Built a prediction model using both ridge and lasso advanced regression methods to predict house prices.
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.
Google colab notebook for the Kaggle Home prices submission
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.
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.
Udacity Machine Learning Nanodegree Capstone Project (Kaggle's House Prices: Advanced Regression Techniques)
This repository contains machine learning tasks for TechnoHacks Internship Program 2023
flask, machine-learning, house-prices, morocco.
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