Tasks in Data Mining for R Language

Anomaly Detection 
→ Identification of unusual patterns, outliers, which help us in understanding the variation in data.
 Example:-

Association Rule Mining
→ Also referred to as market basket analysis, this method is used for discovering interesting "association" patterns among the variables.
Example :- The beer-diaper syndrome

Clustering
→ Identifying groups/classes in data which are similar to each other.
     The similarity inside the "cluster" is high and between the "clusters" is low.

Classification
→ Classification is the process of identifying to which category does an observation belong.
Example:-

 
Regression
→ With the help of regression, we can identify the extent of relationship among variables.
      Understanding how the "dependent" variable varies with respect to the variation in "independent" variable.

  
Who uses R?

1. FACEBOOK :- For behavior analysis related status updates and profile pictures.
2. GOOGLE :- For advertising effectiveness and economic forecasting.
3. TWITTER :- For data visualization and semantic clustering. 

Tasks to be performed

• Data Importing :- Import the "Houses for sale" dataset.
• Data Pre-processing :- Understand the structure of data and find correlation between different data entities.
• Data Mining :- Use Linear Regression to predict the rates of houses.
• Pattern Evaluation :- Evaluate which model fits better for the dataset.

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Data Mining using R Language

Why Data Mining ?

- I have this financial data with me, I need to find out if any of the transactions are fraudulent.
- I have this email data with me, I have need to check how many of the mails are spam.
- I have this telecom data with me, I need to find out how many of the customers will churn out.

Data Mining to the rescue!
 How do I obtain knowledge from this data?
→ Hey, you can use data mining technique to find interesting insights from the data.

What is Data Mining?
→ Data Mining is the computing process of discovering patterns in large datasets involving methods at the intersection of machine learning, statistic, and database systems.

 How should the Mined Information be?

New :- The extracted information should give us new patterns, relationships among the data entities.

Correct :- As everything that glitters is not gold, similarly, all the mined information might not be correct/valid. The mined information needs to be evaluated for it's correctness before we use it for any other purpose.

Potentially useful :- As we extract useful products such as petrol, diesel etc. from crude oil, similarly, the mined information from raw data should be useful and relevant to us. 

Knowledge Discovery in database

Tasks in KDD

1. Data Selection :- a) Data from 

                                b) Data Warehouse

                                c) Target Data

2. Data Pre-Processing :-

      a) The selected data must be appropriate for mining tasks

      b) Simple operations such as summarizing, aggregation, normalization can be done to transform/consolidate the data such that it is suitable for mining.

3. Data Mining :- 

       a) This is the most important step in KDD process

       b) Intelligent operations such as clustering, classification, regression, and applied in order to extract patterns.

4. Pattern Evaluation :-

         Once the data mining technique have been applied, the obtained results need to be evaluated for their accuracy.

5. Knowledge Representation :-

         The identified patterns must be represented using simple, anesthetic graphs.

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Data Visualization in R Language

Data visualization helps the organizations unleash the power of their most valuable assets:
 - Their data and 
 - Their people 

1. Pie Chart :-
      Pie Charts are the best to use when you are trying to compare parts of whole.

2. Bar Chart :-
       Bar graphs are used to compare things between different group or to track changes over time. 

3. Boxplot :-
      Boxplot are used summarize data from multiple source and display the results in a single graph.

4. Histogram :-
       Histogram are used to plot the frequency of score occurrences in a continuous data set that has been divided into classes, called bins.

5. Line Graph :-
       Line graph are used to track changes over short and long periods of time.

6. Scatter Plot :-
       Scatter plot show how much one variable is affected by another.

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Fundamental Concepts of R Language

 Variables in R

A variables are nothing but reserved memory locations to store values. This means that when you create a variable you reserve some space in memory.

Data Operators

1. Arithmetic Operators
2. Assignment Operators
3. Relational Operators
4. Logical Operators
5. Special Operators

1. Arithmetic Operator

    (" + ") → Add two operands or unary plus.
                             >> 2+3
                              5
                              >>+2
    (" - ") → Subtract two operands or unary subtract.
                             >> 3-1
                              2
                              >>-2
    (" * ") → Multiply two operands
                             >> 2*3
                              6
    (" / ") → Divide left operand with the right and results is in float.
                               >> 6/3
                              2.0
    (" ^ ") → Left operand raised to the power of right
                               >> 2^3
                               8
    (" %% ") → Remainder of the division of left operand by the right
                               >>5%%2
                               1
     (" %/% ") →Division that results into whole number adjusted to the left in the number line.
                              >> 7%/%3
                              2
                            
2. Assignment Operators

     (" = ") →  x = <right operand>
                  >>x=5
                  >>x
                   5
     (" <- ") → x <- <right operand>
                    >>5<-15
                     >> x
                      15
     (" <<- ") → x<<-  <right operand>
                       >> x<<-2
                        >> x
                         2
     (" -> ") → <left operand> -> x
                    >> 25 -> x
                      >> x
                       25

3. Relational Operators
     
      (" > ") → True if left operand is greater than the right
                              >> 2>3
                                  False
       (" < ") → True if left operand is less than the right
                               >> 2>3
                                    True
       (" == ") → True if left operand is equal to right
                               >> 2==2
                                     True
        (" != ") → True if left operand is not equal to the right
                                 >> x >>=2
                                  >> print(x)
                                   1
        (" >= ") → True if left operand is greater than or equal to the right operand
                                  >> 2 >=3
                                    False
        (" =< ") → True if left operand is less than or equal to the right operand
                                  >> 2 =<3
                                    True

4. Logical Operators

         (" & ") → Returns x if x is False , y otherwise
                                  >> 2 &3
                                    3
         (" | ") → Returns y if x is False, x otherwise
                                   >> 2|3
                                    2
         (" ! ") → Returns True if x is True, False otherwise
                                   >> !1
                                    False

5. Special Operators
       
         (" : ") → It creates the series of numbers in sequence for a vector
                                      >> x <- 2:8
                                       >> x
                                       [1] 2 3 4 5 6 7 8
         (" %in% ") → This operator is used to identify if an element belongs to a vector
                                        >> x <-2:8
                                        >> y <- 5
                                        >>y %in% x
                                         True

Data Type
 We do not need to declare a variables before using them.
    

Vectors :-
   A Vector is a sequence of data elements of data elements of the same basic type.
      Example :
                 vtr = (1,3,5,7,9)
                  or
                  vtr <- (1,3,5,7,9)
  There are 5 Atomic vectors, also termed as five classes of vectors.

Lists :-

  Lists are the R objects which contain elements of different types like -numbers, strings, vectors and another list inside it.
    > n = c(2,3,5)
    > 5 = c("aa", "bb", "cc", "dd", "ee")
    >x = list(n, s, TRUE)

Arrays :-

Arrays are the R data objects which can store data in more than two dimensions.
It takes vectors as input and uses the values in the dim parameter to create an array.
       vector 1 <- c(5,9,3)
        vector2 <- c(10,11,12,13,14,15)
  result <- array(c(vector1, vector2), dim = c(3,3,2))

Matrices :-

 Matrices are the R objects in which the elements are arranged in a two-dimensional rectangular layout.
A Matrix is created using the matrix() function.
  matrix(data, nrow, ncol, byrow, dimnames)

- data is the input vector which becomes the data elements of the matrix.
- nrow is the number of rows to be created
- ncol is the number of columns to be created.
- byrow is a logical clue. If TRUE then the vector elements are arranged by row.
- dimname is the names assigned to rows and columns.

Factors:-

Factors are the data objects which are used to categorize the data and store it as levels
They can store both strings and integers.
They are useful in data analysis for statistical modeling.

   data <- c("East","West","East","North","North","East","West","West","East")
                   factor_data <- factor(data)

Data Frames :-

  A data frame is a table or two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column.
   emp_id = c(1:5),
emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
salary = c(623.3,515.2,611.0,729.0,843.25),
 emp.data <- data.frame(emp_id, emp_name, salary)

Flow Control Statements

if → It evaluates a single condition
if .. else → It evaluates a group of condition and selects the statements
Switch → It checks the different known possibilities and selects the statements 

Loops :-

Repeat → Repeat things until the loop condition is true
While → Repeat things until the loop condition is true
For → Repeat things till the given number of times.

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Introduction of R Language

Why do we need Analytics ?

→ Data analytics helps organizations harness their data and use it to identify few opportunities.
- Cost reduction
- Better marketing & product analysis
- Organization analysis
- Faster, better decision marketing

What is Business Analytics ?

→ Business Analytics examines large and different types of data to uncover hidden patterns, correlations and other insights.

What is Data Visualizations ?

→ Visualization allows us visual access to huge amounts of data in easily digestible visuals.


→ Well designed data graphics are usually the simplest and at the same time, the most powerful.

Why R ?

→ Programming and Statistical Language
⇒ Apart from used as a statistical language, it can also be used a programming language for analytical purposes.

→ Data Analysis and Visualization
⇒ Apart from being one of the most dominant analytics tools, R also is one of the most popular tools used for data visualization.



 
→ Simplest and Easy to Learn
⇒ R is a simple and easy to learn, read and write.





→ Free and Open Source
⇒ R is an example of a FLOSS (Free/Libre and Open Source Software) which means one can freely distribute copies of this software, read it's source code, modify it, etc.

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Java - The Complete Reference by Herbert Schildt

JAVA BOOK

This book is a comprehensive guide to the Java language, describing its syntax, keywords and fundamental programming principles. Significant portions of the Java API library are also examined. This book is for all programmers, whether you are a novice or an experienced pro. The beginner will find its carefully paced discussions and many examples especially helpful.

Download:

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JOINs

The JOIN is used to combine rows from two or more tables.

SQL Inner Join :-

Select all rows from tables for the match between the columns in tables.
Same as JOIN

Syntax :-
SELECT column FROM table1
INNER JOIN table2
on table1.column = table2.column;

[only matching rows are retrieved]

 Example :-

SELECT emp.eno, emp.ename, dept.dno, dept.dname FROM emp
INNER JOIN dept
on emp.dno = dept.dno; 

SQL LEFT JOIN :-

Returns all rows from the left table, with the matching rows in the right table.
The result is NULL in the right side when there is no match.

Syntax :-

SELECT Columns FROM table1
LEFT [OUTER] JOIN table2
on table1.column = table2.column;
 
Example :-
SELECT emp.eno, emp.ename,dept.dno, dept.dname FROM emp
LEFT JOIN dept
on emp dno = dept.dno; 

SQL RIGHT JOIN :-

Returns all rows from the right table, with the matching rows in the left table.
The result is NULL in the left side when there is no match.

Syntax :-

SELECT columns FROM table1
RIGHT [OUTER] JOIN table2
on table1.column = table2.column;

Example :-

 FULL OUTER JOIN

Returns all rows from the left table and from the right table.
The combines the result of both LEFT and RIGHT joins.

Syntax :-

SELECT columns FROM table1
FULL [OUTER] JOIN table2
on table1.column = table2.column;

Example :-

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UNION Operator

Combines the result of two or more select statement.

Each Select Statement must have same number of columns and columns must have same data types.

Columns should also be in same order.

Syntax :-

[ Removes the duplicate Values ]

SELECT Column1, Column2, FROM table1 
UNION 
SELECT Column1, Column2, FROM table2 

[ Duplicate Values are retained ]

SELECT Column FROM table1
UNION ALL
SELECT Column FROM table2


Example :-

SELECT ename, job FROM emp1
UNION
SELECT ename, job FROM emp2

We have two tables :-

After use UNION  operator we get this table,

a

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FOREIGN KEY

A FOREIGN KEY in one table points to PRIMARY KEY in another table.

A foreign key can have a different name than the primary key it comes from.

The primary key used by a foreign key is also known as a parent key. The table where the primary key is from is known as a parent table.

The foreign key can be used to make sure that the row in one table have corresponding row in another table.

Foreign key value can be null, even though primary key value can't.

Foreign key don't have to be unique in fact, they often aren't.

Create table from use a FOREIGN KEY:-

CREATE TABLE department
(
D_id int NOT NULL AUTO_INCREMENT PRIMARY KEY,
D_Name varchar (40),
E_id int,'
CONSTRAINT employee_Eid_fk
FOREIGN KEY (E_id) REFERENCES employee (E_id)

The CONSTRAINT clause allows to define constraints name for the foreign key constraint. If we omit it MySQL will generate a name automatically. It is optional.

The REFERENCES clause specifies the parent table and its columns to which the columns in the child table refer. The number of columns in the child table and parent table specified in the FOREIGN KEY and REFERENCES must be the same.

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ALTER TABLE

This command is used to Add/Change/Modify/Drop existing structure of the table.

ADD Column
Enable/Disable Constraints
Change Column
Modify Column
Drop Column

ADD Column :- When a new column is to be added to the table structure without constraints.

Syntax :-

ALTER TABLE table_name
ADD COLUMN column_name datatype (size);

Example:-

ALTER TABLE my_tab
ADD COLUMN stu_id integer (5);

Change Column :-This is used to change name and data type of an existing column without constraints.

Syntax:-

ALTER TABLE table_name
CHANGE COLUMN old_column_name new_column_name new_data_type (size);

Example:-

ALTER TABLE my_tab
CHANGE COLUMN name student varchar (5);

Modify Column :- This is used to modify size of the data type or the data type itself of an existing column without changing column name.

Syntax:-

ALTER TABLE table_name
MODIFY COLUMN column_name datatype (size);

Example:-

ALTER TABLE  my_tab
MODIFY COLUMN roll integer (10);

DROP COLUMN :- When a column in a table need to delete

Syntax :- 

ALTER TABLE table_name
DROP COLUMN column_name;

Example:-

ALTER TABLE my_tab
DROP COLUMN roll;

When removing constraints from a column

Syntax:-

ALTER TABLE table_name
DROP constraints_name column_name;

Example:-

ALTER TABLE my_tab
DROP UNIQUE KEY (roll);

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SQL The Complete Reference, 3rd Edition Paperback – 1 Jul 2017 by James Groff (Author), Paul Weinberg (Author), Andy Oppel (Author)

SQL The Complete Reference, 3rd Edition

Get comprehensive coverage of every aspect of SQL from three leading industry experts. Revised with coverage of the latest RDBMS software versions, this one-stop guide explains how to build, populate, and administer high-performance databases and develop robust SQL-based applications. 

SQL: The Complete Reference, Third Edition shows you how to work with SQL commands and statements, set up relational databases, load and modify database objects, perform powerful queries, tune performance, and implement reliable security policies. Learn how to employ DDL statements and APIs, integrate XML and Java scripts, use SQL objects, build web servers, handle remote access, and perform distributed transactions. Techniques for managing in-memory, stream, and embedded databases that run on today's mobile, handheld, and wireless devices are included in this in-depth volume.

• Build SQL-based relational databases and applications
• Create, load, and modify database objects using SQL
• Construct and execute simple, multitable, and summary queries
• Implement security measures with authentication, privileges, roles, and views
• Handle database optimization, backup, recovery, and replication
• Work with stored procedures, functions, extensions, triggers, and objects
• Extend functionality using APIs, dynamic SQL, and embedded SQL
• Explore advanced topics such as DBMS transactions, locking mechanisms, materialized views, and two-phase commit protocol
• Understand the latest market trends and the future of SQL

About the Author

James R. Groff is senior vice president of business strategy at Oracle Corporation. He is a SQL expert whose SQL-oriented software company, TimesTen Performance Software, was acquired by Oracle in 2005.

Paul N. Weinberg is senior vice president of NetWeaver MDM at SAP. He is a SQL expert whose SQL-oriented software company, A2i, Inc., was acquired by SAP in 2004. Weinberg is the bestselling author, with James Groff, of the previous editions of this book.

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Auto Increment

Auto increment is used to  generate an unique, when a new record is inserted into a table.
If use a auto increment than increase by 1. 
In table sequence is increment automatically.
Auto increment ignore null value.

Syntax :-

CREATE TABLE table_name
(
Column_name int NOT NULL AUTO_INCREMENT,
Column_name1 varchar (50) NOT NULL,
Column_name2 varchar (50),
PRIMARY KEY (column_name)
);

Example :-

CREATE TABLE emp
(
Emp_id int NOT NULL AUTO_INCREMENT,
Emp_name varchar (50) NOT NULL,
City varchar (50),
PRIMARY KEY (Emp_id)
);

Insert rule are different 

INSERT INTO emp (emp_name, city)
VALUES
('Subham', 'Delhi'),
('Ankit', 'Mumbai');

INSERT INTO emp (emp_id, emp_name, city)
VALUES
(NULL, 'Subham', 'Delhi'),
(NULL, 'Ankit', 'Mumbai');

INSERT INTO emp (emp_id, emp_name, city)
VALUES
(NULL, 'Subham', 'Delhi'),
(1, 'Ankit', 'Mumbai');

Table:-

Use NULL VALUE

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