Linear regression is a data analysis technique that uses linear functions to predict data. Although the linear regression model is relatively simple, it is a mature statistical technique.
When a model has poor performance, it cannot predict the data accurately. The main cause may be overfitting or underfitting. If it is a case of overfitting, we can use regularization to solve model overfitting.
Overfitting and underfitting are the root causes for poor model accuracy. Only by being able to determine whether a model is overfitting or underfitting can we take the correct approach to improve the performance of the model.
The confusion matrix is a tool used to measure the performances of models. This allows data scientists to analyze and optimize models. Therefore, when learning machine learning, we must learn to use confusion matrix. In addition, this article will also introduce accuracy, recall, precision, and F1 score.
Candlestick charts are often used to show the trend of stock prices. A single candlestick can indicate four prices, which are the highest price, the opening price, the closing price, and the lowest price.
When analyzing data, regression lines can help us understand the trend of data. In this article, we will introduce how to use Seaborn and Plotly Express to plot regression lines.
Choropleth Map displays spatial variations of quantities of data on a map. In this article, we will introduce how to use Python’s Plotly Express package to plot a choropleth map.
Box Plots and Violin Plots are statistical charts that can well represent the distribution of data. In this article, we will introduce how to use Python’s Matplotlib, Seaborn, and Plotly Express packages to draw box plots and violin plots.