João Pedro Soares Piovesan Verdant31

## manual.py
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.preprocessing import StandardScaler
import pandas as pd
from numpy import linalg as LA

df = pd.read_csv("forestfires.csv")
df.drop(columns=["area"], inplace=True)

## forestfires.csv

          
            X
            Y
            Temp
            RH
            Wind
            Rain
            area

            
              7
              5
              8.2
              51
              6.7
              0
              0

            
              7
              4
              18
              33
              0.9
              0
              0

            
              7
              4
              14.6
              33
              1.3
              0
              0

            
              8
              6
              8.3
              97
              4
              0.2
              0

            
              8
              6
              11.4
              99
              1.8
              0
              0

            
              8
              6
              22.2
              29
              5.4
              0
              0

            
              8
              6
              24.1
              27
              3.1
              0
              0

            
              8
              6
              8
              86
              2.2
              0
              0

            
              8
              6
              13.1
              63
              5.4
              0
              0
	import pandas as pd
	import matplotlib.pyplot as plt
	import numpy as np
	import seaborn as sns
	from sklearn.preprocessing import StandardScaler
	import pandas as pd
	from numpy import linalg as LA

	df = pd.read_csv("forestfires.csv")
	df.drop(columns=["area"], inplace=True)
X	Y	Temp	RH	Wind	Rain	area
7	5	8.2	51	6.7	0	0
7	4	18	33	0.9	0	0
7	4	14.6	33	1.3	0	0
8	6	8.3	97	4	0.2	0
8	6	11.4	99	1.8	0	0
8	6	22.2	29	5.4	0	0
8	6	24.1	27	3.1	0	0
8	6	8	86	2.2	0	0
8	6	13.1	63	5.4	0	0