The goal of this project is to build a machine learning model that can predict the price of a house based on various features such as location, number of rooms, square footage, etc. The model will be trained on a dataset of historical house prices and related features.
The dataset will be preprocessed to clean and transform the data, handle missing values, and encode categorical features. Exploratory data analysis (EDA) will be performed to gain insights into the data, identify patterns, and visualize relationships between features and the target variable (house prices).
Various machine learning algorithms will be explored and compared, including regression algorithms such as linear regression, decision tree regression, random forest regression, and others.
The model will be evaluated using various metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared (R2) score. The model with the best performance will be selected and used to make predictions on a test dataset.
inspiration from kaggle