{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "#  CO2 concentration by Donato Tiano"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "#### Loading Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "As first step, I've downloaded the file and I've put it in the GitLab. I've modified the file .csv removing the header.\n",
    "Then, I've download the file via Python and printed every row of the dataset. Moreover, I've created a dataframe in order to organize better the information. \n",
    "The measure extracted from the .csv are converted in Float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Smocker Status   Age\n",
      "0        Yes  Alive  21.0\n",
      "1        Yes  Alive  19.3\n",
      "2         No   Dead  57.5\n",
      "3         No  Alive  47.1\n",
      "4        Yes  Alive  81.4\n",
      "5         No  Alive  36.8\n",
      "6         No  Alive  23.8\n",
      "7        Yes   Dead  57.5\n",
      "8        Yes  Alive  24.8\n",
      "9        Yes  Alive  49.5\n",
      "10       Yes  Alive  30.0\n",
      "11        No   Dead  66.0\n",
      "12       Yes  Alive  49.2\n",
      "13        No  Alive  58.4\n",
      "14        No   Dead  60.6\n",
      "15        No  Alive  25.1\n",
      "16        No  Alive  43.5\n",
      "17        No  Alive  27.1\n",
      "18        No  Alive  58.3\n",
      "19       Yes  Alive  65.7\n",
      "20        No   Dead  73.2\n",
      "21       Yes  Alive  38.3\n",
      "22        No  Alive  33.4\n",
      "23       Yes   Dead  62.3\n",
      "24        No  Alive  18.0\n",
      "25        No  Alive  56.2\n",
      "26       Yes  Alive  59.2\n",
      "27        No  Alive  25.8\n",
      "28        No   Dead  36.9\n",
      "29        No  Alive  20.2\n",
      "...      ...    ...   ...\n",
      "1284     Yes   Dead  36.0\n",
      "1285     Yes  Alive  48.3\n",
      "1286      No  Alive  63.1\n",
      "1287      No  Alive  60.8\n",
      "1288     Yes   Dead  39.3\n",
      "1289      No  Alive  36.7\n",
      "1290      No  Alive  63.8\n",
      "1291      No   Dead  71.3\n",
      "1292      No  Alive  57.7\n",
      "1293      No  Alive  63.2\n",
      "1294      No  Alive  46.6\n",
      "1295     Yes   Dead  82.4\n",
      "1296     Yes  Alive  38.3\n",
      "1297     Yes  Alive  32.7\n",
      "1298      No  Alive  39.7\n",
      "1299     Yes   Dead  60.0\n",
      "1300      No   Dead  71.0\n",
      "1301      No  Alive  20.5\n",
      "1302      No  Alive  44.4\n",
      "1303     Yes  Alive  31.2\n",
      "1304     Yes  Alive  47.8\n",
      "1305     Yes  Alive  60.9\n",
      "1306      No   Dead  61.4\n",
      "1307     Yes  Alive  43.0\n",
      "1308      No  Alive  42.1\n",
      "1309     Yes  Alive  35.9\n",
      "1310      No  Alive  22.3\n",
      "1311     Yes   Dead  62.1\n",
      "1312      No   Dead  88.6\n",
      "1313      No  Alive  39.1\n",
      "\n",
      "[1314 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import requests\n",
    "import pandas as pd\n",
    "CSV_URL = 'https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv'\n",
    "\n",
    "with requests.Session() as s:\n",
    "    download = s.get(CSV_URL)\n",
    "    decoded_content = download.content.decode('utf-8')\n",
    "    cr = csv.reader(decoded_content.splitlines(), delimiter=',')\n",
    "    my_list = list(cr)\n",
    "    datasetDict = {'Smocker': [],\n",
    "        'Status': [],\n",
    "        'Age': []\n",
    "        }\n",
    "    for row in my_list[1:]:\n",
    "        datasetDict['Smocker'].append(row[0])\n",
    "        datasetDict['Status'].append(row[1])\n",
    "        datasetDict['Age'].append(float(row[2]))\n",
    "        \n",
    "\n",
    "df = pd.DataFrame(datasetDict, columns = ['Smocker', 'Status','Age'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### Computation of Mortality of Smocker and Not Smocker Women"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "I'm going to tabulate the total number of women alive and dead over the period according to their smoking habits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "smockerWomen = df[df.Smocker == 'Yes']\n",
    "noSmockerWomen = df[df.Smocker == 'No']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "First of all, I will count the number of women for each groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Smocker Women: 582\n",
      "Number of No Smocker Women: 732\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Smocker Women: \" + str(len(list(smockerWomen.Status))))\n",
    "print(\"Number of No Smocker Women: \" + str(len(list(noSmockerWomen.Status))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "I'm going to compute the mortality rate of each table. The computation is very easy, I just count the number of women \"*Dead*\" for each group, subdivided by the number of women of the same group. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mortality rate of Smocker Women: 0.23883161512027493\n",
      "Mortality rate of No Smocker Women: 0.31420765027322406\n"
     ]
    }
   ],
   "source": [
    "from statistics import mean\n",
    "\n",
    "print(\"Mortality rate of Smocker Women: \" + str((list(smockerWomen.Status).count(\"Dead\"))/len(list(smockerWomen.Status))))\n",
    "print(\"Mortality rate of No Smocker Women: \" + str((list(noSmockerWomen.Status).count(\"Dead\"))/len(list(noSmockerWomen.Status))))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "The Mortality rate of the Smocker Women is lower than the No Smocker Women, but it contains less number of people."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### Mortality Rate by Age Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "I'm going to compute the mortality rate grouped by the age of the women. First of all, I'm going to subdivide the Smocker and NoSmocker Group by the age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "smockerWomen1834 = smockerWomen[smockerWomen.Age < 34]\n",
    "smockerWomen3454 = smockerWomen[(smockerWomen.Age > 34) & (smockerWomen.Age < 54)]\n",
    "smockerWomen5564 = smockerWomen[(smockerWomen.Age > 55) & (smockerWomen.Age < 64)]\n",
    "smockerWomen65 = smockerWomen[smockerWomen.Age > 65]\n",
    "\n",
    "noSmockerWomen1834 = noSmockerWomen[noSmockerWomen.Age < 34]\n",
    "noSmockerWomen3454 = noSmockerWomen[(noSmockerWomen.Age > 34) & (noSmockerWomen.Age < 54)]\n",
    "noSmockerWomen5564 = noSmockerWomen[(noSmockerWomen.Age > 55) & (noSmockerWomen.Age < 64)]\n",
    "noSmockerWomen65 = noSmockerWomen[noSmockerWomen.Age > 65]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Then, it is possible to show the mortality rate for each group of age and smocker/noSmocker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mortality rate of Smocker Women 18-34: 0.027932960893854747\n",
      "Mortality rate of Smocker Women 15-54: 0.1729957805907173\n",
      "Mortality rate of Smocker Women 55-64: 0.43859649122807015\n",
      "Mortality rate of Smocker Women 65: 0.8571428571428571\n",
      "\n",
      "Mortality rate of No Smocker Women 18-34: 0.0273972602739726\n",
      "Mortality rate of No Smocker Women 15-54: 0.09547738693467336\n",
      "Mortality rate of No Smocker Women 55-64: 0.3277310924369748\n",
      "Mortality rate of No Smocker Women 65: 0.859375\n"
     ]
    }
   ],
   "source": [
    "smockerWomen1834Mort = (list(smockerWomen1834.Status).count(\"Dead\"))/len(list(smockerWomen1834.Status))\n",
    "smockerWomen3454Mort = (list(smockerWomen3454.Status).count(\"Dead\"))/len(list(smockerWomen3454.Status))\n",
    "smockerWomen5564Mort = (list(smockerWomen5564.Status).count(\"Dead\"))/len(list(smockerWomen5564.Status))\n",
    "smockerWomen65Mort = (list(smockerWomen65.Status).count(\"Dead\"))/len(list(smockerWomen65.Status))\n",
    "\n",
    "noSmockerWomen1834Mort = (list(noSmockerWomen1834.Status).count(\"Dead\"))/len(list(noSmockerWomen1834.Status))\n",
    "noSmockerWomen3454Mort = (list(noSmockerWomen3454.Status).count(\"Dead\"))/len(list(noSmockerWomen3454.Status))\n",
    "noSmockerWomen5564Mort = (list(noSmockerWomen5564.Status).count(\"Dead\"))/len(list(noSmockerWomen5564.Status))\n",
    "noSmockerWomen65Mort = (list(noSmockerWomen65.Status).count(\"Dead\"))/len(list(noSmockerWomen65.Status))\n",
    "\n",
    "print(\"Mortality rate of Smocker Women 18-34: \" + str(smockerWomen1834Mort))\n",
    "print(\"Mortality rate of Smocker Women 35-54: \" + str(smockerWomen3454Mort))\n",
    "print(\"Mortality rate of Smocker Women 55-64: \" + str(smockerWomen5564Mort))\n",
    "print(\"Mortality rate of Smocker Women 65: \" + str(smockerWomen65Mort))\n",
    "print()\n",
    "print(\"Mortality rate of No Smocker Women 18-34: \" + str(noSmockerWomen1834Mort))\n",
    "print(\"Mortality rate of No Smocker Women 35-54: \" + str(noSmockerWomen3454Mort))\n",
    "print(\"Mortality rate of No Smocker Women 55-64: \" + str(noSmockerWomen5564Mort))\n",
    "print(\"Mortality rate of No Smocker Women 65: \" + str(noSmockerWomen65Mort))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "In the graph below is shown the difference in terms of rate mortality of each range group. It is notable the high difference of mortality rate on the middle group 35-64. Indeed, in the Smocker group the mortality rate is higher. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "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": [
    "# libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# width of the bars\n",
    "barWidth = 0.2\n",
    "\n",
    "# Bars of Smocker Women\n",
    "bars1 = [smockerWomen1834Mort,smockerWomen3454Mort, smockerWomen5564Mort, smockerWomen65Mort]\n",
    "\n",
    "\n",
    "# Bars of No Smocker Women\n",
    "bars2 =[ noSmockerWomen1834Mort, noSmockerWomen3454Mort, noSmockerWomen5564Mort,noSmockerWomen65Mort]\n",
    "\n",
    " \n",
    "# The x position of bars\n",
    "r1 = np.arange(len(bars1))\n",
    "r2 = [x + barWidth for x in r1]\n",
    " \n",
    "# Create blue bars\n",
    "plt.bar(r1, bars1, width = barWidth, color = 'blue', edgecolor = 'black', capsize=7, label='Smockers')\n",
    " \n",
    "# Create cyan bars\n",
    "plt.bar(r2, bars2, width = barWidth, color = 'cyan', edgecolor = 'black',  capsize=7, label='No Smockers')\n",
    " \n",
    "# general layout\n",
    "plt.xticks([r + barWidth for r in range(len(bars1))], ['18-34', '35-54', '54-65','65'])\n",
    "plt.ylabel('Rate Mortality')\n",
    "plt.legend()\n",
    " \n",
    "# Show graphic\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computation of Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've computed the linear regression, but the results appear similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4f4b9ac88>"
      ]
     },
     "execution_count": 162,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# print(smockerWomen5564)\n",
    "labelencoder = LabelEncoder()\n",
    "smockerWomen['Death'] = labelencoder.fit_transform(smockerWomen['Status'])\n",
    "noSmockerWomen['Death'] = labelencoder.fit_transform(noSmockerWomen['Status'])\n",
    "\n",
    "\n",
    "listApp = []\n",
    "\n",
    "listAppNoSmo = []\n",
    "\n",
    "for x in list(smockerWomen['Age']):\n",
    "    listApp.append([x])\n",
    "\n",
    "for x in list(noSmockerWomen['Age']):\n",
    "    listAppNoSmo.append([x])\n",
    "\n",
    "    \n",
    "\n",
    "model = LogisticRegression(solver='liblinear', random_state=0).fit(listApp,list(smockerWomen['Death']))\n",
    "modelNoSm = LogisticRegression(solver='liblinear', random_state=0).fit(listAppNoSmo,list(noSmockerWomen['Death']))\n",
    "\n",
    "resSmok = model.predict_proba(listApp)\n",
    "resNoSmok = model.predict_proba(listAppNoSmo)\n",
    "\n",
    "# print(resSmok[0][0])\n",
    "dictSmokeRate = {'Age':[],'SurviveSmoke':[]}\n",
    "for x in range(0,len(listApp)):\n",
    "    dictSmokeRate['Age'].append(listApp[x][0])\n",
    "    dictSmokeRate['SurviveSmoke'].append(resSmok[x][0])\n",
    "\n",
    "    \n",
    "\n",
    "dictNoSmokeRate = {'Age':[],'SurviveNoSmoke':[]}\n",
    "for x in range(0,len(listAppNoSmo)):\n",
    "    dictNoSmokeRate['Age'].append(listAppNoSmo[x][0])\n",
    "    dictNoSmokeRate['SurviveNoSmoke'].append(resNoSmok[x][0])\n",
    "    \n",
    "dfSmoke = pd.DataFrame(dictSmokeRate, columns = ['Age','SurviveSmoke'])\n",
    "dfSmoke = dfSmoke.sort_values(by=['Age'])\n",
    "\n",
    "# print(dictNoSmokeRate)\n",
    "\n",
    "dfNoSmoke = pd.DataFrame(dictNoSmokeRate, columns = ['Age','SurviveNoSmoke'])\n",
    "dfNoSmoke = dfNoSmoke.sort_values(by=['Age'])\n",
    "\n",
    "ax = dfSmoke.plot(x='Age')\n",
    "dfNoSmoke.plot(x='Age',ax=ax)\n"
   ]
  }
 ],
 "metadata": {
  "hide_code_all_hidden": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}