Pinned Repositories
Anime-Recommendation-Database
Anime Recommendation Engine
Assignment-2
INFO 6210 - Assignment - 2
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
Book_List
Python, Machine Learning, Deep Learning and Data Science Books
Data-Science--Cheat-Sheet
Cheat Sheets
Deep_Learning
INFO-6210-Final-Project
Cloud Based Medical Insurance Database
NYC-Taxi-Fare-Prediction
Spam-Classifier
A basic NLP classification task to classify between spam and ham on a given Email spam dataset of 5,574 emails. The files contain one message per line. Each line is composed by two columns: v1 contains the label (ham or spam) and v2 contains the raw text.
Telco-Customer-Churn-Analysis
Customer Turnover or churn rate, is the percentage of an organization's customer base lost during a given period of time, usually a month or annual basis. It can be an indicator of customer dissatisfaction, cheaper and/or better offers from the competition, better marketing strategies from the competition. The goal of this project is to predict the customer churn in a Telecommunication company. We will deploy over several classification machine learning algorithms to determine the customers' willingness to churn. We will also be exploring various factors that contribute towards the customer dissatisfaction and their willingness to churn.
ashwinjohn3's Repositories
ashwinjohn3/Python
All Algorithms implemented in Python
ashwinjohn3/Anime-Recommendation-Database
Anime Recommendation Engine
ashwinjohn3/Assignment-2
INFO 6210 - Assignment - 2
ashwinjohn3/INFO-6210-Final-Project
Cloud Based Medical Insurance Database
ashwinjohn3/NYC-Taxi-Fare-Prediction
ashwinjohn3/Spam-Classifier
A basic NLP classification task to classify between spam and ham on a given Email spam dataset of 5,574 emails. The files contain one message per line. Each line is composed by two columns: v1 contains the label (ham or spam) and v2 contains the raw text.
ashwinjohn3/Telco-Customer-Churn-Analysis
Customer Turnover or churn rate, is the percentage of an organization's customer base lost during a given period of time, usually a month or annual basis. It can be an indicator of customer dissatisfaction, cheaper and/or better offers from the competition, better marketing strategies from the competition. The goal of this project is to predict the customer churn in a Telecommunication company. We will deploy over several classification machine learning algorithms to determine the customers' willingness to churn. We will also be exploring various factors that contribute towards the customer dissatisfaction and their willingness to churn.
ashwinjohn3/ajc-portfolio
ashwinjohn3/Assignment-3
MongoDB Assignment
ashwinjohn3/barrier
Open-source KVM software
ashwinjohn3/bchiang7.github.io
Third iteration of my personal site built with Jekyll
ashwinjohn3/covid-19
A collection of work related to COVID-19
ashwinjohn3/Cracking-the-Data-Science-Interview
A list of resources for our society members who have upcoming interviews!
ashwinjohn3/first-contributions
🚀✨ Help beginners to contribute to open source projects
ashwinjohn3/Heart-Failure-Prediction
Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.
ashwinjohn3/IE7374_HW_1
Use the housing and yacht dataset to estimate the regression weights by normal equations. Compare the performance (measured through RMSE) with the results obtained using the gradient descent algorithm. In this section you will calculate the analytical solution in Python that we obtained through Normal equations to learn your weight vector, and contrast the performance (train- ing and test RMSE) for your gradient-descent based implementation.
ashwinjohn3/langgraph-agents-with-amazon-bedrock
ashwinjohn3/learning
Becoming 1% better at data science everyday
ashwinjohn3/ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
ashwinjohn3/mml-book.github.io
Companion webpage to the book "Mathematics For Machine Learning"
ashwinjohn3/Mobile-Games-A-B-Testing-with-Cookie-Cats-Datacamp
Cookie Cats is a hugely popular mobile puzzle game developed by Tactile Entertainment. It's a classic "connect three" style puzzle game where the player must connect tiles of the same color in order to clear the board and win the level. It also features singing cats. We're not kidding! As players progress through the game they will encounter gates that force them to wait some time before they can progress or make an in-app purchase. In this project, we will analyze the result of an A/B test where the first gate in Cookie Cats was moved from level 30 to level 40. In particular, we will analyze the impact on player retention.
ashwinjohn3/New-York-City-Airbnb-Open-Data
Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in NYC, NY for 2019. In this project, I would be exploring the dataset learning more about how the listings in New York City are prices in different buroughs. I will also be building a machine learning model predict the price of listings.
ashwinjohn3/pearai-app
The Open Source AI-Powered Code Editor. A fork of VSCode and Continue.
ashwinjohn3/Practical-Deep-Learning-for-Coders-2.0
Notebooks for the "A walk with fastai2" Study Group and Lecture Series
ashwinjohn3/project_cs_v2
This is the second iteration of 'project_cs' using mkdocs framework. This will act as my primary source of keeping notes, blogs. The reason for using mkdocs is that it allows for muti-repo documentation similar to spotify. Much more scalable, extendable and down right easy to use
ashwinjohn3/raman_classifier_challenge
ashwinjohn3/Reggie-s-Linear-Regression
Codeacademy Linear Regression Project
ashwinjohn3/resume
Software developer resume in Latex
ashwinjohn3/resume-template
:page_facing_up::briefcase::tophat: A simple Jekyll + GitHub Pages powered resume template.
ashwinjohn3/Zero-to-Hero-in-NLP
This repository contains A-Z techniques of Natural Language Processing to get started in NLP.