In the Kaggle competition on "House Prices - Advanced Regression Techniques," I participated by analyzing the provided datasets, which included a training set and a test set. These datasets contained information related to various features of houses, such as the number of rooms, size, location, and other relevant attributes.
To tackle the competition, I began by performing a comprehensive analysis of both the training and test datasets. This involved exploring the data, understanding the distributions of variables, identifying missing values, and conducting data preprocessing tasks like data cleaning and feature engineering. By carefully examining the datasets, I gained insights into the relationships between different variables and their potential impact on house prices.
Using the analyzed data, I then applied advanced regression techniques to build predictive models that could accurately estimate house prices based on the given features. This involved selecting appropriate algorithms, tuning model parameters, and evaluating the performance of the models using various metrics such as root mean square error (RMSE) or R-squared. Through this analysis and modeling process, I aimed to create a robust and accurate prediction model to address the challenge posed by the Kaggle competition.