Conteúdo do Curso

Introduction to Scikit Learn

1. The Very First Steps

Loading Data Sets SimpleImputer Ordinal Encoding

2. Scaling Numerical Data

MinMaxScaler MaxAbsScaler StandardScaler

3. Models in Scikit Learn

Prepearing Data Set 1/2 Prepearing Data Set 2/2

Prepearing Data Set 1/2

During this section we will work with one dataset about prices of houses in Amsterdam. Let's get to know more about it. Take a look at first rows.

[object Object]

Here, we have the next columns:

Adress - the detailed adress of the house,
Zip - a postal code,
Price - the price of the house,
Area - the total area of the house,
Room - the number of rooms in the house,
Lon and Lat- a coordinate scheme that can locate or identify geographic positions on the surfaces.

To work with this dataset in the next chapters you have to make some preperation.

Checking and Dropping Duplicates. First, let's find duplicated values in the dataset using the duplicated() function. Then you have to delete these duplicates with the hepl drop_duplicates() function, like in the example below.


              1234
            
# Checking duplicates
dataset.loc[dataset.duplicated()]
# Dropping duplicates
dataset.drop_duplicates()

Checking and replacing null values. To find null values you have to use isnull() function. To replace this values, you have to use SimpleImputer() function.


              12345678
            
# Checking null values
dataset.isnull().sum()
# Replacing null values
imputer = SimpleImputer(missing_values = 0,strategy ='mean')
# Fitting the imputer on your data 
imputer = imputer.fit(dataset)
# Impute all missing values in you data
dataset = imputer.transform(dataset)

We can see that in our dataset not all values are numerals, we have also categorical values, with which it is harder to work.For it is essential to make categorical encoding values, using for instance OrdinalEncoder() function. But in our case, the addresses of each house are different, so there is so little sense to do encoding here. In this case let's take a sub-dataset from the whole dataset without categorical values, only with numerals.


              1234
            
# Create list for categorical variables
categorical_features= list(dataset.select_dtypes(include=['object']).keys())
# Deleting the categorial values
dataset_without_categorical_features = dataset.drop(columns=categorical_features, axis=1)

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

Tarefa

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

Tarefa

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

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

Tudo estava claro?

Seção 3. Capítulo 1

Prepearing Data Set 1/2

During this section we will work with one dataset about prices of houses in Amsterdam. Let's get to know more about it. Take a look at first rows.

[object Object]

Here, we have the next columns:

Adress - the detailed adress of the house,
Zip - a postal code,
Price - the price of the house,
Area - the total area of the house,
Room - the number of rooms in the house,
Lon and Lat- a coordinate scheme that can locate or identify geographic positions on the surfaces.

To work with this dataset in the next chapters you have to make some preperation.

Checking and Dropping Duplicates. First, let's find duplicated values in the dataset using the duplicated() function. Then you have to delete these duplicates with the hepl drop_duplicates() function, like in the example below.


              1234
            
# Checking duplicates
dataset.loc[dataset.duplicated()]
# Dropping duplicates
dataset.drop_duplicates()

Checking and replacing null values. To find null values you have to use isnull() function. To replace this values, you have to use SimpleImputer() function.


              12345678
            
# Checking null values
dataset.isnull().sum()
# Replacing null values
imputer = SimpleImputer(missing_values = 0,strategy ='mean')
# Fitting the imputer on your data 
imputer = imputer.fit(dataset)
# Impute all missing values in you data
dataset = imputer.transform(dataset)

We can see that in our dataset not all values are numerals, we have also categorical values, with which it is harder to work.For it is essential to make categorical encoding values, using for instance OrdinalEncoder() function. But in our case, the addresses of each house are different, so there is so little sense to do encoding here. In this case let's take a sub-dataset from the whole dataset without categorical values, only with numerals.


              1234
            
# Create list for categorical variables
categorical_features= list(dataset.select_dtypes(include=['object']).keys())
# Deleting the categorial values
dataset_without_categorical_features = dataset.drop(columns=categorical_features, axis=1)

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

Tarefa

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

Tarefa

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

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

Tudo estava claro?

Seção 3. Capítulo 1

Prepearing Data Set 1/2

During this section we will work with one dataset about prices of houses in Amsterdam. Let's get to know more about it. Take a look at first rows.

[object Object]

Here, we have the next columns:

Adress - the detailed adress of the house,
Zip - a postal code,
Price - the price of the house,
Area - the total area of the house,
Room - the number of rooms in the house,
Lon and Lat- a coordinate scheme that can locate or identify geographic positions on the surfaces.

To work with this dataset in the next chapters you have to make some preperation.

Checking and Dropping Duplicates. First, let's find duplicated values in the dataset using the duplicated() function. Then you have to delete these duplicates with the hepl drop_duplicates() function, like in the example below.


              1234
            
# Checking duplicates
dataset.loc[dataset.duplicated()]
# Dropping duplicates
dataset.drop_duplicates()

Checking and replacing null values. To find null values you have to use isnull() function. To replace this values, you have to use SimpleImputer() function.


              12345678
            
# Checking null values
dataset.isnull().sum()
# Replacing null values
imputer = SimpleImputer(missing_values = 0,strategy ='mean')
# Fitting the imputer on your data 
imputer = imputer.fit(dataset)
# Impute all missing values in you data
dataset = imputer.transform(dataset)

We can see that in our dataset not all values are numerals, we have also categorical values, with which it is harder to work.For it is essential to make categorical encoding values, using for instance OrdinalEncoder() function. But in our case, the addresses of each house are different, so there is so little sense to do encoding here. In this case let's take a sub-dataset from the whole dataset without categorical values, only with numerals.


              1234
            
# Create list for categorical variables
categorical_features= list(dataset.select_dtypes(include=['object']).keys())
# Deleting the categorial values
dataset_without_categorical_features = dataset.drop(columns=categorical_features, axis=1)

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

Tarefa

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

Tarefa

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

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

Tudo estava claro?

During this section we will work with one dataset about prices of houses in Amsterdam. Let's get to know more about it. Take a look at first rows.

[object Object]

Here, we have the next columns:

Adress - the detailed adress of the house,
Zip - a postal code,
Price - the price of the house,
Area - the total area of the house,
Room - the number of rooms in the house,
Lon and Lat- a coordinate scheme that can locate or identify geographic positions on the surfaces.

To work with this dataset in the next chapters you have to make some preperation.

Checking and Dropping Duplicates. First, let's find duplicated values in the dataset using the duplicated() function. Then you have to delete these duplicates with the hepl drop_duplicates() function, like in the example below.


              1234
            
# Checking duplicates
dataset.loc[dataset.duplicated()]
# Dropping duplicates
dataset.drop_duplicates()

Checking and replacing null values. To find null values you have to use isnull() function. To replace this values, you have to use SimpleImputer() function.


              12345678
            
# Checking null values
dataset.isnull().sum()
# Replacing null values
imputer = SimpleImputer(missing_values = 0,strategy ='mean')
# Fitting the imputer on your data 
imputer = imputer.fit(dataset)
# Impute all missing values in you data
dataset = imputer.transform(dataset)

We can see that in our dataset not all values are numerals, we have also categorical values, with which it is harder to work.For it is essential to make categorical encoding values, using for instance OrdinalEncoder() function. But in our case, the addresses of each house are different, so there is so little sense to do encoding here. In this case let's take a sub-dataset from the whole dataset without categorical values, only with numerals.


              1234
            
# Create list for categorical variables
categorical_features= list(dataset.select_dtypes(include=['object']).keys())
# Deleting the categorial values
dataset_without_categorical_features = dataset.drop(columns=categorical_features, axis=1)

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

Tarefa

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

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

Seção 3. Capítulo 1

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