Fahad Hassan

End-to-end Reddit Data Pipeline: Extracted data using PRAW, stored as CSV, ingested into Delta Lake (Bronze → Silver → Gold) using PySpark. Applied transformations, built a semantic layer, and implemented Delta Lake time travel & rollback.

Unsupervised Machine Learning model on Iris dataset using KMeans algorithm. Aim is to find opimum number of clusters for the dataset.

Laurence Moroney's poem dataset, achieved 71% of accuracy.

The model uses 50K movie reviews labelled as 'positive' and 'negative' that implements a Sentiment Analysis model using LSTM and GloVe(Global Vectors) Word Embeddings. There is 100-dimmensional GloVe(Global Vectors) word Embeddings is used. It is an unsupervised learning algorithm used for the similar representation of words having same meanings. LSTM(Long Short-term Memory) is a type of RNN model used for handling long sequence dependencies for classification task.

A supervised Machine Learning model that predict student score percentage on the basis of study hours. The model uses Linear Regression to predict percentage score of a student. The linear regression is provided by Scikit-learn.

I've tried to find out the weak areas where we can work to make more profit. What all business problems we can derive by exploring the data. Technologies used are Python, Numpy, Pandas, Matplotlib, Seaborn.