richy1996 Goto Github PK

11.4.1-case-study---linear-regression

linear regression case study on wine alcohol content

alphalens

Performance analysis of predictive (alpha) stock factors

artificial-intelligence-for-trading

Content for Udacity's AI in Trading NanoDegree.

backtrader

Python Backtesting library for trading strategies

bioinfokit

Bioinformatics data analysis and visualization toolkit

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

datascienceguidedcapstone

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

documentation

The repository contains files used in the respective Tools & Skills training module.

dx

DX Analytics

hello-world

Just another repository

hmmlearn

Hidden Markov Models in Python, with scikit-learn like API

mvportfolio

Simple portfolio analysis and management.

packaging

The repository contains files used in the respective Tools & Skills training module.

pairs-trading-pipeline

pairs trading pipeline with google big query scripts and graphical lasso

pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

py4fi

Python for Finance (O'Reilly)

pyfolio

Portfolio and risk analytics in Python

qdb

Quantopian Remote Debugger for Python

qgrid

An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks

richy1996.github.io

My Github "Pages" page

springboard-ds

Coding work for the springboard data science bootcamp I started in May of 2023.

springboard_11_3_case_study

11.3 case study

springboard_14_2_logistic_regression

logistic regression case study

springboard_14_3_decision_trees

decision tree case study

springboard_14_4_random_forest

random forest

springboard_14_5_gradient_boosting

gradient boosting case study

springboard_15_2_distances

calculating distances, i.e. euclidean and manhattan

springboard_15_5_cosine

cosine similarity case study

springboard_15_6_k_means

customer segmentation using k-means clustering case study