Collision_Severity_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of Accident Security and Pattern Recognition\n",
    "\n",
    "**Problem Definition:**\n",
    "To model the prediction of severity of an accident based on factors that are included in such an event.\n",
    "\n",
    "**Taks Included in current notebook:**\n",
    "* Loading of data.\n",
    "* Preprocessing.\n",
    "* Visualizations.\n",
    "\n",
    "**Important Observations of this analysis:**\n",
    "* A very small subset of conditions result in most no. of accidents.\n",
    "* Narrow roads contribute to a staggering ~70% of accidents.\n",
    "* As the week progresses saturday being the 1st day of the week, the no. of accidents also increase peaking on friday.\n",
    "\n",
    "Continue reading to have a detailed analysis of the observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import the required packages\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read CSV file to DataFrame (df)\n",
    "\n",
    "The csv file is encoded in latin encoding format hence we will use the encoding parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/train_PHD.csv\", encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the spaces from the column names becomse easy to operate on columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = [\"_\".join(x.split(\" \")) for x in df.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Types Checks\n",
    "\n",
    "* Check if the data types infered by pandas read_csv are correct\n",
    "* Correct the data types to the expected formats\n",
    "\n",
    "    **Note**: Pandas stores **strings as object** datatype (So for strings we will see object as datatype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Collision_Reference_No.                     int64\n",
       "Policing_Area                              object\n",
       "Collision_Severity                          int64\n",
       "Weekday_of_Collision                       object\n",
       "Day_of_Collision                            int64\n",
       "Month_of_Collision                          int64\n",
       "Hour_of_Collision_(24_hour)               float64\n",
       "Carriageway_Type                            int64\n",
       "Speed_Limit                                 int64\n",
       "Junction_Detail                           float64\n",
       "Junction_Control                          float64\n",
       "Pedestrian_Crossing__Human_Control       float64\n",
       "Pedestrian_Crossing__Physical_Control    float64\n",
       "Light_Conditions                            int64\n",
       "Weather_Conditions                          int64\n",
       "Road_Surface_Conditions                   float64\n",
       "Special_Conditions_at_Site                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Collision_Reference_No.</th>\n",
       "      <th>Collision_Severity</th>\n",
       "      <th>Day_of_Collision</th>\n",
       "      <th>Month_of_Collision</th>\n",
       "      <th>Hour_of_Collision_(24_hour)</th>\n",
       "      <th>Carriageway_Type</th>\n",
       "      <th>Speed_Limit</th>\n",
       "      <th>Junction_Detail</th>\n",
       "      <th>Junction_Control</th>\n",
       "      <th>Pedestrian_Crossing__Human_Control</th>\n",
       "      <th>Pedestrian_Crossing__Physical_Control</th>\n",
       "      <th>Light_Conditions</th>\n",
       "      <th>Weather_Conditions</th>\n",
       "      <th>Road_Surface_Conditions</th>\n",
       "      <th>Special_Conditions_at_Site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8562.000000</td>\n",
       "      <td>8562.000000</td>\n",
       "      <td>8562.000000</td>\n",
       "      <td>8562.000000</td>\n",
       "      <td>8307.000000</td>\n",
       "      <td>8562.000000</td>\n",
       "      <td>8562.000000</td>\n",
       "      <td>8317.000000</td>\n",
       "      <td>8286.000000</td>\n",
       "      <td>8326.000000</td>\n",
       "      <td>8294.000000</td>\n",
       "      <td>8562.000000</td>\n",
       "      <td>8562.000000</td>\n",
       "      <td>8321.000000</td>\n",
       "      <td>8331.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6549.375380</td>\n",
       "      <td>2.878416</td>\n",
       "      <td>15.702056</td>\n",
       "      <td>6.641556</td>\n",
       "      <td>13.669556</td>\n",
       "      <td>12.223896</td>\n",
       "      <td>42.463794</td>\n",
       "      <td>6.381027</td>\n",
       "      <td>4.144581</td>\n",
       "      <td>1.008167</td>\n",
       "      <td>2.216301</td>\n",
       "      <td>3.236744</td>\n",
       "      <td>4.743635</td>\n",
       "      <td>1.657133</td>\n",
       "      <td>1.060737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3927.957169</td>\n",
       "      <td>0.364021</td>\n",
       "      <td>8.801282</td>\n",
       "      <td>3.525814</td>\n",
       "      <td>5.075447</td>\n",
       "      <td>2.706829</td>\n",
       "      <td>14.530785</td>\n",
       "      <td>5.017856</td>\n",
       "      <td>2.866551</td>\n",
       "      <td>0.118775</td>\n",
       "      <td>3.237456</td>\n",
       "      <td>2.070228</td>\n",
       "      <td>3.746747</td>\n",
       "      <td>1.453808</td>\n",
       "      <td>0.495025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3029.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6076.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10078.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13147.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Collision_Reference_No.  Collision_Severity  Day_of_Collision  \\\n",
       "count              8562.000000         8562.000000       8562.000000   \n",
       "mean               6549.375380            2.878416         15.702056   \n",
       "std                3927.957169            0.364021          8.801282   \n",
       "min                   1.000000            1.000000          1.000000   \n",
       "25%                3029.250000            3.000000          8.000000   \n",
       "50%                6076.500000            3.000000         16.000000   \n",
       "75%               10078.750000            3.000000         23.000000   \n",
       "max               13147.000000            3.000000         31.000000   \n",
       "\n",
       "       Month_of_Collision  Hour_of_Collision_(24_hour)  Carriageway_Type  \\\n",
       "count         8562.000000                  8307.000000       8562.000000   \n",
       "mean             6.641556                    13.669556         12.223896   \n",
       "std              3.525814                     5.075447          2.706829   \n",
       "min              1.000000                     0.000000          1.000000   \n",
       "25%              4.000000                    10.000000         13.000000   \n",
       "50%              7.000000                    14.000000         13.000000   \n",
       "75%             10.000000                    17.000000         13.000000   \n",
       "max             12.000000                    23.000000         14.000000   \n",
       "\n",
       "       Speed_Limit  Junction_Detail  Junction_Control  \\\n",
       "count  8562.000000      8317.000000       8286.000000   \n",
       "mean     42.463794         6.381027          4.144581   \n",
       "std      14.530785         5.017856          2.866551   \n",
       "min      10.000000         1.000000          1.000000   \n",
       "25%      30.000000         1.000000          1.000000   \n",
       "50%      30.000000         6.000000          4.000000   \n",
       "75%      60.000000        12.000000          7.000000   \n",
       "max      70.000000        12.000000          7.000000   \n",
       "\n",
       "       Pedestrian_Crossing__Human_Control  \\\n",
       "count                          8326.000000   \n",
       "mean                              1.008167   \n",
       "std                               0.118775   \n",
       "min                               1.000000   \n",
       "25%                               1.000000   \n",
       "50%                               1.000000   \n",
       "75%                               1.000000   \n",
       "max                               3.000000   \n",
       "\n",
       "       Pedestrian_Crossing__Physical_Control  Light_Conditions  \\\n",
       "count                             8294.000000       8562.000000   \n",
       "mean                                 2.216301          3.236744   \n",
       "std                                  3.237456          2.070228   \n",
       "min                                  1.000000          1.000000   \n",
       "25%                                  1.000000          1.000000   \n",
       "50%                                  1.000000          3.000000   \n",
       "75%                                  1.000000          5.000000   \n",
       "max                                 12.000000          7.000000   \n",
       "\n",
       "       Weather_Conditions  Road_Surface_Conditions  Special_Conditions_at_Site  \n",
       "count         8562.000000              8321.000000                 8331.000000  \n",
       "mean             4.743635                 1.657133                    1.060737  \n",
       "std              3.746747                 1.453808                    0.495025  \n",
       "min              1.000000                 1.000000                    1.000000  \n",
       "25%              1.000000                 1.000000                    1.000000  \n",
       "50%              3.000000                 1.000000                    1.000000  \n",
       "75%              9.000000                 2.000000                    1.000000  \n",
       "max             10.000000                10.000000                    6.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count missing values and nulls\n",
    "\n",
    "Lets see how much data is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8562 entries, 0 to 8561\n",
      "Data columns (total 17 columns):\n",
      "Collision_Reference_No.                   8562 non-null int64\n",
      "Policing_Area                             8317 non-null object\n",
      "Collision_Severity                        8562 non-null int64\n",
      "Weekday_of_Collision                      8562 non-null object\n",
      "Day_of_Collision                          8562 non-null int64\n",
      "Month_of_Collision                        8562 non-null int64\n",
      "Hour_of_Collision_(24_hour)               8307 non-null float64\n",
      "Carriageway_Type                          8562 non-null int64\n",
      "Speed_Limit                               8562 non-null int64\n",
      "Junction_Detail                           8317 non-null float64\n",
      "Junction_Control                          8286 non-null float64\n",
      "Pedestrian_Crossing__Human_Control       8326 non-null float64\n",
      "Pedestrian_Crossing__Physical_Control    8294 non-null float64\n",
      "Light_Conditions                          8562 non-null int64\n",
      "Weather_Conditions                        8562 non-null int64\n",
      "Road_Surface_Conditions                   8321 non-null float64\n",
      "Special_Conditions_at_Site                8331 non-null float64\n",
      "dtypes: float64(7), int64(8), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# No. of rows for each column\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputation filling NAs\n",
    "\n",
    "If we choose to ignore the na's blindly we are loosing 20% of the data, which is not a very good way to proceed.\n",
    "Instead lets try to inpute continous columns with a strategy.\n",
    "\n",
    "**Strategy**\n",
    "* For Continous variables we will replace the NA values with **MEAN**\n",
    "* For Categorical/Ordinal variables we will replace the NA values with **MODE**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.Speed_Limit = df.Speed_Limit.fillna(df.Speed_Limit.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since rest all features/variables are categorical we will replace the NA's in each column with respective columns MODE values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.fillna(df.mode().iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dtypes conversion\n",
    "\n",
    "There are two columns with dtypes as objects [Strings in PANDAS], we will have to convert them to some numerical form before we could impute those. Hence we will use LabelEncoder class to encode the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "le_PA = preprocessing.LabelEncoder()\n",
    "le_WD = preprocessing.LabelEncoder()\n",
    "\n",
    "le_PA.fit(list(df.Policing_Area.unique()))\n",
    "# le_WD.fit(list(df.Weekday_of_Collision.unique()))\n",
    "le_WD.fit([\"MON\", \"TUE\", \"WED\", \"THU\", \"FRI\", \"SAT\", \"SUN\"])\n",
    "\n",
    "df.Policing_Area = le_PA.transform(df.Policing_Area)\n",
    "df.Weekday_of_Collision = le_WD.transform(df.Weekday_of_Collision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations\n",
    "\n",
    "Lets visualize some data.\n",
    "\n",
    "Start by checking the class distribution, it turns out class distribution is pretty uneven.\n",
    "\n",
    "Bullet Points:\n",
    "* Around 7K points for 1 class.\n",
    "* Other two classes do not even come close to the most frequent class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d92b6a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118aa6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.Collision_Severity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "We observe that the data is not correlated, so we can ignore the dimensionality reduction step in the process.\n",
    "Since all the data available will be usefull to use for model building.\n",
    "\n",
    "**Note**: The column Collision_Reference_No. is not considered as it has unique values and it should just be a serial number of some sort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118a67978>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5oYA5j341C4xdI68CZnN7AT55JXnCLos3R+vLYzN/OlpWXq/XM4oYMxE0RNIc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\nJEmSJEk6kclEkiRJkiSd+P9bflxVfn+qZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a7e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation plot\n",
    "sns.heatmap(df[df.columns[1:]].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "From the grouped frequency plot below we observe that Most of the accidents happen during the weekdays, and the number of accidents increase as the week progresses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c30c7f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c29ed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=le_WD.inverse_transform(df.Weekday_of_Collision), hue=df.Collision_Severity, order=[\"SAT\", \"SUN\", \"MON\", \"TUE\", \"WED\", \"THU\", \"FRI\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c2fe438>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EJEZxBsTFwBVm9jdgG+BBd/8MmEYQAHOA8e6+CrgF2NfM5gFnAVfEWKeIiJDjQ0zu/gHQ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\nCUIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c2f68d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.Weather_Conditions, hue=df.Collision_Severity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "From the above plot we can see that weather conditions 1,2,3, 9 and 10 are contributing to severe accidents.\n",
    "Lets see what those are:\n",
    "* 1: Fine without High Winds\n",
    "* 2: Raining without High Winds\n",
    "* 3: Snowing without High Winds\n",
    "* 9: Other\n",
    "* 10: Unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c463550>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RGAw8APwWWAl0B85y90fM7EPgq+5+Y8r7GZ7UdTVwZVKXTsD5Je9tGHA1sD0w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\nMIuI5IwCs4hIzigwi4jkzP8DEGNTFeLPhMwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c5156d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.pivot_table(index=\"Month_of_Collision\", columns=\"Day_of_Collision\", values=\"Collision_Severity\", aggfunc='count'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c515cc0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7SMLedQlhrzrNhh/GLTJub/AlRhnrPJ8wgFxOCIi3k3WsqbbcLoTDOZ+7+wFV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\nkSgFhIiIRP0/8Z5FMOhpcw8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a8dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.Road_Surface_Conditions, hue=df.Collision_Severity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "Interestingly there is a high chance of server accident if the road conditions are 1 and 2, Since majority of accidents are occured on those two accounts. Lets see what those conditions are:\n",
    "* 1: Dry\n",
    "* 2: Wet / Damp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c696ef0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEFCAYAAAD5bXAgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkccAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c5155c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.Carriageway_Type, hue=df.Collision_Severity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "Most of the accidents happen on single carriageway.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c8f1828>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Sp6bPW870j4zW+zetcf/ez5vNmsK1AKzevJb382b7FKX4oW3TduzfJIv9m2Tx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c46f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lvplot(y=df.Light_Conditions, x=df.Carriageway_Type, hue=df.Collision_Severity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expectation:**\n",
    "Expectation was that most of the accidents would happen on narrow roads(13) which are poorly lit (6,7).\n",
    "\n",
    "**Inference:**\n",
    "Looking at the plot it appears that most of the accidentsly are caused under light conditions (3,4,5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}