After a great deal of hard work and staying behind the scenes for quite a while, we’re excited to now offer our expertise through a collaborative platform, the “Data Science Hub” on Patreon (https://www.patreon.com/TheDataScienceHub). This hub is our way of...
Let’s use the classic Iris dataset as an example (fetchable from the Seaborn plotting library) and limit it to just 15 rows for simplicity: import seaborn as snsiris_data = sns.load_dataset(‘iris’) df = iris_data.head(5).copy() df = pd.concat([df, iris_data..iloc[50:5...
Weka provides a handy tool to load CSV files and save them in ARFF. You only need to do this once with your dataset. Using the steps below you can convert your dataset from CSV format to ARFF format and use it with the Weka workbench. If you do not have a CSV file handy, you can...
Create the following density on the sepal_length of iris dataset on your Jupyter Notebook. import seaborn as sns df = sns.load_dataset('iris') Show Solution Iris Histograms 9. What next Congratulations if you were able to reproduce the plot. You might be interested in the matplotlib tutori...
If you are anR user, you may have used the RStudio Package Manager to install, update, or remove packages. Homebrew is a package manager designed for Mac that is useful to install. You will find that you can utilize Homebrew for data science as it makes it a lot easier to install addi...
Scikit-learnprovides several built-in datasets for learning purposes. One popular dataset is the “Iris” dataset, which contains data about different species of iris flowers. To load theIrisdataset, use the following code: from sklearn.datasets import load_iris ...
At the high-level, the way to build this plot is: (1) Load data and build the model (2) Calculate the Shapley values using the shapley function (introduced in 21a) (3) Create the Shapley summary plot For R2024a and higher: R2024a has new functionality to easily create shapley summary...
from sklearn.datasets import load_iris from numpy import reshape import seaborn as sns import pandas as pd tsne = load_iris() x = tsne.data y = tsne.target tsne = TSNE () z = tsne.fit_transform (x) scikit = pd.DataFrame()
In this case, an R dataframe is converted into a Python Pandas Dataframe which is ideally the object type that the heatmap function would take in to plot the heatmap. Seaborn Pairplot in R #building a seaborn pairplot using pairplot() sns$pairplot(r_to_py(iris), hue = 'Spec...
(__name__) # Load and prepare the model iris = load_iris() X, y = iris.data, iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = RandomForestClassifier(random_state=42) model.fit(X_train, y_train) @app.route('...