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Hello šŸ‘‹, I'm Subhash Dixit

Data is today's fuel. The sheer power of data drove me to initiate my journey into the mysterious world of Data Science.

Like many others, my initial interest lay in building real-world applications, I was fascinated by building logic for simple programming problems. I realized my passion upon switching to Data analytics. Data Science has been my primary point of interest for a year now, and my zest lies in Machine Learning. I am currently working as an Associate Data Scientist at Affine.

My programming, statistics & finance coursework has provided me with the analytical skills I apply to diverse business scenarios. My zeal to learn, coupled with my ability to collaborate and accomplish goals makes me well-suited for a career in the field of Data Science and business analytics. I look forward to the opportunity of being interviewed in order to share more about my experience and the skills that I could bring to any company.

Mobile: 9205979486

Email: [email protected]

  • šŸ”­ Iā€™m currently working on Data Science & Machine Learning.

  • šŸŒ± Iā€™m currently learning Data Science,Machine Learning, Deep Learning, End to End projects and Databricks

  • šŸ‘Æ Iā€™m looking to collaborate on Projects,Tech Articles

  • šŸ’¬ Talk to me about Python, Machine Learning, SQL, Freelancing Opportunites, Open Source, NLP, Deep Learning, Statistics, Mathematics

  • šŸ˜„ Words that describe me - Self-motivated, Focused, Extreme Hardworking & curious.

Languages and Tools:

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Subhash Dixit's Projects

analysis-of-page-industries-stock- icon analysis-of-page-industries-stock-

Performed fundamental and technical analysis of Page Industries. Made candle stick chart,20SMA, 50SMA,20EMA,50EMA using the data available in the NSE website. Forecasted the future target price of the stock using projected EPS and net profit.

credit_risk_modelling_using_pyspark icon credit_risk_modelling_using_pyspark

We are going to build an end-to-end machine learning model using MLlib in pySpark. we are going to use a real world dataset from Home Credit Default Risk competition on kaggle. the objective of this competition was to identify if loan applicants are capable of repaying their loans.

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