Preprocessing data and building some models to predict passengers survival chances.

Summary of Assignment Two from the Second semester of the MSc in Data Analytics program. This repository contains the CA2 assignment guidelines from the college and my submission. To see all original commits and progress, please visit the original repository using the link below.

This project explores the optimal combination of Bag-of-Words and TF-IDF vectorization with Naive Bayes and SVM for sentiment analysis. It evaluates performance using accuracy, precision, recall, and F1-score, addressing ethical concerns like data privacy and bias to improve sentiment classification in real-world applications.

Analyze data of US work Visa applicants, build a predictive model to facilitate approvals, and based on factors that significantly influence visa status, recommend profiles for whom visa should be certified or denied.

This Machine Learning model deals with Exploratory Data Analysis (EDA) for the prediction of housing prices in California. Custom transformation is performed for explicit cleaning of the data and feature extractions. Different regressor models are applied and evaluated using cross-validation scores, and are fine-tuned using Grid Search.

predicts length-of-stay of hospital inpatients