Excersie_3 capstone

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Toronto Police Traffic Case Study - Week 3 By: Arjun - 101391954 Dolly Nair – 101490446 Priyansh Bhardwaj – 101455632 Jay Sehgal – 101453476 Pulkit Patwari - 101401006
1) Sample Data: Due to 59 columns, it is unable to show all the columns. Below is the list of the same with the description. 2) Meta data of dataset: Columns Description 'ACCNUM' Accident Number 'YEAR' Year in which accident occurred 'DATE' Date on which accident occurred. It also contains time. 'TIME' Time of accident occurred 'Hour’ particular hour (this is in 24-hour format) 'STREET1’ One of the street names 'STREET2' Second nearest street 'Intersection' Name of the Intersection if it occurred on that. Not all accident are on intersection. But all has two street name mentioned. 'OFFSET' Precise location with distance and direction i.e. ‘20 m North’ 'ROAD_CLASS' Type of Road i.e. Local, Major Arterial etc. 'District' District in which that location belongs. 'WardNum' Ward number of location 'WardNum_X' If the wardno: is 03,04 than WardNum_X = 04 'WardNum_Y' If the wardno: is 03,04 than WardNum_Y= 03
'Division' Division of that particular location 'Division_X' Division = 22,11 than Division_X = 11 'Division_Y' Division = 22,11 than Division_Y = 22 'LATITUDE' measures of accident’s location position north or south on the Earth's surface, measured in degrees from the equator 'LONGITUDE' measures of accident’s location distance east or west of the prime meridian 'LOCCOORD' Location Coordinates: i.e. ’Intersection’, ‘Mid Block’, ‘Park/Private’, etc. 'ACCLOC' Precise Accident Location i.e. ‘On Intersection’, ‘Parking Lot’, etc. 'TRAFFCTL' Was there any traffic signs/signal available? i.e. ‘Stop sign’ etc. 'VISIBILITY' What was the visibility at the time of accident?. i.e. ‘Clear’, ‘Fog’, etc. 'LIGHT' Type of Light available at that time. i.e. ’Daylight’, ‘Dark’, etc. 'RDSFCOND' It contains information about road surface conditions at the time of accident. i.e.: Dry, Wet, etc. 'ACCLASS' It contains information about the accident classification or severity. I.e.: ’Non-Fatal’ or ‘Fatal’. 'IMPACTYPE' It contains information about the impact types or collision types involved in the accidents. i.e.: ‘Angle’, ‘Turning Movement’, etc. 'INVTYPE' This column contains information about the involvement types or roles of individuals involved in the accidents. i.e. ’Driver’, ‘Passenger’, etc. 'INVAGE' Age (in Range) of injured person. 'INJURY' Type Of injury. i.e. ‘Major’, ‘Fatal’, etc. 'FATAL_NO' No of fatality in that accident. 'INITDIR' This contains information about the initial directions or orientations of the vehicles or objects involved in the accidents 'VEHTYPE' Vehicle type involved. i.e.: ‘Station Wagon’, ‘Truck’, etc. 'MANOEUVER' It contains information about the maneuvers or actions performed by the vehicles. 'DRIVACT' contains information about the driving actions or behaviors
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of the individuals involved in the accidents. 'DRIVCOND' Condition of Driver i.e. ‘ Normal’, ‘Had been drinking’, etc. 'PEDTYPE' information about the type of pedestrian involved in the accidents 'PEDACT' It contains information about the actions or behaviors of the pedestrians involved in the accidents 'PEDCOND' Condition of pedestrians. i.e.: ‘Normal’, ‘Inattentive’, etc. 'CYCLISTYPE' Type of Cyclist i.e.: ‘C’, ‘I’, ‘M’ 'PEDESTRIAN' indicates whether a pedestrian was involved in the accidents. The value "Yes" suggests the presence of a pedestrian, while empty cells indicate no pedestrian involvement. 'CYCLIST' whether a cyclist was involved in the accidents 'AUTOMOBILE' whether an automobile was involved in the accidents 'MOTORCYCLE' whether a motorcycle was involved in the accidents 'TRUCK' whether a truck was involved in the accidents 'TRSN_CITY_’ represents some transportation-related information * 'EMERG_VEH' whether an emergency vehicle was involved in the accidents. Here ‘Yes’ suggest presence of emergency vehicle. 'PASSENGER' a passenger was involved in the accidents 'SPEEDING' whether speeding was a factor in the accidents 'AG_DRIV' refers to aggressive driving behavior. 'REDLIGHT' whether running a red light was a factor in the accidents 'ALCOHOL' whether alcohol was involved in the accidents 'DISABILITY' Was there any disability a factor in the accidents 'Hood_ID' represents the ID or code associated with different neighborhood areas 'Neighbour' name or label of the neighborhood 3) Frequency Distribution: In our dataset most of the field have unique values or multiple numeric values which may not derive any insights in terms of frequency distribution. So, we are only considering some of them which make sense.
Frequency Distribution for Column: YEAR
2012 453 2009 438 2013 429 2008 417 2011 400 2010 400 2017 393 2018 389 2016 388 2015 350 2014 348 Frequency Distribution for Column: Hour 18 301 17 299 15 264 16 254 14 251 19 248 20 234 21 215 13 210 12 200 10 194 11 193 9 186 8 174 22 171 7 155 6 150
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23 148 0 133 2 111 1 105 3 94 5 75 4 40 Here in the case of Street 1 field we have more than 1400 data so representing all individually is not possible so summary of it is provided below. Frequency Distribution for Column: STREET1 YONGE ST 80 BATHURST ST 78 DUNDAS ST W 77 EGLINTON AVE E 71 FINCH AVE W 63 .. MILVERTON BLVD 1 322 THE WESTWAY 1 RICHVIEW Road 1 SAMMON AVE 1 PICKERING TOWN LIN 1 Name: STREET1, Length: 1401, dtype: int64 Frequency Distribution for Column: STREET2
BATHURST ST 39 LAWRENCE AVE E 36 FINCH AVE E 28 YONGE ST 27 EGLINTON AVE E 27 .. SUMMITCREST DR 1 AILEEN AVE 1 SUMMITCREST Driv 1 ROSECLIFFE AVE 1 GORDON MURISON L 1 Name: STREET2, Length: 2091, dtype: int64 Frequency Distribution for Column: Intersection ROSEDALE VALLEY RD,BAYVIEW AVE 3 LAKE SHORE BLVD W,ELLIS AVE 2 KING ST W,BRANT ST 2 DUNDAS ST W,STERLING RD 2 DUFFERIN ST,ST CLAIR AVE W 2 .. DUFFERIN ST,LAPPIN AV 1 DUPONT ST,LANSDOWNE AVE 1 QUEEN ST E,EASTERN AVE 1 QUEEN ST E,KINGSTON RD 1 STEELES AVE E,PICKERING TOWN LINE 1
Name: Intersection, Length: 282, dtype: int64 Frequency Distribution for Column: OFFSET 5 m South of 13 100 m North o 11 1 m North of 11 10 m West of 11 1 m West of 10 .. 66 m North of 1 75 m East 1 220 m South o 1 240 m North o 1 84 m West of 1 Name: OFFSET, Length: 336, dtype: int64 Frequency Distribution for Column: ROAD_CLASS Major Arterial 2922 Minor Arterial 727 Collector 280 Expressway 236 Local 219 Other 8
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Pending 3 Laneway 3 Name: ROAD_CLASS, dtype: int64 Frequency Distribution for Column: District Toronto and East York 1656 Scarborough 1014 Etobicoke York 942 North York 785 Toronto East York 1 Name: District, dtype: int64 Frequency Distribution for Column: WardNum 10 251 1 211 3 210 11 210 5 207 ... 04,09 1 13,14 1 16,19 1 05,04 1
01,07 1 Frequency Distribution for Column: Division 42 414 32 314 22 293 14 275 23 271 41 261 31 242 43 242 53 213 51 206 12 204 11 187 52 185 13 180 33 176 55 162 54 129 54,55 55 14,52 41 51,52 37 11,14 34 12,11 27
33,41 26 41,43 23 33,32 23 23,22 23 13,53 22 54,41 18 33,42 16 32,13 16 32,53 13 51,53 12 33,54 11 14,53 9 33,54,41 6 51,55 5 14,53,52 5 53,52 5 32,13,53 4 55,41 3 13,14 3 00,51 2 23,31 1 42,41,43 1 33,42,41 1 11,13 1 33,32,53 1
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D14UE 1 00,52 1 22,11 1 42,43 1 Name: Division, dtype: int64 Frequency Distribution for Column: VISIBILITY Clear 3752 Rain 492 Snow 86 Other 34 Freez 13 Fog, 12 Drift 6 Stron 1 Name: VISIBILITY, dtype: int64 Frequency Distribution for Column: LIGHT Daylight 2555 Dark, artificial 842 Dark 790 Dusk 65 Dusk, artificial 52
Daylight, artifici 40 Dawn, artificial 33 Dawn 27 Other 1 Name: LIGHT, dtype: int64 Frequency Distribution for Column: RDSFCOND Dry 3478 Wet 783 Oth 44 Loo 39 Slu 26 Ice 13 Pac 11 Spi 1 Frequency Distribution for Column: ACCLASS Non-Fatal Injury 3805 Fatal 600 Frequency Distribution for Column: IMPACTYPE Pedestrian Collisions 1976 Turning Movement 570 Cyclist Collisions 538
SMV Other 432 Rear End 310
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Angle 212 Approaching 159 Sideswipe 104 Other 58 SMV Unattended Vehicle 46 Frequency Distribution for Column: INVTYPE Driver 2603 Vehicle Owner 633 Passenger 364 Pedestrian 287 Motorcycle Driver 255 Cyclist 109 Truck Driver 87 Other 32 Motorcycle Passenger 11 Moped Driver 10 Other Property Owner 4 Driver - Not Hit 4 Runaway - No Driver 3 In-Line Skater 1 Wheelchair 1 Frequency Distribution for Column: INJURY
None 2067 Major 1110 Fatal 231 Minor 190 Minimal 166 Frequency Distribution for Column: INITDIR South 857 East 842 West 828 North 764 Unknown 57 Frequency Distribution for Column: VEHTYPE Automobile, Station Wagon 2379 Other 984 Motorcycle 255 Bicycle 108 Pick Up Truck 64 Passenger Van 51 Municipal Transit Bus (TTC) 40 Truck - Open 35 Delivery Van 26 Truck - Closed (Blazer, etc 22 Truck - Dump 12
Street Car 11 Truck-Tractor 11 Moped 8 Truck (other) 5 Bus (Other) (Go Bus, Gray C 5 Taxi 4 Truck - Tank 4 Fire Vehicle 2 Intercity Bus 2 Tow Truck 2 Construction Equipment 2 Police Vehicle 1 School Bus 1 Off Road - 2 Wheels 1 Frequency Distribution for Column: MANOEUVER Going Ahead 1757 Turning Left 698 Turning Right 182 Changing Lanes 82 Stopped 71 Slowing or Sto 62 Reversing 56 Parked 53 Other 36
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Unknown 36 Overtaking 34 Making U Turn 31 Pulling Away f 13 Pulling Onto S 7 Merging 6 Disabled 1 Frequency Distribution for Column: DRIVACT Driving Properly 994 Failed to Yield Right of Way 700 Lost control 352 Improper Turn 216 Other 187 Disobeyed Traffic Control 144 Following too Close 88 Exceeding Speed Limit 78 Speed too Fast For Condition 75 Improper Lane Change 48 Improper Passing 39 Wrong Way on One Way Road 2 Speed too Slow 2
Frequency Distribution for Column: DRIVCOND Normal 1607 Inattentive 655 Unknown 406 Ability Impaired, 86 Medical or Physic 72 Had Been Drinking 56 Fatigue 23 Other 20 Frequency Distribution for Column: PEDTYPE Pedestrian hit at mid-block 81 Vehicle turns left while ped crosses with ROW at inter. 48 Vehicle is going straight thru inter.while ped cross without ROW 42 Pedestrian involved in a collision with transit vehicle anywhere along roadway 24 Vehicle turns right while ped crosses with ROW at inter. 19 Vehicle is going straight thru inter.while ped cross with ROW 15 Vehicle is reversing and hits pedestrian 13 Pedestrian hit on sidewalk or shoulder 12 Other / Undefined 8 Pedestrian hit a PXO/ped. Mid-block signal 6 Pedestrian hit at private driveway 4 Vehicle turns left while ped crosses without ROW at inter. 4 Unknown 2
Vehicle hits the pedestrian walking or running out from between parked vehicle 2 Pedestrian hit at parking lot 1 Frequency Distribution for Column: PEDACT Crossing with right of way 87 Crossing, no Traffic Contr 78 Crossing without right of 35 Other 31 Running onto Roadway 16 On Sidewalk or Shoulder 12 Crossing, Pedestrian Cross 8 Playing or Working on High 4 Person Getting on/off Vehi 3 Coming From Behind Parked 3 Walking on Roadway with Tr 2 Walking on Roadway Against 2 Person Getting on/off Scho 1 Pushing/Working on Vehicle 1 Frequency Distribution for Column: PEDCOND Normal 138 Inattentive 63 Unknown 41 Had Been Drinking 23
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Ability Impaired, 7 Medical or Physic 7 Other 6 Frequency Distribution for Column: CYCLISTYPE C 83 M 18 I 6 Frequency Distribution for Column: CYCACT D 42 I 21 F 20 L 14 O 9 S 2 Frequency Distribution for Column: CYCCOND N 49 I 29 U 15 H 9 M 2 A 2
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O 2 --- Frequency Distribution for Column: PEDESTRIAN Yes 1974 Name: PEDESTRIAN, dtype: int64 --- Frequency Distribution for Column: CYCLIST Yes 569 Name: CYCLIST, dtype: int64 --- Frequency Distribution for Column: AUTOMOBILE Yes 3893 Name: AUTOMOBILE, dtype: int64 --- Frequency Distribution for Column: MOTORCYCLE Yes 446 Name: MOTORCYCLE, dtype: int64 --- Frequency Distribution for Column: TRUCK Y 226 Name: TRUCK, dtype: int64 --- Frequency Distribution for Column: TRSN_CITY_ Yes 245
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Name: TRSN_CITY_, dtype: int64 --- Frequency Distribution for Column: EMERG_VEH Y 7 Name: EMERG_VEH, dtype: int64 --- Frequency Distribution for Column: PASSENGER Yes 1061 Name: PASSENGER, dtype: int64 --- Frequency Distribution for Column: SPEEDING Yes 639 Name: SPEEDING, dtype: int64 --- Frequency Distribution for Column: AG_DRIV Yes 2145 Name: AG_DRIV, dtype: int64 --- Frequency Distribution for Column: REDLIGHT Y 263 Name: REDLIGHT, dtype: int64 --- Frequency Distribution for Column: ALCOHOL
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Y 160 Name: ALCOHOL, dtype: int64 --- Frequency Distribution for Column: DISABILITY Y 119 Name: DISABILITY, dtype: int64 Frequency Distribution for Column: Neighbourh Waterfront Communities-The Island (77) 168 West Humber-Clairville (1) 132 Bay Street Corridor (76) 100 Woburn (137) 89 South Riverdale (70) 81 ... Lawrence Park North (105) 6 Danforth (66) 5 Elms-Old Rexdale (5) 4 Maple Leaf (29) 4 Lambton Baby Point (114) 3 Name: Neighbourh, Length: 140, dtype: int64 4) Summary of key findings: Upon reviewing the dataset, it became apparent that preprocessing the data is crucial before initiating any analysis. During this preliminary phase, we performed data cleaning tasks, including the identification and elimination of duplicate records. Furthermore, we thoroughly
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investigated the existence of any null or missing values in significant columns.Initially, there were 12,244 records in the dataset. However, after conducting data processing and cleaning, we now have a refined set of 4,405 records ready for further analysis. We've noticed certain descriptive data fields like VEHTYPE and INITDIR that require clarification from our client. We plan to discuss these points in our upcoming meeting to ensure a clear direction for our analysis. Here are some queries we aim to address in the upcoming meeting: 1) Are there particular criteria or specific requirements essential for this analysis? 2) Do you have any preferences or guidelines on how we should handle missing data or outliers in the dataset? 3) Are there particular statistical techniques, models, or methodologies you'd like us to employ during our analysis? 4) Are there any specific insights or patterns that you are specifically interested in uncovering within the data?
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