Interpreting Categorical Data Classifiers using Explanation-based Locality
Categorial and numerical (ordinal and nonordinal) Data Clustering Algorithm
All about emotion recognition.
This is a Shiny App created with the data of climacteric stations in Ottawa, Canada. It helps understand the time series of data recorded and obtain final predictions from some statistical models. Correlation as well can be achieved based also on monthly categories.
Final project program DBA mitra Ruangguru X Studi Independen Bersertifikat Kampus Merdeka batch 2
Life expectancy data processing
Bayesian network analysis in R
Fun Project -: Playing with the statistics. Main Motto -: Trying to understand the statistics underneath the various most commonly used Machine Learning Models. I have used two library i.e. Stats-Model and Scikit-Learn.
Suicides in India 2019: A Case Study on the impact of the societal constructs on Mental Health
CLOPE Clustering Algorithm
MSC Project - Artifical Categorical Datasets
Multivariate analysis (MVA) of high dimensional heterogeneous data
Recurrent Neural Network to model Natural Language data with categorical output. Application: predicting side effects and effectiveness of a drug from user review data.
Galvanize Capstone Project: Gender vs Phobias - The objective of this capstone is to see if a person's gender has a statistical significance on their phobias. This is accomplished using Chi-Squared Hypothesis Testing.
complete case analysis drops the whole column if there are missing values, arbitrary value imputation in this we can use replace (mean or median) with -1 or 99.999, end of the distribution it replaces the values with "missing" term
The convenient loan experience, however, has to be balanced against the fact that the company does charge an origination fee. This is a case study for a company that wants a model developed which will help the agents to visit the right customer looking at the prediction that the model would make depending on the given data fields.
Prepare a classification model using Naive Bayes for salary data
Usage of AutoML library like H2O.ai to create predictive models and interpret them. Found a significant relation for each algorithm in the data. Created multivariate models.
Scikit-klearn compatible BinaryEncoder class capable of handling unseen categories in an automated fashion
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