# Classifying Mechanism of Action

This post is a reflection on my Mechanism of Action Classification project. It describes how I made decisions on exploratory analysis, cleaning and modeling. My code is available on Github. If you want to discuss anything or find an error, please email me at Lvqingchuan@gmail.com :)

This project predicts 206…

# Understanding Normal Distribution

In this post, we’ll walk through the basic concepts , properties, and practical values of Normal distribution. If you want to discuss anything or find an error, please email me at Lvqingchuan@gmail.com :)

Before getting started, I want you to take a close look at the above diagram. This depicts…

# Implementing Random Forest

In this post, we’ll walk through basic concepts behind Random Forest, discuss practical problems of implementation, such as highly correlated features, feature sparsity, imbalanced classes. Then, compare the concepts and performance of Random Forest to Boosting Trees and Decision Tree! If you want to discuss anything or find an error…

# Improving Projects — Part II: Time Series (Sales)

This post is part of efforts on improving my technical projects. Each post describes how I identified suspicious spots and updated them. You can find Part I: Regressors and Part III: NLP here. …

# Understanding Linear Regression Assumptions

In this post, I’ll show you necessary assumptions for linear regression coefficient estimates to be unbiased, and discuss other “nice to have” properties. There are many versions of linear regression assumptions on the internet. Hopefully, this post will make it clear.

“Must have” Assumption 1. …

# Python: Data manipulation and simulation

In this post, we’ll walk through basic techniques of data manipulation and simulation with Python.

Data manipulation

A. Group aggregation

Given a data frame, say we want to summarize customer orders by different genders, we can use a simple groupby and agg function:

`values_gender = csv_file\                       .groupby(['gender']) \                       .agg(avg_order_values=('value','mean'),\ …`

# Power and Level in A/B Test

In this post, we’ll learn what is power, significant level, type I/II errors and how they relate to each other.

Power

Once we setup the null hypothesis and alternative hypothesis, we will collect data and compute test statistics. …

# Simple explanation of p-value

Just wanted to explain p-value in a very simple way.

Idea: p-value is the smallest probability at which you would reject the null hypothesis after seeing data. You reject the null hypothesis if and only if p-value is smaller than a pre-determined probability (usually 0.05).

Example: you want to test…

## Qingchuan Lyu

Data Engineering, Causal Inference & Predictive Analysis

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