Dispersión y Correlación
# Tratamiento de datos
# ==============================================================================
import pandas as pd
import numpy as np
# Gráficos
# ==============================================================================
import matplotlib.pyplot as plt
from matplotlib import style
import seaborn as sns
# Preprocesado y modelado
# ==============================================================================
from scipy.stats import pearsonr
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
import statsmodels.api as sm
import statsmodels.formula.api as smf
# Configuración matplotlib
# ==============================================================================
plt.rcParams['image.cmap'] = "bwr"
#plt.rcParams['figure.dpi'] = "100"
plt.rcParams['savefig.bbox'] = "tight"
style.use('ggplot') or plt.style.use('ggplot')
# Configuración warnings
# ==============================================================================
import warnings
warnings.filterwarnings('ignore')
iris = sns.load_dataset('iris')
iris
sepal_lengthfloat64
4.3 - 7.9
sepal_widthfloat64
2.0 - 4.4
0
5.1
3.5
1
4.9
3.0
2
4.7
3.2
3
4.6
3.1
4
5.0
3.6
5
5.4
3.9
6
4.6
3.4
7
5.0
3.4
8
4.4
2.9
9
4.9
3.1
Vamos a realizar el gráfico de dispersión entre sépalo y pétalo de la base de datos
fig, ax = plt.subplots(1, 1, figsize=(6,4))
ax.set_title('Correlación de longitud de pétalos y Sépalos')
ax.scatter(x=iris.sepal_length, y=iris.petal_length, alpha= 0.8)
ax.set_xlabel('Sépalo (l)')
ax.set_ylabel('Pétalo (l)')
plt.tight_layout();
Calculamos el coeficiente de correlación de Pearson
print('Correlación Pearson: ', iris['sepal_length'].corr(iris['petal_length'], method='pearson'))
Correlación Pearson: 0.8717537758865831
# División de los datos en train y test
# ==============================================================================
X = iris[['sepal_length']]
y = iris['petal_length']
X_train, X_test, y_train, y_test = train_test_split(
X.values.reshape(-1,1),
y.values.reshape(-1,1),
train_size = 0.8,
random_state = 1234,
shuffle = True
)
# Creación del modelo
# ==============================================================================
modelo = LinearRegression()
modelo.fit(X = X_train.reshape(-1, 1), y = y_train)
# Información del modelo
# ==============================================================================
print("Intercepto (b): ", modelo.intercept_)
print("Pendiente (m): ", list(zip(X.columns, modelo.coef_.flatten(), )))
print("Coeficiente de determinación R^2:", modelo.score(X, y))
Intercepto (b): [-7.04315163]
Pendiente (m): [('sepal_length', 1.8469469867521675)]
Coeficiente de determinación R^2: 0.7599004590492431
plt.style.use('seaborn-v0_8-ticks')
fig = plt.figure()
#plt.title('Apr')
plt.scatter(x=iris.sepal_length, y=iris.petal_length)
plt.plot(X, modelo.coef_.flatten()*X + modelo.intercept_, color='blue' )
plt.grid()
fig.show()