Applied Data Science Capstone.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# capstone project"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Data analysis and Pre-processing"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import preprocessing"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (33) have mixed types. Specify dtype option on import or set low_memory=False.\n  interactivity=interactivity, compiler=compiler, result=result)\n"
                }
            ],
            "source": "df=pd.read_csv('https://s3.us.cloud-object-storage.appdomain.cloud/cf-courses-data/CognitiveClass/DP0701EN/version-2/Data-Collisions.csv')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Pre-processing_1"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {
                "scrolled": false
            },
            "outputs": [],
            "source": "df.drop(['X', 'Y', 'OBJECTID', 'INCKEY', 'INTKEY', 'COLDETKEY', 'REPORTNO', 'SEVERITYCODE.1', 'SEVERITYDESC', 'INCDTTM', 'LOCATION', 'ST_COLDESC'\n             ,'SDOTCOLNUM', 'SEGLANEKEY', 'CROSSWALKKEY', 'EXCEPTRSNDESC', 'EXCEPTRSNCODE','SDOT_COLDESC'],axis=1,inplace=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {
                "scrolled": false
            },
            "outputs": [],
            "source": "df['STATUS'].replace(to_replace=['Unmatched','Matched'], value=[0,1],inplace=True)\ndf['INATTENTIONIND'].replace(to_replace=['NaN','Y'], value=[0,1],inplace=True)\ndf['PEDROWNOTGRNT'].replace(to_replace=['NaN','Y'], value=[0,1],inplace=True)\ndf['SPEEDING'].replace(to_replace=['NaN','Y'], value=[0,1],inplace=True)\ndf['UNDERINFL'].replace(to_replace=['N','0','Y','1'], value=[0,0,1,1],inplace=True)\ndf['HITPARKEDCAR'].replace(to_replace=['N','Y'], value=[0,1],inplace=True)\ndf.fillna(0, inplace=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0         10\n1         11\n2         32\n3         23\n4         10\n5         10\n6         10\n7          5\n8         32\n9         10\n10        22\n11        10\n12        14\n13        32\n14        30\n16        28\n17        14\n18        14\n19        32\n20        14\n21        10\n22        32\n23        51\n24        14\n25         5\n26        28\n27        10\n28        23\n29        32\n30        28\n          ..\n194640     6\n194641    32\n194642    28\n194643    20\n194644    10\n194645    16\n194647     0\n194648    10\n194649    28\n194650    32\n194651     0\n194652    32\n194653    32\n194656     2\n194657    11\n194658    14\n194659    32\n194660    52\n194661    29\n194662    32\n194663    10\n194664    10\n194665    10\n194666    10\n194667    50\n194668    24\n194669    13\n194670    28\n194671     5\n194672    14\nName: ST_COLCODE, Length: 189787, dtype: int64"
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['ST_COLCODE']=df['ST_COLCODE'].apply(lambda x: np.NaN if str(x).isspace() else x)\ndf.dropna(inplace=True)\npd.to_numeric(df['ST_COLCODE'])"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [],
            "source": "df['COLLISIONTYPE'].replace(to_replace=['Other'], value=[0],inplace=True)\ndf['JUNCTIONTYPE'].replace(to_replace=['Unknown'], value=[0],inplace=True)\ndf['WEATHER'].replace(to_replace=['Other','Unknown'], value=[0,0],inplace=True)\ndf['ROADCOND'].replace(to_replace=['Other','Unknown'], value=[0,0],inplace=True)\ndf['LIGHTCOND'].replace(to_replace=['Other','Unknown'], value=[0,0],inplace=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "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>SEVERITYCODE</th>\n      <th>STATUS</th>\n      <th>ADDRTYPE</th>\n      <th>COLLISIONTYPE</th>\n      <th>PERSONCOUNT</th>\n      <th>PEDCOUNT</th>\n      <th>PEDCYLCOUNT</th>\n      <th>VEHCOUNT</th>\n      <th>INCDATE</th>\n      <th>JUNCTIONTYPE</th>\n      <th>SDOT_COLCODE</th>\n      <th>INATTENTIONIND</th>\n      <th>UNDERINFL</th>\n      <th>WEATHER</th>\n      <th>ROADCOND</th>\n      <th>LIGHTCOND</th>\n      <th>PEDROWNOTGRNT</th>\n      <th>SPEEDING</th>\n      <th>ST_COLCODE</th>\n      <th>HITPARKEDCAR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>1</td>\n      <td>Intersection</td>\n      <td>Angles</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>At Intersection (intersection related)</td>\n      <td>11</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Overcast</td>\n      <td>Wet</td>\n      <td>Daylight</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Block</td>\n      <td>Sideswipe</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>Mid-Block (not related to intersection)</td>\n      <td>16</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Raining</td>\n      <td>Wet</td>\n      <td>Dark - Street Lights On</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Block</td>\n      <td>Parked Car</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>Mid-Block (not related to intersection)</td>\n      <td>14</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Overcast</td>\n      <td>Dry</td>\n      <td>Daylight</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>32</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Block</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>4</td>\n      <td>Mid-Block (not related to intersection)</td>\n      <td>11</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Clear</td>\n      <td>Dry</td>\n      <td>Daylight</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>23</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>1</td>\n      <td>Intersection</td>\n      <td>Angles</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>At Intersection (intersection related)</td>\n      <td>11</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Raining</td>\n      <td>Wet</td>\n      <td>Daylight</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   SEVERITYCODE  STATUS      ADDRTYPE COLLISIONTYPE  PERSONCOUNT  PEDCOUNT  \\\n0             2       1  Intersection        Angles            2         0   \n1             1       1         Block     Sideswipe            2         0   \n2             1       1         Block    Parked Car            4         0   \n3             1       1         Block             0            3         0   \n4             2       1  Intersection        Angles            2         0   \n\n   PEDCYLCOUNT  VEHCOUNT  INCDATE                             JUNCTIONTYPE  \\\n0            0         2        2   At Intersection (intersection related)   \n1            0         2        2  Mid-Block (not related to intersection)   \n2            0         3        3  Mid-Block (not related to intersection)   \n3            0         3        4  Mid-Block (not related to intersection)   \n4            0         2        2   At Intersection (intersection related)   \n\n   SDOT_COLCODE  INATTENTIONIND  UNDERINFL   WEATHER ROADCOND  \\\n0            11             0.0        0.0  Overcast      Wet   \n1            16             0.0        0.0   Raining      Wet   \n2            14             0.0        0.0  Overcast      Dry   \n3            11             0.0        0.0     Clear      Dry   \n4            11             0.0        0.0   Raining      Wet   \n\n                 LIGHTCOND  PEDROWNOTGRNT  SPEEDING ST_COLCODE  HITPARKEDCAR  \n0                 Daylight            0.0       0.0         10             0  \n1  Dark - Street Lights On            0.0       0.0         11             0  \n2                 Daylight            0.0       0.0         32             0  \n3                 Daylight            0.0       0.0         23             0  \n4                 Daylight            0.0       0.0         10             0  "
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['INCDATE'] = pd.to_datetime(df['INCDATE'])\ndf['INCDATE'] = df['INCDATE'].dt.dayofweek\ndf.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Data Visualization"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "###### Relationship between day of the week and severity\n\nFrom Monday to Friday, the number of car accidents is increasing, while from Friday to Sunday that is decreasing. However, the severity ratio of 1 to 2 is roughly 7:3 no matter what day of week it is. Thus, I think  day of the week has a little effect on the severity of a car accident."
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd370963748>"
                    },
                    "execution_count": 8,
                    "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": "pic=df.groupby(['INCDATE'])['SEVERITYCODE'].value_counts().to_frame()\npic.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd370a62908>"
                    },
                    "execution_count": 9,
                    "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": "pic1=df.groupby(['INCDATE'])['SEVERITYCODE'].value_counts(normalize=True).to_frame()\npic1.plot(kind='bar')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "###### Relationship between collision address type and severity\n\nCollision address type has impact on severity, block is more likely to occur car accident, but intersection is more likely to occur fital car accidents."
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd373d397b8>"
                    },
                    "execution_count": 10,
                    "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": "pic2=df.groupby(['ADDRTYPE'])['SEVERITYCODE'].value_counts().to_frame()\npic2.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd374a12860>"
                    },
                    "execution_count": 11,
                    "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": "pic3=df.groupby(['ADDRTYPE'])['SEVERITYCODE'].value_counts(normalize=True).to_frame()\npic3.plot(kind='bar')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "###### Relationship between collision type/junction place and severity\n\nThese factors have impact on severity."
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3710023c8>"
                    },
                    "execution_count": 12,
                    "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": "pic4=df.groupby(['COLLISIONTYPE'])['SEVERITYCODE'].value_counts().to_frame()\npic4.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3745d5940>"
                    },
                    "execution_count": 13,
                    "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": "pic5=df.groupby(['COLLISIONTYPE'])['SEVERITYCODE'].value_counts(normalize=True).to_frame()\npic5.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd37145cc50>"
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "pic6=df.groupby(['JUNCTIONTYPE'])['SEVERITYCODE'].value_counts().to_frame()\npic6.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd371450748>"
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "pic7=df.groupby(['JUNCTIONTYPE'])['SEVERITYCODE'].value_counts(normalize=True).to_frame()\npic7.plot(kind='bar')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "###### Relationship between weather/road conditions/light conditions and severity\n\nThese factors also have influences on severity; however, there are some conditions are too rare, so I will adjust these conditions before one hot encoding to simplify the madel."
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd374845780>"
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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gZmZ1XDmYmVkdVw5mZlbHlYOZmdUpXDlIGi7pNkkX59trSbpJ0n2SzpW0WC5fPN+enu9fsybjiFx+r6SdasrH57Lpkg4v79czM7N2tHLk8Hng7prb3wF+FBFjgKeB/XL5fsDTEbEO8KO8HZI2APYA3gqMB07KFc5w4ERgZ2ADYM+8rZmZ9UihykHSaOD9wM/zbQHbAb/Nm5wJfChf3zXfJt+/fd5+V+CciHgpIh4ApgNb5Mv0iLg/Il4GzsnbmplZjxQ9cvh/wJeA1/PtFYFnIuLVfHsGsGq+virwCEC+/9m8/Zzyfo8ZqNzMzHqkaeUgaRfg8YiYWlvcYNNocl+r5Y32ZaKkKZKmzJw5c5C9NjOzThQ5ctgG+KCkB0lNPtuRjiSWk7RI3mY08Gi+PgNYDSDfvyzwVG15v8cMVF4nIk6JiHERMW7UqFEFdt3MzNrRtHKIiCMiYnRErEk6oXxlROwFXAXsljebAFyYr1+Ub5PvvzIiIpfvkXszrQWMAW4GbgHG5N5Pi+XnuKiU387MzNqySPNNBnQYcI6kY4DbgFNz+anALyRNJx0x7AEQEXdJOg/4G/AqsH9EvAYg6QDgMmA4cFpE3NXBfpmZWYdaqhwi4mrg6nz9flJPo/7bvAh8dIDHfxP4ZoPyS4BLWtkXMzMbOh4hbWZmdVw5mJlZHVcOZmZWx5WDmZnVceVgZmZ1XDmYmVmdTsY52AJqzcP/2HSbB499fxf2xMx6xUcOZmZWx5WDmZnVceVgZmZ1XDmYmVkdVw5mZlbHlYOZmdVx5WBmZnVcOZiZWR1XDmZmVseVg5mZ1XHlYGZmdVw5mJlZnaaVg6QlJN0s6a+S7pJ0dC5fS9JNku6TdK6kxXL54vn29Hz/mjVZR+TyeyXtVFM+PpdNl3R4+b+mmZm1osiRw0vAdhGxMTAWGC9pK+A7wI8iYgzwNLBf3n4/4OmIWAf4Ud4OSRsAewBvBcYDJ0kaLmk4cCKwM7ABsGfe1szMeqRp5RDJ8/nmovkSwHbAb3P5mcCH8vVd823y/dtLUi4/JyJeiogHgOnAFvkyPSLuj4iXgXPytmZm1iOFzjnkb/jTgMeBScA/gGci4tW8yQxg1Xx9VeARgHz/s8CKteX9HjNQeaP9mChpiqQpM2fOLLLrZmbWhkKVQ0S8FhFjgdGkb/pvabRZ/qkB7mu1vNF+nBIR4yJi3KhRo5rvuJmZtaWl3koR8QxwNbAVsJykvpXkRgOP5uszgNUA8v3LAk/Vlvd7zEDlZmbWI0V6K42StFy+viSwA3A3cBWwW95sAnBhvn5Rvk2+/8qIiFy+R+7NtBYwBrgZuAUYk3s/LUY6aX1RGb+cmZm1p8ga0qsAZ+ZeRcOA8yLiYkl/A86RdAxwG3Bq3v5U4BeSppOOGPYAiIi7JJ0H/A14Fdg/Il4DkHQAcBkwHDgtIu4q7Tc0M7OWNa0cIuJ2YJMG5feTzj/0L38R+OgAWd8Evtmg/BLgkgL7a2ZmXeAR0mZmVseVg5mZ1XHlYGZmdVw5mJlZHVcOZmZWx5WDmZnVceVgZmZ1XDmYmVkdVw5mZlbHlYOZmdVx5WBmZnVcOZiZWR1XDmZmVseVg5mZ1XHlYGZmdVw5mJlZnSIrwZnZAmDNw/846P0PHvv+Lu2JzQ985GBmZnWaVg6SVpN0laS7Jd0l6fO5fAVJkyTdl38un8sl6XhJ0yXdLmnTmqwJefv7JE2oKd9M0h35McdL0lD8smZmVkyRI4dXgUMi4i3AVsD+kjYADgcmR8QYYHK+DbAzMCZfJgInQ6pMgKOALUlrTx/VV6HkbSbWPG5857+amZm1q2nlEBGPRcSt+fos4G5gVWBX4My82ZnAh/L1XYGzIrkRWE7SKsBOwKSIeCoingYmAePzfSMi4oaICOCsmiwzM+uBls45SFoT2AS4CVg5Ih6DVIEAK+XNVgUeqXnYjFw2WPmMBuVmZtYjhSsHScsA5wMHRcRzg23aoCzaKG+0DxMlTZE0ZebMmc122czM2lSocpC0KKli+FVE/C4X/zs3CZF/Pp7LZwCr1Tx8NPBok/LRDcrrRMQpETEuIsaNGjWqyK6bmVkbivRWEnAqcHdE/LDmrouAvh5HE4ALa8r3yb2WtgKezc1OlwE7Slo+n4jeEbgs3zdL0lb5ufapyTIzsx4oMghuG2Bv4A5J03LZkcCxwHmS9gMeBj6a77sEeB8wHZgN7AsQEU9J+gZwS97u6xHxVL7+WeAMYEng0nwx65gHfpm1p2nlEBF/pvF5AYDtG2wfwP4DZJ0GnNagfAqwYbN9MTOz7vAIaTMzq+PKwczM6rhyMDOzOq4czMysjisHMzOr48rBzMzquHIwM7M6rhzMzKzOArNMqEfCmpmVx0cOZmZWx5WDmZnVceVgZmZ1XDmYmVkdVw5mZlbHlYOZmdVx5WBmZnVcOZiZWR1XDmZmVseVg5mZ1Wk6fYak04BdgMcjYsNctgJwLrAm8CDwsYh4WpKA44D3AbOB/46IW/NjJgBfybHHRMSZuXwz4AxgSeAS4PN5HWozq5BmU9SAp6lZkBQ5cjgDGN+v7HBgckSMASbn2wA7A2PyZSJwMsypTI4CtgS2AI6StHx+zMl5277H9X8uMzPrsqaVQ0RcCzzVr3hX4Mx8/UzgQzXlZ0VyI7CcpFWAnYBJEfFURDwNTALG5/tGRMQN+WjhrJosMzPrkXbPOawcEY8B5J8r5fJVgUdqtpuRywYrn9GgvCFJEyVNkTRl5syZbe66mZk1U/YJaTUoizbKG4qIUyJiXESMGzVqVJu7aGZmzbRbOfw7NwmRfz6ey2cAq9VsNxp4tEn56AblZmbWQ+1WDhcBE/L1CcCFNeX7KNkKeDY3O10G7Chp+XwiekfgsnzfLElb5Z5O+9RkmZlZjxTpyvpr4D3ASEkzSL2OjgXOk7Qf8DDw0bz5JaRurNNJXVn3BYiIpyR9A7glb/f1iOg7yf1Z5nZlvTRfzCrB3TdtYdW0coiIPQe4a/sG2waw/wA5pwGnNSifAmzYbD/MzKx7PELazMzquHIwM7M6rhzMzKyOKwczM6vjysHMzOq4cjAzszquHMzMrI4rBzMzq+PKwczM6rhyMDOzOq4czMysjisHMzOr48rBzMzquHIwM7M6rhzMzKyOKwczM6vjysHMzOq4cjAzszqVqRwkjZd0r6Tpkg7v9f6YmS3Mmq4h3Q2ShgMnAu8FZgC3SLooIv7Wzf1otpi8F5I3s4VFJSoHYAtgekTcDyDpHGBXoKuVg5lZt1T9y2hVKodVgUdqbs8AtuzRvlgJmr3xofmbv+r/PGa9NpT/I4qIth9cFkkfBXaKiE/l23sDW0TEgf22mwhMzDfXA+4dJHYk8ESHu9ZpRhX2oSoZVdiHqmRUYR+qklGFfahKRjf2YY2IGFUoKSJ6fgG2Bi6ruX0EcESHmVNK2K+OMqqwD1XJqMI+VCWjCvtQlYwq7ENVMqqwD7WXqvRWugUYI2ktSYsBewAX9XifzMwWWpU45xARr0o6ALgMGA6cFhF39Xi3zMwWWpWoHAAi4hLgkhIjT6lARhX2oSoZVdiHqmRUYR+qklGFfahKRhX2YY5KnJA2M7Nqqco5BzMzqxBXDmZmVqcy5xwWBJKGARsDbwJeAO6KiH93O6MKJK0EbMPc3+NOUje717uZkXOWr8l4sNXHl5XRa1V6TarA74vBLRDnHCSNJnV/fSfzvmn/CFxa5AXrJEPS2sBhwA7AfcBMYAlgXWA28FPgzKHOyDlLALs0+j2K9gDrJEPStsDhwArAbcDjNb/H2sBvgR9ExHNDnLEssD+wJ7AYc/+eKwM3AidFxFVNfpcyMrYGPkH6W67CvO+rX0bEs4M9voyMqrwmNVnjqH9vXRERTzV7bKcZfl8UN99XDpJOJ02/cTEwhXnftNsCmwGHR8S1Q5Uh6dfAycB10e8Pmr9pfRx4OiLOHGQfysj4GvAB4GpgaoPfYwngkIi4fagyJH0P+HFEPNzgvkVIlc7wiDh/kH0oI2MScBbwh4h4pt99mwF7A3dExKlDlSHpUuBR4EIav68+APwwIgYc01NSRlVek/8G/hd4gPr31jakD7b/a/QcZWX4fVHcglA5bBgRdw5y/2LA6hExfSgzqkDS+yNiwMlWciWzekRMGcoMSySNjIhBp0Notk0ZGVUhaX/SGKYXBrh/LLBiREweyoxem1/eF/N95VB1kt4bEZMKbjsCGBUR/+hXvtFg3/bnF5L2jYjTC267Pulo7qaIeL6mfHxE/KnD/Vg/Iu5pYftFI+KVfmXzxQdyM1V5TarA74t5LdC9lfKhV5HtRkg6VtIvJH28330ndbgbAx6e9nuejwH3AOdLukvS5jV3n9HhPiCp0OAYScMlfVrSNyRt0+++r3S4G0cX3If/JR0uHwjcKWnXmru/1eE+AFxecD+2lTQDeFTS5ZLWbCVD0mqSzpF0naQjJS1ac9/vW93pBvl3dJpBRV4TSV9tYdudJO3X7/VA0ic73I2uvC+aZBd6TYf6vQULQG8lSZsOdBcwtmDM6aSTwOcDn5T0EeDjEfESsFWBfRioXU/AigX34Uhgs4h4TNIWwC8kHRkRv8s5TUlaYZD9eF/B/fgpsBRwM3C8pGsi4uB834eBY5rsw0BHOCKdsCvif0h/i+fzP95vJa0ZEcdR/G9x/CD7sVzB/fguabbguyTtBkyStHdE3FhwP04jvaduBPYDrpH0gYh4ElijyA5I+vBAdwFvLJhRidekiU8BX2+2kaRvk84t3AocKen/RcSP890HkP7mgz2+5++LMl5TSnhvNTPfVw6kSfuuofGLUvTFXjsiPpKv/17Sl4ErJX2w4OPfSeo18Hy/cpEWMipieEQ8BhARNyv1DrlYqRdV0ba/mcBDzPu3iHx7pYIZW0TERgCSTgBOkvQ7Us+MIh8CKwM7AU/3KxdwfcF9GN7XbBERD0p6D+nDaI2C+wCwL3AI8FKD+/YsmLFYX++siPitpLuB3yktY1vkNRkVET/J1w+U9Ang2vy+Kvqangv8aoDtlyiYUYnXRNJAPZkELFlwP3YBNok0H9vXgLMlvTkivlBwP6rwvijjNS3jvTW4KGl6115dSL0Txgxw3yMFM+4GhvUrmwDcBTxU4PGXAtsOcN+1BffhelIlVVv2BmAy8FLBjPtIJ4s7+Vvc06Dsq8BfgPsKPP5U4B0D3Hd2wX24Ehjbr2wRUg+R11rIePsA9z1QMGMK8MZ+ZaOBacCsAo+/C1iiX9kOwHTgsYL7MBXYsMPXtCqvycPAyh3+Lnf3uz08/36/IY0Jmh/eF2W8ph2/t5o+RxkhvbwAuwHrDXDfhwpmfBfYoUH5+CIfiCX9HhsD6zQoXxTYq2DG/sDGA9x3YMGMXwLjG5R/CnilS3+L0f3/+Wru26ZgxgrAUh3uxw6N/p7AssCXCzz+C8C7G5RvAkwquA/vZOAKf1w3Xo8SX5NjSEemje77TsGMiwf4mx4DvD6fvC86fk3LeG81u7i3kpnNNyQtCRANurJKWjUi/tn9vVowuXIwM7M6C3RXVjMza48rBzMzq7PAVg6SxklatZcZks6UdLKkDXucsYqkxdt9fBkZkq6QdKmkXXqc8S1Jh0kqOv6k9AxJu0rast3nLzGjEq9JFfh9UW+BrRxIIzkvlnRuDzNOAK4gTcTVrjIyfgHcI+n7PczYB/gKnQ3QKSPjZuBV4Ec9zNgS+IoKjuAfwoxKvCaS7s6XA3qY4fdFPwv8CWlJb4iIWd3IkPTRiPhNs7KhzhggV8AGUXDa7jIycs+S1SPi3g6es6MMSdtExF+alQ11hg1O0khgyxhk0sduZNhcC0TloDS/+njSpGBBmsr2sug3nW4XMm6NiE2blQ1lhtJiQbdHRCfNUGVkfAD4Pmk06VpKs2V+PSKKjjovK6MKr8kU0hQtZ0dE/1HKRfehjIw7qB89+yxpUNcxkaZeGPKMKpA0ijQlyJrUzBQREYXnZ+o0Q42n0XiWNN33493KGMh8P32GpH2Ao0gTXvX1cd4W+JakoyPirKHOkLQzae6iVTXv3C0jSIeZRX6PjjMAIuJ1SX+VtHoMMi/+UGcAXyNNHXJ1zpymfhOlDWWG0kIobwdGSTq45q4RpFGgJYsgAAAgAElEQVS1XcnI9iBN23BLzYf85dHaN7MyMi4FXgPOrskU6cPkDNIaAEOWIWkWg0ztEBEjmj15GRnZhcB1pCbb1wo+puyM/YCtgb6Fgd5DmitpXUlfj4hfdCmjofm+cgC+TJoQrP+iG8sDN5GG9w91xqOkb04fJA2N7zOLNJKxiDIy+qwC3CXpZuA/fYWtfOMuIePViHg2tUS1rZOMxYBlSO/xN9SUP0caVd+tDCKtA/JlSf9HmhvoNOB1SacBx0WBFdDKyCCNZK6dafcOSX+JiG2U5uYpou2MiHgDgKSvA/8inccSsBfz/n2HNCNbKiIOa2H7och4HXhL5GWAJa1MWvBrS+Ba0u/WjYyGFoTKQTT+JvE6xSdp6ygjIv4q6U5gxxhkpbahzqhRaBrmIc64U2n68+GSxpBW7yo6yVvHGRFxDWmmyjMi4iGY01y2TBRYyrKsjD6SNiJ9838faTbNXwHvIM9Z1KWMZSRtGRE35bwtSJUfFD86LSNjp4io7VFzsqSbSNPYFNVpxsWS3hcRl7TwnGVnrBnzrg//OLBuRDwl6ZWBHjQEGQ0tCJXDN4FbJV0OPJLLVgfeC3yjWxkR8ZqkFSUtFhEvF977kjNyzjXtPrbEjANJR2QvkZogLqf461FmxrclfYZ02D8VWFbSDyPie93KkDQVeIY0QdzhkaaCB7hJ/dbMGMoM0vxYp0lahvSl5zngU5KWBr7dxYzXJO0FnEP6UrYnrTfLdJrxedKU3y8Br5C/ILbQLFVGxnWSLiZNGgjwEdLMqkuTXutuZTS0oJyQXp40JfGqpBdoBulkcuETdyVl/BTYFLiIeZtiftjljK2AHwNvITWNDAf+08obv9MMpbn+H+xXtnlE3NLCPpSRMS0ixuYPks2Aw4Cpkacl70aG0pTS9xd9vqHKqMlalvS/3/aHRycZ+bzRcaR1GYI04+9B/V/roc7oNaX20o+QfgcBfwbOb+U8UhkZA5nvjxwkKX+An9NkmwH/WGVkZI/myzBaa/8sO+ME0onC3wDjSH3Rx3Q543dKi4/8E0DSu4ATgbd1OWNRpVWyPgScEBGvSGr1H6etjNqT2I3OmxSp8MvIqHn84qQPkjWBRfryIqLpIjtlZuQP8F2bbTcUGcpLgWqARcIi4tZuZOTtAvhtvrSljIyBzPeVA3CVpPOBC2t71khajNQeO4F0Jv+MIc4gIjpu6y8jI+dMlzQ8Il4DTpfUant/pxmfJi2c9AHSkdC3KL4aXZkZPwUeBP5KOtxeg9QU0o2Mvsp9PWBz0tEgpB491xZ87jIy+lxI6lU0lcaL3XQlQ73tRnowMBH4QYP7AtiuwNOXkdHXDfU7pIW4RBtNW2VkDJg9vzcrSVoC+CSpt8JapHa2JUnfvC8HToyIaUOZobRU4UGS/kCDE9tFeviUkVGTdS1pzvmfk3p0PAb8d0Rs3OWMrUkfrC8C74+ImUUfW2ZGg8xFIqJw9+BOM/K5rI9EHkgp6Q3AbyJifAvPV0bGndHB2JUSM64ndQGdSs15gog4v5sZvSZpOvCBiLi7lxkDZs/vlUOtfOg/Enih3fbUdjIkbRYRUyW9u9H9RU7ulpFRk7UG8G/SuYIvkBYhOSlSd8ghzWhQuW1Aqliezr9HkYqy44x+ee8H3krNEoytNIN0miHpHtLiMC/l24sDf42I9Vt4/jIyTgF+HBGFFrEfwoxpEVF0ffehzNiQ9N6qfU2LdH0vJUO5C3ArzzcUGQNmL0iVQxXkw106+YZbUkZPpq4YqHLrU7Ci7DijJusnwFKkQY0/J41PuDki9utWhtKa5B8DLiBVev8FnBcR32phH8rI+BuwDvAAqUmorwmilZPzZWQcA1wfHXQj7TRD0lGkAWMbAJcAOwN/jojC41c6zZB0HPBG4PfUNNFFxO9a2IeOMwbMduXQudxj4CjgANI/yzBSn+8ft/DtsuOMmqyqTF2xMqmdHNKHacvD+TvNkHR7RGxU83MZ4HcRsWOXMzYlLQ8JaV3x21r5PcrIyEeDdSKP4ehixixgadKHWVvdSDvNUJoGZGPgtojYOL/Pfh4RRUaJl5Ih6fQGxdHiuZeOMwayIJyQroKDSF3JNo+IByB1PSQNzPlCRBSZpbGMjD5fo4dTVwBI+hjwvfx4AT+WdGhEFO5VUUYG0Lec5GxJbwKeJJ1XakVHGZJWB54gfeufUxYtTE3SSYakEZEG7bU9AWUZGX0ij3LuccYLkaaJeVXSCNLgsTd3MyMi9m3x+YYkYyCuHMqxD/DeiHiiryAi7leaTuByik3hW0ZGn15PXQFp8Nrmfd/0c1PZFbTW5a6MjIslLUeqZG4lNcn8vIXHl5HxR+aeQ1mSVLHcSzqH0Y2Ms0lTbkzNGbUvalDsA63jDFWoGykwJb+mPyP9Ts+TptxuRVsZkr4UEd+V9GMadz75325kNOPKoRyL1n6o94mImfkEd7cy+vR06opsWL8moCdpff2QjjMiom9E9flKI0mXiIhnu5kREfOMy8gfbJ9ucR/azoiIXfLPVo+YSs2gQt1II+Jz+epPJP0JGBERtxd5bAkZfT2LprTyfEOQMSifcyiBBpm+ebD7ys6o2X4p0rfuHUnf8C4DvhERL3YrQ9L3gI2AX+ei3UnTCH+phX0oI2N/4FeRe54pjYTfMyJO6mZGg8yWXtOyMpRWNlyDeccGtDReooyMqpG0HvDFiPifbmWoYiPn67JdOXRO0mukqS76T+An0rfMpt/8y8ioGkm1w/qvjYgLmjyk9Aw16PIo6baI2KRbGZp3uu9hpAF9K0bETi3sQxkZ3yFVsH9j7tiAaLGTQRkZ15EG8F0H/CXaWIyr3QylyQu/D7yJ1MPnx8BJpFlMf1Dk3F4ZGTnnWtJ0Pbf0/S7RYhfhMjIGzHblsODQAAPo+hT5By4jo1/eCOb9hllkaunSMiTdThofEPn2cNJCRoXb+zvNUOry2OdV0mjr81s8kisj415go5g7aV/LSsp4M2nmgXcCW5F6HF0XEYWnpm83Q2nm1pOBG0iLe32JdD7l/1o4Ku44oyZrMVJvvPeQmgmXiYgVup3RiM85lEBp8ZW/kBZCubrVN0hZGaRvM50qIwNJnwa+Turp0zf1edGTn6VlkE7mn6c0ViGAzwB/auHxHWdEnhJFaVRzRMTzLT5/KRnA/cCitD91RikZuaPFC8DL+bItaYLHbmQsHhFn5Ov3SvoiaZbbVmZ0LSMDSX2V2zuB5YCLSUdCXc0YMNtHDp2TtAjpW8x40pv0SVIb/aUR8fcuZowCRkXE3/qVvxV4PAoMqisjI29/H7B1o5PsRZWUMYx0AnMHUuVyOakveuF/5E4zlEbR/gLo+zb3BDAhIu5sYR/KyDif1C9/MvMOmCrcs6WkjH+Q9v9s0gfZtIh4vejjO8lQGmm+J3N7W/0K+Hjf7YI9pjrOyDmvkU4ofxu4JNqYpr+MjAGzXTmUT9IqpNGS40kzmd5Q07NhyDIknQOcHP1GEEvaifRB8vECz9txRt7+T8CHI2J2ke2HIiM3/5wZEUVXORuqjOuBL0fEVfn2e4BvRcTbu5wxoVF5tLC4VEkZnyd9EVoNuAe4hnQ+6R9DnSHpqkHujoho2tupjIycsxzpfNq7SM1Cr5P+z/+vyOPLyhgw25XD0MrfOreOiL8MdYakuwZqB1fBCdPKyMjbbkJa5/gm2v+GWUbGZaSJydr+RtVphqS/Rr8JCxuVDXVG1SiNNN8X+CIwOiJaWZe7tIxekvQW4N2kZqG3Aw9HxKDTxwxFRiM+51CCCp0IHqxHU+HxFiVkQJpJ9UrgDtK3mXaUkfEg8BdJbS+eVELG/UprP/et5/sJ0txErWg7Q9J5EfExpekeGg2YajovUhkZNVk/IH3rX4Z0UvertN7W3nFGr+WmsXtJC/T8BNi31S8gZWQMxJVDOfpO4n6YNAnWL/PtPUkfLN3KuE8N1rSVtDPpRGK3MiCNsD64+WZDnlGFBZg+SVqT+3fkLrmkb7vdyvh8/rlLi89ZdkafG4HvxrxrH/cio9fGtHquZYgyGnKzUokkXRsR72pWNlQZktYl9Va4njScH9IqblsDuxQ5sV1GRs75JvAQ8AfmbRJqpRtqxxk1WUtHxH+abzm0GQZKa15Pi4j/KE0PsylwXLQ2eV/HGb0maTRpjETfUqd/Bj4fETO6mTFgtiuH8ki6m7Qgzf359lqkHgSFu+l1mqE0z//Hgb5zA3cBZ7fSNbakjEZNHhERrXRlLSNja+BUUt/v1SVtDHy6lQ4C7WZUqLmxL6vna4vnjNtJPZ42IjWTnUrqeFC4nbzTDDWem+lZ4KEovohTRxmSJpF6W9U2Fe4VEe8t8vxlZQyY7cqhPJLGA6cwt/llTdKHyGXdyJCar3PdbJsyMqpEacDSbsBFkUc0t3JivZMMVW9tiynUrwu+TkR8ucsZt0bEppK+CvwzIk5V61PEdJQh6UbS0cbtpGa6DfP1FYHPRMTlQ52hxiPvW1rEqIyMAUWELyVegMVJ32g2Jg2W6VoGaWrrA0kL9NSWL0aakOxM0lKfQ5ZB6k73xprb+5DWHT4eWKHg79FxRs1jb8o/b6sp+2s3MoBRwAYNyt9KGktS5Lk7zqh5zJT88/aasut7kHENcATwd9L5teGkObO6lgGcA7y15vYGpJ5xbyY1Vw15BmmG4U/kfR+er09u8e/QccZAl1ZnybTmNiP9424M7C5pny5mjCfNd/NrSY9K+ltumrmPdGL7RzF3ZOdQZfyUNGIVSe8CjgXOIh1un1Lw9ygjo88jkt4OhKTFlEaztrrebrsZPyZ9uPc3Gjiu4HOXkdFnttJUC9MkfVfSF0gL5nQ7Y3fSOaT9IuJfpLmBvtfljPUj4q6+G5EGfW4SrU1i12nGJ0mr+/Wt0b5bLmtFGRkNuVmpRJJ+AawNTGPeScla6ZffcUbO6dV62nP63ks6EZgZEV/Ltwsd7paRUZM1kvQhWju6+fMR8eRQZ6hC407y9j1bW7xfxtLAixHxWu4AsT5pJoBXupUh6VzgKdK3f0iVzUhgb9JSn5sP9NgyM6rMlUOJ8snkDaKDP2oZGb0k6U5gbES8qjTNwMTI0zm38IHYcUZN1qjoYC3uTjIk/T0i1h3gvnsjYr1uZDTJ3yY6GKDZToakqaQBW8uTuqROAWZHxF7dylBaH/1zpLESIvXyOQl4EVgqCsxd1WmG0lQ1/0M6r1g7sWQry4R2nDEQj3Mo152k9s/HepzRS78GrpH0BGnCvOsAJK1DahbqVkaf63Oz2LmkWUzbOYpqN6MS406UpgD5GKnp5U8RcaekXYAjSavKNZ16vIyM2riImC1pP9Ia6d+VNK2Fx3ecEREvkBYMarRoUKFJDUvIuJD03r6Cua0ErSojoyEfOZRIac6VsaSlAmv75bfS3bDjjF7L3R1XAS6PPC4gH/ovE8UnJes4oyZrC1IPmw+R1iE4JyJ+OfijOs9QRcadSDqDNAfRzaQ1Bx7Kjz88In7f7PFlZdRk3Ub6xv0j0jmDuyTdEf1WuxvKDKVxEl+jftGiVrpJd5RRRq+i0nomNcp25VCegbodRmvdDTvO6CVVZ/ryRrkjgR+S+oG3NQdPqxmqwLiT3Ey3UUS8LmkJ0mym6+QTuUX3oeOMmqx3A4eQFun5jtLaDAe1eG6uo4zcXPkFUoU75xt3i+eiOsqQdAypp9clTTcewowBs105WJlUkenLa7JGAP9F+ta/NnABcF5ETB30gSVn9JL69f/vf7tbGQ0yO1mboqMMSTdFxJbtPm8ZGZJmkXp6vURawAnS79LKgMKOMwbMduVQHlVkBGqVqEfTl9c89gHSUo7nRcQNrTxvmRm9JGk20NebSKQKbnq+HlFs4r2OM2qy3kbqmrxCfvxMYJ+o6RY61BmSjiX9b/2OeZtvCzdZlpFRZa4cSqTGo0fHRMSR3cyoKnVx+vKa7UX6ZhXR5rxIZWT0Uu5+OqAoMB9RGRk1WT1f30KN12SIKLgWQycZ+W/5TEQ8m29vSzqX9SBwYhSYVbWMjKbP4cqhPJKmRMQ4Sbf3fZOSdH2Lb/qOM6pAjecEepbU5fCnRdrLO82Q9DngMNK0zgJmAd+JiJMK/RLlZexCmh+r7dkzO8mQqjWtihby9S2UpmP5r4h4VNJYUk+jb5PmiXolIj7VjYxm3JW1XPOMHiV1R+1oBGqbGVVwP2lk76/z7d1Jg6fWBX5GGig0ZBmSvkJa+GTbmDuJ4ZuB4yStEBHHNHvyMjKyPfJjzgdOj4hWR2h3mnFVftyFEfFwX2F+n70DmABcBZwxxBl9era+haRPRMQvJTWcCj4KrNFRQsaSEfFovv4J4LSI+EE+Ki7aHbeMjEG5cijX3qQ5/w8g9WJYDfhIDzKqYJOYd5rxPyhPPS6paNtyJxl7AxvXHl1EWpT+Y8BfgSIf7GVkEBGfyCe19wROlxSkOXh+HRGzupAxnjSlwq+VZvl9BliC1F5+OWlKlGYfKGVk9KldmwI6X9+ilYy+L1rtru1RRoZqrm9HmiOK3BOsmxmDixImaPKlbjKsRUmDglbqZUaP/wZ3UzN5H7A68Ld8/bahzgDuHeS+ewo+f8cZ/R4zEjiI1C58KWm+qgO7mZHfV6sAy3Xw2radQapMvtfhe6uMjNUalL2xGxmkqVjOyz8fABbN5auQJzbsRkbT5ygjZGG/kJbne2u+vixpkNQdwD+BPbuVUaUL8D7gYVJTw9WkQVPvJ33rOmioM4DJwPYNyrcDrir4/B1n5O0/QOr+ejtwKLnCB5Yizf3flYyqXIAre50BvEJqrlyqpuzWbmSQvvXvQWoZWLWmfBNgp4LP3XFGs4tPSJdANZOjSToIeE9EfEjSG0l984tMT9BxRtUoDd5an/RGvifaGxDXVoakt5KmFvgzaZBSkKYC3wbYNQp0eSwjI+ecBfw88vxQ/e7bPiImdyOjKpTWfx5D6pFXuyb37wZ8UMkZSiOsfwbsB3wsIv4h6bZW/s/azahaB4GB+JxDOWq7jb2X9IYlIv7VQvtfGRmVoTSj66eBvnMGV0v6abQ282bbGZGmU9iQNLL4rTBn3eVPF61gysjIOQNOuV70Q72MjApZgTSwsbbLZzD3/EE3MiIiTpL0V9K5rMMYZMW9kjOq1kGgIR85lCD3d/4BqQnoKtI87/9SGul7Z0Ss342MKpH0c1Lb9Jm5aG/gtWihi10nGVX4dqY0erXRfX0Dx5oObCwjw+rVfsNXGmR5LjAuIpYa6gyl6Uc+CewFNDq5f2I0OblfRkZTZbRNLewXUtfKP5G6kP13TflOwA+6lVGlCw1WSmtUNlQZVGBVPF/q/p7fJS2f2b/8C6SxI13JyNuv0u/2IsC7Wvx9ysjoaQeBQXN7/YbxZcG8ALcCa9fcfjOtn/BrO4P0LepzpAn8HiWd4H+AdFL7Z6T1IoY8I+f8okjZUGf0+pL/fsMalA8jHR13JSNvvzJwKul8HqQlPvdr8ffpOKPKF59zsKFyKKld9H5SE8gatN6Xve2MSOcETgJOUpur4pWRkc2zkltuKtysBxm9FtFghHekvvlFT6yVkQGpLf504Mv59t9JzUKndjmjslw52JCIiMmSxgDrMben0UtNHlZ6Rs55hQ4XT2onQ9IR5MVwJD3XV0zqfFBoLewyMipktqQxEXFfbWF+jV/oYgbAyIg4L/99ibTqYKuL5ZSRUVmuHGxIKK0cthNzly/cXhJRYHqCMjN6KSK+Lek7pC6obS3bWEZGhXwVuFRpDYLahYuOIA3s61YGwH8krUg+2a80G3KrqwyWkVFZrhxKNMBcK88CU6Ngz4EyMiriD6S1dO8A2p1wroyMnsrNHR1NBldGRhVExKWSPkRqLjwwF98JfCQi7uhWRnYwcBGwtqS/kObw2q2Fx5eVUVnuyloiSWeTvsX8IRe9H7iFNIjrNxHx3W5kVIFqZpXtZUYVSDoROCMibullhs0rn7fpa7K8N1oYg1NmRlW5ciiRpMtI32Cez7eXAX5LWkVsakRs0I2MKshNIZMj4vJeZlSBpL+Ruio/RBrN284COR1n2FySliJ9818jIv6n79xWRFzczYwqc7NSuVZn3pHOr5DeOC9IKnoitYyMKrgRuEBpCuFXaG/QVhkZVbBzRTJsrtNJ5yy2zrdnkGYlaOWDvYyMynLlUK6zgRslXZhvf4A0xfHSpP7Z3cqogh+Q/mnuiPYPT8vI6LnIq6RJWok0dqInGTaPtSNid0l7AuQvX63OU1NGRmUN6/UOLEgi4hvARNJQ9mdJIzm/HhH/iYi9upVREfeRBiV18qFeRkbPSfqgpPtIA+iuYe50213NqApJ60qaLOnOfHsjpYWVupnxsqQlmdvTaG1q1oHuYkZl+ZxDyXL3y5WpOSqLmomxupXRa5LOII1ovpR5F19vpStrxxlVoDQx23bAFRGxidJ6v3tGxMRuZlSFpGtIvY1+GnPnJrozIjbsVoak9wJfIY1qvpw00+5/R8TVLexDxxlV5malEkk6EDiKtJTla+Q2ctK6rl3LqIgH8mWxfOlVRhW8EhFPShomaVhEXJVPtnc7oyqWioib+7XAvNqtjNz0cw/wYWAr0v/Y5yPiiaJPXkZG1blyKNfnSb0VnuxxRs9FxNF91yUtDzzTavNQGRkV8UzudXYt8CtJj9P6h2EZGVXxRG6C6WuO2Y3WR7C3nRERIen3EbEZ8McWn7e0jKrzOYdyPULnIyTLyOgZSV+VtH6+vrikK4F/AP+WtEO3MipmV2A2aebQP5F+lw/0IKMq9gd+Cqwv6Z+kkc2f6XLGjZI2b/E5hyKjsnzOoUSSTiUNiPkj7bezd5zRS5LuAjbM36wmkhbK2Z7UR//MiNiiGxlVkUfzrkPqcXVZrzKqIndL3i3PSbQ0aYbVWT3I8NiTJtysVK6H86WTNvIyMnrp5Zqmn52AX0fEa8DdeTRptzJ6TtJJpNlUrwe+IWmL3ButqxlVkqcCOQA4LyL+0/QBQ5SBx5405SMHK5WkG4FPkU6o3wtsFhEP5PvuiWKr4nWcUQW5m+XGEfFaHk17XW6j7mpG1Uj6P9IMqucy7/rPTw11hqQPR15nWtLyEfF0a3tfTsb8YL75FlZlkv5fRBwk6Q80WNIxIj7YjYyK+Dxpuo9RwI9qPtTfB9zWxYwqeDkf8RARs9scIFVGRtX0zS67f01ZkLotD3XGV5i7zvRkYNMWnrPMjMpz5VCOX+Sf3+9xRhUMA97Sv1dRRFwCXNLFjCpYX9Lt+bpIs3feTmtt02VkVEpErNXDDA1wvdsZlefKoQQR0Tev/HDgxoiY3YuMipgAnCjp76ReNX+KiH/1IKMK3lKRjErR3AnrVo+Iieps0rtWM5aUtAnpC8gS+fqcD/iIuLXA05eRUXk+51AiSWeRBsQ8CVyXL39upU2yjIwqyF1RdyadUF4WuIr0Qf+XvmaSbmT0kiQ1G5fRbJsyMqpG0rmkCev2iYgNlaaguCEixg51hqSrBrk7ImK7As/dccb8wJXDEJD0JtKiH18E3hQRLR+hlZFRFfkfd1vSB/3WETGuFxndJulq4HzgwqiZ/kTSYsA7SEdIV0XEGUOZUTWSpkTEOEm3xdypL/4aEYUXNGo3Q9IqEdHRkrFlZMwP5tsPnCqS9AngncDbgCeAE0jf/LuaUQWSVuhXdCMwKVpYDKWMjB4bTzpx+mtJa5EmU1yC1HR4Oelke7PV/crIqJpeTnp3mtJo+6tJR6F/johWR5qXkVF5PnIokaQnSCNXf0L6NvdgLzKqQNKDwGrA06T22OVI0xs8DvxPzTmWIc2oCkmLAiOBFyLimV5lVIGkHYEv09mkd21nSFoCeA/pKHQb0riivnNbhSa4LCOj6lw5lEzSW4F3kQ75x5CWDty72xm9JuknwAWRR/Tmf+bxwHnAcRGxZTcyrJokrcjcCetujDYmrCsjI+esRfqQHw+8MdoYgV9GRtW4ciiRpBGkbxHvJjUNjSS9aSd0M6MK+tqEG5VJmlbk5GMZGVY9ki4Cfg1cFG2OcC4jY4DcxSLi5eZbDm1GFficQ7n+XHM5ISJm9CijCp6SdBhwTr69O/C00loVr3cxw6rnB6TX8lhJN5NGOV8cES8OdYakWcw7yLRvSvzCS9CWkTE/8JHDEJD0BtKb5PleZvSSpJGkdSneQfqn+TNwNGnG2dUjYno3Mqy6ciW/HfA/wPh2PlTLyLDGXDmUSNKGpJHOK5A+zGYCEyLizm5mVEluJnu9w4qy4wyrltzT6AOkb/+bkr71HzjUGZJGRMRzDXrCAcXmdyojY37gyqFEkq4HvhwRV+Xb7wG+FRFv72ZGFUh6G3AWqZKD1C231Yqy4wyrnjyAbUtS757zgKsjoqVmwnYzJF0cEbtIeoC5TUF9IiKazu9URsb8wJVDiRoNwmljcE/HGVXgitIGImk8abxK26Pcy8iwwfmEdLnuV5pKuG8SvU+Q1kDudkYVLN33oQ4QEVcrLczS7QyrnmuBIyS1PbdSGRl5INsY0qBCACLi2hb2oZSMqvIyoeX6JGma6d8BF+Tr+/Ygowrul/R/ktbMl6/QZkXZYYZVz+nAy0DfEeAM4JhuZkj6FKmCuYzUyeEy4Gut7EAZGVXmZiUbEvkb1dGknkaQ/omOjtYmIew4w6qnl3Mr1Tz+DmBz0hiisUqTPB4dEbu3sA8dZ1SZm5VKoAEW6OkTxRb76TijCiQtEhGv5g/w/+1VhlVaL+dW6vNiRLwoCUmLR8Q9ktZrcR/KyKgsVw7lKGOBnvl9kZ8+N5NXxpL041a7J5aYYdV1FKmX0WqSfkWeF6nLGTMkLQf8Hpgk6Wng0Rb3oYyMynKzkpWq32H+rRHR8hKKZWRYtVVsbqV3k9YL+VO7016UkVE1PiFdAkljJJ0u6YeSRku6VNLzkv4qqdC6A2VkVEQZ3zb8jWUBJGkNScsCRMSTwGzgvcA+SutTDHmGpGIvkj4AAAnDSURBVBX6X4A7SKPvlym4Dx1nzA985FACSX8mDdYaAXwBOAj4A2nivGMKzkDacUYVSJoNTCd9m1s7XweKr3lcRoZVj6SbgP+KiEcljQWuAL4NbAS8EhGfGuqMfgPXVmFuM1Dfe6vIILiOM+YHrhxKoJoZQiVNj4h1Gt031BlVIGmNwe6PiIe6kWHVI+n2vopd0vdJU6J8SdIwYFrBLw4dZ9RkzWm+bFcZGVXlE9LlqB22/9wg9w11RhU8HE2+cUhN1zwuI8Oqp3aaie2AIwAi4nVJjR8xNBl93AQ6CFcO5Vhf0u3kZpB8nXy76CFmGRlVcJWkpmseA2cMcYZVz5WSziOt5rc8cCWApFVIA9q6lWEFuFmpBG5KmUtp+cRPAnsBjdY8PjGarHlcRoZVj9JX+91J7fTnRcQ/c/kmwEqRV/wbygxJB9fcPBj4Ye39EfFDmigjY37gyqEERZo4mm1TRkbVyOsmW40q/J9IOmqwx0bE0YPdX1bG/MDNSuVwU0oDEfEK6fC/pxlWGVX4P7kPuCx3g21XGRmV5yOHErgpxay5KvyfSDoc2BFYFJgMXArc3MoReRkZ8wNXDiVzU4pZc73+P1FahncHYDywBXA3aTqOyyLi393KqDJXDma20JO0AbAzsGNE7NSrjCrx9BlmttCRNLn2dkT8DXhfKx/qZWRUmU9Im9lCI5+zWAoYqbReSN/IuRHAm7qVMT9w5WBmC5NPk+YtexNwa035c8CJXcyoPJ9zMLOFjqQDI+LHvc6oMlcOZrbQkbQ0afbj1SNioqQxwHoRcXE3M6rMJ6TNbGF0Gmkuprfn2zOAY3qQUVmuHMxsYbR2RHwXeAUgIl5g3hlfu5VRWa4czGxh9LKkJclTbktaG3ipBxmV5d5KZrYwOoo0mnk1Sb8CtgH+uwcZleUT0ma2UJK0IrAVqSnoxoh4ohcZVeVmJTNb6OR1IXYGNsu9i5aStEW3M6rMRw5mttCRdDJp+d3tIuIteaTz5RGxeTczqsznHMxsYbRlRGwq6TaAiHg6rwnR7YzKcrOSmS2MXpE0nLk9jUaRjgK6nVFZrhzMbGF0PHABsJKkbwJ/Br7Vg4zK8jkHM1soSVof2J7U02hyRNzdi4yqcuVgZgsNSSsMdn9EPNWNjPmBKwczW2hIeoB0jqDRNBcREW/uRsb8wJWDmZnV8QlpM1toSFpD0rI1t7eVdJykLxTthlpGxvzAlYOZLUzOA5YGkDQW+A3wMDAWOKmLGZXnQXBmtjBZMiIezdc/AZwWET+QNAyY1sWMyvORg5ktTGpPIm8HTAaIiFYGr5WRUXk+cjCzhcmVks4DHgOWB64EkLQKaVW3bmVU3v9v785j7RjDOI5/f/Y2CCGW2JfG0qBNGuVK7bGlCWItQSP+QGwltQVBCLHVGmvsVFDLHyKVSFFtiaXXrYaqai0VQmlpXEL788f7Hl5zzrk91R7tdZ9PcpMz7zvvO+9MbuaZeWbOe+JtpRBCn5FnUj0O2BR42vbcXD4Y2Mj2+P+ij94ggkMIoc+QJC/hpLekdZZHH71BPHMIIfQlEySdLWnLslDSGpL2l/QIcMp/0MdKL+4cQgh9hqS1gFOBE4FtgPnAWsCqwCvAXbZ7fONoefTRG0RwCCH0SZJWBzYEum3PX1F9rKwiOIQQQqgTzxxCCCHUieAQQgihTgSHsNxJGiPpvGJ5vKQHiuWbJZ0vqVtSZ/F3crHOYEmWdHBe3qBY7xtJc4vlNSQtrIxhpKQ78+crK+t3SlpP0r6SFkiaKuljSTc12Z/+kp6QNE3Sh5LelLR2rltU6ffivL3rKn0MkvRR/jwn91Vrc3suf1jS7Fz2gaQDivavSRoi6e1c/4Wk74o+npR0RrH+UEldklaTtLakeyXNkjRd0huShub1Npf0oqSZuf622uRxleMzI7cbXmyj4XFt/T8lrMziG9KhHSYDxwC3Ks03syGwblHfAZwHzLI9qEkfI0g/uzgCGG97HmliMyRdCSy0/dfJPH0vqUdjyvWLNhNtD5fUD5gq6XnbkyptzwW+tb1LbrcD8Huu667uQ65/GbikKD4eeLJY3s/29w3GOdr2s5L2A+4DBpSVtmsn9ZHAENtn5eWNgSmSngXmAXcCZ9r+Iwfm2cAA24slbQvspHQAngPutn240u8h3wdcC4zOm5xoe3jexiDgBUndtl9tdlzD/0PcOYR2mEQKAAADgQ+BnyWtL2lNYCfgx2aN80nraGAkcFB+dbCtbHeTJk3brEH1psDcYt0Ztn/roa8ZwPza1Xl2LPDUUgxpSpOxNNvmt8BNwA3A6UCX7TclbQcMBS6rzf1j+zPbL5HmBfrV9kO5fBEwCjhVUv8G2+gErgbOWor9CL1UBIew3OUZK/9Q+pJQB+lE9zawJzAE6CLNQbNdJSUxLHexFzDb9izgNeCwFjbbr+yLdBIrjSrqJ1QbS1qfdJX+RoO+HwQukjRF0jWSyqv5fpV9OC6XjyXdLSBpD2Ce7ZlFuwlFm1ENtnkI8EIL+126B9iZdNV/YS4bCHTmE3/VQOC9ssD2T6Tpp7dvso33gR2L5R6Pa+i9Iq0U2qV299AB3EK6Cu4AFpDSTtA8rTSCv6+ynwJOIqU/evKP9E4t7VLUN0t/DJPUBewAXG/7m+oKtjtzKuYg4EDgHUl75h+Tr0srFeOeLOkCUpAYW6lvlla6UdINwEbAHs12tpGcMrqXlG6a10ITkX7ustXyWl0p0kr/U3HnENplMikY7EJKK71FunPoIAWOhnLe+yjgCklzgDuAQyWt06ZxTrS9ax7nGTmvXsf2QtvP2T4TeJwl3M3Y/hKYA+xD2p+nWxzPaNJV+2XAIy22KS3OfzXTgd3ys5+q6fwzgCJpXWALYFaT/gcDH/2LcYVeJoJDaJdJwHDgB9uLbP8ArEcKEFN6aHcg8IHtLWxvbXsrYBxwRDsHa/sT4DrgIgBJu0t6NH/eK6edyG/y7Ax83kK3Y4ExpDukr5ZiLIuB24BVlN/W+rdyau5d4Kr8LAdJAyQdTvodgv7Kb4nlwHwz8LDtX6p9SdoVuBy4a1nGFHqHCA6hXaaR3lJ6q1K2oEinVJ85nENKKT1f6WsccMIyjmdUZVtbN1jnHmBvSdsAWwLdtXECr0uaBkwlnWzH5brqM4fri/6eIeX1Gz2ILp85PFqtzDN6XsPfzw6WxWnAJsCneR/uB77O2zgSOEbSTOAT4Ffg0qLtsNqrrKSgcE7xphK0dlxDLxTTZ4TQgKQbgcdsd63osYSwIkRwCCGEUCfSSiGEEOpEcAghhFAngkMIIYQ6ERxCCCHUieAQQgihTgSHEEIIdSI4hBBCqPMnKqQAH/iNoQwAAAAASUVORK5CYII=\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "pic8=df.groupby(['WEATHER'])['SEVERITYCODE'].value_counts().to_frame()\npic8.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3746991d0>"
                    },
                    "execution_count": 17,
                    "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": "pic9=df.groupby(['WEATHER'])['SEVERITYCODE'].value_counts(normalize=True).to_frame()\npic9.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd37468cf98>"
                    },
                    "execution_count": 18,
                    "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": "pic10=df.groupby(['ROADCOND'])['SEVERITYCODE'].value_counts().to_frame()\npic10.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd37490e208>"
                    },
                    "execution_count": 19,
                    "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": "pic11=df.groupby(['ROADCOND'])['SEVERITYCODE'].value_counts(normalize=True).to_frame()\npic11.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd37490f4e0>"
                    },
                    "execution_count": 20,
                    "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": "pic12=df.groupby(['LIGHTCOND'])['SEVERITYCODE'].value_counts().to_frame()\npic12.plot(kind='bar')"
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3737885f8>"
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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GwGMR8UREzAGuAHao3yAiJkTEq+nLu4DR+e6mmZm1o5XiPgp4pu71zHRZT/YDbuhkp8zMrDOttLmrybJouqH0OWAc8NEe1u8P7A+wwgortLiLZmbWrlbO3GcCy9e9Hg38vXEjSVsAXwe2j4jXmwVFxPiIGBcR40aOHJllf83MrAWtFPd7gdUkrSRpCLArcE39BpLWA84hKezP57+bZmbWjl6Le0S8CRwM3AhMA34ZEVMlnSRp+3SzU4ElgV9JmiLpmh7izMysD7TUzz0irgeub1h2Qt3zLXLeLzMz64DvUDUzqyAXdzOzCnJxNzOrIBd3M7MKcnE3M6sgF3czswpycTczqyAXdzOzCnJxNzOrIBd3M7MKcnE3M6sgF3czswpycTczqyAXdzOzCnJxNzOrIBd3M7MKcnE3M6sgF3czswpycTczqyAXdzOzCnJxNzOrIBd3M7MKcnE3M6sgF3czswpycTczqyAXdzOzCnJxNzOroEX6ewe6bcwx17W87YxTtuninpiZ9R2fuZuZVZCLu5lZBbm4m5lVkIu7mVkFubibmVWQi7uZWQW5uJuZVVBLxV3SVpKmS3pM0jFN1i8m6cp0/d2SxuS9o2Zm1rpei7ukwcBZwNbAWGA3SWMbNtsP+HdErAqcDnw/7x01M7PWtXLmvhHwWEQ8ERFzgCuAHRq22QG4MH3+a+DjkpTfbpqZWTtaKe6jgGfqXs9MlzXdJiLeBF4ClsljB83MrH2KiIVvIH0G2DIivpC+3hPYKCIOqdtmarrNzPT14+k2LzZk7Q/sn75cA5je4n6OAP7Z4rb9kedMZzpz4GT29z6uGBEje9uolYHDZgLL170eDfy9h21mSloEWBr4V2NQRIwHxrfwmfORNCkixrX7vr7Kc6YznTlwMsuwj9Bas8y9wGqSVpI0BNgVuKZhm2uAvdPnOwO3Rm9fCczMrGt6PXOPiDclHQzcCAwGzouIqZJOAiZFxDXAL4CLJT1Gcsa+azd32szMFq6l8dwj4nrg+oZlJ9Q9nw18Jt9dm0/bTTl9nOdMZzpz4GSWYR97v6BqZmbl4+EHzMwqyMXdzKyCKj+HqiUkLQHMjoi3ipop6Z3Ae4DXgBkR8XbRMiUNAtapy5waEf9XtEyzwrW5SxpN0ttmU+b9sj8EXAfckOUfZ5cyxzXJuzkiFujf3x+ZacHYFdgD2BB4HVgMeIHk4vj4iHi0AJlLAwcBuwFD0qyhwP8D7gLOjogJBchcBTga2AJ4tC5zdeBV4BzgwnZ+l7qRmeZ+EPgcye/Scsz/+35JRLzUTl4XM4cC29Lk32VETG03b6BnLvAZRSruks4nGcrgWmAS8Dzzftk3BzYAjomIP/VXpqTPA4cCTwKTG/I+TPIX9I2IeLqNfexG5u3AzcDVwEO1AiHpXSQ/9+7AbyPikn7OvAm4CPh9RPynYd0GwJ7AgxHxi37OvBz4KTCx8R4OScuS/Oz/jogLm72/DzNvILnJ8Gqa/75vB/ww7cLcn5knpu+7jQV/5zdPnx8REX91ZkYRUZgHsFYv64cAq/ZnJskZ4bCFrF8X+Hib+9iNzEXz2KbbmX7k+wBG5LFNH2Ru08v6ZYFxzsz+KNSZu/UNSUtGxCv9vR+9kbRmRDzSwfsXjYg3GpaNiIhcxwWR9ImIuCnje4cDIyPi8Ybla0enZ242oJWmt0z61TDL+4ZLOkXSxZJ2b1h3dj57NzfvhN636vG9W0rar3GiE0n7drpfTTyc5U2S1pZ0l6RnJI1PL1bW1t2T3+7N9ccsb5K0uaSZwN8l/bHhmGbK7EXLzTv1JH0WeAS4StJUSRvWrb4gjx1r+LwHM75veUlXSJoo6ThJi9at+11+ezg3M9MNPZIGSzpA0rclfbhh3fH57N18mYXez0L1lpG0fk+rSJomsjif5GLVVcC+knYCdo+I14GNM2b25AvASe2+SdLJJG3r9wHHSfpRRPwkXX0wcF6GzMN7WgUs2W5e6mzgRJKLkl8A/ixp+/Ssc9GFvbEnks7oaRXwjiyZwA9IRimdKmln4CZJe0bEXWlulv3sqT1ZZB/e+jhgg4h4TtJGJEN4HBcRv+lgP3dcyH6+O+N+nkfy7+cukol5bpe0XSSjvq6YJTC9TtN0FfCpTHuZXIBeHLgHOEPS7RFR+3ewI/CddgPLsp/NFKq4kwxSdjvNf7Gz/kNfJSJ2Sp//TtLXgVslbZ8lTNLLPa0ChmXJJLlqvl4k4/icCFwmaeWI+CoZ/5ED3wNOBd5ssi7rN7YlI+IP6fPTJE0G/qBkGOis7Xv7AEeQ9LxptFvGzCGR9jiIiF9Lmgb8RskUkVn3c1OS3iKNzVkimdAmi8ER8Vy6n/dI2hy4Nu3dlXU/rwQu7eH9QzNmjoyIn6XPD5H0OeBP6b+hrPv5AvAU8/9+R/p62YyZG0XE2gCSzgTOlvQbkt+jrP+OyrKfC+q00T7PB0mvkNV6WPdMxsxpwKCGZXsDU4GnMuQ9Dfy/vPex4fVgkq/6vyLp85wl8y8kZ4V57ucDwNINy9Ym+Wb0YsbMW4EP9bDuyYyZk4B3NywbDUwBZmXMvAHYvId1f+rg72iVhmVLAbcAr2fMnEwPnQg6+HufCgxtWLYF8BjwXMbMR4EVct7PR5osOwG4A3i0yvvZ9HPyCsplZ5LhgtfoYd3/ZMz8AbBFk+VbZTmQJF+ZNuph3fcz7uO1wEd7+Ky3M2auQQ89GHr6z6mFzN2BjZssXwE4N2Pmu4DFc/492gJYp8nypYGv5/lZHe7nOjTpqUXSxLVHxsxNF1KMMvXAAL7aw+/nesBNGTMPavZ3lK47JGPmJcBWTZZ/AXijyvvZ7OHeMgUgaRhARLzWZN2oiHi27/fKzMrMxd3MrIJK0xXSzMxa5+JuZlZBpSjuksZJGlX0zDKQdKCkXZRMZF7kzO9JOlpS1j7kfZV5oaSfSlqr4Jk7SPpAXnldzFxO0mLO7FwpijtwCEn/3yuLmilpWvo4OI+8bmWS9KPdBPhNwTPvIemjf3rBM88kGUxtz4JnfgA4Puud3n2YeTHwiKTTnNmZUl1QlbRURMwqamZ6RrhxRFyXR14nmZK+EhE/lvThiLgjp33JPdOskSQBYyOnoW8HambhiruSsbi3IhmmN0iGGr0xGoZuLUDmEsBrEfG2pNWBNUnGhn+jl7f2SaakKRGxrqT7IqKnYR36PbMueyTwRWAMdXdOR0TmsXW6lPl7Frwr8yWSG6fOiWSy+H7PTHM/xII/+0VZsrqVKeniiNizt2UFyNwvGoaIlnRKRBxTpMx6hRp+QNJewDdJBneq9e3eHPiepG9l+SXqRmbqT8CmSgbPuoXkH+IuJBNZZJVn5jRJM4CRkupHFxQQkd7+XIDMmquBiSTNEXnNFtWNzCeAkcDl6etdgP8jGYv7XLI1peSeKeliYBWSu3JrP3uQjHOfSTcygfc1fMYiJHMsdKIbmTtLmh0Rl6aZZ5NMVFO0zHnyuhsqjwcwHXhHk+XvBP5WlMz0/felfx4CfC19fn+HP3+umSQDRT1AMrjTfI8iZaa5U7rw+9SNzAWGGqgtI/tQEd3InEb6zTzHnz23TOBYYBbJNZCX08cs4EXg5KJk1mUPA24iGf/lIuBHORyD3DPrH0W7oCqaD0T0NtkH1OlGJiTNYx8kOauutYd3+k0ot0xJt0TEP0ian55qfBQls861krKOsteXmSMlrVB7kT4fkb6cU6DMh8g+CmTXMyPi5IhYCjg1Ioanj6UiYpmIOLYomZLepWRkyGEkwwN8jeQ/jZPU84iRfZ7ZTKGaZYDvAvdJ+iPwTLpsBeATwLcLlAnwFZIzhd9GMrTsysCEDvLyzlxO0keB7ZRM5zbff2QRcV9BMmu+QjLc8RySglZr6hlesMwjSIY6fjzNWwk4ML1e0vJ0eH2QOQJ4WMk4+3NH3IyITKOhdiszIo5V0iV5ReZvx295Ks0uZ05m3iiQtT+3SR8BrFyQzAUU8YLqO4EtSS5+CphJcqb474JlfiYiftXbsv7KVDKO+X4kXRQnNayOiPhYETLLKO2LvCbJ79IjkfGCZzcz0/+EFxARtxcs8xSSSdcfpq4dv5P/MLqRWUaFKu6SFL3sUCvbdDszfc8CPUY67UXSpcxvREQn31C6nilpCElT1PtIzlweBi6NiKxNEl3JrMsuQy+UfUkm3n60k/3qg8zpwNqRTJ5T5MzPAH+IiFlKZktaH/h2RNxfpMx6RWuWmSDpKuDqiHi6tjD9h7oJyTjsE2hvCrJcMyVtTTIDyyjNP4vQcJpPjNEvmWnuEOBZJTdF1ArcZZ380uedKWkscA3JWNaTSc5cNwO+LmmHyNDftxuZddll6YUyBvicpBVJjsFEksI8pWCZT5AMcZxbIe5S5jci4leSNiFpBTgN+BnJjVxFypyraGfuQ4F9Sc64VgL+Q3LRYRBJV8az2v1FyjtT0jokU/6dRDLAfs0sYEKWpp4uZTYrcOuTTOeXZ9HsNPMW4JRomGBa0hYkY69vXoTMuoxpJDeZ5PYPpxuZddnDSPr6HwmMiojBRciU9BOS/8BGkYxrfwvzt+MfWoTMuuz7I2I9JVNiPhgRl9WWFSlzvvwiFfd6SibhHUFyU0/mm426lSlp0ejghqVuZ5alaEp6JCLW7GHdtIh4bxEy697/K+DQSKfHy0OXMo8n+U93SeB+4M8kZ9mZPyPPTEl7L2x9RLR9IbkbmXXZ15LcJ7MFSZ/514B7ImKdImXOl1/U4l50SmYtP5F5V+RrPTEyX+nOM7MsRVPS34D3NzbrpN+4HoyI1YqQWZcxgeRbVm49RrqUeR9Jk951JPMS35XDRdrcM8tC0uIkd7k/GBGPSlqO5Hfsj0XKrFe0Nvcy+QXJ9GOTye/uxzwzB0larIcCl/XvvRuZFwFXSTo4ImakeWOAM0gGUipKZs2JHb6/TzIjYn1JS5FcV/oEcK6k/4uITYqUKelBeh564TsR8WIRMkm+8U9K82v3JDySIafbmXO5uGf3UkTkORpe3pmlKJoR8R0lo17+KT2TAfgvcFpE/KQomXXZmbv99WWmkuGCNwU+CowjucdjYtEySSYefwu4LH29K8k31pdIOjlsV5DM65jXJ30oyfW76TQMdVCAzLncLNMmSbVuiZ8FBpMMc1v/VbrtG3m6kZnmHkxy91tuBa4bmXXZSwFEjiN/5pUp6c8RsYmkWcx/Vpj5xqhuZNZlX0cyVtFE4N48ruV0KfOOiPhws2WSHoyI9xchs8lnrA8cEBEHdJrVrUwX9zal7aM9ich2c1DumQ35hS2aNrBJegDYPyLuTl9vBJwbEetk7TnSjcwePqcbo6PmlulmmTZ10o2uLzMb8nMvwAO1qKs8Q9SuBpwMjCX5yg9Ahxf8c88kGVvlPElLknxjeRn4gpKhF04uSqakw+teDiLpAvxCxv3rWmY9F/eMGv5ial4CJkfGmzq6kWm5K8sQteeTDHV9OskQ1/tARwPldSUzIu4F3q9kzgU1dFH+ZVEygaXqntd6DF2VMaubmXO5WSYjSZeRXFT6fbpoG+BekvFBfhURPyhCZlmo+a3Y38l6vSHvTEnHAseR3AD3am0xyYBk4yPDqIPdyKzLnhwRG9S3MUuaGBGbFiFT0uci4pIeTmiIiB8WIbPMijbkb5ksA6wfEUdExBEkRXkk8BHg80XJlPSZWvu4pOMl/abuAm4m3cgkuRV7lubdin0h8NOiZEZJhqitM1vSIOBRSQdL+jSwbIEyl0j/XKqHR1EykbS3pPsk/Td9TFIyCVBm3chcQOQ4OPxAepBMXDCk7vViwLT0eaYJNrqU+df0z01IejnsANzd4c/ejcz70z9PBnbv5GfuZmaaMQr4EMl/uh8BPlK0TGBDkjtJR5M0p/yGZC7eQmUW/QHsRXI37ubA0sA7gI+R3IuyV1Eymz3c5p7dZcBdkq5OX28HXJ5etHm4QJm1m6G2AX4aEVdLOjFjVjczn5V0Dsmt2N9XMgRup98sc89UD8PJknQRLExmJO3OAK+QtI13LM9MzT9AXrPPyjK2TO6ZwIHApyO9ryN1q6SdgCvINrhbNzIX4Db3DkjagOTsVcCfI6JxjPN+z1RJxsRQSW7vVgmGqFUyxspXgDXSRdOAM6L+4wS0AAAXcklEQVSzIYRzzVRJxpaR9HBEjG13XV9nNtXfX3vK9gCGp3++q9mjKJl12YsDOwKrpa+XAz5ZwMyLW1lWgMwbgCVz/p3KLZOSNSMAa+V5LPPOJOmp1va6vs5s9vCZe5skXRsR20p6kuZ3FWYZ5Cv3zLrssvTLnu/mDUmDSc64M5/F5JmpkgxRK+kuYNeY/yt/bZiIKyJi4yJk1mX8GRhCMizAZZHDCLB5Zkp6FXis2Spg5YhYosm6Ps9sxm3ubYqIbdM/V2pcp2TexkJk1mnsQz2Y/PtlZ86s7w4o6eXaYtLugEXJZN60gpNJxrTPQzcyhzcWYYCImCEp63AG3cisZWyS3hy1LzBJyfysF0QHTWc5Z2YeHrqPMxeU91eigfwAni5KJslE27NIbo54OX3MAl4ETi5KZl12R+/vq8yiPyhpMwLJmEo7kVzLmUYyOuKO/Z1Jel2y0226ndns4WaZHEl6JiKWL1KmpJOj8z7TXc9Mc/Oasb5rmSr4ELVla0aQtDZJz5ttgJuAX0TEfZLeA9wZESv2Z6ak20juGl3oNJ0RcUF/Zjb9HBf3/Eh6OiJW6H3Lvs0sSdHMfcb6LmX+gJ6Hk90kItoeTjbPTCXzm/YoIp7KsH+5Z9Zl/wk4F/h1RLzWsG7PiGh7KOk8M9V8ms6hJN8K8pz6s6PMpp/j4t6euotgC6wC9o5sQ7/mnlmXXZaiWfguhmlmoYeolaTo5R91K9t0O7OMVPCpPxv5gmr7FtbvPGuf9G5k1nwaWCPPAtelzG7MWN+NzCUlfSDmH052yXTdmwXInCCp16/8JD1J+jOzllGW0SuJZPz63Oa57VZmjYt7m6KDSXb7MrNOoYtm3beWV4EpSibhzquLYW6ZdYo+RO1WJF/5L5fU7Cv/6Rm+8ncjs6YUo1eWkZtlKqpE/bJLcadik89oNpxsoTLL0Iyggo9eWWY+c6+uUvTLLss3IfUwnKyk2mfmNkRtJ5n1StKMMN9IkyTdFnMdvTKnzNJxcc9I0ocj4o7elvVXZlmKZk3Ruxgy/3CyjbJ+/e1GZtkcRjKcxaHAt0mGNVjoN69+yiwdN8tkpCZzHTZbVoDMohfNWmahuxj28jmHRcSP8sjqZqYNLD5zb5OkD5KMuz2y4ev0cJILTIXIrHMDPRe4C0iGFS5C5ocbugM+WNcd8HMZ8rqV2czhQN6FuBuZhaISjF5ZZi7u7RtC0k1tEeb/Ov0ysHOBMmvKUjSL3sVwYbrRE6PSvTuUzDp0GMl/YveR/LzrA6dKIksx7kZmqUUO40IMxAewYvrnEgXPfAD4QN3rjYAH0udZZ3fqRuaGwIPAk8AM4K9p7hLAZ4uS2cPnFGZMobI8gLuAMU2WjwHuKkpmmR9uc88obUr5Bck43CtIWgc4ICIOLFjmhsB5JGesc/tQA1OBbSKi7dngu5FZl13ILoaSZtHzXcTDIqLtb8HdyCwLlXkSjJJwcc9I0t0kTSbXRMR66bKHImKtImXWZRe1aOY+Y303Mi1ftb7o7a7r68wyq+yZQV+IiGdqfZJTb/W0bV9nlqhftrsYDkzvlfTXJssFZB0moBuZpeXint0zkj4ERDrGxqEkV+aLklmKohkR56R/fqtxnaTDipJpuSvvJBgl4WaZjCSNAH5MMkm0SMbY+Epk6OPdzcwePqcU/bJV0CGUrXOSR6/sNhf3AagsRVMFnPzE8qEST4JRFoP6ewfKStLqkm6R9FD6em1Jxxcts6ePKklmN848OsqUtKOkRyW9JOllSbM0b57WwmSWwFYk15Mul/R3SQ9LegJ4FNiNZKTJCwqQWVo+c89I0u3AUcA5OfaWyT2zh88pzJl72boYSnoM2C4iOr2+0tXMMlEJRq8sI19QzW7xiLinoWdLp3c95pbZW4ErSmZENLs425FuZNb5vy4U4W5klkaUY/TK0nFxz+6fklYhLXaSdqbzX6bcMktYNMtikqQrgd8x/1j2vylYpg1wbpbJSNLKwHiSAb/+TXKL+x7R2WTBuWdaviSd32RxRMS+Rco0c3HPQMlEADtHxC+VTIU2KCJmFS3T8idpaETMLnqmmXvLZBARbwMHp8//m0cR7kZmmUj6fivL+jsTeEjSHZJOkfSpdAiGTnUj0wY4F/fsbpJ0pKTlJb2r9ihaZomK5ieaLNu6aJkRsSpJt7oHgW2BByRlnRy6a5lmbpbJSNKTTRZHRGQew6JLmc1md/prRKxdhExJXwYOJBn74/G6VUsBd0RE2+PDdyOzLns0sCnwUZJJwv8F/DkiTi5SppmLe0bN2kk7bTvNM7MsRTNtgngncDJwTN2qWRHxr3bzupVZl/02cC/wvYi4upOsbmaaubhn1MPZa2HmUC1b0UzzNwFWi4jzlYyzs1RENPs202+ZSsbY3wT4CLACyd2Pt0fEL4qUaebi3iZJ7wZGAZcAuzPvtvvhwM8iYs0iZDbkl6FofhMYB6wREatLeg/wq5h/Or9+z0xzlyQpxpsCnyNpOhtTtEwb2HwTU/u2BD4PjAb+l3mFeBZwXIEygfkLHHA+yXytlwC5FM28MoFPA+uRzH1JRPxdUqc3TeWeKWkSsBjwF+DPwEc6vQ+hG5lmLu5tiogLgQsl7RQRVxU1s04piiYwJyJCUu3u3CV6e0M/ZW4dES/kkNPtTBvg3BWyTZK2k7RirQhLOkHSA5KukbRSUTLrzImk7S33oplz5i8lnQO8Q9IXgZuBcwuYOUfSDyVNSh//m0O/9G5k2gDnNvc2KZnGa+OIeFXStsAPSfoorwd8JiK2LEJmXfaRwGokfb5PBvYFLouInxQpM839BPBJkmapGyPipk7yupEp6SrgIeDCdNGewDoRsWORMs1c3Nsk6YGIWCd9fh4wPSK+n77O2rMl98yG/MIXzTRzRZKLtDdLWhwYnMOwDrlmSpoSEev2tqy/M83cLNM+SVpSyVgwHwduqVs3tECZ9f5GUoCPBO7IoX0898y02eTXwDnpolEkoyQWKhN4Le0pVPuMDwOvFTDTBjhfUG3fj4ApwMvAtIiYBCBpPbIP+duNTNKMLwL7A+8CViEpcD8j+U+kMJnAQcBGwN0AEfGopGU7yOtW5peAi9I2cZHcTfr5AmbaAOfi3qaIOE/SjcCywAN1q/4B7FOUzDplKZqvR8QcpROVSFqEzqfZyz0zIh4A1pE0PH3d8XR43cg0c3HPICKeBZ5tWNbRGXY3MlOlKJrA7ZKOA4al7fkHAr8vWqakxYCdgDHAIrVjEBEnFSnTzG3u1ddY4H5F/kUzj8xjgBdIRkY8ALge6HRy8G5kXg3sQDL94X/rHkXLtAHOvWUqLr1Iux91PVuAn0cHf/F5Z0oaDFzYyWiNfZGZ5nZjwvLcM83cLNMBJQM+bZq+nJi2nRYms6HAdXrzTtcyI+ItSSMlDYmIOUXNTP1F0vsj4sGCZ9oA5+KekaSvAF8EapMYXyJpfIc3B+WaWbKiOYOkS+U11DVJRMQPC5a5CfB5JWPvv07yzSWig/Hxu5RpA5yLe3b7AR+IiP/C3JmI7gQ6uUuzG5kzKEfR/Hv6GEQyPjx0fpG2G5mdzg7VV5k2wLm4ZyfgrbrXbzFvNMciZZalaD4cEb+qXyDpMwXM3CIaxlmXdArzj29fhEwb4FzcszsfuFvSb9PX/wN0OrlCNzLLUjSPJel109uy/s7cWdLsiLgUQNLZJMP1dqIbmTbAubdMByRtQDKGuYA/RcT9RctsNjZNp+PV5JkpaWvgU8BngSvrVg0HxkbERkXIrMseBlwDnEfSnPKviDgsa163Ms185t6BiJgMTC5iZl2BGyXpjLpVw0n6Uxcik6R5ZxKwPfP/3LOArxYlU9K76l5+gWSMmjuAkyS9KzJMM9iNTLMan7m3SdIs5rUvq+75IsCQiGj7P8wuZa4DrAucBJxQt2oWMCEi/l2EzLrsRSPiDUmLAmsBz0bE81nz8s5Me7IE8/5+6q+FRESsXIRMsxoX9w6loyEeSHIH5G8j4ogiZZagaP4M+ElETE0HzrqT5ELyu4AjI+LyImSalY2Le0aS3gEcBuwFXAacHhEvFiWzLEVT0tSIeF/6/DBgs4j4HyWTht8QEesVIbMh/0Ok48DUlkXERUXLtIHNbe5tkjQCOALYheQC2HoR8VLRMoFNI+JL6fN9gL/VFzggy9lrNzLrb4SqjVNDRPyjNoBWQTIBkHQxyTDHU5jXbTWAzIW4G5lmLu7te4pkMKrzgVeB/eoLRsYbebqRWZai+R8lUws+S9JLaD+YO9LksAJl1owj6XGT51febmTaAOfi3r5TmXfBM48ZjbqVWZaieQBwBvBu4LCI+Ee6/OPAdQXKrHkozc1jOOZuZtoA5zb3ipK0OvMK3I8i4oJ0+ZbAJ7NcpO1GZtlImkDSY+geknFgAIiI7YuUaebibtYGSR9ttjwibi9SppmLu5lZBbnN3awFDTeakT7/JzABODpLl9VuZJrVeJq9HEi6tgyZZVHE4xkRS0XE8LrH0iS9XKYCPytKplmNi3s+RpUhs4hFswelOJ4R8e+IOJ2kj3phM21gcnHPR8ejQfZRZimKJuU5nqRDMOTavNmNTBt4/AuUg4jYtwyZlKRoFvF4StqxyeJ3ktxV/OuiZJrVuLeMWQsknd+wKIAXgdsiItONUd3INKtxcTczqyC3uWckaUyTZRsWLbMsfDzN8uXint1vJM29mJjeZXhe0TJLVDRLcTzNysLFPbsDgN9JerekTwE/JpmCrmiZZSmaZTmeZqXgNvcOSPogcA4wG9gmIl4oWmZ6Rn02sB2wPvA9YLuIeKZImWlu4Y9nQ/a1EbFtXnndyrSBycW9TZJ+z/y3jI8lGar135BtJL9uZDbkF7ZolvF41n3O/Z3O6tQXmTYwuZ97+04rQ2aTArc48BLwC0l5Fc2OMynJ8exBKe4bsIHJZ+4ZSBoM3BgRWxQ1s6dhZGuyDCfbjcw0t/DH06xsfOaeQUS8JelVSUvnMNdpVzIj4va8C1w3MtPcwh9Ps7Jxcc9uNvCgpJuA/9YWRsShRcksWdEs/PE0KxMX9+yuo/P5OPsisyxFsxTHU9KYiJjRsGzDiLi3SJlmbnOvOEl7N1seERcWKbMsJN1H0u3z2fT1R4EzI+L9Rco0c3HPSNJqwMkk3eyG1pZHxMpFyiyLshzPMt03YAObm2WyOx/4JnA6sDmwD6CiZZalaFKS4xkR90o6FPgjSfPUJzq9b6AbmWY+c89I0uSI2EDSg7Wvz5ImRsSmBcv8M/MK3HakBS4ivlmwzEIfzzLfbGUDk8/cs5staRDwqKSDgWeBZQuYOSwibpGkiHgKOFHSRJLiXKTMoh/PMt9sZQOQi3t2h5HcoXko8G3gY0DTC439nFn0ollT6ONZpvsGzMDNMpWXXqybBryDpMAtDfwgIu4qUmZZSLoG2DPPPv7dyDRzcc8g7Qr4FWCNdNE04IyIuKhImWVRpuMp6ZfAxkBuffy7kWnmZpk2SdqL5Ov+4cB9JL0v1gdOTQfPart4dCMzzS180SzT8UyV4mYrMyLCjzYewF3AmCbLxwB3FShzL5IRBjcnaTZ5B0mb82RgrwJlluJ4+uFH2R5ulmmTpIcjYmy76/oh8y5g11jwtvYxwBURsXFBMktxPOveX5b7BmyA8zR77Xst47q+zhzeWIQB0mXDC5RZluNZcz7wU+BNkm8wFwEXFzDTBji3ubfvvZL+2mS5gKxnWt3ILEvRLMvxrCnLfQM2wLm4t++9ZcksSdEsy/GsKct9AzbAuc29TenZ1UIPWivb9EHmigtbn54htqVLmaU4nnXv830DVgou7m2SdBtwFXB1RDxdt3wIsAnJHZATIuKCfs4sRdEsy/E0KxtfUG3fVsBbwOWS/i7pYUlPAI8CuwGnZyga3cicIOkQSSvUL5Q0RNLHJF1I+7fidyOzLMcTSXtLuk/Sf9PHpLRPfWbdyDQDn7l3RNKiwAjgtYj4T5EyJQ0F9gX2AFYC/kPSzW4wydCyZ0XElP7ObMgv8vHcC/gqTW6MAn4c2W+2yjXTrMbFfQAoctEsi7LcN2BW494yA0BEvEEyTnihMwuuxz7+knK/b6CDTDPAbe5mrSrLfQNmgJtlzFoi6VXgsWargJUjYokiZJrVuFnGrDVlu9nKBjifuZu1oCz3DZjVuM3drDVluW/ADPCZu1lLynjfgA1sLu5mbfJ9A1YGLu5mZhXkNnczswpycTczqyAXd0PSK02WnSjpyLrXh0t6RNKDkh6Q9MO0nRhJMySNqNt2M0nXStpH0pT0MSd97xRJp6TbbZ2OgjgtzT6tLmP/dNkjku6RtEndutskTap7PS4d5rf22S9Jul/SdEl/krRtDz/3/0v384F05Mjr0+VjJL1Wt+9TJO0l6QJJBzRk/E/d+95qeM8xdfs7Pf2ceyWtW/f+Gel+1N7zD0nPps8fkPQXSVvXbf9ZSX9In79b0hWSHq/tv6TV03Xvk3SrpL9JelTSNyQpXfd5SS+kx+hRSTdK+lDdZ1wg6cm6ffpL018cK7a8Ztr2o7wP4JUmy04Ejkyffwn4A/CO9PUQ4BiSsVEAZgAj6t67GXBtQ17jNmsBjwNrpq8XAQ5Mn28LTK5tTzJS4tPAu9PXt6Wvt05fjwNua/bZwLrpZ3+8yc94DvCVutdrp3+OAR5qsv2WJOPA1y+7Atizp+NYt7/j0uf7ADct5LjMPe51x2kaSS+aJUiGLV6F5C7WO4EvNfysmwLD0mP7yXT54sANwEHp688DZ9a9b3PgH8B709cXADv39++lH509fOZurfg68OVIe3FExJyIOCUiXu4g82vAdyPikTTzzYg4O113NHBURPwzXXcfcCFwUN37TwWO7+1DIulKeBJwcJPVywEz67ZtNoVgvZuBNSUtByBpcWAL4He97UedO4FRrW4cEQ8Bvyc5Jt8ELoqIx0kK8hsR8bO6badExERgd+COiPhjuvxVkp//mB4+YwIwHti/jZ/DCs7F3RZK0lLAkhHxZC+bTqh9jQd+3kL0WiRn5828r8m6SenymjuB1yVt3sJn3Qes2WT5WcAvJE2Q9HVJ76lbt0pDE8umEfEW8Bvgs+k225Ocyc9KXw9reM8uTT5zK9r7zwDgWyQFe2vgB+myto5f+h/Ckup5tMnGY3Rq3c9xaZv7awXgsWWsNwLm9peVtCXwfZL5PnePiFp77Oa1M21JmwFHkq/59iP1HZKz96NbeO8CIuJGSSuTFNytgfslrZWufjwi1m3ytstJJ9MAdgXqJ9R4rYf3AFwqaQmSG5TW72V/G/fzv5KuJGn2eb2FtzQ7VnPjFvKeekdFxK9b3UcrHp+520KlTS//lbRS+vrGtIA9RNL2ntVUYIMe1j3cZN366fL6fbuVpC26t0kt1iNpt15ARPwrIi6LiD2Be4GP9JJ1B7CcpHWADwHX97J9Te0u1MtIvjG06+30UbOw4zeV5DrEXOl/Yq/Ufcto1OMxsnJycbdWnAz8VNI7IBnMiqSoduJU4Li63h2DJB2ervsB8H1Jy6Tr1iW5CHh2k5zvkrTfNyVpbeAbpAVV0qclnZw+/1jabl5rflqF5EJtjyIigF+SXAO4PiJmt/TTMneCk+OBjSV1OiLkrcBikr5YWyBpQ0kfBS4FNpG0Rbp8GHAG85p05pO+Z3/g3A73yQrEzTIGsLikmXWvf9iw/qckPS7ulvQ68ArJGez9WT8wIv4q6TCSSawXJ2kuuC5dd42kUcBfJAUwC/hcRCww81NEXC/phYbFm0q6P93n54FDI+KWdN0qQO1C8AbAmZLeJDnR+XlE3KtkmrtV0usHNedFxBnp88uBo1jwAuWwhvf8ISLm2yYiXpP0vyTNVvv1eIB6EREh6dPAj9Iul7NJet4cln7GDsBPJJ1F0hR0MXBmXcQuSrqXLg48CewUEfVn7qdKqr9gvVFEzMm6v9b3PPyADSiSLgG+GhGN/yGYVYqLu5lZBbnN3cysglzczcwqyMXdzKyCXNzNzCrIxd3MrIJc3M3MKsjF3cysgv4/pQgpqk7AvxEAAAAASUVORK5CYII=\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "pic13=df.groupby(['LIGHTCOND'])['SEVERITYCODE'].value_counts(normalize=True).to_frame()\npic13.plot(kind='bar')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Pre-processing_2"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [],
            "source": "df.drop(['INCDATE'],axis=1,inplace=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [],
            "source": "df['WEATHER'].replace(to_replace=['Snowing','Fog/Smog/Smoke','Sleet/Hail/Freezing Rain','Blowing Sand/Dirt','Severe Crosswind','Partly Cloudy'], value=[0,0,0,0,0,0],inplace=True)\ndf['ROADCOND'].replace(to_replace=['Ice','Snow/Slush','Standing Water','Sand/Mud/Dirt','Oil'], value=[0,0,0,0,0],inplace=True)\ndf['LIGHTCOND'].replace(to_replace=['Dawn','Daylight','Dusk'], value=['Light','Light','Light'],inplace=True)\ndf['LIGHTCOND'].replace(to_replace=['Dark - Street Lights On','Dark - No Street Lights','Dark - Street Lights Off','Dark - Unknown Lighting'], value=['Dark','Dark','Dark','Dark'],inplace=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [],
            "source": "X=df[['STATUS', 'PERSONCOUNT', 'PEDCOUNT', 'PEDCYLCOUNT', 'VEHCOUNT', 'SDOT_COLCODE',\n           'INATTENTIONIND', 'UNDERINFL', 'PEDROWNOTGRNT', 'SPEEDING', 'ST_COLCODE', 'HITPARKEDCAR']]\ny=df['SEVERITYCODE'].values"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "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>STATUS</th>\n      <th>PERSONCOUNT</th>\n      <th>PEDCOUNT</th>\n      <th>PEDCYLCOUNT</th>\n      <th>VEHCOUNT</th>\n      <th>SDOT_COLCODE</th>\n      <th>INATTENTIONIND</th>\n      <th>UNDERINFL</th>\n      <th>PEDROWNOTGRNT</th>\n      <th>SPEEDING</th>\n      <th>...</th>\n      <th>Mid-Block (but intersection related)</th>\n      <th>Mid-Block (not related to intersection)</th>\n      <th>Ramp Junction</th>\n      <th>Clear</th>\n      <th>Overcast</th>\n      <th>Raining</th>\n      <th>Dry</th>\n      <th>Wet</th>\n      <th>Dark</th>\n      <th>Light</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>11</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>16</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>14</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>11</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>11</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 37 columns</p>\n</div>",
                        "text/plain": "   STATUS  PERSONCOUNT  PEDCOUNT  PEDCYLCOUNT  VEHCOUNT  SDOT_COLCODE  \\\n0       1            2         0            0         2            11   \n1       1            2         0            0         2            16   \n2       1            4         0            0         3            14   \n3       1            3         0            0         3            11   \n4       1            2         0            0         2            11   \n\n   INATTENTIONIND  UNDERINFL  PEDROWNOTGRNT  SPEEDING  ...  \\\n0             0.0        0.0            0.0       0.0  ...   \n1             0.0        0.0            0.0       0.0  ...   \n2             0.0        0.0            0.0       0.0  ...   \n3             0.0        0.0            0.0       0.0  ...   \n4             0.0        0.0            0.0       0.0  ...   \n\n  Mid-Block (but intersection related)  \\\n0                                    0   \n1                                    0   \n2                                    0   \n3                                    0   \n4                                    0   \n\n   Mid-Block (not related to intersection)  Ramp Junction  Clear  Overcast  \\\n0                                        0              0      0         1   \n1                                        1              0      0         0   \n2                                        1              0      0         1   \n3                                        1              0      1         0   \n4                                        0              0      0         0   \n\n   Raining  Dry  Wet  Dark  Light  \n0        0    0    1     0      1  \n1        1    0    1     1      0  \n2        0    1    0     0      1  \n3        0    1    0     0      1  \n4        1    0    1     0      1  \n\n[5 rows x 37 columns]"
                    },
                    "execution_count": 25,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X=pd.concat([X,pd.get_dummies(df['ADDRTYPE'])], axis=1)\nX.drop([0],axis=1,inplace=True)\nX=pd.concat([X,pd.get_dummies(df['COLLISIONTYPE'])], axis=1)\nX.drop([0],axis=1,inplace=True)\nX=pd.concat([X,pd.get_dummies(df['JUNCTIONTYPE'])], axis=1)\nX.drop([0],axis=1,inplace=True)\nX=pd.concat([X,pd.get_dummies(df['WEATHER'])], axis=1)\nX.drop([0],axis=1,inplace=True)\nX=pd.concat([X,pd.get_dummies(df['ROADCOND'])], axis=1)\nX.drop([0],axis=1,inplace=True)\nX=pd.concat([X,pd.get_dummies(df['LIGHTCOND'])], axis=1)\nX.drop([0],axis=1,inplace=True)\nX.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64, object were all converted to float64 by StandardScaler.\n  return self.partial_fit(X, y)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:1: DataConversionWarning: Data with input dtype uint8, int64, float64, object were all converted to float64 by StandardScaler.\n  if __name__ == '__main__':\n"
                },
                {
                    "data": {
                        "text/plain": "array([[ 0.00459094, -0.33875237, -0.18991352, -0.17182116,  0.05343064,\n        -0.42686777, -0.43161881, -0.22468962, -0.1587787 , -0.22741933,\n        -0.88556073, -0.1944711 , -0.06286131, -1.36752505,  1.40925415,\n         2.11505725, -0.17137669, -0.1038246 , -0.27896382, -0.58173298,\n        -0.18993158, -0.46792191, -0.12578468, -0.32971412, -0.10475391,\n         1.447159  , -0.24246593, -0.36570527, -0.92118765, -0.02922871,\n        -1.18868238,  2.41827524, -0.4599969 , -1.38107401,  1.73138788,\n        -0.60825721,  0.72381211],\n       [ 0.00459094, -0.33875237, -0.18991352, -0.17182116,  0.05343064,\n         0.30230973, -0.43161881, -0.22468962, -0.1587787 , -0.22741933,\n        -0.81667581, -0.1944711 , -0.06286131,  0.73124803, -0.70959521,\n        -0.47280044, -0.17137669, -0.1038246 , -0.27896382, -0.58173298,\n        -0.18993158, -0.46792191, -0.12578468,  3.03293048, -0.10475391,\n        -0.69100907, -0.24246593, -0.36570527,  1.08555516, -0.02922871,\n        -1.18868238, -0.41351786,  2.17392771, -1.38107401,  1.73138788,\n         1.64404135, -1.38157402],\n       [ 0.00459094,  1.13734759, -0.18991352, -0.17182116,  1.84629486,\n         0.01063873, -0.43161881, -0.22468962, -0.1587787 , -0.22741933,\n         0.62990757, -0.1944711 , -0.06286131,  0.73124803, -0.70959521,\n        -0.47280044, -0.17137669, -0.1038246 , -0.27896382,  1.71900174,\n        -0.18993158, -0.46792191, -0.12578468, -0.32971412, -0.10475391,\n        -0.69100907, -0.24246593, -0.36570527,  1.08555516, -0.02922871,\n        -1.18868238,  2.41827524, -0.4599969 ,  0.72407416, -0.57757133,\n        -0.60825721,  0.72381211],\n       [ 0.00459094,  0.39929761, -0.18991352, -0.17182116,  1.84629486,\n        -0.42686777, -0.43161881, -0.22468962, -0.1587787 , -0.22741933,\n         0.00994327, -0.1944711 , -0.06286131,  0.73124803, -0.70959521,\n        -0.47280044, -0.17137669, -0.1038246 , -0.27896382, -0.58173298,\n        -0.18993158, -0.46792191, -0.12578468, -0.32971412, -0.10475391,\n        -0.69100907, -0.24246593, -0.36570527,  1.08555516, -0.02922871,\n         0.84126762, -0.41351786, -0.4599969 ,  0.72407416, -0.57757133,\n        -0.60825721,  0.72381211],\n       [ 0.00459094, -0.33875237, -0.18991352, -0.17182116,  0.05343064,\n        -0.42686777, -0.43161881, -0.22468962, -0.1587787 , -0.22741933,\n        -0.88556073, -0.1944711 , -0.06286131, -1.36752505,  1.40925415,\n         2.11505725, -0.17137669, -0.1038246 , -0.27896382, -0.58173298,\n        -0.18993158, -0.46792191, -0.12578468, -0.32971412, -0.10475391,\n         1.447159  , -0.24246593, -0.36570527, -0.92118765, -0.02922871,\n        -1.18868238, -0.41351786,  2.17392771, -1.38107401,  1.73138788,\n        -0.60825721,  0.72381211]])"
                    },
                    "execution_count": 26,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Modeling and Evaluating"
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import metrics\nfrom sklearn.metrics import jaccard_similarity_score\nfrom sklearn.metrics import f1_score"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### 70% for training, 30% for evaluating"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [],
            "source": "X, test_X, y, test_y = train_test_split( X, y, test_size=0.3, random_state=4)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### K Nearest Neighbor and Support Vector Machine"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Since the dataset is too large, KNN and SVM are not suitable."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Decision Tree"
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "4        0.7963697277829946\n5        0.7963221169195861\n6        0.7967959928418549\n7        0.7998976942425119\n8        0.8002969008090273\n9        0.7988271432605216\n"
                }
            ],
            "source": "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=4)\nfor i in range(4,10):\n    DT=DecisionTreeClassifier(criterion=\"entropy\", max_depth = i)\n    DT.fit(X_train, y_train)\n    DT_pre=DT.predict(X_test)\n    print(i, '      ', (jaccard_similarity_score(y_test, DT_pre)+f1_score(y_test, DT_pre))/2)\n    \n#max_depth=8 has the best accuracy"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=8,\n            max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n            splitter='best')"
                    },
                    "execution_count": 30,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "DT=DecisionTreeClassifier(criterion=\"entropy\", max_depth = 8)\nDT.fit(X,y)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Logistic Regression"
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='warn',\n          n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n          tol=0.0001, verbose=0, warm_start=False)"
                    },
                    "execution_count": 31,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "LR = LogisticRegression(C=0.01, solver='liblinear')\nLR.fit(X,y)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Evaluating"
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [],
            "source": "#predict\nDT_pre=DT.predict(test_X)\nLR_pre=LR.predict(test_X)\n\n\n# Jaccard\nDT_J=jaccard_similarity_score(test_y, DT_pre)\nLR_J=jaccard_similarity_score(test_y, LR_pre)\n\n# F1-score\nDT_F=f1_score(test_y, DT_pre)\nLR_F=f1_score(test_y, LR_pre)"
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "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>Algorithm</th>\n      <th>Jaccard</th>\n      <th>F1-score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Decision Tree</td>\n      <td>0.757838</td>\n      <td>0.847295</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>LogisticRegression</td>\n      <td>0.757363</td>\n      <td>0.845823</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "            Algorithm   Jaccard  F1-score\n0       Decision Tree  0.757838  0.847295\n1  LogisticRegression  0.757363  0.845823"
                    },
                    "execution_count": 33,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "pd.DataFrame([['Decision Tree',DT_J,DT_F],\n                          ['LogisticRegression',LR_J,LR_F]],\n                        columns=['Algorithm','Jaccard','F1-score'])"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Result"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The best machine learning is Decision Tree when max_depth=8"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6",
            "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": "3.6.9"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 1
}