RAJ NARAYANAN B

• Data Scientist familiar with gathering, cleaning and organizing data for use by technical and non-technical personnel.
• Advanced understanding of statistical, algebraic and other analytical techniques. Highly organized, motivated and diligent.
• Meticulous Data Scientist accomplished in compiling, transforming and analyzing complex information through software.
• Expert in machine learning and large dataset management. Demonstrated success in identifying relationships and building solutions to business problems.
• Communicates clearly with stakeholders and builds consensus around well-founded models. Talented in writing applications and reformulating models.
• To seek and maintain full-time position that offers professional challenges utilizing interpersonal skills, excellent time management and problem-solving skills.

• Music
• Reading
• Cooking
• Video Games

• Python
• R
• C
• C++
• Maching Learning Algorithms
• Deep Learning Algorithms

• MySQL
• MongoDB
• Tableau
• PowerBI
• MS Excel

• Communication
• Problem solving
• Strategic Planning
• Teamwork
• Active Listening
• Adaptability

• Pandas
• NumPy
• SciPy
• Seaborn
• Matplotlib
• Plotly
• Tensorflow
• NLP
• Scikit-learn
• BeautifulSoup
• Statsmodels
• Flask
• Django
• Streamlit

• AWS
• Azure
• Heroku
• GCP
• RStudio
• Anaconda
• VSCode
• PyCharm
• Jupyter Notebook/Jupyter Lab
• Google Colab

Time Series Analysis:

• Explore the data (ECDF, handle missing values etc) on Sales Data.
• Analysis per store type and correlational analysis of stores activity.
• Perform extensive Time Series Analysis (seasonal decomposition, trends, autocorrelation).
• Predict next 6 weeks of sales using Prophet (Facebook methodology)

Scania Truck Failures (Internship Project):

• Load the data from the database (Cassandra Database).
• Analyze the data for missing values, outliers, data imbalance.
• Perform feature selection.
• Standardize the data for modelling.
• Use different models to check for maximum AUC score.
• Perform hyperparameter tuning
• Deploy using flask on AWS

Wine Quality: (Using MLFLOW & Docker Containers)

• Perform data ingestion, validation, transformation
• Create Models and model evaluation pipelines
• Create tracking for MLFlow
• Create Flask app and testing done locally in docker desktop.
• Once, successful, deployment done in AWS.
• Link for code: https://github.com/Raj-Narayanan-B/End_to_End_ML_Project.git

Student Performance: (Deployment done using Azure and AWS)

• Perform data ingestion, validation, transformation and create pipelines for training and prediction
• Create params.yaml file for all parameters related to project (incl. model hyperparamters)
• Create model and hyper-parameter tuning done using params.yaml file
• Created Flask app and dockerized it - tested locally and then deployed to cloud.
• For Azure code: https://github.com/Raj-Narayanan-B/MLOPS_Azure_Docker_StudentPerformance_Data.git
• For AWS code: https://github.com/Raj-Narayanan-B/MLOPS_student_performance.git

Real Estate Analysis: (Capstone)

• Read the data and perform EDA task
• Identify the primary key and from EDA, answered the business requirements
• Link for code: https://github.com/Raj-Narayanan-B/CapstoneML.git