I am a final year student of GGSIP University, New Delhi, pursuing B.Tech in Information Technology. I am a cheerful, always optimistic guy, with a knack for achieving the set target at any cost. I am an avid learner, and more than willing to learn any new tool or software if the requirement be. I never shy off from working hard or even working till late. I am also a passionate reader, and love reading thriller novels, Jeffrey Archer being my favorite writer. The negative part of my personality is that sometimes I tend to procrastinate if the project I am working on is not really intriguing to me. However, if the project is indeed very interesting, then I can spend days and nights without count working on it.
I was introduced to the field of Data Analytics last year -during my internship with Landmark Insurance Brokers Pvt Ltd, Mumbai. I was quite fascinated by the power of analytical tools which help in determining the trends and uncovering insights from the company's customer database. Since then, I have been learning new tools and techniques in this field. I have done various online courses on Python and Data Science.
D-20, Lord Krishna Road,
Adarsh Nagar, Delhi 110033 India
Maharaja Surajmal Institute of Technology, Delhi • August 2018
Percentage (till 6th semester): 83.15
St. Xavier's Sr. Sec. School, Delhi • March 2014
CBSE Board Percentage: 90.40
Probus Insurance Brokers, Mumbai • June 2017 - July 2017
Responsible for Risk Scoring of customers, calculating the percentage of renewals, mapping, etc.
Presenting the analysis reports to the Management.
Landmark Insurance Brokers, Mumbai • June 2016 - July 2016
Responsible for studying the customer databases and creating visual insights (pivot charts, graphs, etc.)
Reporting the findings to the Management for better decision-making.
Predicting the Stock Prices of Tesla.
Stock Price data of 5 years used to predict the prices after one month.
Language used: Python
Algorithm used: Random Forests
Making a predictive model to automate the loan eligibility process (real time) based on customer details provided while filling online application form.
Simple implementation of Random Forest algorithm using ScikitLearn in Python, rendering a score of 0.75120 on Kaggle.
The Data set concerns with Housing Prices in suburbs of Boston. Problem is to predict the median value of owner occupied homes
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