Scatter plot for binary class dataset with two features in python I have my dataset that has multiple features and based on that the dependent variable is defined to be 0 or 1.
I want to get a scatter plot such that all my positive examples are marked with 'o' and negative ones with 'x'.
I am using python and here is the code for the beginning.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('/home/Dittu/Desktop/Project/creditcard.csv')


now I know how to make scatter plots for two different classes.

fig = plt.figure()
ax1 = fig.add_subplot(111)

ax1.scatter(x[:4], y[:4], s=10, c='b', marker="s", label='first')
ax1.scatter(x[40:],y[40:], s=10, c='r', marker="o", label='second')
plt.show()


but how to segregate both class of examples and the plot them or plot them with distinct marks without separating?
One approach is to plot the data as a scatter plot with a low alpha, so you can see the individual points as well as a rough measure of density.

from sklearn.datasets import load_iris
iris = load_iris()
features = iris.data.T

plt.scatter(features[0], features[1], alpha=0.2,
            s=100*features[3], c=iris.target, cmap='viridis')
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1]);




We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data: 


the (x, y) location of each point corresponds to the sepal length and width,
the size of the point is related to the petal width, and 
the color is related to the particular species of flower, i.e the Target Variable...


Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data.