supervised-machine-learning

KO | EN

gitlite — search

#typescript #ai-agents #ai #dsh-plugin #deepseek-harness #open-source #cli #claude-code #codex #developer-tools #react #windows

supervised-machine-learning

★ 51 Open GitHub ↗

This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using Lasso and Ridge regressions.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## Supervised Machine Learning Projects

![image title](https://img.shields.io/badge/python-v3.6-green.svg) ![image title](https://img.shields.io/badge/ntlk-v3.2.5-yellow.svg) ![Image title](https://img.shields.io/badge/sklearn-0.19.1-orange.svg) ![Image title](https://img.shields.io/badge/BeautifulSoup-4.6.0-blue.svg) ![Image title](https://img.shields.io/badge/pandas-0.22.0-red.svg) ![Image title](https://img.shields.io/badge/matplotlib-v2.1.2-orange.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

<a href="#nb"> Notebooks and descriptions </a> •
 <a href="#ci"> Contact Information </a>

<a id = 'nb'></a>
### Notebooks and descriptions
| Notebook | Brief Description |
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|[predicting-comments-on-reddit](http://nbviewer.jupyter.org/github/marcotav/deep-learning/blob/master/painters-identification/notebooks/capstone-models-final-model-building.ipynb) | In this project I determine which characteristics of a post on Reddit contribute most to the overall interaction as measured by number of comments.|
|[tennis-matches-prediction](http://nbviewer.jupyter.org/github/marcotav/deep-learning/blob/master/bitcoin/notebooks/deep-learning-LSTM-bitcoins.ipynb) | The goal of the project is to predict the probability that the higher-ranked player will win a tennis match. I will call that a `win`(as opposed to an upset).|
|[churn-analysis](http://nbviewer.jupyter.org/github/marcotav/deep-learning/blob/master/keras-tf-tutorial/notebooks/neural-nets-digits-mnist.ipynb) | This project was done in collaboration with [Corey Girard](https://github.com/coreygirard/). A mobile device company is having a major problem with customer retention. Customers switching from one company to another is called churn. Our goal in this analysis is to understand the problem, identify behaviors which are strongly correlated with churn and to devise a solution.|
|[click-prediction](http://nbviewer.jupyter.org/github/marcotav/deep-learning/blob/master/transfer-learning/notebooks/transfer-learning.ipynb) | Many ads are actually sold on a "pay-per-click" (PPC) basis, meaning the company only pays for ad clicks, not ad views. Thus your optimal approach (as a search engine) is actually to choose an ad based on "expected value", meaning the price of a click times the likelihood that the ad will be clicked [...] In order for you to maximize expected value, you therefore need to accurately predict the likelihood that a given ad will be clicked, also known as "click-through rate" (CTR). In this project I will predict the likelihood that a given online ad will be clicked.|
| [retail-store-expansion-analysis-with-lasso-and-ridge-regressions](http://nbviewer.jupyter.org/github/marcotav/deep-learning/blob/master/painters-identification/notebooks/capstone-models-final-model-building.ipynb) | Based on a dataset containing the spirits purchase information of Iowa Class E liquor licensees by product and date of purchase this project provides recommendations on where to open new stores in the state of Iowa. To devise an expansion strategy, I first needed to understand the data and for that I conducted a thorough exploratory data analysis (EDA). With the data in hand I built multivariate regression models of total sales by county, using both Lasso and Ridge regularization, and based on these models, I made recommendations about new locations.|

<a id = 'ci'></a>
## Contact Information

Feel free to contact me:

* Email: [marcotav65@gmail.com](mailto:marcotav65@gmail.com)
* GitHub: [marcotav](https://github.com/marcotav)
* LinkedIn: [marco-tavora](https://www.linkedin.com/in/marco-tavora)
* Website: [marcotavora.me](http://www.marcotavora.me)

supervised-machine-learning

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README