IRJET - An User Friendly Interface for Data Preprocessing and Visualization using Machine Learning Models

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 948
An User Friendly Interface for Data Preprocessing and Visualization
using Machine Learning Models
Mr. S. Yoganand1, Bharathi Kannan R2, Daya Meenakshi B2
1Assistant Professor, Department of Computer Science and Engineering, Agni College of Technology Chennai-130,
Tamil Nadu, India.
2,3UG Student, Department of Computer Science and Engineering,Agni College of Technology Chennai-130,
Tamil Nadu, India.
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Abstract – Machine learning is one of the most efficient
techniques for prediction and classification related problems.
In this modern era, most of the industries all over the world
depend upon the machine learning models which leadintothe
data analytics century. There is no properandefficienttool for
handling the datasets which use machine learning models for
data prediction and Visualization. So, in this paper a novel
idea is proposed for making the user-friendly approach to
handle the machine learning models for data prediction and
visualisation. A tool is developed, such that it performs data
cleaning which will be a prerequisite for data analysis and
then provides a visible representation of the cleansed data.
The developed tool will take the input as structured dataset
that contains both textual and numerical data which are then
processed using machine learning algorithms to obtain a pre-
processed dataset. This process may undergo series of steps to
produce visualized and predicted data as per the chosen
effective algorithm to obtain efficient result.
Key Words: Machine learning, visualization, pre-
processing, Tool, user interface.
1. INTRODUCTION
An organisation uses the dataset for predictive
analysis and an important concern in these cases is data
quality. Using noisy data can hamper with the correctness of
analysis. The common errors are missing values, duplicates
and other errors. These errors need to be corrected for
reliable decisions and analytics. The users must know that
the effects of using the noisy data before proceeding with the
cleaning process. Noise removal will improve the model
performance, due to the fact that noises may disturb the
discovery of important information.
Machine learning is the appreciated application of
Artificial Intelligence. It is used to learn automatically
without any human assistancethatprovideshugedataset for
analysing with a large number of data fields. With the data
provided by the system after implementing the machine
learning algorithms, organizations are able to work more
effective and acquire profit over their competitors. The
system that uses machine learning technique will be able to
predict how the structure looks like and adjust the data
according to their structure. The mainchallengesinmachine
learning model is to deal with large data sources for data
cleaning process. Data cleaning process is carried by taking
in huge datasets which are checkedforthepossible errorsby
using data pre-processing techniques. The other challenges
include avoiding learning process from noisy data, avoiding
building a prejudiced model, not giving reasons for
compromising with the qualityofthedata.The bestpractices
for data cleaning using machine learning techniquesthatare
filling missing values, removing unnecessary rows,reducing
the size of the data and implementing a good quality plan.
The success of machine learning applications
depends on the amount of good quality data that is given to
it. But this process of cleaning may not be considered as a
main area in data pre-processing. The system that uses
powerful algorithms to process the noisy data can yield bad
results if irrelevant or wrong training set of data is given. In
the proposed model ML algorithms to find out the different
patterns in the data and group it by itself into clean and
noisy data which will help in reducing execution time.
2. Related Work:
Data Pre-processing is used to convert the raw data
into pre-processed data set. [1] In Machine Learning, the
data pre-processing is used to transform or encode the data
easily by their algorithm. It consists of interactive steps as
follows. Data cleaning is used to detect and correct
inaccurate records from a record or tables, and then
replacing, modifying or deleting this noisy data. Data
integration will combines the data residing indifferent
sources that provides user with a unified view of these data
[2]. The process of selecting suitable data for a research
project will impact data integritywhereData transformation
converts data from a source data format into resultant data
[3].
The tools which are available to process the data in
data processing and visualizing are Knime, Shogun, Oryx 2,
Tensor flow, Weka, RapidMiner, Trifacta Wrangler, Python
[12] [13]. In this paper, we will focus on removing the noisy
data that identifies the numerical values, predicting and
filling in missing values and detect outliers which hamper
with data analysis [11]. We propose a system that simplifies
the process for the user and allows for better processing. In
summary, Machine learning for data cleaning might be the
only way to provide complete and trustworthy data sets for

effective analytics, so we provide an user friendly interface
for pre-processing and model analysis with visualizationfor
the ease of user.
3. System Design:
The Data Pre-processingisdone withthreemethods
they are Data Cleaning, Data Transformation and Data
Reduction. The data cleaning application is to process the
raw dataset containing both textual and numerical data that
convert it into a cleaned dataset which can be used for data
analysis. Initially, users must upload the dataset in which
they perform the analysis. They can choose the operations
that they want to perform on their dataset from themodules
provided. This application performs a series of operations
which includes removing columns with less information or
no information, removing unnecessary rows, identifyingthe
numerical values, filling in the missing fields and identifying
the outliers. Some columns may contain less information or
no information that makes it hard to rely on such columns
for analysis and so such columns can be removed and they
don’t cause significant damage to the data.
Some rows may contain empty fields which will
again tamper with the proper pre-processing of the dataset.
Hence such values are identified and removed. The dataset
will contain categorical features ranging from numerical to
non-numerical values. This application requires only
numerical data which is used for analysis and prediction,
such that the fields containing numeric values areidentified.
If you try to remove them, you might reduce the amount of
data that is available. So, these fields need to be filled in
appropriate values.
4. Implementation:
The outliers with data points are really far from the
rest of your data points. Mathematically, an outlierisusually
defined as an observation over three standard deviations
from the mean. They can show up due to errors in data entry
or measurement, or just because there's a variation in the
population. Identifying and handlingoutliersisanimportant
part of data cleaning.
In Data Analysis we are using the subsequent
algorithms to analyse the cleansed data. Linear regression,
SVM (Support Vector Machine), KNN (K-Nearest
Neighbours), Logistic Regression, Decision Tree, K-Means,
Random Forest, Naive Bayes, Dimensional Reduction
Algorithms, Gradient Boosting Algorithms.
Linear Regression algorithm will use the
info points to seek out the simplest fit line to model the
info. A line can be represented by the equation, y = m*x +
c where y is the dependent variable and x is the
independent variable. Basic calculus theories are
applied to seek out the values for m and c using the given
data set. The SVM will separate the data points using a line.
The KNN will predict unknown data point with its k nearest
neighbours. The value of k is a critical factor regarding the
accuracy of prediction. It determines the nearest distance
using basic distance functions like Euclidean. Thisalgorithm
has to be a high computation power and that we have to
normalize the information initially to bring every datum
within the same range. The Decision Tree algorithm is used
to solve classification problems.Sometechniquesareusedto
categorize the data they are Gini, Chi-square, entropy etc. K-
Mean is an unsupervised algorithm that provides a solution
for clustering problem. The algorithm will follow the
procedure to form a cluster which contains homogeneous
data.
Random forest is identified as a collection of
decision trees. Every tree will try to estimate a classification
and this is called as a vote. We consider each votefromevery
tree and chose the maximum voted classification. Naive
Bayes can be applied only if the features are independent to
each other. Gradient Boosting Algorithm usesmultipleweak
algorithms to form accurate algorithm. Instead of using the
single estimator, will create a more stable and robust
algorithm. Based on the data set the algorithm is predicted
and provides an efficient result for data analysing process.
5. Results:
The user can click on the Submit button that is
provided and then select the operations they wish to
perform on their dataset from the list of operations
provided. The user can then upload the dataset into the
application by click on the Upload button to start the pre-
processing. Initially the original dataset is displayed and
then dataset after operation 1 will be displayed as cleansed
dataset. The selected operations are performed with the

Cleansed dataset; finally the user will perform the data
analysis with the required algorithm to obtain the result in
visualization and it can be download by the user.
Upload Noisy Dataset:
Displaying the noisy Dataset:
Preprocessing the Data:
Applying Machine Learning Modal:
Output:
6. Conclusion:
Our developed systemperformsData Cleaning,Data
Transformation and Data Reduction in data pre-processing.
Our system which takes the rawdatasetsintotheapplication
which are then pre-processed to clean up all the noisy data
using pre-processing techniques and the cleansed data is
visualized to the users after all the pre-processing is done.
This system saves a lot of time since manual cleaning can be
avoided. After cleansing the user can choose or select the
machine learning model which will provide efficient results
as plots. This serves as an effective purpose for the users
who wants to clean huge datasets and visualizestheanalysis
of pre-processed data. In future the accuracy and
comparison of the machine learning algorithms can be done
within the friendly user interface.
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