Many people tend to say that machine learning models are black boxes because they can make good predictions but cannot understand the logic behind those predictions.

Images from Unsplash Disclaimer: This article is my learning note from the courses I took from Kaggle. In this course, we will learn on how to: determine which features are the most important with mutual information invent new features in several real-world problem domains encode high-cardinality categoricals with a target encoding create segmentation features with k-means clustering decompose a dataset’s variation into features with principal component analysis 1. Introduction The reason we perform feature engineering is we want to make our data more suited to the problem at hand....

Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. It is a key part of data science, and it can be deeply frustrating. What should we do to the missing values? Why the dates are not in the correct format? How to clean up inconsistent data entry? These are some of the problems that we will learn to tackle in this course.

The most common application of machine learning in the real world is forecasting. For example, businesses forecasting product demand, governments forecasting economic growth and meteorologists forecasting the weather.

In this course, we will explore on data visualization using seaborn, a Python package to visualize data with a variety of plot types. The package is powerful yet easy to use, check out the below images on the plot types that seaborn is able to generate

In this course, we will explore on the Python pandas module which is a popular library for data analysis. With pandas, we can use it to create data and also work or manipulate the existing data.

In this project, I make use of Streamlit, which is an open-source Python library that allows us to build and deploy powerful apps with speed and efficiency. It also offers a cloud deployment feature for you to host the Streamlit app that you created online publicly through Streamlit Community Cloud.

In econometrics, the ordinary least square (OLS) model is widely used to estimate the parameter of a linear regression model.

Google Sheet IMPORTXML() function is a wonderful tool to allow you to scrape for website information by just identifying the corresponding Xpath. But it has a limit, when you have hundreds or thousands of data items to be gathered, your Sheet’s cell will get stuck at endless loading.

Geospatial analysis is the gathering, display and manipulation of imagery, GPS, satellite photography and historical data, described explicitly in terms of geographic coordinates. This course will learn on methods to visualize geospatial data and perform some analysis concerning a particular geographic location or region.