liyangbit / Pydataroad

Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]

👨🏽‍🏫You can learn about what data science is and why it's important in today's modern world. Are you interested in data science?🔋

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018

A Portfolio of my Data Science Projects

🛠 All-in-one web-based IDE specialized for machine learning and data science.

Data Science Hacks consists of tips, tricks to help you become a better data scientist. Data science hacks are for all - beginner to advanced. Data science hacks consist of python, jupyter notebook, pandas hacks and so on.

This repository is my attempt to help Data Science aspirants gain necessary Data Visualization skills required to progress in their career. It includes all the types of plot offered by Seaborn, applied on random datasets.

Source code accompanying book: Data Science on the Google Cloud Platform, Valliappa Lakshmanan, O'Reilly 2017

repo for code published on pythondata.com

Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm. Capstone Project for Machine Learning Engineer Nanodegree by Udacity.

📢 Ready to learn or review your knowledge!

Resources for weekly Data Science Lunch & Learns

Open-Source Tutorial For Analyzing and Visualizing Cryptocurrency Data

Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.

A Python library for easy data analysis, visualization, exploration and modeling

Materials for GWU DNSC 6279 and DNSC 6290.

Visualizer for pandas data structures

Introduction to Data Science

General Assembly's 2015 Data Science course in Washington, DC