This project is part of the Coursera Practical Machine Learning course. It involves building a predictive model to classify how individuals perform barbell lifts using data collected from accelerometers on various parts of the body. The project demonstrates the application of machine learning techniques to real-world sensor data.

Survival Analysis of Autonomous Vehicle Components When I analyzed the reliability of autonomous vehicle components using survival analysis, I focused on cleaning and preparing a simulated large dataset. I know that reliability depends on consistent and robust data, so I took a systematic approach to data preparation. I started by loading the dataset, inspecting it for structural issues, and checking for missing values. Missing data was present in several critical variables, and I used predictive imputation to handle it effectively. I also renamed columns for clarity and reformatted data types to ensure consistency. Finally, I filtered rows and removed outliers to focus on meaningful trends. Through this process, I was able to simulate and clean a large dataset for survival analysis. I used survival time data for various autonomous system components and analyzed the failure patterns using Kaplan-Meier survival curves. This analysis helped me identify the components most prone to early failures, which I can prioritize for improvement. Analyzing the comparison plot for the original and imputed values of the variables—FailureTime, StressLevel, and Temp—I observed several key statistical insights. Most notably, nearly 95% of the data points align closely with or directly on the dashed diagonal line. This line indicates perfect correspondence between the original and imputed values, suggesting a high level of accuracy in the imputation process.

When I analyzed the scaled and unscaled PCA plots for data scientist skills, I noticed distinct patterns in how the variables contributed to the principal components. In the scaled PCA plot, the first principal component (PC1) accounted for approximately 55% of the variance. The second principal component (PC2) explained another 30%, bringing the cumulative explained variance to 85%. This showed me that two components were sufficient to capture most of the variability in the dataset. The balance in contributions between variables became clear because of scaling, where each variable was standardized to have a mean of 0 and a standard deviation of 1. In the scaled PCA, I saw that PythonProficiency and MachineLearning had the strongest loadings on PC1, with loadings of 0.72 and 0.68, respectively. These high loadings told me that these two skills were the primary contributors to the overall variability in data scientist profiles. BigDataTools and DataVisualization, with loadings of 0.58 and 0.55, still contributed but to a lesser degree. This statistical balance confirmed that when I equalize the scale of the variables, Python and Machine Learning dominate in determining differences among data scientists.

When I looked at the scree plot, I noticed that the first principal component (PC1) explained about 27% of the variance. This immediately told me that PC1 represents the most significant factor influencing logistic warehouse shipping. I think it likely captures critical elements like shipment volume or warehouse efficiency, as these often dominate variability in shipping data. The second principal component (PC2) explained another 25%, and when I added them together, they accounted for 52% of the total variance. I realized that focusing on these two components would help me understand the majority of the trends in the dataset. The third and fourth components (PC3 and PC4) explained 24% and 23% of the variance, respectively. While they added up to 100% of the variance when combined with PC1 and PC2, I noticed they contributed less individually. I figured they might capture smaller or less significant patterns in the data that are not as impactful for my analysis. Statistically, I knew that the first two components alone captured over half the dataset’s variability. I felt confident that reducing the dataset to two dimensions would allow me to focus on the key drivers of shipping efficiency and costs without losing much information. I saw the diminishing returns from adding more components after PC2, so I decided it wasn’t worth including them in my primary analysis. By focusing on PC1 and PC2, I could simplify my approach and concentrate on optimizing the most critical aspects of the shipping process.

The creation of a US Economic Index and comparing it to The Chicago Fed National Activity Index

When I analyzed the PCA plot of the simulated crime data, I noticed clear patterns related to urbanization and crime rates. The First Principal Component (PC1) captured the majority of the variance in the data, and I saw it was heavily influenced by variables like UrbanPop, AssaultRate, and RapeRate. This told me that urban areas are strongly linked to higher occurrences of certain crimes, particularly assault and rape. The Second Principal Component (PC2) showed patterns that PC1 didn’t explain. I noticed that MurderRate had a unique relationship, moving in a different direction compared to the other variables. This made me think that murder might not always follow the same trends as assault or rape and could be influenced by other factors beyond urbanization.