Machine Learning: Data Preprocessing

Data preprocessing is preparing raw data suitable for machine learning models. It is the most important status. Are you interested in Machine Learning: Data Preprocessing, one of the hottest fields in technology today? Our Machine Learning Course in Pune is the perfect way to dive deep into this exciting field and gain hands-on experience with cutting-edge tools and technologies.

Data Preprocessing involves the following status

1. Data Import 
2. Data Exploration
3. Data Wrangling
4. Data Manipulation
5. Encoding Categorical Data
6. Splitting Data Set
7. Feature Scaling

 

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Methods of Machine Learning: Data Preprocessing

 

1. Data Import

Before starting with the dataset, the first step is to load the dataset.

Code : 

df  = pandas.read_csv(“filepath”) : to load .csv file

df  = pandas.read_excel(“filepath”) : to load .xlsx file

df  = pandas.read_json(“filepath”) : to load .json file

df  = pandas.read_sql(“filepath”) : to load .sql file

 

1. Data Exploration Techniques 
   1. Dimensionality Check : df.shape
   2. Type of Dataset : type(df)
   3. Slicing and Indexing : df.iloc[ : , : ]
   4. Mean : df.mean()
   5. Median : df.median()
   6. Mode : df.mode()
   7. Identifying Unique Elements : df.unique()
   8. Value Extraction : df.values()

 

1. Data Wrangling
   1. Missing Value and missing value treatment.
   2. Inconsistent Data
   3. Presence of noisy Data
   4. Developing a more accurate model.

 

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1. Data Manipulation 

 

A data object is a two dimensional data structure on which following functions can be applied.

1. Returns first n rows : df.head()
2. Returns last n rows : df.tail()
3. Returns actual data in a series : df.values()
4. Returns data frame in groped format : df.group()
5. Concatenate combines two or more data structures : df.concatenate()
6. Merging is the Pandas operation that perform database joins on object 

 

5. Encoding Categorical Data

 

The machine learning model completely works on mathematics and numbers, but if our dataset has a categorical variable, then it may create trouble while building the model.

To perform it we apply LabelEncoder() and One Hot Encoding.

 

6. Splitting Data Set

In machine learning data preprocessing, we divide our dataset into training and testing datasets.

 

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7. Feature Scaling 

 

It is a technique to standardize the independent variable of the dataset in a  specific  range

 

Author:-

Aniket Kulkarni

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