DSA enthusiast...Full stack Egnineer...React, Ruby on Rails...Python
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Pramata Knowledge Solutions
Data Scientist
June 25, 2026 – Present
tdd_assessment_incubyte
December 28, 2024 – December 28, 2024
tdd_assessment_incubyte — GitHub repository
View ProjectPredict-house-sales-prices
March 15, 2018 – March 15, 2018
The above prediction is made by bagging three regressors and using PCA (for reducing dimensions). Takes user input and predicts price. Also tested on 20% of available dataset. Accuracy of 88.45% achieved
View ProjectData-Mining
March 15, 2018 – March 15, 2018
Predicts opening stock prices of 469 S&P companies. Uses five different datasets merged into one using the date column. Three regression techniques are used and also reinforcement learning is used among these algorithms to determine weights among these regressor predictions. Accuracy for each company on an average is 97%.
View ProjectPredict-stock-prices-with-reinforcement-learning
March 15, 2018 – March 15, 2018
Uses five different datasets merged into one using the date column. Three regression techniques are used and also reinforcement learning is used among these algorithms to determine weights among these regressor predictions. Accuracy for each company on an average is 97%.
View Projectdual-core-simulation-c-
November 14, 2017 – September 18, 2018
In this project, comparision between time taken to run a given set of processes in a single core and a dual core CPU is simulted using C++. The details of each process is given by the user with each process having process ID, and a sequence of running times where each time given is run either by the CPU or waiting queue.
View ProjectOS-Project
May 30, 2017 – May 30, 2017
For a given set of processes, their placing in the ready queue and waiting queue will be shown along with status of CPU. This will be shown per unit time. For ready queue, multilevel feedback queue will be implemented. For each process, a unique ID will be given along with total I/O and computational runtime. Status of each process in both the queues will be shown.
View ProjectDSA-Project
May 30, 2017 – May 30, 2017
Implementing stacks using binary trees and solving common ancestor problem in O(log(n))
View ProjectCultural Fit Analysis
The candidate's projects show a strong inclination towards data science and machine learning, which aligns with a Data Scientist role. However, the projects are all personal, and there's only one current professional experience listed with no details on responsibilities or team collaboration, making it difficult to fully assess cultural fit. The diversity of technologies (C++, Python, Java, Ruby) indicates a broad interest but also a potential lack of deep specialization in a single ecosystem relevant to the target role.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The psychometric test score is 0, providing no insights.