import sklearn
import pandas as pd
import numpy as np
from sklearn.utils import shuffle
from sklearn.linear_model import LinearRegression


#import the read set
def read_dataset():
    df = pd.read_csv("C:\\Users\\BADSHAH\\PycharmProjects\\edureka1.csv")
    x = df[df.columns[0:4]].values
    y = df[df.columns[4]]
    # to convert categorical data to numerical
    obj_df = df.select_dtypes(include=['object']).copy()
    obj_df["State"].value_counts()
    cleanup_nums = {"State": {'California': 2 , 'New York': 1 , 'Florida': 3}}  # type:
    obj_df.replace(cleanup_nums, inplace=True)

    print(x.shape)
    return(x,y)


# read data set
x,y = read_dataset()

# shuffle the dataset
x,y = shuffle(x,y, random_state=1)

#break data into test and train part
x_train,x_test,y_train,y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.20, random_state=5)

print(x_train.shape)
print(x_test.shape)
print(y_train.shape)
print(y_test.shape)

lm = LinearRegression()

lm.fit(x_train,y_train)

y_train_predict = lm.predict(x_train)
y_test_predict = lm.predict(x_test)
cf=pd.DataFrame(y_test_predict,x_test)
print(cf)

Error:
C:\Users\BADSHAH\PycharmProjects\thesis\venv\Scripts\python.exe C:/Users/BADSHAH/PycharmProjects/thesis/krish
(50, 4)
Traceback (most recent call last):
(40, 4)
  File "C:/Users/BADSHAH/PycharmProjects/thesis/krish", line 41, in <module>
    lm.fit(x_train,y_train)
(10, 4)
  File "C:\Users\BADSHAH\PycharmProjects\thesis\venv\lib\site-packages\sklearn\linear_model\base.py", line 482, in fit
(40,)
    y_numeric=True, multi_output=True)
(10,)
  File "C:\Users\BADSHAH\PycharmProjects\thesis\venv\lib\site-packages\sklearn\utils\validation.py", line 573, in check_X_y
    ensure_min_features, warn_on_dtype, estimator)
  File "C:\Users\BADSHAH\PycharmProjects\thesis\venv\lib\site-packages\sklearn\utils\validation.py", line 433, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: could not convert string to float: 'California'

Process finished with exit code 1