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- 95%Đúng ngân sách
- 88%Đúng giờ
- 22%Tỉ lệ thuê lại
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Data mining with R project
“He is very friendly and is clear about the concepts of machine learning and data mining. Surey will prefer him for the next time.”satyamandava kemarin
Project for prajwalbhatt
“Mr. Prajwal is very helpful and I am very satisfy with his did my project same as my requirements. Even after submission his work, he always help me to solve my project problems.I am really thankful to him to complete my project on time and helping me a again Thanks a lot Sir.”SimriDhindsa minggu lalu
Develop Models from the specified specimens with species classification that you can use to predict the species membership of the remaining unclassified speciments.
“Really great work and delivered on time.”vishnusdsu minggu lalu
Utilities work Using Python - 20/11/2017 17:58 EST
“Great work . I highly recommend him.”sti59cab1a1916ca 2 minggu yang lalu
Machine learning neural deep neural network
“Delivered”Bindvi N. 2 minggu yang lalu
Stanford NLP Entity Extraction.
“Prajwal seems to know about the subject. But delivery time wasnt quite good.”waloar 2 minggu yang lalu
Data Science InternNov 2016 - Jan 2017 (2 months)
• Built supervised and unsupervised Lead Score Models which assign a scalable confidence score to the merchants who sign up on the platform. • The score is reflective of the intent & potential of the merchant to produce transaction volume on the platform. • The data was collected from various REST APIs and the Authentication form submitted by the on-board merchants.
Data Science Research InternAug 2016 - Sep 2016 (1 month)
• R script to access Google Maps geocode API to validate address accuracy and proximity to landmarks. • Mercator projection and density-based clustering of geocodes, based on the business requirement. • Remote sensing techniques based on Google Earth engine to determine land cover, population density, route optimisation etc.
Data Science InternMay 2016 - Jul 2016 (2 months)
• Built the Machine Learning risk engine, which assigns a default score to the loan applicants. Introduced to the real-life data sets and the associated problems; missing value imputation, noise removal and imbalanced data. • Involved in building a classifier, based on Natural Language Processing which categorises the bank transactions into various pre-defined categories and thus, finding patterns in the user expenditure.
Bachelor of Technology2013 - 2016 (3 years)
US English Level 192%
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