Titanic: Machine Learning from Disaster https://www.kaggle.com/c/titanic
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Updated
Mar 5, 2016 - Jupyter Notebook
Titanic: Machine Learning from Disaster https://www.kaggle.com/c/titanic
TensorFlow implementations of a Restricted Boltzmann Machine and an unsupervised Deep Belief Network, including unsupervised fine-tuning of the Deep Belief Network.
My Machine Learning First Project on Github
A tutorial for Kaggle's Titanic: Machine Learning from Disaster using Octave and Logistic Regression Modeling.
Supervised Machine Learning methods (Random Forest and SGD Classifier) to classify short conversations extracted from Reddit
Predict which passengers of the Titanic would have survived the disaster of 1912
Movie Sentiment predictor using decision tree classifier and random forests.
Data Mining Project: Classification of the Car Evaluation Dataset.
Classification of various products into different categories is a very important task. Doing this classification, one can get various types of insights about the specific product. This also helps in doing product matching when you try and search a product on a eCommerce site.
This is an example for how handwritten digits can be learnt with random forests
Case study of the Risk Assesment GmbH
Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using Python
Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using R
RandomForest classifier to build a predictive model for Blue Book for Bulldozer Kaggle competion
Football Player Transfer Prediction Using Different Classifiers
A single-layer Random Forest model for pixel classification (image segmentation).
CSE601 Course Projects - Fall 2017
Review Classification using NLP
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