Project using multiple linear regression to model prices of houses in Ames, IA.

This is a house price prediction study which utilized Exploratory Data Analysis, Dealing with Missing Values, Linear Regression with LASSO and Ridge regularization to predict house prices in the Ames Housing Data Set

MSDS 410 Data Modeling for Supervised Learning (R)

my attempt to train a model on Ames housing dataset.

Prediction of house sale prices based on the Ames Housing dataset

This repository contains my assignments and projects related to deep learning, including implementations of fundamental concepts such as Linear Regression, Gradient Descent, Multi-Layer Perceptron (MLP), and more. Each section includes code, explanations, and relevant documentation. The goal of this repository is to showcase my learning journey.

🏡 Built linear regression model to predict house prices in Ames dataset with applied tools such as scikit-learn pipeline

Ames dataset: House Price prediction for kaggle competition (advanced regression, supervised ML)

Data-driven analysis of the Ames Housing Dataset, combining advanced feature engineering and Stochastic Gradient Descent (SGD) regression model tuning. This repository showcases predictive modeling, hyperparameter optimization, and actionable insights for real estate analytics.

A collection of small data science projects to predict house pricing for two different datasets

Predicting sales prices for Ames housing dataset

Predicting house prices using advanced regression techniques

Prediction of Sales Prices of Houses

This repo contains my attempts at developing machine learning algorithms in python to predict the house prices in Ames, Iowa as part of the following Kaggle competition: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques

In depth EDA on Ames Housing dataset from Kaggle and Regression model to predict house prices.