After a long time, got a chance to share somethings with you guys, so feeling awesome :-), Today we are gonna see the Graphical Display of Data Stats or sometime we call it Exploratory Data Analysis as well, This is the best way to understand your data in very less time and set your analysis path for it. So without doing more chats, let's start -
1. Scatter Plot
Very Basic, Very Easy and Most Used EDA(Exploratory Data Analysis) technique. It is 2-D plot between X and Y variables where X or Y can be numeric data features or columns.With this plot we can easily see if there is any relationship, pattern or trends between between these 2 features or any data outlier existing. It is also useful to explore possibility of correlation relationships. Correlation can be positive, negative or neutral.
Now, let's look into a scatter plot -
I am using IRIS dataset and Python matplotlib library for this illustration - -
2. Histogram
Histogram plot is one of the oldest plotting technique to summarize the data distribution of a attribute X. X can be numerical feature and height of bar is frequency. Resulting plot is also called Bar Chart.
3. Quantile Plot (Bar Charts)
Quantile Plot or Bar Charts also used to display the uni-variate variables data distribution as well as plot the percentile information with outlier detection.
Keep looking for this space for further update.
Happy Learning
https://www.facebook.com/datastage4you
https://twitter.com/datagenx
https://plus.google.com/+AtulSingh0/posts
https://datagenx.slack.com/messages/datascience/
