Fish data linear regression

regression.py

# **Regresión Lineal Multivariable**
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
#from sklearn.preprocessing import PolynomialFeatures
Ahora, la descripción de la venta de coches se realizó por medio del número de la semana y el día de la semana (dos variables)
**Set de datos**

data = pd.read_csv('Fish.csv')
train_size = int(data.shape[0]*0.8)
train_data = data.iloc[:train_size]
test_data  = data.iloc[train_size:]
X = train_data[['Length1',	'Length2',	'Length3',	'Height',	'Width']]
Y = train_data[['Weight']]
data = pd.read_csv('Fish.csv')
num_rows = data.shape[0]
random_indices = np.random.permutation(num_rows)
data_shuffled = data.iloc[random_indices]
data_shuffled = data_shuffled.reset_index(drop=True)

train_size = int(data_shuffled.shape[0]*0.8)
train_data = data_shuffled.iloc[:train_size]
test_data  = data_shuffled.iloc[train_size:]
X = train_data[['Length1',	'Length2',	'Length3',	'Height',	'Width']]
Y = train_data[['Weight']]
**Modelo**
model = LinearRegression()
**Entrenamiento**
model.fit(X, Y)
**Evaluación**
r_sq = model.score(X, Y)

print('Coeficiente de determinación:', r_sq)

print('Intercepción:', model.intercept_)

print('Pendiente:', model.coef_)
# **Predicciones**
Xt = test_data[['Length1',	'Length2',	'Length3',	'Height',	'Width']]
y_pred = model.predict(Xt)
print('Predicción:', y_pred)
y_pred = model.intercept_ + np.sum(model.coef_ * X, axis=1)
print('Predicción = interception + pendiente*X:', y_pred)
# **Predicción con un dato nuevo**
x_new = np.array([[23.9,	26.5,	31.1,	12.3778,	4.6961]])
y_new = model.predict(x_new)
print(y_new)
print('Error cuadrático medio: %.2f'
      % mean_squared_error(Y['Weight'], y_pred))