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eda-process-with-feature-selection-boston-housing-dataset

EDA and Feature Selection on Boston Housing Dataset This project demonstrates the process of exploratory data analysis (EDA) and feature selection on the Boston Housing Dataset. It includes steps for data loading, exploration, handling missing values, outlier treatment, univariate and bivariate analysis, and using linear regression for feature

feature_selection_kbest-_lasso-_and_decision_trees.ipynb

gradientanalysis

Simple gradient boosting on the Iris dataset.

hult_la_airbnb_regression

We are trying to predict the price of properties in LA. I did it with a team of two other people for a final assignment in my Business Analytics class.

logistic-regression-on-breast-cancer-dataset

nlp

Simple NLP exploration

restaurantcompliance

A predictive model using logistic regression to analyze and improve restaurant compliance with health regulations in New York City. This project leverages the DOHMH New York City Restaurant Inspection Results dataset to help restaurant owners and the Department of Health and Mental Hygiene enhance public health and safety.

titanicdataset_naivebayes_decisiontrees

A comprehensive analysis of the Titanic dataset using Logistic Regression, Naive Bayes, and Decision Tree models to predict passenger survival. This project includes data preprocessing, visualization, and model evaluation.