The study utilises the California housing prices dataset and employs a variety of machine learning techniques such as data preprocessing, feature engineering, model training and hyperparameter tuning. The end result showcase the effectiveness of the Random Forest algorithm in comparison to Linear Regression, providing valuable insight into the effectiveness of machine learning in order to garner accurate house price predictions.linear regression as a supervised machine learning algorithm used for predicting a linear trend between a dependent variable and one or more independent variables. It assumes a consistent correlation and aims to find a best fit line minimizing the difference between predicted and actual values. The algorithm originates from statistics, where a literal line is drawn to depict the relationship mathematically. Linear regression finds applications in various domains like stock market analysis, price prediction, medical research, and sales forecasting, making it a reliable method for predicting future outcomes in diverse fields.The name “Random Forest” derives from its very consisting of numerous interconnected decision trees resembling that of a forest. The Random forest algorithm is an ensemble machine learning algorithm that operates through the combination of multiple independent decision trees brought together for the purpose of increasing the accuracy of predictions. [IF ANYONE LIKES TO ADD A NEW MODEL OR MAKE SOME CHANGES TO MAKE THE MODEL BETTER WOULD BE HELPFUL, THANKS].
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View Code? Open in Web Editor NEWPredicting Canada house prices using logistic regression and random forest model and making a comparison between them.