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A/B Testing : Pitfalls, Bayesian & Math Behind

Posted on 2020-07-16 | In Product Sense
Symbols count in article: 24k | Reading time ≈ 22 mins.

My study note of various A/B testing resources. I do not own any credit for the any idea, knowledge, code below.

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Probability & Statistics Fundamental

Posted on 2020-07-15 | In Math
Symbols count in article: 29k | Reading time ≈ 26 mins.

Basic Probability & Statistics knowledge.

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A/B Testing Final Project

Posted on 2020-07-03 | In Product Sense
Symbols count in article: 17k | Reading time ≈ 16 mins.

My implementation & answer of Udacity A/B Testing course's final project.

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Study Notes of Udacity A/B Testing

Posted on 2020-07-03 | In Product Sense
Symbols count in article: 47k | Reading time ≈ 43 mins.

My study note of Udacity A/B Testing course.

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Study Note: Exploratory Data Analysis

Posted on 2020-04-29 | In Machine Learning , Practical Statistics for DS
Symbols count in article: 11k | Reading time ≈ 10 mins.

My study note of the book Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python - Chapter 1 Exploratory Data Analysis .

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October 2019

Posted on 2019-11-10 | In Journal
Symbols count in article: 992 | Reading time ≈ 1 mins.

 

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CS229 Note: Generative Learning

Posted on 2019-10-22 | Edited on 2020-09-11 | In Machine Learning , CS229
Symbols count in article: 1.7k | Reading time ≈ 2 mins.

Generative Learning algorithms

Discriminative learning algorithms: Algorithms that try to learn \(p(y|x)\) directly (such as logistic regression), or algorithms that try to learn mappings directly from the space of inputs \(X\) to the labels {0, 1}, (such as the perceptron algorithm)

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CS229 Note: Linear Regression, Logistic regression, Generalized Linear Models

Posted on 2019-10-20 | Edited on 2019-10-23 | In Machine Learning , CS229
Symbols count in article: 21k | Reading time ≈ 19 mins.

linear function: \(h_θ(x) = θ_0 + θ_1x_1 + θ_2x_2+...+θ_dx_d=θ^Tx\)

cost function: \(J(θ)=\frac{1}{2}\sum_{i=1}^n(h_θ(x^{(i)})-y^{(i)})^2\)

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Realtime Financial Market Data Visualization and Analysis

Posted on 2019-10-19 | In Project
Symbols count in article: 5.9k | Reading time ≈ 5 mins.

Introduction

In this project, I developed a financial data processing and visualization platform using Apache Kafka, Apache Cassandra, and Bokeh. I used Kafka for realtime stock price and market news streaming, Cassandra for historical and realtime stock data warehousing, and Bokeh for visualization on web browsers. I also wrote a web crawler to scrape companys' financial statements and basic information from Yahoo Finance, and played with various economy data APIs.

Please check the GitHub repo of this project, and most importantly, please check this platform's website and play with each plot \(\rightarrow\) magiconch.me

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Data Analysis of K-POP: Playing with Spotify API

Posted on 2019-10-19 | Edited on 2020-04-29 | In Project
Symbols count in article: 9k | Reading time ≈ 8 mins.

Introduction

K-pop has become a phenomenon in the U.S, as evidenced by bombing number of K-pop shows across the nation. In Spotify's K-pop genre, there are more than 500 K-pop artists. Among them, the wildly popular male group BTS is certainly worth mentioning. According to the data of Top 100 selling artists in the first half of 2019, out of 10 album copies sold out, there are 4 of BTS's albums (Ref).

BTS is certainly a reason for the rise of K-pop, but as more and more K-pop artists successfully hit the Billboard charts and took massive world tour, K-pop is definitely a hot topic to dig into.

This K-Pop sensation draws my attention to perform data analysis using data provided by Spotify API which could be used to answer some interesting questions like:

  • Besides BTS, who are the best K-pop artists in the U.S. market?
  • How do their popularity change?
  • How did the market of K-pop music evolve in size and audio features?
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Kamel Chehboun

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