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OmniSkills.ai
Data Scientist
June 16, 2026 – Present
Table_localisation
November 6, 2023 – November 8, 2023
Table_localisation — GitHub repository
View Projectvideo_summarizer
October 5, 2023 – July 6, 2024
Get concise text summary of video/audio by pasting the media URL.
View ProjectChatWith_PDFs
August 12, 2023 – January 2, 2024
Chat with PDFs is a Python web application hosted on Streamlit, designed to empower users to seamlessly upload multiple PDFs and engage in insightful interactions through a conversational interface that revolves around the content of these PDFs.
View ProjectText-Solution
July 18, 2023 – August 18, 2023
A streamlit application built in Python to summarize, analyze sentiment and compare the semantic similarity of texts.
View ProjectSentiment-Analysis-of-Tweets
December 19, 2022 – January 17, 2023
Runs sentiment analysis using VADER approach on about 25k tweets to label each tweet as positive, negative or neutral followed by an EDA.
View ProjectMini-Projects-
December 8, 2022 – June 8, 2023
Contains some useful data science snippets in Kaggle notebooks
View ProjectCredit-Card-Fraud-Detection
August 30, 2022 – August 30, 2022
The goal of this project is to classify a credit card transaction as legit or fraud. The data is trained on LogisticRegression model. Under-sampling technique is utilized to treat the imbalance in the dataset.
View ProjectMovie-Recommendation-System
August 30, 2022 – December 8, 2022
A movie recommendation system - It takes one movie name as user input and recommends 10 movies which the user might like based on his/her input.
View ProjectHouse-Price-Prediction
August 30, 2022 – August 30, 2022
This project aims to build a house price prediction system using python. The XGBRegressor model has been trained on sklearn's Boston house price dataset.
View ProjectFake-News-Detection-Using-NLP
August 30, 2022 – December 20, 2022
The goal of this project is to classify a news text as Fake or Real. The LogisticRegression model has been trained on kaggle Fake News dataset
View ProjectCultural Fit Analysis
The candidate's projects are diverse within the data science domain, covering NLP, traditional ML, and application development. However, all projects are personal, and the listed experience is current with a future start date, suggesting limited real-world team collaboration or corporate environment exposure. This might indicate a need for mentorship and adaptation to a structured team environment. The experience level is 0, which suggests this candidate is an entry-level candidate, not a senior. This is a significant mismatch for a senior role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a focus on practical problem-solving and application development. However, without psychometric test results or interview data, it is difficult to assess soft skills like teamwork, communication, or stress handling. The operational fit is moderate, given the candidate's experience level and the target role.