Kureishi Shivanand

Showcase of my projects along with my technical knowledge and abilities.


This section displays my projects that illustrate my technical skills and knowledge.
The title of the project, a picture correlating to which set of skills it correlates to,
and a description of accomplishments of such project.

There are 5 sets of technical skills that are obvious from the provided picture:
Android Development, Computer Networking, Computer Software, Computer Hardware, Technological Communication.

Engineering Capstone (Android App)

  • Engineered ad-hoc text messaging android app to facilitate multi-device communication
  • Improved coding solutions to be most optimal per client’s requirements
  • Tracked and reported progress via constant reports and meetings to supervisor
  • Organized and led frequent team meetings to discuss direction of project and requirements to be fulfilled

Anonymous Message Broadcaster

  • Established anonymous communication between multiple client and server using multi-threading principles
  • Implemented socket programming using Java Networking and Encryption to simulate network sessions

Routing Control System for Inter-domain Routing

  • Designed controller to compute shortest path to other networks via link costs
  • Generated packet routes through various networks
  • Integrated knowledge of BGP and inter-domain routing

Ticketing System Software

  • Collaborated with team members to create interactive console for ticketing service
  • Executed multiple black and white box testing through vigorous regression testing
  • Integrated Test Driven Development using different levels of testing (unit cases, integration, system etc.)
  • Incorporated Sprint planning within Agile environment
  • Utilized SDLC layout and documented along each stage with improvements

Network File Transfer Application

  • Created client that was able to upload and download file from server using Java Networking library
  • Client request to change directories or list files in directory from server
  • Utilized knowledge in TCP protocols and socket programming

Embedded Systems Media Center

  • Produced an audio player, photo gallery and game on NXP LPC17xx board
  • Enhanced knowledge in programming embedded systems and C using uVision IDE

1-bit Full Adder (IC Design)

  • Designed full-adder circuit using logical effort method to receive a specific load capacitance
  • Developed schematic of circuit and executed parametric and DC analysis to ensure correct functionality
  • Created PCB layout of schematic using Virtuoso Layout Editor Turbo
  • Generated testbench to compare schematic and extracted view from layouts

Cache Controller

  • Programmed cache controller to interface SRAM units with other devices using Xilinx Spartan-3E FPGA
  • Incorporated VHDL coding techniques in Xilinx ISE CAD to implement controller
  • Executed program on FPGA and monitored using built-in performance tools


  • Designed and implemented 1-bit semi-RISC CPU on DE2 board
  • Enhanced practical experience using VHDL as an HDL
  • Administered appropriate control signals to data-path elements to achieve desired operation

Function Generator

  • Designed and implemented function generator using Operational Amplifiers
  • Generated desired square/triangle waveform per requirements
  • Incorporated Voltage-controlled frequency, Frequency Range Select and Amplitude Control

eCommerce System

  • Created interactive GUI using Java and JFrame library
  • Designed program flow using UML User and Class Diagrams
  • Implemented design and developed unit test cases for desired functionality
  • Improved object-oriented coding techniques

BJT Amplifier

  • Designed and implemented inverting 50V Amplifier with 20kHz bandwidth using 2 stages
  • Generated functional simulations using NI Multisim
  • Analyzed physical circuit using oscilloscope and multimeter

Coffee Maker Hackathon

  • Redesigned household coffee maker for smart pouring addition
  • Strengthened collaboration and communication skills
  • Improved documentation skills through constant progress reports

Predicting College Admission using R

  • Analyzed historical data for college admission and determined driving factors using R
  • Conducted descriptive analysis on variables in dataset to demonstrate correlation with admission
  • Performed predictive analysis using Logistic Regression, SVM and Decision Trees models on admission
  • Determined significant variables to the target variable as well as choose a champion prediction model

Determine Source of Complaints by Customers using SAS

  • Created trend chart to track number of complaints by day and month
  • Categorized complaint types and determined which occurred the most frequent
  • Visualized percentage of resolved/unresolved complaints by State
  • Investigated if company utilized resources equally to resolve complaints by frequency plot

Building User-based Recommendation Model for Amazon using Python

  • Utilized Python libraries to explore movie rating’s dataset with missing values
  • Extracted basic statistics of dataset to gain deeper insight into its composition
  • Decided on logical method to fill missing values in dataset to allow the training of the model
  • Imported Surprise library to predict user ratings for movies using SVD algorithm
  • Tested the recommendation model and achieved a high prediction accuracy

Mercedes-Benz Greener Manufacturing

  • Utilized Python resources to explore Mercedes-Benz dataset to reduce the time cars spend on the test bench
  • Reduced training features by removing zero-variance columns and applied label encoders to categories
  • Conducted dimensionality reduction on training features using PCA to reduce training time for the model
  • Developed highly efficient and accurate model to predict the time it takes a car to pass the testing

Sales Performance Analysis

  • Utilized Tableau Desktop capabilities to visualize data in comprehensive manner to display to clients
  • Blended separate data sources together to incorporate separate measures into a single visualization
  • Created Bullet Chart using 2 measures from different data sources
  • Implemented calculated field on color to determine if the monthly sales had exceeded the sales target
  • Incorporated filter to show visualizations by the selected year

K-Means Clustering for Telecommunication Domain

  • Utilized Apache Spark to implement K-Means cluster algorithm on telecommunication complaint dataset
  • Incorporated Scala language in order to interact with Apache Spark through spark-shell
  • Evaluated clusters based on training data and tweaked input parameters to improve model accuracy

Data Science Capstone (Diagnosing Diabetes in Patients)

  • Pre-processed data by imputing missing data with variable mean
  • Performed descriptive analysis by visualizing utilizing histograms, scatter plots, bubble charts and pie charts
  • Conducted correlation analysis using a heatmap to determine factors that affect diabetes diagnosis
  • Devised strategies to build a prediction model to classify whether a patient had diabetes
  • Decided on cross-validation as an appropriate validation framework
  • Tested various models using recall, AUC(ROC) curve, accuracy score etc. to choose the most accurate model

Lending Club Loan Data Analysis

  • Utilized TensorFlow and Keras to implement ANN to classify if a customer meets loan criteria
  • Conducted EDA on all variables to analyze their distribution and assess if outliers were present
  • Performed Feature Transformation and Extraction on dataset to allow faster computation of the neural network
  • Optimized hyperparameters using GridSearchCV to achieve an acceptable classification accuracy

Artificial Intelligence Capstone (Finance – Credit Card Fraud)

  • Performed Exploratory Data Analysis (EDA) on entire dataset
  • Utilized resampling techniques to remedy imbalanced dataset in during model training
  • Assessed Naïve Bayes, Logistic Regression and SVM models using various evaluation metrics
  • Tuned in-built parameters of tree-based models to help with the imbalanced dataset
  • Conducted hyper-parameter tuning on an ANN model to attain the highest performance
  • Implemented anomaly detection systems to differentiate fraud transactions from valid ones
  • Transformed anomaly scores to engineered features to re-assess models for difference in performance