Correlation plot
Correlation plot is a multi-variate analysis which comes very handy to have a look at relationship with data points. Scatter plots helps to understand the affect of one variable over the other. Correlation could be defined as the affect which one variable has over the other.
Correlation could be calculated between two variables or it could be one versus many correlations as well which we could see the below plot. Correlation could be positive, negative or neutral and the mathematical range of correlations is from -1 to 1. Understanding the correlation could have a very significant effect on the model building stage and also understanding the model outputs.
Python3
# Finding and plotting the correlation for # the independent variables# import required moduleimport seaborn as sns# adjust plotsns.set(rc={'figure.figsize': (14, 5)})# assign dataind_var = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']# illustrate heat map.sns.heatmap(diabetes.select_dtypes(include='number').corr(), cmap=sns.cubehelix_palette(20, light=0.95, dark=0.15)) |
Output Correlation Plot
Data Visualisation in Python using Matplotlib and Seaborn
It may sometimes seem easier to go through a set of data points and build insights from it but usually this process may not yield good results. There could be a lot of things left undiscovered as a result of this process. Additionally, most of the data sets used in real life are too big to do any analysis manually. This is essentially where data visualization steps in.
Data visualization is an easier way of presenting the data, however complex it is, to analyze trends and relationships amongst variables with the help of pictorial representation.
The following are the advantages of Data Visualization
- Easier representation of compels data
- Highlights good and bad performing areas
- Explores relationship between data points
- Identifies data patterns even for larger data points
While building visualization, it is always a good practice to keep some below mentioned points in mind
- Ensure appropriate usage of shapes, colors, and size while building visualization
- Plots/graphs using a co-ordinate system are more pronounced
- Knowledge of suitable plot with respect to the data types brings more clarity to the information
- Usage of labels, titles, legends and pointers passes seamless information the wider audience