├── DATA.py
└── README.md


/DATA.py:
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 1 | import pandas as pd
 2 | import numpy as np
 3 | df=pd.read_csv("data.csv")
 4 | print(df)
 5 | df['nst']=df['nst'].replace(np.nan, 0)
 6 | df['magNst']=df['magNst'].replace(np.nan, 0)
 7 | check_nan = df['nst'].isnull().values.any()
 8 | check_nan1= df['latitude'].isnull().values.any()
 9 | check_nan2= df['longitude'].isnull().values.any()
10 | check_nan3= df['depth'].isnull().values.any()
11 | check_nan4= df['magNst'].isnull().values.any()
12 | print(check_nan)
13 | print(check_nan1)
14 | print(check_nan2)
15 | print(check_nan3)
16 | print(check_nan4)
17 | x=df[['latitude','longitude','depth','nst','magNst']]
18 | y=df['mag']
19 | print(x)
20 | print(y)
21 | from sklearn.linear_model import LinearRegression
22 | from sklearn.metrics import mean_squared_error
23 | from sklearn.model_selection import train_test_split
24 | x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
25 | print("x_test",x_test.shape)
26 | print("x_train",x_train.shape)
27 | print("y_test",x_test.shape)
28 | print("y_train",y_train.shape)
29 | print(df.shape)
30 | lr=LinearRegression()
31 | lr.fit(x_train,y_train)
32 | y_pred=lr.predict(x_test)
33 | print("y_pred",y_pred)
34 | print("y_test",y_test)
35 | print("MEAN SQUARED ERROR ",mean_squared_error(y_test,y_pred))
36 | result=pd.DataFrame({'Actual':y_test,'Predicted':y_pred})
37 | print(result)
38 | import matplotlib.pyplot as plt
39 | import seaborn as sns
40 | plt.scatter(range(len(y_test)),result['Actual'],label='Actual')
41 | plt.scatter(range(len(y_test)),result['Predicted'],label='Predicted')
42 | plt.legend(loc='best')
43 | plt.show()
44 | sns.scatterplot(x='latitude',y='longitude',data=df,hue='magSource')
45 | plt.show()
46 | 


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/README.md:
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1 | # data


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