Fatal Accidents in Arizona.ipynb

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    "# Arizona Fatal Accidents from 2012-2016\n",
    "\n",
    "### BRIEF\n",
    "* This is a basic version of the two main tables in the Fatality Accident Reporting System as published on NHTSA's.\n",
    "### SOURCE:\n",
    "<b> This data contains two datasets one is about accidents that is accident.tsv and another is about vehicles that is vehicle.tsv.</b>\n",
    "\n",
    "https://data.world/cronkite-data/azfars\n",
    "* Analytic manual and detailed record layouts and instructions: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812447\n",
    "\n",
    "* Auxiliary file layouts:\n",
    "https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/AUXF_A\n",
    "\n",
    "### DATA SELECTION AND OBJECTIVE:\n",
    "<b> I have taken this data in consideration due to following reasons:</b>\n",
    "* Firstly open source data ,so one can download without any restrictions.\n",
    "* It fulfills the requirements of this project\n",
    "* It is an interesting data as I have two datasets which i am going to merge for further detailed EDA\n",
    "* I will find out famous city for accident \n",
    "* contribution of weather and speed in accident will be analysed in this.\n",
    "* Hypothesis testing to prove aur finding.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### DATA PROPERTIES:\n",
    "### Accident Data\n",
    "* Number of columns:79\n",
    "* Number of Rows:3904\n",
    "#### Columns detail:\n",
    "\n",
    "* accident_id - id related to the accident\n",
    "* st_case - Consecutive Number\n",
    "* ve_total - Number of Vehicle Forms Submitted\n",
    "* ve_forms - Number of Vehicle Forms Submitted for MV In Transport\n",
    "* peds - Number of Forms Submitted for Persons Not in Motor Vehicles\n",
    "* persons - Number of MV Occupant\n",
    "* county - county name\n",
    "* country - country name\n",
    "* city - city\n",
    "* city_name - city name\n",
    "* day - Crash Date (Day)\n",
    "* month - Crash Date (Month)\n",
    "* year - Crash Date (Year)\n",
    "* hour - Crash Time (Hour)\n",
    "* minute - Crash Time (Minute)\n",
    "* nhs - National Highway System\n",
    "* func_sys_lit - Functional System\n",
    "* road_fnc_lit - Roadway Function Class\n",
    "* rd_owner_lit - Ownership\n",
    "* tway_id - Trafficway Identifier (1)\n",
    "* tway_id2 - Trafficway Identifier (2)\n",
    "* latitude - Global Position (Latitude)\n",
    "* longiture - Global Position (Longitude)\n",
    "* sp_jur_lit - Special Jurisdiction\n",
    "* harm_ev_lit - First Harmful Event\n",
    "* man_col_lit - Manner of Collision\n",
    "* reljct1 - Relation to Junction Within Interchange Area\n",
    "* reljct2 - Relation to Junction - Specific Location\n",
    "* typ_int - Type of Intersection\n",
    "* wrk_zone - Work Zone\n",
    "* rel_road - Relation To Trafficway\n",
    "* lgt_cond_lit - Light Condition\n",
    "* weather_lit - Atmospheric Conditions\n",
    "* sch_bus - School Bus Related\n",
    "* cf1_lit - Related Factors (1)\n",
    "* cf2_lit - Related Factors (2)\n",
    "* cf3_lit - Related Factors (3)\n",
    "* fatals - Fatalities\n",
    "* a_inter_lit - interstate\n",
    "* a_road_fc_lit - roadway function class\n",
    "* a_tod_lit - Time of Day\n",
    "* a_dow_lit - day of week\n",
    "* a_lt_lit - involving a large truck\n",
    "* a_spcra_lit - Involving Speeding\n",
    "* a_ped_lit - Involving a Pedestrian\n",
    "* a_ped_f_lit - Involving a Pedestrian Fatality\n",
    "* a_pedal_lit - Involving a Pedalcyclist\n",
    "* a_pedal_f - Involving a Pedalcyclist Fatality\n",
    "* a_polpur_lit - Involving a Police Pursuit\n",
    "* a_posbac - Involving a Driver With Positive BAC\n",
    "* a_dist_lit - Involving a Distracted Driver\n",
    "* a_drowsy_lit - Involving a Drowsy Driver\n",
    "* indian_res - Indian Reservation based on special jurisdiction and geographic location data\n",
    "\n",
    "\n",
    "### Vehicle data\n",
    "\n",
    "* Number of rows:5889\n",
    "* Number of columns:131\n",
    "\n",
    "### Columns detail:\n",
    "\n",
    "* state - State Number\n",
    "* st_case - Consecutive Number\n",
    "* veh_no - Vehicle Number\n",
    "* ve_forms - Number of Vehicle Forms Submitted for MV In Transport\n",
    "* numoccs - Number of Occupants\n",
    "* day - Crash Date (Day)\n",
    "* month\t- Crash Date (Month)\n",
    "* hour - Crash Time (Hour)\n",
    "* minute - Crash Time (Minute)\n",
    "* harm_ev - First Harmful Event\n",
    "* man_coll - Manner of Collision\n",
    "* unittype - Unit Type\t\n",
    "* hit_run - Hit and Run\n",
    "* reg_stat - Vehicle Registration State\n",
    "* owner - Registered Vehicle Owner\n",
    "* make - Vehicle Make\n",
    "* model - Vehicle Model\n",
    "* mak_mod - Make Model Combined\n",
    "* body_type - Body Type\t\n",
    "* mode_year - Vehicle Model Year\n",
    "* vin_vechile - Identification Number\n",
    "* vin_1 - VIN Character(1)\n",
    "* vin_2 - VIN Character(2)\n",
    "* vin_3 - VIN Character(3)\n",
    "* vin_4 - VIN Character(4)\n",
    "* vin_5 - VIN Character(5)\n",
    "* vin_6 - VIN Character(6)\n",
    "* vin_7 - VIN Character(7)\n",
    "* vin_8 - VIN Character(8)\n",
    "* vin_9 - VIN Character(9)\n",
    "* vin_10 - VIN Character(10)\n",
    "* vin_11 - VIN Character(11)\n",
    "* vin_12 - VIN Character(12)\n",
    "* tow_veh - Vehicle Trailing\n",
    "* j_knife - Jackknife\t\n",
    "* mcarr_i1 - MCID Issuing Authority\n",
    "* mcarr_i2 - MCID Identification Number\n",
    "* mcarr_id - Motor Carrier Identification Number\n",
    "* gvwr - Gross Vehicle Weight Rating\n",
    "* v_config - Vehicle Configuration\n",
    "* cargo_bt - Cargo Body Type\n",
    "* haz_inv - Hazardous Materials Involvement/Placard-HM1 -Involvement\n",
    "* haz_plac - Hazardous Materials Involvement/Placard-HM2 Placard\n",
    "* haz_id - Hazardous Materials Involvement/Placard- HM3 Identification Number\n",
    "* haz_cno - Hazardous Materials Involvement/Placard-HM4 Class Number\n",
    "* haz_rel - Hazardous Materials Involvement/Placard-HM5 Released\n",
    "* bus_use - Bus Use\t\n",
    "* spec_use - Special Use\n",
    "* emer_use - Emergency Use\n",
    "* trav_sp - Travel Speed\n",
    "* underride - Underride/Override\n",
    "* rollover - Rollover Type\n",
    "* rolinloc - Location of Rollover\n",
    "* impact1 - Areas of Impact - Initial Contact Point\n",
    "* deformed - Extent of Damage\n",
    "* towed - Vehicle Removal\n",
    "* m_harm - Most Harmful Event\n",
    "* veh_sc1 - Vehicle Related Factors (1)\n",
    "* veh_sc2 - Vehicle Related Factors (2)\n",
    "* fire_exp - Fire Occurrence\n",
    "* dr_pres - Driver Presence\n",
    "* l_state - Driver License State\n",
    "* dr_zip - Driver ZIP Code\n",
    "* l_status - Non-CDL License Status\n",
    "* l_type - Non-CDL License Type\n",
    "* cdl_stat - Commercial MV License Status\n",
    "* l_endors - Compliance with CDL Endorsements\n",
    "* l_cmpl - License Compliance with Class of Vehicle\n",
    "* l_restri - Compliance with License Restrictions\n",
    "* dr_hgt - Driver Height\n",
    "* dr_wgt - Driver Weight\n",
    "* prev_acc - Previous Recorded Crashes\n",
    "* prev_sus - Previous Recorded Suspensions and Revocations\n",
    "* prev_dwi - Previous DWI Convictions\n",
    "* prev_spd - Previous Speeding Convictions\n",
    "* prev_oth - Previous Other Moving Violation Convictions\n",
    "* first_mo - Date of First Crash, Suspension or Conviction (Month)\n",
    "* first_yr - Date of First Crash, Suspension or Conviction (Year)\n",
    "* last_mo - Date of Last Crash, Suspension or Conviction (Month)\n",
    "* last_yr - Date of Last Crash, Suspension or Conviction (Year)\n",
    "* speedrel - Speeding Related\n",
    "* dr_sf1 - Driver Related Factors (4 choices)\n",
    "* dr_sf2 - Driver Related Factors (4 choices)\n",
    "* dr_sf3 - Driver Related Factors (4 choices)\n",
    "* dr_sf4 - Driver Related Factors (4 choices)\n",
    "* vtrafway - Trafficway Description"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "from datetime import datetime as dt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "## getting path\n",
    "path = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Import Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Import Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Accident data\n",
    "\n",
    "Below are the accident data for each accident happened from 2012 through 2016.\n",
    "* For many fields, the original code and the translation of that code as provided by NHTSA are shown next to each other, with the original field name followed by that field name with the suffix “lit”, for “literal”.Example, if the original record layout contains a field called WEATHER, this file will include WEATHER as a numeric, coded field, and WEATHER_LIT as its translation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File accident.tsv does not exist: 'accident.tsv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-a6dfeab19a5e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0macci_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr\"accident.tsv\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'\\t'\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m## importing accident.tsv data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Shape of the accident data is {}.\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0macci_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m## getting shape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0macci_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0macci_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m## converting all columns into lowercase data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0macci_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File accident.tsv does not exist: 'accident.tsv'"
     ]
    }
   ],
   "source": [
    "acci_df=pd.read_csv(r\"accident.tsv\",sep='\\t')  ## importing accident.tsv data\n",
    "print(\"Shape of the accident data is {}.\".format(acci_df.shape))  ## getting shape\n",
    "acci_df = acci_df.rename(columns=str.lower) ## converting all columns into lowercase data\n",
    "acci_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset information\n",
    "#acc_df.info()\n",
    "#acc_df.a_drowsy_lit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 vehicle data\n",
    "\n",
    "The data represents the vehicle involves in the accident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the vehicle data is (5889, 131).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accident_id</th>\n",
       "      <th>state</th>\n",
       "      <th>st_case</th>\n",
       "      <th>veh_no</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>numoccs</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>...</th>\n",
       "      <th>displace</th>\n",
       "      <th>mcycl_cy</th>\n",
       "      <th>mcycl_wt</th>\n",
       "      <th>tire_sze</th>\n",
       "      <th>ton_rat</th>\n",
       "      <th>trk_wt</th>\n",
       "      <th>trkwtvar</th>\n",
       "      <th>vin_rest</th>\n",
       "      <th>whldrwhl</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016040001</td>\n",
       "      <td>4</td>\n",
       "      <td>40001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016040002</td>\n",
       "      <td>4</td>\n",
       "      <td>40002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016040003</td>\n",
       "      <td>4</td>\n",
       "      <td>40003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>6</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016040004</td>\n",
       "      <td>4</td>\n",
       "      <td>40004</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016040004</td>\n",
       "      <td>4</td>\n",
       "      <td>40004</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   accident_id  state  st_case  veh_no  ve_forms  numoccs  day  month  hour  \\\n",
       "0   2016040001      4    40001       1         1        5    2      1    13   \n",
       "1   2016040002      4    40002       1         1       99    2      1    21   \n",
       "2   2016040003      4    40003       1         1        6    3      1     6   \n",
       "3   2016040004      4    40004       1         2        2    7      1    22   \n",
       "4   2016040004      4    40004       2         2        3    7      1    22   \n",
       "\n",
       "   minute  ...  displace  mcycl_cy  mcycl_wt  tire_sze  ton_rat  trk_wt  \\\n",
       "0      54  ...       NaN       NaN       NaN       NaN      NaN     NaN   \n",
       "1      26  ...       NaN       NaN       NaN       NaN      NaN     NaN   \n",
       "2      56  ...       NaN       NaN       NaN       NaN      NaN     NaN   \n",
       "3      22  ...       NaN       NaN       NaN       NaN      NaN     NaN   \n",
       "4      22  ...       NaN       NaN       NaN       NaN      NaN     NaN   \n",
       "\n",
       "   trkwtvar  vin_rest  whldrwhl  year  \n",
       "0       NaN       NaN       NaN  2016  \n",
       "1       NaN       NaN       NaN  2016  \n",
       "2       NaN       NaN       NaN  2016  \n",
       "3       NaN       NaN       NaN  2016  \n",
       "4       NaN       NaN       NaN  2016  \n",
       "\n",
       "[5 rows x 131 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vech_df=pd.read_csv(r\"vehicle.tsv\", sep='\\t') ## importing vehicle.tsv data\n",
    "print(\"Shape of the vehicle data is {}.\".format(vech_df.shape))  ## getting shape\n",
    "vech_df = vech_df.rename(columns=str.lower) ## converting all columns into lowercase data\n",
    "vech_df.head(5)  ## getting head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5889 entries, 0 to 5888\n",
      "Columns: 131 entries, accident_id to year\n",
      "dtypes: float64(14), int64(89), object(28)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "vech_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Merging the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data after merging all columns are (5889, 205).\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accident_id</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>county</th>\n",
       "      <th>county_name</th>\n",
       "      <th>city</th>\n",
       "      <th>city_name</th>\n",
       "      <th>...</th>\n",
       "      <th>displace</th>\n",
       "      <th>mcycl_cy</th>\n",
       "      <th>mcycl_wt</th>\n",
       "      <th>tire_sze</th>\n",
       "      <th>ton_rat</th>\n",
       "      <th>trk_wt</th>\n",
       "      <th>trkwtvar</th>\n",
       "      <th>vin_rest</th>\n",
       "      <th>whldrwhl</th>\n",
       "      <th>year_y</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>2012040001</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>PIMA</td>\n",
       "      <td>0</td>\n",
       "      <td>NONE LISTED</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012040002</td>\n",
       "      <td>40002</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>YAVAPAI</td>\n",
       "      <td>90</td>\n",
       "      <td>CLARKDALE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 205 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   accident_id  st_case  ve_total  ve_forms  peds  persons  county  \\\n",
       "0   2012040001    40001         1         1     0        1      19   \n",
       "1   2012040002    40002         1         1     0        2      25   \n",
       "\n",
       "  county_name  city    city_name  ...  displace  mcycl_cy  mcycl_wt  tire_sze  \\\n",
       "0        PIMA     0  NONE LISTED  ...       NaN       4.0     600.0       NaN   \n",
       "1     YAVAPAI    90    CLARKDALE  ...       NaN       4.0     723.0       NaN   \n",
       "\n",
       "   ton_rat  trk_wt  trkwtvar vin_rest  whldrwhl year_y  \n",
       "0      NaN     NaN       NaN      NaN       NaN   2012  \n",
       "1      NaN     NaN       NaN      NaN       NaN   2012  \n",
       "\n",
       "[2 rows x 205 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## merging accident and vehicle data on accident id\n",
    "finalised_df = pd.merge(acci_df, vech_df, on=[\"accident_id\", \"st_case\", \"ve_forms\", 'harm_ev', 'man_coll'])\n",
    "### getting final shape and head\n",
    "print(\"Shape of the data after merging all columns are {}.\".format(finalised_df.shape))\n",
    "### getting head\n",
    "finalised_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Duplicate Column Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1 removing all columns with encoded values and have _lit as other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after dropping the encoded columns is (5889, 179).\n"
     ]
    }
   ],
   "source": [
    "## as we can read above all columns with \"lit\" is the lireral meaning of the corresponding columns\n",
    "col_remove = [i.replace(\"_lit\", \"\") for i in list(finalised_df.columns) if \"_lit\" in i]\n",
    "col_remove.remove('a_road_fc'); col_remove.remove('a_polour')  ## remove \n",
    "col_remove.append(\"a_roadfc\"); col_remove.append(\"a_polpur\")   ## appending same columns with different name\n",
    "\n",
    "finalised_df_m1 = finalised_df.drop(columns = col_rem)   ## drop columns\n",
    "## sanity check\n",
    "print(\"Shape after dropping the encoded columns is {}.\".format(finalised_df_m1.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 removal of redundent columns present in both accident and vehicle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>day_x</th>\n",
       "      <th>day_y</th>\n",
       "      <th>hour_x</th>\n",
       "      <th>hour_y</th>\n",
       "      <th>minute_x</th>\n",
       "      <th>minute_y</th>\n",
       "      <th>month_x</th>\n",
       "      <th>month_y</th>\n",
       "      <th>year_x</th>\n",
       "      <th>year_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day_x  day_y  hour_x  hour_y  minute_x  minute_y  month_x  month_y  year_x  \\\n",
       "0      1      1       2       2        13        13        1        1    2012   \n",
       "1      1      1      16      16        33        33        1        1    2012   \n",
       "2      1      1      18      18         4         4        1        1    2012   \n",
       "3      1      1      20      20         3         3        1        1    2012   \n",
       "4      1      1      20      20         3         3        1        1    2012   \n",
       "\n",
       "   year_y  \n",
       "0    2012  \n",
       "1    2012  \n",
       "2    2012  \n",
       "3    2012  \n",
       "4    2012  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## we saw there were many columns with duplicate information in all three columns now it has come as _x, _y\n",
    "## we need to check and remove the redundent one\n",
    "dup_column = sorted([i for i in list(finalised_df_m1.columns) if (i[-2:] in \"_x\") or (i[-2:] in \"_y\")])\n",
    "## checking values\n",
    "finalised_df_m1[dup_col].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after removing redundent columns are (5889, 174).\n"
     ]
    }
   ],
   "source": [
    "## removing all redundent columns\n",
    "dup_col_remove = sorted([i for i in list(finalised_df_m1.columns) if (i[-2:] in \"_y\")])\n",
    "finalised_df_m2 = finalised_df_m1.drop(columns = dup_col_remove)\n",
    "## sanity check\n",
    "print(\"Shape after removing redundent columns are {}.\".format(final_df_m2.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Null Values Analysis\n",
    "\n",
    "* The dataset contains null values in the form of None,NONE LISTED,UNKNOWN and \".\" inside the columns. Before proceeding into analysis, we must replace these values as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## there are some columns with null values as none and \".\"\n",
    "for col in finalised_df_m2.columns:\n",
    "    finalised_df_m2[col] = finalised_df_m2[col].replace(\"None\", np.nan)\n",
    "    finalised_df_m2[col] = finalised_df_m2[col].replace(\".\", np.nan)\n",
    "    finalised_df_m2[col] = finalised_df_m2[col].replace(\"NONE LISTED\", np.nan)   ## city name\n",
    "    finalised_df_m2[col] = finalised_df_m2[col].replace(\"UNKNOWN\", np.nan)   ## city name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#getting missing value counts\n",
    "count = finalised_df_m2.isnull().sum()\n",
    "\n",
    "#getting missing value percentage\n",
    "percent = 100 * ((finalised_df_m2.isnull().sum()) / (final_df_m2.shape[0]))\n",
    "\n",
    "#creating dataframe and renaming column\n",
    "table = pd.concat([count, percentage], axis=1).rename(columns = {0:\"missing_count\", 1:\"missing_percentage\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>func_sys_lit</th>\n",
       "      <th>road_fnc_lit</th>\n",
       "      <th>rd_owner_lit</th>\n",
       "      <th>tway_id2</th>\n",
       "      <th>cf1_lit</th>\n",
       "      <th>cf2_lit</th>\n",
       "      <th>cf3_lit</th>\n",
       "      <th>trlr1vin</th>\n",
       "      <th>trlr2vin</th>\n",
       "      <th>trlr3vin</th>\n",
       "      <th>...</th>\n",
       "      <th>cylinder</th>\n",
       "      <th>displace</th>\n",
       "      <th>mcycl_cy</th>\n",
       "      <th>mcycl_wt</th>\n",
       "      <th>tire_sze</th>\n",
       "      <th>ton_rat</th>\n",
       "      <th>trk_wt</th>\n",
       "      <th>trkwtvar</th>\n",
       "      <th>vin_rest</th>\n",
       "      <th>whldrwhl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>missing_count</th>\n",
       "      <td>3317.00</td>\n",
       "      <td>2572.00</td>\n",
       "      <td>3317.00</td>\n",
       "      <td>3938.00</td>\n",
       "      <td>5190.00</td>\n",
       "      <td>5807.00</td>\n",
       "      <td>5879.00</td>\n",
       "      <td>4563.00</td>\n",
       "      <td>4563.00</td>\n",
       "      <td>4563.00</td>\n",
       "      <td>...</td>\n",
       "      <td>4892.00</td>\n",
       "      <td>5023.00</td>\n",
       "      <td>5759.00</td>\n",
       "      <td>5759.00</td>\n",
       "      <td>5335.00</td>\n",
       "      <td>5680.00</td>\n",
       "      <td>5577.0</td>\n",
       "      <td>5577.0</td>\n",
       "      <td>5694.00</td>\n",
       "      <td>5577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing_percentage</th>\n",
       "      <td>56.33</td>\n",
       "      <td>43.67</td>\n",
       "      <td>56.33</td>\n",
       "      <td>66.87</td>\n",
       "      <td>88.13</td>\n",
       "      <td>98.61</td>\n",
       "      <td>99.83</td>\n",
       "      <td>77.48</td>\n",
       "      <td>77.48</td>\n",
       "      <td>77.48</td>\n",
       "      <td>...</td>\n",
       "      <td>83.07</td>\n",
       "      <td>85.29</td>\n",
       "      <td>97.79</td>\n",
       "      <td>97.79</td>\n",
       "      <td>90.59</td>\n",
       "      <td>96.45</td>\n",
       "      <td>94.7</td>\n",
       "      <td>94.7</td>\n",
       "      <td>96.69</td>\n",
       "      <td>94.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    func_sys_lit  road_fnc_lit  rd_owner_lit  tway_id2  \\\n",
       "missing_count            3317.00       2572.00       3317.00   3938.00   \n",
       "missing_percentage         56.33         43.67         56.33     66.87   \n",
       "\n",
       "                    cf1_lit  cf2_lit  cf3_lit  trlr1vin  trlr2vin  trlr3vin  \\\n",
       "missing_count       5190.00  5807.00  5879.00   4563.00   4563.00   4563.00   \n",
       "missing_percentage    88.13    98.61    99.83     77.48     77.48     77.48   \n",
       "\n",
       "                    ...  cylinder  displace  mcycl_cy  mcycl_wt  tire_sze  \\\n",
       "missing_count       ...   4892.00   5023.00   5759.00   5759.00   5335.00   \n",
       "missing_percentage  ...     83.07     85.29     97.79     97.79     90.59   \n",
       "\n",
       "                    ton_rat  trk_wt  trkwtvar  vin_rest  whldrwhl  \n",
       "missing_count       5680.00  5577.0    5577.0   5694.00    5577.0  \n",
       "missing_percentage    96.45    94.7      94.7     96.69      94.7  \n",
       "\n",
       "[2 rows x 34 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## show table with only missing percentage more than 40%\n",
    "table[table['missing_percentage'] > 40].round(2).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "* To keep all the values with more than 40% null values is not logical. So removing all columns at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after dropping the null columns is (5889, 140).\n"
     ]
    }
   ],
   "source": [
    "to_drop = list(table[table['missing_percentage'] > 40].transpose().columns)  ## getting column names to drop\n",
    "finalised_df_m3 = finalised_df_m2.drop(columns = to_drop)  ### dropping all selected columns\n",
    "## sanity check\n",
    "print(\"Shape after dropping the null columns is {}.\".format(finalised_df_m3.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Duplicate rows analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accident_id</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>county</th>\n",
       "      <th>county_name</th>\n",
       "      <th>city</th>\n",
       "      <th>city_name</th>\n",
       "      <th>...</th>\n",
       "      <th>vtrafcon</th>\n",
       "      <th>vtcont_f</th>\n",
       "      <th>p_crash1</th>\n",
       "      <th>p_crash2</th>\n",
       "      <th>p_crash3</th>\n",
       "      <th>pcrash4</th>\n",
       "      <th>pcrash5</th>\n",
       "      <th>acc_type</th>\n",
       "      <th>deaths</th>\n",
       "      <th>dr_drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [accident_id, st_case, ve_total, ve_forms, peds, persons, county, county_name, city, city_name, day_x, month_x, year_x, hour_x, minute_x, nhs, tway_id, latitude, longitud, sp_jur_lit, harm_ev_lit, man_coll_lit, reljct1, reljct2, typ_int, wrk_zone, rel_road, lgt_cond_lit, weather_lit, sch_bus, fatals, a_inter_lit, a_road_fc_lit, a_tod_lit, a_dow_lit, a_lt_lit, a_spcra_lit, a_ped_lit, a_ped_f_lit, a_pedal_lit, a_pedal_f_lit, a_polour_lit, a_posbac_lit, a_dist_lit, a_drowsy_lit, indian_res_lit, state, veh_no, numoccs, unittype, hit_run, reg_stat, owner, make, model, mak_mod, body_typ, mod_year, vin, vin_1, vin_2, vin_3, vin_4, vin_5, vin_6, vin_7, vin_8, vin_9, vin_10, vin_11, vin_12, tow_veh, j_knife, mcarr_i1, mcarr_i2, mcarr_id, gvwr, v_config, cargo_bt, haz_inv, haz_plac, haz_id, haz_cno, haz_rel, bus_use, spec_use, emer_use, trav_sp, underide, rollover, rolinloc, impact1, deformed, towed, m_harm, veh_sc1, veh_sc2, fire_exp, dr_pres, l_state, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 140 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking for duplicate rows\n",
    "finalised_df_m3[finalised_df_m3.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result:\n",
    "* no duplicate data is present."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Redundent Feature Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## as we have date column we can map it into one\n",
    "finalised_df_m3[\"date_time\"] = finalised_df_m3[\"day_x\"].apply(str) + \"-\" + finalised_df_m3[\"month_x\"].apply(str) + \"-\"+ finalised_df_m3[\"year_x\"].apply(str)\n",
    "\n",
    "## dropping day, month, year, minutes columns\n",
    "## also city and county are redundent with city name and county name\n",
    "finalised_df_m4 = finalised_df_m3.drop(columns = [\"day_x\", \"month_x\", \"year_x\", \"minute_x\", \"county\", \"city\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vin</th>\n",
       "      <th>vin_1</th>\n",
       "      <th>vin_2</th>\n",
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       "      <th>vin_4</th>\n",
       "      <th>vin_5</th>\n",
       "      <th>vin_6</th>\n",
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       "      <th>vin_9</th>\n",
       "      <th>vin_10</th>\n",
       "      <th>vin_11</th>\n",
       "      <th>vin_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JY4AM09Y67C0</td>\n",
       "      <td>J</td>\n",
       "      <td>Y</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Y</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "            vin vin_1 vin_2 vin_3 vin_4 vin_5 vin_6 vin_7 vin_8 vin_9 vin_10  \\\n",
       "0  JY4AM09Y67C0     J     Y     4     A     M     0     9     Y     6      7   \n",
       "\n",
       "  vin_11 vin_12  \n",
       "0      C      0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### vin_vechile - Identification Number\n",
    "### the number is present indivilually as we can see\n",
    "finalised_df_m5 = finalised_df_m4.drop(columns = list(final_df_m4.iloc[:,53:65].columns))\n",
    "## showing output\n",
    "finalised_df_m4.iloc[:,52:65].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after dropping the unnecessary columns are (5889, 123).\n"
     ]
    }
   ],
   "source": [
    "## sanity check\n",
    "print(\"Shape after dropping the unnecessary columns are {}.\".format(finalised_df_m5.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "### copy the final dataframe into another one\n",
    "df = finalised_df_m5.copy(deep = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result:\n",
    "* Redundancy is removed.\n",
    "* two columns has made compact such rather than using 12 different columns for vehicle identification ,we can use one compact identification number vin only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis with Visualization for easy understanding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 details about accident "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The total number of accident details present in the dataset is 3904, where as the dataset contains accident id multiple times as there are multiple vehicle involved in the accident. \n",
    "* Let's analyse that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "### bar plot function\n",
    "def bar_plot(data, xlabel_, ylabel_, title_, num, size, color):\n",
    "    \"\"\"\n",
    "    This function produces bar plot using pandas plot attribute\n",
    "    \"\"\"\n",
    "    plot = data.value_counts()[0:num].plot(kind=\"bar\", figsize=size, color=color)\n",
    "    plt.xlabel(xlabel_, fontsize=15)\n",
    "    plt.ylabel(ylabel_, fontsize=15)\n",
    "    plt.title(title_, fontsize=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3904 accident id present in the dataset.\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {} accident id present in the dataset.\".format(len(df[\"accident_id\"].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot(df[\"accident_id\"], \"accident id\", \"count\", \"Total number of vehicle involved in each accident\", 30, (10,5), color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "* Although its weird to see that more than 16 vehicles are associated with one accident id,but sometimes due sudden breaking multiple cars bumps into each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accident_id</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>county_name</th>\n",
       "      <th>city_name</th>\n",
       "      <th>hour_x</th>\n",
       "      <th>nhs</th>\n",
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       "      <th>vtcont_f</th>\n",
       "      <th>p_crash1</th>\n",
       "      <th>p_crash2</th>\n",
       "      <th>p_crash3</th>\n",
       "      <th>pcrash4</th>\n",
       "      <th>pcrash5</th>\n",
       "      <th>acc_type</th>\n",
       "      <th>deaths</th>\n",
       "      <th>dr_drink</th>\n",
       "      <th>date_time</th>\n",
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       "      <th>2165</th>\n",
       "      <td>2013040743</td>\n",
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       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "      <th>2166</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
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       "      <td>1</td>\n",
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       "      <td>26-10-2013</td>\n",
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       "      <th>2167</th>\n",
       "      <td>2013040743</td>\n",
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       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2168</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
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       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
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       "      <td>...</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
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       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2170</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
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       "      <td>26-10-2013</td>\n",
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       "      <th>2171</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
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       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
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       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
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       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>5</td>\n",
       "      <td>13</td>\n",
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       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
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       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>99</td>\n",
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       "      <td>9</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
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       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2176</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2177</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
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       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2178</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
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       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>99</td>\n",
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       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2179</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
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       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>2013040743</td>\n",
       "      <td>40743</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>PINAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26-10-2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      accident_id  st_case  ve_total  ve_forms  peds  persons county_name  \\\n",
       "2165   2013040743    40743        17        17     0       22       PINAL   \n",
       "2166   2013040743    40743        17        17     0       22       PINAL   \n",
       "2167   2013040743    40743        17        17     0       22       PINAL   \n",
       "2168   2013040743    40743        17        17     0       22       PINAL   \n",
       "2169   2013040743    40743        17        17     0       22       PINAL   \n",
       "2170   2013040743    40743        17        17     0       22       PINAL   \n",
       "2171   2013040743    40743        17        17     0       22       PINAL   \n",
       "2172   2013040743    40743        17        17     0       22       PINAL   \n",
       "2173   2013040743    40743        17        17     0       22       PINAL   \n",
       "2174   2013040743    40743        17        17     0       22       PINAL   \n",
       "2175   2013040743    40743        17        17     0       22       PINAL   \n",
       "2176   2013040743    40743        17        17     0       22       PINAL   \n",
       "2177   2013040743    40743        17        17     0       22       PINAL   \n",
       "2178   2013040743    40743        17        17     0       22       PINAL   \n",
       "2179   2013040743    40743        17        17     0       22       PINAL   \n",
       "2180   2013040743    40743        17        17     0       22       PINAL   \n",
       "2181   2013040743    40743        17        17     0       22       PINAL   \n",
       "\n",
       "     city_name  hour_x  nhs  ... vtcont_f  p_crash1  p_crash2 p_crash3  \\\n",
       "2165       NaN      12    1  ...        0         2        61        1   \n",
       "2166       NaN      12    1  ...        0         1        50        7   \n",
       "2167       NaN      12    1  ...        0         5        53        1   \n",
       "2168       NaN      12    1  ...        0         5        60        1   \n",
       "2169       NaN      12    1  ...        0         1        13        1   \n",
       "2170       NaN      12    1  ...        0         5        13        1   \n",
       "2171       NaN      12    1  ...        0         1        13        1   \n",
       "2172       NaN      12    1  ...        0         1        50        1   \n",
       "2173       NaN      12    1  ...        0         5        13        1   \n",
       "2174       NaN      12    1  ...        0         1        99       99   \n",
       "2175       NaN      12    1  ...        0         1        99       99   \n",
       "2176       NaN      12    1  ...        0         1        99        1   \n",
       "2177       NaN      12    1  ...        0         1        99       99   \n",
       "2178       NaN      12    1  ...        0         1        99       99   \n",
       "2179       NaN      12    1  ...        0         1        99       99   \n",
       "2180       NaN      12    1  ...        0         1        99       99   \n",
       "2181       NaN      12    1  ...        0         2        99       99   \n",
       "\n",
       "     pcrash4 pcrash5  acc_type  deaths  dr_drink   date_time  \n",
       "2165       1       1        98       0         0  26-10-2013  \n",
       "2166       1       3        32       0         0  26-10-2013  \n",
       "2167       1       1        32       0         0  26-10-2013  \n",
       "2168       1       4        98       0         0  26-10-2013  \n",
       "2169       1       4        98       0         0  26-10-2013  \n",
       "2170       1       5        98       0         0  26-10-2013  \n",
       "2171       1       1        98       0         0  26-10-2013  \n",
       "2172       1       1        98       0         0  26-10-2013  \n",
       "2173       1       5        98       1         0  26-10-2013  \n",
       "2174       1       9        98       0         0  26-10-2013  \n",
       "2175       9       9        98       0         0  26-10-2013  \n",
       "2176       1       3        98       0         0  26-10-2013  \n",
       "2177       9       9        98       0         0  26-10-2013  \n",
       "2178       9       9        98       1         0  26-10-2013  \n",
       "2179       9       9        98       0         0  26-10-2013  \n",
       "2180       9       9        98       1         0  26-10-2013  \n",
       "2181       9       9        98       0         0  26-10-2013  \n",
       "\n",
       "[17 rows x 123 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"accident_id\"] == 2013040743]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "* As we can see above 2013040743 accident id present 17 times in the dataset.\n",
    "."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Accident in a particular day\n",
    "\n",
    "* To check the number accidents happened on the particular dates we must take unique data to analyse it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creation of a temporary dataframe to get the unique accident id only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## dropping all redundent columns with accident_id and keeping the first row\n",
    "temporary_df = df.drop_duplicates(subset = [\"accident_id\"], keep=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot(temporary_df[\"date_time\"], \"day_month_year\", \"count\", \"Accident happened on dates\", 50, (15,3), color = \"green\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### observation:\n",
    "* The maximum accident happend on a single date is 8. There are 5 accident id present with 8 accidents in a single day only.\n",
    "* In 2014 maximum accident happend on one day is 7 ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Here's the yearwise count of accidents?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'2012': 738, '2013': 782, '2014': 708, '2015': 811, '2016': 865}\n"
     ]
    }
   ],
   "source": [
    "## getting years from date time column to check how many accidents happening year wise\n",
    "temporary_df[\"year\"] = temporary_df[\"date_time\"].apply(lambda x : x[-4:])\n",
    "## getting dictionary of thr year and number of accidients\n",
    "getting_dict = temporary_df[\"year\"].value_counts().sort_index().to_dict()\n",
    "print(getting_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## getting key and values from the dictionaries\n",
    "labels = getting_dict.keys(); values = getting_dict.values()\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.pie(values, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "plt.title(\"Number of accidents per year\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "* As we see the last 5 years of trend, we can say that accident rate has increased from 2014 to 2016 constantly.\n",
    "* In 2016 the most number of accidents recorded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 During which month-year the accident rate is high?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "### bar plot function unsorted\n",
    "def bar_plot_unsorted(data, xlabel_, ylabel_, title_, num, size, color):\n",
    "    \"\"\"\n",
    "    This function produces bar plot using pandas plot attribute\n",
    "    \"\"\"\n",
    "    plot = data.value_counts().sort_index()[0:num].plot(kind=\"bar\", figsize=size, color=color)\n",
    "    plt.xlabel(xlabel_, fontsize=15)\n",
    "    plt.ylabel(ylabel_, fontsize=15)\n",
    "    plt.title(title_, fontsize=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "temporary_df[\"my\"] = temporary_df[\"date_time\"].apply(lambda x : x.split(\"-\")[2] + \"-\" + x.split(\"-\")[1])\n",
    "temporary_df[\"my\"] = temporary_df[\"my\"].apply((lambda x : dt.strptime(x, '%Y-%m')))\n",
    "temporary_df[\"my\"] = temporary_df[\"my\"].dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot_unsorted(temporary_df[\"my\"], \"month&year combination\", \"count\", \"Accident rate\", 48, (15,3), color = \"magenta\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "* The most of the accidents recored during the month of march 2015.\n",
    "* I didn't find any particular pattern here of the accidents. But the volume of accidents during the month of march is very high yearly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 During which hour accident rates are high?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot_unsorted(temporary_df[\"hour_x\"], \"hours\", \"count\", \"accident count per hours\", 24, (15,4), \"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "* As we can see the accident mostly happened during evening to night time, from 5PM to 10PM.\n",
    "* This may happen due to the rush in the roads during the night time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 Travel speed of vehicle\n",
    "\n",
    "* The data is given with a travel speed of the vehicle column as well. We must analyse it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "## distribution plot\n",
    "plot = sns.distplot(df[\"trav_sp\"], color=\"red\", kde=True)\n",
    "plt.title(\"Distribution plot of travel speed of the vehicle\", fontsize=20)\n",
    "plt.show()\n",
    "\n",
    "## bot plot\n",
    "plot = plt.boxplot(df[\"trav_sp\"].values)\n",
    "plt.ylabel(\"range\", fontsize=15)\n",
    "plt.title(\"Travel Speed Analysis\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "* As we can see above the travel speed starts from \"0\" unit but it had values more than 900 as well. \n",
    "* Though the unit is not present I am generalizing it with km/hr.\n",
    "* It seems weird, as boxplot shows there is no outlier. We must do percentile analysis here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9, 100]\n",
      "[0.0, 15.0, 34.0, 40.0, 50.0, 65.0, 75.0, 998.0, 998.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0, 999.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### percetile analysis\n",
    "###sanity check\n",
    "\n",
    "ls1 = [(val, np.percentile(df[\"trav_sp\"].values, val)) for val in range(0,91,10)]\n",
    "ls2 = [(val, np.percentile(df[\"trav_sp\"].values, val)) for val in range(91,100)]\n",
    "ls3 = [(val, np.percentile(df[\"trav_sp\"].values, val)) for val in [99.1,99.2,99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9, 100]]\n",
    "## extending class\n",
    "ls1.extend(ls2); ls1.extend(ls3)\n",
    "## getting x and y\n",
    "percent = [val[0] for val in ls1]\n",
    "print(percent)\n",
    "values = [val[1] for val in ls1]\n",
    "print(values)\n",
    "fig, ax = plt.subplots(ncols=3, figsize=(15,3))\n",
    "## getting for 0-90\n",
    "ax[0].plot(percent[0:10], values[0:10], 'r-o')\n",
    "ax[0].set_xticks(percent[0:10])\n",
    "ax[0].set_title(\"Percentile Analysis for 0 to 90\")\n",
    "## getting for 90-99\n",
    "ax[1].plot(percent[10:19], values[10:19], 'g-o')\n",
    "ax[1].set_xticks(percent[10:19])\n",
    "ax[1].set_title(\"Percentile Analysis for 91 to 99\")\n",
    "## getting for 99.1 to 99.9\n",
    "ax[2].plot(percent[19:], values[19:], 'b-o')\n",
    "ax[2].set_xticks(percent[19:])\n",
    "ax[2].set_title(\"Percentile Analysis for 99.1 to 99.9\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 63rd percentile of the travel speed is 117.88 units.\n",
      "The 64rd percentile of the travel speed is 998.00 units.\n"
     ]
    }
   ],
   "source": [
    "print(\"The 63rd percentile of the travel speed is {:.2f} units.\".format(np.percentile(df[\"trav_sp\"].values, 63)))\n",
    "print(\"The 64rd percentile of the travel speed is {:.2f} units.\".format(np.percentile(df[\"trav_sp\"].values, 64)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "* From above analysis we conclude that 34 percentile of the values present in the travel speed section is junk and full of outliers which 998. This anomaly coz a vehicle can't go more than 1000km/hr which is impossible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df[df[\"trav_sp\"] < 120][\"trav_sp\"]\n",
    "\n",
    "### after removal of outliers\n",
    "plot = plt.boxplot(x)\n",
    "plt.ylabel(\"range\", fontsize=15)\n",
    "plt.title(\"Travel Speed Analysis\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.distplot(x, color=\"red\", kde=True)\n",
    "plt.title(\"Distribution plot of travel speed of the vehicle, after removing outliers\", fontsize=15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The average vehicle speed was betwen 30 to 60 units when accident happened."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1 what is the probability of death and chances of to be alive according to the speed of vehicle?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* using temp_df to analyse speed with death rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>accident_id</th>\n",
       "      <th>st_case</th>\n",
       "      <th>ve_total</th>\n",
       "      <th>ve_forms</th>\n",
       "      <th>peds</th>\n",
       "      <th>persons</th>\n",
       "      <th>county_name</th>\n",
       "      <th>city_name</th>\n",
       "      <th>hour_x</th>\n",
       "      <th>nhs</th>\n",
       "      <th>...</th>\n",
       "      <th>p_crash1</th>\n",
       "      <th>p_crash2</th>\n",
       "      <th>p_crash3</th>\n",
       "      <th>pcrash4</th>\n",
       "      <th>pcrash5</th>\n",
       "      <th>acc_type</th>\n",
       "      <th>deaths</th>\n",
       "      <th>dr_drink</th>\n",
       "      <th>date_time</th>\n",
       "      <th>bins_of_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012040001</td>\n",
       "      <td>40001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PIMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1-1-2012</td>\n",
       "      <td>(-0.001, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012040003</td>\n",
       "      <td>40003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>MARICOPA</td>\n",
       "      <td>PHOENIX</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-1-2012</td>\n",
       "      <td>(-0.001, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012040005</td>\n",
       "      <td>40005</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>PIMA</td>\n",
       "      <td>TUCSON</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4-1-2012</td>\n",
       "      <td>(30.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012040005</td>\n",
       "      <td>40005</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>PIMA</td>\n",
       "      <td>TUCSON</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>62</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4-1-2012</td>\n",
       "      <td>(30.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012040006</td>\n",
       "      <td>40006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>MARICOPA</td>\n",
       "      <td>MESA</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4-1-2012</td>\n",
       "      <td>(30.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5883</th>\n",
       "      <td>2016040877</td>\n",
       "      <td>40877</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>MARICOPA</td>\n",
       "      <td>GLENDALE</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15-11-2016</td>\n",
       "      <td>(-0.001, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5884</th>\n",
       "      <td>2016040877</td>\n",
       "      <td>40877</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>MARICOPA</td>\n",
       "      <td>GLENDALE</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15-11-2016</td>\n",
       "      <td>(30.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5885</th>\n",
       "      <td>2016040878</td>\n",
       "      <td>40878</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>MARICOPA</td>\n",
       "      <td>PHOENIX</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12-9-2016</td>\n",
       "      <td>(-0.001, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5886</th>\n",
       "      <td>2016040878</td>\n",
       "      <td>40878</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>MARICOPA</td>\n",
       "      <td>PHOENIX</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12-9-2016</td>\n",
       "      <td>(-0.001, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5887</th>\n",
       "      <td>2016040879</td>\n",
       "      <td>40879</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>MARICOPA</td>\n",
       "      <td>PHOENIX</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10-12-2016</td>\n",
       "      <td>(-0.001, 30.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3711 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      accident_id  st_case  ve_total  ve_forms  peds  persons county_name  \\\n",
       "0      2012040001    40001         1         1     0        1        PIMA   \n",
       "2      2012040003    40003         1         1     1        0    MARICOPA   \n",
       "5      2012040005    40005         2         2     0        4        PIMA   \n",
       "6      2012040005    40005         2         2     0        4        PIMA   \n",
       "7      2012040006    40006         1         1     1        1    MARICOPA   \n",
       "...           ...      ...       ...       ...   ...      ...         ...   \n",
       "5883   2016040877    40877         2         2     0        3    MARICOPA   \n",
       "5884   2016040877    40877         2         2     0        3    MARICOPA   \n",
       "5885   2016040878    40878         2         2     0        5    MARICOPA   \n",
       "5886   2016040878    40878         2         2     0        5    MARICOPA   \n",
       "5887   2016040879    40879         1         1     1        2    MARICOPA   \n",
       "\n",
       "     city_name  hour_x  nhs  ... p_crash1  p_crash2  p_crash3 pcrash4 pcrash5  \\\n",
       "0          NaN       2    0  ...       10         6         1       1       3   \n",
       "2      PHOENIX      18    0  ...        0        19         0       0       0   \n",
       "5       TUCSON       0    0  ...       14         9         1       9       4   \n",
       "6       TUCSON       0    0  ...       14        62         6       1       3   \n",
       "7         MESA       9    0  ...        1        80         4       2       1   \n",
       "...        ...     ...  ...  ...      ...       ...       ...     ...     ...   \n",
       "5883  GLENDALE      11    0  ...       10        16         1       1       1   \n",
       "5884  GLENDALE      11    0  ...        1        65         6       1       1   \n",
       "5885   PHOENIX      17    1  ...        1        50         1       1       1   \n",
       "5886   PHOENIX      17    1  ...        5        53         1       1       1   \n",
       "5887   PHOENIX      19    1  ...        1        80        99       1       1   \n",
       "\n",
       "     acc_type  deaths  dr_drink   date_time   bins_of_speed  \n",
       "0          98       1         1    1-1-2012  (-0.001, 30.0]  \n",
       "2          98       0         0    1-1-2012  (-0.001, 30.0]  \n",
       "5           2       1         0    4-1-2012    (30.0, 60.0]  \n",
       "6          98       0         0    4-1-2012    (30.0, 60.0]  \n",
       "7          13       0         0    4-1-2012    (30.0, 60.0]  \n",
       "...       ...     ...       ...         ...             ...  \n",
       "5883       78       0         0  15-11-2016  (-0.001, 30.0]  \n",
       "5884       79       1         0  15-11-2016    (30.0, 60.0]  \n",
       "5885       20       1         0   12-9-2016  (-0.001, 30.0]  \n",
       "5886       21       0         0   12-9-2016  (-0.001, 30.0]  \n",
       "5887       13       0         0  10-12-2016  (-0.001, 30.0]  \n",
       "\n",
       "[3711 rows x 124 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## creating dataframe for speed below 120 which is acceptable\n",
    "speedo_df = df[df[\"trav_sp\"] < 120]\n",
    "## creating bins using panndas cut\n",
    "speedo_df[\"bins_of_speed\"] = pd.cut(x, bins=[0, 30, 60, 90, 120], include_lowest=True)\n",
    "speedo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Yes\n",
       "2        No\n",
       "5       Yes\n",
       "6        No\n",
       "7        No\n",
       "       ... \n",
       "5883     No\n",
       "5884    Yes\n",
       "5885    Yes\n",
       "5886     No\n",
       "5887     No\n",
       "Name: is_dead?, Length: 3711, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### creating a new column of death or no death as yes or no\n",
    "speedo_df[\"is_dead?\"] = [\"No\" if i < 1 else \"Yes\" for i in speed_df[\"deaths\"].values]\n",
    "speedo_df[\"is_dead?\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "plot = sns.countplot(x=\"bins_of_speed\", hue=\"is_dead?\", data=speed_df)\n",
    "plt.title(\"mortality rate with the speed of vehicle analysis\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "* As we can see above vehicle in speed 0 to 60, the probability of death is less.\n",
    "* But if the speed is more than 60 it is severe and death rate is high.\n",
    "* Comparatively chances of to be alive are more when speed is less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.7 Fatality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 3604, 2: 236, 3: 40, 4: 16, 5: 7, 7: 1}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getting_dict = temp_df[\"fatals\"].value_counts().sort_index().to_dict()\n",
    "print(getting_dict)\n",
    "\n",
    "## getting key and values from the dictionaries\n",
    "labels = getting_dict.keys(); values = getting_dict.values()\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(values, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "plt.title(\"Fatality range\", fontsize=15)\n",
    "plt.legend(labels, loc='upper center', bbox_to_anchor=(1.45, 0.8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion:\n",
    "\n",
    "* As we can see that 92.3% accidents were not severe,means fatality  is only 1\n",
    "* In one accident it is 7 which shows that only one  accident was quite severe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### what was the weather condition during accidents?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### getting plot for weather list\n",
    "plot = df[\"weather_lit\"].value_counts().plot(kind=\"bar\", xlabel =\"weather condition\", ylabel=\"total count\", figsize=(15,5), color=\"skyblue\")\n",
    "plt.title(\"What was the weather condition during accidents?\", fontsize=15,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "\n",
    "* Most of the accidents happend when weather was clear.\n",
    "* It is observed that worse weather conditions did not contribute in accidents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In which city and corresponding country has the higher accident rate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"city_name\", \"county_name\"]].value_counts()[0:20].plot(kind=\"bar\", xlabel = \"City with corressponding county\", figsize=(15,5))\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Number of accident happen in perticular county\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "* Above analysis conclude that the most of the accidents happend in PHOENIX city's MARICOPA county,which were around 1400."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How drinking and driving are related to death rate?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"is_dead?\"] = [\"No\" if i < 1 else \"Yes\" for i in df[\"deaths\"].values]\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plot = sns.countplot(x=\"dr_drink\", hue=\"is_dead?\", data=df, palette=\"BuGn\")\n",
    "plt.title(\"mortality rate with the condition of drinking and driving\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## selecting some particlular columns for correaltion which makes sense\n",
    "column_list = [\"accident_id\", \"nhs\", \"hour_x\", \"wrk_zone\", \"fatals\", \"deaths\", \"mak_mod\", \"mod_year\", \"trav_sp\", \"underide\", \"dr_drink\"]\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "sns.heatmap(df[column_list].corr(), annot=True, cmap=\"ocean\",  fmt='.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "* There is some relation between fatal accidents and deaths,so if accident is fatal then deaths would be high(38% chances).\n",
    "* From selected columns we plotted the correaltion and we didn't find any strong correaltion with any thing.\n",
    "* There is some correlation between make_mod(which is model of the vehicle) and mod_year(vechicle build on which year), but not enough to form a strong relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NULL HYPOTHESIS\n",
    "## 6. Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption: There is no significant difference between deaths and people escaped in an accident  with respect to speed. It means severity of accident independent of speed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* the alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=15.285057349553153, pvalue=4.1109245287280895e-51)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind, norm\n",
    "\n",
    "## to avoid the outliers issue in speed we are taking the speed_df as created above.\n",
    "death_yes = speedo_df[speedo_df['is_dead?'] == \"Yes\"]\n",
    "death_no = speedo_df[speedo_df['is_dead?'] == \"No\"]\n",
    "\n",
    "yes_speed = death_yes[\"trav_sp\"].values\n",
    "no_speed = death_no[\"trav_sp\"].values\n",
    "\n",
    "ttest_ind(yes_speed, no_speed, equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As p-value is very very less than alpha, that means we reject the null hypothesis using t-test.\n",
    "* That mean there is a significant difference between death and alive rate according to the speed.\n",
    "* This means death rate or severity of accident depends upon speed of the vehicle ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=14.066065986436195, pvalue=1.2001958378657282e-40)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind, norm\n",
    "\n",
    "## to avoid the outliers issue in speed we are taking the speed_df as created above.\n",
    "drink_n_drive_yes = speedo_df[speedo_df['dr_drink'] == 1]\n",
    "drink_n_drive_no = speedo_df[speedo_df['dr_drink'] == 0]\n",
    "\n",
    "yes_speed = drink_n_drive_yes[\"trav_sp\"].values\n",
    "no_speed = drink_n_drive_no[\"trav_sp\"].values\n",
    "\n",
    "ttest_ind(yes_speed, no_speed, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "plot = sns.countplot(x=\"bins_of_speed\", hue=\"dr_drink\", data=speedo_df, palette=\"prism\")\n",
    "plt.title(\"mortality rate with the speed of vehicle analysis\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3111\n",
       "1     600\n",
       "Name: dr_drink, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speedo_df[\"dr_drink\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.185"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3111/600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30.0, 60.0]      306\n",
       "(60.0, 90.0]      182\n",
       "(-0.001, 30.0]     87\n",
       "(90.0, 120.0]      25\n",
       "Name: bins_of_speed, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drink_n_drive_yes[\"bins_of_speed\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30.0, 60.0]      1471\n",
       "(-0.001, 30.0]    1015\n",
       "(60.0, 90.0]       601\n",
       "(90.0, 120.0]       24\n",
       "Name: bins_of_speed, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drink_n_drive_no[\"bins_of_speed\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11.666666666666666, 4.80718954248366, 3.302197802197802, 0.96)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1015/87, 1471/306, 601/182,  24/25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary:\n",
    "<b> After doing extensive analysis I came down to following conclusion</b>:\n",
    "\n",
    "* we consider some factors such as weather,speed to check their impact on mortality and frequency of accidents and analysis concluded that most of accidents happened in clear weather ,but speed has a significant effect on mortality of accident .It means death rate is increases with surge in speed and more severe is accident.\n",
    "\n",
    "* T-test done for hypothesis testing also rejects the null hypothesis as p value considerably less than alpha value and reveals that fatality depends upon speed.\n",
    "\n",
    "* data was very vast as encoded information was present ,so i simplify the data by keeping relevent information which provided me substanial visualisation and made a compact dataset.\n",
    "* outliers are detected and removed to clean the data. main outlier was about speed ,which was showing car speed around 1000 which is impossible.\n",
    "* It is concluded that accidents are increasing incessantly.\n",
    "* Around 93% accidents are not much harsh.mortality is negligble ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## what I learned so far?\n",
    "* I performed statistical analysis and used advance visualisation with Seaborn and Matplotlib, which are crucial skills for a an aspiring Data scientist .\n",
    "* I learned about timeseries analysis and got idea about Extensive EDA and made some interesting insights.\n",
    "* Got to know,how important is data visualisation for a buisness. \n",
    "* how to deal with  complex pattern and find out meaningful insight from them."
   ]
  },
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    "### Objective:\n",
    "* This analysis can be useful in reducing the number of accidents. As we all know, the majority of accidents occur between the hours of 5 and 10 p.m., which means more traffic on the roads. As a result, the NHS can take steps to regulate traffic to avoid accidents.\n",
    "* Since speed is directly linked to the seriousness of an accident, the NHS could create a new speed limit regulation."
   ]
  }
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