Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. To determine how much a house is worth based on the property's location and characteristics.

Built a prediction model using both ridge and lasso advanced regression methods to predict house prices.

This repository contains a machine learning algorithm that trains a Random Forest model to predict house prices based on specified features of the homes, using the California Housing Dataset. The dataset used to train and evaluate the Random Forest model to predict median housing prices.

🏡 Predict house prices using machine learning (Linear, Random Forest) with full EDA, preprocessing and model evaluation.

A full-fledged approach to make predictions about the future sale prices of houses.This approach consists in: Descriptive statistics about the data, Data cleaning and pre-processing, Defining a modeling approach to the problem, Build such a statistical model and Validate the outcome of the model.

This repository contains a machine learning algorithm that trains a model to predict house prices based on specified features of the homes, using the California Housing Dataset.

Google colab notebook for the Kaggle Home prices submission

Udacity Machine Learning Nanodegree Capstone Project (Kaggle's House Prices: Advanced Regression Techniques)

No description provided.

This project is a complete practical implementation of the Machine Learning Lifecycle (ML Lifecycle), combining supervised modeling with modern MLOps practices.

This repository contains machine learning tasks for TechnoHacks Internship Program 2023

flask, machine-learning, house-prices, morocco.

No description provided.

Comprehensive exploratory data analysis of Kaggle's House Prices dataset using Python, pandas, seaborn, and matplotlib. Uncovers pricing patterns, feature relationships, and data insights through visualizations.

eda-house-prices-prediction

Predict house prices using machine learning