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Data Scientist
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Xelpmoc Design and Tech
Data Scientist
June 13, 2026 – Present
ml-pipeline-using-stroke-data
January 31, 2023 – January 31, 2023
This project demonstrates the implementation of a ML pipeline and CI/CD using data on heart strokes. The pipeline includes data preprocessing, model training and evaluation, and deployment. The project leverages GitHub for version control and integration with GitHub actions for efficient and automated model updates.
View Projectvideo-transcript-summarizer
January 28, 2023 – March 21, 2024
Video Transcription and Summarization Web App using OpenAI's Whisper and BART.
View Projectlanguage_identification-using-cnn-and-audio-processing
January 22, 2023 – January 22, 2023
An Web application Language Identification project uses Pytorch and Torchaudio to accurately identify spoken language from audio files.
View Projectsearch-engine-prediction-endpoint
September 10, 2022 – December 16, 2022
search-engine-prediction-endpoint — GitHub repository
View Projectsearch-engine-training-endpoint
September 10, 2022 – December 7, 2022
This Repository is responsible for model training
View Projectsearch-engine-data-collection
September 7, 2022 – December 10, 2022
# This Repository Is for data collection for Embedding Based Search Engine
View Projectused-car-price-prediction-using-ml
September 6, 2022 – April 26, 2023
This project utilizes machine learning techniques to predict the price of a used car in the Indian market using the Cardheko dataset.
View Projectforest-fire-prediction
June 1, 2022 – February 22, 2023
Project for Predicting Algerian Forest Fires and Fire Weather Index Using Machine Learning with Python.
View Projectcourse_web_scrapping
April 27, 2022 – January 22, 2023
iNeuron Webscraper Python Project using beautifulsoup and flask
View Projectphising-classifaction
January 23, 2022 – February 22, 2023
An end to end machine learning system with mlflow to predict whether a website is a phising website or not based on given set of indicators
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and demonstrate a strong interest in data science and machine learning. The breadth of project types (prediction, summarization, classification, web scraping, MLOps) indicates a versatile and curious individual. However, the lack of team projects or professional experience details makes it difficult to fully assess cultural fit beyond technical alignment. The single professional experience entry is future-dated, suggesting limited real-world team collaboration experience.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to learning and applying various data science techniques. The focus on end-to-end ML systems suggests an understanding of operational aspects, though specific soft skills like teamwork or problem-solving cannot be directly assessed from the provided data.