infecting_disease_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3EMBYHQpHTCu"
   },
   "source": [
    "**The features of the dataset are described as follows:**\n",
    "\n",
    "*F1 Date            \n",
    "F9 Temperature2  \n",
    "F2 Lat             \n",
    "F10 Albedo  \n",
    "F3 Long              \n",
    "F11 GeopotentialHS  \n",
    "F4 Temperature1    \n",
    "F12 Pressure1  \n",
    "F5 Humidity1       \n",
    "F13 Pressure2  \n",
    "F6 LWdown          \n",
    "F14 Humidity2  \n",
    "F7 SWdown          \n",
    "F15 Wind speed  \n",
    "F8 Aaerosolindex   \n",
    "Value Number of infected people*\n",
    "\n",
    "**Required**  \n",
    "Please build and evaluate two models:\n",
    "1. A regression model for the output values.\n",
    "2. A classification model for the categorized output values.  \n",
    "\n",
    "Write a report (for both parts) to explain your work and to show your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "A_yhOuMG14ta"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pYByDIi92cs8"
   },
   "outputs": [],
   "source": [
    "# !pip install patool\n",
    "# import patoolib\n",
    "# patoolib.extract_archive(\"/content/drive/MyDrive/DatasetHW1.rar\", outdir=\"/content/drive/MyDrive/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "IAv82AEB2U2o",
    "outputId": "fa6bd939-f787-40e7-e8f8-76c869054211"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>F11</th>\n",
       "      <th>F12</th>\n",
       "      <th>F13</th>\n",
       "      <th>F14</th>\n",
       "      <th>F15</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3051</td>\n",
       "      <td>3052</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>47.4914</td>\n",
       "      <td>-121.8346</td>\n",
       "      <td>4.6849</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>305.8819</td>\n",
       "      <td>305.5443</td>\n",
       "      <td>-1.6057</td>\n",
       "      <td>275.9406</td>\n",
       "      <td>35.1916</td>\n",
       "      <td>1044.5486</td>\n",
       "      <td>90351.2768</td>\n",
       "      <td>90288.1143</td>\n",
       "      <td>0.004573</td>\n",
       "      <td>90288.1143</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3389</td>\n",
       "      <td>3390</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>1.3147</td>\n",
       "      <td>0.004022</td>\n",
       "      <td>313.3366</td>\n",
       "      <td>286.8496</td>\n",
       "      <td>-1.8308</td>\n",
       "      <td>281.6387</td>\n",
       "      <td>24.9399</td>\n",
       "      <td>309.8847</td>\n",
       "      <td>98497.8600</td>\n",
       "      <td>98975.0940</td>\n",
       "      <td>0.004071</td>\n",
       "      <td>98975.0940</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390</td>\n",
       "      <td>3391</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>40.1824</td>\n",
       "      <td>116.4142</td>\n",
       "      <td>0.9599</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>310.4150</td>\n",
       "      <td>355.6478</td>\n",
       "      <td>-1.0110</td>\n",
       "      <td>283.7595</td>\n",
       "      <td>36.3201</td>\n",
       "      <td>314.4100</td>\n",
       "      <td>98271.7222</td>\n",
       "      <td>98835.0176</td>\n",
       "      <td>0.003854</td>\n",
       "      <td>98835.0176</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3391</td>\n",
       "      <td>3392</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>30.0572</td>\n",
       "      <td>107.8740</td>\n",
       "      <td>6.9995</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>325.5123</td>\n",
       "      <td>413.2223</td>\n",
       "      <td>-2.1141</td>\n",
       "      <td>282.6847</td>\n",
       "      <td>14.9820</td>\n",
       "      <td>515.6411</td>\n",
       "      <td>95626.8403</td>\n",
       "      <td>96287.3399</td>\n",
       "      <td>0.002510</td>\n",
       "      <td>96287.3399</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3392</td>\n",
       "      <td>3393</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>26.0789</td>\n",
       "      <td>117.9874</td>\n",
       "      <td>7.3174</td>\n",
       "      <td>0.005321</td>\n",
       "      <td>325.3152</td>\n",
       "      <td>515.3695</td>\n",
       "      <td>-1.7381</td>\n",
       "      <td>284.0376</td>\n",
       "      <td>14.4931</td>\n",
       "      <td>590.4265</td>\n",
       "      <td>94457.6359</td>\n",
       "      <td>95359.5988</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>95359.5988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    ID          F1       F2        F3      F4        F5  \\\n",
       "0        3051  3052  22/01/2020  47.4914 -121.8346  4.6849  0.004741   \n",
       "1        3389  3390  22/01/2020  31.8257  117.2264  1.3147  0.004022   \n",
       "2        3390  3391  22/01/2020  40.1824  116.4142  0.9599  0.004042   \n",
       "3        3391  3392  22/01/2020  30.0572  107.8740  6.9995  0.005700   \n",
       "4        3392  3393  22/01/2020  26.0789  117.9874  7.3174  0.005321   \n",
       "\n",
       "         F6        F7      F8        F9      F10        F11         F12  \\\n",
       "0  305.8819  305.5443 -1.6057  275.9406  35.1916  1044.5486  90351.2768   \n",
       "1  313.3366  286.8496 -1.8308  281.6387  24.9399   309.8847  98497.8600   \n",
       "2  310.4150  355.6478 -1.0110  283.7595  36.3201   314.4100  98271.7222   \n",
       "3  325.5123  413.2223 -2.1141  282.6847  14.9820   515.6411  95626.8403   \n",
       "4  325.3152  515.3695 -1.7381  284.0376  14.4931   590.4265  94457.6359   \n",
       "\n",
       "          F13       F14         F15  Value  \n",
       "0  90288.1143  0.004573  90288.1143      1  \n",
       "1  98975.0940  0.004071  98975.0940      1  \n",
       "2  98835.0176  0.003854  98835.0176     14  \n",
       "3  96287.3399  0.002510  96287.3399      6  \n",
       "4  95359.5988  0.006967  95359.5988      1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df=pd.read_csv('/content/drive/MyDrive/DatasetHW1.csv')\n",
    "df=pd.read_csv('Infection_Dataset_cleaned.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "3honyMWN2aLr",
    "outputId": "275dbdf9-9e2c-4292-a1e6-0a26cf266191"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
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       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>F11</th>\n",
       "      <th>F12</th>\n",
       "      <th>F13</th>\n",
       "      <th>F14</th>\n",
       "      <th>F15</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>278236.000000</td>\n",
       "      <td>2.782360e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>394671.997254</td>\n",
       "      <td>394672.997254</td>\n",
       "      <td>36.800401</td>\n",
       "      <td>-81.788603</td>\n",
       "      <td>22.269169</td>\n",
       "      <td>0.010276</td>\n",
       "      <td>373.812955</td>\n",
       "      <td>670.471084</td>\n",
       "      <td>-1.751293</td>\n",
       "      <td>294.698662</td>\n",
       "      <td>14.796148</td>\n",
       "      <td>445.720166</td>\n",
       "      <td>96622.990512</td>\n",
       "      <td>96634.110702</td>\n",
       "      <td>0.010451</td>\n",
       "      <td>96634.110702</td>\n",
       "      <td>1.037856e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105467.708348</td>\n",
       "      <td>105467.708348</td>\n",
       "      <td>9.111459</td>\n",
       "      <td>40.789082</td>\n",
       "      <td>8.607790</td>\n",
       "      <td>0.004515</td>\n",
       "      <td>45.869090</td>\n",
       "      <td>190.091631</td>\n",
       "      <td>0.771562</td>\n",
       "      <td>8.197379</td>\n",
       "      <td>5.399802</td>\n",
       "      <td>506.725057</td>\n",
       "      <td>5475.575368</td>\n",
       "      <td>5401.860377</td>\n",
       "      <td>0.004610</td>\n",
       "      <td>5401.860377</td>\n",
       "      <td>1.264460e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3051.000000</td>\n",
       "      <td>3052.000000</td>\n",
       "      <td>-42.882100</td>\n",
       "      <td>-164.035400</td>\n",
       "      <td>-25.555700</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>143.419500</td>\n",
       "      <td>3.923500</td>\n",
       "      <td>-10.923600</td>\n",
       "      <td>231.244900</td>\n",
       "      <td>6.592200</td>\n",
       "      <td>-1.696600</td>\n",
       "      <td>67185.384800</td>\n",
       "      <td>67160.147300</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>67160.147300</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>311686.500000</td>\n",
       "      <td>311687.500000</td>\n",
       "      <td>33.719000</td>\n",
       "      <td>-96.223200</td>\n",
       "      <td>16.460850</td>\n",
       "      <td>0.006448</td>\n",
       "      <td>340.538950</td>\n",
       "      <td>511.526625</td>\n",
       "      <td>-2.188500</td>\n",
       "      <td>289.258500</td>\n",
       "      <td>12.796000</td>\n",
       "      <td>135.425200</td>\n",
       "      <td>95668.107150</td>\n",
       "      <td>95799.816050</td>\n",
       "      <td>0.006471</td>\n",
       "      <td>95799.816050</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>400429.500000</td>\n",
       "      <td>400430.500000</td>\n",
       "      <td>37.752200</td>\n",
       "      <td>-88.096700</td>\n",
       "      <td>23.522600</td>\n",
       "      <td>0.009891</td>\n",
       "      <td>377.185850</td>\n",
       "      <td>714.510950</td>\n",
       "      <td>-1.677700</td>\n",
       "      <td>295.860750</td>\n",
       "      <td>14.229600</td>\n",
       "      <td>284.922600</td>\n",
       "      <td>98326.517850</td>\n",
       "      <td>98328.924250</td>\n",
       "      <td>0.010195</td>\n",
       "      <td>98328.924250</td>\n",
       "      <td>2.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>484108.250000</td>\n",
       "      <td>484109.250000</td>\n",
       "      <td>41.459900</td>\n",
       "      <td>-81.124900</td>\n",
       "      <td>28.931100</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>410.339325</td>\n",
       "      <td>832.605625</td>\n",
       "      <td>-1.265100</td>\n",
       "      <td>301.029450</td>\n",
       "      <td>15.412300</td>\n",
       "      <td>506.630600</td>\n",
       "      <td>100064.056600</td>\n",
       "      <td>99921.871825</td>\n",
       "      <td>0.014407</td>\n",
       "      <td>99921.871825</td>\n",
       "      <td>1.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>566029.000000</td>\n",
       "      <td>566030.000000</td>\n",
       "      <td>71.706900</td>\n",
       "      <td>178.065000</td>\n",
       "      <td>49.630700</td>\n",
       "      <td>0.024560</td>\n",
       "      <td>541.365300</td>\n",
       "      <td>1212.071200</td>\n",
       "      <td>5.360300</td>\n",
       "      <td>322.377600</td>\n",
       "      <td>79.314600</td>\n",
       "      <td>3429.221100</td>\n",
       "      <td>104780.254600</td>\n",
       "      <td>103915.247100</td>\n",
       "      <td>0.023407</td>\n",
       "      <td>103915.247100</td>\n",
       "      <td>1.274974e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          Unnamed: 0             ID             F2             F3  \\\n",
       "count  278236.000000  278236.000000  278236.000000  278236.000000   \n",
       "mean   394671.997254  394672.997254      36.800401     -81.788603   \n",
       "std    105467.708348  105467.708348       9.111459      40.789082   \n",
       "min      3051.000000    3052.000000     -42.882100    -164.035400   \n",
       "25%    311686.500000  311687.500000      33.719000     -96.223200   \n",
       "50%    400429.500000  400430.500000      37.752200     -88.096700   \n",
       "75%    484108.250000  484109.250000      41.459900     -81.124900   \n",
       "max    566029.000000  566030.000000      71.706900     178.065000   \n",
       "\n",
       "                  F4             F5             F6             F7  \\\n",
       "count  278236.000000  278236.000000  278236.000000  278236.000000   \n",
       "mean       22.269169       0.010276     373.812955     670.471084   \n",
       "std         8.607790       0.004515      45.869090     190.091631   \n",
       "min       -25.555700       0.000316     143.419500       3.923500   \n",
       "25%        16.460850       0.006448     340.538950     511.526625   \n",
       "50%        23.522600       0.009891     377.185850     714.510950   \n",
       "75%        28.931100       0.014019     410.339325     832.605625   \n",
       "max        49.630700       0.024560     541.365300    1212.071200   \n",
       "\n",
       "                  F8             F9            F10            F11  \\\n",
       "count  278236.000000  278236.000000  278236.000000  278236.000000   \n",
       "mean       -1.751293     294.698662      14.796148     445.720166   \n",
       "std         0.771562       8.197379       5.399802     506.725057   \n",
       "min       -10.923600     231.244900       6.592200      -1.696600   \n",
       "25%        -2.188500     289.258500      12.796000     135.425200   \n",
       "50%        -1.677700     295.860750      14.229600     284.922600   \n",
       "75%        -1.265100     301.029450      15.412300     506.630600   \n",
       "max         5.360300     322.377600      79.314600    3429.221100   \n",
       "\n",
       "                 F12            F13            F14            F15  \\\n",
       "count  278236.000000  278236.000000  278236.000000  278236.000000   \n",
       "mean    96622.990512   96634.110702       0.010451   96634.110702   \n",
       "std      5475.575368    5401.860377       0.004610    5401.860377   \n",
       "min     67185.384800   67160.147300       0.000063   67160.147300   \n",
       "25%     95668.107150   95799.816050       0.006471   95799.816050   \n",
       "50%     98326.517850   98328.924250       0.010195   98328.924250   \n",
       "75%    100064.056600   99921.871825       0.014407   99921.871825   \n",
       "max    104780.254600  103915.247100       0.023407  103915.247100   \n",
       "\n",
       "              Value  \n",
       "count  2.782360e+05  \n",
       "mean   1.037856e+03  \n",
       "std    1.264460e+04  \n",
       "min    1.000000e+00  \n",
       "25%    6.000000e+00  \n",
       "50%    2.900000e+01  \n",
       "75%    1.510000e+02  \n",
       "max    1.274974e+06  "
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    "id": "ypaQRqcC3LyV",
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       "2           14\n",
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       "4            1\n",
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   "metadata": {
    "id": "VZbh0K9d3PFP"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "AIR4j7P249Cf"
   },
   "outputs": [],
   "source": [
    "X=df.drop(columns='Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "f3BPpeDC5a0z",
    "outputId": "019f95bf-b12c-43c2-b97e-ec8f421bc426"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>F11</th>\n",
       "      <th>F12</th>\n",
       "      <th>F13</th>\n",
       "      <th>F14</th>\n",
       "      <th>F15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3051</td>\n",
       "      <td>47.4914</td>\n",
       "      <td>-121.8346</td>\n",
       "      <td>4.6849</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>305.8819</td>\n",
       "      <td>305.5443</td>\n",
       "      <td>-1.6057</td>\n",
       "      <td>275.9406</td>\n",
       "      <td>35.1916</td>\n",
       "      <td>1044.5486</td>\n",
       "      <td>90351.2768</td>\n",
       "      <td>90288.1143</td>\n",
       "      <td>0.004573</td>\n",
       "      <td>90288.1143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3389</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>1.3147</td>\n",
       "      <td>0.004022</td>\n",
       "      <td>313.3366</td>\n",
       "      <td>286.8496</td>\n",
       "      <td>-1.8308</td>\n",
       "      <td>281.6387</td>\n",
       "      <td>24.9399</td>\n",
       "      <td>309.8847</td>\n",
       "      <td>98497.8600</td>\n",
       "      <td>98975.0940</td>\n",
       "      <td>0.004071</td>\n",
       "      <td>98975.0940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390</td>\n",
       "      <td>40.1824</td>\n",
       "      <td>116.4142</td>\n",
       "      <td>0.9599</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>310.4150</td>\n",
       "      <td>355.6478</td>\n",
       "      <td>-1.0110</td>\n",
       "      <td>283.7595</td>\n",
       "      <td>36.3201</td>\n",
       "      <td>314.4100</td>\n",
       "      <td>98271.7222</td>\n",
       "      <td>98835.0176</td>\n",
       "      <td>0.003854</td>\n",
       "      <td>98835.0176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3391</td>\n",
       "      <td>30.0572</td>\n",
       "      <td>107.8740</td>\n",
       "      <td>6.9995</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>325.5123</td>\n",
       "      <td>413.2223</td>\n",
       "      <td>-2.1141</td>\n",
       "      <td>282.6847</td>\n",
       "      <td>14.9820</td>\n",
       "      <td>515.6411</td>\n",
       "      <td>95626.8403</td>\n",
       "      <td>96287.3399</td>\n",
       "      <td>0.002510</td>\n",
       "      <td>96287.3399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3392</td>\n",
       "      <td>26.0789</td>\n",
       "      <td>117.9874</td>\n",
       "      <td>7.3174</td>\n",
       "      <td>0.005321</td>\n",
       "      <td>325.3152</td>\n",
       "      <td>515.3695</td>\n",
       "      <td>-1.7381</td>\n",
       "      <td>284.0376</td>\n",
       "      <td>14.4931</td>\n",
       "      <td>590.4265</td>\n",
       "      <td>94457.6359</td>\n",
       "      <td>95359.5988</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>95359.5988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       F2        F3      F4        F5        F6        F7  \\\n",
       "0        3051  47.4914 -121.8346  4.6849  0.004741  305.8819  305.5443   \n",
       "1        3389  31.8257  117.2264  1.3147  0.004022  313.3366  286.8496   \n",
       "2        3390  40.1824  116.4142  0.9599  0.004042  310.4150  355.6478   \n",
       "3        3391  30.0572  107.8740  6.9995  0.005700  325.5123  413.2223   \n",
       "4        3392  26.0789  117.9874  7.3174  0.005321  325.3152  515.3695   \n",
       "\n",
       "       F8        F9      F10        F11         F12         F13       F14  \\\n",
       "0 -1.6057  275.9406  35.1916  1044.5486  90351.2768  90288.1143  0.004573   \n",
       "1 -1.8308  281.6387  24.9399   309.8847  98497.8600  98975.0940  0.004071   \n",
       "2 -1.0110  283.7595  36.3201   314.4100  98271.7222  98835.0176  0.003854   \n",
       "3 -2.1141  282.6847  14.9820   515.6411  95626.8403  96287.3399  0.002510   \n",
       "4 -1.7381  284.0376  14.4931   590.4265  94457.6359  95359.5988  0.006967   \n",
       "\n",
       "          F15  \n",
       "0  90288.1143  \n",
       "1  98975.0940  \n",
       "2  98835.0176  \n",
       "3  96287.3399  \n",
       "4  95359.5988  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "pUh_GOaS5b1E"
   },
   "outputs": [],
   "source": [
    "Y=df.Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8T-tULTQ5gaz",
    "outputId": "8d59402a-ebe8-4c2a-9262-c9989525fb38"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     1\n",
       "2    14\n",
       "3     6\n",
       "4     1\n",
       "Name: Value, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "oyUDDZ4M5hcB"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "h-a10dYI5zn2"
   },
   "outputs": [],
   "source": [
    "X_norm= normalize(X , axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "flBRpmKc6igy"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train , X_test , y_train , y_test = train_test_split(X_norm,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_ngMh9g460p_",
    "outputId": "9af53dd4-e642-44c4-c21c-0330577ca7db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)== len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZsRNWbdh7luo",
    "outputId": "6e35e6d7-c8f4-4aee-921d-b24f68fd508a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3333333333333333"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)/ len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "X9lLZCRq7rsS"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FRBPVkZT7_i7",
    "outputId": "1d25157b-3def-446e-c92d-cfca2092be77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_model=LinearRegression()\n",
    "tmp_model.fit(X= X_train, y= y_train) \n",
    "#tmp_model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gpo_5TIU8Jru",
    "outputId": "b80a98e1-8d02-482d-9653-44b51105842d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015185804692479543"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_model.score(X_test,y_test )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "1O8nJh7o91FX"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4eN0pjAE-hIi",
    "outputId": "03afa9d0-37e1-4ee5-cc97-626534565fa0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=20)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest= RandomForestRegressor(20)\n",
    "forest.fit(X= X_train, y= y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kxxsenbn-_4S",
    "outputId": "32c7a34f-e372-455c-f3b8-f2d2277ab596"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9920629623466443"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iYxL4KAIAP24"
   },
   "source": [
    "# using clustering methods to cluster the output into 3 categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "iY0aOkjb_0m8"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yArqdxZyAnEx",
    "outputId": "40256b90-64a1-4a17-8945-c39d48cc0730"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pca=PCA(3).fit(X_train)\n",
    "clusters=KMeans(3)\n",
    "clusters.fit(data_pca.transform(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "vb2K5s2EA328"
   },
   "outputs": [],
   "source": [
    "xlabels= clusters.predict(data_pca.transform(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YNTTqYiNBlOb",
    "outputId": "7d2a0bef-d0f9-4201-f3ad-680fd2048bdf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 1, ..., 0, 0, 2])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "eNLmeD67BVnB",
    "outputId": "5941c013-7e03-41ae-9394-092370fc2f33"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Getting unique labels\n",
    " \n",
    "u_labels = np.unique(xlabels)\n",
    " \n",
    "#plotting the results:\n",
    " \n",
    "for i in u_labels:\n",
    "    plt.scatter( X_test[xlabels == i] , X_test[xlabels == i ] , label = i )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fV_WOYaEDney"
   },
   "source": [
    "# Classification based on deviding the output range into 3 parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "KbsJUWzsDtaP"
   },
   "outputs": [],
   "source": [
    "yt_cat= pd.qcut( y_train , q=3 , duplicates='drop', labels=[0,1,2] ) #, labels=['low','medium','high'] )\n",
    "ys_cat=pd.qcut( y_test , q=3 , duplicates='drop', labels=[0,1,2] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kVAfLtz5EIAX",
    "outputId": "66b475ca-f06a-4f5d-ce1e-be9d670a7614"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154397    0\n",
       "249395    1\n",
       "156335    0\n",
       "201741    2\n",
       "235477    2\n",
       "         ..\n",
       "248028    1\n",
       "145994    2\n",
       "160431    1\n",
       "58159     1\n",
       "13948     2\n",
       "Name: Value, Length: 208677, dtype: category\n",
       "Categories (3, int64): [0 < 1 < 2]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yt_cat # y train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IUgfqp-3GZFo",
    "outputId": "cd46bacb-f584-4f5a-ac67-4a989d59082e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129403    2\n",
       "135710    2\n",
       "259096    2\n",
       "68095     2\n",
       "192122    2\n",
       "         ..\n",
       "177084    0\n",
       "37043     1\n",
       "201538    1\n",
       "199791    1\n",
       "77902     0\n",
       "Name: Value, Length: 69559, dtype: category\n",
       "Categories (3, int64): [0 < 1 < 2]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys_cat # y test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "fRLAkZc0Fz_A"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest_classifier= RandomForestClassifier(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u1ky2waeGBJG",
    "outputId": "379e88a1-1620-44af-c196-623b5cecce8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=20)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_classifier.fit(X_train,yt_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zVUOF2KTGMn3",
    "outputId": "a86efd3c-7b28-4ce7-d9c6-d5542710cee6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9159418623039434"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_classifier.score(X_test,ys_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "infecting disease.ipynb",
   "provenance": []
  },
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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