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Getting 'ValueError: Expected 2D array, got 1D array instead: array=[-0.27861589].' in python for predicting SVR regrssion

I am having trouble to solve the array dimension problem showing in the code. When I am trying to figure out the y_predict, the valueerror problem is showing. here is the code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#importing dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:,1:2].values
y = dataset.iloc[:,2].values
y=np.reshape(y,(10,1))
#Spliting dataset into training set and test set
'''from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 0)'''
#Feature scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)
######## SVR regression
from sklearn.svm import SVR
svr_regressor = SVR(kernel='rbf') #rbf = gaussian kernel
svr_regressor.fit(X, y)
#Prediction of given value using SVR regression
X = np.reshape(X,(-1, 1))
y_predict = sc_y.inverse_transform(svr_regressor.predict(sc_X.transform([[6.5]])))
########### Visulization of svr model
plt.scatter(X, y, color = 'blue')
plt.plot(X, svr_regressor.predict(X), color = 'red')
plt.show()
I am getting error:
ValueError: Expected 2D array, got 1D array instead:
array=[-0.27861589].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

ValueError: Expected a 2D array, but received a 1D array instead:

I received this error while practicing the Simple Linear Regression Model; I assume there is an issue with my set of data.
Here is the Error
ValueError: Expected 2D array, got 1D array instead:
array=[1140. 1635. 1755. 1354. 1978. 1696. 1212. 2736. 1055. 2839. 2325. 1688.
2733. 2332. 2159. 2133.].
Here is my Dataset
Here the code
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
df = pd.read_csv('C:/Users/AgroMech/Desktop/ASDS/data.csv')
df.shape
print(df.duplicated())
df.isnull().any()
df.isnull().sum()
df.dropna(inplace = True)
x=df["Area"]
y=df["Price"]
df.describe()
reg = linear_model.LinearRegression()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=4)
x_train.head()
reg=LinearRegression()
reg.fit(x_train,y_train)
LinearRegression(copy_x=True, fit_intercept=True, n_jobs=1, normalize=False)
reg.coef_
reg.predict(x_test)
np.mean((reg.predict(x_test) - y_test)**2)

As the error suggests when executing reg.fit(x_train, y_train):
ValueError: Expected 2D array, got 1D array instead:
array=[1140. 1635. 1755. 1354. 1978. 1696. 1212. 2736. 1055. 2839. 2325. 1688.
2733. 2332. 2159. 2133.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
This means your arrays don't have the right shape for reg.fit(). You can reshape them explicitly:
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=4)
x_train = x_train.values.reshape(-1,1)
x_test = x_test.values.reshape(-1,1)
y_train = y_train.values.reshape(-1,1)
y_test = y_test.values.reshape(-1,1)
or you can reshape your original x and y values:
x = df[['Area']]
y = df[['Price']]
Also note that LinearRegression takes a copy_X argument and not copy_x.

The easiest way to reshape your x variable (from a 1D array to a 2D) is:
x = df[["Area"]]

How to fix error with train_test_split in Python?

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
x=np.array([0.1,0.2,0.7,8.0,45.0,56.0,66.0,0.7,0.6,64.0])
y=np.array([0,0,0,1,1,1,1,0,0,1])
x = np.array(x).reshape((1, -1))
y = np.array(y).reshape((1, -1))
x_train, x_test, y_train, y_test = train_test_split(x, y,test_size=0.4, train_size=0.5, random_state=7, stratify=y)
knn = KNeighborsClassifier()
knn.fit(y_train, x_train)
y_train_predict = knn.predict(x_train)
y_test_predict = knn.predict(x_test)
print(y_train_predict)
print(y_test_predict)
Error:
With n_samples=1, test_size=0.4 and train_size=0.5, the resulting train set will be empty. Adjust any of the aforementioned parameters.

Try:
x = np.array(x).reshape(-1, 1)
y = np.array(y).reshape(-1, 1)

How to avoid Collection Error Python Numpy

I am trying to train a Linear Regression Qualifier to continue a grap.
I have a couple of thousand lines of data in my csv file that I import into numpy arrays. Here is my code :
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import csv
import math
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
def predict():
sample_data = pd.read_csv("includes\\csv.csv")
x = np.array(sample_data["day"])
y = np.array(sample_data["balance"])
for x in x:
x = x.reshape(1, -1)
#lol
for y in y:
y.reshape(1, -1)
#lol
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
clf = LinearRegression()
clf.fit(x_train, y_train)
clf.score(x_test, y_test)
When I run this, the error is:
TypeError: Singleton array 6014651 cannot be considered a valid collection.
Any ideas why that's a thing?

After discussion in comments:
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import csv
import math
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
def predict():
sample_data = pd.read_csv("includes\\csv.csv")
x = np.array(sample_data["day"])
y = np.array(sample_data["balance"])
x = x.reshape(-1,1)
y = y.reshape(-1,1)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
clf = LinearRegression()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)

X_train, X_test should be capitals, python variables are case sensitive

Expected 2D array, got 1D array instead, any solution?

I'm new using Machine Learning and I am trying to predict the price of the stocks in 30 days.
This is my code:
import pandas as pd
import matplotlib.pyplot as plt
import pymysql as MySQLdb
import numpy as np
import sqlalchemy
import datetime
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
forecast_out = int(30)
df['Prediction'] = df[['LastPrice']].shift(-forecast_out)
df['Prediction'].fillna(0)
X = np.array(df['Prediction'].fillna(0))
X = preprocessing.scale(X)
X_forecast = X[-forecast_out:]
X = X[:-forecast_out]
y = np.array(df['Prediction'].fillna(0))
y = y[:-forecast_out]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
X_train, X_test, y_train, y_test.reshape(-1,1)
# Training
clf = LinearRegression()
clf.fit(X_train,y_train)
# Testing
confidence = clf.score(X_test, y_test)
print("confidence: ", confidence)
forecast_prediction = clf.predict(X_forecast)
print(forecast_prediction)
I got this error:
ValueError: Expected 2D array, got 1D array instead:
array=[-0.46939923 -0.47076913 -0.47004993 ... -0.42782272 3.07433019 -0.46573474].
Reshape your data either using
array.reshape(-1, 1) if your data has a single feature
or
array.reshape(1, -1) if it contains a single sample.

It's expecting a 2D Array when you're only passing in a 1D Array. You can solve this by putting another set of brackets around where you're getting the probelm. For example
x = [1,2,3,4]
Foo(x)
If that throws the error, you could just do
Foo([x])