ADS Capstone Chronicles Revised
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Traffic_Calming, and Turning_Loop. This analysis helped uncover patterns, such as the most common weather conditionsandroadfeatures,providingvaluable insights into the dataset’s structure and trends. The analysis of categorical variables revealed severalkeyinsightsintoaccidentpatternsinSan Diego County. The city of San Diegorecorded the highest number of accidents, with 49,361 incidents, followed by Escondido with 5,815, Fallbrook with 4,414,andCarlsbadwith3,361. Weatherconditionsshowedthat“Clear”wasthe mostfrequentlyreportedcondition,with46,354 observations, followed by “Cloudy” at 39,644 and “Rainy” at 5,767. Regarding street names, the most accident-prone locations included I-5 N with 9,989 accidents, I-5 S with 6,573, EscondidoFwySwith5,442,andI-805Nwith 4,269 accidents. Other frequently recorded streets were Escondido Fwy N, I-805 S, I-8 E, I-8 W, CA-78 W, CA-78 E, and I-15 N. Additionally, the Street Name distribution revealedseveralhigh-trafficroadswithfrequent accidents. Old Highway 395, US 395 Traffic_Signal,
HISTORIC recorded the highest number of accidentsat3,788,followedbyCaminodelRio North (2,614), Market Street (2,282), and Clairemont Mesa Boulevard (1,836). Other streets with notable accident counts include Home Avenue (1,804), Kearny Villa Road (1,601),andVistaWay(1,549),withmanyother streets displaying varying accident frequencies. These patterns provide valuable insights into accident distribution, highlighting the cities, weatherconditions,andstreetsmostaffectedby traffic incidents. Univariate graphical analysis followed, using bar charts and histograms to visualize the distribution, outliers, and counts of numerical and categorical variables. Figure4.1presentsasetofchartsthatvisualize the distributionsofvariousnumericalvariables. Thesechartsprovideinsightsintothefrequency distribution of the numerical data, highlighting potential outliers, skewness, and trends across the dataset.
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