Python Programming Examples
Summary
The purpose of this page is to display some of my Python projects/examples of previous work. All of these files are uploaded to my Github Repository (Portfolio-Projects).
Peer Graded Assignment Questions
This assignment was part of my completion of the IBM Data Analyst Professional Certificate. I used the pandas, dash, dash_html_components, dash_core_components, plotly.graph_objects, and plotly.express libraries to create an interactive dash application. The webpage was created to be able to analyze flight delay data through visualizations the user could interact with. The source code is linked below.
Automobile Dashboard
This dashboard was created as part of my completion of the IBM Data Analyst Professional Certificate. I used the pandas, dash, dash_html_components, dash_core_components, plotly.graph_objects, and plotly.express libraries to create an interactive dashboard. Data was read into a pandas dataframe and I created a dash application to allow users to analyze automobile component data through interactive visualizations. The source code is linked below.
Dashboard Basics
This dashboard was created as part of my completion of the IBM Data Analyst Professional Certificate. In order to create a basic interactive dashboard, I used the pandas, plotly.express, dash, dash_html_components, and dash_core_components libraries. I created the dashboard by reading airline data into a pandas dataframe and randomly sampling 500 data points to normalize the sample. From there, I set up a dash application allowing users to analyze the airline data through interactive visualizations. The source code is linked below.
House Sales in King County
This analysis was part of my completion of the IBM Data Science Professional Certificate. In order to conduct my analysis, I imported the pandas, matplotlib.pyplot, numpy, seaborn, sklearn.pipeline, sklearn.preprocessing, and sklearn.linear_model libraries. Data on home sales in King County was read into pandas dataframes and plotted as part of the exploratory data analysis. Further visualizations were plotted and descriptive statistics were calculated to further analyze the data. The source code is linked below.
Machine Learning with Python
This machine learning model was created as part of my completion of the IBM Data Science Professional Certificate. In order to create the model, I imported pandas, sklearn.linear_model, sklearn, numpy, sklearn.neighbors, sklearn.model_selection, sklearn.tree, and sklearn.metrics libraries. I used data from the Australian Government's Bureau of Meteorology to analyze weather metrics. Algorithms used included Logistic Regression, KNN, SVM, Decision Trees, and Linear Regression. The models I created were evaluated using their Accuracy Score, Jaccard Index, F1-Score, LogLoss, Mean Absolute Error, Mean Squared Error, and R2-Score. The source code is linked below.