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airbnb-new-user-booking

Prediction of where airbnb users are likely to make a booking next. This is my submission to the kaggle competition titled "Airbnb New User Booking" using pyspark and spark ml.

airflow-maintenance-dags

A series of DAGs/Workflows to help maintain the operation of Airflow

algorithms-to-solve-the-rod-cutting-problem

Three approaches to solve the classic Rod Cutting Problem: brute force, bottom-up dynamic programing and memoization. A brief analysis is included for the approaches

audio-preprocessing-and-classification

.wav files from two different speakers classified into two classes

backpropagation-and-neural-networks-from-scratch

churn-prediction-using-scikit-learn

Prediction of churn rate for the "Telco Customer Churn" from Kaggle. In-depth analysis of the models is included.

cnn-fasion-mnist

covid-19-scholarly-articles-recommendation-engine

This is a project based on the COVID-19 Open Research Dataset Challenge (CORD-19). We built a reccomendation engine using pyspark and spark-ml. This project utilizes Natural language processing and clustering techniques.

exploratory-analysis-of-telco-customer-churn-dataset

Prediction of customer churn based on telecommunication data. simple logistic regression model used.

fisher-discriminant-implementation

An implementation of Fisher's classifier from scratch.

htmxflask

All the HTMX samples redone using a flask server

implementation-of-a-policy-gradient-model-using-keras-and-a-custom-gym-environment

This is an implementation of a policy gradient model to make predictions using reinforcement learning. This implementation is made using Keras with a custom loss function. The model is trained on a custom gym environment.

kmeans_from_scratch

An Implementation of the KMeans ++ algorithm from Scratch

linear-discriminant-analysis-model

A Fisher's Linear Discriminant model

logistic_regression_classifier_from_scratch

A Logistic regression classifier built from scratch and tested on multiple datasets

mass-spring-physics-simulation

mostafa-k-m

n-queens-problem-solution

An algorithm to solve classic N Queens problem written in python. Solutions are illustrated on up to n = 20.

post-office-problem-solution

This is a solution to the classic Post Office problem in python. it includes 4 algorithms to minimize the average distance and to minimize the maximum distance in both vanilla python and numpy. Also includes mathematical and empirical analysis of the time complecity of these algorithms.

predicting-flights-taxi-out-time-using-deep-q-learning

A deep Q-Learning Model built using Keras and tested on the Marketing Carrier On-Time Performance dataset to predict taxi-out time

prediction-of-flight-delay-using-random-forests

Exploratory Analysis on the Marketing Carrier On-Time Performance database and prediction of flight delay using scikit learn.

predictions-of-the-2019-2020-premier-league

Predictions of the premier league's 2020/2021 table based on the matches played upto the half way point. Plus an interactive dashboard that predicts on live data

scientific-computing-courswork-1

tpc-di-etl-using-pyspark

Using pyspark Extract-Transform-Load TPC-DI data and save them in the efficient parquet format. For more information about TPC-DI http://www.tpc.org/tpcdi/