Statistics class project aimed at studying the relationship between temperature and other attributes such as humidity, pressure, etc in Szeged, Hungary to build effective predictive models.
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Updated
Feb 18, 2019 - R
Statistics class project aimed at studying the relationship between temperature and other attributes such as humidity, pressure, etc in Szeged, Hungary to build effective predictive models.
Built LDA and QDA models on variables obtained from Principal Component Analysis (PCA) and Kolmogorov-Smirnov (KS) and tuned by leave-one-out cross-validation (LOOCV) to predict fraudulent online advertising click traffic
Simple Gaussian process in R.
Amazon_product_sentiment_analysis_project_data_mining
A multi-label classification model for classifying comments from Wikipedia talk page edits into different types of toxicity(insult, threat, identity hate, etc).
An Introduction to Statistical Learning exercise solution
Exploring consumer behaviour through Expedia sales.
As part of this project, I have developed algorithms from scratch using Gradient Descent method. The first algorithm developed will be used to predict the average GPU Run Time and the second algorithm will be used to classify a GPU run process as high or low time consuming process.
Supervised and Unsupervised statistical learning techniques applied on Pima Indians Diabetes Dataset
[R code] Application of the random forest machine learning classification algorithm
Comparison of the logistic regression, decision tree, and random forest models to predict red wine quality in R.
Fire Incident risk classification Data Mining project
Using the 'neuralnet' package in R for machine learning classification
It's a little knowledge base using some scripts that I faced in my professional life.
Using Decision Tree to predict Employee Attribution
Regular Expression Counts of Terms and Substrings
Built a logistic regression model and a classification tree model for predicting the final status of a loan based on various variables available. Confusion matrix and misclassification rate for each model for a test dataset. Variables that appear to be important for predicting outcome. Plotted and described the ROC curves and AUC for the four mo…
This project was conducted at UT Tyler Data Analytics Lab with the goal of using historical patient data and neural networks to predict future opioid abuse.
R, Classification, Stepwise Variable, A/B Testing
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