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analysis-and-data-visualization-based-on-transportation

analysis-on-district-students-dropout

analysis-on-shooting-events

analyze-casino-strategy

There is a problem: when a person in casino and he has 1000 dollars per day. The strategy is the original bet is 5 dollars. When he loses one game, he will increase his bet by timing two. When he wins the game, he can win two times bet. After this win, he will come back to the original bet and continue game. The probability to win the game is 18/37. He can play 72 times per day. We want to know after 1000 days, can the person win money or lose money.

article-web-scraping-and-subject-analysis

build-song-keywords-api

using SQL and flask to build API for searching target songs

create-two-hidden-layers-neural-network

Create two hidden layers neural network not using packages

dash

data-analysis-of-d-penicillamine-treatment-in-scleroderma-

In this analysis, we will consider two independent sample t-test, longitudinal analysis, GEE models and Pearson's chi-squared test. For the model construction, we will use logistic regression and ordinal logistic regression. Also, MLR, Poisson Regression, Negative Binomial Regression, Zero-Inflated Poisson Regression and Zero-Inflated Negative Binomial Regression will be considered.

data-science-with-python

data preprocessing

digital-voice-detection

Using CNN and MLP to do voice classifacation

exercise-on-matplotlib

extract-keywords-and-entities-from-ai-news

facial-recognition

food-recognition-using-cnn

Food recognition and build it into webapp

html2latex

Convert HTML/CSS to LaTeX, fully customizable

islr-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

jena_climate_prediction

k-nearest-neighbors-classifier-using-sql-and-datalog

K-Nearest Neighbors classifier4 is a non-parametric machine learning classification model that predicts the class of a test instance based on the class labels k training instances closest to it. Your task will be to write the K-NN classifier in SQL and Datalog.

knn-smoothing

K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

los-angeles-pay-roll-visualization-using-r-shiny-

• Data preprocessing using tidyverse and dplyr library • Integrated data visualizations into an interative web app using Shiny

m280

Class projects for BIOSTAT M280 at UCLA

mnist_exercise

music_partial_classification_nn

using NN, optimizer is Adam, plot multiple lines(using legend, plt.legend(label))

naive-bayes-model-in-iris

nlp-in-python-tutorial

comparing stand up comedians using natural language processing

synthetic-financial-datasets-for-fraud-detection

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets. We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

time-series-analysis

timeseriesanalysiswithpython

using-perceptron-to-train-data-in-casino

first step: define dealer and players' original method on blackjack, simulating 100 data; second: building a perceptron model: x1=sum(players' cards' number) x2=dealer's first card number y=-1/1 (1 stands for win) using this data to make a perceptron third: using this hyperplane to decide whether to hit a card or not; fourth: using the above method to find the winning rate