Course Content

Preprocessing Data

1. Data Exploration

Data Extracting Explore the Dataset Data Types

2. Data Cleaning

Missing and Wrong Data Work with NaNs Replace Numerical Missing Data with Values Dealing with Missing Ages Replace Categorical Missing Data with Values Replace Missing Values with Interpolation Removing Duplicates Removing Outliers Visualizing Outliers

3. Data Validation

Types Conversion Data Binning

4. Normalization & Standardization

Data Normalization Data Standardization

5. Data Encoding

Label Encoding One Hot Encoding

Data Normalization

Data Normalization & Standardization provides rescaling numerical data into the appropriate interval. For example, ML models usually process the values in the interval [0; 1]. It's much more convenient to process the finite data, and also the data that all scaled to the same interval. There are two approaches:

to normalize data: move it to the interval [0; 1]
to standartizate data.

Data normalization

After normalization, each value will be represented as the value from the interval [0;1]. This allows us to easily understand how close the value is to the left or right bound. There is a quick demo.

For example, you got the 4th value from column fare:

How can you know, how many it is? How close is it to the mean value? Is it many or not?

You have to know the bounds for the fare price. If you normalized the data, you would get the value:

which is more informative: the value is small enough, only 10% out of the max price.

Normalization formula is:

Let's implement it manually:


              1234
            
x_min, x_max = data['Fare'].min(), data['Fare'].max()
normalized_fare = (data['Fare'] - x_min) / (x_max-x_min)
print(normalized_fare[3])
 # output: 0.10364429745562033

Great! Now we see that 4th value is 0.10364, so this price is only 10% out of the maximum one.

Of course, there is a built-in way that does all the work: let's use the MinMaxScaler() from sklearn.


              1234567
            
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
# reading data
# creating a scaler
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)
# now the data is normalized

Task

Use the fit_transform() method with Fare data as an argument. Compare if the normalized fare value of 4th record is equal to the received manually one.

Note that your data should be a 2D-array.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 1

Data Normalization

to normalize data: move it to the interval [0; 1]
to standartizate data.

Data normalization

After normalization, each value will be represented as the value from the interval [0;1]. This allows us to easily understand how close the value is to the left or right bound. There is a quick demo.

For example, you got the 4th value from column fare:

How can you know, how many it is? How close is it to the mean value? Is it many or not?

You have to know the bounds for the fare price. If you normalized the data, you would get the value:

which is more informative: the value is small enough, only 10% out of the max price.

Normalization formula is:

Let's implement it manually:


              1234
            
x_min, x_max = data['Fare'].min(), data['Fare'].max()
normalized_fare = (data['Fare'] - x_min) / (x_max-x_min)
print(normalized_fare[3])
 # output: 0.10364429745562033

Great! Now we see that 4th value is 0.10364, so this price is only 10% out of the maximum one.

Of course, there is a built-in way that does all the work: let's use the MinMaxScaler() from sklearn.


              1234567
            
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
# reading data
# creating a scaler
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)
# now the data is normalized

Task

Use the fit_transform() method with Fare data as an argument. Compare if the normalized fare value of 4th record is equal to the received manually one.

Note that your data should be a 2D-array.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 1

Data Normalization

to normalize data: move it to the interval [0; 1]
to standartizate data.

Data normalization

After normalization, each value will be represented as the value from the interval [0;1]. This allows us to easily understand how close the value is to the left or right bound. There is a quick demo.

For example, you got the 4th value from column fare:

How can you know, how many it is? How close is it to the mean value? Is it many or not?

You have to know the bounds for the fare price. If you normalized the data, you would get the value:

which is more informative: the value is small enough, only 10% out of the max price.

Normalization formula is:

Let's implement it manually:


              1234
            
x_min, x_max = data['Fare'].min(), data['Fare'].max()
normalized_fare = (data['Fare'] - x_min) / (x_max-x_min)
print(normalized_fare[3])
 # output: 0.10364429745562033

Great! Now we see that 4th value is 0.10364, so this price is only 10% out of the maximum one.

Of course, there is a built-in way that does all the work: let's use the MinMaxScaler() from sklearn.


              1234567
            
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
# reading data
# creating a scaler
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)
# now the data is normalized

Task

Use the fit_transform() method with Fare data as an argument. Compare if the normalized fare value of 4th record is equal to the received manually one.

Note that your data should be a 2D-array.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

to normalize data: move it to the interval [0; 1]
to standartizate data.

Data normalization

After normalization, each value will be represented as the value from the interval [0;1]. This allows us to easily understand how close the value is to the left or right bound. There is a quick demo.

For example, you got the 4th value from column fare:

How can you know, how many it is? How close is it to the mean value? Is it many or not?

You have to know the bounds for the fare price. If you normalized the data, you would get the value:

which is more informative: the value is small enough, only 10% out of the max price.

Normalization formula is:

Let's implement it manually:


              1234
            
x_min, x_max = data['Fare'].min(), data['Fare'].max()
normalized_fare = (data['Fare'] - x_min) / (x_max-x_min)
print(normalized_fare[3])
 # output: 0.10364429745562033

Great! Now we see that 4th value is 0.10364, so this price is only 10% out of the maximum one.

Of course, there is a built-in way that does all the work: let's use the MinMaxScaler() from sklearn.


              1234567
            
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
# reading data
# creating a scaler
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)
# now the data is normalized

Task

Use the fit_transform() method with Fare data as an argument. Compare if the normalized fare value of 4th record is equal to the received manually one.

Note that your data should be a 2D-array.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 4. Chapter 1

Switch to desktop for real-world practiceContinue from where you are using one of the options below