Applied ML algorithms on Enron Dataset to predict Person of Interest (POI)
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Updated
Apr 20, 2017 - Jupyter Notebook
Applied ML algorithms on Enron Dataset to predict Person of Interest (POI)
We compared the predictive accuracy and sparsity of support vector machines and relevance vector machines for a range of synthetic data sets differing in signal-to-noise ratio and other measures of difficulty.
Machine Learning Classification on Unbalanced Real World Dataset
Elegant Mathematica-style model manipulation, fitting and exploration in MATLAB.
An sklearn type class for testing multiple models on data with sequential hyper-parameter tuning for the models
Assignments, Projects and other course related material.
Logistic Regression in Python is used to study the classification problem of heights & weights in men and women
a case study on deep learning where tuning simple SVM is much faster and better than CNN
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and s…
This is a solution to a Kaggle competition on predicting claim severity for Allstate Insurance using the Extreme Gradient Boosting (XgBoost) algorithm in R
Tuning of parameters of ML algorithms to optimise precision/f-score for fault detection in softwares
Churn Modelling Using ANN with Parameter tuning for best accuracy
Resample, parameter tuning, meta-learning, clustering, and mining algorithms for the purpose of data mining and machine learning.
Home price prediction with Lasso and Ridge Regression with parameter tuning
The application of machine learning techniques (such as linear regression) to classify a frequency filter from its spectrum
Design and Implementation of Pac-Man Strategies with Embedded Markov Decision Process in a Dynamic, Non-Deterministic, Fully Observable Environment
My submission for the DeepTraffic Competition by MIT 6.S094: Deep Learning for Self-Driving Cars
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