ipynb-jupyter-notebook

Jupyter Notebooks from the old UnsupervisedLearning.com (RIP) machine learning and statistics blog

Python crawling tutorial

A repository for sharing ipynb's of my experiments with GAN

A jupyter notebook server via HEROKU web without Password.

Rock Paper Scissors with OpenCV and Neural Networks

This repository provides a complete set of python starter code for beginners learning python. It consists of '.py' and '.ipynb' for hands-on working with the python code that I have created.

Interactive Machine Learning Examples

Parch and Posey database was used to explore different functional aspects of SQL from basic to advanced.

This project is on a data set from Prosper, which is America’s first marketplace lending platform, with over $7 billion in funded loans. This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, borrower employment status, borrower credit history, and the latest payment information. The main purpose of this project is to summarize the characteristics of variables that can affect the loan status and to get some ideas about the relationships among multiple variables using summary statistics and data visualizations.

A predictive model for Order Cancellation among customers to try to predict suspicious bookings in the hotel industry

For CCBR - ML Lab

The project aims to perform various data cleaning strategies to handle data quality problems like handling missing values, standardizing attribute values, correcting typos.

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

House Prices: Análisis exploratorio de datos con Python

This project includes scraping and machine learning processes regarding actresses or actors who are getting divorced

Comprehensive Analysis of the Android app market by comparing over a thousand apps in Google Play across different categories.

This project is performed on 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in the data. The project tries to find out the factors that predicts if a patient will show up for their scheduled appointment

Created as a demonstration to showcase the usefulness of RNN's with LSTM, in the pursuit of accurate stock price prediction models. Creating using Tensorflow. Written in Python-3 using Jupyter Notebook

A list of data science projects completed by me for academic, self learning, and creative purposes

Repository contains neural network for classification using softmax as an activation function

Report on World Happiness Data (Data Analysis and Visualisation of the data)

IBM Coursera Capstone: Seattle Car Collision Severity Factors