Pavan kalyan reddy Kamasaani

This project is to predict whether one has diabetes or not, simply by the responses given by the individual on a health survey, without a blood test or lab work. My dataset was a CDC dataset named BRFSS 2024 containing the answers of nearly 457,670 individuals residing in the United States of America regarding their lifestyle, health, and demographics. I cleaned data, investigated it to identify patterns and trained six different machine learning models to determine which model performed well in identifying diabetic patients. The models continued to predict the type no diabetes to all so that the accuracy was an invalid measure as this was not true to the majority of the dataset. Therefore, rather than aiming at accuracy, I aimed at ensuring that the model captures as many cases of the diabetics as possible. With testing and tuning, I determined that XGBoost was the most suitable model, and by using its decision threshold of 0.50, reducing it to 0.13, the model could catch a minimum of 12 percent of the diabetics and a maximum of 83 percent. The most significant variables that were indicative of diabetes were age, BMI, general health, and physical health. This project has demonstrated that constructing a good model is not only what algorithm you choose, but also the way in which you get to know the issue inside and out and then make the appropriate changes to make it actually work in real life.

This project focus is to detect the anomalies in the RF (radio frequencies) spectrum using the spectrum data collected through sensors. The main idea here is to understand the behaviour of the spectrum that works wirelessly over time and to identify if there are any suspicious patterns which can tell about the abnormal activity, interference, or unexpected spectrum usage. Using wireless systems has become part of almost everyone’s life as a way of communication, for example ‘WiFi’, which is the most widely used wireless system used by almost everyone in the world. So monitoring the spectrum and understanding its behaviour does have many applications, and this can be done with the help of data science to further study more about these spectrums. The plan here is to use a real measured dataset like the ElectroSense platform; this provides power spectral density data. This data is collected through spectrum sensors in real environments [1], [2]. The project will follow a workflow that includes data understanding, cleaning, exploratory analysis, feature preparation, anomaly definition, model development, and evaluation. Since the problem is about detecting anomalies, the first focus is on learning the normal spectrum behavior and then identifying deviations from it rather than only using standard classification ideas. The overall goal of this project is to build a clear and practical workflow for RF spectrum monitoring using real-world data.