Parikshit Parihar is pursuing a Certificate Course in Data Science and Machine Learning at Scaler Academy and holds a Dual Degree (BS+MS) in Chemistry from the Indian Institute of Science Education and Research. He has e…
Parikshit Parihar is pursuing a Certificate Course in Data Science and Machine Learning at Scaler Academy and holds a Dual Degree (BS+MS) in Chemistry from the Indian Institute of Science Education and Research. He has experience in data analysis, Python-based research, SQL, BigQuery, Tableau, and machine learning concepts through academic projects and research internships.
Utilized Python to scrape an extensive collection of tweets from Twitter, specifically targeting discussions related to the topic of demonetisation.
Constructed a robust sentiment analysis algorithm capable of evaluating individual tweet sentiments on a detailed scale ranging from -1 to 1, with 0 denoting neutral sentiment, enabling nuanced understanding of public opinion.
Provided a comprehensive analysis of public sentiment during the demonetisation period, offering insights into the diverse opinions surrounding this economic event with a precision rate of 90%.
Analyzed 12 distinct protein detecting platforms, assessing over 20,000 protein structures, resulting in a comprehensive comparison that enhanced algorithmic understanding and choice for research by 25%.
Utilized Python to meticulously scrape data from various servers, accelerating the data collection process by 50% and ensuring a robust dataset for analysis.
Identified patterns in content consumption, highlighting preferred genres, user viewing habits, and much more.
Recommended Friday releases for heightened viewership.
Identified top content-producing countries: US, India, UK, Japan, South Korea.
Identified a shift in 2021 towards a heightened emphasis on TV shows, deviating from historical trends.
Analyzed data indicates that top directors and actors significantly boost content viewership.
Highlighted the dominance of films over TV shows, suggested prioritizing movies for optimal outcomes.
Identified e-commerce patterns, highlighting seasonal peaks, buying habits, and much more.
Identified increased orders in recent years, prioritizing top-performing states and cities for strategic focus.
Recommended to optimize server functionality during peak website traffic in the Afternoon, Night, and Midnight for enhanced customer engagement.
Analyzed data reveals higher freight values in smaller cities or states.
Recommending a focus on reducing freight values in these areas to not only optimize costs but also drive increased orders.
Recommended to enhance payment infrastructure by diversifying payment options beyond credit cards to accommodate broader customer preferences.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
