrPEVd7Cprv3ufvv3P0MYBbwBHC3mX2hwnFtAvZJuz4BeK7Crzkk847an5ZE86BjUWzO3t7RyezFy5k8fxnd27aTaCq+kXf41PEFj4e9Txk+JQaJq4K9Xma2k5mdBFwNfA74PnBTheP6MzDFzCab2UjgFCBv/0dUTpzRxqKTpg0a/1/tP/jMOQyvdPeCJbfnzCX9ttvWPR96n/TjYe9TMxWG5/I505UYJLYKdUj/EngHcAdwobs/WI2g3H17MJ/iTpJDWX9ehVLWkJVji83hCO1Q7nPG7DSChSccwLzr1w2aj5BoMhaecMDA9a6e8P2YM49nvs9J85eVI/yG09qSKLh1qkjUCvU5fAx4neQy3V+0HdNdDXB336VSgbn77cDtlXr+epKvU3woW3fmkjmfQwobNzqBO2zp6dUieVJT8iYHd9dg62GqxgS5Qh3Kw2nZpCbRhc1zyCfRbOCNu7Um+K8AABGSSURBVAKrlrmQWlfsDGkZgmInyA03gRTaj6GQ1pZEztJSahLdNSs3Fr0qKyTLWiX0ideNOG+2E/VMfqktSg4VVMwEuXLMsB5u6SisXyLTUL7/N1KjIe7rIMVhJr/UFiWHCipmgly5ZlgPp3Q0lEl0kmwlPLno2KjDKEocZvJLbVGfQgXl6rRNPx6HGdaQTBD3zj+Cy+ZMpzlsnW20gUemUw/ep/CdYiIuv2dSO9RyqKBi+gKinGGdWYNOdTqH7eCWaDLmHLQPKx55ceD+o0c28fjm1yseZ9yU0q8Qlzp/XGbyS+1QcqigYvoChtuZPFRhNei8nc4GM/fdddAJcfbi5RWNMW5K7VeIU50/qt8zqV1KDhVWqC+gnPMQSpFrK9Bcevs8qz7dKCWJPXceyarzjyz5cXGq80f1eya1S8khBqo9w7q9o3NIi+h1dvUM2uznTXmGwNaLkc02pMQA8avzRz2TX2qLOqQbTKrUMVTpe1C/+sb28gUWQwZ898PvHPLjixmQIBJXSg4NJqzUMVR9dTiRIX1hwcuGuTBeXFbsFRkKlZUaTKP0EwxFsxn3zj+ibM+nOr/UMiWHBqNNe3KrxLwF1fmlVqmsFIH0jXlmL15Oe0dn1V4718Y+jazZTAvliWRQy6HKoh77ftOaTRV/jbiL8+J4InGh5FBlUY997+7tr/hrxFXcF8cTiRMlhyqr1tj30356H/c++fLA9UY9MZrBU4uOizoMkZqj5FBl5VzjJmzdHoB5168ls4Fw75Mvc9pP78MMQpZOqlunHTwx6hBEapI6pKusXGPfU30X6ZPS5t2wLtiXIfwx9z75csOcLNXJLDI8ajlUWbnGvof1XfT2FW4SrHjkxZJep5Y0m/FfH3mnho6KlIGSQwTKMfZ9qHMV6nWOQ0uimUUnTVNiECkTJYeYy7UfQLNZ6L4LjajZTIlBpMyUHGIs15yI1c+8rMQQUItBpDKUHGIs15yIa1ZujCiieGnTWkUiFaPkEFP59lxolDbDlD3G8LnDp4TuYKbWgkhlKTnE0HD3XKgHU/YYw11fPmzgulY2FakuJYcYyrfnglG/LYeWRBMPf/OYrONa2VSk+jQJLobyLaVx2qyJWZPo6kETsOikA6MOQ0QCSg4xlGspjWYzZu67K4tOmkazWZWjKp/Z++3K5XOmD9p17dJh7romIuVlXgdDImfOnOmrV6+OOoyyyRzCmi7RZFxycnJf41z3ibNxoxN0XPD+qMMQEcDM1rj7zLDb1OdQJbkms4U5cUYbq595matDhqz29jtzl66tyUlwiWZjwQcOiDoMESmCkkMVlLrBT3tHJzeuyb87XC0khnGjE7jDlp5ejTISqTFKDlVQ6gY/+UYr1YK21hbunX9E1GGIyDAoOVRYvsls1dr4p9o6u3qYNH/ZwPWnF2uznVpTShm00OMz27h77jySVecfCcCRl97N45tfz/k8U/YYwz96ennh1W2DHv+WPcYO2swKsmfM53sP32hfz5JVz9LnXtS2scP9PGqRkkMFFZrMFjYqqb0jfzmpFk2av0wJooYMd5/zfAMqAF54dRsHf+sudmlJ5E0MQOjtL7y6jRdefTnreHqcQM73kNmf1+c+cD0sQUS973tUNJS1gvKVh8I2+En9Esa/N2Gw02dN1Mm/juQrgw718ZleeHVbwcQwFKk4872HJaueDX1sruPD/TxqlVoOFZSvPJS+NlCqyVqLey0Y4d+2pHYNt9wZdVk03+uHlblScg3yqNfybyFKDhWUa7/ottYWTpzRRntHJxfeuoFXunsjiK48hrL3tcTbcPc5z/X4aknFmes9/G3L1tBEkGtiaTn3fa8lkZSVzOwSM3vEzB4ws5vNrDXttvPM7Akze9TMjooivnLJt190qoRUy4lhKHtfS/wNd5/zsMdn2nPnkUzZY8yQY8wlFWe+93DqwfuEPjbX8XLt+15roupzuAt4h7sfCDwGnAdgZm8HTgEOAI4G/tvManYhoRNntLHopGmDlolIlZNqfbhq2O5rufod1B9RW/L93g7l8ZlSo5Xu+vJhBRPElD3GsOfOI7MeP3u/XbPumx5nvvdw8YnTOH3WxIGWQrMZp8+amLM8OtzPo1ZFvnyGmX0I+LC7n2Zm5wG4+6LgtjuBhe5+X77niPPyGen9CalZzW0RN7vLwYCndNIXqWn5ls+Iw2ils4A7gsttQPqQgU3BsSxm9ikzW21mq1988cUKhzg0qdJRKhGk6py1lBhyLe9X7/VWkUZXseRgZn8wswdDfj6Ydp/zge3ANalDIU8V2rRx95+4+0x3nzl+/Pjyv4EyqPXS0ZiRzaFLhDdCvVWk0VVstJK7vy/f7WZ2BnA88F7fUdvaBKT3Ck0AnqtMhJVXa0PdUmWvzBmjM/fdteFmh4o0ukiGsprZ0cDXgH9z9+60m24BfmNmlwJ7A1OA+yMIsSyiHtJXqicXHRt6XDuxiTSeqOY5XAHsBNxlyREDK939M+6+wcyuAx4iWW76nLvXZF2mvaOT7m3bow6jaKMTceh+EpG4iCQ5uPtb8tz2LeBbVQynJMUswFVobZkoJJqNOf+8D8seeD5rbkWTwbe1RaeIpNEM6RIUuwBXXDqimwz6ffBqlRefOK0hV5gUkdJEPs+hHKo1z2H24uU5+xDST8CT5y+LxeJ52ldBRPLRNqElyvXNOt/oo/RWRFw6ouMQg4jUJvVCZkifuObsOOm3d3QWnPjV09vHudet4/Cp4wuuLVMNuRYSExEpRMkhQ6612y+8dQNd3dtyPGqHPnduXNPJv7+7cA2/uamyJ+9a2GdaROJJySFDrtLRK929vL6tuE7mnt4+VjySf0mP5ibj1IPCV4HMpa21hcvnTM9aNGzMyPBWSpuWuBCRIVKfQ4Zy9Rc819XDuNGJnEty9/UntyZMzUrOxwyeWrRjkbvUqKOUsKGzWuJCRIZDLYcMxaxFX4zRI5tZ8IEDSDTnLx0VU/opdJdGXVJYRCpHLYcMqRNq+mil19/YTldPaZvyvL6tj4W3bKC3b/h1/2I6lrXEhYiUk5JDiMwT7VBnPJeaUHLJtUOViEilKDkUIZUo5i5dW9XXzVwdVUSkWtTnUKRql2xOnzWRJxcdq8QgIpFQcoiAGbS2JAY6j2fvt2vR+9mKiFSDykpV1pJo1kgiEYk9tRxKMNxJZc1mSgwiUhPUckiTWnCvs6tnYHJa+mqr847an3k3rBvS8FS1GESklig5kEwKC2/ZMGjoaWpyWmdXD/NuWLfjzkOYttCmPRNEpMY0fHIoZg5Db59z4a0bGD1yBL39xWcHtRZEpFY1fJ9Dsbu2vdLdm3c/h7bWFk6fNVFLWIhIXWj4lkO+E36mXIvyacc1Eak3Dd9yKLSBT7rubdtJZOzBYCT7JWYvXk57R2eZoxMRiUbDJ4dSVmF9pbsXgglskEwMqR6I9B3jRERqXcMnh9Ry18Xq7XPG7DSCttaWrIFLPb19XHLno+UNUEQkAg3b55Ca05Balrsl0URPb39Rj83XT1FKH4aISFw1ZHLIHL5a6s5vqX6KsMeV0ochIhJXDVlWKnb4apjU9pthfRXamlNE6kVDthxKKf2MG51g9MgRA+WnzJnO6aUpzYIWkXrRkMkh13yF9NFHkGwJLPjAATlP+NqaU0TqVUOWlXKVhE7TDGcREaBBWw6pE75KQiIi4RoyOYBKQiIi+TRkWUlERPJTchARkSxKDiIikkXJQUREsig5iIhIFnMfwqbIMWNmLwLP5LnL7sDfqxTOcCjO8lKc5VULcdZCjBCfOPd19/FhN9RFcijEzFa7+8yo4yhEcZaX4iyvWoizFmKE2ohTZSUREcmi5CAiIlkaJTn8JOoAiqQ4y0txllctxFkLMUINxNkQfQ4iIlKaRmk5iIhICZQcREQkS0MkBzP7ipm5me0eXDcz+76ZPWFmD5jZuyKO75tBHGvN7PdmtndM47zEzB4JYrnZzFrTbjsviPNRMzsq4jhPNrMNZtZvZjMzbotTnEcHcTxhZvOjjCWdmf3czDab2YNpx3Y1s7vM7PHg33FRxhjEtI+ZrTCzh4P/73PiGKuZjTKz+81sXRDnhcHxyWa2KohzqZmNjDLOLO5e1z/APsCdJCfJ7R4cOxa4g+Tmb7OAVRHHuEva5S8CP4ppnO8HRgSXvwN8J7j8dmAdsBMwGXgSaI4wzrcB+wN3AzPTjscmTqA5eP03AyODuN4e5f9vWmz/CrwLeDDt2HeB+cHl+an/+4jj3At4V3B5Z+Cx4P84VrEGf79jg8sJYFXw93wdcEpw/EfA/4n6M03/aYSWw2XAVxm8A+gHgV950kqg1cz2iiQ6wN3/kXZ1DDtijVucv3f37cHVlcCE4PIHgWvd/Q13fwp4AjgoihgB3P1hd3805KY4xXkQ8IS7/9XdtwHXBvFFzt3vAV7OOPxB4JfB5V8CJ1Y1qBDu/ry7/yW4/CrwMNBGzGIN/n5fC64mgh8HjgBuCI5HHmemuk4OZnYC0Onu6zJuagOeTbu+KTgWGTP7lpk9C5wGXBAcjl2cac4i2aqBeMeZLk5xximWYuzp7s9D8qQM7BFxPIOY2SRgBslv5bGL1cyazWwtsBm4i2SrsSvty1bs/v9rfic4M/sD8E8hN50PfJ1kKSTrYSHHKjqmN1+c7v5bdz8fON/MzgM+DywghnEG9zkf2A5ck3pYyP0jjzPsYSHHohrLHadYapqZjQVuBOa6+z/Mwj7aaLl7HzA96Ke7mWTpM+tu1Y0qv5pPDu7+vrDjZjaNZF15XfDLMgH4i5kdRDJL75N29wnAc1HEGeI3wDKSySF2cZrZGcDxwHs9KJYSwzhzqHqcNRJLMV4ws73c/fmgtLk56oAAzCxBMjFc4+43BYdjGSuAu3eZ2d0k+xxazWxE0HqI3f9/3ZaV3H29u+/h7pPcfRLJP8Z3ufvfgFuAjwejgWYBW1LN0CiY2ZS0qycAjwSX4xbn0cDXgBPcvTvtpluAU8xsJzObDEwB7o8ixgLiFOefgSnBiJWRwClBfHF1C3BGcPkMIFfrrGos+a3vSuBhd7807aZYxWpm41Mj+8ysBXgfyf6RFcCHg7tFHmeWqHvEq/UDPM2O0UoG/IBk3W89aSNaIortRuBB4AHgVqAtpnE+QbJOvjb4+VHabecHcT4KHBNxnB8i+WXgDeAF4M6YxnksyRE2T5Ish0UWS0ZcS4Dngd7gczwb2A34I/B48O+uMYjzUJKlmAfSfiePjVuswIFARxDng8AFwfE3k/xy8gRwPbBT1J9p+o+WzxARkSx1W1YSEZGhU3IQEZEsSg4iIpJFyUFERLIoOYiISBYlB5FAsHLvr9OujzCzF83stuD6CflWTzWzacHKumvN7GUzeyq4/IdqxC9SThrKKhIws9dIjo0/xN17zOwYYBGwyd2PL/G5rgJuc/cbCt1XJI7UchAZ7A7guODyqSQnhAFgZp8wsyuCy1cFe238r5n91cw+HPJcg5jZ/GBd/wfM7ILg2FvM7MFgD4UNZvYrMzsqeN7HUvtRmNnFZvbLYP+Cx83srLK/c5E0Sg4ig11LcomNUSRntq7Kc9+9SM7SPR5YnO9JzexYYCJwMDAdOMTMDglu3h/4v8C04DU/7O6HAOeR3I8gZRpwDDAbuMjM9iztrYkUT8lBJI27PwBMItlquL3A3dvdvd/dHwIKnajfT/LE3gH8BXgL8Nbgtifc/SF37wceAlJ9FOuDWNJfb6u7bwbuAf65qDclMgQ1vyqrSAXcQvKb/GEk1+nJ5Y20y4XWiTbgYne/ctBBs7dkPE9/2vV+Bv+NZnYQqsNQKkYtB5FsPwcucvf1ZXzOO4GzzWwMgJlNsGBP8xKcGKwouzvwL8DqMsYnMohaDiIZ3H0T8L0yP+ftZjYVWBnsL/Iq8NESn+bPJDvM9wEWuPsL5YxRJJ2GsorUADO7GPi7u18edSzSGFRWEhGRLGo5iIhIFrUcREQki5KDiIhkUXIQEZEsSg4iIpJFyUFERLL8f12jxPrTWsyHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data set\n",
    "dataset.plot(x='MinTemp', y='MaxTemp', style='o')  \n",
    "plt.title('MinTemp vs MaxTemp')  \n",
    "plt.xlabel('MinTemp')  \n",
    "plt.ylabel('MaxTemp')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show MaxTemp distribution\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.tight_layout()\n",
    "seabornInstance.distplot(dataset['MaxTemp']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n",
    "In scikit-learn a random split into training and test sets can be quickly computed with the \n",
    "[train_test_split](https://scikit-learn.org/stable/modules/cross_validation.html)\n",
    "helper function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Attributes are the independent variables \n",
    "# while labels are dependent variables (predicted)\n",
    "X = dataset['MinTemp'].values.reshape(-1,1)\n",
    "y = dataset['MaxTemp'].values.reshape(-1,1)\n",
    "np.reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y=0.92*x+10.7\n"
     ]
    }
   ],
   "source": [
    "# select algorithm and train the algorithm\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# resulting linear regression function\n",
    "print('y={m}*x+{b}'.format(\n",
    "    m=round(regressor.coef_[0][0],2), \n",
    "    b=round(regressor.intercept_[0],1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict values from test data\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.888889</td>\n",
       "      <td>33.670351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.111111</td>\n",
       "      <td>30.091251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.222222</td>\n",
       "      <td>26.512151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28.888889</td>\n",
       "      <td>31.113851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.333333</td>\n",
       "      <td>15.774852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted\n",
       "0  28.888889  33.670351\n",
       "1  31.111111  30.091251\n",
       "2  27.222222  26.512151\n",
       "3  28.888889  31.113851\n",
       "4  23.333333  15.774852"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create data frame with actual and predicted values\n",
    "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar chart with actual and predicted values (1st 25 values)\n",
    "df1 = df.head(25)\n",
    "df1.plot(title='comparison of Actual and Predicted values', kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot regression function with test data\n",
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.2\n",
      "Root Mean Squared Error: 4.2\n",
      "RMSE: 16.0%\n"
     ]
    }
   ],
   "source": [
    "# evaluate prediction quality using MAE and RMSE metrics\n",
    "rmse = round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)), 2)\n",
    "print('Mean Absolute Error:', round(\n",
    "    metrics.mean_absolute_error(y_test, y_pred), 2))   \n",
    "print('Root Mean Squared Error:', rmse)\n",
    "print('RMSE: {}%'.format(round(100*rmse/dataset.MaxTemp.mean())))"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}