SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Playground Series Season 3 Episode 6 Dataset is a regression modeling situation where we are trying to predict the value of a continuous variable.
INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions to give the Kaggle community a variety of reasonably lightweight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science. The dataset for this competition was generated from a deep learning model trained on the Paris Housing Price Prediction dataset. Feature distributions are close to but different from the original.
ANALYSIS: The performance of the preliminary TensorFlow models achieved an RMSE benchmark of 165,145. When we tested the final model using the test dataset, the model achieved an RMSE score of 269,283.
CONCLUSION: In this iteration, TensorFlow appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Playground Series Season 3, Episode 6
Dataset ML Model: Regression with numerical features
Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e6
One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e6/leaderboard
The HTML formatted report can be found here on GitHub.