Software Engineer [Check out my GitHub Wrapped for 2023](https://www.githubwrapped.io/sandeepyadav10011995)
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20p95
Data Scientist
June 20, 2026 – Present
humbee-bb-survey
June 8, 2026 – Present
HUMBEE Partner Profiler & Lead Intelligence Dashboard
View Projectgithub-slideshow
February 17, 2022 – February 17, 2022
A robot powered training repository :robot:
View ProjectThyroid-Detection
December 19, 2020 – December 19, 2020
To build a classification methodology to predict the type of Thyroid based on the given training data. Deployment work in progress
View ProjectMachine-Learning-Templates
April 25, 2020 – November 2, 2021
Machine Learning Models Templates
View ProjectFace-Detection
April 2, 2020 – April 4, 2020
Fun Project : Just playing with OpenCV to implement a Face Detection Algorithm
View ProjectData-Science-Advanced-Statistics-ML
March 22, 2020 – April 25, 2020
Fun Project -: Playing with the statistics. Main Motto -: Trying to understand the statistics underneath the various most commonly used Machine Learning Models. I have used two library i.e. Stats-Model and Scikit-Learn.
View ProjectEmployee-Attrition-Prediction-Model
March 19, 2018 – April 24, 2020
Bill Gates was once quoted as saying, "You take away our top 20 employees and we [Microsoft] become a mediocre company". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and demonstrate an interest in exploring various data science applications. However, the lack of team-based projects or contributions to open-source initiatives makes it difficult to fully assess cultural fit. The 'HLD' and 'LLD' projects, while named, lack descriptions or technologies, limiting insight into design thinking. The current role as 'Data Scientist' is listed with a future start date (2026), which is unusual and suggests a lack of current professional experience in the target role.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The psychometric test score is 0, providing no insights.