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Student at Bharati Vidyapeeth University Pune
Data
June 13, 2026 – Present
sql_questions_Answers_R1
May 13, 2026 – Present
sql_questions_Answers_R1 — GitHub repository
View ProjectData-Pipeline-CSV-to-Postgre-SQL
January 31, 2026 – Present
Flow: CSV (raw) → Spark (clean & transform) → PostgreSQL (employees_clean) Tech Stack Docker & Docker Compose Apache Spark (PySpark or Scala) PostgreSQL JDBC for Spark ↔ Postgres
View ProjectData-Engineering-Case-Study
May 9, 2024 – May 9, 2024
This Ad Tech case study scenario focuses on the challenges and data formats commonly encountered in the digital advertising industry. Candidates can use this information to design a data engineering solution that addresses the specific data processing and analysis needs of AdvertiseX.
View ProjectFake-News-Detection-Platform
March 3, 2022 – March 6, 2022
Fake-News-Detection-Platform — GitHub repository
View ProjectIMDB-SENTIMENT-ANALYSIS
September 2, 2021 – September 2, 2021
Classified the users movie review from IMDB and analyzed the sentiment of the users regarding the movie.
View ProjectNETFLIX-SHOWS-ANALYSIS
September 2, 2021 – September 2, 2021
This dataset consists of tv shows and movies available on Netflix as of 2019. The dataset is collected from Flixable which is a third-party Netflix search engine. In 2018, they released an interesting report which shows that the number of TV shows on Netflix has nearly tripled since 2010. The streaming service’s number of movies has decreased by more than 2,000 titles since 2010, while its number of TV shows has nearly tripled. It will be interesting to explore what all other insights can be obtained from the same dataset. Integrating this dataset with other external datasets such as IMDB ratings, rotten tomatoes can also provide many interesting findings.
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong inclination towards data-related fields, aligning with a 'Data' target role. The diversity of projects (Fake News Detection, Sentiment Analysis, Netflix Analysis, Self-Driving Car, Data Pipeline) indicates a broad interest in various data applications. However, the projects are all personal and lack team collaboration context, making it difficult to assess cultural fit beyond technical interest.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate is currently a student with no professional experience listed beyond academic enrollment.