Data Scientist | Deep Learning | Reinforcement Learning
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EY GDS
Data Scientist
June 16, 2026 – Present
Plant_Seedlings_Classification
January 15, 2019 – January 15, 2019
Helped farmers in increasing productivity of crop and assisted in identifying the weed plants in initial stages by designing an intelligent model. Achieved 94% accuracy by effectively using deep learning techniques.
View Projectquadcopter_project
December 27, 2018 – December 27, 2018
In this project, I will train a quadcopter to perform certain tasks using Reinforcement Learning.
View Projectdog_breed_classification
December 24, 2018 – December 24, 2018
In this project, my code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling.
View ProjectPredict_Damage_to_a_building_ML_Challenge
November 1, 2018 – December 2, 2018
Determining the degree of damage that is done to buildings post an earthquake can help identify safe and unsafe buildings, thus avoiding death and injuries resulting from aftershocks.We Leverage the power of machine learning to predict the damage grade of building and thus potentially preventing massive loss of lives while simultaneously making rescue efforts easy and efficient.
View Projectopenai_Taxi_v2
October 26, 2018 – October 26, 2018
There are four designated locations in the grid world indicated by R(ed), B(lue), G(reen), and Y(ellow). When the episode starts, the taxi starts off at a random square and the passenger is at a random location. The taxi drive to the passenger's location, pick up the passenger, drive to the passenger's destination (another one of the four specified locations), and then drop off the passenger. Once the passenger is dropped off, the episode ends.
View Projectcustomer_segmentation
August 5, 2018 – August 5, 2018
In this project, I will analyze a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.
View ProjectPredicting_Donors_for_CharityML
July 21, 2018 – December 24, 2018
CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. My goal was to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.
View Projectnyc_311_data_analysis
March 23, 2018 – December 23, 2018
This Project is analysing NYC 311 calls dataset from the year 2009.
View ProjectWebcam-Motion-Detector
July 12, 2017 – July 12, 2017
This Python Code detects the motion of Objects in webcam and records start and end time of that objects and stores in a .csv file
View ProjectThe-Bookstore
July 11, 2017 – July 11, 2017
Bookstore is a simple python desktop application made with help of tkinter. Bookstore provides its users to store, search,update and delete book's information
View ProjectCultural Fit Analysis
The candidate has a strong portfolio of personal projects, indicating initiative and a passion for data science. The projects cover a good breadth of machine learning applications, which aligns with a dynamic and learning-oriented culture. However, the candidate's professional experience is listed as current with a start date in the future (2026), which is inconsistent and makes it difficult to assess real-world team collaboration or corporate cultural fit. The lack of diverse professional experience beyond personal projects limits the assessment of cultural fit in a structured organizational environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on diverse problems, suggesting adaptability. However, without psychometric test results or interview data, it's difficult to assess specific soft skills like teamwork, stress handling, or communication clarity in a professional setting.