Abhi Patel
Software Developer
About Me
My IntroductionI'm Abhi Patel, a driven computer science graduate adept in software development, AI, and ML. Proficient in various programming languages like Java, Python, and JavaScript, I bring hands-on experience in web development and cutting-edge technologies. During my tenure at MedianV Development & Marketing, I led performance optimization strategies, boosting web application efficiency by 30%. Managing a diverse portfolio of 250 websites, I integrated WordPress plugins, themes, and custom coding, resulting in a remarkable 25% increase in user engagement. My meticulous security audit fortified these websites against malware attacks, reducing lines of code by 70% for heightened data accuracy.
As a Web Development Intern at Fab AF OPC Private Limited, I revamped CMS and E-commerce websites, reducing loading times by 20% through innovative HTML, CSS, and JavaScript solutions. Additionally, my Summer Internship at BrainyBeam Technologies honed my expertise in PHP with Laravel, optimizing CRUD operations and enhancing database functionalities. Alongside professional experiences, I've spearheaded impactful academic projects, such as Sentiment Analysis using NLP and an AR-based shopping solution, integrating AI, ML, and computer vision tools for real-time customer insights and enhanced online shopping experiences during the COVID-19 pandemic. Proficient in cloud platforms like Google Cloud and skilled in various development tools, I'm committed to contributing innovative solutions and driving tangible impact within the tech industry.
Currently pursuing my Master's in Computer Science at Arizona State University, I am fueled by a profound passion for technological innovation. My relentless dedication during my undergraduate years not only resulted in the attainment of a Bachelor of Engineering in Computer Engineering but also merited the honor of a Bronze medal for academic excellence. Furthermore, my pursuit of excellence was recognized through scholarships such as the NAmU University Scholarship and the Shardashish Scholarship, underscoring my commitment and proficiency in the field. These achievements validate my drive and perseverance, motivating me to continually push boundaries in the realm of computer science. My success in exams like GRE and IELTS serves as a testament to my unwavering commitment to academic and professional growth, propelling me toward further achievements and contributions in this dynamic field.
experience
certifications
worked
Skills
My technical levelProgramming Languages and Technologies
More than 4 LanguagesPython
60%Java
70%HTML
80%CSS/Bootstrap
90%PHP
50%JavaScript/jQuery
50%C/C++
60%Development Tools and Environments
worked in different environments with different tools
Google Cloud PlatformMySQLVisual Studio CodeIntelliJ IDEA (Java)NetBeans IDESublime Text |
Anaconda NavigatorJupyter NotebookWordPressAndroid StudioFileZillaFigma |
Software Proficiencies and Applications
Implementation of knowledgeMastery of various programming languages (Java, JavaScript, Python, C, C++, etc.)
Expertise in web development technologies (HTML, CSS, jQuery, PHP, Bootstrap)
Proficient use of development environments and tools (Google Cloud Platform, MySQL, Visual Studio Code, etc.)
Experience with content management systems (WordPress)
Skilled in Android app development using Android Studio
Competency in file transfer and management through FileZilla
Qualification
My personal journeyMaster of Computer Science
Arizona State University, USA- Recieved NAmU University Scholarship GRAD (ASU)
- Shardashish Scholarship Recipient (UN Mehta Foundation)
- GRE: 316/340,
IELTS: 7.5
B.E. in Computer Engineering
Gujarat Technological University, India- Bronze Medalist - Bachelor's college
- CGPA: 9.04/10
Jr. Web Developer
MediaNV Development & Marketing, IndiaJr. Software Developer
Fab Advertising Firm, IndiaSummer intern - PHP with Laravel
BrainyBeam Technologies, IndiaCertifications
What I learntGenerative AI
Issued Aug 2023
Human Research
Issued Aug 2023
Java
Issued Jan 2023
Google Cloud
Issued Oct 2021
Python
Issued Apr 2020
RHCSA
Issued Mar 2020
Projects
Worked On
Automated Warehouse Project
Developed a sophisticated scheduling algorithm for warehouse robots to optimize product delivery and order fulfillment. Leveraged robotics, AI, and IoT technologies to enhance operational efficiency and reduce processing time. Key contributions included designing the algorithm to navigate spatial limitations, storage unit placement, and pickup policies. Extensive testing and simulation validated the solution's effectiveness in improving warehouse operations, contributing to the optimization of logistics in supply chain management. Skills: Algorithm Development · Robotics · AI and IoT Integration · Supply Chain Optimization · Simulation and Testing
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Creating an ERC-721 Token Standard Smart Contract on Ethereum
Developed and deployed an ERC-721 compliant smart contract for token creation and management on the Ethereum blockchain. Key Highlights: Implemented a fully functional smart contract using Solidity to create unique, non-fungible tokens. Deployed the contract to the Ethereum test network and integrated with Web3.js for interaction. Submitted deliverables, including formatted documentation and contract address, per project specifications. Technologies Used: Solidity, Ethereum, Remix IDE, Metamask, Web3.js Skills: Blockchain Development · Ethereum Smart Contracts (Solidity) · Decentralized Applications (DApps) · web3.js · Remix IDE
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MovieMatcher: Movie Recommender System using MovieLens Dataset
Developed a robust movie recommendation system leveraging the MovieLens dataset with over 32 million user ratings. The project utilized advanced techniques like Collaborative Filtering, Matrix Factorization, Content-Based Filtering, and Hybrid Models to address challenges such as data sparsity, cold start problems, and over-specialization. Technologies & Tools: Python, Pandas, NumPy, Scikit-learn, Surprise Library, Matplotlib, Seaborn Skills: Machine Learning · Data Analysis · Recommender Systems · Python (Programming Language) · ML Models · Collaborative Filtering
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Traffic Accidents in Arizona - Data Mining Project
As part of my Data Mining course (CSE 572), I worked on a project that involved analyzing fatal traffic accidents in Arizona from 2012 to 2016. The goal was to uncover patterns and factors influencing the severity of injuries sustained in these accidents and to develop predictive models for improving road safety. Tools Used: Python, Pandas, Scikit-learn, XGBoost, Matplotlib, Kaggle Skills: Machine Learning · Data Analysis & Preprocessing · Feature Engineering · Predictive Modeling · Data Visualization
Hyperledger Fabric Private Blockchain and Chaincodes
Built a supply chain management solution on a private blockchain using Hyperledger Fabric. Key Highlights: Designed and implemented a smart contract (smartcontract.go) tailored to supply chain management needs. Set up a private blockchain network using Docker containers for peers and ordering nodes. Tested and validated chaincode functionality to ensure secure and transparent transaction recording. Technologies Used: Hyperledger Fabric, Go, Docker, VS Code Skills: Hyperledger Fabric · Chaincode Development (Go) · Docker for Blockchain Networks · Private Blockchain Architecture · Supply Chain Management with Blockchain
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Gym Performance and User Analysis Using Yelp Dataset
Conducted business-level and user-level analysis of gym performance in Arizona using Spark SQL and visualization techniques. Analyzed city-level gym distributions, top-performing categories, customer review trends, and geographical hotspots using the Yelp dataset. Explored user engagement by investigating average ratings, review counts, review lengths, and yearly review trends for gyms. Derived actionable insights, including top cities for gym expansion, niche service preferences, and high-engagement ZIP codes. Generated over 10 detailed reports visualized through bar and line charts using PySpark for data queries and Matplotlib/Seaborn for visualizations. Skills: PySpark · Spark SQL · Data Analysis · Visualization · Python (Programming Language)
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UI/UX Design Project - Accommodation and Roommate Finder Application
Worked as part of a team to design a user-centered platform that integrates apartment searches and roommate matching into a seamless experience. Focused on enhancing usability and accessibility to streamline the process of finding accommodation and compatible roommates. Key contributions include: User-Centered Design, Wireframes and Prototypes, Information Architecture, Interactive Elements, Usability Testing, Responsive Design. Skills: UI/UX Design · Wireframing & Prototyping · Usability Testing · User Research · Interaction Design · Responsive Design
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Twitter Analysis
This project focuses on analyzing Twitter data to uncover trends, sentiments, and insights about topics, hashtags, and user interactions. Using Python and various libraries, it processes large datasets to perform text mining and sentiment analysis on tweets. The project involves collecting data from Twitter using the Twitter API, performing sentiment analysis with TextBlob, and identifying trends by analyzing tweet frequency over time. It also includes visualizing the data with Matplotlib and Seaborn to illustrate trends, sentiment distributions, and engagement metrics. The analysis provides actionable insights into public sentiment and Twitter activity patterns. Technologies used include Python, Pandas, Matplotlib, Seaborn, TextBlob, and the Twitter API.
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AI and Blockchain for Social Media Authentication
This project explores the potential of combining AI and blockchain technologies to enhance authentication mechanisms in social media applications. The focus is on using AI-driven anomaly detection for behavioral biometrics to prevent identity theft and leveraging blockchain’s decentralized architecture to ensure tamper-resistant, transparent authentication records. The goal is to improve user privacy and security within mobile social networks, making the authentication process more robust, personalized, and trustworthy. For citation, please refer to: PATEL, A. D. (2024). Improve Authentication in Social Media Applications Using AI and Blockchain (1.0). Zenodo. https://doi.org/10.5281/zenodo.13892125
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Sentiment Analysis without NLTK
This Python repository performs sentiment analysis on textual data without using the NLTK library. It reads text data from a file, preprocesses it, and matches words with predefined sentiments for classification. The analysis is applied to Steve Jobs' speech from the Stanford News blog, involving text preprocessing, tokenization, and sentiment extraction based on a custom dictionary. The results are visualized using Matplotlib, generating a bar graph of sentiment distribution. The project allows users to upload their own text files and sentiment dictionaries for analysis on various types of textual data. Technologies used include Python and Matplotlib.
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Java Inheritance Employee Management
This project demonstrates the concept of inheritance in Java through an employee management system, focusing on code reduction and reuse. The system showcases how inheritance can be used to model different types of employees, such as Analysts and Salespersons, while managing common functionalities like salary raises and bonuses.The project consists of a base Employee class that defines common attributes and behaviors, which are then inherited by subclasses Analyst and SalesPerson. The Analyst subclass includes additional methods for calculating annual bonuses, while the SalesPerson subclass includes methods for managing commission percentages. This structure illustrates how inheritance promotes code reuse and reduces redundancy by defining common behaviors in a superclass.
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Automated ML Pipeline for Iris Dataset
This project implements an automated machine learning pipeline for classifying the Iris dataset using a Decision Tree classifier. It demonstrates the process of loading data, splitting it, applying dimensionality reduction with PCA, standard scaling of features, training the classifier, and evaluating its performance. The goal is to streamline and automate the machine learning workflow using scikit-learn, ensuring reproducibility and optimal model performance. The pipeline prepares the data through PCA and scaling, then trains and evaluates the model, showcasing an efficient approach to machine learning tasks.
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Breast Cancer Classification using SVM
This project focuses on classifying breast cancer using a Support Vector Machine (SVM) with hyperparameter tuning, leveraging the Breast Cancer Wisconsin (Diagnostic) Dataset. It demonstrates the application of machine learning for medical diagnosis by optimizing the SVM classifier's performance using GridSearchCV. The project evaluates the initial SVM model performance without tuning, followed by optimization of key parameters like C, gamma, and kernel using GridSearchCV, and compares the model's performance before and after tuning with evaluation metrics such as classification reports.
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Classification ML Project: Diabetes Prediction
This machine learning project predicts diabetes based on health indicators using multiple classification algorithms, following the entire lifecycle of a machine learning project. It starts with data preparation, where the diabetes.csv dataset is loaded, checked for missing values, and split into features and target variables. The project tests three classification algorithms: Logistic Regression, Decision Tree Classifier, and Random Forest Classifier. Each model is trained and evaluated using accuracy as the performance metric. The repository includes Python code for running the models, allowing users to explore different algorithms, experiment with data, and analyze the results.
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Machine Learning Regression Lifecycle Project
This repository provides a comprehensive guide through the full lifecycle of a machine learning project focused on solving a regression problem, including data acquisition, preprocessing, model training, testing, and evaluation. The project uses a sample dataset (insurance.csv) and applies three regression models: Linear Regression, Decision Tree Regression, and Random Forest Regression. Each model is trained and evaluated using the R² score as the performance metric, allowing users to compare models and identify the most suitable one. The process begins with data preparation and exploratory data analysis, followed by encoding categorical features for modeling. The models are then trained and evaluated, with their performance compared based on the R² score to determine the best fit for the use case.
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Snake Game in Java
This is a simple Snake game implemented in Java using Swing. The game involves controlling a snake using arrow keys to eat red apples, causing the snake to grow. The game ends if the snake collides with itself or the boundaries of the grid. It showcases basic game development principles, such as graphics rendering, user input handling, and event-driven programming in Java. The project includes features like responsive keyboard controls, basic shape-based graphics, and a game over message with a restart option. The game is controlled using the arrow keys, with 'R' to restart after the game ends.
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