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become-a-wise-investor-on-lending-club-using-ann

LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform. Given historical data on loans given out with information on whether or not the borrower defaulted (charge-off), I will build a model that can predict wether or nor a borrower will pay back their loan. This way in the future when there is a new potential customer I can assess whether or not they are likely to pay back the loan. The datset can be obtained from [Kaggle](https://www.kaggle.com/wordsforthewise/lending-club)

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

fastai

The fastai deep learning library, plus lessons and tutorials

handwritten-digits-classifier-using-ann

In this project, I will build models that classify handwritten digits. The load_digits() function returns a copy of the hand-written digits dataset from UCI.

hummingbird

Hummingbird compiles trained ML models into tensor computation for faster inference.

iclr2021637

ICLR 2021 Submission 637

models

Models and examples built with TensorFlow

neural_prophet

NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch

open3d

Open3D: A Modern Library for 3D Data Processing

predicting-bike-rentals

In this project, I will try to predict the total number of bikes people rented in a given hour. The data was collected by Washington, D.C. and compiled by Hadi Fanaee-T at the University of Porto. The data can be downloaded [here](http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset)

predicting-car-prices-using-k-nearest-neighbors

In this project, I will use the K-nearest neighbors model to predict a car's market price using its attributes. The data set I will be working with contains information on various cars. For each car I have information about the technical aspects of the vehicle such as the motor's displacement, the weight of the car, the miles per gallon, how fast the car accelerates, and more. The data set can be downloaded [here](https://archive.ics.uci.edu/ml/datasets/automobile)

predicting-house-sale-prices-using-linear-regression

In this project, I will be working with housing data for the city of Ames, Iowa, United States from 2006 to 2010. The dataset was originally compiled by Dean De Cock for the primary purpose of having a high quality dataset for regression. His paper can be found here

predicting-survival-on-the-titanic

predicting-the-stock-market

I'll be using historical data on the price of the S&P500 Index to make predictions about future prices. Predicting whether an index will go up or down will help us forecast how the stock market as a whole will perform.

qlib

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies.

scikit-learn

scikit-learn: machine learning in Python

sktime

A unified framework for machine learning with time series

spam-filter-using-nlp-and-naive-bayes

In this project, I'm going to build a spam filter for SMS messages using the multinomial Naive Bayes algorithm. My goal is to write a program that classifies new messages with an accuracy greater than 80% — so I expect that more than 80% of the new messages will be classified correctly as spam or ham (non-spam). To train the algorithm, I'll use a dataset of 5,572 SMS messages that are already classified by humans. The dataset was put together by Tiago A. Almeida and José María Gómez Hidalgo, and it can be downloaded from the The UCI Machine Learning Repository.

tensorflow

An Open Source Machine Learning Framework for Everyone

winning-jeopardy

Jeopardy is a popular TV show in the US where participants answer questions to win money. It's been running for a few decades, and is a major force in popular culture. Let's say a friend of mine want to compete on Jeopardy, and my job is to look for any edge I can get to help him win. In this project, I will work with a dataset of Jeopardy questions to figure out some patterns in the questions that could help my friend win. The dataset is available on reddit.