SiweiMa's repositories

Ames-House-Prices-Multiple-Linear-Regression-Project-in-Python

Given Ames Housing dataset, the project started with an exploratory data analysis (EDA) to identify the missing values, suspicious data, and redundant variables. Then I performed a mixed stepwise selection to reduce the set of variables and select the best model based on AIC, BIC, and adjust R-squared. With the best model selected, the model assumptions were checked regarding normality, homoscedasticity, collinearity, and linearity between response and predictors. Several solutions were proposed to solve the assumption violation. The model was then tested on unseen data and scored on Root-Mean-Squared-Error (RMSE).

Language:Jupyter NotebookStargazers:2Issues:0Issues:0

concrete-strength-prediction

Concrete is the single most widely used man-made material in the world. Construction workers rely on experiments to determine the strength of concrete. The app presents an attempt to predict the strength based on the information of raw materials by machine learning methods.

Language:Jupyter NotebookStargazers:2Issues:0Issues:0

Ames-House-deep-learning

revisit Ames house prices project by using PyTorch

Language:PythonStargazers:0Issues:1Issues:0
Language:PythonStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

is-test

Implementation of an awesome trick to determine if training and test are from the same distribution

Language:PythonStargazers:0Issues:0Issues:0

machine-learning-snippets

code snippet I created for machine learning workflow

Language:PythonStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

police-stop-eda

The How, Who, When, and Where of Police Stops in San Francisco: a case study of visualization and EDA

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

SiweiMa.github.io

Siwei Ma's personal website

Language:HTMLStargazers:0Issues:1Issues:0

snake-game

A classic snake game with some modifications.

Language:PythonStargazers:0Issues:1Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

user-search

Given a customer ID, the tool will return the customer's segment/clustering, CLV, the conditional expected number of purchases for next month, the probability of the customer being alive, detailed purchase history.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0