End-to-end machine learning regression model for predicting housing prices in Bengaluru, with Heroku deployment.

This model trains according to the data and makes a Polynomial Regression curve of degree 16. The model is regularized using Ridge regression. It also compares the predicted values with original outputs and for different alphas.

School exercise - Multivariate Statistical Methods subject

Approach to some basic Machine Learning Techniques.

Predictive Analytics for Real Estate Investment: A Regression Model Approach for Surprise Housing in the Australian Market using Regularization methods (Ridge and Lasso)

[2024 KGU CSE Advanced Capstone Design🌱] A service that utilizes AI and Seoul city data to provide customized convenience functions to users looking for popular destinations in Seoul. (TEAM3-피어나)

Exploring World Development Indicators: Identifying relationship between Health Indicators using Linear Regression & Classification of Income Group based on Health Indicators using Logistic Regression.

Regression models(lasso, ridge, DT) using NumPy.

In this project, I build 20+ models predicting Spotify song popularity. These include neural networks, Lasso and Ridge regression models. I also leverage OpenAI chat-completion API to engineer features from song lyrics.

Building Advanced regression models (Lasso and Ridge) for house price prediction in the Australian market

As part of the UCSanDiego online course "Machine Learning Fundamentals"

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

This repository contains projects completed during during my Udacity Data Science Nanodegree course.

Developed regularization and tree-based machine learning models to predict remission status in a cohort of 5059 patients. Elastic net and Random Forest models were compared on F1 scores accuracy, sensitivity, specificity, and AUC ROC.

Machine Learning Algorithms

A collection of multiple projects involving tasks such as classification, time series forecasting , regression etc. on a number of datasets using different machine learning algorithms such as random forest, SVM, Naive Bayes, Ensemble, perceptron etc in addition to data cleaning and preparation.

Sale trending

Regresión Lineal Múltiple con Modelos Regularizados (Lasso y Ridge) y Sin Regularizar

This is a sample ML Regression Project whose web part is created by Flask and it involves AWS Deployment