Customer Segmentation Using Unsupervised Machine Learning Algorithms

Outlier detection based on random forest models

METEOR: Outlier detection for longitudinal data using Dynamic Bayesian Networks

Micro-clusters-based Outlier Explanations for Data Streams

Scripts and notes related to the manuscript: Stuart KC & Cardilini APA 2021 Signatures of selection in a recent invasion reveal adaptive divergence in a highly vagile invasive species. Molecular Ecology 30(6):1419-1434, doi.org/10.1111/mec.15601 † joint first author ‡ joint last author.

Credit Card Fraud Detection Using Unsupervised Outlier Analysis

To identify customers who are more likely to default loan repayment

Data Preprocessing for Machine Learning

Online Retail Market Customer Analysis of over 0.5M+ points to find the target customers based on Recency, Frequency and Total Revenue contributed by a customer using K-Means & Agglomerative Clustering.

This repo includes regression analysis on AAPL stock. Basic statistics, visualization, outlier detection and regression modeling steps are included.

Contains R code used to analyse SNP data of the alcon blue butterfly and the marsh gentian

X Education Organization wants to identify if a customer registered on their website for enquiry is a potential customer or not. Using past data to build a machine learning algorithm

Business analysis and Business prediction with Pandas on tabular data using statistics and sklearn

Create a model to tracking AOV.

Perfomed exploratory data analysis (EDA) using Tableau and analysed 48,895 listings to map demand by borough (Manhattan 44%, Brooklyn 41%), decode booking behaviour (1–6 nights peak + 30-day spike), and identify the optimal price band ($40–$190) to guide acquisition and pricing strategy.

Vault of variety of topics taught for Rayan Contest

Scripts and notes related to the manuscript: Stuart KC et al. 2022. Historical museum samples enable the examination of divergent and parallel evolution during invasion. Molecular Ecology, 31(1): 1836-1852, doi.org/10.1111/mec.16353

Handling Missing Values and Outliers

Given the details of cell nuclei taken from breast mass, objective is to predict whether or not a patient has breast cancer using the Ensembling Techniques. This is a classification problem. The dataset consists of several predictor variables and one target variable, Diagnosis. The target variable has values 'Benign' and 'Malignant', where 'Beni…

Comparing the performances of simple linear, multiple linear, multi-layer perceptron and k-nearest neighbors regressions on abalone data to predict the age.