Flight Delay Prediction
The Flight Delay Prediction Model was my first dive into Data Analytics and Machine Learning in general. The Flight Delay Prediction model utilizes a dataset of flight-related information to predict the likelihood of flight delays. The project employs Python for data preprocessing, model building, and analysis, incorporating both Linear Regression and Logistic Regression algorithms to achieve accurate predictions. Additionally, data visualization plays a pivotal role, accomplished using tools like Tableau to effectively explore and represent insights from the dataset. The final model successfully categorizes flights as delayed or not delayed, offering valuable assistance to airlines and travelers in planning and making informed decisions about their journeys.
Breast Cancer Prediction
The Breast Cancer Prediction Model used Logistic Regression similar to the Flight Delay Prediction Model; however, focused on a major real-world issue. The project utilized multiple Python libraries, specifically Matplotlib, Sci-kit Learn, Pandas and Numpy. The model utilized Matplotlib to effectively visualize correlations within data that allowed only the variables that correlated well with Breast Cancer cases to be used in the final model. The final model using Logistic Regression, like the previous Flight Prediction Model and showed amazing results. The model attained a 99% accuracy; however, the dataset was very little and I would need to collaborate with a medical instiution to collect more data to build a more accurate and effective model for patients suffering from Breast Cancer.