lecritch

lecritch's Stars

• fastai/fastbook

  The fastai book, published as Jupyter Notebooks

  Language:Jupyter Notebook22.3k5102388.5k

• dask/dask-tutorial

  Dask tutorial

  Language:Jupyter Notebook1.8k90104710

• capitalone/datacompy

  Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!

  Language:Python49623138131

• zygmuntz/evaluating-recommenders

  Compute and plot NDCG for a recommender system

  Language:Python9611135

• georgezoto/TensorFlow-in-Practice

  TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

  Language:Jupyter Notebook678029

• mattnedrich/CentralLimitTheoremDemo

  Language:Python253015

• djjonno/kerala

  Distributed KV Streams

  Language:Kotlin18311

• rikturr/getting-up-to-speed-with-dask

  Gentle introduction to Dask

  Language:Jupyter Notebook11206

• RTongs/Kondenser

  Game Engine written in Rust on the Metal API

  4310

• lecritch/king_county_housing_prices_linear_regression

  This repo contains EDA of 2019 King County housing data. It also contains iterations of generating linear regression models to predict home sale prices.

  Language:Jupyter Notebook3204

• chum46/customer_churn_prediction

  Predicting customer churn with telecommunications data

  Language:Jupyter Notebook2209

• dannmorr/opportunity_youth_statistical_analysis

  Estimating the number of Opportunity Youth in South King County, WA using the 2017 5-year American Community Survey (ACS) Public Use Microdata Survey (PUMS).

  Language:Jupyter Notebook2102

• lecritch/Rate-5-Get-5-Movie-Recommendation-System

  We aim to create a recommendation system based on the MovieLens dataset from the GroupLens research lab at the University of Minnesota. Furthermore, we would like to deploy a web app that will alloy a user to enter some ratings for movies that they have seen, and then, based on the model we have implemented, it will reccomend movies that align with their interests.

  Language:Jupyter Notebook2103

• learn-co-students/mod_4_project-chi-sea

  Language:Jupyter Notebook1209

• learn-co-students/quality-deliverables-chi-seattle-051120

  Repo for quality deliverables gone over in Mod 1 project

  Language:Jupyter Notebook12404

• lecritch/capstone2020

  Language:Jupyter Notebook1100

• lecritch/respones_to_resistance_model_analysis

  This project analyses response to resistance data for the Dallas police municipality. A model is created to predict whether citizens are injured if they resist police arrest.

  Language:Jupyter Notebook1200

• mesterhammerfic/x_ray_classification

  An image classifcation project using x-ray data from mendelay.

  Language:Jupyter Notebook1102