About Me

Engineering graduate, Attentive to Details and a data enthusiast. Passionate about studying how to improve performance. Seeking to leverage my analytical skills to improve corporate performance as an analyst.

My Certifications

Vamstar - Data Analyst

March 2021 - Present

  • An Analyst with the Data Management Team to provide Clean and Accurate data for Advance Analytics
  • Sourced Pharmaceutical tender and award data from multiple sources, with research experience in the MENA region & the European Region
  • Structured the QA Process & tackled the issues in the existing data, hence reduced the data drop rate by 30%
  • Researched the Products and the Market according to the need of the Client

    • MedTourEasy - Data Analyst Trainee

    October 2020 - November 2020

  • Worked with various Guided Projects on Machine Learning
  • Trained with multiple Analytical Tools
  • Developed a Blood Donation Prediction System using Auto-ML
  • Prepared a Report and Presented for the final certification

    • Projects

    Coffee Chain Analytics with Tableau

    The Data set contains 3 Tables - The Fact Table, The Area Table and the Product Table Fact Table contains the data for every dealing, whereas the Product Table and the Area Table contains the information about the Product and the Area. The Product ID is the Key between the Fact Table and Product Table, the Area Code is the Key between the Fact Table and Area Table Before Starting to Visualise the Tables need to join, I have used Excel's VLOOKUP function to join the Product Table and Area Table to the Fact Table and then Loaded it to Tableau.

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    Blood Donation Prediction

    Blood transfusion saves lives - from replacing lost blood during major surgery or a serious injury to treating various illnesses and blood disorders". TPOT will automatically explore hundreds of possible pipelines to find the best one for our dataset. Note, the outcome of this search will be a scikit-learn pipeline, meaning it will include any pre-processing steps as well as the model. We are using TPOT to help us zero in on one model that we can then explore and optimize further.

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    More Projects Comming Soon!

    Check Out Github