Bebeto Nyamwamu

Reporting, Data Preprocessing, Model Training and Deployment -Tech Stack & Skills: Python - Data Analysis and Report writing using Excel for over 10 clients - Quarterly development of survey questions for benchmark data collection and reporting for 10 industries - Lead in the technical development of NLP pipelines and services with performance accuracies of over 80% - Data Warehousing - Batched ETL Pipelines with at least 50000 records processed every hour.

Data Processing, Behaviour Analysis and Modelling for Healthcare Delivery Optimization - Tech Stack & Skills: Python - Data Processing Pipelines using pySpark for healthcare delivery optimization - Cleaning, Feature Engineering, and Analysis of partners' data using pySpark, Azure Databricks, seaborn, matplotlib - Time Series, Survival Analysis, and Modelling to understand and predict user churn and time to associated events using pySurvival and Lifelines with over at least 60% model accuracies

Tech: Python, Tensorflow Developing an AI Model to Identify School Locations in Sudan from Satellite Imagery

Tech: Tensorflow | Geopandas | Numpy Using Satellite Imagery to Assess FAW - Collaborated in a team of 50 to collect relevant data - Collaborated in a team to preprocess satellite images - Built a Resnet-50 model to predict the area affected by Armyworm pest and classify level of infestation - Accuracy of over 90% and RMSE of 0.36

Tech: XGBoost, Scikit-Learn, Streamlit - Cleaned data - Processed data to fit machine learning - Classified startups (closure, operating, ipo or acquisition) with an accuracy of over 89%. - Startup Recommender App up and running. Link: https://start-upp.herokuapp.com

Managed the task of collecting, cleaning and organizing data from online sources for Biofoods, hotels and restaurants.

- Extract data from SQL and NoSQL databases - Clean authentic, messy datasets - Build predictive models for regression and classification - Create compelling visualizations to explain data characteristics and model performance. - Discuss the ethical implications of deploying models in the real world. - Consider the environmental impact of machine learning models. - Learn how to apply machine learning to business problems.