Machine learning and data science are two areas of computer science that are used to analyze, understand, and make predictions about data. One of the most popular techniques for machine learning and data science is Gradient Boosting Machine (GBM). GBM is a type of ensemble method that combines multiple decision trees to make predictions about data.
The Ames Housing dataset from UCI (University of California, Irvine) is a collection of data that is used to predict the sale prices of houses in Ames, Iowa. The dataset contains a wide range of features that describe the properties of the houses, such as the number of bedrooms, bathrooms, square footage, etc. The goal of this dataset is to train a model that can accurately predict the sale prices of houses based on these features.
The first step is to load the data into Python. The UCI dataset contains information about the houses and can be downloaded from the UCI website. Once the data is loaded, it’s important to make sure that the variables are in the correct format, such as numeric for continuous variables.
The next step is to prepare the data for the model. This includes cleaning the data, handling missing values and splitting the data into a training set and a test set. The training set is used to train the model, while the test set is used to evaluate the performance of the model.
The next step is to fit the GBM model to the data. This involves specifying the dependent and independent variables and selecting the appropriate model parameters, such as the learning rate, the number of trees, and the depth of the trees. It’s important to evaluate the performance of the model using the test set and adjust the parameters of the model if necessary.
Once the model is fitted, the next step is to make predictions. The model can be used to make predictions on new data, and it’s important to remember that the model is only as good as the data it was trained on, and it’s important to keep updating the model with new data and retraining it as necessary.
In conclusion, Machine Learning and Data Science in Python using GBM with Ames Housing Dataset from UCI is a multi-step process that includes loading the data, preparing the data, fitting the GBM model, evaluating its performance, and using the model to make predictions. GBM is a powerful model that is widely used in Machine Learning and Data Science, and it’s particularly useful for regression problems such as predicting sale prices of houses. The Ames Housing dataset is a valuable resource for researchers and practitioners who want to gain experience in machine learning and data science using GBM. It’s important to note that GBM models can be used to forecast future values but they might not generalize well to unseen data, and they might also be prone to overfitting if the parameters are not chosen correctly. Therefore, it’s important to evaluate the performance of the model using different metrics such as Mean Absolute Error, Mean Absolute Percentage Error and visualize the results to have a better understanding of the model. Furthermore, it’s important to compare the performance of the GBM model with other models such as Random Forest and Linear Regression and select the model that performs best based on the evaluation metrics. Additionally, it’s important to perform feature engineering and feature selection to select the most important features that affect the sale prices of houses. Finally, it’s important to keep in mind that machine learning and data science is a process that requires constant learning and experimenting with different models and techniques to achieve the best results.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Machine Learning and Data Science in Python using GBM with Ames Housing Dataset .
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R: