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How to Model Data in R? 1.car – car’s ANOVAs function is popular for making type II and type III ANOVAs tables. 2.mgcv – Generalised Additive Models 3. lme4/nlme – Used to fit and compare Gaussian linear and non-linear mixed-effects models. Linear and Non-linear mixed effects models. 4.randomforest – Random_Forest method from Machine learning. 5.multcomp
How to visualize Data ? ggplot2 – ggplot2 is R’s famous package for making beautiful graphics. You can use ggplot2 for graphics to build layered, customizable plots. ggvis -It is used for Interactive, web-based graphics built with the grammar of graphics. rgl – Interactive 3D visualisation with R. htmlwidgets – A fast way to build
HTML web page using JSP tags. How to make Web Page using JSP tags Steps: Create three files named as form.html,web.xml and E_register.java. Define all the form input in form.html. Write entries in web.xml. Create a table student_detail in ‘Shivani’ database as follows: mysql>create table user_details ( Roll_no varchar(11), Name varchar(20), marks
Simple servlet web page with HTML form. Steps: Create an html page named as “Inform.html” -> Define different tags for user input using form and set the method to post Create a servlet file named as E_Register.java Write entries in web.xml. Inform.html : E_Register.java: Web.xml: <?xml version=”1.0″ encoding=”UTF-8″?> <web-app xmlns:web=”http://java.sun.com/xml/ns/javaee/web-app_2_5.xsd”> <servlet> <servlet-name>E_Register</servlet-name>
Difference between Machine Learning & Data Science Machine Learning Data Science Machine Learning is a subset of Data Science that provides machines with the ability to learn automatically Data Science is a process of extracting useful insights from data by using a variety of tools, algorithm and ML Machine Learning Stages1.Import Data Data Science Stages-1.
Difference between Data Analyst & Data Scientist Data Scientist Data Analyst Use Current data to discover opportunities Use pre-existing data to solve a problem Data Integration Data Acquisition & Maintenance Ad-hoc analyses Pattern Identification & Analysis Develop Operational Model Optimize Statical Efficiency & Quality Develop Analytical Methods and Machine Learning Models Used to create Reports