Build A Machine Learning Web App From Scratch

This paragraph introduces Patrick from the Python Engineer channel, who will guide viewers through the process of building a machine learning web application from scratch. The focus is on using real-world data from the Stack Overflow Developer Survey and cleaning, analyzing this data. Patrick explains that they will build a machine learning model using the scikit-learn library and later create a web application in Python with the Streamlit library, which is beginner-friendly and easy to use. The video showcases the final application, a software developer salary prediction app, which predicts salaries based on user-inputted information such as country, education level, and years of experience. Patrick emphasizes the app's interactivity and the underlying machine learning model's predictive capabilities based on the provided data points.

In this paragraph, Patrick delves into the exploration and cleaning of the Stack Overflow data. He demonstrates the vast amount of information available in the dataset, highlighting the number of data points and the variety of columns. Patrick discusses the importance of selecting the relevant columns for the model and cleaning the data by removing unnecessary information and missing values. He also explains the process of setting up a virtual environment, installing necessary packages like Streamlit, and preparing for the project. The focus is on creating a streamlined dataset that retains only the essential features for the machine learning model.

Patrick continues the process of cleaning and preparing the data for modeling. He explains how to remove small countries with few data points to prevent potential confusion for the model. Patrick also discusses the importance of keeping only full-time employed data points and dropping unnecessary columns. He then focuses on cleaning the country data by eliminating countries with fewer samples and combines them into a new 'other' category. The paragraph emphasizes the need to inspect the salary range and clean the 'years code pro' data by converting string values to floats and applying cutoffs. Patrick also discusses the process of cleaning the education level data by defining and applying functions to categorize the different education levels.

This paragraph covers the training and evaluation of machine learning models using the cleaned data. Patrick explains the process of splitting the data into features and labels, with the salary as the label and other attributes as features. He introduces the concept of regression problems and the use of the scikit-learn library for this task. Patrick walks through training different models, including linear regression, decision tree regressor, and random forest regressor. He discusses evaluating the models using mean squared error and explores the use of grid search with cross-validation to find the best model and parameters. The paragraph highlights the iterative process of testing different models and parameters to achieve the best predictive performance.

Patrick explains the importance of saving the trained model for future use and demonstrates how to save the model, label encoders, and other necessary components using pickle. He details the process of creating a dictionary of the model components, opening a binary file, and using the 'pickle dump' function to save the data. Patrick also shows how to load the saved model back into the environment using 'pickle load', ensuring that the loaded model functions as expected. This step is crucial for deploying the model in a web application and making predictions based on new input data.

In this paragraph, Patrick begins building the 'Predict' page of the Streamlit app. He outlines the process of importing necessary libraries, defining functions to load the model, and creating a user interface with select boxes for countries and education levels, as well as a slider for years of experience. Patrick demonstrates how to use Streamlit's widgets to create an interactive prediction form and explains the process of defining a function to display the prediction page. He emphasizes the ease of use and beginner-friendliness of the Streamlit library in creating web applications for machine learning tasks.

Patrick focuses on implementing data visualization in the 'Explore' page of the Streamlit app. He explains how to load and clean the data similarly to the previous steps and then create different types of plots, such as a pie chart for the number of data points from different countries, a bar chart for mean salaries based on country, and a line chart for mean salary based on years of experience. Patrick demonstrates the use of Matplotlib for plotting and Streamlit's built-in chart functions for displaying the charts within the app. The paragraph highlights the importance of visualizing data to gain insights and better understand the relationships between different variables.

In the final paragraph, Patrick wraps up the tutorial by showcasing the complete machine learning web application. He explains how to add a sidebar for navigation between the 'Predict' and 'Explore' pages and how to finalize the 'Explore' page with data visualizations. Patrick encourages viewers to experiment with the app and model, suggesting potential improvements such as incorporating additional data points like age. He concludes the tutorial by expressing hope that viewers have learned how to build a machine learning web app from scratch using real data and looks forward to future tutorials.