Car insurance by Elena Peña.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a data case with 20 car insurance companies values. I had to explain the total ganancies of them during one year.\n",
    "\n",
    "The variables\n",
    "- Monaten= Month\n",
    "- Total Einzahlungen = Total deposits \n",
    "- Total Schäden= Total Damages costs\n",
    "- Total Single Damage Number \n",
    "- Monaten Differenzen= Diffence between damages and depositis by mothts\n",
    "\n",
    "- Have the entrepreneurs had more in damage than in deposit?\n",
    "- Can certain effects be predicted?\n",
    "- Does the number of damages have something to do with the damage behavior.\n",
    "\n",
    "Tip, the claims ratio is an important value (claims per month/deposit per month) over 100% bad.\n",
    "\n",
    "Below is a list of individual claims per month.\n",
    "\n",
    "An entrepreneur pays per vehicle 194€ insurance per month.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic preprocessing packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization packages\n",
    "\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "plt.rcParams['figure.figsize'] = (16, 9)\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "## Packages for Linear regression\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Total Einzahlungen</th>\n",
       "      <th>Total Schäden</th>\n",
       "      <th>Total Single Damage Number</th>\n",
       "      <th>Monaten Differenzen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>April</td>\n",
       "      <td>44929.670000</td>\n",
       "      <td>35486.16</td>\n",
       "      <td>18</td>\n",
       "      <td>9443.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>August</td>\n",
       "      <td>57948.186667</td>\n",
       "      <td>85720.92</td>\n",
       "      <td>34</td>\n",
       "      <td>-27772.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>September</td>\n",
       "      <td>69937.000000</td>\n",
       "      <td>131726.48</td>\n",
       "      <td>29</td>\n",
       "      <td>-61789.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>November</td>\n",
       "      <td>131920.000000</td>\n",
       "      <td>158322.00</td>\n",
       "      <td>54</td>\n",
       "      <td>-26402.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>December</td>\n",
       "      <td>146664.000000</td>\n",
       "      <td>183647.93</td>\n",
       "      <td>78</td>\n",
       "      <td>-36983.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Februar</td>\n",
       "      <td>26973.000000</td>\n",
       "      <td>67306.00</td>\n",
       "      <td>11</td>\n",
       "      <td>-40333.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Januar</td>\n",
       "      <td>15083.500000</td>\n",
       "      <td>15169.89</td>\n",
       "      <td>11</td>\n",
       "      <td>-86.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Juli</td>\n",
       "      <td>55643.600000</td>\n",
       "      <td>94979.64</td>\n",
       "      <td>26</td>\n",
       "      <td>-39336.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Juni</td>\n",
       "      <td>55122.610000</td>\n",
       "      <td>146391.68</td>\n",
       "      <td>38</td>\n",
       "      <td>-91269.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mai</td>\n",
       "      <td>49563.430000</td>\n",
       "      <td>67628.27</td>\n",
       "      <td>26</td>\n",
       "      <td>-18064.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Marz</td>\n",
       "      <td>41088.020000</td>\n",
       "      <td>84713.44</td>\n",
       "      <td>29</td>\n",
       "      <td>-43625.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Oktober</td>\n",
       "      <td>102044.000000</td>\n",
       "      <td>237573.26</td>\n",
       "      <td>57</td>\n",
       "      <td>-135529.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Total general</td>\n",
       "      <td>796917.016667</td>\n",
       "      <td>1308665.67</td>\n",
       "      <td>411</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   Total Einzahlungen  Total Schäden  \\\n",
       "0           April         44929.670000       35486.16   \n",
       "1          August         57948.186667       85720.92   \n",
       "2       September         69937.000000      131726.48   \n",
       "3        November        131920.000000      158322.00   \n",
       "4        December        146664.000000      183647.93   \n",
       "5         Februar         26973.000000       67306.00   \n",
       "6          Januar         15083.500000       15169.89   \n",
       "7            Juli         55643.600000       94979.64   \n",
       "8            Juni         55122.610000      146391.68   \n",
       "9             Mai         49563.430000       67628.27   \n",
       "10           Marz         41088.020000       84713.44   \n",
       "11        Oktober        102044.000000      237573.26   \n",
       "12  Total general        796917.016667     1308665.67   \n",
       "\n",
       "    Total Single Damage Number  Monaten Differenzen   \n",
       "0                           18           9443.510000  \n",
       "1                           34         -27772.733333  \n",
       "2                           29         -61789.480000  \n",
       "3                           54         -26402.000000  \n",
       "4                           78         -36983.930000  \n",
       "5                           11         -40333.000000  \n",
       "6                           11            -86.390000  \n",
       "7                           26         -39336.040000  \n",
       "8                           38         -91269.070000  \n",
       "9                           26         -18064.840000  \n",
       "10                          29         -43625.420000  \n",
       "11                          57        -135529.260000  \n",
       "12                         411                   NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel (r'EPEÑA-Data Case.xlsx',sheet_name='Total Montlich', header=3)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## I change \"Unnamed: 0\" colummn name for \"Monat\"\n",
    "\n",
    "# df.columns.values\n",
    "df= df.rename(columns= {'Unnamed: 0': \"Monat\", ' Total Einzahlungen': \"Total Einzahlungen\", 'Total Schäden':'Total Schäden','Total Single Damage Number':'Total Single Damage Number',\n",
    "       'Monaten Differenzen':'Monaten Differenzen '})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monat</th>\n",
       "      <th>Total Einzahlungen</th>\n",
       "      <th>Total Schäden</th>\n",
       "      <th>Total Single Damage Number</th>\n",
       "      <th>Monaten Differenzen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>April</td>\n",
       "      <td>44929.670000</td>\n",
       "      <td>35486.16</td>\n",
       "      <td>18</td>\n",
       "      <td>9443.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>August</td>\n",
       "      <td>57948.186667</td>\n",
       "      <td>85720.92</td>\n",
       "      <td>34</td>\n",
       "      <td>-27772.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>September</td>\n",
       "      <td>69937.000000</td>\n",
       "      <td>131726.48</td>\n",
       "      <td>29</td>\n",
       "      <td>-61789.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>November</td>\n",
       "      <td>131920.000000</td>\n",
       "      <td>158322.00</td>\n",
       "      <td>54</td>\n",
       "      <td>-26402.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>December</td>\n",
       "      <td>146664.000000</td>\n",
       "      <td>183647.93</td>\n",
       "      <td>78</td>\n",
       "      <td>-36983.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Februar</td>\n",
       "      <td>26973.000000</td>\n",
       "      <td>67306.00</td>\n",
       "      <td>11</td>\n",
       "      <td>-40333.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Januar</td>\n",
       "      <td>15083.500000</td>\n",
       "      <td>15169.89</td>\n",
       "      <td>11</td>\n",
       "      <td>-86.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Juli</td>\n",
       "      <td>55643.600000</td>\n",
       "      <td>94979.64</td>\n",
       "      <td>26</td>\n",
       "      <td>-39336.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Juni</td>\n",
       "      <td>55122.610000</td>\n",
       "      <td>146391.68</td>\n",
       "      <td>38</td>\n",
       "      <td>-91269.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mai</td>\n",
       "      <td>49563.430000</td>\n",
       "      <td>67628.27</td>\n",
       "      <td>26</td>\n",
       "      <td>-18064.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Marz</td>\n",
       "      <td>41088.020000</td>\n",
       "      <td>84713.44</td>\n",
       "      <td>29</td>\n",
       "      <td>-43625.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Oktober</td>\n",
       "      <td>102044.000000</td>\n",
       "      <td>237573.26</td>\n",
       "      <td>57</td>\n",
       "      <td>-135529.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Total general</td>\n",
       "      <td>796917.016667</td>\n",
       "      <td>1308665.67</td>\n",
       "      <td>411</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Monat  Total Einzahlungen  Total Schäden  \\\n",
       "0           April        44929.670000       35486.16   \n",
       "1          August        57948.186667       85720.92   \n",
       "2       September        69937.000000      131726.48   \n",
       "3        November       131920.000000      158322.00   \n",
       "4        December       146664.000000      183647.93   \n",
       "5         Februar        26973.000000       67306.00   \n",
       "6          Januar        15083.500000       15169.89   \n",
       "7            Juli        55643.600000       94979.64   \n",
       "8            Juni        55122.610000      146391.68   \n",
       "9             Mai        49563.430000       67628.27   \n",
       "10           Marz        41088.020000       84713.44   \n",
       "11        Oktober       102044.000000      237573.26   \n",
       "12  Total general       796917.016667     1308665.67   \n",
       "\n",
       "    Total Single Damage Number  Monaten Differenzen   \n",
       "0                           18           9443.510000  \n",
       "1                           34         -27772.733333  \n",
       "2                           29         -61789.480000  \n",
       "3                           54         -26402.000000  \n",
       "4                           78         -36983.930000  \n",
       "5                           11         -40333.000000  \n",
       "6                           11            -86.390000  \n",
       "7                           26         -39336.040000  \n",
       "8                           38         -91269.070000  \n",
       "9                           26         -18064.840000  \n",
       "10                          29         -43625.420000  \n",
       "11                          57        -135529.260000  \n",
       "12                         411                   NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As appeared in the table April is the only one profit month, meanwhile the worst profit month is Oktober. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(12,axis=0)## I drop the \"Total general\" row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # I check the dataframe shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 12 entries, 0 to 11\n",
      "Data columns (total 5 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   Monat                       12 non-null     object \n",
      " 1   Total Einzahlungen          12 non-null     float64\n",
      " 2   Total Schäden               12 non-null     float64\n",
      " 3   Total Single Damage Number  12 non-null     int64  \n",
      " 4   Monaten Differenzen         12 non-null     float64\n",
      "dtypes: float64(3), int64(1), object(1)\n",
      "memory usage: 576.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info() # The data types look well for working with them. I don't have to do changes here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()# Are there missing values? No, there are not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Einzahlungen</th>\n",
       "      <th>Total Schäden</th>\n",
       "      <th>Total Single Damage Number</th>\n",
       "      <th>Monaten Differenzen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66409.751389</td>\n",
       "      <td>109055.472500</td>\n",
       "      <td>34.250000</td>\n",
       "      <td>-42645.721111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40345.676649</td>\n",
       "      <td>64270.279413</td>\n",
       "      <td>19.936832</td>\n",
       "      <td>39336.834848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15083.500000</td>\n",
       "      <td>15169.890000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-135529.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43969.257500</td>\n",
       "      <td>67547.702500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>-48166.435000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55383.105000</td>\n",
       "      <td>90350.280000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>-38159.985000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77963.750000</td>\n",
       "      <td>149374.260000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>-24317.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>146664.000000</td>\n",
       "      <td>237573.260000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>9443.510000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Einzahlungen  Total Schäden  Total Single Damage Number  \\\n",
       "count           12.000000      12.000000                   12.000000   \n",
       "mean         66409.751389  109055.472500                   34.250000   \n",
       "std          40345.676649   64270.279413                   19.936832   \n",
       "min          15083.500000   15169.890000                   11.000000   \n",
       "25%          43969.257500   67547.702500                   24.000000   \n",
       "50%          55383.105000   90350.280000                   29.000000   \n",
       "75%          77963.750000  149374.260000                   42.000000   \n",
       "max         146664.000000  237573.260000                   78.000000   \n",
       "\n",
       "       Monaten Differenzen   \n",
       "count             12.000000  \n",
       "mean          -42645.721111  \n",
       "std            39336.834848  \n",
       "min          -135529.260000  \n",
       "25%           -48166.435000  \n",
       "50%           -38159.985000  \n",
       "75%           -24317.710000  \n",
       "max             9443.510000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"Total Schäden\", \"Total Einzahlungen\"]].plot.box(figsize=(8,6));\n",
    "df[['Total Single Damage Number']].plot.box(figsize=(8,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preview visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Total Single Damage Number']].plot.line(figsize=(14,12));\n",
    "plt.xticks(rotation=360, fontsize=11)\n",
    "plt.xticks([0,1,2,3,4,5,6,7,8,9,10,11],[\"April\", \"August\" ,\"September\", \"November\", \"December\", \"Februar\",\"Januar\", \"Juli\", \"Juni\", \"Mai\", \"Marz\", \"Oktober\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In December there were the highest number of Damages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"Total Schäden\", \"Total Einzahlungen\"]].plot.line(figsize=(14,12))\n",
    "plt.yticks(range(0,250000,25000)); \n",
    "plt.xticks(rotation=360, fontsize=11)\n",
    "plt.xticks([0,1,2,3,4,5,6,7,8,9,10,11],[\"April\", \"August\" ,\"September\", \"November\", \"December\", \"Februar\",\"Januar\", \"Juli\", \"Juni\", \"Mai\", \"Marz\", \"Oktober\"]);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Linear Relation between Total Single Damage Number and Total Schäden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Simple Linear regression is a useful statistical method used to understand the relationship between two variables, x and y. However, before I conduct linear regression, I must first make sure that four assumptions are met:\n",
    "\n",
    "1. Linear relationship: There exists a linear relationship between the independent variable, x, and the dependent variable, y. It can be observed with a scatter plot.\n",
    "\n",
    "\n",
    "2. Independence: The residuals are independent. In particular, there is no correlation between consecutive residuals in the data. It is checkeed with Durbin-Watson test.\n",
    "\n",
    "\n",
    "3. Homoscedasticity: The residuals have constant variance at every level of x.I will check it with the Q-Qplot in the next section\n",
    "\n",
    "\n",
    "4. Normality: The residuals of the model are normally distributed. I will check it with the Shapiro-Will Test. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='Total Single Damage Number', y='Total Schäden', style='o', figsize=(10,8))\n",
    "plt.title('Total Single Damage Number vs Total Schäden')  \n",
    "plt.xlabel('Total Single Damage Number')  \n",
    "plt.ylabel('Total Schäden')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How these two variables are distribuited?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total Single Damage Number'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sb.distplot(df['Total Single Damage Number'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The median value is 29.0\n",
      "The mean value is 34.25\n"
     ]
    }
   ],
   "source": [
    "import statistics\n",
    "median = statistics.median(df[\"Total Single Damage Number\"])\n",
    "mean = statistics.mean(df[\"Total Single Damage Number\"])\n",
    "print(f\"The median value is {median}\")\n",
    "print(f\"The mean value is {mean}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sb.distplot(df['Total Schäden'])\n",
    "plt.xticks(range(0,350000,25000)); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The median value is 90350.28\n",
      "The mean value is 109055.4725\n"
     ]
    }
   ],
   "source": [
    "import statistics\n",
    "median = statistics.median(df[\"Total Schäden\"])\n",
    "mean = statistics.mean(df[\"Total Schäden\"])\n",
    "print(f\"The median value is {median}\")\n",
    "print(f\"The mean value is {mean}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normality test\n",
    "\n",
    "\n",
    "\n",
    "Sample inferior to 50. I proceed to do the Shapiro-Wilk normality test\n",
    "\n",
    "[Shapiro Wilk documentation](https://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test)\n",
    "[scipy.stats](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from scipy.stats import shapiro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Single Damage Number:\n",
      " The Shapiro normality test is 0.91 and its p-value is bigger than 0.05 (0.2113)\n",
      " it means the null hypothesis should be retained: \n",
      " The Total Single Damage Number distribuition doesn't have differences wiht a normal distribuition \n",
      "\n",
      "Total Schäden:\n",
      " The Shapiro normality test is 0.97 and its p-value is bigger than 0.05 (0.8654)\n",
      " it means the null hypothesis should be retained:\n",
      "  The Total Schäden distribuition doesn't have differences wiht a normal distribuition.\n"
     ]
    }
   ],
   "source": [
    "y = df[\"Total Schäden\"]\n",
    "x= df[\"Total Single Damage Number\"]\n",
    "shapiro_testX = stats.shapiro(x)\n",
    "shapiro_testY = stats.shapiro(y)\n",
    "print(f\"Total Single Damage Number:\\n The Shapiro normality test is {np.round(shapiro_testX[0],2)} and its p-value is bigger than 0.05 ({np.round(shapiro_testX[1],4)})\\n it means the null hypothesis should be retained: \\n The Total Single Damage Number distribuition doesn't have differences wiht a normal distribuition \\n\")\n",
    "\n",
    "print(f\"Total Schäden:\\n The Shapiro normality test is {np.round(shapiro_testY[0],2)} and its p-value is bigger than 0.05 ({np.round(shapiro_testY[1],4)})\\n it means the null hypothesis should be retained:\\n  The Total Schäden distribuition doesn't have differences wiht a normal distribuition.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some preview conclusions\n",
    "\n",
    "\n",
    "At this point I can observe some facts:\n",
    "              \n",
    "              - The general performance of all companies in 2020. \n",
    "              \n",
    "                 -It's notory that there were some profit values just in April.\n",
    "                 - There were more losses in Oktober than the rest of the year.\n",
    "                 - Meanwhile in Dezember there were more single damage number \n",
    "                 - The Total Single Damage Number and Total Schäden met normality \n",
    "                 assumption in order to do the simple linear regression\n",
    "              \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much it changes the damage costs when a singular damage case occurs?\n",
    "\n",
    "In order to predict this effect I'm going to use a Simple linear regression\n",
    "\n",
    "[Simple linear regression](https://en.wikipedia.org/wiki/Simple_linear_regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = df[\"Total Schäden\"].values.reshape(-1,1)\n",
    "y = df[\"Total Schäden\"]\n",
    "x1= df[\"Total Single Damage Number\"].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression().fit(x1, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Elena Peña\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=12\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>Total Schäden</td>  <th>  R-squared:         </th> <td>   0.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.714</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   28.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 09 Apr 2021</td> <th>  Prob (F-statistic):</th> <td>0.000333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:54:59</td>     <th>  Log-Likelihood:    </th> <td> -141.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    12</td>      <th>  AIC:               </th> <td>   286.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    10</td>      <th>  BIC:               </th> <td>   287.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  1.41e+04</td> <td> 2.04e+04</td> <td>    0.691</td> <td> 0.505</td> <td>-3.13e+04</td> <td> 5.95e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td> 2772.4278</td> <td>  520.179</td> <td>    5.330</td> <td> 0.000</td> <td> 1613.398</td> <td> 3931.458</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.950</td> <th>  Durbin-Watson:     </th> <td>   2.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.622</td> <th>  Jarque-Bera (JB):  </th> <td>   0.746</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.523</td> <th>  Prob(JB):          </th> <td>   0.689</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.368</td> <th>  Cond. No.          </th> <td>    80.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          Total Schäden   R-squared:                       0.740\n",
       "Model:                            OLS   Adj. R-squared:                  0.714\n",
       "Method:                 Least Squares   F-statistic:                     28.41\n",
       "Date:                Fri, 09 Apr 2021   Prob (F-statistic):           0.000333\n",
       "Time:                        12:54:59   Log-Likelihood:                -141.28\n",
       "No. Observations:                  12   AIC:                             286.6\n",
       "Df Residuals:                      10   BIC:                             287.5\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        1.41e+04   2.04e+04      0.691      0.505   -3.13e+04    5.95e+04\n",
       "x1          2772.4278    520.179      5.330      0.000    1613.398    3931.458\n",
       "==============================================================================\n",
       "Omnibus:                        0.950   Durbin-Watson:                   2.052\n",
       "Prob(Omnibus):                  0.622   Jarque-Bera (JB):                0.746\n",
       "Skew:                           0.523   Prob(JB):                        0.689\n",
       "Kurtosis:                       2.368   Cond. No.                         80.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "x = sm.add_constant(x1)\n",
    "results = sm.OLS(y,x).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Total Single Damage Number p-value coefficient is less than 0.05, so It's not possible to reject the null hypothesis: the Damage cases number predict the Damages in Euros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Independence and the Homoscedasticity are met.\n",
    "\n",
    "The Durbin-Watson test checked there is no serial correlation in the residuals.(Must be between 0-4) \n",
    "\n",
    "[Durbin-Watson test](https://www.statsmodels.org/stable/generated/statsmodels.stats.stattools.durbin_watson.html)\n",
    "\n",
    "The Q-Q plot, short for quantile-quantile plot, is a type of plot that it is used to determine whether or not the residuals of a model follow a normal distribution. \n",
    "The points on the plot roughly form a straight diagonal line, then the normality assumption is met.\n",
    "[QQplot Statsmodels](https://www.statsmodels.org/stable/generated/statsmodels.graphics.gofplots.qqplot.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The residual values are well distribuited p-value(0.75)is superior than 0.05.\n",
      " The normality assumption in the residual values is met\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEJCAYAAABohnsfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAA14ElEQVR4nO3deXwU9f3H8dfmgtxRA0riga1aD/qzWgtereKBRz1T/Shar5ZirWitXBFBDkFCFBFBBVQEVMRPNeKFigrW1qPFsx5otYoCQSCEXORO9vfHbGqIOSZhd2c3+TwfDx7szszOvBlgP/nOd+b79fn9fowxxhg3YrwOYIwxJnpY0TDGGOOaFQ1jjDGuWdEwxhjjmhUNY4wxrsV5HSAM7PYwY4zpPF9rC3tC0aCwsLDTn8nMzKSoqCgEaYIrWnKCZQ2FaMkJ0ZM1WnJC8LLGbNlC+s03k7hiBbUDBpDw0Udtb7vLRzPGGBOd/H4SH3+cvoMH0/vVVym76SaKnnuu3Y/0iJaGMcaYncWuX0/62LH0/tvfqBk4kJLbb6fhgAM6/JwVDWOM6UkaG0letIjU6dPB56Nk2jQqL78cYtxdeLKiYYwxPUTcl1+SPmoUvdasoXrwYErz8mjYe+/O7SNE2YwxxkSKujpS7ruP1Fmz8CclsX32bKp+8xvwtXqDVLusI9wY06MUFCQycGBf9t67HwMH9qWgINHrSCEV/9FH9Pn1r0mbMYPqIUPY8tprVF1wQZcKBlhLwxjTgxQUJDJmTDpVVc7Pyxs3xjFmTDoAOTlVXkYLvqoqUu+6i5T77qNxjz0ofuABqs84Y5d3ay0NY0yPkZeX+r+C0aSqKoa8vFSPEoVGwr/+Rd8hQ0idO5fKCy9ky+rVQSkYYC0NY0wPUlgY26nl0cZXUUHa9OkkL1pE/T77UPTYY9T+6ldBPYa1NIwxPUZWVkOnlkeTXqtW0WfwYJIWL6Zi2DC2rloV9IIBEdLSEJF9gCXAXkAjsEBVZ7fYxgfMBs4EKoErVfW9cGc1xkSv3Nzynfo0ABITG8nNLfcw1a7xFReTPmkSSU8+Sd2BB1K0fDl1Rx0VsuNFSkujHhipqocARwPXisihLbY5Azgw8Gs4cF94Ixpjol1OThX5+aVkZ9fj8/nJzq4nP780OjvB/X58Tz5J38GDSXz6acpvuIGtL70U0oIBEdLSUNVNwKbA63IRWQtkA5822+xcYImq+oG3RSRDRPoFPmuMMa7k5FRFZ5FoJmbzZtLHjSP+xRep/b//Y9vSpdQfdlhYjh0RRaM5EekPHAH8s8WqbGB9s/cbAst+UDREZDhOawRVJTMzs9M54uLiuvS5cIuWnGBZQyFackL0ZI3onH4/MYsXEztmDNTU0DhjBowYQUZc+L7KI6poiEgK8CRwg6qWtVjd2pMorc6VoaoLgAVN23Rl6OBoGR45WnKCZQ2FaMkJ0ZM1UnPGfvstGWPGEPf3v1Nz9NGU3H47uw0cGJKsWVlZba6LmKIhIvE4BeNRVS1oZZMNwD7N3u8NdH6iDGOMiSYNDSQ/9BCpeXkQG0vJ9OlU/va3rgcYDLaIKBqBO6MeBNaq6p1tbPYMMEJElgGDgFLrzzDGdGdx//kPGSNHkvDee1SfdBIleXk0Zmd7m8nTo3/vOOAy4CMR+SCwbBywL4CqzgNW4Nxu+yXOLbdXhT+mMcaEQW0tKffcQ+rdd9OYnMz2OXOoOv/8Lo8XFUwRUTRU9R+0MR9ts238wLXhSWSMMd6I//BDMkaOJH7tWirPPZeyKVNojKCO+YgoGsYY0+NVVZE2cybJ8+fT2Lcv2x56iJohQ7xO9QNWNIwxxmMJb71FxqhRxK1bx45LL6Xs5pvxp6d7HatVVjSMMcYjvvJy0qZNI/nhh6nfbz+KHn+c2uOP9zpWu6xoGGOMB3q98goZubnEbN5MxfDhlI8Zgz8x8ieEsqJhjDFhFFNcTNrEiSQVFFD3k59QvGABdUce6XUs16xoGGNMOPj99H7mGdLHjyemvJzyG2+k/LrrICHB62SdYkXDGGNCLGbTJtLHjSNx5Upqf/Yztt1xB/WHHOJ1rC6xomGMMaHi95O0dClpt94KdXWU3nILO4YNg9jonSnQioYxxoRA7Lp1ZIweTa8336Tm2GMpuf12Gvr39zrWLrOiYYwxwdTQQPIDD5Canw/x8ZTk51N5ySURMQRIMFjRMMaYIIn77DMyRo0i4f33qT71VEqmT6exXz+vYwWVFQ1jjNlVtbWkzJ3rDDCYmkrxvfdSfc453aZ10ZwVDWOM2QXx779PxqhRxH/2GZU5OZRNnkzj7rt7HStkrGgYY0wX+KqqSM3PJ/mBB2jcc0+2LV5MzSmneB0r5KxoGGNMJyW88QYZo0cT98037LjsMmeAwdRUr2OFhRUNY4xxyVdWRtrUqSQ/+ij1/ftT9MQT1B5zjNexwsqKhjHGuNBr5UoybrqJmC1bqLjmGspHjoyKAQaDzYqGMca0I2bbNtImTCDp6aepO+QQihcupO7ww72O5ZmIKRoishA4C9iiqgNaWX8i8DTwdWBRgapOCV9CY0yP4veTuHw5aRMmEFNRQdmoUVRce23UDTAYbBFTNIBFwFxgSTvb/F1VzwpPHGNMTxWzcSMZN91E71dfpfbII9k2cyb1Bx3kdayIEON1gCaq+jpQ7HUOY0wP1thIzIIF9D3pJBLefJPSyZMpWr7cCkYzkdTScOMYEfkQKARGqeonXgcyxnQPsV99RcaYMcS99RY1xx9PSX4+Dfvt53WsiBNNReM9YD9VrRCRM4HlwIGtbSgiw4HhAKpKZmZmpw8WFxfXpc+FW7TkBMsaCtGSEyI4a309MbNnEztlCvTqReP99+O77DJ2i4IhQLw4pz6/3x/WA7ZHRPoDz7XWEd7KtuuAo1S1qINN/YWFhZ3OkpmZSVFRR7v2XrTkBMsaCtGSEyIza9ynnzoDDH74IVWnnUbpbbex+4ABEZezLaE6p1lZWQCtVs2oaWmIyF7AZlX1i8hAnP6YbR7HMsZEo5oaUu++m5S5c2nMyKB43jyqzzqrWw4wGGwRUzRE5DHgRCBTRDYAE4F4AFWdB1wAXCMi9UAVcLGqRk4zyRgTFeLfeccZYPCLL6i84AJKJ07E340HGAy2iCkaqjq0g/VzcW7JNcaYTvNVVpKal0fywoU09OvHtocfpuakk7yOFXUipmgYY0yoJLz+unNn1Pr17LjySspuugl/SorXsaKSFQ1jTLflKy0lbcoUkpcto37//SkqKKB20CCvY0U1KxrGmG6p94svkj5uHDFFRZSPGEH5DTdADxxgMNisaBhjupWYrVtJHz+exOeeo+6wwyhevJi6n/7U61jdhhUNY0z34PeT+OSTpE+ciK+ykrKxY6m45hqIj/c6WbdiRcMYE/ViN24kfexYeq9eTe1RR1Eycyb1BxzgdaxuyYqGMSZ6NTaStGQJabfdBn4/pbfeyo4rr4SYiBmLtduxomGMiUqxX35JxujR9PrXv6j+1a8ozc+nYZ99vI7V7VnRMMZEl/p6UubNI/XOO/H37s32O++kSsSGAAkTa8MZY0KuoCCRAw+MZ++9+zFwYF8KCrp262vcxx+TedZZpE2fTvXJJ7PltdeouugiKxhh5KqlISKDgXWq+rWI9APygAZgnKp+F8qAxpjoVlCQyJgx6VRVOV/sGzfGMWZMOgA5OVXudlJdTepdd5Fy77007r47xQsWUP3rX4cqsmmH25bGvThFAmAmzkCCfmBBKEIZY7qPvLxUqqp2/qqpqoohLy/V1ecT1qyhz5AhpM6ZQ9VvfsOW1autYHjIbZ9Gtqp+KyJxwGnAfkAtzgx6xhjTpsLC2E4tb+LbscMZYPChh2jIzmbb0qXUnHBCKCKaTnDb0igTkT2BE4BPVbUisNyemjHGtCsrq6FTywF6/e1v9DnpJJIfeogdV13F1lWrrGBECLdFYw6wBngUuCew7Djgs1CEMsZ0H7m55SQmNu60LDGxkdzc8h9s69u+nYy//IU9LrkEf69ebHvqKcpuvRV/cnK44poOuCoaqjoDOAU4TlWXBRZvBIaFKpgxpnvIyakiP7+Ufff14/P5yc6uJz+/9Aed4L2ff56+gweT+OSTlF9/PVtXrqT2F7/wKLVpS2ee0/gaOFpEjlLVx3GKhjHGdCgnp4rhw5Nbnc86ZssW0m++mcQVK6gdMIBtjzxC/YABHqQ0brhqaYjIT4H/APcDDwYWnwAsDFEuY0x35/eT+Pjj9D3xRHq/+ipl48ZR9PzzVjAinNs+jfuAW1T1YKAusOxvwPEhSWWM6dZi169n90suYbcbb6TuJz9hy8qVVFx7LcTZIBWRzu3f0GHAI4HXfgBV3SEiQZvRREQWAmcBW1T1Bz9qiIgPmA2cCVQCV6rqe8E6vjEmDBobSV60iNTp08Hno2TaNCovv9wGGIwibv+m1gE/b75ARAYCXwYxyyLg9HbWnwEcGPg1HKf1Y4yJFmvXknn++aRPmEDtoEFsXbWKShuRNuq4/duaADwvIpOBBBG5CfgrMD5YQVT1daC4nU3OBZaoql9V3wYyAkOaGGMiWV0dKXffTfzAgcR9+SXbZ8+m+OGHadh7b6+TmS5wdXlKVZ8TkTNwbrH9G84T4Tmq+m4ow7WQDaxv9n5DYNmmlhuKyHCc1giqSmZmZqcPFhcX16XPhVu05ATLGgqRntP3/vvEDh9OzL//jf/CC6mfOZPkPfckkp+6iPRz2pwXWV33OgX6D/4UwiwdaW0YS39rG6rqAr4fF8vf2m1+HcnMzGz19sBIEy05wbKGQsTmrKoiddYsUubNo3GPPSh+8EFSfvtbJ2sk5m0mYs9pK0KVNSsrq811bRYNEZniZueqeksXMnXFBqD5DCt7Y2NfGRNxEv75TzJGjSLuq6/YMXQoZePH48/IIMXrYCYo2mtpRNoUWM8AI0RkGTAIKFXVH1yaMsZ4w1dRQdptt5G8eDH1++xD0WOPUfurX3kdywRZm0VDVa8KZxAReQw4EcgUkQ3ARAIDIqrqPGAFzu22X+LcchvWfMaYtvVatYr0sWOJ3bSJimHDKB87Fn9SktexTAi0d3mqv6quC7z+UVvbqepXwQiiqkM7WO8Hrg3GsYwxOysoSCQvL5XCwliyshrIzS13NUGSr7iY9EmTSHrySeoOOoii5cupO+qoMCQ2Xmnv8tRHQNMsKV/idDq37Iz2A+0Pim+MiWjfz6zn3IHvamY9v5/ezz5L+vjxxJSWUn7DDZRffz306hWu2MYj7V2eSm322p6+Maabam9mvdaKRsx33zkDDL74IrX/939sW7aM+kMPDVdc4zG3Axbe3cbyu4KaxhgTdq5n1vP7SXrsMfoOHkzv116jdPx4ip591gpGD+O2BXFlG8svC1IOY4xH3MysF/vNN+xx8cVkjBpF3aGHsuXll9lxzTU2wGAP1O7fuIj8rmm7Zq+b/AiIjidgjDFtys0t36lPA5rNrNfQQPLChaTOmAGxsZRMn07lb39r40X1YB39mNDUkkhg51aFH9gMXBGKUMaY8Gnqt2h595QM+JCM80aS8N57VJ98MiXTp9OYne1xWuO1douGqg4GEJGpqhq0wQmNMZElJ6fq+07v2lpS7rmH1Btn05iSwva5c6k67zzwtTaSj+lp3A5YOB5ARPrCzqMBBOs5DWOMezs/V9HX9XMVHYn/4AMyRo0ifu1aKs89l7Jbb6Vxjz2CkNh0F66KhoichjO1a8uhyO05DWPCrEvPVXTAV1VF6syZJM+fT2Pfvmx76CFqhgwJWmbTfbi99eFe4FZgsaru+o8zxpgu6+xzFR1JeOstZ4DBdevYcemlzgCDaWnBimu6GbdFYzdgfmAoD2OMh1w/V9EBX3k5adOmkfzww9Tvtx9Fjz9O7fHHByOi6cbc3jf3IDZAoDERwc1zFR3p9cor9B08mKRHH6Xi6qvZ+uqrVjCMK25bGkcD14tILvBd8xWqamMfGxNG7T5X0YGYbdtImziRpKeeou7ggym+/37qjjgilHFNN+O2aDwQ+GWM8Vhbz1W025/h99P7mWecAQbLyykbOZKKESMgISFMqU134faW28WhDmKMca/puQo3033GbNpExk030fvll6k94gi23XEH9QcfHKakprtxPXCMiOwJDAQyaTZEuqouDEEuY8yu8vtJWrqUtFtvhbo6Sm+5hR3DhkGs3SVvus7tcxrnAY8AXwCHAZ8AA4B/4Dy/YYyJILHr1pExejS93nyTmmOPpeT222no39/rWKYbcHv31FTgKlU9AtgR+H048G7IkhljOq+hgeR58+hz8snEf/QRJfn5bFO1gmGCxu3lqX1V9a8tli3GuZNqVHAjGWO6Iu6zz8gYOZKEDz6g+tRTnQEG+7UcxMGYXeO2aGwRkT1VdTOwTkSOwRkWPWgXR0XkdGB2YJ8PqGpei/UnAk8DXwcWFajqlGAd35ioVVtL6pw5pMyZQ2NaGsX33kv1OefYAIMmJNwWjfuB44EngVnAaqARmBmMECISC9wDnApsANaIyDOq+mmLTf+uqmcF45jGdAe+NWvo8/vfE//551Tm5FA2eTKNu+/udSzTjbm95XZGs9dLROQ1IFlV1wYpx0Dgy6YRc0VkGXAu0LJoGGMIDDCYn0/cAw/QuOeebFu8mJpTTvE6lukBujRXo6p+G+Qc2cD6Zu83AINa2e4YEfkQKARGqeonre1MRIbjdNSjqmRmZnY6UFxcXJc+F27RkhMsa7D4Vq8m7ppr8H39Nf6rr6Zh6lRS09JI9TpYByL5nDYXLTnBm6xub7ldjzMM+g+o6r5ByNHaxdeWx3sP2E9VK0TkTGA5cGAbmRYAC5r209HDT61x89BUJIiWnGBZd5WvtJS0qVNJXrqU+v79KXniCdLOPtvJGWFZWxOJ57Q10ZITQpc1KyurzXVuWxq/bfG+H/BnYFkXM7W0Adin2fu9cVoT/6OqZc1erxCRe0UkU1Wj42/XmF3Qa+VKMm66iZgtWyj/058ov/FGSEz0Opbpgdz2afyt5bJAv8aLOHc87ao1wIEisj+wEbgYuKTF8fYCNquqX0QG4jxjsi0IxzYmYsUUFZE+YQKJzzxD3SGHULxwIXWHH+51LNODuX24rzU1wP7BCKGq9cAI4CVgrbNIPxGRP4rIHwObXQB8HOjTuBu42Ob3MN2W309iQQF9TjyR3i++SNno0WxdscIKhvGc2z6Nls9DJAFnAi8EK4iqrgBWtFg2r9nrucDcYB3PmEgVs3GjM8Dgq69Se+SRbJs5k/qDDvI6ljGA+z6NfVq83wHcCTwc3DjG9GCNjSQ98ghp06ZBQwOlkyez46qrbIBBE1Hc9mnYrH3GhFDsV185Awy+/TY1v/wlJfn5NOwbjBsTjQmuDotG4GntS4AhQB9gK/AasFRVOz+LvTHme/X1pCxYQOrMmfgTEtg+cyZVF11kQ4CYiNVu0RCRNGAlTof3C8D7OLfb3gbcICKnBvZxrKpqiLMa0y0UFCSSl5dK5saPWRz/e7Lq3qPq9NMpnTaNxr328jqeMe3qqKUxHee21pNVdUfTQhFJBhRYCvwYGBeyhMZ0IwUFiYwf3ZuR1ZPJJY/iut25NGEZx5x5Kjl7VXsdz5gOdXTL7fnANc0LBkDg/bXAicBEVX00NPGM6V5WTv6YN6t/zgSmspRLOJRPWVp7EXkz0ryOZowrHbU00nEetmvNBqBGVRcFNZEx3ZBvxw5SZ8xgedFCNrA3Z7CCFznjf+sLC+0OKRMdOmpp/Bc4qY11JwNfBjeOMd1Pr9dfp8/JJ5Py4IMsSf4jh/HJTgUDICurwaN0xnRORy2NO4ElIjICeEpVG0UkBsgB5mB9Gca0yVdSQtqtt5K8bBn1P/oRRQUFVGw8kYYxydDsvsPExEZyc8u9C2pMJ7Tb0ghceroDWARUi0ghUA08BNypqg+FOqAx0aj3Cy/Qd/Bgkv76V8pHjGDLyy9TO2gQOTlV5OeXkp1dj8/nJzu7nvz8UnJy7O51Ex06fE5DVWeKyALgWCATZ5rXt5qPOmuMccRs3Ur6+PEkPvccdYcdRvGSJdT99Kc7bZOTU2VFwkQtt0+El+MMJmiMaY3fT+ITT5A+aRK+ykrKxo6l4pprID7e62TGBFWXZu4zxnwvdsMG0nNz6b16NbVHHUXJzJnUH3CA17GMCQkrGsZ0VWMjSUuWkHbbbeD3UzJ1KpVXXAExuzLjgDGRzYqGMV0Q++WXzgCD//oX1SecQOmMGTTs03IwaGO6H9dFQ0T2wJlDo5+q5otIFhCjqhtCls6YSFNXR8r8+aTeeSf+xES2z5pF1YUX2gCDpsdw1Y4WkROAz4FLgQmBxQcC94UolzERJ+7jj8k86yzSpk+n+uST2bJ6NVUiVjBMj+L24utdwEWqejpQH1j2T2BgKEIZE1Gqq0nNy6PPmWcSu3kzxQsWsP3++2ns29frZMaEndvLU/1V9dXA66Z5uWs78fkOicjpwGwgFnhAVfNarPcF1p8JVAJXqup7wTq+Ma1JWLOG9JEjif/vf6m86CJKJ0zAv9tuXscyxjNuWxqfishpLZadAnwUjBCBiZ7uAc4ADgWGisihLTY7A+eS2IHAcOzSmAml8nLSxo9nj/PPx1dTw7alSym5804rGKbHc1s0RgKPishiIFFE5uMMLTI6SDkGAl+q6leqWgssA85tsc25wBJV9avq20CGiPQL0vGN+Z9er71G/JFHkrxoETt+9zu2rlpFzQkneB3LmIjgqmgEvqQPBz4BFgJfAwNVdU2QcmQD65u93xBY1tltjOky3/btZNxwA3tceikkJrLtqacomzIFf3Ky19GMiRiu+yRUdSOQH6Icrd1+4u/CNgCIyHCcS1ioKpmZmZ0OFBcX16XPhVu05ITIzup76ini/vxnKCqiITcX34QJpMVF/mNMkXxOW4qWrNGSE7zJ2ub/ChF5mDa+lJtT1cuDkGMD0PzJqL2Bwi5s05RpAbAg8NZfVFTU6UCZmZl05XPhFi05ITKzxmze7AwwuGIFtQMGULJkCfUDBpAZFxdxWVsTiee0LdGSNVpyQuiyZmVltbmuvR+lwjnB0hrgQBHZH2emwIuBS1ps8wwwQkSWAYOAUlXdFMaMpjvx+0lUJX3yZHzV1ZSNG0fF1VdDFLQujPFSm/9DVHVyuEKoan1goqeXcG65Xaiqn4jIHwPr5wErcG63/RLnlturwpXPdC+x69eTPmYMvV9/nZpBgyi5/XYafvxjr2MZExV8fn+HV6AAEJGTgKFAFs5loWXNnt2IZP7CwlavYrUrWpqo0ZITIiBrQwPJixaRmpcHPh9l48ZRefnlrQ4w6HlWl6IlJ0RP1mjJCSG/PNXqUAduhxG5Eec22GLgeWAbsFRERgYpozEhFffFF2Tm5JB+yy3UDhrE1tWrqbzyShuR1phOcnsBdyRwkqp+3LQg0FH+MjAzFMGMCYq6OlLuvZfUu+7Cn5TE9tmzqfrNb7o8XlRBQSJ5eakUFsaSldVAbm65zcJnepTO9Pq17Bj/Chd3Vxnjlfh//5uMkSOJ//RTqs4+m9Jbb6WxT58u76+gIJExY9KpqnJaJxs3xjFmTDqAFQ7TY7gtGpOAB0VkEt/f+joBmCgi/2vfq2pjsAMa02lVVaTOmkXKvHk0ZmZS/OCDVJ9++i7vNi8v9X8F4/tDxZCXl2pFw/QYbovG/MDvQ3FaF01t+0sD63yB5bFBTWdMJyW8/TYZo0YR9/XX7Bg6lLIJE/Cnpwdl34WFrf/zbmu5Md2R26Kxf0hTGLOLfOXlpE2fTvLixdTvuy9Fy5ZR+8tfBvUYWVkNbNz4w/8yWVkNQT2OMZHMVdFQ1W9CHcSYruq1ahXpY8cSu2kTFcOGUT52LP6kpKAfJze3fKc+DYDExEZyc8uDfixjIpWroiEi6cD1wBFASvN1qjokBLmM6ZCvuJj0SZNIevJJ6g46iKKnn6bu5z8P2fGa+i3s7inTk7m9PPVXnP6KpwD7H2K85ffT+9lnSR8/npjSUspvuIHy66+HXr1CfuicnCorEqZHc1s0jgb2UNW6UIYxpiMx331H+rhxJL70ErWHH862ZcuoP7TlfF2ts2csjNl1bovGP4BDgH+HMIsxbfP7SVq2jLQpU/DV1lI6YQI7hg1zPcCgPWNhTHC4LRpXAitE5J/A5uYrVHVKsEMZ01zsN9+QMXo0vd54g5pjjnEGGNy/czf02TMWxgSH26IxDeeBvnVAWrPl9kS4CZ2GBpIXLiR1xgyIjaUkL4/KSy/t0nhR9oyFMcHhtmhcDBxk81eYcIn7/HMyRo4k4f33qT75ZEry8mhsZ2KYjtgzFsYEh9sf2b4CrBPchF5tLSmzZtHntNOIXbeO7XPnUrx48S4VDHCesUhM3HmUG3vGwpjOc9vSeBh4RkTm8MM+jVVBT2V6pPgPPiBj1Cji166l8rzzKJsyhcY99gjKvu0ZC2OCw23RuDbw+20tlvuBHwUvjumJfFVVpN5xB8kLFtDYty/bHnqImiHBf2bUnrEwZte5HUbExp4yIZHw5ptkjB5N3Lp17Lj0UsrGj8efltbxB40xnujMfBrGBI2vrIy0adNIfuQR6vv3p0iV2uOO8zqWMaYDbseeSsOZU+MEIJNmc8eq6r67EkBEdgceB/rj3NIrqrq9le3WAeVAA1CvqkftynGNd3q98goZY8cSs2ULFVdfTfno0fgTE72OZYxxwe3dU/cCRwJTgN2B64BvgVlByJALvKqqBwKvBt63ZbCq/swKRpTaupWMESPY44oraMzIoOiZZyi75RYrGMZEEbdFYwjwG1V9GmgI/H4RcFkQMpwLLA68XgycF4R9mkji95O4fDnxP/sZic89R9nIkWx94QXqjjjC62TGmE5y26cRA5QGXleISAawCTggCBn2bHpoUFU3iUjfNrbzAytFxA/MV9UFbe1QRIYDwwP7JDMzs9Oh4uLiuvS5cIv4nBs2EHfddcSsWIF/4EDq582j92GH0dvrXB2I+PMaEC05IXqyRktO8Car26LxIU5/xqvA34F7gArgP24+LCKvAHu1supml8cHOE5VCwNF5WUR+UxVX29tw0BBaSoq/qKiok4cxpGZmUlXPhduEZuzsZGkpUtJmzoV6uoonTiRxLFjKdq+HSIxbwsRe15biJacED1ZoyUnhC5rVjsP07otGn/g+87v64E8IAO43M2HVfWUttaJyGYR6RdoZfQDtrSxj8LA71tE5ClgINBq0TDeiv36a2eAwbfeoubYY50BBvv3JzF218Z5sqHNjfGe2+c0vmr2eivw+yBmeAa4AqcQXQE83XIDEUkGYlS1PPB6CE6nvIkkDQ0k338/qbffDvHxlNx+O5VDh4LP1/FnO2BDmxsTGdotGiLyc6BGVT8OvO8D3AUMAN4CRqlqxS5myANURH6Pc0fWhYFjZQEPqOqZwJ7AUyLSlHmpqr64i8c1QRS3di0Zo0aR8MEHVJ96KiXTp9PYr1/Q9m9DmxsTGTpqadwFTAY+Drx/AMjC6S8YCuQDf9qVAKq6DTi5leWFwJmB118Bh+/KcUyI1NSQOmcOKXPm0JieTvG991J9zjlBaV00Z0ObGxMZOrrl9hCcjm8Cd0ydAVyqqvfgFI2zQ5rORLT4996jzxlnkDprFlXnnMPW116j+txzg14woO0hzG1oc2PCq6OiEQfUBl4fDXynqv8BUNX1OJ3hpofxVVaSNmkSmeecQ0xZGdsWL6Zkzhwad989ZMe0oc2NiQwdFY1PCPQx4EzE9ErTChHJ5vtnN0wPkfCPf9DnlFNIuf9+Ki+7jC2rV1NzSps3xwVNTk4V+fmlZGfX4/P5yc6uJz+/1PozjAmzjvo0xgLPisg8nDGfjm+27iLgjVAFM5HFV1pK2tSpJC9d6gww+MQT1B5zTFgz2NDmxniv3ZaGqv4D2Bc4FfiRqn7ebPXzwF9CmM1EiF4rV9L3pJNIWraM8j/9iS2vvNKpglFQkMjAgX3p3TuegQP7UlBgY00ZE606fE5DVcuBd1tZ/nkrm5tuJKaoiPQJE0h85hnqDjmE4oULqTu8czex2fMVxnQvbgcsND2J30/ik0/S94QT6P3ii5SNHu0MMNjJggHtP19hjIk+NgmT2UnMxo1k5ObSe9Uqao88kpKZM6k/6KAu78+erzCme7GiYRyNjSQ9/DBpt90GDQ2UTp7Mjquugl0cLyorq4GNG3/4z8yerzAmOtnlKUPsf//LHhdeSMa4cdQdcQRbV61ix7Bhu1wwwJ6vMKa7sZZGT1ZfT8qCBaTOnIk/IYHtM2dSddFFQX2iu6mz20anNaZ7sKLRQ8V98gkZI0eS8NFHVJ1+OqXTptG4V2tTnuy6pucrommeAmNM66xo9DQ1NaTOnk3KPffQmJFB8fz5VP/61yEZL8oY0/1Y0ehB4t95h4xRo4j/4gsqL7iA0okT8YdwvChjTPdjHeE9gG/HDtJuuYXM887DV1nJtkceoWT27DYLRtMT3Hvv3c+e4DbG7MSKRhRq/qV+4IHx7X6p93r9dfqcfDIpDz5I5RVXsHXVKmoGD25332PGpLNxYxx+v+9/T3Bb4TDGgBWNqNPyS/3bb32tfqn7SkrIuPFG9hg6FOLjKSoooHTaNPwpKe3u357gNsa0x4pGlHHzpd77hRfoO3gwiU88QfmIEWx5+WVqBw1ytX97gtsY0x7rCI8y7X2px2zZQvr48SQ+/zx1hx1G8ZIl1P30p53avz3BbYxpj+dFQ0QuBCbhTC07UFXfaWO704HZQCzwgKrmhS1kBGn9S93PdRmL6Dv4RnxVVZTl5lLxxz9CfHyn95+bW77TqLRgT3AbY74XCZenPgZygNfb2kBEYoF7cOYoPxQYKiKHhideZGk5LMe+fMPKmNOZvf131B9wAFtXrqTiuuu6VDDAZsgzxrTP85aGqq4FEJH2NhsIfKmqXwW2XQacC3wa8oARpunLe8b0ZM4tXECe7yYS4vyU3DKVyiuugJhd/znAZsgzxrTF86LhUjawvtn7DUCbPbsiMhwYDqCqZGZmdvqAcXFxXfpcOAw/4XP+9NgfiSl8E/8pp1I3dy5J/fuT5HWwDkTyOW0pWrJGS06InqzRkhO8yRqWoiEirwCtDWx0s6o+7WIXrY1x4W9rY1VdACxo2q4r4x1F5DhJdXWkzJtH6qxZ+BMT2T5rFsnXXEPRtm0QaVlbEZHntA3RkjVackL0ZI2WnBC6rFlZWW2uC0vRUNVTdnEXG4B9mr3fGyjcxX1GlbiPP3YGGPz4Y6p+/WtKp06lsW9fkm3MKGNMGEXL5ak1wIEisj+wEbgYuMTbSGFSXU3qrFmk3HcfjbvvTvH991N95plepzLG9FCe3z0lIueLyAbgGOB5EXkpsDxLRFYAqGo9MAJ4CVjrLNJPvMocLglr1tBnyBBS586l6oIL2PLaa1YwjDGe8vn9bXYNdBf+wsLOX8ny8rqmr6KC1Lw8khctoiE7m9L8fGpOOKHVbe36a2hES9ZoyQnRkzVackLI+zRavfYdLZeneoxer71G+pgxxBYWsuN3v6N87Fj8yclexzLGGMCKRsTwbd9O+qRJJD3xBHUHHEDRU09R94tfeB3LGGN2YkUjAvR+7jnSb76ZmJISyq+/nvI//xl69/Y6ljHG/IAVDQ/FbN7sDDC4YgW1P/0p2x59lPoBA7yOZYwxbbKi4QW/n0RV0idPxlddTdm4cVRcfTXE2V+HMSay2bdUmMV++y3pY8fS+/XXqRk0iJLbb6fhxz/2OpYxxrhiRSNcGhpIXrSI1OnTISaGkmnTqLz88qAMMGiMMeFiRSMM4r74whkC5N13qT7pJErz8mjIzvY6ljHGdJoVjVCqqyPl3ntJvesu/ElJbL/7bqpycsDGizLGRCkrGiES/+9/k3HjjcSvXUvV2Wc7AwxGyXDLxhjTFisawVZVReqdd5Iyfz6NmZkUP/gg1aef7nUqY4wJCisaQZTw9ttkjBpF3Ndfs2PoUMomTMCfnu51LGOMCRorGkHgKy8n7bbbSF6yhPp996Vo2TJqf/lLr2MZY0zQWdHYRb1efZX03FxiN22i4g9/oHzMGPxJkT7xqjHGdI0VjS6KKS4mbeJEkgoKqDvoIIqefpq6n//c61jGGBNSVjQ6y++n97PPkj5+PDGlpZT/5S+UX3cd9OrldTJjjAk5KxqdEPPdd6SPG0fiSy9Re/jhbFu2jPpDD/U6ljHGhI0VDTf8fpIee4y0W2/FV1tL6YQJ7Bg2zAYYNMb0OJ5/64nIhcAk4BBgoKq+08Z264ByoAGoV9WjwpEv9ptvyBg9ml5vvMFbCb/i8toHqFq4P7l9y8nJqQpHBGOMiRieFw3gYyAHmO9i28GqGp7JexsaSF6wgNQZM6gnjuvi7+We2qvxEwMbYcwY5/kLKxzGmJ7E8yFWVXWtqn7udY7m4j7/nLgTTyR98mRqjzuO4zI+Ym7dNU7BCKiqiiEvL9XDlMYYE36eF41O8AMrReRdERkekiPU1pIyaxZ9TjsN31dfsf2eeyhevJh3N+/X6uaFhbEhiWGMMZEqLJenROQVYK9WVt2sqk+73M1xqlooIn2Bl0XkM1V9vY3jDQeGA6gqmS4GCvS98w6xw4cT88knNFx0Eb7Zs0nebTeSgX32gW+//eFn9tkHV/sOpbi4OM8zuGVZgy9ackL0ZI2WnOBNVp/f7w/rAdsiIq8Bo9rqCG+x7SSgQlXvcLFrf2FhYZsrfVVVpN5+O8n3309j376UTJ9OzZAhZGZmUlTkdJ8UFCQyZkw6VVXfN8wSExvJzy/1vE+jec5IZ1mDL1pyQvRkjZacELqsWVlZAK3O4RAVl6dEJFlEUpteA0NwOtB3ScKbb9LnlFNImT+fyqFD2bJ6NTVDhvxgu5ycKvLzS8nOrsfn85OdXR8RBcMYY8LN87unROR8YA7QB3heRD5Q1dNEJAt4QFXPBPYEnhIRcDIvVdUXu3pMX1kZaVOnkvzoo9T370+RKrXHHdfuZ3JyqqxIGGN6vIi5PBVCO12e6vXyy2Tk5hKzZQs7hg+nfNQo/ImJP/hQtDRRoyUnWNZQiJacED1ZoyUneHN5yvOWRrjEbNtG2i23kLR8OXUHH0zxAw9Qd8QRXscyxpio0iOKRuLy5aRNmEBMeTllo0ZRce21kJDgdSxjjIk6PaJo7HbttdQecQTb7riD+oMP9jqOMcZErR5RNEonTmTH738PsfYwnjHG7Ioe0RHudQBjjIlC0fucxi7ydeWXiLzb1c+G81e05LSsPTtnNGWNlpxhyNqqnlA0jDHGBIkVDWOMMa5Z0WjbAq8DuBQtOcGyhkK05IToyRotOcGDrD2hI9wYY0yQWEvDGGOMa1Y0jDHGuNYjHu5zQ0QuBCYBhwAD25rXQ0TWAeVAA1CvqkeFK2Pg+G5zng7MBmJxRgvOC1vI7zPsDjwO9AfWAaKq21vZbh0enNOOzpGI+ALrzwQqgStV9b1wZGvJRdYTgaeBrwOLClR1SlhDOjkWAmcBW1R1QCvrI+Kcush5IhFwPgNZ9gGW4Exk1wgsUNXZLbYJ23m1lsb3PgZygFZnA2xhsKr+LNwFI6DDnCISC9wDnAEcCgwVkUPDE28nucCrqnog8GrgfVvCek5dnqMzgAMDv4YD94UjW0ud+Pv8e+Ac/syrLzhgEXB6O+sj4pzScU6IjPMJUA+MVNVDgKOBa738t2pFI0BV16rq517n6IjLnAOBL1X1K1WtBZYB54Y+3Q+cCywOvF4MnOdBhra4OUfnAktU1a+qbwMZItIv3EGJnL/PDgWmYC5uZ5OIOKcuckYMVd3U1GpQ1XJgLZDdYrOwnVcrGp3nB1aKyLuBucgjUTawvtn7DfzwH1k47Kmqm8D5hw/0bWM7L86pm3MUKefRbY5jRORDEXlBRA4LT7ROi5Rz6kbEnU8R6Q8cAfyzxaqwndce1achIq/gXBds6WZVfdrlbo5T1UIR6Qu8LCKfBX5qCZog5GxtCICQ3FvdXtZO7Cbk57QVbs5R2M5jB9zkeA/YT1UrRORMYDnOpYpIEynntCMRdz5FJAV4ErhBVctarA7bee1RRUNVTwnCPgoDv28RkadwLh0E9QsuCDk3APs0e783UNjGtrukvawisllE+qnqpkBTeUsb+wj5OW2Fm3MUtvPYgQ5zNP8SUdUVInKviGSqaqRNQRcp57RdkXY+RSQep2A8qqoFrWwStvPao4rGrhKRZCBGVcsDr4cAXnaQtWUNcKCI7A9sBC4GLvEgxzPAFUBe4PcftJI8PKduztEzwAgRWQYMAkqbLreFWYdZRWQvYLOq+kVkIM6l521hT9qxSDmn7Yqk8xm4M+pBYK2q3tnGZmE7r1Y0AkTkfGAO0Ad4XkQ+UNXTRCQL5xbHM4E9gadEBJxzt1RVX4y0nKpaLyIjgJdwbtFcqKqfhDNnQB6gIvJ74FvgwsCfwfNz2tY5EpE/BtbPA1bg3ML4Jc5tjFeFOtcuZL0AuEZE6oEq4GJVDftlHxF5DDgRyBSRDcBEIL5Zzog4py5yRsT5DDgOuAz4SEQ+CCwbB+wL4T+vNoyIMcYY1+zuKWOMMa5Z0TDGGOOaFQ1jjDGuWdEwxhjjmhUNY4wxrlnRMBFLRCaJyCNe52iNiFwqIiuDsB+/iBwQjEzhICKfBEaAjei/HxM69pyG8YyIVDR7mwTU4AyPDnB1+BO1LjDez9dAvKrWA6jqo8CjYTj2WcAtwGFANfACMFZVN4bh2IuADao6vmmZqkbEGEzGO9bSMJ5R1ZSmXzgP/53dbFnIv5CbiEhE/vAkIhcAS3HmScjEKRy1wN9FJMPDaKYHi8j/LMY0kyAiS4DzcQrLFU0TTwWeLJ8D/AqoAGap6t2Bdb2AGYAE9qM4P6HXBC6vPBL47F9wBkm8AhgD/AHIwJn/44+qWsz342CVBJ5cPxX4CTBMVY8PHO8w4C7g50AdMFtVbwsMQTEbZ9KsKpzxg24MDHHepsDQETOBqc0KaJWIDAP+DfwZmCwik4ADVPW3gc/1p1mrSESuCvy59ga2AjNUdX5g26bzMAsYi9PKG6eqDwVGG74U8IvIDcBqVT1bnAmzhqnqK61kPhq4E2fOj2+AP6vqa4F1V+K0mPoARcD4cP5gYILHWhom0p2DM39EBs74OnMBRCQGeBb4EGcI6JOBG0TktMDnbsaZsOZnwOE4gyCOb7bfvYDdgf1wJq25Hme+jxOALGA7zsRH4BQlgIxAK+it5gFFJBV4BXgx8NkDcIoOOF/Ef8FpKRwTyPknF3/un+AME/HX5gtVtRGn8AxxsQ9wBok8C0jDGVpilogc2Wz9XkA6zjn8PXCPiOymqgtwLr/lB/7MZ7d3EBHJBp4HpuKc11HAkyLSJzCm2N3AGaqaChwLfOAyv4kw1tIwke4fqroCQEQeBm4ILP8F0KfZjGpficj9OIP5vYTzU/J1qrol8NnJwHxgQmD7RmCiqtYE1l8NjFDVDYH3k4BvReQyFxnPAr5T1ZmB99UE5jtQ1XebbbdORObjFKa7OthnZuD31gad24TzE3uHVPX5Zm//Fui8/yXO0N/gtIqmBPpqVgT6mX4CvO1m/838FljR9HeF03p7B2c8pCdwzvcAEfk2MJBexA1SaNyxomEi3XfNXlcCvQN9EPsBWSJS0mx9LPD3wOssnEskTb4JLGuyVVWrm73fD2fgxMZmyxpwBlTsyD7Af1tbISIH4VyyOQqnsz8OeLe1bVtoGoK7H9/PU02zZVtd7AMROQNnML6DcK4sJAEfNdtkW1PnfkAlkOJm3y3sB1woIs1bJPE4l7V2iMhFOK2PB0XkDZzpSz/rwnGMx6xomGi1HvhanfnHW1OI80XWNLrvvuw8v0DLkTrXA79T1Tda7khE9nORZWgb6+4D3geGBoZ/vwFnBNWOfI4zR8KFQH6zLDHAb3Au1QHswCkETfZqtm0vnEtZlwNPq2qdiCyn9Ql7WtOZ0UzXAw+r6h9aW6mqLwEviUgiziWs+3FaPCbKWNEw0epfQJmIjMW5Xl6L09mcqKprgMeA8SKyBufL7xacTt+2zAOmicgVqvqNiPQBjg3MlLgV5/LKj4D/tPLZ54A7AwXhPiABOFRV/wmkAmVAhYgcDFyDi1ZCYB6HUcD9gaG7n8Lpe7gN59LVnMCmHwBjRWRfoBS4qdluEoBegePVB1odQ4CPOzp+wObAn9mNR4A1gT6lV3BaGUfjDNVdhzPHw6s4NwNU8P2t1SbKWEe4iUqq2gCcjdPR/TXO5ZwHcL5Ywflp9h2cO40+wrmGP7WdXc7G+el9pYiU41zTHxQ4ViUwDXhDREoCdwk1z1KOc0fV2TiX074ABgdWj8KZMKkc56frxzvxZ3wcZx6Fv+BMALQJpy/nhGZzr78c2Oe/cS57Pdci1/U4d45tD+R4BvceBA4N/JmXd5B1PXAuzjwPW3FaHqNxvmNigJE4Lb1inD4dNzcDmAhk82kYEyVEZAhOC+pkVf3A4zimh7KWhjFRQlVXAlfiXPYxxhPW0jDGGOOatTSMMca4ZkXDGGOMa1Y0jDHGuGZFwxhjjGtWNIwxxrhmRcMYY4xr/w+YPzQoa4ogrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = results.resid # residuals\n",
    "fig = sm.qqplot(res, fit=True, line=\"45\")\n",
    "shapiro_res = stats.shapiro(res);shapiro_res \n",
    "print(f\"The residual values are well distribuited p-value({np.round(shapiro_res[1], 2)})is superior than 0.05.\\n The normality assumption in the residual values is met\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot\n",
    "plt.scatter(x1,y)\n",
    "# Define the regression equation, so we can plot it later\n",
    "yhat =2772.4278*x1 + 1.41e+04 \n",
    "#Schadenhat= SDNcoefficient * SDNOriginalValues + SchadenConstant\n",
    "\n",
    "# Plot the regression line against the independent variable (SAT)\n",
    "fig = plt.plot(x1,yhat, lw=2, c='red', label ='regression line')\n",
    "# Label the axes\n",
    "plt.ylabel('Total Schäden')\n",
    "plt.xlabel('Total Single Damage Number')\n",
    "plt.yticks(range(0,250000,50000));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When it happens one unit increase in the number of Damage cases, the Damage price increases by average of 2772.43 units."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
    "y = df[\"Total Schäden\"].values.reshape(-1,1)\n",
    "x= df[\"Total Single Damage Number\"].values.reshape(-1,1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=0, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 15169.89]\n",
      " [237573.26]\n",
      " [183647.93]]\n"
     ]
    }
   ],
   "source": [
    "# print(X_train)\n",
    "# print(X_test)\n",
    "# print(y_train)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regressor = LinearRegression()  \n",
    "# regressor.fit(X_train, y_train) #training the algorithm\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X = X_train.reshape(-1, 1), y = y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17226.20241845]\n",
      "[[2706.56138956]]\n",
      "0.6608128924143306\n"
     ]
    }
   ],
   "source": [
    "#To retrieve the intercept:\n",
    "print(regressor.intercept_)\n",
    "\n",
    "#For retrieving the slope:\n",
    "print(regressor.coef_)\n",
    "\n",
    "print(regressor.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6268941816557636"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def adj_r2(x,y):\n",
    "    r2 = regressor.score(X_train, y_train)\n",
    "    n = x.shape[0]\n",
    "    p = x.shape[1]\n",
    "    adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
    "    return adjusted_r2\n",
    "\n",
    "adj_r2(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15169.89</td>\n",
       "      <td>46998.377704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>237573.26</td>\n",
       "      <td>171500.201623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183647.93</td>\n",
       "      <td>228337.990804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual      Predicted\n",
       "0   15169.89   46998.377704\n",
       "1  237573.26  171500.201623\n",
       "2  183647.93  228337.990804"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "df.plot(kind='line',figsize=(6,4))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.title('Total Single Damage Number vs Total Schäden (Test set)')\n",
    "plt.xlabel(\"Total Single Damage Number\") # adding the name of x-axis\n",
    "plt.ylabel(\"Total Schäden\")\n",
    "plt.yticks(range(0,250000,50000));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 47530.54\n",
      "Mean Squared Error: 2458634402.47\n",
      "Root Mean Squared Error: 49584.62\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred).round(2))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred).round(2))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)).round(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train, y_train,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.title('Total Single Damage Number vs Total Schäden (Train set)')\n",
    "plt.xlabel(\"Total Single Damage Number\") # adding the name of x-axis\n",
    "plt.ylabel(\"Total Schäden\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "\n",
    "https://www.kdnuggets.com/2019/03/beginners-guide-linear-regression-python-scikit-learn.html\n",
    "\n",
    "https://jakevdp.github.io/PythonDataScienceHandbook/05.06-linear-regression.html\n",
    "\n",
    "https://realpython.com/linear-regression-in-python/\n",
    "\n",
    "https://www.cienciadedatos.net/documentos/py10-regresion-lineal-python.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Anaconda3",
   "language": "python",
   "name": "anaconda3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}