video1.0<iframe src="https://www.loom.com/embed/c402a1d1e00240c0a35c02ec338ebb67" frameborder="0" width="1818" height="1363" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>13631818Loomhttps://www.loom.com13631818https://cdn.loom.com/sessions/thumbnails/c402a1d1e00240c0a35c02ec338ebb67-e5d47f5f6f865e63.gif162.448Hi, I’m Purvansh Dave, a third-year ICT student, and this video is my submission for the PM Xerator data science assessment. I utilized the Global Weather Repository dataset from Kaggle, focusing on data cleaning, exploratory data analysis, and anomaly detection using Isolation Forest, which flagged about 5% of the data as anomalous. I explored various models, including ARIMA, Facebook Prophet, and XGBoost, with Facebook Prophet performing the best for seasonal predictions. I also conducted a unique analysis on feature importance, highlighting temperature, humidity, and wind speed as significant factors. You can find all the outputs and details in my GitHub repo, and I encourage you to check it out.