preprocessing .mlpy

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

df=pd.read_csv("data.csv")
df.head()
     
country	age	salary	puchased
0	France	NaN	7200	NO
1	Spain	27.0	4800	yes
2	Germany	30.0	5400	yes
3	UK	49.0	98000	NO

from google.colab import files

uploaded = files.upload()
     
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving data.csv to data.csv

df.tail()
     
country	age	salary	puchased
0	France	44.0	7200.0	NO
1	Spain	27.0	4800.0	yes
2	Germany	30.0	5400.0	yes

df.info()
     
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   country   3 non-null      object 
 1   age       3 non-null      float64
 2   salary    3 non-null      float64
 3   puchased  3 non-null      object 
dtypes: float64(2), object(2)
memory usage: 224.0+ bytes

df.describe()
     
age	salary
count	3.000000	3.0000
mean	33.666667	5800.0000
std	9.073772	1248.9996
min	27.000000	4800.0000
25%	28.500000	5100.0000
50%	30.000000	5400.0000
75%	37.000000	6300.0000
max	44.000000	7200.0000

df.isnull().sum()
     
country     0
age         1
salary      0
puchased    0
dtype: int64
 df["age"].fillna(df["age"].mean(),inplace=True)
df["salary"].fillna(df["salary"].mean(),inplace=True)
     

df.isnull().sum()
     
country     0
age         0
salary      0
puchased    0
dtype: int64

from sklearn.impute import SimpleImputer
x=df.iloc[:,:-1].values
x


     
array([['France', 35.333333333333336, 7200],
       ['Spain', 27.0, 4800],
       ['Germany', 30.0, 5400],
       ['UK', 49.0, 98000]], dtype=object)

y=df.iloc[:,3:].values
y

     
array([['NO'],
       ['yes'],
       ['yes'],
       ['NO']], dtype=object)

imp=SimpleImputer(missing_values=np.nan,strategy="mean")

x[:,1:3]=imp.fit_transform(x[:,1:3])
x
     
array([['France', 35.333333333333336, 7200.0],
       ['Spain', 27.0, 4800.0],
       ['Germany', 30.0, 5400.0],
       ['UK', 49.0, 98000.0]], dtype=object)


     

y
     
array([['NO'],
       ['yes'],
       ['yes'],
       ['NO']], dtype=object)


     

from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
h=le.fit_transform(x[:,0])
h
     
array([0, 2, 1, 3])

y=le.fit_transform(y)
y
     
/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
array([0, 1, 1, 0])

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
transform=ColumnTransformer([("norm1",OneHotEncoder(),[0])],remainder='passthrough')
x=transform.fit_transform(x)
x
     
array([[1.0, 0.0, 0.0, 0.0, 44.0, 7200.0],
       [0.0, 0.0, 1.0, 0.0, 27.0, 4800.0],
       [0.0, 1.0, 0.0, 0.0, 30.0, 5400.0],
       [0.0, 0.0, 0.0, 1.0, 49.0, 98000.0]], dtype=object)

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)
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
x_train[:,4:6]=sc.fit_transform(x_train[:,4:6])
x_train
     
array([[0.0, 0.0, 0.0, 1.0, 0.9557896392964647, 1.4138527953056175],
       [0.0, 0.0, 1.0, 0.0, -1.3805850345393378, -0.7345887349522664],
       [1.0, 0.0, 0.0, 0.0, 0.4247953952428732, -0.679264060353351]],
      dtype=object)

plt.bar(df["country"],df["salary"])
plt.xlabel("country")
plt.ylabel("salary")
plt.show()
     


import seaborn as sns
sns.pairplot(df)
     
<seaborn.axisgrid.PairGrid at 0x7e40d00ff1c0>


df
     
country	age	salary	puchased
0	France	44	7200	NO
1	Spain	27	4800	yes
2	Germany	30	5400	yes
3	UK	49	98000	NO

from sklearn.preprocessing import OneHotEncoder
import numpy as np

# Sample data
colors = np.array(['red', 'green', 'blue', 'red', 'blue']).reshape(-1, 1)

colors.shape
colors
     
array([['red'],
       ['green'],
       ['blue'],
       ['red'],
       ['blue']], dtype='<U5')

encoder = OneHotEncoder()

# Fit and transform the data
one_hot_encoded = encoder.fit_transform(colors)
one_hot_encoded
# Convert the sparse matrix to a dense array for visualization
one_hot_encoded_array = one_hot_encoded.toarray()

# Display the one-hot encoded array
print(one_hot_encoded_array)

     
[[0. 0. 1.]
 [0. 1. 0.]
 [1. 0. 0.]
 [0. 0. 1.]
 [1. 0. 0.]]