Applying Analytical Math with Python
These projects are field work from my Applied Probability & Statistics class, I used Python to apply the mathematical and statistical concepts to real-world data and problems. I worked with datasets to perform data cleaning, statistical analysis, and visualization, while also building predictive models using regression modeling. Probability theory and calculus concepts, such as distributions and integration, were used to analyze continuous random variables and compute probabilities. You can view the full reports by clicking on the ⌞ ⌝ full-screen icon.
Project 1
Basic Data Analysis
Used Python (Pandas, NumPy, and Matplotlib) to process and analyze real-world datasets by importing CSV sets, cleaning and converting data into usable formats, and performing statistical analysis. I calculated averages, quartiles, and distributions, and created visualizations like box plots and time-series graphs to identify trends and patterns in the data.
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Project 3
Probability Analysis
Used Python to analyze probability distributions and continuous random variables by applying integration to verify valid probability density functions. I computed areas under curves over specific intervals, visualized distributions, and worked with standard normal distributions to calculate probabilities.
Project 2
Predictive Modeling
Used Python (Pandas and Matplotlib) to analyze economic datasets by building regression models and evaluating relationships between variables inflation and unemployment. I calculated correlation coefficients, constructed least-squares lines, and visualized data through scatterplots and residual plots to assess the accuracy of their relationship. My analysis showed that current unemployment has a strong predictive relationship with future unemployment, but that inflation has little to no predictive power.
Project 4
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Coming Soon!