Plotly is an open-source Python library for creating charts. You can use the features available in Plotly to set multiple figures as subplots. In this tutorial, we will use plotly.graph_objects to generate the figures. It contains a lot of methods to customize chart and render a chart into HTML format. For example, plotly.subplots() method can be used to add subplots. Follow the steps given below to create subplots with Plotly express. Step 1 Import plotly.graphs_objs module and alias as go. import plotly.graphs_objs as go Step 2 Import make_subplots to create subplots. from plotly.subplots import make_subplots Step 3 Create subplots ... Read More

Plotly supports two different libraries "Plotly graphs in a Dash app" and "Plotly Graph objects and Plotly Express". Dash is a Python framework and it is used to create interactive web-based dashboard applications. For example, dash library adds all the required libraries to web-based dashboard applications. Import dash core components and HTML components. Add plotly.express method to generate graphs. Use the Dcc.Graph() method to set the style for height and width coordinates. Follow the steps given below to open a URL by clicking a data point. Step 1 Import the Dash library. import dash Step 2 Import the Dash ... Read More

Linear classification is one of the simplest machine learning problems. To implement linear classification, we will be using sklearn’s SGD (Stochastic Gradient Descent) classifier to predict the Iris flower species. Steps You can follow the below given steps to implement linear classification with Python Scikit-learn − Step 1 − First import the necessary packages scikit-learn, NumPy, and matplotlib Step 2 − Load the dataset and build a training and testing dataset out of it. Step 3 − Plot the training instances using matplotlib. Although this step is optional, it is good practice to plot the instances for more clarity. Step 4 − Create ... Read More

Iris, a multivariate flower dataset, is one of the most useful Pyhton scikit-learn datasets. It has 3 classes of 50 instances each and contains the measurements of the sepal and petal parts of three Iris species namely Iris setosa, Iris virginica, and Iris versicolor. Along with that Iris dataset contains 50 instances from each of these three species and consists of four features namely sepal_length (cm), sepal_width (cm), petal_length (cm), petal_width (cm). We can use Principal Component Analysis (PCA) to transform IRIS dataset into a new feature space with 2 features. Steps We can follow the below given steps to ... Read More

Sklearn DIGITS dataset has 64 features as each image of the digit is of size 8 by 8 pixels. We can use Principal Component Analysis (PCA) to transform Scikit-learn DIGITS dataset into new feature space with 2 features. Transforming 64 features dataset to 2-feature dataset will be a big reduction in the size of data and we’ll lose some useful information. It will also impact the classification accuracy of ML model. Steps to Transform DIGITS Dataset to 2-feature Dataset We can follow the below given steps to transform DIGITS dataset to 2-feature dataset using PCA − First, import the ... Read More

Dimensionality reduction, an unsupervised machine learning method is used to reduce the number of feature variables for each data sample selecting set of principal features. Principal Component Analysis (PCA) is one of the popular algorithms for dimensionality reduction available in Sklearn. In this tutorial, we perform dimensionality reduction using principal component analysis and incremental principal component analysis using Python Scikit-learn (Sklearn). Using Principal Component Analysis (PCA) PCA is a statistical method that linearly project the data into new feature space by analyzing the features of original dataset. The main concept behind PCA is to select the “principal” characteristics of the ... Read More

Random projection is a dimensionality reduction and data visualization method to simplify the complexity of highly dimensional data. It is basically applied to the data where other dimensionality reduction techniques such as Principal Component Analysis (PCA) can not do the justice to data. Python Scikit-learn provides a module named sklearn.random_projection that implements a computationally efficient way to reduce the data dimensionality. It implements the following two types of an unstructured random matrix − Gaussian Random Matrix Sparse Random Matrix Implementing Gaussian Random Projection For implementing Gaussian random matrix, random_projection module uses GaussianRandomProjection() function which reduces the dimensionality by ... Read More

Naïve Bayes classification, based on the Bayes theorem of probability, is the process of predicting the category from unknown data sets. Scikit-learn has three Naïve Bayes models namely, Gaussian Naïve Bayes Bernoulli Naïve Bayes Multinomial Naïve Bayes In this tutorial, we will learn Gaussian Naïve Bayes and Bernoulli Naïve Bayes classifiers using Python Scikit-learn (Sklearn). Gaussian Naïve Bayes Classifier Gaussian naïve bayes classifier is based on a continuous distribution characterized by mean and variance. With the help of an example, let’s see how we can use the Scikit-Learn Python ML library to build a Gaussian Naïve Bayes classifier. ... Read More

Random forest is a supervised machine learning algorithm that is used for classification, regression, and other tasks by creating decision trees on data samples. After creating the decision trees, a random forest classifier collects the prediction from each of them and selects the best solution by means of voting. One of the best advantages of a random forest classifier is that it reduces overfitting by averaging the result. That is the reason we get better results as compared to a single decision tree. Steps to Create Random Forest Classifier We can follow the below steps to create a random forest ... Read More

With the help of the Scikit-learn python library, we can get the dictionary-like objects of a dataset. Some of the interesting attributes of dictionary-like objects are as follows − data − It represents the data to learn. target − It represents the regression target. DESCR − The description of the dataset. target_names − It gives the target names on of the dataset. feature_names − It gives the feature names from the dataset. Example 1 In the example below we use the California Housing dataset to get its dictionary-like objects. # Import necessary libraries import sklearn import pandas as ... Read More