These projects are part of the IBM Data Science course and cover various aspects of data analysis, from data wrangling to model development. Each project utilizes Python and Jupyter Notebooks to analyze and visualize data, providing hands-on experience in data science tasks.
- This project demonstrates data wrangling techniques to clean and prepare raw data for analysis. Various operations such as handling missing values, filtering data, and merging datasets were performed to ensure the data is in the best form for analysis.
- In this project, stock data was extracted from public APIs, and the data was then cleaned and visualized. The goal was to explore the historical trends of stock prices and present the findings using graphical representations for better decision-making.
- This notebook focuses on exploratory data analysis (EDA) to discover patterns, trends, and anomalies within the dataset. It involves summary statistics, data visualizations, and preliminary insights that help form hypotheses for deeper analysis.
- This project involves developing machine learning models to predict outcomes based on a given dataset. It includes the selection of relevant features, model training, evaluation, and fine-tuning for optimal performance.