mmmarciel Goto Github PK

datacamp

DataCamp data-science courses

embeddedsystemsclass

Repository for Embedded Systems class at griloSchool of Engeneering of São Carlos - USP

grupo_07

hello-world

jupyter-treinamento-bigdata

Material Disponibilizado para Laboratórios do Curso de Big Data

opencv-object-face-tracking

Tracking objects and Face with OpenCV and Python

titanic-machine-learning-from-disaster

Titanic test and train dataset consists of unique features like passengerID, Passenger Class, name of each passenger, sex, age, number of siblings on the ship, parents, ticket number, fare of each ticket, Cabin number, and totall number of Embarked in different sections. Only train dataset consists a label of number of people Survived as this label is used to train the neural network.Submission format is a .CSV file which contains two main aspects of dataset i.e. PassengerId and Survived which give values of number of people survived. Results of this project is converted into a .csv format which is provided in the zip folder with the code and then it is compared with Kaggle results so the accuracy what we get over here might slightly vary from the accuracy we achieve in Kaggle submission. Totally 3 diverse types of neural network operations are tried on this dataset i.e. 1) Single Hidden MLP 2) RNN 3) DNN each Neural Net is processed in different ipynb files before combining it. DNN is my baseline model because I achieved my maximum accuracy of 88.53% while processing data with it and rank achieved in it is 1335 out of 9,803.

titanic_kaggle

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data wrangling, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.