1. PROJECTS SUMMARY
PRESNETED BY
HARISH KUMAR
JANUARY 10,2018

2. OUTLINE PROJECT I: Identification of Geo Objects In Lidar Data
PROJECT II: Implementation of C4.5 Algorithm In MatLab
PROJECT III: s Retrieval From account In UI Path
PROJECT IV: Information Retrieval System for UNT Domain
PROJECT V: Periodic Update of Account Balance To Excel Doc
PROJECT VI: Implementation of Brill’s Tagger In Python
PROJECT VII: Market Basket Analysis On Large Data Sets

3. PROJECT I: Identification Of Geo Objects In Lidar Data(In progress)
Objectives
Proposed Strategies
Challenges
Tasks Performed
To identify Objects in a LIDAR data collected from a sample geographical location
Creation of learning model which is efficient in classification and takes less time in execution
Comparing the classifier with previous classifiers for accuracy more than 95 percent
Process:
Extraction of attributes that helps in classification of objects in LIDAR data
Use this Attributes to train the model and then test the model on testing data
Tools Used:
MatLab
Less information in LIDAR data compared to high spectral images
Large complex data which increase execution time exponentially
Inconsistency and missing values in LIDAR data can cause incorrect predictions
Preprocessing of LIDAR data for missing values using interpolation
Identifying set of attributes for training the classifier using SVM and Neural Network
Testing classifier on testing data for predictions and labels
Calculating accuracy of model

4. PROJECT II: Implementation Of C4.5 Algorithm In MatLab
Objectives
Proposed Strategies
Challenges
Tasks Performed
Implement C4.5 algorithm for fraud credit card detection.
Creating training and testing samples
Create a learning model which can classify input into fraud and non fraud classifications.
Creating a classifier with minimum false positive and false negatives
Process:
Create a C4.5 learning algorithm for the training data set
Tools Used:
MatLab
Accuracy increase by 97 percent from 92 percent compared to ID3 algorithm
Getting good training data set
Building classifier on large data set which tends to increase in false negative and false positives
No references for C4.5 algorithms in MatLab library
Identifying good tributes for decision making
Calculating information gain for all the attributes at each level
Identification of good training sample data which enough attributes for classification
Generation of decision tree based on attributes selected at each level
Calculation of accuracy for testing data.

5. PROJECT III: Email Retrieval From Account In UIPath
Objectives
Proposed Strategies
Challenges
Tasks Performed
Automate the process of retrieval from an account in UIPath
Creating a automation model which works independent on type of browser
The goal was to automate the stuff without any functionality errors and in less execution time
Transfer of recent s to an excel file.
Process:
Error handling techniques for different type of browsers and accounts
Tools Used:
UI Path editor
Increase in speed of process by 60 percent compared to human doing the same process
Having different logistics for different type of browsers and different systems
Rewriting of old s with the new s.
Request deny by some of the providers.
Identified objects in different browser and different systems
Constructed a flow chart for the automation process.
Developed a model from above flow chart for the automation process.

6. PROJECT IV: Information Retrieval System For UNT Domain
Objectives
Proposed Strategies
Challenges
Tasks Performed
To create a Information Retrieval System For University data
Creating a GUI which allows user to retrieve top information related to the search words
Specifying good inbound conditions for search techniques which increases efficiency of search result and decrease in search time
Process:
We construct a dictionary of web links as keys and corresponding words, frequency in that web link and as Items in dictionary
Then use this dictionary and a good search algorithm to retrieve the result of search by user
Tools Used:
Python IDLE
Increase in accuracy by 97 percent compared to 92 percent using traditional mapping
Identification of all the possible web links under University domain.
Handling dynamic and redundant websites
Denial in request of XML object for some of secure websites.
Documented Project description and requirements
Identified all the web links under UNT domain using web scrapping and JSON
Created a dictionary for all the search key words by eliminating adjectives and pro nouns.
Assigning ranks for each of he word in corresponding web link
GUI for user to enter search words

7. PROJECT V: Periodic Update Of Account Balance to Excel document
Objectives
Proposed Strategies
Challenges
Tasks Performed
Automate the process of going through account balance every day.
Creating a automation model which retrieves account balance periodically and stores it in a excel document
The goal was to automate the stuff without any functionality errors and in less execution time
Process:
Ensuring security of the account details is required.
Tools Used:
UI Path editor
Increase in speed of process by 60 percent compared to human doing the same process
Flexibility for user to check his Account balance without Internet connectivity
Choosing browser security options before login in to an account to view for account balance.
Deciding up on the period time.
Request deny for some of the connection settings.
Identified objects in different browser and different Bank Account websites.
Constructed a flow chart for the automation process.
Developed a model from above flow chart for the automation process.

8. PROJECT VI: Implementation Of Brill’s Tagger In Python
Objectives
Proposed Strategies
Challenges
Tasks Performed
Implementation of Brill’s Tagger algorithm in Python
Effectively assigning parts of speech tags to given corpus.
Training good classifier model using hand written or Gold corpus
Minimization of miss classifications
Process:
In this we train the model based on given corpus and their parts of speech. Then used this model to tag unknown corpus
Tools Used:
Python interpreter
Achieved accuracy of 97 percent in correct prediction of parts of speech for a given unknown corpus
Having a good known tagged corpus
Large training corpus can increase execution time.
Miss classification in training data can lead to decrease in the efficiency of model
Preprocessing of training corpus.
Creating model for training data
Testing test data using model created
Calculated the accuracy for testing corpus
Submitted a detailed report and working model

9. PROJECT VII: Market Basket Analysis
Objectives
Proposed Strategies
Challenges
Tasks Performed
Main objective is to identify affinity between products that are purchased by the customer
Deliverable analysis report
A clear and concise report for financial assistance and marketing
Process:
In this, we analysis induvial customer purchases and try to globalize the common purchases and rank them induvial. This rank of subset of items helps us to identify items that are sold frequently together and to identify store layout
Tools Used:
JAVA Compiler
JAVA Editor
Collection of Large Transaction from customer and merging them as a single data unit
Large set of data and identification of subsets in each transaction can lead to enormous computation power
Initially we transform induvial transactions into a single data unit
Removing attributes or items of less interest which decreased our computation time by 40 percent
Identifying subsets of items from total list of items ranging from (2,3,4…..). We carried till 15th subset.
Use this information to generate a detail report.