Data Mining: Algorithms, Tools, and Real-World Applications Project

This data mining project comprehensively addresses various aspects of data mining, starting with an overview of its scopes and significance in handling large datasets across diverse industries. It delves into common data mining algorithms such as C4.5, K-means, Support Vector Machines, Apriori, and EM (Expectation-Maximization), explaining their functionalities and applications. The project then investigates data mining tools, with a detailed focus on Rapid Miner, highlighting its features and advantages. Additionally, it examines Python as a programming language for data mining, including an example of implementing Support Vector Regression. Finally, the project explores the application of data mining in real-world scenarios, covering healthcare, marketing, education, engineering, banking, and crime investigation, demonstrating its broad impact and utility across different sectors.

P3 Demonstrate various scopes of data mining.
The procedure by which the basic data and necessary information can be shifted is called data
mining. We know that many multinational companies and organization has their large amount of
data sources. That data is controlled in altered locations. Every location can yield huge amount
of data even in petabytes. It is very important for the companies to take the productive decision
to use the data sources. For measuring, preparation and making rapid choice it is necessary to
shift in every areas. Basically, the main motive of data mining is to get the necessary data
sources.
Fig: Trend of Data Analytics Job
The opportunity of using data mining are:
1. It uses the tree-shaped structure for describing and characterize the dataset of
database.
2. It can easily enhances large databases in a short time which is important for
administrative progress.
3. We can easily take an inherent way to find out the different data sets of data objects that
shows the data.
4. Data mining creates a new sector of job. Also it can enhance the productivity of a
company.
5. Data mining tools easily give us data with efficiency.

P4 Investigate a range of data mining algorithms and their uses.
Basically data mining is a computing method of determining large data sets. Data mining is
executed by algorithms. Some common Algorithms are:
C4.5:
C4.5 makes the classifier in a decision tree form. It represents things that are already classified.
Rose Quinian has developed this.
Fig: C4.5 algorithm
It is also known as statistical classifier which is an augmentation of Quinlan’s ID3 algorithm. The
decision tree are generated by C4.5 for later use. It is also called “a landmark decision tree
program.”
K-means
Fig: K-Means
K-means method is basically popular for cluster analysis. It is also known as centroid
classifier. This is a method of vector quantization. This algorithm is used for creating K group of
data set. Through this algorithm dataset can be explored.

Support Vector Machines:
Fig: Support Vector Machines
Support Vector Machines is used in machine learning. It is also recognized as support vector
networks. It is basically controlled learning models. It analyses those data that use for analyzing
in regression and classification.
Apriori:
Apriori is a common item set mining. It is an organized rule acquiring of relational databases.
The process is worked by finding out the separate items and extend that in sufficient database.
Fig: Apriori
This algorithm was first introduced by Agarwal and Srikant in 1994. Basicaslly it operates the
database transaction.

EM (Expectation-Maximization):
EM (Expectation-Maximization) works in iterative method. It is used for finding out the extreme
posteriori of parameters in numerical model. This model is varies on suppressed dormant
variables.
M2 Investigate a tool or programming language that can support data mining.
Through data mining, we can get many kinds of data with a useful pattern in a big data sets. It
helps to find out the relations in data for business achievements.
There are many tools that can be used for data mining. Some common data mining tools are:
1) SAS Data mining
2) Teradata
3) R-Programming
4) BOARD
5) Dundas
6) Inetsoft
7) H3O
8) Qlik
9) Rapid Miner
10) Oracle BI
11) KNIME
12) Tanagra
13) Solver:
14) Sisense
15) Data Melt
16) ELKI:
17) SPMF
18) Alteryx
19) Enterprise Miner
20) Datawatch
21) Advanced miner
22) Analytic Solver
23) Poly Analyst
24) Civis
25) Viscovery:

Among all, I want to describe about the Rapid Miner Tool.
Rapid Miner
Rapid Miner is used for machine learning, data prep, and model placement. It is also user
friendly and free to use.
Features of Rapid miner:
 GUI or batch treating
 Variety of data management systems
 Suitable for in-house and shareable databases.
 Open source for data miners.
 User friendly tool.
For its wonderful features, Rapid Miner is very famous for well-known companies. This tool
become beneficial over 40,000 companies in minimizing costs, earning and lessen risks. It is a
group of depth and simplified algorithms.
Fig: Diagram of depth and simplified view
There are many programming languages That are used for data mining. Some Common
programming languages are:

1. Python
2. SQL
3. Ruby
4. R-Programming
To me, the best programming language is Python.
Fig: Python
Now a days, python becomes so much popular. It because it is the latest programming
language. It has exceptional attributes that makes it easy to operate. Python is being used in
many sectors like engineering, software development, banking etc. World famous application
YouTube is created by using Python.
Python is open source and very easy to use. If anyone has a knowledge in C, C++, Visual
Basic, Java can easily operate Python. The programmer can do many difficult codes within a
short time.
M3 Apply an appropriate tool or programming language to demonstrate how data mining
algorithms work.
Now a days, data mining is using in different kinds of sectors. They basically gathers raw data
from the customers. Then they implement it as their needs.
Now I am implementing a tool by using Python.
Implementing Support vector Regression by using Python:
1. State a training = {X, Y}ꞇ
2. Make a kernel and parameter and regularization if needed. (Gaussian Kernel and noise
Regularization are possible for both steps)
3. Using the correlation matrix:

4. Training the machine, exactly or just about to get contraction coefficient by using the main
part
of the algorithm.
5. Use this constant for make an estimator program.In SVR, the target is to clarify the errors that
are not exceed the threshold.
Steps of implementing algorithm in Python IDE ( JUPITER )
1. Importing Libraries
2. Introducing Dataset [ **WE NEED TO CREATE DATASET NAMED AS
(Position_Salaries.csv)**]
3. Feature Scaling

The feature of scaling and comparison is somewhere looking like this-
4. Enhance the SVR Model to the dataset
5. Announce SVR parameter or Kernel type
6. Imagining the SVR Outcomes code
The outcome of SVR Model Algorithm.

D2 Develop a complete data mining application for a real world issue.
Data mining has a great use in our life. We have to use it in many sectors. Here are some uses
of data mining in our regular and practical life.
1. HealthCare
Data mining is very much important in health sector. It can assure the best analysis of data.
Rather than it also helps for increasing the quality of treatment and also cost reduction. The
motto of using data mining in this sector to get right service within a right time. It also helps to
identify health insurance fraud and abuses.
2. Marketing:
We all know that marketing is the way to promote the goods and services of any companies.
Through data mining, the company can get the idea of the promotion of the company. They can
inspect which product is selling at a good amount. Also, the company can know the needs of the
product to the customer. They can also use data mining for the comparison of their services.
3. Education:
Educational data mining is a new sector. It helps the institution to get the information on various
tasks. They can observe the condition of their institute. Also they can get the decision on how to
improve the educational quality of the students. They can take the decision on learning and
teaching method of a student.
4. Engineering:
In engineering the book manufacturing to find out the process of manufacturing. Also, it also
helps for the development of product, expense and etc.
5. Banking:
For banking sector a huge amount of is needed for the betterment of their bank. They can
inspect their transaction.
6. Crime Investigation:
For crime analysis, data mining gives a big impact. It helps them for further investigation and
solve the cases. Through data mining the crime department can make crime databases and use
it for further time.