Covid_19_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Covid-19_Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPJItE4i3O7PC4n7QYu88IU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SBMagar/Covid-19_Prediction/blob/main/Covid_19_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zzd3Vaxz1-rI"
      },
      "source": [
        "I am using  a sigmoidal function to fit the historical data of Covid-19 and predict or forecast. And also use LinearRegression and RandomForestRegressor to predict.\n",
        "I thought of a sigmoidal function first because China's data resembled a sigmoidal shape. Therefore, I try to fit sigmoid functions onto Nepal also.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlDKlKnO3PyJ"
      },
      "source": [
        "**Step-1: Load Dataset from s3 Bucket: (downloaded from https://covid.ourworldindata.org/data/owid-covid-data.csv)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DIbYxTa0Enf"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "df1=pd.read_csv('s3://sagemaker-studio-im065hy7nj/owid-covid-data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhM_OXiP0MGJ"
      },
      "source": [
        "# df1.head(5)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fP2az5p2_8p"
      },
      "source": [
        "df1.columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFL7vzGR3hrc"
      },
      "source": [
        "**Step-2: Subsetting only those rows that have \"NPL\" in the \"location\" column and plot y-axis total_cases over x-axis as day-count.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsQgYFjR3DMY"
      },
      "source": [
        "Nepal_df = df1[df1['location']=='Nepal'].groupby('date')[['total_cases','total_deaths']].sum()\n",
        "Nepal_df['day_count'] = list(range(1,len(Nepal_df)+1))\n",
        "ydata = Nepal_df.total_cases\n",
        "xdata = Nepal_df.day_count\n",
        "Nepal_df['rate'] = (Nepal_df.total_cases-Nepal_df.total_cases.shift(1))/Nepal_df.total_cases\n",
        "Nepal_df['increase'] = (Nepal_df.total_cases-Nepal_df.total_cases.shift(1))\n",
        "# Nepal_df = Nepal_df[Nepal_df.total_cases>100]\n",
        "\n",
        "plt.plot(xdata, ydata, 'o')\n",
        "plt.title(\"Nepal\")\n",
        "plt.ylabel(\"Population Infected\")\n",
        "plt.xlabel(\"Days\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NogI3L1F-mMw"
      },
      "source": [
        "![Nepal(infected).png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAdwAAAE0CAYAAACGtIIaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeViUVf/48feIG+IyLiwi4gYPuGYuDGpagmloZm5Z2de0CBVXvuFWmeaXJ8R9CadSaXtcIU1twfKREhXFejJMSxEfSUwhsFGYIBXm9we/GRnZBhxgmPm8rsvrkvs+c9+fw618OOfc5xyFRqPRIYQQQogqVaemAxBCCCFsgSRcIYQQohpIwhVCCCGqgSRcIYQQohpIwhVCCCGqgSRcIYQQohpIwhVCWLz4+HiUSiXh4eE1HYoQlSYJVwgrpFQqUSqVdOnShb/++qvEMkOGDEGpVJKamlrN0QlhmyThCmHFfv/9dzZu3FjTYQghkIQrhNVq2rQprVq1YsOGDVy/fr2mwxHC5knCFcJKNWzYkEWLFqHVagkLC6vQZy9dusSsWbPo1q0bTk5OdOrUiYkTJ3L69OliZcPDw1EqlWzbto3Y2Fgef/xxXF1dad++PZMnT+a///1vsc9cvHiRpUuX8thjj9GpUyecnJzo1q0bs2fP5sqVK5WusxCWTBKuEFbsxRdfxMvLi+3bt3PmzBmTPvPdd98xaNAgduzYwUMPPcS0adPw8/Pj3//+N8OGDePf//53iZ87cOAAL7zwAm5ubkybNo0+ffrw2WefMWTIEFJSUoqVjYqKok2bNowdO5agoCC8vLz45JNP8PPz4+rVqw9cdyEsTd2aDkAIUXXq1q3LsmXLmDBhAosXL+azzz4rs/zNmzeZMmUK9erV49ChQ3h7exvOnT9/Hn9/f2bMmMFPP/1EgwYNjD4bGxvLrl27GDZsmOHYxo0bWbx4MfPmzWPPnj2G4xMmTCA4OLjYNb755hsmTJjAqlWrWLt27YNUXQiLIy1cIazcsGHDeOyxx/j22285ePBgmWV37tzJjRs3WLBggVGyBfDy8mLSpElcv36db7/9tthnBw0aZJRsAaZPn46bmxuHDx/m999/Nxx3dXUtlmwBHn/8cby9vTl8+HAFaihE7SAtXCFsQFhYGIMGDeLNN9/E39+funVL/q9/8uRJAM6ePVvinNeLFy8CcOHChWLJdcCAAcXK161bF5VKRVpaGklJSbi6ugKg0+nYvXs327dv5+eff0aj0ZCfn2/4XP369StXUSEsmCRcIWxAt27deP755/nXv/7Fhx9+SGBgYInlbty4AcAnn3xS5vW0Wm2xY05OTiWWdXR0BODWrVuGY6+99hpqtRoXFxf8/f1p3bo1DRs2BGD79u3y4pSwSpJwhbARb7zxBnv37mX58uU888wzJZZp2rQpAN9++y09e/as0PUzMjJKPP7HH38YXfuPP/7gvffeo0uXLhw8eJAmTZoYlf/0008rdF8hagsZwxXCRri4uDB79mwyMzNZs2ZNiWX69u0LQEJCQoWvf+zYsWLH7t69a+im7tGjBwCXL1+moKCAwYMHF0u2V69e5fLlyxW+txC1gSRcIWzIrFmzcHV1Ra1Wl7gYxgsvvIBSqWTlypUkJiYWO6/T6UhISOD27dvFzh05cqTYS1lqtZq0tDQGDx5sGL91d3cH4MSJE0bjtjk5OcyZM4e7d+8+UB2FsFTSpSyEDWnUqBFvvPEGwcHBpKWlFTvfvHlzPv74Y1544QWGDh3KoEGD8Pb2pl69ely9epXvv/+etLQ0Ll++XOzFpoCAACZOnMhTTz1F+/btSUpK4tChQ7Ro0YJVq1YZyjk7OzN27Fg+/fRTBg4cyODBg7l16xZxcXE0bNiQ7t27mzxnWIjaRFq4QtiY5557joceeqjU84MGDeLYsWNMnTqV33//nY8//piPPvqIM2fO0LdvXzZv3mwYjy3qySefZNu2bVy5coV3332XU6dOMWrUKA4dOkSnTp2Mym7cuJFXX32V3NxctmzZwuHDh3niiSf4+uuvS7y2ENZAodFodDUdhBCi9goPDyciIoLIyEgmTpxY0+EIYbGkhSuEEEJUA0m4QgghRDWQhCuEEEJUAxnDFUIIIaqBtHCFEEKIaiAJVwghhKgGknCFEEKIaiAJ1wIlJyfXdAjVxpbqCrZVX6mrdZK6Vl6NJtxjx47x7LPP0rlzZ5RKJdu2bTOcu3PnDkuWLKF///64urri5eVFYGBgsW27/v77b+bNm0fHjh1xdXXl2Wef5erVq0Zlrly5woQJE3B1daVjx47Mnz+/2FqwR48e5dFHH8XZ2ZmHHnqIqKioYvFu2bKFHj164OzszKOPPsrx48fN+N0QQghhzWo04Wq1Wrp06cLy5cuxt7c3OvfXX3/x008/ERoaynfffcf27du5evUq48aNM1rcfNGiRRw4cICtW7fy5Zdfkp2dzYQJEwyLoufn5zNhwgRycnL48ssv2bp1K/v37+f11183XOPy5cs888wz+Pj4cOTIEf73f/+X+fPns2/fPkOZPXv2sHDhQl599VWOHDmCj48P48ePl307hRBCmMRipgW1adOGFStWlLk03K+//oqvry/Hjh2ja9eu3Lx5Ew8PDyIjIw37e6alpdG9e3diYmLw9/fnm2++4ZlnnuHMmTO4ubkBsGvXLmbPnk1ycjJNmzZlyZIlHDhwgP/85z+Ge82aNYtff/2Vb775BgB/f3+6du3Khg0bDGV69erFqFGjWLJkiVm/F8nJyXh6epr1mpbKluoKtlVfqat1krpWXq0aw83OzgZAqVQCcPr0ae7cuYOfn5+hjJubG15eXoY9OBMTE/Hy8jIkWyhMnn///TenT582lCl6DX2ZH3/8kTt37nD79m1Onz5drIyfn5/hPkIIIURZas32fLdv3+aNN97giSeeoE2bNgBkZGRgZ2dHy5Ytjco6OjqSkZFhKOPo6Gh0vmXLltjZ2RmVeeyxx4pd4+7du2RlZaHT6cjPzy92naL3KcmDDLjLiwnWy5bqK3W1TrZa1wdt7daKhHv37l2CgoK4efMmO3bsKLe8TqdDoVAYvi7696LKKqPT6QzHi/69rPvcr7IPR7psrJct1Vfqap1qY12jU7Qs+yGbNG0+bg52vNm7CeM7OZT7OZvrUr579y4vv/wyZ8+eZd++fbRo0cJwzsnJifz8fLKysow+k5mZaWiNOjk5FWuFZmVlGbVYSyqTmZlJ3bp1adGiRbEWcUn3EUIIYXmiU7TMPnaTK9p8dMAVbT6zj90kOkVb7bFYdMK9c+cOU6ZM4ezZsxw4cABnZ2ej8z179qRevXrExcUZjl29epXz58+jUqkA8PHx4fz580ZTheLi4mjQoAE9e/Y0lPn222+Nrh0XF8fDDz9MvXr1qF+/Pj179jS6j76M/j5CCCEsz7IfssnNN343ODdfx7Ifsqs9lhrtUs7JyeHSpUsAFBQUkJaWRlJSEs2bN6d169a8+OKL/Pjjj+zYsQOFQkF6ejoATZs2xd7enmbNmvE///M/vPnmmzg6OtK8eXNef/11unbtahiT9fPzo3PnzkybNo2wsDD+/PNP3nzzTSZNmkTTpk0BmDJlCps3b2bhwoVMmTKFkydPsn37drZs2WKIdcaMGUydOpXevXujUqmIiori+vXrTJkypXq/aUIIIUyWps2v0PGqVKMJ98cff2TkyJGGr8PDwwkPD+e5555j4cKFfPnllwDFXmiKjIw0TB96++23sbOzY8qUKeTl5TFo0CDeffdd7OzsALCzs2PXrl2EhobyxBNP0LBhQ8aNG0dYWJjheu3bt2f37t289tprREVF4eLiQkREBKNGjTKUGTNmDDdu3GDlypWkp6fTuXNndu/ejbu7e1V9e4QQQjyg5g0U3Pi7+OxXNwe7ao/FYubhintq40sJlWVLdQXbqq/U1TrVprpGp2gJjtdw574sV78ORD6iLPfFKZt7aUoIIYSojGU/ZBdLtgCN6ypMekvZ3CThCiGEsEqljdP+ebtmOnYl4QohhLBKpY3T1sT4LUjCFUIIYaXe7N0EezvjxYns7RS82btJjcQjCVcIIYRVGt/JgQ0DmtHWwQ4F0NbBjg0DmtXI+C1IwhVCCGGlKrukY1WpFWspCyGEEBWhX9JRv8qUfklHQFq4QgghhLlY0pKOepJwhRBCWB1LWtJRTxKuEEIIq2NpU4JAEq4QQggrZGlTgkASrhBCCCtkaVOCQBKuEEIIK2RpU4JApgUJIYSwMpY4JQikhSuEEMLKWOKUIJCEK4QQwspY4pQgkIQrhBDCikSnaKmjKPlcTU4JAkm4QgghrIR+7Da/hO1ua3pKEEjCFUIIYSVKGrsFsFNQ41OCQBKuEEIIK1HaGG2BrmbfTtaThCuEEMIqWOJyjkVJwhVCCGEVLHE5x6Ik4QohhLAKlricY1GlrjS1Y8eOSl3wueeeq3QwQgghxIMY38nBYhLs/UpNuMHBwcWOKRSFTXWdTlficZCEK4QQQpSk1C7ln376yejP0aNH6dq1K76+vnz44YccPXqU+Ph4PvjgA1QqFd26dePo0aMVuvmxY8d49tln6dy5M0qlkm3bthmd1+l0hIeH4+3tjYuLCyNGjOCXX34xKqPRaAgKCsLd3R13d3eCgoLQaDRGZc6ePcvw4cNxcXGhc+fOREREFPulYd++fahUKpycnFCpVBw4cKDCsQghhKg50Slauu++TvMPrtJ993WiU7Q1HZKRUhOuPoHp/7z33ns0b96cL774glGjRtG1a1e6devG008/zRdffEGzZs147733KnRzrVZLly5dWL58Ofb29sXOr1+/nsjISCIiIjh8+DCOjo6MHj2a7Ox762EGBgaSlJREdHQ0MTExJCUlMXXqVMP5W7duMXr0aJycnDh8+DDLly9n48aNvPPOO4YyiYmJvPTSS4wfP574+HjGjx/P5MmT+f777ysUixBCiJqhX/TiijYfHfc2LLCkpGvyS1MHDhxg5MiRRt3HhovUqcNTTz1VrFVYnqFDh/Lmm28yatQo6tQxDkWn06FWq5k7dy6jRo2iS5cuqNVqcnJyiImJAeD8+fMcOnSIdevWoVKp8PHxYe3atRw8eJDk5GQAoqOjyc3NRa1W06VLF0aNGsWcOXPYtGmToZWrVqsZOHAgoaGheHl5ERoayiOPPIJarTY5FiGEEDXHUjcsKMrkhHv37l1+++23Us9fvnyZO3fumCUogNTUVNLT0/Hz8zMcs7e3p3///pw8eRIobJk2btwYlUplKOPr64uDg4NRmX79+hm1oP39/bl27RqpqakAnDp1yug++jL6a5gSixBCiJpzxUI3LCjK5P1w/fz8eP/99+nRowfjxo0zeoEqOjqaLVu2EBAQYLbA0tPTAXB0dDQ67ujoyLVr1wDIyMigZcuWRq1uhUJBq1atyMjIMJRxdXUtdg39ufbt25Oenl7iffTXMCWWkuhb2ZXxIJ+tbWyprmBb9ZW6WidLq+tXGXZAfaB4D6xzgwKz/Sz29PSs9HWgAgl3xYoV/Prrr0ydOpU33niDDh06oFAouHTpEhkZGXh6erJ8+fIHCqYk93dh63S6Ygn2fuWV0Xcll1fm/mOmlCmqsg8nOTn5gR9sbWFLdQXbqq/U1TpZYl3H/HgdKN6SVQD/p2qBZyWnCZm7riZ3KTs7O3PkyBHCw8Pp1q0bN27cIDMzk27durF8+XKOHDmCi4uL2QJzdnYGMLQy9TIzMw0tTScnJzIzM43eONbpdGRlZRmVKekacK/F6uzsXOZ9TIlFCCFEzSit21iHZayhrFehlaYaNGjA1KlT+fTTT0lMTOTUqVN8+umnTJ06lYYNG5o1sHbt2uHs7ExcXJzhWF5eHgkJCYYxWx8fH3JyckhMTDSUSUxMRKvVGpVJSEggLy/PUCYuLo7WrVvTrl07APr27Wt0H30Z/TVMiUUIIUTNKG2t5LYWsoayXoWXdszNzeX48ePs27fP0FKsrJycHJKSkkhKSqKgoIC0tDSSkpK4cuUKCoWC6dOns27dOvbv38+5c+cIDg7GwcGBcePGAeDl5cWQIUMICQnh1KlTJCYmEhISwrBhwwzdAOPGjcPe3p7g4GDOnTvH/v37WbduHcHBwYbu4GnTpnHkyBHWrFnDhQsXWLNmDfHx8UyfPh3ApFiEEELUDEtfQ1mvQgn33XffxcvLixEjRjBlyhTOnj0LQFZWFu7u7nz88ccVuvmPP/7IoEGDGDRoELm5uYSHhzNo0CDefvttAObMmUNwcDDz5s1j8ODBXL9+nT179tCkyb1v4ubNm+nWrRtjxoxh7NixdOvWzWg+cLNmzdi7dy/Xrl1j8ODBzJs3jxkzZjBz5kxDGZVKRVRUFDt27GDAgAHs3LmTqKgo+vTpYyhjSixCCCGqn6Wvoayn0Gg0xXfrLcG2bduYOXMmo0ePxt/fn5kzZ/LZZ5/x6KOPAvDiiy9y69Yt9u7dW6UB2wJLfCmhqthSXcG26it1tU5S18ozuYUbGRnJsGHDiIqKKnH6T8+ePTl//rzZAhNCCCFMYelLOuqZnHBTUlIYNmxYqedbtmxJVlaWWYISQgghTFEblnTUMznhNmnShJs3b5Z6PiUlhVatWpklKCGEEMIUtWFJRz2TE+6gQYPYtm0bf//9d7FzV69e5aOPPmLIkCFmDU4IIYQoS2lzcC1pSUc9kxPuG2+8QWZmJo899hibN29GoVDwzTffsHTpUgYMGEC9evWYP39+VcYqhBBCGCltDm5px2uSyQm3Y8eOxMbG4uLiYthPNjIykvXr1/PQQw8RGxtLmzZtqjJWIYQQwkhtmYMLFVhLGQoXmti7dy8ajYZLly5RUFBA+/btZexWCCFEjbG3g9z/34PcooGCCJXlzcGFCrRwIyIiOHfuHABKpZJevXrRp08fQ7L95ZdfiIiIqJoohRBCiPvo31C+cfveS1O5d2swoHKYnHCXL19uWFmqJJJwhRBCVKfa9IYyVGIt5dLk5ORQr149c11OCCGEKFNt2HS+qDLHcH/++WfOnDlj+DohIYG7d4u31zUaDVFRUXh4eJg/QiGEEOI+0SlaFBRuwXc/S3xDGcpJuJ9//rmhm1ihUPDBBx/wwQcflFi2SZMmbN682fwRCiGEEPdZ9kN2iclWARb5hjKUk3AnTZrEkCFD0Ol0DB06lIULF+Lv729URqFQ0KhRIzp16kT9+vWrNFghhBACas+m80WVmXBdXV1xdXUF4MCBA3h7e8sUICGEEDXOzcGuxDFcS9t0viiTX5ry8PDgwoULpZ4/fvw46enpZglKCCGEKE10ihbtnYJixy11wQs9kxe+WLx4MWlpaXz11Vclnv/nP/+Jm5ub0ebvQgghhDnp597ePx3Ikhe80DO5hXvs2DEef/zxUs8PGTKEY8eOmSUoIYQQoiQlzb0FcKhbx6KTLVQg4WZlZdG8efNSzyuVSv744w+zBCWEEEKUpDbtDnQ/kxNu69at+fHHH0s9/5///AdHR0ezBCWEEEKUpHkDRYnHLXXubVEmJ9yRI0eyfft2Pv3002Ln9u7dy44dOxg5cqRZgxNCCCH0olO0ZN8u3p1cv47lzr0tyuSXpubNm0dcXByvvPIKq1evpnPnzigUCs6dO8evv/6Kt7c3CxcurMpYhRBC2LBlP2Rzp4TVLhrXVVj8+C1UoIXbtGlTvv76a+bNmwfAl19+yRdffAHA/PnzOXToEM2aNauaKIUQQti80sZp/yyh1WuJKrQfbqNGjVi0aBGLFi2qqniEEEKIEjVvoODG38WTa20Yv4VK7haUlpbG6dOnycnJMXc8QgghRDG1ffwWKphwP//8c3r16kWPHj3w8/Pjhx9+AAqnDPXv358DBw5USZBCCCFsW20fv4UKJNyDBw8yadIkWrVqxYIFC9Dp7tW8ZcuWuLm5sX37drMGl5+fT1hYGD169MDZ2ZkePXoQFhZmtEWgTqcjPDwcb29vXFxcGDFiBL/88ovRdTQaDUFBQbi7u+Pu7k5QUBAajcaozNmzZxk+fDguLi507tyZiIgIozoC7Nu3D5VKhZOTEyqVSn7BEEKIalLbx2+hAgl3xYoVqFQqvv76a1555ZVi5/v27Wu0d645rFu3ji1bthAREUFiYiLLly9n8+bNrFmzxlBm/fr1REZGEhERweHDh3F0dGT06NFkZ2cbygQGBpKUlER0dDQxMTEkJSUxdepUw/lbt24xevRonJycOHz4MMuXL2fjxo288847hjKJiYm89NJLjB8/nvj4eMaPH8/kyZP5/vvvzVpnIYQQxdXm+bd6Jr80de7cOZYtW1bqeWdnZzIzM80SlF5iYiJPPPEEAQEBALRr146AgABDV7ZOp0OtVjN37lxGjRoFgFqtxtPTk5iYGKZMmcL58+c5dOgQsbGxqFQqANauXUtAQADJycl4enoSHR1Nbm4uarUae3t7unTpwoULF9i0aRMzZ85EoVCgVqsZOHAgoaGhAHh5eREfH49arWbr1q1mrbcQQoh7rGH8FirQwq1fvz5///13qeevXLlC06ZNzRKUnq+vL0ePHjXsUvTrr78SHx9vWNM5NTWV9PR0/Pz8DJ+xt7enf//+nDx5EihM2o0bNzYkW/11HRwcjMr069cPe3t7Qxl/f3+uXbtGamoqAKdOnTK6j76M/hpCCCGqhjWM30IFWri+vr7s3buXmTNnFjt369Yttm3bxsCBA80a3Ny5c8nJyUGlUmFnZ8fdu3cJDQ0lMDAQwLAd4P1LSjo6OnLt2jUAMjIyaNmyJQrFve4IhUJBq1atyMjIMJTR7/tb9Br6c+3btyc9Pb3E++ivUZLk5OTKVPuBP1vb2FJdwbbqK3W1TtVd1zStPVC8S/nP2wVVHkvR63t6ej7QtUxOuAsXLiQgIICnn36a8ePHA5CUlERKSgobN27k1q1bzJ8//4GCud+ePXvYuXMnW7ZswdvbmzNnzrBw4ULc3d2ZNGmSoVzRZAqFXc33J9j7lVdG/8JUeWVKurZeZR+OvqvbFthSXcG26it1tU41Udfmp34vZf5t3SqNxdx1NTnhPvzww8TExBASEmJo5b755psAdOrUiZiYGLy8vMwWmP76M2fOZOzYsQB07dqVK1eusHbtWiZNmoSzszNQ2Ap1c3MzfC4zM9PQGnVyciIzM9MoOep0OrKysozK3N9S1Y9H68s4OzuXWEY2bBBCiKpjLeO3UMF5uI888ginTp3iyJEjfPDBB2zdupXDhw9z6tQpfH19zR7cX3/9hZ2d8RtodnZ2FBQUAIUvUTk7OxMXF2c4n5eXR0JCgmHM1sfHh5ycHBITEw1lEhMT0Wq1RmUSEhLIy8szlImLi6N169a0a9cOKHwLu+h99GWKjg0LIYQwL2sZv4UyEm6LFi2Ijo42fD1jxgzDFJju3bvz9NNPM2bMGB5++OEyu1UfxBNPPMG6des4ePAgqampHDhwgMjISJ588kmgsIt3+vTprFu3jv3793Pu3DmCg4NxcHBg3LhxQOHbxEOGDCEkJIRTp06RmJhISEgIw4YNM3QVjBs3Dnt7e4KDgzl37hz79+9n3bp1BAcHG+o2bdo0jhw5wpo1a7hw4QJr1qwhPj6e6dOnV0ndhRDC1kWnaLliBfNv9UrtUq5bty537twxfL19+3Yee+wx+vTpUy2BQeHc33/+85+8+uqrZGZm4uzszIsvvmg0Vjxnzhxyc3OZN28eGo2G3r17s2fPHpo0udfVsHnzZhYsWMCYMWMACAgIYMWKFYbzzZo1Y+/evYSGhjJ48GCUSiUzZswwekFMpVIRFRVFWFgY4eHhdOjQgaioqGr9fgghhK2ITtEy+9jNUs/Xpvm3egqNRlPirwm+vr60bt2asLAwmjZtSo8ePVi+fDnDhw8v84Jt27atkkBtibyAYb1sqb5SV+tUXXXtuL3kF6UA7O0UbBjQrMq7lKvtpanQ0FCmT59umOqjUChM2inoxo0bZgtOCCGE7YlO0ZaabIFqSbZVodSEO27cOPr06cPx48f5448/WLp0KWPGjKF79+7VGZ8QQggbs+yH7FLPtXWwq5XJFsqZFtS+fXvat28PwNatWxk7dmy5XcpCCCHEgyjtRSmofVOBijJ5Hm5SUlJVxiGEEEIQnaJFAZTUodyifu2bClSUyQlX7/z581y+fJk///yz2PZ1AM8995xZAhNCCGF7lv2QXWKyVQARvs2qOxyzMjnhpqamMnXqVBITE0tMtFD4YpUkXCGEEJVVWneyDmp16xYqkHBDQkJISkrin//8JwMGDECpVFZlXEIIIWxMWd3JbWvhvNv7mZxwExISmD17tqysJIQQokosOHmz1O7k2vyylJ7Jayk3a9aMli1bVmUsQgghbFRZc2+toTsZKpBwn3/+eT777LOqjEUIIYSNWnCy9GUcraE7GSrQpfz4448TFxfHyJEjmTJlCm5ubsV28gHo3bu3WQMUQghh3cpbWcoaupOhAgm36IIXx44dK3Zev9+sLO0ohBCiIspaWaq2z70tyuSEGxkZWZVxCCGEsEFlbcEHtX/ubVEmJ9znn3++KuMQQghhY8rbgs+aWrdQgZemhBBCCHNacPImufmlb8FnTa1bKKeFu379+gpdTKFQMHv27AcKSAghhPWz1i34ylJmwl26dGmFLiYJVwghhCnKmwZkbckWykm4P/30U3XFIYQQwkbYyjSg+5WZcN3d3asrDiGEEDairNattb0oVZS8NCWEEKLalNe6tbYXpYqShCuEEKLa2MoiFyWRhCuEEKLa2MoiFyWRhCuEEKJa6Pe7LYm1t25BEq4QQohqEJ2iZVq8ptT9bq29dQuScIUQQlQx/RKOpSwqZTX73ZanQgk3Pz+fb775hs2bN7NixQoiIiKM/qxYscLsAV6/fp1p06bRqVMnnJ2dUalUHD161HBep9MRHh6Ot7c3Li4ujBgxgl9++cXoGhqNhqCgINzd3XF3dycoKAiNRmNU5uzZswwfPhwXFxc6d+5MREQEOp3xv459+/ahUqlwcnJCpVJx4MABs9dXCCGszbIfsktdwhGsZ7/b8pi8eUFSUhIvvPACaWlpxRKRnkKhYP78+WYLTqPRMDZxFyUAACAASURBVGzYMHx9fdm9ezctW7YkNTUVR0dHQ5n169cTGRlJZGQknp6erFixgtGjR3Pq1CmaNCmcPB0YGEhaWhrR0dGG1bCmTp3Krl27ALh16xajR4+mf//+HD58mOTkZGbMmEGjRo2YNWsWAImJibz00kssWrSIkSNHcuDAASZPnszBgwfp06eP2eoshBDWJq2MF6Xs7RRWu9DF/UxOuKGhoeTk5PDJJ58wYMAAlEplVcYFwIYNG3BxceG9994zHGvfvr3h7zqdDrVazdy5cxk1ahQAarUaT09PYmJimDJlCufPn+fQoUPExsaiUqkAWLt2LQEBASQnJ+Pp6Ul0dDS5ubmo1Wrs7e3p0qULFy5cYNOmTcycOROFQoFarWbgwIGEhoYC4OXlRXx8PGq1mq1bt1b590IIIWqr5g0UJc69tVNY55rJpTG5SzkpKYk5c+YwYsSIakm2AF988QW9e/dmypQpeHh48Mgjj/D+++8bWtipqamkp6fj5+dn+Iy9vT39+/fn5MmTQGHLtHHjxoZkC+Dr64uDg4NRmX79+mFvb28o4+/vz7Vr10hNTQXg1KlTRvfRl9FfQwghRHHRKVqybxdPtvXrwLsDlTaTbKECLVwnJyfq1jW5uFlcvnyZrVu3EhwczNy5czlz5gwLFiwAICgoiPT0dACjLmb919euXQMgIyODli1bolDcexldoVDQqlUrMjIyDGVcXV2LXUN/rn379qSnp5d4H/01SpKcnFyZaj/wZ2sbW6or2FZ9pa7WqSJ1DT1hzx1d8clADRUF9Cz4HUv/thWtq6en5wNdy+QMGhQUxM6dOwkKCqJevXoPdFNTFRQU8PDDD7NkyRIAHnroIS5dusSWLVsICgoylCuaTKGwq/n+BHu/8sroW9HllSnp2nqVfTj6rm5bYEt1Bduqr9TVOlWkrtEpWm7e1ZR4Lju/jsV/z8z9XE1OuK6urtStW5d+/frxwgsv4Obmhp1d8TfLRo8ebbbgnJ2d8fLyMjr2j3/8g7S0NMN5KGyFurm5GcpkZmYaWqNOTk5kZmYaJUedTkdWVpZRmftbqpmZmcC9lq6zs3OJZe5v9QohhChU1jKObjbyZnJRJifcl19+2fD3t956q8QyCoXCrAnX19eXixcvGh27ePEibdu2BaBdu3Y4OzsTFxdHr169AMjLyyMhIYFly5YB4OPjQ05ODomJiYZx3MTERLRareFrHx8fli5dSl5eHg0bNgQgLi6O1q1b065dOwD69u1LXFyc0X6/cXFxRmPDQggh7ilrGUdbeTO5KJMTbk3MOQ0ODmbo0KGsWrWKMWPGkJSUxPvvv8/ixYuBwgQ/ffp0Vq9ejaenJx4eHqxatQoHBwfGjRsHFL5NPGTIEEJCQli/fj06nY6QkBCGDRtm6CoYN24cERERBAcHExoaysWLF1m3bh3z5883tIqnTZvG8OHDWbNmDU8++SSff/458fHxxMbGVvv3RQghLJ1+GceSJpHawjKOJTE54T7yyCNVGUeJevXqxbZt21i2bBkrV67Ezc2N1157jcDAQEOZOXPmkJuby7x589BoNPTu3Zs9e/YY5uACbN68mQULFjBmzBgAAgICjBbpaNasGXv37iU0NJTBgwejVCqZMWMGM2fONJRRqVRERUURFhZGeHg4HTp0ICoqSubgCiFECRacvGnTyziWRKHRaEpf/qMUZ8+e5bfffgMKN6nv2rWr2QOzZfIChvWypfpKXa2TKXWNTtHyypGSX5YC0ExpY+6wqkSNvTQFhfNiFy1aZHhpSf8iUtu2bXn77bcZMWKE2QITQghROy04ebPUc7ayjGNJTE64hw4dYtKkSbi6urJ48WK8vLzQ6XRcuHCBDz74gBdffJFdu3bh7+9flfEKIYSwYNEp2hJXldKzxZel9ExOuCtWrMDLy4uDBw8ajY+OGDGCwMBAhg0bxsqVKyXhCiGEjdJvwVcaW31ZSs/kpR1//vlnJk6caJRs9Zo0acLEiRNJSkoya3BCCCFqh/K24APbfVlKz+SEW69ePf76669Sz2u12mpbgUoIIYRlWXDyZplb8Nl66xYqkHD79evH5s2bSUlJKXZOv9xi//79zRqcEEIIy1feuK29ncLmW7dQgTHcJUuWMGzYMPr160dAQIDhVekLFy5w8OBBGjZsaFjzWAghhO0oawlHW9uCrywmJ9zOnTsTFxfHW2+9xb///W/2798PgIODA0888QSLFy/Gw8OjygIVQghhmcpawtHWtuArS4Xm4Xbq1ImPP/6YgoICw+L+rVq1ok4dk3umhRBCWBFZwtF0ldrgtk6dOjg5OZk7FiGEELWMLOFoulIT7o4dOwB49tlnUSgUhq/L89xzz5knMiGEEBatrJeldCCt2/uUmnCDg4NRKBSMHTuW+vXrExwcXO7FFAqFJFwhhLARsoRjxZSacH/66ScA6tevb/S1EEIIIUs4VlypCdfd3b3Mr4UQQtgmWcKxckx+vfihhx7iyy+/LPV8bGwsDz30kFmCEkIIYZm+yrCTJRwryeSE+9tvv6HVaks9r9VquXLlilmCEkIIYZlWX6ovSzhWUoUm0CoUilLPXbx4scSNDYQQQliHV4/f4Obd0s/LEo5lK3Me7vbt242mA61atYqPPvqoWDmNRsO5c+cYNmyY+SMUQghR46JTtESdz6Vwhm1xsoRj+cpMuFqtlvT0dMPXN2/epKCgwKiMQqGgUaNGvPjiiyxcuLBqohRCCFGjSlvgQk+WcCxfmQn3lVde4ZVXXgGgR48eLF++nOHDh1dLYEIIISxDeVOAZNzWNCYv7SibywshhG0qazcgWcLRdJVaSzk7O5tbt24V614GaNu27QMHJYQQwnKUtRvQS1720ro1UYUS7scff8yGDRu4dOlSqWVu3LjxwEEJIYSwDOXtBrS6f4vqDqnWMnla0CeffMKcOXNo27Ytb7zxBjqdjunTpxMSEoKTkxPdu3dn48aNVRmrEEKIaia7AZmPyQlXrVYzcOBA9u7dy+TJkwEYOnQoixcv5sSJE2g0Gm7dulVVcQKwevVqlEol8+bNMxzT6XSEh4fj7e2Ni4sLI0aM4JdffjH6nEajISgoCHd3d9zd3QkKCkKjMV6W7OzZswwfPhwXFxc6d+5MREQEOp3xP7N9+/ahUqlwcnJCpVJx4MCBqqusEELUMNkNyLxMTriXLl3iySefLPzQ/99w/s6dOwAolUomTZrEli1bqiDEQqdOneKjjz6ia9euRsfXr19PZGQkERERHD58GEdHR0aPHk129r1B/sDAQJKSkoiOjiYmJoakpCSmTp1qOH/r1i1Gjx6Nk5MThw8fZvny5WzcuJF33nnHUCYxMZGXXnqJ8ePHEx8fz/jx45k8eTLff/99ldVZCCFqSnnrJctuQBVncsJ1cHAwtPgaN26MnZ0d169fN5xv0aIFv//+u/kjpHD+7yuvvMLGjRtRKpWG4zqdDrVazdy5cxk1ahRdunRBrVaTk5NDTEwMAOfPn+fQoUOsW7cOlUqFj48Pa9eu5eDBgyQnJwMQHR1Nbm4uarWaLl26MGrUKObMmcOmTZsMdda38ENDQ/Hy8iI0NJRHHnkEtVpdJXUWQoiaEp2iLXe9ZNkNqOJMTrienp6cO3cOgLp169K9e3d27tzJnTt3yMvLY9euXbRr165KgtQn1EcffdToeGpqKunp6fj5+RmO2dvb079/f06ePAkUtkwbN26MSqUylPH19cXBwcGoTL9+/bC3tzeU8ff359q1a6SmpgKFLeyi99GX0V9DCCGsxYKTN2W95CpgcsIdMWIE33zzDXl5eQCEhoZy/Phx2rdvj4eHBydPniQkJMTsAX700UdcunSJ119/vdg5/SpYjo6ORscdHR3JyMgAICMjg5YtWxqtA61QKGjVqpVRmZKuoT+nv1dZ9xFCCGvw6vEbZS5y0bCOTl6WqiSTpwXNmjWLWbNmGb4eMWIEX375Jfv27cPOzo4nnniCRx55xKzBJScns2zZMr766ivq169farn7N1XQ6XTFEuz9yiuj70our0xZGzrou6wr40E+W9vYUl3Btuorda1dvsqwY+uF+pS2XnIddLzmcZueBb9jBdU1SdHn6unp+UDXqtTCF3q+vr74+vo+UABlSUxMJCsri379+hmO5efnc/z4caKiojhx4gRQ2Ap1c3MzlMnMzDS0Rp2cnMjMzDRKjjqdjqysLKMy97dUMzMzgXstXWdn5xLL3N/qLaqyDyc5OfmBH2xtYUt1Bduqr9S19hnz43Wg9EUu3hvUnJ4Fv1tFXU1h7udaoe35qtuIESM4fvw48fHxhj8PP/wwY8eOJT4+Hg8PD5ydnYmLizN8Ji8vj4SEBMOYrY+PDzk5OSQmJhrKJCYmotVqjcokJCQYussB4uLiaN26tWFcum/fvkb30ZcpOjYshBC1WVkrSsm47YMrtYU7cuTICl9MoVCwf//+BwqoKKVSafRWMkCjRo1o3rw5Xbp0AWD69OmsXr0aT09PPDw8WLVqFQ4ODowbNw4ALy8vhgwZQkhICOvXr0en0xESEsKwYcMMv7mMGzeOiIgIgoODCQ0N5eLFi6xbt4758+cbWsXTpk1j+PDhrFmzhieffJLPP/+c+Ph4YmNjzVZfIYSoKa8eL32VQFnkwjxKTbgFBQVljk+W5P6FIqrDnDlzyM3NZd68eWg0Gnr37s2ePXto0uTeK+ubN29mwYIFjBkzBoCAgABWrFhhON+sWTP27t1LaGgogwcPRqlUMmPGDGbOnGkoo1KpiIqKIiwsjPDwcDp06EBUVBR9+vSpvsoKIUQVuLfXbclkvWTzUGg0murPkqJM1jIeZApbqivYVn2lrrWDfoGLsubcaqa0Mfy9Nte1omxqDFcIIUTVMWWBC1lRynxMfkv5ypUrJpWT7fmEEKJ2KG+BCwWyopQ5mZxwe/ToYdKYrmzPJ4QQlq+8BS4UyNituZmccN95551iCTc/P5/U1FR27tyJk5MTgYGBZg9QCCGEeZX3kpSdAt4dqJRka2YmJ9yJEyeWem7u3Ln4+fmRk5NjlqCEEEJUndL2uNWTZFs1zPLSVOPGjZk4cSKbNm0yx+WEEEJUkfK6kmWBi6pjtreU69Wrx7Vr18x1OSGEEGZWXleyLHBRtcyScM+cOcO7776Ll5eXOS4nhBCiCpTXlSwvSVWtB35L+ebNm9y6dYvGjRsTGRlp1uCEEEI8uOgULQtO3OTG7bK7klf3b1GNUdkekxPugAEDiiVchUKBUqmkY8eOjB07tti6x0IIIWrWq8dvEHU+t8yWrXQlVw+TE65ara7KOIQQQpiZfsy2vPV7pSu5elR6DDc7O5vs7GxzxiKEEMKMyhuzBelKrk4VSriXLl0iKCiIDh060K5dO9q1a0eHDh0ICgri4sWLVRWjEEKICipv+g+AvZ1CupKrkcldyj/88AOjR48mNzcXPz8/PDw80Ol0pKSksHfvXr766iv27t0r29UJIUQNK2/6D0CLBgoiVM2kK7kamZxwFy5cSOPGjYmLi6NTp05G5y5evMjIkSN57bXX+Prrr80epBBCCNOV15X8spe9dCPXAJO7lH/++WcCAwOLJVsADw8PAgMDOXPmjFmDE0IIYbroFC0dt/1e7kpSkmxrhsktXCcnJ+rUKT0/29nZ4ezsbJaghBBCVIxM/7F8JrdwZ8yYwZYtW0hLSyt27rfffmPLli3MmDHDrMEJIYQo36vHb7BVpv9YPJNbuLm5uTRr1ow+ffowfPhwOnbsCEBKSgqxsbF06tSJv/76i/Xr1xs+o1AomD17tvmjFkIIAdxLtuWRruSaZ3LCXbp0qeHve/fuLXb+7NmznD171uiYJFwhhKg6piZbmf5jGUxOuD/99FNVxiGEEKICTG7ZyvQfi2FywnV3d6/KOIQQQpTDlE0IipLpP5bF5ISrl52dzdGjR/ntt9+AwkT8yCOP0KRJE7MHJ4QQopCpLVo9SbaWp0IJ97333iMsLAytVotOd+83LAcHBxYvXszUqVPNHqAQQtg6SbbWweSEu3PnThYuXEjv3r2ZPn06Xl5e6HQ6Lly4wLvvvsuiRYto3rw5zzzzTFXGK4QQNiE6RcuyH7K5os2v0Ock2Vouk+fhRkZGolKpiI2NZezYsXTr1o3u3bszduxYvvrqK3x8fNi4caNZg1uzZg2DBw+mbdu2dOrUiQkTJnDu3DmjMjqdjvDwcLy9vXFxcWHEiBH88ssvRmU0Gg1BQUG4u7vj7u5OUFAQGo3GqMzZs2cZPnw4Li4udO7cmYiICKNWPMC+fftQqVQ4OTmhUqk4cOCAWesrhBDRKVrafHyVV45oKpRsFUiytXQmJ9zk5GTGjBlD3brFG8V169ZlzJgxZt8x6OjRo7z88sscPHiQ/fv3U7duXZ5++mn+/PNPQ5n169cTGRlJREQEhw8fxtHRkdGjRxttHRgYGEhSUhLR0dHExMSQlJRk1P1969YtRo8ejZOTE4cPH2b58uVs3LiRd955x1AmMTGRl156ifHjxxMfH8/48eOZPHky33//vVnrLISwXa8ev8ErRzRUsFFLiwYK3h+klGRr4UzuUnZwcCA9Pb3U8+np6TRq1MgsQent2bPH6Ov33nsPd3d3Tpw4QUBAADqdDrVazdy5cxk1ahQAarUaT09PYmJimDJlCufPn+fQoUPExsaiUqkAWLt2LQEBASQnJ+Pp6Ul0dDS5ubmo1Wrs7e3p0qULFy5cYNOmTcycOROFQoFarWbgwIGEhoYC4OXlRXx8PGq1mq1bt5q13kII2xKdomXusYonWoe6Ctb1lyk/tYXJLVw/Pz/ee+894uPji507evQo77//Pv7+/mYN7n45OTkUFBSgVCoBSE1NJT09HT8/P0MZe3t7+vfvz8mTJ4HClmnjxo0NyRbA19cXBwcHozL9+vXD3t7eUMbf359r166RmpoKwKlTp4zuoy+jv4YQQphKv8mA8oOrKD+4WqlW7cte9lz9H1dJtrWIyS3cJUuWcPz4cUaNGkWPHj34xz/+AcCFCxdISkqidevWLFmypMoChcItArt3746Pjw+AocXt6OhoVM7R0ZFr164BkJGRQcuWLVEoFIbzCoWCVq1akZGRYSjj6upa7Br6c+3btyc9Pb3E++ivIYQQpansC1AlUVC4JrJ0H9c+JidcNzc34uPjWbNmDV9//TX79+8HCufhzpgxg5CQEFq0qLp/AK+99honTpwgNjYWOzs7o3NFkykUvkh1f4K9X3ll9C9MlVempGvrJScnl3quPA/y2drGluoKtlVfW63rVxl2rEqpx638oj8fSv9ZUb7Cn0cuDXQEt7tDgONfJCdnPcD1HoytPldPT88HulaF5uG2aNGCsLAwwsLCHuimFbVo0SL27NnDgQMHaN++veG4fjvAjIwM3NzcDMczMzMNrVEnJycyMzONkqNOpyMrK8uozP0t1czMTOBeS9fZ2bnEMve3eouq7MPRjy3bAluqK9hWfW2lrtEpWl49doNb+SaP0FVI4Tit0mK6jm3luYL561ruv5C///6bvXv3snbtWj766COuX79utpubYsGCBcTExLB//35DN7Zeu3btcHZ2Ji4uznAsLy+PhIQEw5itj48POTk5JCYmGsokJiai1WqNyiQkJJCXl2coExcXR+vWrWnXrh0Affv2NbqPvkzRsWEhhHW7f+xVP/5aVclWxmmtS5kt3PT0dIYPH85///tfQxdro0aN2L17NwMGDKjy4EJDQ9m1axf/+te/UCqVhjFbBwcHGjdujEKhYPr06axevRpPT088PDxYtWoVDg4OjBs3Dih8m3jIkCGEhISwfv16dDodISEhDBs2zPCby7hx44iIiCA4OJjQ0FAuXrzIunXrmD9/vqFVPG3aNIYPH86aNWt48skn+fzzz4mPjyc2NrbKvw9CiOpVdMxVAeXuM2tusuGAdSoz4YaFhXH58mWCg4MZNGgQly5dYuXKlcyfP59jx45VeXBbtmwBMEz50VuwYAGLFi0CYM6cOeTm5jJv3jw0Gg29e/dmz549Rms7b968mQULFjBmzBgAAgICWLFiheF8s2bN2Lt3L6GhoQwePBilUsmMGTOYOXOmoYxKpSIqKoqwsDDCw8Pp0KEDUVFR9OnTp8rqL4SoPqVtDFBdyVaSrPVTaDSaUv89de3alcGDBxstALFnzx4CAwM5c+YMbdq0qZYgbY2MkVgvW6pvbairOd8erozamGRrw3M1F3PXtdwu5fvHKH19fdHpdKSlpUnCFULUWtEpWmYfu0lufvV1GNfGBCvMp8yEm5+fT8OGDY2O6b8u+oKREELUJtEpWqYe0VBQRdevAxQAbR3seLN3E0mwAjBhWtDly5f54YcfDF/funULKGxqN27cuFj53r17mzE8IYQwr4pudWcaHS0a1JHWqyhTuQk3PDyc8PDwYsfnz59v9LV+nuuNGzfMF50QQpiRuZLt/V3DtjSuKSqvzIQbGRlZXXEIIUSVqkyylTFXYU5lJtznn3++uuIQQogqE52iJcrEZCvjrqKqVGhpRyGEqE1Km1tbkvp1IPIRy1lCUVgfSbhCCKtT0f1lZV9ZUR0k4QohrEp0ipbgeA13TJxe+7JsdSeqiSRcIYRVWfZDtknJVvaVFdVNEq4QotaryFgtgJ0C3h0o47WieknCFULUahWd7iMvR4maUjWbOAohRDWoaLJ1qKuQZCtqjLRwhRC1UkWT7eZBkmhFzZIWrhCi1qlosm3rYCfJVtQ4aeEKIWqNir4cBWBvp+DN3k2qMCohTCMJVwhh8Sq6kIUC0CHLNArLIglXCGHRKtp9LAtZCEslCVcIYXGiU7Qs+yGbK6Y2af8/SbbCkknCFUJYjIp2HevJqlGiNpCEK4SocZVNtCB71oraQxKuEKJGVLbbuCjpQha1iSRcIUS1e/X4DaLO52L65B5jsp2eqI0k4QohqkVl5tDeT8ZqRW0mCVcIUWXMkWT1ZE6tqO0k4VbQli1b2LBhA+np6Xh7exMeHk7//v1rOiwhalTR8Vj9ohNgD2ge6LrSdSysiSTcCtizZw8LFy5k9erV+Pr6smXLFsaPH8+JEydo27ZtTYcnRKWYsxUKFBmXVVT6GpJohTVSaDQa8/wvswH+/v507dqVDRs2GI716tWLUaNGsWTJErPdZ8OxS6y73NBsPwAtm44H+cFc+9hafSumtnYbJycn4+npWdNhVAupa+VJC9dEt2/f5vTp08yaNcvouJ+fHydPnjTbfaJTtCy7UJ+7lX5/s7axteRja/U1jUzvEbZAEq6JsrKyyM/Px9HR0ei4o6MjGRkZZrvPsh+yuSs/lIWNkEUrhC2RhFtBCoVxMtTpdMWO6SUnJ1f4+mlae6QVJKyTDvs6sMjjNgFORRa7KNBSif8qFqcy/99rK1ut64N2L0vCNVHLli2xs7Mr1prNzMws1urVq8zDcfvx+gOtvCOE5bg3Xl3YklVabUtWxjWtk7nrWsdsV7Jy9evXp2fPnsTFxRkdj4uLQ6VSme0+b/ZuQl2bGb8V1qZFAwWbBynRTGnDqUdy0Uxpg2ZKGy4972q1yVYIU0kLtwJmzJjB1KlT6d27NyqViqioKK5fv86UKVPMdo/xnRy4dj1d3lK2WpZZXxlLFaLqScKtgDFjxnDjxg1WrlxJeno6nTt3Zvfu3bi7u5v1PgFO+cwe4GrWa1oqW+qeAturrxDiHkm4FRQYGEhgYGBNhyGEEKKWkTFcIYQQohpIwhVCCCGqgSRcIYQQohrIWspCCCFENZAWrhBCCFENJOEKIYQQ1UASrhBCCFENJOEKIYQQ1UASrhBCCFENJOFamC1bttCjRw+cnZ159NFHOX78eE2H9MDCw8NRKpVGf/7xj38Yzut0OsLDw/H29sbFxYURI0bwyy+/1GDEpjt27BjPPvssnTt3RqlUsm3bNqPzptRNo9EQFBSEu7s77u7uBAUFodFoqrMaJimvrtOnTy/2nIcMGWJU5u+//2bevHl07NgRV1dXnn32Wa5evVqd1TDJmjVrGDx4MG3btqVTp05MmDCBc+fOGZWxlmdrSl2t5dlu3ryZ/v3707ZtW9q2bcvjjz/OwYMHDeer+plKwrUge/bsYeHChbz66qscOXIEHx8fxo8fz5UrV2o6tAfm6enJ+fPnDX+K/iKxfv16IiMjiYiI4PDhwzg6OjJ69Giys7NrMGLTaLVaunTpwvLly7G3ty923pS6BQYGkpSURHR0NDExMSQlJTF16tTqrIZJyqsrwGOPPWb0nKOjo43OL1q0iAMHDrB161a+/PJLsrOzmTBhAvn5lrUl5dGjR3n55Zc5ePAg+/fvp27dujz99NP8+eefhjLW8mxNqStYx7N1dXXlrbfe4rvvviMuLo5BgwYxceJEfv75Z6Dqn6nMw7Ug/v7+dO3alQ0bNhiO9erVi1GjRrFkyZIajOzBhIeHs3//fhISEoqd0+l0eHt788orrxAaGgpAbm4unp6e/N///Z9Zd2Kqam3atGHFihVMnDgRMK1u58+fR6VSERsbi6+vLwAJCQkEBARw6tQpi93o4P66QmEr6MaNG+zatavEz9y8eRMPDw8iIyN55plnAEhLS6N79+7ExMTg7+9fLbFXRk5ODu7u7mzbto2AgACrfrb31xWs+9m2b9+eJUuWMHny5Cp/ptLCtRC3b9/m9OnT+Pn5GR338/Pj5MmTNRSV+Vy+fJnOnTvTo0cPXnrpJS5fvgxAamoq6enpRvW2t7enf//+tb7eptQtMTGRxo0bG+2p7Ovri4ODQ62sf0JCAh4eHvTu3ZvZs2fzxx9/GM6dPn2aO3fuGH0/3Nzc8PLysvi65uTkUFBQgFKpBKz72d5fVz1re7b5+fl8+umnaLVafHx8quWZym5BFiIrK4v8/HwcHR2Njjs6OpKRkVFDUZlHnz592LRpE56enmRmZrJy5UqGDh3KNgQ4OAAACUZJREFUiRMnSE9PByix3teuXauJcM3GlLplZGTQsmVLFIp7e+QqFApatWpV6577kCFDGDlyJO3ateO3334jLCyMp556im+//ZYGDRqQkZGBnZ0dLVu2NPpcbfg3vnDhQrp3746Pjw9g3c/2/rqCdT3bs2fPMnToUPLy8nBwcOBf//oXXbt2NSTMqnymknAtTNEHCYXdkvcfq20ef/xxo6/79OlDz5492b59O3379gWss9565dWtpHrWxvqPHTvW8PeuXbvSs2dPunfvzsGDB3nqqadK/Zyl1/W1117jxIkTxMbGYmdnZ3TO2p5taXW1pmfr6elJfHw8N2/eZP/+/UyfPp3PP//ccL4qn6l0KVuIli1bYmdnV+y3pMzMzGK/cdV2jRs3xtvbm0uXLuHs7AxglfU2pW5OTk5kZmai0917lUKn05GVlVXr69+6dWtcXV25dOkSUFjX/Px8srKyjMpZ8rNetGgRn376Kfv376d9+/aG49b4bEura0lq87OtX78+HTt25OGHH2bJkiV0796dTZs2VcszlYRrIerXr0/Pnj2Ji4szOh4XF2c0XmAN8vLySE5OxtnZmXbt2uHs7GxU77y8PBISEmp9vU2pm4+PDzk5OSQmJhrKJCYmotVqa339s7KyuHbtmuEHWc+ePalXr57R9+Pq1auGF1EszYIFC4iJiWH//v1G09jA+p5tWXUtSW1/tkUVFBRw+/btanmmdgsXLlxq9hqISmnSpAnh4eG4uLjQsGFDVq5cyfHjx3nnnXdo1qxZTYdXaW+88Qb169enoKCAixcvMm/ePC5dusTatWtRKpXk5+ezdu1aPDw8yM/P5/XXXyc9PZ1169bRoEGDmg6/TDk5Ofz666+kp6fzySef0KVLF5o2bcrt27dp1qxZuXVr1aoV33//PTExMfTo0YOrV68SEhJCr169LG76SFl1tbOzY9myZTRu3Ji7d+9y5swZZs2aRX5+PitXrqRBgwY0bNiQ69evs3nzZrp168bNmzcJCQmhadOmvPXWW9SpYzm//4eGhrJz504+/PBD3Nzc0Gq1aLVaoPCXY4VCYTXPtry65uTkWM2zXbp0qeFn0dWrV1Gr1ezevZulS5fSqVOnKn+mMi3IwmzZsoX169eTnp5O586defvttxkwYEBNh/VAXnrpJY4fP05WVhatWrWiT58+vP7663h7ewOFXTLLly/nww8/RKPR0Lt3b1atWkWXLl1qOPLyxcfHM3LkyGLHn3vuOdRqtUl1+/PPP1mwYAFfffUVAAEBAaxYsaLYW6I1ray6rlmzhokTJ5KUlMTNmzdxdnZm4MCBvP7667i5uRnK5uXlsXjxYmJiYsjLy2PQoEGsXr3aqIwlKO17v2DBAhYtWgSY9u+2Njzb8uqam5trNc92+vTpxMfHk5GRQdOmTenatSuzZ882TFuq6mcqCVcIIYSoBpbRzhdCCCGsnCRcIYQQohpIwhVCCCGqgSRcIYQQohpIwhVCCCGqgSRcIYQQohpIwhVCCCGqgSRcIWzQtm3bUCqVhj/Ozs54e3szZswY3n33XaMNt4UQ5iG7BQlhwxYuXEiHDh24c+cOGRkZHD16lEWLFhEZGcmOHTvo1q1bTYcohNWQhCuEDfP39zdskQjwv//7v3z33Xc8++yzPPfccyQmJmJvb1+DEQphPaRLWQhh5NFHH2XevHlcuXKF3bt3A/Dzzz8zffp0evbsibOzM506deLll18mLS3N8LmUlBSUSiWRkZHFrvnrr7+iVCp5//33Abh79y4rV66kd+/euLi40LFjR4YOHcq+ffuqp5JC1ABJuEKIYiZMmADA4cOHgcJtIpOTk3nmmWdYsWIFL7zwAt988w0jR44kNzcXgE6dOvH/2ru/V/b+OA7gTx/RodmYGzUjU5QLSkKLmwnjBsVycseVJLvYxfgHbCkXU7ggxYXNLhgXcjEiPy5IW2lciJvtitbZWjPRfC5kfdbUt2/5HOrzfNztnNf73Xnd7NXrfbZeTU1NcLlcGfu5XC7k5OSkBpnbbLbUYA673Q6LxQKdToeLiwuZMiSSH4+UiSiDRqOBUqnE/f09AGBkZATj4+NpMUajEV1dXdjZ2YHJZALwPjnIbDbj5uYmbRqU2+1GW1sbiouLAQB7e3vo6OiAw+GQMSui78UOl4g+pVAoEIvFAAD5+fmp67FYDOFwGFVVVVCpVPD5fKl7fX19EAQhrcs9Pj5GMBjE4OBg6lpBQQGur69xe3srQyZEPwMLLhF9KhaLQaFQAAAkSYLZbEZFRQVKS0uh0+lQWVmJSCSCSCSSWqNSqdDd3Q232423t/fJnxsbG1AqlTAajam4yclJRCIRNDQ0oLm5GVNTU7i8vJQ3QSKZseASUYZQKIRoNAqdTgcAGB4ehtPpxMjICFZXV7G5uYmtrS2o1Wokk8m0taIoIhgM4uTkBM/Pz/B4POjt7YUgCKmY1tZW+P1+LCwsoLa2Fk6nE21tbZidnZU1TyI58R0uEWX4OBI2GAyQJAn7+/uwWq2wWq2pmEQiAUmSMtYaDAaUlJTA5XLh8fER0Wg09SOsPxUWFkIURYiiiKenJ/T398Nut2NiYgLZ2dl/Lzmib8IOl4jSHB4eYmZmBuXl5TCZTPj16/1r4uOI+MP8/HxGdwsA2dnZGBgYgMfjwdraGsrKyqDX69NiwuFw2ue8vDxUV1fj+fkZ8Xj8izMi+hnY4RL9w7xeL+7u7vD6+oqHhwccHR3h4OAAWq0W6+vrEAQBgiCgpaUFDocDLy8v0Gq1ODs7w+npKdRq9af7iqKIubk5eL1eWCwWZGVlpd1vbGyEXq9HfX091Go1rq6usLq6is7OThQUFMiROpHsWHCJ/mE2mw0AkJubi6KiItTU1GB6ehpDQ0NphW9paQlWqxUrKyt4fX2FXq/H9vY2enp6Pt23pqYGdXV18Pv9nx4nj46OYnd3F0dHR0gkEtBoNDCbzTCbzX8nUaIfIEuSpLf/DiMi+n/a29uRTCbh9Xq/+1GIfgS+wyWiLxcIBHB+fg5RFL/7UYh+DHa4RPRlAoEAfD4fFhcXEQqF4Pf7U//lJfrXscMloi/j8XgwNjaGeDyO5eVlFluiP7DDJSIikgE7XCIiIhmw4BIREcmABZeIiEgGLLhEREQyYMElIiKSAQsuERGRDH4DMEUQZcIPQhYAAAAASUVORK5CYII=)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zh9br2YV4rCk"
      },
      "source": [
        "**Step-3: Sigmoidal Function:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwxBv0SN4zfd"
      },
      "source": [
        "from scipy.optimize import curve_fit\n",
        "import pylab\n",
        "\n",
        "\n",
        "def sigmoid(x,c,a,b):\n",
        "     y = c*1 / (1 + np.exp(-a*(x-b)))\n",
        "     return y\n",
        "\n",
        "xdata = np.array([1, 2, 3,4, 5, 6, 7])\n",
        "ydata = np.array([0, 0, 13, 35, 75, 89, 91])\n",
        "\n",
        "popt, pcov = curve_fit(sigmoid, xdata, ydata, method='dogbox',bounds=([0.,0., 0.],[100,2, 10.]))\n",
        "print(popt)\n",
        "\n",
        "x = np.linspace(-1, 10, 50)\n",
        "y = sigmoid(x, *popt)\n",
        "\n",
        "pylab.plot(xdata, ydata, 'o', label='data')\n",
        "pylab.plot(x,y, label='fit')\n",
        "pylab.ylim(-0.05, 105)\n",
        "pylab.legend(loc='best')\n",
        "pylab.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ykJS05a-ZqQ"
      },
      "source": [
        "![sigmoidalFunction.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wi1e56oW4-28"
      },
      "source": [
        "Sigmoid function,\n",
        "Here is a snap of how I learnt to fit Sigmoid Function - y = c/(1+np.exp(-a*(x-b))) and 3 coefficients [c, a, b]:\n",
        "\n",
        "* c - the maximum value (eventual maximum infected people, the sigmoid scales to this value eventually)\n",
        "* a - the sigmoidal shape (how the infection progress. The smaller, the softer the sigmoidal shape is)\n",
        "* b - the point where sigmoid start to flatten from steepening (the midpoint of sigmoid, when the rate of increase start to slow down)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eesAsKPG5J61"
      },
      "source": [
        "**Step-4: Since our dataset doesn't have recovered cases column so I am gonna import and read next dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yX9t4Mo85Dcb"
      },
      "source": [
        "df2 = pd.read_csv('s3://sagemaker-studio-im065hy7nj/time_series_covid19_recovered_global.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ycou-Dxz5fWQ"
      },
      "source": [
        "# Extract list of values of 'recovered' column and insert that column to our first data-subset with proper arrangement. \n",
        "# Since few pairs of row doesn't exist in column of new dataset, so we gonna manually add values referencing other sites.\n",
        "list(df2.iloc[172, 4:])\n",
        "input_values = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 4, 4, 4, 7, 10, 11, 12, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 22, 22, 31, 31, 31, 33, 33, 35, 35, 36, 36, 36, 36, 37, 45, 49, 70, 70, 87, 112, 155, 183, 187, 206, 219, 220, 221, 266, 278, 290, 333, 365, 467, 488, 584, 674, 861, 877, 913, 974, 1041, 1158, 1167, 1186, 1402, 1578, 1772, 2148, 2224, 2338, 2650, 2698, 2834, 3013, 3134, 3194, 4656, 5320, 6143, 6415, 6547, 6811, 7499, 7752, 7891, 8011, 8442, 8589, 10294, 10328, 11025, 11249, 11534, 11637, 11695, 11868, 12477, 12684, 12840, 12947, 13053, 13128, 13754, 13875, 14021, 14248, 14399, 14492, 14603, 14961, 15026, 15156, 15389, 15814, 16313, 16353, 16493, 16664, 16728, 16837, 17077, 17201, 17335, 17495, 17580, 17700, 17964, 18214, 18350, 18631, 18806, 19119, 19504, 20073, 20242, 20555, 20822, 21410, 22178, 23290, 24207, 25561, 27127, 28941, 30677, 32964, 33882, 35700, 36672, 37524, 38697, 39576, 40638, 41706, 42949, 43820, 45267, 46233, 47238, 48061, 49954, 50411, 51866, 53013, 53898, 54640, 55371, 56428, 57389, 60696, 62740, 64069, 65202, 67542, 68668, 71343, 73023, 74252, 75804, 77277, 78780, 80954]\n",
        "len(input_values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcfVHxQM7YeB"
      },
      "source": [
        "**step-5: Creating new subset with required columns and rows only:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGqtFtee7CaB"
      },
      "source": [
        "in_df1 = df1[df1['location']=='Nepal'].groupby('date')[['total_cases', 'new_cases', 'total_deaths', 'new_deaths', 'total_tests']].sum().reset_index(False)\n",
        "in_df1['recovered']= np.array(input_values).copy()\n",
        "in_df1['Active']=in_df1['total_cases']-in_df1['new_deaths']-in_df1['recovered']\n",
        "# in_df1 = in_df1[in_df1.Active>=20]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYcPqg9K7JE3"
      },
      "source": [
        "in_df1.head(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnIS0R8w7OUf"
      },
      "source": [
        "in_df1.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSC-5s_N9aBh"
      },
      "source": [
        "**Step-6: Sigmoidal fitting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBhSsatj7PDr"
      },
      "source": [
        "in_df1['day_count'] = list(range(1, len(in_df1)+1))\n",
        "in_df1['increase'] = (in_df1.total_cases-in_df1.total_cases.shift(1))\n",
        "in_df1['rate'] = (in_df1.total_cases-in_df1.total_cases.shift(1))/in_df1.total_cases\n",
        "\n",
        "\n",
        "def sigmoid(x,c,a,b):\n",
        "     y = c*1 / (1 + np.exp(-a*(x-b)))\n",
        "     return y\n",
        "\n",
        "xdata = np.array(list(in_df1.day_count)[::2])\n",
        "ydata = np.array(list(in_df1.total_cases)[::2])\n",
        "\n",
        "# population=29136808\n",
        "# popt, pcov = curve_fit(sigmoid, xdata, ydata, method='dogbox',bounds=([0., 0., 0.], [population, 6, 180.]))\n",
        "# print(popt)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGQ7MyWf88rg"
      },
      "source": [
        "**Step-7: Prediction Using Mannual sigmoidal fitting:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUGk1cBw7SvO"
      },
      "source": [
        "import pylab\n",
        "\n",
        "est_a = 145000\n",
        "est_b = 0.04\n",
        "est_c = 270\n",
        "x = np.linspace(-1, Nepal_df.day_count.max()+50, 50)\n",
        "y = sigmoid(x,est_a,est_b,est_c)\n",
        "pylab.plot(xdata, ydata, 'o', label='data')\n",
        "pylab.plot(x,y, label='fit',alpha = 0.8)\n",
        "pylab.ylim(-0.05, est_a*1.05)\n",
        "pylab.xlim(-0.05, est_c*2.05)\n",
        "pylab.legend(loc='best')\n",
        "plt.xlabel('days from day 1')\n",
        "plt.ylabel('Infection Cases')\n",
        "plt.title('Nepal')\n",
        "pylab.show()\n",
        "\n",
        "print('model start date:',Nepal_df[Nepal_df.day_count==1].index[0])\n",
        "print('model start infection:',int(Nepal_df[Nepal_df.day_count==1].total_cases[0]))\n",
        "print('model fitted max infection at:',int(est_a))\n",
        "print('model sigmoidal coefficient is:',round(est_b,2))\n",
        "print('model curve stop steepening, start flattening by day:',int(est_c))\n",
        "print('which is date:', Nepal_df[Nepal_df.day_count==int(est_c)].index[0])\n",
        "print('model curve flattens by day:',int(est_c)*2)\n",
        "\n",
        "display(Nepal_df.head(3))\n",
        "display(Nepal_df.tail(3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GamUrd4S-9rG"
      },
      "source": [
        "![outputsigmoidal.png](data:image/png;base64,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)\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYLbmyLGAFE_"
      },
      "source": [
        "**Nepal,**\n",
        "\n",
        "- The b coefficient is 270, which means that the model starts to flatten 270 days,\n",
        "after 25th of September, and really flattern significantly after 540 days.\n",
        "\n",
        "- The c coefficient is 145000, which is the predicted amount of infected people.\n",
        "\n",
        "- The a coefficient is 0.04 is smaller than China's 0.22, which means the sigmoid is even softer in China. This means that Italy will take even longer than China to fight Covid-19."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9R0liPx7y_0"
      },
      "source": [
        "From this, its seen that in Nepal if the graph goes like that:\n",
        "\n",
        "max Active case: 145000,\n",
        "\n",
        "curve stop steepening, start flattening by day: 270,\n",
        "\n",
        "which is: 2020-09-25,\n",
        "\n",
        "curve flattens by day: 540"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njcn7I49B2p2"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "# **Linear Regression and Random Forest for prediction:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uski9iSHCOHm"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ON0n68PAFWcb"
      },
      "source": [
        "**Step-1:** Load dataset from - https://covid.ourworldindata.org/data/owid-covid-data.csv\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou7AYLnmCUBn"
      },
      "source": [
        "df = pd.read_csv('s3://sagemaker-studio-im065hy7nj/owid-covid-data.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKJ54LBfCaL3"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0khsKMcCdAC"
      },
      "source": [
        "df.info"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5vBtaElCc1m"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiJjNGK-Cggq"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chy1Bpa0Frp9"
      },
      "source": [
        "**Step-2:** Subsetting only those rows that have \"NPL\" in the \"location\" column and plot y-axis total_cases over x-axis as day-count.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1yET6ggCnWI"
      },
      "source": [
        "df=df[df['location']=='Nepal']\n",
        "df.head()\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73Aza4AQCu92"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SGVBzM2CxAW"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2LkZ1gACy0W"
      },
      "source": [
        "cols = ['total_cases', 'new_cases', 'total_deaths', 'new_deaths', 'total_cases_per_million', 'new_cases_per_million',\n",
        "           'new_deaths_per_million', 'new_tests', 'total_tests', 'total_tests_per_thousand', 'new_tests_per_thousand',\n",
        "           'population', 'population_density', 'median_age', 'aged_65_older', 'aged_70_older', 'gdp_per_capita', 'extreme_poverty',\n",
        "           'cardiovasc_death_rate', 'diabetes_prevalence', 'handwashing_facilities', 'hospital_beds_per_thousand',\n",
        "           'life_expectancy', 'human_development_index'\n",
        "          ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0tdf1GHhF4jG"
      },
      "source": [
        "**Step-3:** Univariate Analysis:\n",
        "\n",
        "a. Draw histograms of each numerical variable \n",
        "\n",
        "b. Find mean, median and mode of each column "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8kZc-QiC0jo"
      },
      "source": [
        "for i in cols:\n",
        "  sns.distplot(df[i],kde=False,hist=True,bins=11,hist_kws=dict(edgecolor=\"k\", linewidth=1))\n",
        "  plt.title(\"Histogram\")\n",
        "  plt.ylabel(\"Frequency\")\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFHtkFjaDfqv"
      },
      "source": [
        "# Plotting Boxplots\n",
        "\n",
        "for i in cols:\n",
        "  sns.boxplot(y=i, data = df)\n",
        "  #sns.boxplot(y=i, data = df)\n",
        "  plt.title(\"Boxplot 11\")\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khVS7gxgDjSd"
      },
      "source": [
        "df.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xgc5gc7aDmT9"
      },
      "source": [
        "df.mode()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flMXVmsPDlUf"
      },
      "source": [
        "df.median()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZFpE83xGFh1"
      },
      "source": [
        "**Bivariate Analysis:\n",
        "\n",
        "a. Draw scatter plots of each numerical column versus one another \n",
        "b. Draw line plots of each numerical column versus one another"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PrBcpt6DpCd"
      },
      "source": [
        "# Plotting ScatterPlots\n",
        "\n",
        "for i in cols:\n",
        "    sns.scatterplot(x='total_cases', y=i, data=df)\n",
        "    plt.title(\"Scatter Plot\")\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqEGK79iDxS2"
      },
      "source": [
        "# Plotting Lineplots\n",
        "\n",
        "for i in cols:\n",
        "    sns.lineplot(x='total_cases',y=i,data=df)\n",
        "    plt.title(\"Line Plots\")\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKgbgQ2UGRRG"
      },
      "source": [
        "**Step-5:** Handle Missing values:\n",
        "\n",
        "a. If there are null values in numerical column, replace the null values by the mean of that column  \n",
        "b. If there are null values in categorical column, replace the null values by the mode of that column \n",
        "c. If more than 50%the values in a column are null, then drop that entire column "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSEAo6ZoD09l"
      },
      "source": [
        "# fill null value with mean value\n",
        "\n",
        "df = df.fillna(df.mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7jAcY8CD_LY"
      },
      "source": [
        "# Since 'tests_units' columns is null catagorical columns so we replace them with their mode\n",
        "\n",
        "df['tests_units'].fillna(df['tests_units'].mode()[0], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YIBIh5t9EBLD"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NxKAbBFEDQ9"
      },
      "source": [
        "# For datetime convert to ordinal(since we need float value)\n",
        "\n",
        "import datetime as dt \n",
        "df[\"date\"]=pd.to_datetime(df[\"date\"]) \n",
        "df[\"date\"]=df[\"date\"].map(dt.datetime.toordinal)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ngUX7_-EFqv"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pabqdekGvUt"
      },
      "source": [
        "Step-6: Drop all categorical columns and Select “total_cases” column as the target variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aF_o9mtoEIFV"
      },
      "source": [
        "# Droping the categorical columns to prepare the dataset for training\n",
        "\n",
        "df.drop(['iso_code', 'continent','location','tests_units'], axis=1, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6pLMqhDEKA8"
      },
      "source": [
        "# Create arrays for the features and the response variable\n",
        "y = df[\"total_cases\"].values\n",
        "X = df.drop([\"total_cases\"],axis=1).values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cR2KhP-9HIWr"
      },
      "source": [
        "***Next we will train the model using LinearRegression and RandomForestRegressor as mentioned in the project.***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wR5l36YAELx4"
      },
      "source": [
        "# Train and Test splitting of data \n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_tGv_HyENn8"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Create the regressor: reg\n",
        "reg= LinearRegression(normalize=True, fit_intercept=True)\n",
        "\n",
        "# Fit the regressor to the training data\n",
        "reg.fit(X_train,y_train)\n",
        "\n",
        "# Predict on the test data: y_pred\n",
        "y_pred=reg.predict(X_test)\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(y_pred)\n",
        "\n",
        "\n",
        "#Score the model\n",
        "reg.score(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnQ45ncwHbiA"
      },
      "source": [
        "**Output: 0.9999999999982944**\n",
        "\n",
        "\n",
        "From the LinearRegression classifier we get a accuracy score of 99.99% and that was quiet good for a model .For a buisness model a accuracy percentage of more than 80% is good . So the accuracy score is quiet good for these model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTmEPI3qEY-H"
      },
      "source": [
        "# Compute and print R^2 and RMSE\n",
        "\n",
        "print(\"R^2: {}\".format(reg.score(X_test, y_test)))\n",
        "rmse = np.sqrt(mean_squared_error(y_test,y_pred))\n",
        "print(\"Root Mean Squared Error: {}\".format(rmse))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrF_Mt72HuVT"
      },
      "source": [
        "**Output:**\n",
        "\n",
        "    R^2: 0.9999999999982944\n",
        "    Root Mean Squared Error: 0.35114644816412"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G37PlcZ9Eb9U"
      },
      "source": [
        "# RMS Error is too less... 0.01 only.\n",
        "\n",
        "# Now for RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# Instantiate rf\n",
        "rf = RandomForestRegressor()\n",
        "            \n",
        "# Fit rf to the training set    \n",
        "rf.fit(X_train, y_train) \n",
        "\n",
        "# Predict on the test data: y_pred\n",
        "y_pred=rf.predict(X_test)\n",
        "\n",
        "#Score the model\n",
        "rf.score(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mxp1Yr9_JG_E"
      },
      "source": [
        "Output: \n",
        "\n",
        "    0.91785465582245"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zi8xnnrE9ZP"
      },
      "source": [
        "Accuracy score for RandomForestRegressor is 99.92% which is quite good, although less than LinearRegression model which is 99.99%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkvnY7LBEiia"
      },
      "source": [
        "# Now we can predict using our data\n",
        "\n",
        "reg.predict([[737714.0, 3556.0,3391.857, 675.0, 12.0, 13.857, 4049.723, 90.538, 116.411, 23.167, 0.412, 0.476, 14530.0, 1191514.0, 40.894, 0.499, 14755.0, 0.506, 4.934, 0.203, 76.85, 29136808.0, 204.43, 25.0, 5.809, 3.2119999999999997, 2442.804, 15.0, 260.797, 7.26, 9.5, 37.8, 47.782, 0.3, 70.78, 0.574]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNC4baEvEtW_"
      },
      "source": [
        "output: \n",
        "    \n",
        "    array([118028.49372836])\n",
        "\n",
        "Which is total cases predicted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdSgM7V1Eu5B"
      },
      "source": [
        "rf.predict([[737714.0, 3556.0,3391.857, 675.0, 12.0, 13.857, 4049.723, 90.538, 116.411, 23.167, 0.412, 0.476, 14530.0, 1191514.0, 40.894, 0.499, 14755.0, 0.506, 4.934, 0.203, 76.85, 29136808.0, 204.43, 25.0, 5.809, 3.2119999999999997, 2442.804, 15.0, 260.797, 7.26, 9.5, 37.8, 47.782, 0.3, 70.78, 0.574]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNoxN6cIJfWk"
      },
      "source": [
        "Output:\n",
        "\n",
        "    array([117825.2568475])"
      ]
    }
  ]
}