exploratory.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2020-04-27 12:58:59--  http://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/News_Final.csv\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 30338277 (29M) [application/x-httpd-php]\n",
      "Saving to: ‘news-final.csv’\n",
      "\n",
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      " 11350K .......... .......... .......... .......... .......... 38% 11.1M 7s\n",
      " 11400K .......... .......... .......... .......... .......... 38% 2.47M 7s\n",
      " 11450K .......... .......... .......... .......... .......... 38% 1.39M 7s\n",
      " 11500K .......... .......... .......... .......... .......... 38% 3.25M 7s\n",
      " 11550K .......... .......... .......... .......... .......... 39% 2.59M 7s\n",
      " 11600K .......... .......... .......... .......... .......... 39% 5.24M 7s\n",
      " 11650K .......... .......... .......... .......... .......... 39% 11.2M 7s\n",
      " 11700K .......... .......... .......... .......... .......... 39% 3.01M 7s\n",
      " 11750K .......... .......... .......... .......... .......... 39% 1.68M 7s\n",
      " 11800K .......... .......... .......... .......... .......... 39% 6.74M 7s\n",
      " 11850K .......... .......... .......... .......... .......... 40% 1.25M 7s\n",
      " 11900K .......... .......... .......... .......... .......... 40% 20.0M 7s\n",
      " 11950K .......... .......... .......... .......... .......... 40% 8.97M 7s\n",
      " 12000K .......... .......... .......... .......... .......... 40% 1.73M 7s\n",
      " 12050K .......... .......... .......... .......... .......... 40% 4.44M 7s\n",
      " 12100K .......... .......... .......... .......... .......... 41% 3.21M 7s\n",
      " 12150K .......... .......... .......... .......... .......... 41% 1.31M 7s\n",
      " 12200K .......... .......... .......... .......... .......... 41% 8.41M 7s\n",
      " 12250K .......... .......... .......... .......... .......... 41% 11.9M 7s\n",
      " 12300K .......... .......... .......... .......... .......... 41% 1.65M 7s\n",
      " 12350K .......... .......... .......... .......... .......... 41% 3.94M 7s\n",
      " 12400K .......... .......... .......... .......... .......... 42% 3.55M 7s\n",
      " 12450K .......... .......... .......... .......... .......... 42% 1.54M 7s\n",
      " 12500K .......... .......... .......... .......... .......... 42% 11.8M 7s\n",
      " 12550K .......... .......... .......... .......... .......... 42% 3.26M 7s\n",
      " 12600K .......... .......... .......... .......... .......... 42% 9.77M 7s\n",
      " 12650K .......... .......... .......... .......... .......... 42% 1.37M 7s\n",
      " 12700K .......... .......... .......... .......... .......... 43% 11.2M 7s\n",
      " 12750K .......... .......... .......... .......... .......... 43% 1.18M 7s\n",
      " 12800K .......... .......... .......... .......... .......... 43% 11.5M 7s\n",
      " 12850K .......... .......... .......... .......... .......... 43% 11.4M 7s\n",
      " 12900K .......... .......... .......... .......... .......... 43% 11.0M 7s\n",
      " 12950K .......... .......... .......... .......... .......... 43% 1.40M 7s\n",
      " 13000K .......... .......... .......... .......... .......... 44% 4.36M 7s\n",
      " 13050K .......... .......... .......... .......... .......... 44% 2.16M 7s\n",
      " 13100K .......... .......... .......... .......... .......... 44% 3.41M 7s\n",
      " 13150K .......... .......... .......... .......... .......... 44% 3.95M 6s\n",
      " 13200K .......... .......... .......... .......... .......... 44% 4.71M 6s\n",
      " 13250K .......... .......... .......... .......... .......... 44% 2.11M 6s\n",
      " 13300K .......... .......... .......... .......... .......... 45% 3.59M 6s\n",
      " 13350K .......... .......... .......... .......... .......... 45% 1.60M 6s\n",
      " 13400K .......... .......... .......... .......... .......... 45% 4.14M 6s\n",
      " 13450K .......... .......... .......... .......... .......... 45% 11.3M 6s\n",
      " 13500K .......... .......... .......... .......... .......... 45% 3.19M 6s\n",
      " 13550K .......... .......... .......... .......... .......... 45% 5.48M 6s\n",
      " 13600K .......... .......... .......... .......... .......... 46% 1.54M 6s\n",
      " 13650K .......... .......... .......... .......... .......... 46% 3.03M 6s\n",
      " 13700K .......... .......... .......... .......... .......... 46% 1.98M 6s\n",
      " 13750K .......... .......... .......... .......... .......... 46% 9.54M 6s\n",
      " 13800K .......... .......... .......... .......... .......... 46% 8.41M 6s\n",
      " 13850K .......... .......... .......... .......... .......... 46% 3.71M 6s\n",
      " 13900K .......... .......... .......... .......... .......... 47% 1.81M 6s\n",
      " 13950K .......... .......... .......... .......... .......... 47% 7.25M 6s\n",
      " 14000K .......... .......... .......... .......... .......... 47% 1000K 6s\n",
      " 14050K .......... .......... .......... .......... .......... 47% 11.4M 6s\n",
      " 14100K .......... .......... .......... .......... .......... 47% 11.3M 6s\n",
      " 14150K .......... .......... .......... .......... .......... 47% 11.0M 6s\n",
      " 14200K .......... .......... .......... .......... .......... 48% 2.32M 6s\n",
      " 14250K .......... .......... .......... .......... .......... 48% 3.47M 6s\n",
      " 14300K .......... .......... .......... .......... .......... 48% 1.12M 6s\n",
      " 14350K .......... .......... .......... .......... .......... 48% 2.96M 6s\n",
      " 14400K .......... .......... .......... .......... .......... 48% 11.0M 6s\n",
      " 14450K .......... .......... .......... .......... .......... 48% 7.58M 6s\n",
      " 14500K .......... .......... .......... .......... .......... 49% 11.4M 6s\n",
      " 14550K .......... .......... .......... .......... .......... 49% 3.17M 6s\n",
      " 14600K .......... .......... .......... .......... .......... 49% 4.59M 6s\n",
      " 14650K .......... .......... .......... .......... .......... 49% 1.01M 6s\n",
      " 14700K .......... .......... .......... .......... .......... 49% 3.12M 6s\n",
      " 14750K .......... .......... .......... .......... .......... 49% 11.4M 6s\n",
      " 14800K .......... .......... .......... .......... .......... 50% 11.3M 6s\n",
      " 14850K .......... .......... .......... .......... .......... 50% 10.9M 6s\n",
      " 14900K .......... .......... .......... .......... .......... 50% 3.42M 6s\n",
      " 14950K .......... .......... .......... .......... .......... 50% 1.43M 6s\n",
      " 15000K .......... .......... .......... .......... .......... 50% 1.18M 6s\n",
      " 15050K .......... .......... .......... .......... .......... 50% 11.3M 6s\n",
      " 15100K .......... .......... .......... .......... .......... 51% 11.0M 6s\n",
      " 15150K .......... .......... .......... .......... .......... 51% 11.5M 6s\n",
      " 15200K .......... .......... .......... .......... .......... 51% 11.3M 6s\n",
      " 15250K .......... .......... .......... .......... .......... 51% 7.52M 5s\n",
      " 15300K .......... .......... .......... .......... .......... 51% 1.19M 5s\n",
      " 15350K .......... .......... .......... .......... .......... 51% 1.35M 5s\n",
      " 15400K .......... .......... .......... .......... .......... 52% 11.1M 5s\n",
      " 15450K .......... .......... .......... .......... .......... 52% 5.12M 5s\n",
      " 15500K .......... .......... .......... .......... .......... 52% 11.4M 5s\n",
      " 15550K .......... .......... .......... .......... .......... 52% 11.0M 5s\n",
      " 15600K .......... .......... .......... .......... .......... 52% 11.5M 5s\n",
      " 15650K .......... .......... .......... .......... .......... 52% 1.51M 5s\n",
      " 15700K .......... .......... .......... .......... .......... 53% 1.35M 5s\n",
      " 15750K .......... .......... .......... .......... .......... 53% 7.19M 5s\n",
      " 15800K .......... .......... .......... .......... .......... 53% 10.9M 5s\n",
      " 15850K .......... .......... .......... .......... .......... 53% 4.81M 5s\n",
      " 15900K .......... .......... .......... .......... .......... 53% 7.56M 5s\n",
      " 15950K .......... .......... .......... .......... .......... 54% 3.55M 5s\n",
      " 16000K .......... .......... .......... .......... .......... 54% 1.19M 5s\n",
      " 16050K .......... .......... .......... .......... .......... 54% 5.00M 5s\n",
      " 16100K .......... .......... .......... .......... .......... 54% 3.17M 5s\n",
      " 16150K .......... .......... .......... .......... .......... 54% 11.2M 5s\n",
      " 16200K .......... .......... .......... .......... .......... 54% 11.5M 5s\n",
      " 16250K .......... .......... .......... .......... .......... 55% 3.08M 5s\n",
      " 16300K .......... .......... .......... .......... .......... 55% 4.54M 5s\n",
      " 16350K .......... .......... .......... .......... .......... 55% 1.27M 5s\n",
      " 16400K .......... .......... .......... .......... .......... 55% 11.5M 5s\n",
      " 16450K .......... .......... .......... .......... .......... 55% 1.74M 5s\n",
      " 16500K .......... .......... .......... .......... .......... 55% 11.0M 5s\n",
      " 16550K .......... .......... .......... .......... .......... 56% 11.1M 5s\n",
      " 16600K .......... .......... .......... .......... .......... 56% 7.91M 5s\n",
      " 16650K .......... .......... .......... .......... .......... 56% 3.29M 5s\n",
      " 16700K .......... .......... .......... .......... .......... 56%  878K 5s\n",
      " 16750K .......... .......... .......... .......... .......... 56% 14.9M 5s\n",
      " 16800K .......... .......... .......... .......... .......... 56% 9.58M 5s\n",
      " 16850K .......... .......... .......... .......... .......... 57% 3.04M 5s\n",
      " 16900K .......... .......... .......... .......... .......... 57% 12.2M 5s\n",
      " 16950K .......... .......... .......... .......... .......... 57% 5.55M 5s\n",
      " 17000K .......... .......... .......... .......... .......... 57% 11.1M 5s\n",
      " 17050K .......... .......... .......... .......... .......... 57%  845K 5s\n",
      " 17100K .......... .......... .......... .......... .......... 57% 11.5M 5s\n",
      " 17150K .......... .......... .......... .......... .......... 58% 11.2M 5s\n",
      " 17200K .......... .......... .......... .......... .......... 58% 11.2M 5s\n",
      " 17250K .......... .......... .......... .......... .......... 58% 11.3M 5s\n",
      " 17300K .......... .......... .......... .......... .......... 58% 11.4M 5s\n",
      " 17350K .......... .......... .......... .......... .......... 58% 2.98M 5s\n",
      " 17400K .......... .......... .......... .......... .......... 58%  804K 5s\n",
      " 17450K .......... .......... .......... .......... .......... 59% 11.4M 5s\n",
      " 17500K .......... .......... .......... .......... .......... 59% 9.60M 4s\n",
      " 17550K .......... .......... .......... .......... .......... 59% 11.2M 4s\n",
      " 17600K .......... .......... .......... .......... .......... 59% 10.9M 4s\n",
      " 17650K .......... .......... .......... .......... .......... 59% 11.6M 4s\n",
      " 17700K .......... .......... .......... .......... .......... 59% 6.27M 4s\n",
      " 17750K .......... .......... .......... .......... .......... 60%  831K 4s\n",
      " 17800K .......... .......... .......... .......... .......... 60% 3.13M 4s\n",
      " 17850K .......... .......... .......... .......... .......... 60% 11.0M 4s\n",
      " 17900K .......... .......... .......... .......... .......... 60% 7.13M 4s\n",
      " 17950K .......... .......... .......... .......... .......... 60% 11.7M 4s\n",
      " 18000K .......... .......... .......... .......... .......... 60% 11.4M 4s\n",
      " 18050K .......... .......... .......... .......... .......... 61% 11.1M 4s\n",
      " 18100K .......... .......... .......... .......... .......... 61% 11.2M 4s\n",
      " 18150K .......... .......... .......... .......... .......... 61%  749K 4s\n",
      " 18200K .......... .......... .......... .......... .......... 61% 3.06M 4s\n",
      " 18250K .......... .......... .......... .......... .......... 61% 11.3M 4s\n",
      " 18300K .......... .......... .......... .......... .......... 61% 10.1M 4s\n",
      " 18350K .......... .......... .......... .......... .......... 62% 12.7M 4s\n",
      " 18400K .......... .......... .......... .......... .......... 62% 11.3M 4s\n",
      " 18450K .......... .......... .......... .......... .......... 62% 8.55M 4s\n",
      " 18500K .......... .......... .......... .......... .......... 62% 1.47M 4s\n",
      " 18550K .......... .......... .......... .......... .......... 62% 1.84M 4s\n",
      " 18600K .......... .......... .......... .......... .......... 62% 2.16M 4s\n",
      " 18650K .......... .......... .......... .......... .......... 63% 12.6M 4s\n",
      " 18700K .......... .......... .......... .......... .......... 63% 5.97M 4s\n",
      " 18750K .......... .......... .......... .......... .......... 63% 11.1M 4s\n",
      " 18800K .......... .......... .......... .......... .......... 63% 11.2M 4s\n",
      " 18850K .......... .......... .......... .......... .......... 63% 5.85M 4s\n",
      " 18900K .......... .......... .......... .......... .......... 63% 1.14M 4s\n",
      " 18950K .......... .......... .......... .......... .......... 64% 3.04M 4s\n",
      " 19000K .......... .......... .......... .......... .......... 64% 3.49M 4s\n",
      " 19050K .......... .......... .......... .......... .......... 64% 11.2M 4s\n",
      " 19100K .......... .......... .......... .......... .......... 64% 11.5M 4s\n",
      " 19150K .......... .......... .......... .......... .......... 64% 11.1M 4s\n",
      " 19200K .......... .......... .......... .......... .......... 64% 11.3M 4s\n",
      " 19250K .......... .......... .......... .......... .......... 65% 11.3M 4s\n",
      " 19300K .......... .......... .......... .......... .......... 65% 1000K 4s\n",
      " 19350K .......... .......... .......... .......... .......... 65% 1.94M 4s\n",
      " 19400K .......... .......... .......... .......... .......... 65% 11.4M 4s\n",
      " 19450K .......... .......... .......... .......... .......... 65% 7.83M 4s\n",
      " 19500K .......... .......... .......... .......... .......... 65% 11.3M 4s\n",
      " 19550K .......... .......... .......... .......... .......... 66% 10.6M 4s\n",
      " 19600K .......... .......... .......... .......... .......... 66% 11.9M 4s\n",
      " 19650K .......... .......... .......... .......... .......... 66% 9.20M 4s\n",
      " 19700K .......... .......... .......... .......... .......... 66% 1.20M 4s\n",
      " 19750K .......... .......... .......... .......... .......... 66% 2.16M 4s\n",
      " 19800K .......... .......... .......... .......... .......... 66% 3.37M 4s\n",
      " 19850K .......... .......... .......... .......... .......... 67% 4.65M 3s\n",
      " 19900K .......... .......... .......... .......... .......... 67% 10.2M 3s\n",
      " 19950K .......... .......... .......... .......... .......... 67% 13.0M 3s\n",
      " 20000K .......... .......... .......... .......... .......... 67% 10.7M 3s\n",
      " 20050K .......... .......... .......... .......... .......... 67% 9.20M 3s\n",
      " 20100K .......... .......... .......... .......... .......... 68% 1.51M 3s\n",
      " 20150K .......... .......... .......... .......... .......... 68% 5.59M 3s\n",
      " 20200K .......... .......... .......... .......... .......... 68% 1.59M 3s\n",
      " 20250K .......... .......... .......... .......... .......... 68% 5.27M 3s\n",
      " 20300K .......... .......... .......... .......... .......... 68% 11.6M 3s\n",
      " 20350K .......... .......... .......... .......... .......... 68% 5.24M 3s\n",
      " 20400K .......... .......... .......... .......... .......... 69% 11.2M 3s\n",
      " 20450K .......... .......... .......... .......... .......... 69% 11.3M 3s\n",
      " 20500K .......... .......... .......... .......... .......... 69% 8.17M 3s\n",
      " 20550K .......... .......... .......... .......... .......... 69% 2.12M 3s\n",
      " 20600K .......... .......... .......... .......... .......... 69% 1.18M 3s\n",
      " 20650K .......... .......... .......... .......... .......... 69% 11.1M 3s\n",
      " 20700K .......... .......... .......... .......... .......... 70% 11.3M 3s\n",
      " 20750K .......... .......... .......... .......... .......... 70% 7.03M 3s\n",
      " 20800K .......... .......... .......... .......... .......... 70% 26.7M 3s\n",
      " 20850K .......... .......... .......... .......... .......... 70% 11.5M 3s\n",
      " 20900K .......... .......... .......... .......... .......... 70% 5.53M 3s\n",
      " 20950K .......... .......... .......... .......... .......... 70% 5.57M 3s\n",
      " 21000K .......... .......... .......... .......... .......... 71%  759K 3s\n",
      " 21050K .......... .......... .......... .......... .......... 71% 11.6M 3s\n",
      " 21100K .......... .......... .......... .......... .......... 71% 11.1M 3s\n",
      " 21150K .......... .......... .......... .......... .......... 71% 11.3M 3s\n",
      " 21200K .......... .......... .......... .......... .......... 71% 11.6M 3s\n",
      " 21250K .......... .......... .......... .......... .......... 71% 10.8M 3s\n",
      " 21300K .......... .......... .......... .......... .......... 72% 11.4M 3s\n",
      " 21350K .......... .......... .......... .......... .......... 72% 9.23M 3s\n",
      " 21400K .......... .......... .......... .......... .......... 72% 4.78M 3s\n",
      " 21450K .......... .......... .......... .......... .......... 72%  797K 3s\n",
      " 21500K .......... .......... .......... .......... .......... 72% 12.6M 3s\n",
      " 21550K .......... .......... .......... .......... .......... 72% 11.3M 3s\n",
      " 21600K .......... .......... .......... .......... .......... 73% 9.83M 3s\n",
      " 21650K .......... .......... .......... .......... .......... 73% 10.9M 3s\n",
      " 21700K .......... .......... .......... .......... .......... 73% 7.31M 3s\n",
      " 21750K .......... .......... .......... .......... .......... 73% 11.3M 3s\n",
      " 21800K .......... .......... .......... .......... .......... 73% 9.02M 3s\n",
      " 21850K .......... .......... .......... .......... .......... 73% 3.89M 3s\n",
      " 21900K .......... .......... .......... .......... .......... 74%  918K 3s\n",
      " 21950K .......... .......... .......... .......... .......... 74% 13.9M 3s\n",
      " 22000K .......... .......... .......... .......... .......... 74% 11.2M 3s\n",
      " 22050K .......... .......... .......... .......... .......... 74% 7.61M 3s\n",
      " 22100K .......... .......... .......... .......... .......... 74% 11.0M 3s\n",
      " 22150K .......... .......... .......... .......... .......... 74% 11.2M 3s\n",
      " 22200K .......... .......... .......... .......... .......... 75% 11.2M 3s\n",
      " 22250K .......... .......... .......... .......... .......... 75% 11.3M 3s\n",
      " 22300K .......... .......... .......... .......... .......... 75% 11.2M 3s\n",
      " 22350K .......... .......... .......... .......... .......... 75%  785K 3s\n",
      " 22400K .......... .......... .......... .......... .......... 75% 7.47M 2s\n",
      " 22450K .......... .......... .......... .......... .......... 75% 11.6M 2s\n",
      " 22500K .......... .......... .......... .......... .......... 76% 11.0M 2s\n",
      " 22550K .......... .......... .......... .......... .......... 76% 11.4M 2s\n",
      " 22600K .......... .......... .......... .......... .......... 76% 11.2M 2s\n",
      " 22650K .......... .......... .......... .......... .......... 76% 11.0M 2s\n",
      " 22700K .......... .......... .......... .......... .......... 76% 11.3M 2s\n",
      " 22750K .......... .......... .......... .......... .......... 76% 5.73M 2s\n",
      " 22800K .......... .......... .......... .......... .......... 77%  915K 2s\n",
      " 22850K .......... .......... .......... .......... .......... 77% 5.35M 2s\n",
      " 22900K .......... .......... .......... .......... .......... 77% 11.0M 2s\n",
      " 22950K .......... .......... .......... .......... .......... 77% 11.3M 2s\n",
      " 23000K .......... .......... .......... .......... .......... 77% 11.4M 2s\n",
      " 23050K .......... .......... .......... .......... .......... 77% 11.0M 2s\n",
      " 23100K .......... .......... .......... .......... .......... 78% 11.4M 2s\n",
      " 23150K .......... .......... .......... .......... .......... 78% 11.4M 2s\n",
      " 23200K .......... .......... .......... .......... .......... 78% 11.0M 2s\n",
      " 23250K .......... .......... .......... .......... .......... 78%  945K 2s\n",
      " 23300K .......... .......... .......... .......... .......... 78% 4.26M 2s\n",
      " 23350K .......... .......... .......... .......... .......... 78% 4.69M 2s\n",
      " 23400K .......... .......... .......... .......... .......... 79% 11.4M 2s\n",
      " 23450K .......... .......... .......... .......... .......... 79% 8.28M 2s\n",
      " 23500K .......... .......... .......... .......... .......... 79% 11.3M 2s\n",
      " 23550K .......... .......... .......... .......... .......... 79% 11.2M 2s\n",
      " 23600K .......... .......... .......... .......... .......... 79% 11.4M 2s\n",
      " 23650K .......... .......... .......... .......... .......... 79% 11.3M 2s\n",
      " 23700K .......... .......... .......... .......... .......... 80% 1015K 2s\n",
      " 23750K .......... .......... .......... .......... .......... 80% 6.24M 2s\n",
      " 23800K .......... .......... .......... .......... .......... 80% 11.1M 2s\n",
      " 23850K .......... .......... .......... .......... .......... 80% 3.41M 2s\n",
      " 23900K .......... .......... .......... .......... .......... 80% 5.55M 2s\n",
      " 23950K .......... .......... .......... .......... .......... 81% 11.3M 2s\n",
      " 24000K .......... .......... .......... .......... .......... 81% 10.9M 2s\n",
      " 24050K .......... .......... .......... .......... .......... 81% 11.4M 2s\n",
      " 24100K .......... .......... .......... .......... .......... 81% 11.3M 2s\n",
      " 24150K .......... .......... .......... .......... .......... 81% 11.1M 2s\n",
      " 24200K .......... .......... .......... .......... .......... 81% 1.03M 2s\n",
      " 24250K .......... .......... .......... .......... .......... 82% 5.83M 2s\n",
      " 24300K .......... .......... .......... .......... .......... 82% 11.1M 2s\n",
      " 24350K .......... .......... .......... .......... .......... 82% 8.47M 2s\n",
      " 24400K .......... .......... .......... .......... .......... 82% 4.38M 2s\n",
      " 24450K .......... .......... .......... .......... .......... 82% 11.4M 2s\n",
      " 24500K .......... .......... .......... .......... .......... 82% 5.94M 2s\n",
      " 24550K .......... .......... .......... .......... .......... 83% 11.9M 2s\n",
      " 24600K .......... .......... .......... .......... .......... 83% 11.1M 2s\n",
      " 24650K .......... .......... .......... .......... .......... 83% 2.27M 2s\n",
      " 24700K .......... .......... .......... .......... .......... 83% 1.56M 2s\n",
      " 24750K .......... .......... .......... .......... .......... 83% 11.3M 2s\n",
      " 24800K .......... .......... .......... .......... .......... 83% 11.4M 2s\n",
      " 24850K .......... .......... .......... .......... .......... 84% 4.95M 2s\n",
      " 24900K .......... .......... .......... .......... .......... 84% 6.98M 2s\n",
      " 24950K .......... .......... .......... .......... .......... 84% 11.3M 2s\n",
      " 25000K .......... .......... .......... .......... .......... 84% 11.0M 2s\n",
      " 25050K .......... .......... .......... .......... .......... 84% 3.16M 1s\n",
      " 25100K .......... .......... .......... .......... .......... 84% 11.2M 1s\n",
      " 25150K .......... .......... .......... .......... .......... 85% 11.2M 1s\n",
      " 25200K .......... .......... .......... .......... .......... 85% 1.14M 1s\n",
      " 25250K .......... .......... .......... .......... .......... 85% 11.0M 1s\n",
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      " 25350K .......... .......... .......... .......... .......... 85% 10.8M 1s\n",
      " 25400K .......... .......... .......... .......... .......... 85% 4.94M 1s\n",
      " 25450K .......... .......... .......... .......... .......... 86% 11.3M 1s\n",
      " 25500K .......... .......... .......... .......... .......... 86% 9.27M 1s\n",
      " 25550K .......... .......... .......... .......... .......... 86% 11.2M 1s\n",
      " 25600K .......... .......... .......... .......... .......... 86% 11.3M 1s\n",
      " 25650K .......... .......... .......... .......... .......... 86% 3.36M 1s\n",
      " 25700K .......... .......... .......... .......... .......... 86% 1.08M 1s\n",
      " 25750K .......... .......... .......... .......... .......... 87% 11.5M 1s\n",
      " 25800K .......... .......... .......... .......... .......... 87% 11.1M 1s\n",
      " 25850K .......... .......... .......... .......... .......... 87% 8.44M 1s\n",
      " 25900K .......... .......... .......... .......... .......... 87% 5.10M 1s\n",
      " 25950K .......... .......... .......... .......... .......... 87% 11.1M 1s\n",
      " 26000K .......... .......... .......... .......... .......... 87% 11.3M 1s\n",
      " 26050K .......... .......... .......... .......... .......... 88% 11.4M 1s\n",
      " 26100K .......... .......... .......... .......... .......... 88% 10.9M 1s\n",
      " 26150K .......... .......... .......... .......... .......... 88% 11.4M 1s\n",
      " 26200K .......... .......... .......... .......... .......... 88% 1.52M 1s\n",
      " 26250K .......... .......... .......... .......... .......... 88% 1.76M 1s\n",
      " 26300K .......... .......... .......... .......... .......... 88% 11.4M 1s\n",
      " 26350K .......... .......... .......... .......... .......... 89% 6.06M 1s\n",
      " 26400K .......... .......... .......... .......... .......... 89% 8.46M 1s\n",
      " 26450K .......... .......... .......... .......... .......... 89% 11.7M 1s\n",
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      " 26550K .......... .......... .......... .......... .......... 89% 11.1M 1s\n",
      " 26600K .......... .......... .......... .......... .......... 89% 11.6M 1s\n",
      " 26650K .......... .......... .......... .......... .......... 90% 10.9M 1s\n",
      " 26700K .......... .......... .......... .......... .......... 90% 11.5M 1s\n",
      " 26750K .......... .......... .......... .......... .......... 90% 1.35M 1s\n",
      " 26800K .......... .......... .......... .......... .......... 90% 1.66M 1s\n",
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      " 27150K .......... .......... .......... .......... .......... 91% 13.9M 1s\n",
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      " 27950K .......... .......... .......... .......... .......... 94% 74.4M 1s\n",
      " 28000K .......... .......... .......... .......... .......... 94% 6.40M 0s\n",
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      " 28950K .......... .......... .......... .......... .......... 97% 8.47M 0s\n",
      " 29000K .......... .......... .......... .......... .......... 98% 1.19M 0s\n",
      " 29050K .......... .......... .......... .......... .......... 98% 4.86M 0s\n",
      " 29100K .......... .......... .......... .......... .......... 98% 6.54M 0s\n",
      " 29150K .......... .......... .......... .......... .......... 98% 11.1M 0s\n",
      " 29200K .......... .......... .......... .......... .......... 98% 7.02M 0s\n",
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      " 29350K .......... .......... .......... .......... .......... 99%  599K 0s\n",
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      " 29500K .......... .......... .......... .......... .......... 99%  117M 0s\n",
      " 29550K .......... .......... .......... .......... .......... 99% 3.54M 0s\n",
      " 29600K .......... .......... .......                         100% 42.7M=9.2s\n",
      "\n",
      "2020-04-27 12:59:09 (3.14 MB/s) - ‘news-final.csv’ saved [30338277/30338277]\n",
      "\n",
      "--2020-04-27 12:59:09--  http://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/Facebook_Microsoft.csv\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 6776871 (6.5M) [application/x-httpd-php]\n",
      "Saving to: ‘facebook-microsoft.csv’\n",
      "\n",
      "     0K .......... .......... .......... .......... ..........  0%  266K 25s\n",
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      "  5800K .......... .......... .......... .......... .......... 88% 5.64M 0s\n",
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      "  5900K .......... .......... .......... .......... .......... 89% 8.83M 0s\n",
      "  5950K .......... .......... .......... .......... .......... 90% 2.02M 0s\n",
      "  6000K .......... .......... .......... .......... .......... 91% 3.03M 0s\n",
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      "  6100K .......... .......... .......... .......... .......... 92% 6.74M 0s\n",
      "  6150K .......... .......... .......... .......... .......... 93% 12.9M 0s\n",
      "  6200K .......... .......... .......... .......... .......... 94% 5.01M 0s\n",
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      "  6550K .......... .......... .......... .......... .......... 99% 11.5M 0s\n",
      "  6600K .......... ........                                   100% 4.07M=2.4s\n",
      "\n",
      "2020-04-27 12:59:12 (2.74 MB/s) - ‘facebook-microsoft.csv’ saved [6776871/6776871]\n",
      "\n",
      "--2020-04-27 12:59:12--  http://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/GooglePlus_Microsoft.csv\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 6646480 (6.3M) [application/x-httpd-php]\n",
      "Saving to: ‘googleplus-microsoft.csv’\n",
      "\n",
      "     0K .......... .......... .......... .......... ..........  0%  229K 28s\n",
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      "  1150K .......... .......... .......... .......... .......... 18% 11.7M 4s\n",
      "  1200K .......... .......... .......... .......... .......... 19% 2.50M 4s\n",
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      "  1300K .......... .......... .......... .......... .......... 20% 9.38M 3s\n",
      "  1350K .......... .......... .......... .......... .......... 21%  787K 4s\n",
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      "  1500K .......... .......... .......... .......... .......... 23% 9.75M 3s\n",
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      "  1650K .......... .......... .......... .......... .......... 26%  774K 3s\n",
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      "  1750K .......... .......... .......... .......... .......... 27% 5.78M 3s\n",
      "  1800K .......... .......... .......... .......... .......... 28% 11.3M 3s\n",
      "  1850K .......... .......... .......... .......... .......... 29% 5.75M 3s\n",
      "  1900K .......... .......... .......... .......... .......... 30%  819K 3s\n",
      "  1950K .......... .......... .......... .......... .......... 30% 4.34M 3s\n",
      "  2000K .......... .......... .......... .......... .......... 31% 4.06M 3s\n",
      "  2050K .......... .......... .......... .......... .......... 32% 12.8M 2s\n",
      "  2100K .......... .......... .......... .......... .......... 33% 11.4M 2s\n",
      "  2150K .......... .......... .......... .......... .......... 33% 2.53M 2s\n",
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      "  2250K .......... .......... .......... .......... .......... 35% 10.9M 2s\n",
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      "  2350K .......... .......... .......... .......... .......... 36% 11.3M 2s\n",
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      "  2500K .......... .......... .......... .......... .......... 39% 2.53M 2s\n",
      "  2550K .......... .......... .......... .......... .......... 40% 4.35M 2s\n",
      "  2600K .......... .......... .......... .......... .......... 40% 11.8M 2s\n",
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      "  2700K .......... .......... .......... .......... .......... 42% 11.2M 2s\n",
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      "  2850K .......... .......... .......... .......... .......... 44% 3.35M 2s\n",
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      "  2950K .......... .......... .......... .......... .......... 46% 11.5M 2s\n",
      "  3000K .......... .......... .......... .......... .......... 46%  505K 2s\n",
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      "  6450K .......... .......... .......... ..........           100% 11.3M=2.8s\n",
      "\n",
      "2020-04-27 12:59:15 (2.25 MB/s) - ‘googleplus-microsoft.csv’ saved [6646480/6646480]\n",
      "\n",
      "--2020-04-27 12:59:15--  http://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/LinkedIn_Microsoft.csv\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 7198020 (6.9M) [application/x-httpd-php]\n",
      "Saving to: ‘linkedin-microsoft.csv’\n",
      "\n",
      "     0K .......... .......... .......... .......... ..........  0%  257K 27s\n",
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      "  3950K .......... .......... .......... .......... .......... 56% 17.3M 1s\n",
      "  4000K .......... .......... .......... .......... .......... 57% 10.9M 1s\n",
      "  4050K .......... .......... .......... .......... .......... 58% 10.2M 1s\n",
      "  4100K .......... .......... .......... .......... .......... 59% 5.46M 1s\n",
      "  4150K .......... .......... .......... .......... .......... 59% 35.5M 1s\n",
      "  4200K .......... .......... .......... .......... .......... 60%  924K 1s\n",
      "  4250K .......... .......... .......... .......... .......... 61% 2.05M 1s\n",
      "  4300K .......... .......... .......... .......... .......... 61% 11.4M 1s\n",
      "  4350K .......... .......... .......... .......... .......... 62% 11.4M 1s\n",
      "  4400K .......... .......... .......... .......... .......... 63% 4.97M 1s\n",
      "  4450K .......... .......... .......... .......... .......... 64%  125M 1s\n",
      "  4500K .......... .......... .......... .......... .......... 64% 9.28M 1s\n",
      "  4550K .......... .......... .......... .......... .......... 65% 11.0M 1s\n",
      "  4600K .......... .......... .......... .......... .......... 66%  832K 1s\n",
      "  4650K .......... .......... .......... .......... .......... 66% 4.50M 1s\n",
      "  4700K .......... .......... .......... .......... .......... 67% 3.82M 1s\n",
      "  4750K .......... .......... .......... .......... .......... 68% 11.4M 1s\n",
      "  4800K .......... .......... .......... .......... .......... 68% 11.1M 1s\n",
      "  4850K .......... .......... .......... .......... .......... 69% 10.1M 1s\n",
      "  4900K .......... .......... .......... .......... .......... 70% 11.4M 1s\n",
      "  4950K .......... .......... .......... .......... .......... 71%  919K 1s\n",
      "  5000K .......... .......... .......... .......... .......... 71% 2.85M 1s\n",
      "  5050K .......... .......... .......... .......... .......... 72% 11.1M 1s\n",
      "  5100K .......... .......... .......... .......... .......... 73% 4.42M 1s\n",
      "  5150K .......... .......... .......... .......... .......... 73% 11.8M 1s\n",
      "  5200K .......... .......... .......... .......... .......... 74% 11.2M 1s\n",
      "  5250K .......... .......... .......... .......... .......... 75% 11.2M 1s\n",
      "  5300K .......... .......... .......... .......... .......... 76% 11.3M 1s\n",
      "  5350K .......... .......... .......... .......... .......... 76%  928K 1s\n",
      "  5400K .......... .......... .......... .......... .......... 77% 3.42M 1s\n",
      "  5450K .......... .......... .......... .......... .......... 78% 11.5M 1s\n",
      "  5500K .......... .......... .......... .......... .......... 78% 8.07M 1s\n",
      "  5550K .......... .......... .......... .......... .......... 79% 11.3M 1s\n",
      "  5600K .......... .......... .......... .......... .......... 80% 11.3M 1s\n",
      "  5650K .......... .......... .......... .......... .......... 81% 3.51M 1s\n",
      "  5700K .......... .......... .......... .......... .......... 81% 13.0M 0s\n",
      "  5750K .......... .......... .......... .......... .......... 82% 1.12M 0s\n",
      "  5800K .......... .......... .......... .......... .......... 83% 2.07M 0s\n",
      "  5850K .......... .......... .......... .......... .......... 83% 11.5M 0s\n",
      "  5900K .......... .......... .......... .......... .......... 84% 10.9M 0s\n",
      "  5950K .......... .......... .......... .......... .......... 85% 11.4M 0s\n",
      "  6000K .......... .......... .......... .......... .......... 86% 11.4M 0s\n",
      "  6050K .......... .......... .......... .......... .......... 86% 4.31M 0s\n",
      "  6100K .......... .......... .......... .......... .......... 87% 11.3M 0s\n",
      "  6150K .......... .......... .......... .......... .......... 88% 3.75M 0s\n",
      "  6200K .......... .......... .......... .......... .......... 88%  854K 0s\n",
      "  6250K .......... .......... .......... .......... .......... 89% 10.8M 0s\n",
      "  6300K .......... .......... .......... .......... .......... 90% 12.0M 0s\n",
      "  6350K .......... .......... .......... .......... .......... 91% 11.1M 0s\n",
      "  6400K .......... .......... .......... .......... .......... 91% 11.2M 0s\n",
      "  6450K .......... .......... .......... .......... .......... 92% 10.9M 0s\n",
      "  6500K .......... .......... .......... .......... .......... 93% 4.10M 0s\n",
      "  6550K .......... .......... .......... .......... .......... 93% 11.8M 0s\n",
      "  6600K .......... .......... .......... .......... .......... 94% 1.87M 0s\n",
      "  6650K .......... .......... .......... .......... .......... 95% 1.90M 0s\n",
      "  6700K .......... .......... .......... .......... .......... 96% 3.82M 0s\n",
      "  6750K .......... .......... .......... .......... .......... 96% 4.52M 0s\n",
      "  6800K .......... .......... .......... .......... .......... 97% 11.4M 0s\n",
      "  6850K .......... .......... .......... .......... .......... 98% 11.3M 0s\n",
      "  6900K .......... .......... .......... .......... .......... 98% 10.8M 0s\n",
      "  6950K .......... .......... .......... .......... .......... 99% 11.6M 0s\n",
      "  7000K .......... .......... .........                       100% 6.47M=2.5s\n",
      "\n",
      "2020-04-27 12:59:18 (2.70 MB/s) - ‘linkedin-microsoft.csv’ saved [7198020/7198020]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "wget -nc -O news-final.csv http://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/News_Final.csv \n",
    "\n",
    "wget -nc -O facebook-microsoft.csv http://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/Facebook_Microsoft.csv\n",
    "wget -nc -O googleplus-microsoft.csv http://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/GooglePlus_Microsoft.csv\n",
    "wget -nc -O linkedin-microsoft.csv http://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/LinkedIn_Microsoft.csv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data and set index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "news = pd.read_csv('news-final.csv')\n",
    "news = news[news['Topic']=='microsoft']\n",
    "news = news.drop('Topic', 1)\n",
    "\n",
    "news['IDLink'] = news['IDLink'].astype(int)\n",
    "news = news.set_index('IDLink')\n",
    "\n",
    "\n",
    "facebook = pd.read_csv('facebook-microsoft.csv')\n",
    "facebook = facebook.set_index('IDLink')\n",
    "\n",
    "google = pd.read_csv('googleplus-microsoft.csv')\n",
    "google = google.set_index('IDLink')\n",
    "\n",
    "linkedin = pd.read_csv('linkedin-microsoft.csv')\n",
    "linkedin = linkedin.set_index('IDLink')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restrict to articles appearing on all 3 sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "posted_everywhere = news[['Facebook', 'GooglePlus', 'LinkedIn']].min(axis=1) > -1\n",
    "\n",
    "news = news[posted_everywhere]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restrict to sources with at least 50 articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of sources with at least 50 articles: 75\n"
     ]
    }
   ],
   "source": [
    "greater_than_50 = (news['Source'].value_counts() > 50)\n",
    "\n",
    "sources_50 = greater_than_50[greater_than_50 > 0].index\n",
    "\n",
    "print(\"Number of sources with at least 50 articles: {}\".format(greater_than_50.sum()))\n",
    "\n",
    "news = news[news['Source'].isin(sources_50)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "news.loc[:, 'PublishDate'] = pd.to_datetime(news['PublishDate'])\n",
    "\n",
    "news.loc[:, 'Year'] = news['PublishDate'].dt.year\n",
    "news.loc[:, 'Month'] = news['PublishDate'].dt.month\n",
    "news.loc[:, 'DayOfWeek'] = news['PublishDate'].dt.weekday\n",
    "news.loc[:, 'Hour'] = news['PublishDate'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime.datetime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "news['PublishDate'].max()\n",
    "\n",
    "may_1_2016 = datetime.datetime(2016, 5, 1)\n",
    "\n",
    "news_historical = news[news['PublishDate'] <= may_1_2016]\n",
    "news_new = news[news['PublishDate'] > may_1_2016]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "news_new.to_csv('news-new.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Headline</th>\n",
       "      <th>Source</th>\n",
       "      <th>PublishDate</th>\n",
       "      <th>SentimentTitle</th>\n",
       "      <th>SentimentHeadline</th>\n",
       "      <th>Facebook</th>\n",
       "      <th>GooglePlus</th>\n",
       "      <th>LinkedIn</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDLink</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Microsoft offering £100 discount on original B...</td>\n",
       "      <td>Just ahead of the market rollout of Microsoft'...</td>\n",
       "      <td>International Business Times via Yahoo UK &amp; Ir...</td>\n",
       "      <td>2015-11-08 05:25:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.132812</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Microsoft’s OneDrive debacle shows its cloud c...</td>\n",
       "      <td>When Microsoft announced earlier this week tha...</td>\n",
       "      <td>Digital Trends via Yahoo! News</td>\n",
       "      <td>2015-11-08 12:15:00</td>\n",
       "      <td>-0.166139</td>\n",
       "      <td>-0.259052</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Title  \\\n",
       "IDLink                                                      \n",
       "311     Microsoft offering £100 discount on original B...   \n",
       "299     Microsoft’s OneDrive debacle shows its cloud c...   \n",
       "\n",
       "                                                 Headline  \\\n",
       "IDLink                                                      \n",
       "311     Just ahead of the market rollout of Microsoft'...   \n",
       "299     When Microsoft announced earlier this week tha...   \n",
       "\n",
       "                                                   Source         PublishDate  \\\n",
       "IDLink                                                                          \n",
       "311     International Business Times via Yahoo UK & Ir... 2015-11-08 05:25:00   \n",
       "299                        Digital Trends via Yahoo! News 2015-11-08 12:15:00   \n",
       "\n",
       "        SentimentTitle  SentimentHeadline  Facebook  GooglePlus  LinkedIn  \\\n",
       "IDLink                                                                      \n",
       "311           0.000000          -0.132812         0           0         0   \n",
       "299          -0.166139          -0.259052         6           0         1   \n",
       "\n",
       "        Year  Month  DayOfWeek  Hour  \n",
       "IDLink                                \n",
       "311     2015     11          6     5  \n",
       "299     2015     11          6    12  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Year published**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1192e7990>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "news['PublishDate'].dt.year.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Day of week**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1192badd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "news['PublishDate'].dt.weekday.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sentiment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "news['ones'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "news.plot.scatter('SentimentTitle', 'ones', alpha=0.1, figsize=(14, 0.5))\n",
    "\n",
    "plt.axvline(news['SentimentTitle'].mean(), alpha = 0.4, ls='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn import base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ColumnSelectTransformer(base.BaseEstimator, base.TransformerMixin):\n",
    "    \n",
    "    def __init__(self, col_names):\n",
    "        self.col_names = col_names  # We will need these in transform()\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        # This transformer doesn't need to learn anything about the data,\n",
    "        # so it can just return self without any further processing\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        # Return an array with the same number of rows as X and one\n",
    "        # column for each in self.col_names\n",
    "        return X[self.col_names].values  #REMOVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = news_historical.drop(['PublishDate', 'Title', 'Headline'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<7941x101 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 30127 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_pipe = Pipeline([\n",
    "    ('cst', ColumnSelectTransformer(['Source'])),\n",
    "    ('oh', OneHotEncoder())\n",
    "]) \n",
    "\n",
    "source_pipe.fit_transform(df)\n",
    "\n",
    "hour_pipe = Pipeline([\n",
    "    ('cst', ColumnSelectTransformer(['Hour'])),\n",
    "    ('oh', OneHotEncoder())\n",
    "]) \n",
    "\n",
    "sentiment_pipe = Pipeline([\n",
    "    ('cst', ColumnSelectTransformer(['SentimentTitle', 'SentimentHeadline']))\n",
    "])\n",
    "\n",
    "features = FeatureUnion([\n",
    "    ('source', source_pipe),\n",
    "    ('hour', hour_pipe),\n",
    "    ('sentiment', sentiment_pipe)\n",
    "])\n",
    "\n",
    "\n",
    "features.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('features',\n",
       "                 FeatureUnion(n_jobs=None,\n",
       "                              transformer_list=[('source',\n",
       "                                                 Pipeline(memory=None,\n",
       "                                                          steps=[('cst',\n",
       "                                                                  ColumnSelectTransformer(col_names=['Source'])),\n",
       "                                                                 ('oh',\n",
       "                                                                  OneHotEncoder(categories='auto',\n",
       "                                                                                drop=None,\n",
       "                                                                                dtype=<class 'numpy.float64'>,\n",
       "                                                                                handle_unknown='error',\n",
       "                                                                                sparse=True))],\n",
       "                                                          verbose=False)),\n",
       "                                                ('hour',\n",
       "                                                 Pipeline(memory=None,\n",
       "                                                          steps=[('cst',\n",
       "                                                                  C...\n",
       "                                                                                dtype=<class 'numpy.float64'>,\n",
       "                                                                                handle_unknown='error',\n",
       "                                                                                sparse=True))],\n",
       "                                                          verbose=False)),\n",
       "                                                ('sentiment',\n",
       "                                                 Pipeline(memory=None,\n",
       "                                                          steps=[('cst',\n",
       "                                                                  ColumnSelectTransformer(col_names=['SentimentTitle',\n",
       "                                                                                                     'SentimentHeadline']))],\n",
       "                                                          verbose=False))],\n",
       "                              transformer_weights=None, verbose=False)),\n",
       "                ('model',\n",
       "                 LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "                                  normalize=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facebook_model = Pipeline([\n",
    "    ('features', features),\n",
    "    ('model', LinearRegression())\n",
    "])\n",
    "\n",
    "facebook_model.fit(df, df['Facebook'])\n",
    "\n",
    "google_model = Pipeline([\n",
    "    ('features', features),\n",
    "    ('model', LinearRegression())\n",
    "])\n",
    "\n",
    "google_model.fit(df, df['GooglePlus'])\n",
    "\n",
    "linkedin_model = Pipeline([\n",
    "    ('features', features),\n",
    "    ('model', LinearRegression())\n",
    "])\n",
    "\n",
    "linkedin_model.fit(df, df['LinkedIn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = news_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training: 0.0776331812620148\n",
      "testing: -0.05171044995624863\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('training: {}'.format(facebook_model.score(df, df['Facebook'])))\n",
    "print('testing: {}'.format(facebook_model.score(df_new, df_new['Facebook'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15523238347408452"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print('training: {}'.format(google_model.score(df, df['GooglePlus'])))\n",
    "print('testing: {}'.format(google_model.score(df_new, df_new['Facebook'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1992734891028325"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linkedin_model.score(df_new, df_new['LinkedIn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "facebook_predict = facebook_model.predict(df_new)\n",
    "\n",
    "google_predict = google_model.predict(df_new)\n",
    "\n",
    "linkedin_predict = linkedin_model.predict(df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new['FacebookPrediction'] = facebook_predict\n",
    "\n",
    "df_new['GooglePrediction'] = google_predict\n",
    "\n",
    "df_new['LinkedInPrediction'] = linkedin_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare predicted resutls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Source</th>\n",
       "      <th>PublishDate</th>\n",
       "      <th>FacebookPrediction</th>\n",
       "      <th>GooglePrediction</th>\n",
       "      <th>LinkedInPrediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDLink</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>46462</th>\n",
       "      <td>Get a $50 Microsoft Store gift card with Xbox ...</td>\n",
       "      <td>Windows Central</td>\n",
       "      <td>2016-05-01 08:47:22</td>\n",
       "      <td>11.570160</td>\n",
       "      <td>2.577800</td>\n",
       "      <td>-7.440152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46791</th>\n",
       "      <td>Flow is Microsoft’s answer to IFTTT</td>\n",
       "      <td>Digital Trends via Yahoo! News</td>\n",
       "      <td>2016-05-01 11:10:00</td>\n",
       "      <td>13.221109</td>\n",
       "      <td>0.030471</td>\n",
       "      <td>6.861964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46538</th>\n",
       "      <td>Microsoft's IE loses top browser spot to Googl...</td>\n",
       "      <td>Computer World Australia</td>\n",
       "      <td>2016-05-01 11:31:00</td>\n",
       "      <td>44.621996</td>\n",
       "      <td>0.909720</td>\n",
       "      <td>9.187982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46542</th>\n",
       "      <td>This Microsoft legend says the the company is ...</td>\n",
       "      <td>Business Insider via Yahoo! Finance</td>\n",
       "      <td>2016-05-01 11:39:00</td>\n",
       "      <td>23.466905</td>\n",
       "      <td>0.650514</td>\n",
       "      <td>16.039449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46569</th>\n",
       "      <td>Windows 10 Mobile Build 10586.306 now being te...</td>\n",
       "      <td>MSPoweruser.com</td>\n",
       "      <td>2016-05-01 14:00:21</td>\n",
       "      <td>-2.763371</td>\n",
       "      <td>-1.326493</td>\n",
       "      <td>10.164756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Title  \\\n",
       "IDLink                                                      \n",
       "46462   Get a $50 Microsoft Store gift card with Xbox ...   \n",
       "46791                 Flow is Microsoft’s answer to IFTTT   \n",
       "46538   Microsoft's IE loses top browser spot to Googl...   \n",
       "46542   This Microsoft legend says the the company is ...   \n",
       "46569   Windows 10 Mobile Build 10586.306 now being te...   \n",
       "\n",
       "                                     Source         PublishDate  \\\n",
       "IDLink                                                            \n",
       "46462                       Windows Central 2016-05-01 08:47:22   \n",
       "46791        Digital Trends via Yahoo! News 2016-05-01 11:10:00   \n",
       "46538              Computer World Australia 2016-05-01 11:31:00   \n",
       "46542   Business Insider via Yahoo! Finance 2016-05-01 11:39:00   \n",
       "46569                       MSPoweruser.com 2016-05-01 14:00:21   \n",
       "\n",
       "        FacebookPrediction  GooglePrediction  LinkedInPrediction  \n",
       "IDLink                                                            \n",
       "46462            11.570160          2.577800           -7.440152  \n",
       "46791            13.221109          0.030471            6.861964  \n",
       "46538            44.621996          0.909720            9.187982  \n",
       "46542            23.466905          0.650514           16.039449  \n",
       "46569            -2.763371         -1.326493           10.164756  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[['Title', 'Source', 'PublishDate', 'FacebookPrediction', 'GooglePrediction', 'LinkedInPrediction']]\\\n",
    ".head()"
   ]
  }
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