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.
<p>Utilized Python to scrape an extensive collection of tweets from Twitter, specifically targeting discussions related to the topic of demonetisation.<br>
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.<br>
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%.</p>
<p>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%.<br>
Utilized Python to meticulously scrape data from various servers, accelerating the data collection process by 50% and ensuring a robust dataset for analysis.</p>
<p>Identified patterns in content consumption, highlighting preferred genres, user viewing habits, and much more.<br>
Recommended Friday releases for heightened viewership.<br>
Identified top content-producing countries: US, India, UK, Japan, South Korea.<br>
Identified a shift in 2021 towards a heightened emphasis on TV shows, deviating from historical trends.<br>
Analyzed data indicates that top directors and actors significantly boost content viewership.<br>
Highlighted the dominance of films over TV shows, suggested prioritizing movies for optimal outcomes.</p>
<p>Identified e-commerce patterns, highlighting seasonal peaks, buying habits, and much more.<br>
Identified increased orders in recent years, prioritizing top-performing states and cities for strategic focus.<br>
Recommended to optimize server functionality during peak website traffic in the Afternoon, Night, and Midnight for enhanced customer engagement.<br>
Analyzed data reveals higher freight values in smaller cities or states.<br>
Recommending a focus on reducing freight values in these areas to not only optimize costs but also drive increased orders.<br>
Recommended to enhance payment infrastructure by diversifying payment options beyond credit cards to accommodate broader customer preferences.</p>
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