Course Content
Data Preprocessing
Data Preprocessing
Data Augmentation: Synthetic Data
Data augmentation - is an important step in training machine learning models. This method is understood as an increase in the data sample for training through the modification of existing data. Generating “synthetic” data can be useful in a variety of situations where real-world data may be difficult to obtain, insufficient, or sensitive.
This method is used when there is not enough data to train a machine-learning model. Under the lack of data, we can understand that the dataset may not be representative of the underlying population or phenomenon being studied. The sample size should be large enough to provide sufficient statistical power to detect meaningful relationships or differences. The required sample size depends on factors such as the analysis's complexity, the data's variability, and the desired level of precision. Generating synthetic data can help to supplement real-world data and provide additional training examples.
The pandas library can be used to create synthetic data with a specific structure or format. Here's an example of how to use pandas to create a synthetic dataset:
import pandas as pd import numpy as np # Create a sample dataset dataset = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.choice(['male', 'female'], 10), 'C': np.random.randint(1, 100, 10)}) # Generate synthetic data using Pandas synthetic_data = pd.concat([dataset, dataset.sample(frac=0.5)]) print(synthetic_data)
We use the pd.concat() method to concatenate the original dataframe with a randomly sampled subset of the dataframe. By setting the frac parameter to 0.5, we sample 50% of the rows from the original dataframe and append them to the end of the dataframe, effectively doubling the size of the dataframe and generating synthetic data.
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Generate a dataset with 4 columns and 5 rows using pandas.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Data Augmentation: Synthetic Data
Data augmentation - is an important step in training machine learning models. This method is understood as an increase in the data sample for training through the modification of existing data. Generating “synthetic” data can be useful in a variety of situations where real-world data may be difficult to obtain, insufficient, or sensitive.
This method is used when there is not enough data to train a machine-learning model. Under the lack of data, we can understand that the dataset may not be representative of the underlying population or phenomenon being studied. The sample size should be large enough to provide sufficient statistical power to detect meaningful relationships or differences. The required sample size depends on factors such as the analysis's complexity, the data's variability, and the desired level of precision. Generating synthetic data can help to supplement real-world data and provide additional training examples.
The pandas library can be used to create synthetic data with a specific structure or format. Here's an example of how to use pandas to create a synthetic dataset:
import pandas as pd import numpy as np # Create a sample dataset dataset = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.choice(['male', 'female'], 10), 'C': np.random.randint(1, 100, 10)}) # Generate synthetic data using Pandas synthetic_data = pd.concat([dataset, dataset.sample(frac=0.5)]) print(synthetic_data)
We use the pd.concat() method to concatenate the original dataframe with a randomly sampled subset of the dataframe. By setting the frac parameter to 0.5, we sample 50% of the rows from the original dataframe and append them to the end of the dataframe, effectively doubling the size of the dataframe and generating synthetic data.
Swipe to show code editor
Generate a dataset with 4 columns and 5 rows using pandas.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Data Augmentation: Synthetic Data
Data augmentation - is an important step in training machine learning models. This method is understood as an increase in the data sample for training through the modification of existing data. Generating “synthetic” data can be useful in a variety of situations where real-world data may be difficult to obtain, insufficient, or sensitive.
This method is used when there is not enough data to train a machine-learning model. Under the lack of data, we can understand that the dataset may not be representative of the underlying population or phenomenon being studied. The sample size should be large enough to provide sufficient statistical power to detect meaningful relationships or differences. The required sample size depends on factors such as the analysis's complexity, the data's variability, and the desired level of precision. Generating synthetic data can help to supplement real-world data and provide additional training examples.
The pandas library can be used to create synthetic data with a specific structure or format. Here's an example of how to use pandas to create a synthetic dataset:
import pandas as pd import numpy as np # Create a sample dataset dataset = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.choice(['male', 'female'], 10), 'C': np.random.randint(1, 100, 10)}) # Generate synthetic data using Pandas synthetic_data = pd.concat([dataset, dataset.sample(frac=0.5)]) print(synthetic_data)
We use the pd.concat() method to concatenate the original dataframe with a randomly sampled subset of the dataframe. By setting the frac parameter to 0.5, we sample 50% of the rows from the original dataframe and append them to the end of the dataframe, effectively doubling the size of the dataframe and generating synthetic data.
Swipe to show code editor
Generate a dataset with 4 columns and 5 rows using pandas.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Data augmentation - is an important step in training machine learning models. This method is understood as an increase in the data sample for training through the modification of existing data. Generating “synthetic” data can be useful in a variety of situations where real-world data may be difficult to obtain, insufficient, or sensitive.
This method is used when there is not enough data to train a machine-learning model. Under the lack of data, we can understand that the dataset may not be representative of the underlying population or phenomenon being studied. The sample size should be large enough to provide sufficient statistical power to detect meaningful relationships or differences. The required sample size depends on factors such as the analysis's complexity, the data's variability, and the desired level of precision. Generating synthetic data can help to supplement real-world data and provide additional training examples.
The pandas library can be used to create synthetic data with a specific structure or format. Here's an example of how to use pandas to create a synthetic dataset:
import pandas as pd import numpy as np # Create a sample dataset dataset = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.choice(['male', 'female'], 10), 'C': np.random.randint(1, 100, 10)}) # Generate synthetic data using Pandas synthetic_data = pd.concat([dataset, dataset.sample(frac=0.5)]) print(synthetic_data)
We use the pd.concat() method to concatenate the original dataframe with a randomly sampled subset of the dataframe. By setting the frac parameter to 0.5, we sample 50% of the rows from the original dataframe and append them to the end of the dataframe, effectively doubling the size of the dataframe and generating synthetic data.
Swipe to show code editor
Generate a dataset with 4 columns and 5 rows using pandas.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowSwitch to desktop for real-world practiceContinue from where you are using one of the options below