https://github.com/beimingliu/AdvancedMachineLearning
A collection of machine learning projects
https://github.com/beimingliu/AdvancedMachineLearning
machine-learning predictive-analysis predictive-modeling time-series
Last synced: about 1 month ago
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A collection of machine learning projects
- Host: GitHub
- URL: https://github.com/beimingliu/AdvancedMachineLearning
- Owner: beimingliu
- License: apache-2.0
- Created: 2018-06-10T19:27:00.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-06-11T05:43:06.000Z (almost 7 years ago)
- Last Synced: 2024-10-24T03:37:20.037Z (6 months ago)
- Topics: machine-learning, predictive-analysis, predictive-modeling, time-series
- Language: Jupyter Notebook
- Homepage: https://www.beimingliu.me
- Size: 10.2 MB
- Stars: 48
- Watchers: 2
- Forks: 6
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Advanced Machine Learning Related Projects
This is a collection of ML projects that I did, topics include:
+ Regression: Linear Regression, Lasso, Ridge
+ Time Series: ARIMA, SARIMA (Box-Jenkins Approach), Exponential Smoothing (Holt-Winters Approach), VAR, VARX (Vector Autoregression Approach) etc
+ Recommender System: collaborative filtering and matrix factorization
+ NLP: embeddings, Tf-idf etc
+ XGBoost, Random Forest implementation
+ Neural Network: Image classification## [Here's an Ad, are you gonna click it?](CTR%20Prediction)
Use Random Forest with feature engineering to predict Click-Through Rate (CTR) with [Avazu data](https://www.kaggle.com/c/avazu-ctr-prediction). Final result with Log Loss ≈ 0.4.## [Stop Spamming my Email!](Spam)
Use Adaboost and XGBoost methods to predict if a email is a spam with 97% accuracy rate.## [Teach Computers to recognize Digits](NN%20MNIST%20dataset)
Pytorch: implement simple 2-Layer and 3-Layer neural network using [MNIST dataset](http://yann.lecun.com/exdb/mnist/) to predict hand written digit with an accuracy rate of 98.29%## [So you watched a movie: Yay or Nay? ](Movie%20Review%20Sentiment%20Analysis)
Use [Glove embeddings](https://nlp.stanford.edu/projects/glove/) on [movie review dataset](http://ai.stanford.edu/~amaas/data/sentiment/) of 50,000 reviews from IMDB. Predict if a review is positive or negative given the content: use XGBoost to achieve an 86.7% accuracy rate.## [How much is my House Worth?](House%20Price)
Use Linear Regression to predict the house prices in [Ames, Iowa](https://www.kaggle.com/c/house-prices-advanced-regression-techniques). Compare regression models of OLS, Ridge, Lasso and Elastic Net techniques and generate a business report.## [BBC Article Recommendations](BBC%20Article%20Recommendations)
Replicate the recommendation system on blog-based website: provide 5 articles based on what the user is reading now using word2vec on [data](w).## [Build your own Netflix recommendation engine!](Recommender%20System)
Build a movie rating recommendation system from scratch using collaborative filtering with matrix factorization.## [Twitter Sentiment Analysis!](Twitter%20Sentiment)
I know, I know, I know. You've seen this project a million times, as a ML student, I just had to do it like everyone else :)
Part of the code is modified from Prof. [Yannet Interian's](https://github.com/yanneta) USF Advanced ML class.
Part of the code is modified from Prof. [Terence Parr's](https://github.com/parrt) USF data acquisition class..