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Data Scientist and Artificial Intelligent Developer

About Me

I am Emmanuel from Nigeria. I studied Industrial Chemistry at the University of Ilorin, one of the top universities in Nigeria. I emerged as the best graduating student from the Department of Industrial Chemistry. I enjoy learning data science and Machine Learning. I pretty much enjoy working on big data. My goal is to improve results, make the right decisions and save costs. I am experienced in machine learning, supervised and unsupervised algorithms, and data visualization techniques to present results. I use SQL, Python, Excel, and Cognos Analytics for my data projects. I am also passionate about catalysis/material science. I have learned how data science and how various machine learning techniques involving supervised, unsupervised, deep learning, and artificial neural networks are helping to build better models, understand materials research, and generate new knowledge about materials science and catalysis. In addition, I have learned how computational materials modeling tools such as Density Functional Theory (DFT) calculations are helping in predicting and understanding material properties through atomistic simulations. I desire to develop new advanced and innovative materials for clean energy and a green economy.

Skills

  • Image classification
  • Natural language processing
  • OpenCVs
  • IBM Watson AI services
  • Excel/ Cognos / Python / SQL /Machine Learning
  • šŸ”­ Iā€™m currently working on deploying Machine Learning models to web platforms
  • šŸŒ± Iā€™m currently learning Applied AI
  • šŸ‘Æ Iā€™m looking to collaborate on Data Science projects and anything related to catalysis/material science
  • šŸ“« How to reach me: +2348138663119, [email protected]
  • āš” Fun fact: I love the applications of data science/AI

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You can check out my linkedin at https://linkedin.com/in/emmanuel-adeniyi68

Anurag's github stats

Emmanuel Adeniyi's Projects

kpmg-virtual-internship icon kpmg-virtual-internship

Task 1 Data Quality Assessment Assessment of data quality and completeness in preparation for analysis. Task 2 Data Insights Targeting high value customers based on customer demographics and attributes. Task 3 Data Insights and Presentation Using visualizations to present insights.

machine-learning-for-materials-science-projects icon machine-learning-for-materials-science-projects

Querying databases, Organizing and Plotting Data: Query Pymatgen for properties like Young's modulus and melting temperature Organize data into Pandas dataframes and python dictionaries and plot using Plotly Linear Regression to predict material properties: Performed linear regression using the scikit learn package and predict Young's modulus Visualize trends in data and 'goodness of fit' of linear model Neural Network Regression to predict material properties: Used neural networks to perform non-linear, higher order regression Visualize trends and compare non-linear model to linear regression Neural Network Classification to predict crystal structures: Used neural networks to classify elements according to their crystal structures

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