Conteúdo do Curso

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

Removing Outliers

Outliers are some extra values that do not fit the defined interval. These values can affect some metrics(like mean, mode, etc.) or model weights. Let's explore what are the methods how to remove the outliers.

There are some popular ways to define the allowable interval (limits of acceptable value for some distribution):

remove all the values that form the first and the last 1% of values (presented in ascending order).
Leave the values that fit the interval [q25 - 1.5*IQR; q75 + 1.5*IQR], where IQR = q75 - q25. IQR is Inter Quartile Range. Remove other values.
leave all data that fits the interval [mean - std; mean + std]. Remove other values.

Let's explore the data distribution for each continuous numerical column (Age, SibSp, Parch, and Fare):

Note that Age feature is cleaned using interpoltaion.

Age distribution is close to Normal, and other features distributions look like Exponential ones.

There is a demo of removing the Age data outliers using two approaches.

Remove Outliers Using Mean and Std

Let's remove all the data outside the range [mean - std; mean + std] and check how the distribution changed. After running the following code:


              123456
            
ages = new_data['Age']
mean, std = ages.mean(), ages.std()
# data without outliers
ages_wo = ages.loc[(ages > mean-std) & (ages < mean +std)]

print('Removed data:', (1 - ages_wo.size/ages.size)*100, '%')

The expected amount of removed data is 26.936 %, which is quite a lot. On the plot, the orange area matches the outliers, and blue area matches the other observations:

Maybe you should think about another approach.

Removing Outliers Using IQR

The following code removes all data outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR]:


              1234567
            
ages = new_data['Age']
q25, q50, q75 = ages.quantile(q=[0.25, 0.5, 0.75])
iqr = q75 - q25
# ages column without outliers
ages_wo = ages.loc[(ages > q25 - 1.5*iqr) & (ages < q75 + 1.5*iqr)]

print('Removed data:', (1 - ages_wo.size/ages.size)*100, '%')

The expected amount of removed data is 1.571 % now, looks pretty enough. The distribution is pictured below:

Your goal is to apply these two approaches to the Fare column, explore the amount of outliers, and make some visualization.

Tarefa

For the Fare column:

Find the amount of outliers out the range [mean - std; mean + std]
Find the amount of outliers outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR] (Be careful and do not use the data after the previous task execution).

Tarefa

For the Fare column:

Find the amount of outliers out the range [mean - std; mean + std]
Find the amount of outliers outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR] (Be careful and do not use the data after the previous task execution).

The expected results are 8.193% and 13.019% respectively.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 2. Capítulo 8

Removing Outliers

There are some popular ways to define the allowable interval (limits of acceptable value for some distribution):

remove all the values that form the first and the last 1% of values (presented in ascending order).
Leave the values that fit the interval [q25 - 1.5*IQR; q75 + 1.5*IQR], where IQR = q75 - q25. IQR is Inter Quartile Range. Remove other values.
leave all data that fits the interval [mean - std; mean + std]. Remove other values.

Let's explore the data distribution for each continuous numerical column (Age, SibSp, Parch, and Fare):

Note that Age feature is cleaned using interpoltaion.

Age distribution is close to Normal, and other features distributions look like Exponential ones.

There is a demo of removing the Age data outliers using two approaches.

Remove Outliers Using Mean and Std

Let's remove all the data outside the range [mean - std; mean + std] and check how the distribution changed. After running the following code:


              123456
            
ages = new_data['Age']
mean, std = ages.mean(), ages.std()
# data without outliers
ages_wo = ages.loc[(ages > mean-std) & (ages < mean +std)]

print('Removed data:', (1 - ages_wo.size/ages.size)*100, '%')

The expected amount of removed data is 26.936 %, which is quite a lot. On the plot, the orange area matches the outliers, and blue area matches the other observations:

Maybe you should think about another approach.

Removing Outliers Using IQR

The following code removes all data outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR]:


              1234567
            
ages = new_data['Age']
q25, q50, q75 = ages.quantile(q=[0.25, 0.5, 0.75])
iqr = q75 - q25
# ages column without outliers
ages_wo = ages.loc[(ages > q25 - 1.5*iqr) & (ages < q75 + 1.5*iqr)]

print('Removed data:', (1 - ages_wo.size/ages.size)*100, '%')

The expected amount of removed data is 1.571 % now, looks pretty enough. The distribution is pictured below:

Your goal is to apply these two approaches to the Fare column, explore the amount of outliers, and make some visualization.

Tarefa

For the Fare column:

Find the amount of outliers out the range [mean - std; mean + std]
Find the amount of outliers outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR] (Be careful and do not use the data after the previous task execution).

Tarefa

For the Fare column:

Find the amount of outliers out the range [mean - std; mean + std]
Find the amount of outliers outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR] (Be careful and do not use the data after the previous task execution).

The expected results are 8.193% and 13.019% respectively.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 2. Capítulo 8

Removing Outliers

There are some popular ways to define the allowable interval (limits of acceptable value for some distribution):

remove all the values that form the first and the last 1% of values (presented in ascending order).
Leave the values that fit the interval [q25 - 1.5*IQR; q75 + 1.5*IQR], where IQR = q75 - q25. IQR is Inter Quartile Range. Remove other values.
leave all data that fits the interval [mean - std; mean + std]. Remove other values.

Let's explore the data distribution for each continuous numerical column (Age, SibSp, Parch, and Fare):

Note that Age feature is cleaned using interpoltaion.

Age distribution is close to Normal, and other features distributions look like Exponential ones.

There is a demo of removing the Age data outliers using two approaches.

Remove Outliers Using Mean and Std

Let's remove all the data outside the range [mean - std; mean + std] and check how the distribution changed. After running the following code:


              123456
            
ages = new_data['Age']
mean, std = ages.mean(), ages.std()
# data without outliers
ages_wo = ages.loc[(ages > mean-std) & (ages < mean +std)]

print('Removed data:', (1 - ages_wo.size/ages.size)*100, '%')

The expected amount of removed data is 26.936 %, which is quite a lot. On the plot, the orange area matches the outliers, and blue area matches the other observations:

Maybe you should think about another approach.

Removing Outliers Using IQR

The following code removes all data outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR]:


              1234567
            
ages = new_data['Age']
q25, q50, q75 = ages.quantile(q=[0.25, 0.5, 0.75])
iqr = q75 - q25
# ages column without outliers
ages_wo = ages.loc[(ages > q25 - 1.5*iqr) & (ages < q75 + 1.5*iqr)]

print('Removed data:', (1 - ages_wo.size/ages.size)*100, '%')

The expected amount of removed data is 1.571 % now, looks pretty enough. The distribution is pictured below:

Your goal is to apply these two approaches to the Fare column, explore the amount of outliers, and make some visualization.

Tarefa

For the Fare column:

Find the amount of outliers out the range [mean - std; mean + std]
Find the amount of outliers outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR] (Be careful and do not use the data after the previous task execution).

Tarefa

For the Fare column:

Find the amount of outliers out the range [mean - std; mean + std]
Find the amount of outliers outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR] (Be careful and do not use the data after the previous task execution).

The expected results are 8.193% and 13.019% respectively.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

There are some popular ways to define the allowable interval (limits of acceptable value for some distribution):

remove all the values that form the first and the last 1% of values (presented in ascending order).
Leave the values that fit the interval [q25 - 1.5*IQR; q75 + 1.5*IQR], where IQR = q75 - q25. IQR is Inter Quartile Range. Remove other values.
leave all data that fits the interval [mean - std; mean + std]. Remove other values.

Let's explore the data distribution for each continuous numerical column (Age, SibSp, Parch, and Fare):

Note that Age feature is cleaned using interpoltaion.

Age distribution is close to Normal, and other features distributions look like Exponential ones.

There is a demo of removing the Age data outliers using two approaches.

Remove Outliers Using Mean and Std

Let's remove all the data outside the range [mean - std; mean + std] and check how the distribution changed. After running the following code:


              123456
            
ages = new_data['Age']
mean, std = ages.mean(), ages.std()
# data without outliers
ages_wo = ages.loc[(ages > mean-std) & (ages < mean +std)]

print('Removed data:', (1 - ages_wo.size/ages.size)*100, '%')

The expected amount of removed data is 26.936 %, which is quite a lot. On the plot, the orange area matches the outliers, and blue area matches the other observations:

Maybe you should think about another approach.

Removing Outliers Using IQR

The following code removes all data outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR]:


              1234567
            
ages = new_data['Age']
q25, q50, q75 = ages.quantile(q=[0.25, 0.5, 0.75])
iqr = q75 - q25
# ages column without outliers
ages_wo = ages.loc[(ages > q25 - 1.5*iqr) & (ages < q75 + 1.5*iqr)]

print('Removed data:', (1 - ages_wo.size/ages.size)*100, '%')

The expected amount of removed data is 1.571 % now, looks pretty enough. The distribution is pictured below:

Your goal is to apply these two approaches to the Fare column, explore the amount of outliers, and make some visualization.

Tarefa

For the Fare column:

Find the amount of outliers out the range [mean - std; mean + std]
Find the amount of outliers outside the range [q25 - 1.5*IQR; q75 + 1.5*IQR] (Be careful and do not use the data after the previous task execution).

The expected results are 8.193% and 13.019% respectively.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Seção 2. Capítulo 8

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo