
Vinosh Kanna K R
Technology / Internet
About Vinosh Kanna K R:
I am an experienced R&D Analyst with a strong passion for data science. I have a proven track record of using my analytical, research, and problem-solving skills to solve complex problems. I am also proficient in a variety of data science tools and techniques, including statistics, Excel, MySQL, Python, Machine Learning, and Tableau.I am a highly motivated and results-oriented individual with a strong desire to learn and grow. I am also a team player and I am always willing to help others.
Experience
As a Research and Development (R&D) Analyst, I have been at the forefront of innovation and ongoing technological development within our organization since July 2022. My role is critical in supporting cutting-edge AI technologies to drive insights the value and utility of Python for data analysis and machine learning.
Education
I completed my Master's Programme Certification in Data Science on Simplilearn. During my course period, I undertook the projects,
1. Healthcare Insurance Analysis: Tech Stack: Python, Machine Learning, Tableau
- Performed data modelling operations like handling missing values, label encoding, one-hot encoding, and standardisation using Python.
- predicted hospital charges in each city with an RMSE of 3776.65 by deploying KFold crossvalidation in linear regression, random forest, and XGBoost via machine learning in Python.
- Analysed hospital costs city- and hospital-wise and identified the cost for a case scenario with a predicted model.
- Various charts, like the dual chart, scatter chart, and bar chart, are made on a dashboard for clear data visualisation via Tableau.
2. Share Market Data Analysis: Tech Stack: Python, Machine Learning, Tableau
- Performed data analytics to support the insights by checking the impact that occurred on stock prices due to COVID using Python.
- Performed principal component analysis (PCA) and K-means clustering to check the different sectors falling into the same cohort.
- The ARIMA model forecasting was performed and obtained a MAPE of 1.37. A dashboard was made for clear data visualisation via Tableau.
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