Matplotlib.pyplot Error "ValueError: x and y must be the same size" Regression Analytic-CodePudding

I'm new to Data Analytic I've been working on the Regression Analysis Coding using matplotlib, numpy and pandas. However, I got some trouble with and try my best to find the way to resolve the problem via Stackoverflow and other websites, still, I could not.

Here's the code

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

Training_Dataset = pd.read_csv("https://raw.githubusercontent.com/kuroisep/Problem-1-Data-Analytic/main/A-train.csv?token=GHSAT0AAAAAABZ7O6G34ZTK4PMQGGZLRVS4Y2LAJSQ")
Training_Dataset = Training_Dataset.dropna()
X_train = np.array(Training_Dataset.iloc[:, :-1].values) # Independent Variable
y_train = np.array(Training_Dataset.iloc[:, 1].values) # Dependent Variable

Testing_Datatset = pd.read_csv("https://raw.githubusercontent.com/kuroisep/Problem-1-Data-Analytic/main/A-test.csv?token=GHSAT0AAAAAABZ7O6G2PRKTCT6YBKLZZSRWY2LAK2Q")
Testing_Dataset = Testing_Dataset.dropna()
X_test = np.array(Testing_Dataset.iloc[:, :-1].values) # Independent Variable
y_test = np.array(Testing_Dataset.iloc[:, 1].values) # Dependent Variable

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

accuracy = regressor.score(X_test, y_test)
print('Accuracy = '  str(accuracy))

plt.style.use('seaborn')
plt.scatter(X_test, y_test, color = 'red', marker = 'o', s = 35, alpha = 0.5,
          label = 'Test data')
plt.plot(X_train, regressor.predict(X_train), color = 'blue', label='Model Plot')
plt.title('Predicted Values vs Inputs')
plt.xlabel('Inputs')
plt.ylabel('Predicted Values')
plt.legend(loc = 'upper left')
plt.show()

And Here's the Syntax Error Detail

/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)
   4389         y = np.ma.ravel(y)
   4390         if x.size != y.size:
-> 4391             raise ValueError("x and y must be the same size")
   4392 
   4393         if s is None:

ValueError: x and y must be the same size

my X_test value is (shape = (6,8))

array([[-2.474000e 01, -1.550000e 00,  9.105000e 01,  2.401980e 03,
        -1.520000e 00,  1.360000e 01,  5.660000e 00,  1.059000e 01],
       [ 1.075400e 02, -2.869000e 01, -8.259000e 01,  2.578915e 04,
         5.290000e 00, -8.560000e 00,  1.490000e 00, -4.730000e 00],
       [ 4.508000e 01,  9.662000e 01,  5.185000e 01,  1.280000e 00,
         3.580000e 00,  5.200000e-01, -6.860000e 00, -7.800000e-01],
       [-1.228100e 02,  1.779000e 01, -1.828500e 02,  2.928970e 03,
        -1.210000e 00, -2.060000e 00,  9.680000e 00, -8.590000e 00],
       [ 7.761000e 01, -7.230000e 01,  9.728000e 01,  1.917394e 04,
        -9.290000e 00,  8.600000e-01,  7.060000e 00, -8.060000e 00],
       [-4.401000e 01,  1.316500e 02,  6.988000e 01,  1.778310e 03,
        -1.375000e 01, -1.475000e 01, -1.227000e 01, -8.300000e-01]])

my y_test value is (shape=(6,))

array([ -1.55, -28.69,  96.62,  17.79, -72.3 , 131.65])

I'm sorry if my question seems old. Thank you for your kindness

CodePudding user response：

For the second plot, regressor.predict(X_train) will give the predicted y_train values so again compare predicted values with actual values:

plt.plot(y_train, regressor.predict(X_train), color = 'blue', label='Model Plot')

Output:

By the way, your train-test split isn't right. You set x2 as the target variable but x2 is also included in the training set, that is the reason why the model has perfectly fitted and gives 100% accuracy, but that is the beyond the scope of this question.