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Implementation of two major ensemble learning methodologies, Bagging and Stacking, over the tasks of classification and regression. Also, compared the results of Random Forests with multiple Boosting Techniques.
My capstone project for the Institute of Data investigates the metadata of songs on Spotify, building a predictive model to project a track's popularity on Spotify using its audio features.
This project aims to predict the Taxi-trip duration within NYC based on several factors as predictors. Various combinations of relevant features are explored as iterations. After analysing the dataset, important and necessary features are selected. Several regression models are implemented & evaluated based on R2 & RMSE, & predictions visualised
Application of learnings in the Machine Learning course , this project mainly gives first hand idea of elaborative exploratory data analysis performed on data sets and various advanced regressions models are used for predicting House Prices.
A Novel Approach for Alzheimer's Classification Utilizing Ensemble Learning on Pre-trained Neural Networks Fine-tuned on Pre-processed and Augmented Alzheimer's Dataset
Visa approval process by leveraging machine learning on OFLC's extensive dataset, aiming to recommend suitable candidate profiles for certification or denial based on crucial drivers.
This project aims to enhance the accuracy and efficiency of stock market predictions by employing a sophisticated machine learning methodology. This project leverages the power of PySpark, a robust framework for distributed data processing, to handle large datasets and perform complex computations.
In this problem statement, a sequence of genetic mutations and clinical evidences, i.e. descriptive texts as recorded by domain experts are used to classify the mutations to conclusive categories, to be used for diagnosis of the patient.