Statistical analysis tools. I have done courses related to analytics such as Data Analytics, Recommender System in E-Business, Safety Analytics and Quality Engineering: Design of Experiments as part of main curriculum and submitted assignments concerning to the above subjects , and also took online courses on Data analytics, Machine learning.
I have participated in Data Science competition Predict the Criminal conducted on Hacker Earth platform in which a class imbalance problem was given in which more than 98% of data point belong to one class and less than 2% were other class data, and we have to predict whether a person is going to commit a crime in the future or not. If we apply neural network, logistics regression etc. to predict the category of a person (criminal or not criminal) then we got to know that the trained model, when applied on test data to predict the category, wrongly predict one category that is over fit the trained model.
In order to solve above class imbalance problem, I have applied Extreme gradient boosting (XG-Boosting) algorithm and successfully predict whether a person will commit a crime or not with an accuracy of 0.80267 (Leader board score username: mahendra147).
Similar class imbalance problem was given in ZS associate data science competition in which we to predict a rare disease. As mentioned above model applied and successfully predict the rare disease with an accuracy 59.02.
Built a Movie recommender system that would recommend movies and predict rating of the movie by user. Implemented user based and item based Celebrative filtering to predict the top 5 movies to recommend to users as part of class assignment cum project in Recommender systems in E-Business curriculum subject.
As an internship project at infibeam (May 2017 to July 2017), I have worked on customer behaviour analytics for that I have designed a Recency frequency and Monetary (RFM) analysis model to segregate the customers as per their buying behaviour in order bring back the valuable customers by running certain promotion discounts.
As an internship project at Mebelkart (May 2016 to July 2016), I have applied cohort analysis by which customers can be grouped as a one unit when they share common characteristics in terms of buying behaviour. It helps to recommend the product to a certain group of customers based on common characteristics. Customer life time value analysis also assess the financial value of each customer and predict the expected future revenue and potential customers.
Supply chain Management: I have some experience in operations and supply chain management. Logistics and supply chain Management, management of inventory systems and operation research was the part of main curriculum subject.
I have also worked in supply chain management during my internship at infibeam (May 2017 to July 2017). Working as an intern at infibeam I have developed a mathematical model to obtain distance travelled by a driver and compare the same with sample of actual distance and identified the optimal number of drivers needed to make deliveries, and obtained expected Logistics cost of per delivery

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