This repository contains examples of best practices for solving Machine Learning Regression problems throughout the entire machine learning product life cycle, from data analysis to placing the final machine learning model in production. The code is organized into Jupyter notebooks, with each notebook focusing on a specific regression technique or dataset.
My goal is to provide a clear and concise set of examples that can help you learn how to effectively tackle regression problems, and to guide you through each step of the process, from data exploration and model training to deployment and monitoring. I believe that understanding the entire process of developing a machine learning model, from data cleaning and preprocessing to deploying and maintaining the model in a production environment, is crucial for creating successful machine learning products.
In addition to the notebooks, I've also included a set of scripts and utilities that can help you automate common tasks in the machine learning product life cycle. These tools are designed to help you streamline your workflow and save time, allowing you to focus on building better models.
Whether you're just getting started with Machine Learning or you're a seasoned practitioner, I believe you'll find something of value in this repository. I encourage you to explore the notebooks, try out the code for yourself, and let me know if you have any questions or feedback.
The dataset used in this project is the "Diamonds" dataset from OpenML. This dataset contains information about over 53,000 diamonds, including their carat weight, cut, color, clarity, depth, table, price, and dimensions such as length, width, and depth.
The diamonds dataset is a valuable resource for data exploration and analysis in the field of gemology and jewelry. With its large size and rich feature set, it provides ample opportunity for studying relationships between various diamond characteristics and their market value. The inclusion of multiple variables such as cut, color, and clarity allows for sophisticated analyses, including regression modeling and predictive analytics.
The first step in solving a machine learning regression problem is to preprocess the dataset. Using the Pandas library, we load the "diamonds.csv" file and then analyze it to determine which features are numerical, which are categorical, and which is the target variable. It's essential to identify and handle any missing values or NaNs in the dataset, either by replacing or removing them. In our case, since there are no NaNs, we can go further.
The next critical step is to transform categorical features into numerical ones using OneHotEncoder and scale numerical features using Standarscaler. After completing these preprocessing steps, we can save the datasets ready for use by the Sklearn machine learning libraries.
Once we have preprocessed our diamomnds dataset, the next step is to train and test various regression models with Sklearn. By loading the dataset in the X and y format, we can create a training set and a testing set, ensuring that our models can generalize to unseen data.
We can then instantiate multiple regression models and fit them to our training set. Some of the models we can use include Linear Regression, Decision Trees, Random Forests and MLPRegressor.
After training the models, we can evaluate their performances on the testing set by calculating metrics such as R2, MAE and MQE score. We can then visualize the performance of different models by plotting histograms.
Through this process, we can identify the most promising machine learning models for our dataset and select the one that performs best in terms of R2, MAE, and MQE. By selecting the best-performing model, we can build a reliable and effective machine learning regressor that can make predictions in production environments.
Once we have identified the most promising model, it's time to integrate it into a pipeline to achieve two results:
- Transform the input data that we want to predict using our machine learning model.
- Perform a Grid Search process to search for the best hyperparameters.
By integrating the model into a pipeline, we can streamline the process of making predictions on new data and optimize the model's performance through hyperparameter tuning.
The Grid Search process involves selecting a set of hyperparameters and exhaustively searching for the combination of hyperparameters that result in the best performance of the model. This process can be computationally expensive, but Sklearn provides efficient tools to perform Grid Search on a range of hyperparameters.
Once we have completed this process, we can compare the performance of the base model and the optimized model to see if the hyperparameter tuning has improved the model's performance. This comparison can be done by evaluating metrics such as R2, MAE and MQE.
In summary, model optimization involves integrating the most promising model into a pipeline, performing hyperparameter tuning through Grid Search, and comparing the performance of the base and optimized models. This process can significantly improve the performance of the machine learning model, making it more accurate and suitable for production-level predictions.
In the final step, we reconstruct the pipeline of our model by including the necessary input data transformations and the previously optimized model with the best hyperparameters. We can then train the pipeline one last time on the X and y data of our diamonds dataset and save it to disk using the joblib library.
At this point, our regression predictive model is ready to be integrated into any production application. The pipeline can be easily deployed to a cloud environment, such as Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure, or to an on-premises server. Once deployed, the pipeline can receive input data and provide predictions in real-time, enabling us to make accurate decisions and automate processes based on the predictions made by our machine learning model.
You can use these four steps outlined in the Jupyter Notebook worksheets for any regression work. Of course, these should be understood as a solid starting point that needs to be adapted to the needs of your project. However, from personal experience, they can significantly accelerate the workflow for the entire life cycle of the predictive machine learning model.
Happy regressioning!