Hello I'm
Shreyansh Sinha
Data Analyst @ FedEx
- shreyanshsinha1999@gmail.com
- +91 9818110793
- Gurgaon, Haryana
Hello, I’m Shreyansh, analytical and results-driven Data Analyst specializing in leveraging SQL, Python, and Power BI to drive business growth and operational efficiency. Proven track record in conducting strategic analysis for multimillion-dollar initiatives, developing dashboards for senior stakeholders, and delivering actionable insights to optimize profitability and customer experience.
Downlaod CVInterpret data from multiple sources and analyze results. Filter and “clean” data gathered from multiple sources. Analyze complex datasets and identify the hidden patterns in them.
Predictive Analytics helps us to forecast future results by analysing current and historical facts by data mining, predictive modelling and machine learning techniques.
Dashboards are dynamic displays of the most recent results. You can explore trends and patterns for yourself by clicking and drilling.
It is a Case study of a bike share company, Cyclistic who wants to maximize the annual members because financial analyst have concluded that annual members are much more profitable than casual members and it'll be key to future growth. So the scenario is like this that I'm a junior data analyst working in the marketing analyst team at Cyclistic, Chicago. My team will derive insights from the data and design a new marketing strategy to convert casual rider into annual members.
It's a Case study and the scenario is that I'm a junior data analyst in marketing analyst team of Bellabeat , a wellness company. My Stakeholders has asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information to get high-level recommendations for how these trends can improve Bellabeat marketing strategy.
Hospital readmission costs make for a significant amount of hospital inpatient care expenses. Diabetes is not just one of the world’s top 10 leading causes of mortality, but it is also the most costly chronic disease in the United States. Diabetes patients in the hospital have a greater risk of readmission than those who do not have diabetes. As a result, lowering readmission rates for diabetes patients has the potential to considerably cut medical costs. The goal of this research is to forecast the possibility of a diabetic patient being readmitted. The dataset was collected from the University of California, Irvine’s Center for Machine Learning and Intelligent Systems and comprises over 100,000 characteristics and 50 variables such as the number of operations, drugs, and time spent in hospital etc. In this Project I have identified which factors are the strong predictors for predicting the readmission of diabetic patients and build a classification model with decent accuracy for predicting readmission.
