from sklearn.preprocessing import MinMaxScaler
data = [[-1, 2], 
        [-0.5, 6], 
        [0, 10], 
        [1, 18]]
print(data)
print(max(data))
print(min(data))
scaler = MinMaxScaler()#implement

print(scaler.fit(data))

print(scaler.data_max_)

print(scaler.transform(data))

print(scaler.transform([[2, 2]]))

'''
1.Fit the scaler using available training data. 
For normalization, this means the training data will be used to 
estimate the minimum and maximum observable values.
This is done by calling the fit() function.

2.Apply the scale to training data. This means you can use the 
  normalized data to train your model. This is done by calling the 
  transform() function.
  
3.Apply the scale to data going forward. This means you can prepare 
 new data in the future on which you want to make predictions.
'''

import pandas as pd
data_marks = pd.read_csv("Marks.csv")
print(data_marks.max())

scaler = MinMaxScaler()

print(scaler.fit(data_marks))

print(scaler.data_max_)

print(scaler.transform(data_marks))


import numpy as np 
from sklearn.preprocessing import StandardScaler
data = [[0, 0], 
        [0, 0], 
        [1, 1],
        [1, 1]]


print(np.mean(data,axis=0))
scaler = StandardScaler()
print(scaler.fit(data))
print(scaler.mean_)

print(scaler.transform(data))

data = [[-1, 2], 
        [-0.5, 6], 
        [0, 10], 
        [1, 18]]

print(scaler.transform(data))


import pandas as pd
data_marks = pd.read_csv("Marks.csv")
print(data_marks.mean())

scaler = StandardScaler()
print(scaler.fit(data_marks))

print(scaler.mean_)
print(scaler.transform(data_marks))

import pandas as pd 
import seaborn as sn
import matplotlib.pyplot as plt
data = {'A': [45,37,42,35,39], 
        'B': [38,31,26,28,33], 
        'C': [10,15,17,21,12] } 
df = pd.DataFrame(data,columns=['A','B','C']) 
corrMatrix = df.corr() 
print (corrMatrix)
sn.heatmap(corrMatrix,annot=True) 


import pandas as pd 
import seaborn as sn
import matplotlib.pyplot as plt
data_marks = pd.read_csv("Marks.csv")
corrMatrix = data_marks.corr() 
print(corrMatrix)
sn.heatmap(corrMatrix, annot=True)