MachineLearning/main.py at main · Bananek96/MachineLearning · GitHub

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import pandas as pd
import matplotlib.pyplot as plt
# %matplotlib inline
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
import seaborn as sns

iris = pd.read_csv(r"D:\github\MachineLearning\data\iris.data",
                   header=None,
                   names=['petal length', 'petal width',
                          'sepal length', 'sepal width', 'species'])

iris.head()
iris.shape
iris.shape[0]
iris.shape[1]

x_min, x_max = iris['petal length'].min() - .5, iris['petal length'].max() + .5
y_min, y_max = iris['petal width'].min() - .5, iris['petal width'].max() + .5

colors = {'Iris-setosa': 'red', 'Iris-versicolor': 'blue', 'Iris-virginica': 'green'}

fig, ax = plt.subplots(figsize=(8, 6))

for key, group in iris.groupby(by='species'):
    plt.scatter(group['petal length'], group['petal width'],
                c=colors[key], label=key)

ax.legend()
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
ax.set_title("IRIS DATASET CATEGORIZED")

plt.show()

x_min, x_max = iris['sepal length'].min() - .5, iris['sepal length'].max() + .5
y_min, y_max = iris['sepal width'].min() - .5, iris['sepal width'].max() + .5

colors = {'Iris-setosa': 'red', 'Iris-versicolor': 'blue', 'Iris-virginica': 'green'}

fig, ax = plt.subplots(figsize=(8, 6))

for key, group in iris.groupby(by='species'):
    plt.scatter(group['sepal length'], group['sepal width'],
                c=colors[key], label=key)

ax.legend()
plt.xlabel('sepal length')
plt.ylabel('sepal width')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
ax.set_title("IRIS DATASET CATEGORIZED")

plt.show()

fig, ax = plt.subplots(2, 2, figsize=(10, 6))

plt_position = 1

feature_x = 'petal width'

for feature_y in iris.columns[:4]:

    plt.subplot(2, 2, plt_position)

    for species, color in colors.items():

        plt.scatter(iris.loc[iris['species'] == species, feature_x],
                    iris.loc[iris['species'] == species, feature_y],
                    label=species,
                    alpha=0.45,  # transparency
                    color=color)

    plt.xlabel(feature_x)
    plt.ylabel(feature_y)
    plt.legend()
    plt_position += 1

plt.show()

pd.plotting.scatter_matrix(iris, figsize=(8, 8),
                           color=iris['species'].apply(lambda x: colors[x]));
plt.show()

sns.set()
sns.pairplot(iris, hue="species")