Shailendra Kumar Joshi is a M.Tech student at IIIT Hyderabad with over 7 years of experience in programming and data science projects. He has worked on projects involving product data extraction and analysis, search engine implementation, movie recommendation systems, house price prediction models, and an HTTP proxy cache server. He is proficient in C, C++, and Python and has knowledge of technologies including Scrapy, Beautiful Soup, MongoDB, Flask, Java, NumPy, Pandas, and Scikit-learn.

1. SHAILENDRA KUMAR JOSHI
Email: shailendra.joshi@students.iiit.ac.in
ofy.143@gmail.com
Mobile No.: +91-9581845835
Education
Year Degree/Certificate Institute CGPA/Percentage
2017 (expected) M.Tech(CSE). IIIT, Hyderabad 7.2/10.0
2014 B.Tech(CSE). Birla Institute, Nainital 72.22%
2010 Class XII Vivekanand Vidhya Mandir, Bageshwar 80%
2008 Class X Vivekanand Vidhya Mandir, Bageshwar 84%
Major Projects
Product Cataloging And Intelligence March’16 - April’16
• Built a system that extract product data from product website and enrich master dump,from which further
extraction of product dump and vendor dump has been done.
• Performed analytics over gathered data on the basis of prices and ratings to get the best price and vendor for the
product in search.
Technologies Used : Python, Scrapy, Beautiful Soup, MongoDB, Flask.
Search Engine for 53GB English Wikipedia dump Jan’16 - Feb’16
• Implemented TF-IDF model for retrieving the top 10 results for any query search within 0.2-0.5 seconds.
• Supports field search on - title, infobox, text, categories.
Technologies Used : Java.
Movie Recommendation System Oct’16 - Nov’16
• Implemented a model for Recommendation System that is used to predict movies (or ratings for movies) that the
user may have an interest.
• Content-based filtering approach is used which utilize a series of discrete characteristics of an item in order to
recommend additional items with similar properties.
• Achieved accuracy of 30 %.
Technologies Used : Python, Numpy, Pandas, Flask.
House Price Prediction Using Advanced Regression Techniques Sep’16 - Oct’16
• Predicted house prices using Lasso and Xgboost Regression models.
• Weights are also given to models to get better accuracy.
Technologies Used : Python, Numpy, Pandas, Scikit-learn.
HTTP Proxy Cache Server Oct’16 - Nov’16
• Request coming in from the browser are intercepted and parsed and caches the resource (if possible).
• Works for both HTTP/S traffic.
Technologies Used : C++.
Mini Projects
Mini Course Portal
• Built a mini course portal which provides a subset of functions of IIIT Course Portal using Web2py Framework.
Linux Mini Shell
• Implemented a shell in C that handles basic commands, multilevel pipelines, i/o redirection, signal handling and
job handling.
Mini SQL Engine
• A mini SQL engine implemented in C++ capable of handling basic queries including aggregate functions.
Scholastic Achievements
• All India Rank 31 in JEST-2015(Joint Entrance Screening Test).
• Among Top 30 students of Uttarakhand Board Intermediate Examination (3rd in District).
• Among Top 50 students of Uttarakhand Board High School Examination (1st in District).
• Participated in State Level Quizzes And Science Fairs.
Computer Skills
Programming Languages: C, C++ (Proficient), Python, Java (Working Knowledge).
Platforms: Linux, Windows.
Database: SQlite, MongoDB.
Software/Frameworks: Web2py, Flask.
Other Tools and API’s: Pandas, Scikit-learn.