Data Preprocessing

What is Data Preprocessing?

Data Preprocessing Is the Process Of Transforms Data Into Algorithm Knowing Data. RealWord [Raw ]Data Is In incomplete and inconsistent Not Always. Make Raw-data Useful Using Data Preprocessing.

Data Preprocessing Step By Step

Step 1 : Import the libraries

Step 2 : Import the data set

Step 3 : Data Cleaning

Step 4 : Data Transformation

Step 4 : Data Reduction

Step 5: Feature Scaling

Import the libraries in python

First, I Import pandas and NumPy libraries and give alias.

import pandas as pd
import numpy as np

Import the data set

Import Data Using Pandas. You Can Import Data: CVS, Excel, SAS, delimited, SQL And URL.

# Import CSV File
Data = pd.read_csv("Train.csv")
# Import CSV File Using URL
Data = pd.read_csv("")
# import TXT File
Data = pd.read_table("train1.txt")
# Import Excel File
Data = pd.read_excel("train.xls",sheetname="June", skiprows=2)
#Sqlite 3 db
import sqlite3
from import sql
conn = sqlite3.connect('forecasting.db')
query = "SELECT * FROM forecasting"
results = pd.read_sql(query, con=conn)
print results.head()

Data Cleaning

Find Missing Data

First, check In Data Set Have Missing Value Or Not.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Data = pd.read_csv("~/Downloads/Data Science/data set/train.csv")

In Huge Data Set Use isnull().sum() Not Always.


Some Time Null Value In Different Value As: ?, Blank Than Need To Convert Them NAN Format For Further Algorithm Use.

#Eliminate the NAN
for col in Data.columns:
    Data.loc[Data[col] == '?', col] = np.nan

Visualizing Null Values

In seaborn library Use For statistical graphics visualisation

import seaborn as sns
sns.heatmap(Data.isnull(), cbar=False)

Drop Null Value

Using dropna()


Using Column name

When See Graph, and No More Use Column Or Have A 75% Null Value No fill null value For Imputation then Better Option is Remove Column.

Data.drop(['Unnamed: 0'], axis=1, inplace=True)

Filling null values using mean

# Find mean
result = Data.category_ID.mode()
#Then Fill null value 
Data.category_ID = Data.loc[Data.category_ID == '?', col] = 'Category 26'
Data["category_ID"].fillna("Category", inplace = True)  

fill null values with the previous and next ones

Data.fillna(method ='bfill') # for next values as
Data.fillna(method ='pad') # for previous values as 

fill na value using replace()

Data['category_ID'].replace(to_replace = np.nan, value = 'Category 26') 

Data Transformation

When Your data is mixtures attributes Then Need Transformation them, Not Always. Example : currency, kilograms and sales volume.

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Normalization is rescaling real numeric value into the range 0 and 1.

When you don’t know data distribution or know distribution is not Gaussian distribution(bell curve). Example k-nearest neighbors and artificial neural networks.

#import sklearn library for Normalization
from sklearn import preprocessing
#need all value in number, not convert non number
normalized_Data = preprocessing.normalize(Data)


Standardization is shifting the distribution of every attribute with a zero mean and one standard deviation.

When your data is Gaussian distribution (bell curve). This does not require compulsory, but the technique is more effective if your attribute is Gaussian distribution and varying scale data. Example linear regression, logistic regression

standardized_Data = preprocessing.scale(Data)

Data Discretization

When Your Data is Continuous and need to convert them discrete then use Discretization.


Data Reduction

In data reduction (Dimension Reduction) techniques example: Filter method using Pearson correlation, Wrapper method using pvalue, the Embedded method using Lasso regularization. Lasso regularization is an iterative method. Each iteration extract features to check which features contribute the most to the training. If the feature is irrelevant, lasso penalizes 0(zero) and removes it. PCA(Principal Component Analysis).

#Filter method
corr = Data.corr()
drop_cols = []
for col in Data.columns:
    if sum(corr[col].map(lambda x: abs(x) > 0.1)) <= 4:
Data.drop(drop_cols, axis=1, inplace=True)

Feature Scaling

Feature scaling is also known as Data Transformation. It is applied to independent variables for Given data in a particular range. This is also used for algorithm speeding up the calculation.

from sklearn.preprocessing import StandardScaler 
scaler = StandardScaler() 

Use Feature Scaling

Use feature scaling when data is a big scale, irrelevant or misleading, and your algorithm is Distance based. Example: K-Means, K-Nearest-Neighbours, PCA.

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