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amazon-food-review-sentiment-classification

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories. We perform basic NLP and data cleaning and move on to perform sentiment classification. We predict the sentiment using different algorithms and each has its own seperate notebook

analysing-haberman-survival-dataset

The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer.

analysing-iris-dataset

The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.

analyzing-atlantic-hurricanes

Explores the distribution of Category 4 and 5 Atlantic Hurricanes

aws-dotnet-deploy

Opinionated tooling that simplifies deployment of .NET applications to AWS.

aws-dotnet-messaging

aws-sdk-net

The official AWS SDK for .NET. For more information on the AWS SDK for .NET, see our web site:

backtobackswe

Code Examples For Back To Back SWE Lessons

bootstrapping-ks_test-q-q-_plots

This repository contains python code to estimate confidence interval using Boostrapping and implementing the KS test and Q-Q plots

consoleapp

cookbook

The demo project for the "How Git Works" and "Mastering Git" trainings.

copilot-cli

The AWS Copilot CLI is a tool for developers to build, release and operate production ready containerized applications on AWS App Runner, Amazon ECS, and AWS Fargate.

data-science-cheatsheet

data-wrangling-final-project

Analysis of Video Game Sales

dataquest-projects

This repository contains all the projects that I did as part of an online data science course on Dataquest.io

dev-ops-with-docker-21

The docker course by University of Helsinki

dimensionality-reduction-

Implement PCA and T-SNE on MNIST Dataset

greenlight

Greenlight API server from https://lets-go-further.alexedwards.net

implementing-linear-regression

This is a short tutorial to implement simple Linear Regression using scikit-learn on the Boston Housing dataset

interview-prep

Code snippets for questions asked in technical interviews

knn-decision-boundaries-and-cross-vaidation

understanding and visualizing the decision boundary generated by KNN on different values of K

leetcode

LeetCode Problems' Solutions

leetcode-python

Leetcode Python Solution and Explanation. Also a Guide to Prepare for Software Engineer Interview.

logistic-regression-and-hyper-parameter-tuning

Code example to implement Logistic Regression and using GridSearch to find optimal hyperparameters

lrucache

LRU Cache Implementation from LeetCode

path-finding-and-collision-detection

The Environment is a randomly generated grid with a user-defined probability of each cell being an obstacle or not. Implemented Local Search algorithms like Breadth First Search and Depth First Search to create an AI bot that navigates its way through this environment in order to reach the goal node .Used the A* Algorithm with Euclidean and Manhattan distance based heuristics to improve the time and space complexity . Used a Genetic algorithm to generate progressively harder mazes and benchmarked their performance and summarize the results

predicting-the-success-of-kickstarter-projects

Kickstarter is one of the most popular crowdfunding platform on the internet. The aim of this project is to predict the success or failure of a Kickstarter campaign at launch time.

python-for-data-science

This repository contains code snippets of working with data using Python and its associated libraries (numpy, pandas and matplotlib)

quora-question-pair-similarity

Over 100 million people visit Quora every month, so it's no surprise that many people ask similarly worded questions. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question, and make writers feel they need to answer multiple versions of the same question. Quora values canonical questions because they provide a better experience to active seekers and writers, and offer more value to both of these groups in the long term. Currently, Quora uses a Random Forest model to identify duplicate questions. In this competition, Kagglers are challenged to tackle this natural language processing problem by applying advanced techniques to classify whether question pairs are duplicates or not. Doing so will make it easier to find high quality answers to questions resulting in an improved experience for Quora writers, seekers, and readers.

review_aspect_extraction

CNN-based model to realize aspect extraction of restaurant reviews based on pre-trained word embeddings and part-of-speech tagging