Welcome to my Data Science Portfolio! This collection of projects represents my skills and knowledge in data science using Python, including data wrangling, visualization, machine learning, and statistical analysis. The projects culminate in my experience working with various datasets and applying different techniques to solve real-world problems.
In this portfolio, you will find various projects that showcase my ability to extract meaningful insights from data, build predictive models, and communicate results effectively using Python. I have included projects ranging from exploratory data analysis to machine learning models.
I have used various Python libraries and frameworks, including NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and TensorFlow. I have also worked with various datasets, and more is coming as I learn!
I hope that my Data Science Portfolio will demonstrate my technical skills and highlight my ability to think critically, problem-solve, and communicate effectively using Python. I am excited to share my work with you and look forward to any feedback or suggestions you may have.
This notebook demonstrates an end-to-end process for forecasting Cardano (ADA) cryptocurrency prices using an LSTM model. It begins by importing the necessary libraries, retrieving historical ADA-USD price data, and visualizing the closing price history. The data is then preprocessed and divided into training and testing sets, with 95% of the data used for training. The LSTM model, built using Keras, is trained on this dataset and evaluated using the root mean squared error (RMSE) metric. The model's predictions are visualized along with the actual price data, providing insights into its performance.
The Nobel Prize, an esteemed accolade recognized globally, acknowledges the accomplishments of preeminent scholars and scientists in chemistry, literature, physics, medicine, economics, and peace. Since its inception in 1901, the award has evolved from its initial Eurocentric and male-dominated disposition to a more equitable and unbiased recognition of excellence.
To scrutinize the nature of this prestigious honour, one may pose several inquiries: What attributes are shared amongst laureates? Which nation boasts the highest frequency of recipients? And have any individuals been distinguished with multiple Nobel Prizes? The onus of answering these questions falls upon the inquisitive mind.
For this analysis, data has been procured from the Nobel Foundation's repository on the Kaggle platform.
Commercial banks often face numerous credit card applications, many of which are rejected due to high loan balances or insufficient income. Manually analyzing these applications is time-consuming and prone to errors. Fortunately, machine learning has allowed for automating this process, which is now widely adopted by modern banks. In this project, I will develop an automated credit card approval predictor using machine learning techniques, simulating the methods used by real-world banking institutions. The dataset for this project will be the Credit Card Approval dataset from the UCI Machine Learning Repository.
In 1847, the distinguished Hungarian physician Ignaz Semmelweis made a pivotal discovery that would revolutionize medical practices: hand hygiene. He discerned that contaminated hands posed a significant threat to patients suffering from childbed fever. Through the implementation of rigorous handwashing protocols at his hospital, he safeguarded the lives of numerous individuals. His pioneering work is a testament to the crucial role that conscientious hygiene practices play in delivering effective healthcare.