Dyimah

Achieved an 80% accuracy rate in classifying celestial objects as Stars, Galaxies, or Quasi-Stellar Objects using a machine learning model that analyzes solar data. It was done through Data Collection, Data Preprocessing, Model Optimization, Iterative Improvement and Documentation.I Gathered a comprehensive dataset of solar data, including various features and attributes. I then Cleaned the dataset by handling missing values and outliers. Further Chosing appropriate machine learning algorithms for classification tasks and then Fine-tune hyperparameters of the model using techniques like grid search or random search to improve its performance. Lastly, Documented the entire process, including data sources, preprocessing steps, model architecture, and results, to facilitate reproducibility and collaboration.

In collaboration with a fellow Data Scientist, we developed a powerful machine learning model. This model proved to be highly effective in analyzing medical data and accurately predicting whether a patient had a medical condition, achieving a remarkable accuracy rate of over 90%. Using Machine Learning and TensorFlow we facilitated the model's development. Data Analysis and Data Visualization techniques enabled us to gain valuable insights from the medical data, while Python played a pivotal role in implementing and fine-tuning the model, resulting in its impressive predictive accuracy.

With extensive team discussions I employed three distinct methodologies, namely the Vader sentiment analysis, Bag of Words, and a Hugging Face BERT language model, for the purpose of tweet classification into two categories: "Disaster Tweets" and "Non-Disaster Tweets.". Using BERT-based language models, natural language processing (NLP), data analysis, TensorFlow, and Python programming, i made an in-depth evaluation to determine the optimal approach for the given task.