Aishat Ojikutu's starred repositories
COVID-19-Predictive-Analysis-using-Supervised-and-Ensemble-Learning-Models
Utilizing Python's pandas, scikit-learn, seaborn, and matplotlib, this repository showcases a COVID-19 predictive analysis using supervised and ensemble models. Achieved a high F1 score of 0.98 and 98.0% accuracy, with potential to refine false positives and negatives.
AlexTheAnalyst
All documents from AlexTheAnalyst YouTube videos
WeRateDogs-Data-Wrangling-and-Analysis-Project
Explore the journey of data wrangling and analysis in the WeRateDogs project. Using Python, gather, assess, and clean data from the Twitter archive of @dog_rates. Unveil insights through visualizations and uncover trends like decreasing retweets over time. For details, refer to wrangle_act.ipynb and wrangle_report.pdf.
Data-exploration-and-data-cleaning-in-SQL
Explore COVID-19 data using SQL techniques like joins, CTEs, window functions, and aggregate functions. Also, clean Nashville Housing data with SQL scripts. Repository includes queries, skills used, and dataset sources.
Exploratory-and-Explanatory-Data-Visualization-Project
The data contains information from the 1990 California census. Although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.
TMDB-Movie-Dataset-Analysis
This analysis examines a dataset of 10,000 movies from a movie database, revealing insights and trends in the industry. Notably, drama is the most popular genre, and factors like budget and popularity impact revenue. However, limited data, replaced null values, outliers, and correlation-causation considerations call for cautious interpretation.