Datascience hands on code
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Updated
Oct 18, 2018 - Jupyter Notebook
Datascience hands on code
Implementing Decision Trees, Bagging Trees and Random Forest
This project was an attempt to use ML techniques to identify and prevent DDOS attacks.
for the publication "A network approach to understanding interspecific interactions within a multi-species flock " (in prep)
CS760: Machine Learning
Building classification models to predict if a loan application is approved. Using under-sampling, bagging and boosting to tackle the problem of with unbalanced dataset
Classification problem using Ensemble Techniques
This repository contains algorithmic implementation of machine learning algorithms and also usage of ML algorithms using scikit.
Machine Learning Framework for Estimating Efficiency of Organic Solar Cells using Extreme Random Forests
Analyse the factors which lead to online shopping on a website and building predictive models for it.
An example repo for how PU Bagging and TSA works.
bagging and hyperparameter tuning on spam vs not spam dataset
Comparing different tree-based algorithms to find the best model for cancelation prediction
Regression Analysis - Toyota Corolla price prediction
Nonlinear Regression Models
Classification and regression using Decision Trees, Bagged Decision Trees and Random Forests from scratch in python.
A Survey on ML Techniques for Airbnb Price Prediction
This repository will help in understanding the basic concept of Random Forest algorithm and will also learn how to optimize the hyperparameters and prevent overfitting.
These are coding assignments and projects for the CS 675 Machine Learning course.
Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio
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