Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Learn Prepearing Data Set 2/2 | Models in Scikit Learn
Introduction to Scikit Learn

bookPrepearing Data Set 2/2

Let's make the last preperation of the prices houses in Amsterdam dataset. If you take a look one more time at this dataset...

You will see that, for example, the values in price and room columns are different orders. We know, that it is better to work with data, which are reduced to one range of values. Let's do it with standardization. We will do it in two ways. Firslty without built-in functions, just using fomula.

  1. Let's find mean and variance values.

              
1234

            
# Calculating mean values
print('The mean value of each column in the dataset:', dataset.mean())
# Calculating variance values
print('The std value of each column in the dataset:', dataset.var())
copy
  1. Then we calculate standardized values using the following formula:


              
12

            
# Checking null values
dataset.apply(lambda x: (x-x.mean())/ x.std(), axis=0)
copy
  1. Or we can do it, just using StandardScaler() function in the follwing way:

              
12345678

            
scaler = StandardScaler()
scaler.fit(dataset)
# Calculating mean value
print(scaler.mean_)
# Calculating variance value
print(scaler.var_)
scaled_data = scaler.transform(dataset)
print(scaled_data)
copy

It is time to make all this steps on the dataset in the task. Let's start!

Task

Swipe to start coding

  1. Importing libraries and loading dataset.
  2. Finding and dropping duplicated values.
  3. Finding and replacing null values with mean value.
  4. Delete categorial values, leaving only numerals.

Solution

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 2

Ask AI

expand

Ask AI

ChatGPT

Ask anything or try one of the suggested questions to begin our chat

Suggested prompts:

Ask me questions about this topic

Summarize this chapter

Show real-world examples

Awesome!

Completion rate improved to 12.5

bookPrepearing Data Set 2/2

Swipe to show menu

Let's make the last preperation of the prices houses in Amsterdam dataset. If you take a look one more time at this dataset...

You will see that, for example, the values in price and room columns are different orders. We know, that it is better to work with data, which are reduced to one range of values. Let's do it with standardization. We will do it in two ways. Firslty without built-in functions, just using fomula.

  1. Let's find mean and variance values.

              
1234

            
# Calculating mean values
print('The mean value of each column in the dataset:', dataset.mean())
# Calculating variance values
print('The std value of each column in the dataset:', dataset.var())
copy
  1. Then we calculate standardized values using the following formula:


              
12

            
# Checking null values
dataset.apply(lambda x: (x-x.mean())/ x.std(), axis=0)
copy
  1. Or we can do it, just using StandardScaler() function in the follwing way:

              
12345678

            
scaler = StandardScaler()
scaler.fit(dataset)
# Calculating mean value
print(scaler.mean_)
# Calculating variance value
print(scaler.var_)
scaled_data = scaler.transform(dataset)
print(scaled_data)
copy

It is time to make all this steps on the dataset in the task. Let's start!

Task

Swipe to start coding

  1. Importing libraries and loading dataset.
  2. Finding and dropping duplicated values.
  3. Finding and replacing null values with mean value.
  4. Delete categorial values, leaving only numerals.

Solution

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 2
some-alt