Repository containing portfolio of data science projects completed by me for self learning. Presented in the form of Jupyter notebooks.
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Zindi Financial Inclusion Challenge (Ranked 380 out of 1088) The objective of this challenge was to identify high accuracy machine learning model to predict which individuals are most likely to have or use a bank account. Extensive use of Python libraries such as Plotly, NumPy, Seaborn, Matplot, Pandas for data analysis and ML libraries such as Scikit learn, XGB Classifier and LGBM Classifier.
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Income Classification Worked with a team of young women on a Machine learning project to determine whether a US citizen will make over US$50,000.00 a year or not using 4 models- Random Forest, Logistic Regression, Support Vector Machine and Decision Tree models. Together with the team, we cleaned, explored, visualized, analyzed and modeled data to classify income levels of US citizens.
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Profitable English Apps Analyzed 15795 app profiles from the App Store and Google Play Store. Employed data cleaning and analysis techniques. Extensive use of Pandas libraries. The aim of this project was to recommend profitable and free mobile English apps profiles for the Google Playstore and App store.
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Hacker News Post An analysis of over 20,000 Hacker News posts. Extensive data manipulation and visualisation with Plotly. Also explored the Linear Regression Model to predict the number of comments or upvotes a post on the website will receive.
- Riby Financials Data Visualization Data Analysis and Visualization of the Riby Dataset. The Dataset contains 14 columns and 500,000 rows.
- Data Visualisation Tutorial Video with Power BI using the Riby Financial Dataset (Part 1 - 3) This is the first of 4 videos series describing how I created data visualizations with the Riby Financial Dataset in Power BI.
- 50-day Python Challenge In the course of learning how to code using python, I attempted a 50-day Python challenge by Benjamin Bennett Alexander. I ended up solving 40 Python challenges plus I had a lot of fun doing this.