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Data Preprocessing:
1.Import libraries 2.Load dataset 3.Handle missing data 4.Encoding categorical data 5.Split dataset into train and test data 6.Feature Scaling -
Regression
1.Simple Linear Regression 2.Multi Linear Regression -
Classification
1.K-Nearest Neighbor 2.Logistic Regression -
Neural Networks
1.Single Layer Neural Network 2.Multi Layer Neural Network
1. What does it take to make visualization in Python?
Not much ! Python has already made it easy for you – with two exclusive libraries for visualization, commonly known as matplotlib and seaborn. Heard of them?
I. matplotlib:
Python based plotting library offers matplotlib with a complete 2D support along with limited 3D graphic support. It is useful in producing publication quality figures in interactive environment across platforms. It can also be used for animations as well. To know more about this library, check this link.
II.Seaborn:
Seaborn is a library for creating informative and attractive statistical graphics in python. This library is based on matplotlib. Seaborn offers various features such as built in themes, color palettes, functions and tools to visualize univariate, bivariate, linear regression, matrices of data, statistical time series etc which lets us to build complex visualizations. To know more about this library, check this link.
2. What are the different visualizations we can make?
There are five key plots that you need to know well for basic data visualization. They are:
I.Line Plot
II.Bar Chart
III.Histogram Plot
IV.Box and Whisker Plot
V.Scatter Plot