Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to:
maximize insight into a data set; uncover underlying structure; extract important variables; detect outliers and anomalies; test underlying assumptions; develop parsimonious models; and determine optimal factor settings. Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.
The particular graphical techniques employed in EDA are often quite simple, consisting of various techniques of:
Plotting the raw data (such as data traces, histograms, bihistograms, probability plots, lag plots, block plots, and Youden plots.
Plotting simple statistics such as mean plots, standard deviation plots, box plots, and main effects plots of the raw data.
Positioning such plots so as to maximize our natural pattern-recognition abilities, such as using multiple plots per page.
-> The Analysis performed on road accidents data happened in 5 years in US
-> This can be helpful in preventing accidents.
Some of the Insights from the analysis:
- Population is a factor of accidents
- The number of accidents per city decreases exponentially
- Around 5% of cities have more than 1000 yearly accidents.
- Over 1200 cities have reported just one accident (need to investigate)
- High number of accidents occur in weekdays than in weekends
- High percentage of accidents occur between 6am to 10 am in Weekdays unlike Weekends ie 10am - 6pm
- Increasing tread of accidents year over year.
- Year 2020 has the highest number of accidents
- Sep-Dec have highest accidents
- Accidents in coastal regions are higher than land bounded regions