2-Machine learning, code for identifying the outliers in jupyter notebook
Here’s an example Python code that can be used to identify outliers in a machine learning dataset using Jupyter Notebook:
import pandas as pd import numpy as np # Load the dataset dataset = pd.read_csv('path/to/dataset.csv') # Calculate Z-score z_score = np.abs(stats.zscore(dataset)) # Set a threshold for outlier detection threshold = 3 # Find indices of outliers outlier_indices = np.where(z_score > threshold) # Remove the outliers clean_dataset = dataset.drop(outlier_indices, axis=0) # Save the cleaned dataset to a new CSV file clean_dataset.to_csv('path/to/cleaned_dataset.csv', index=False)
In this example code, we first import the necessary libraries such as
numpy. We then load the dataset from a CSV file using the
Next, we calculate the Z-score of each data point in the dataset using the
np.abs(stats.zscore(dataset)) function. The Z-score measures the distance between a data point and the mean of the dataset in units of standard deviation.
We set a threshold of 3 standard deviations for outlier detection using the
threshold variable. We then find the indices of the outliers in the dataset using the
np.where(z_score > threshold) function.
Finally, we remove the outliers from the dataset using the
drop method of the
pd.DataFrame object. The
axis=0 parameter specifies that we want to drop rows containing outliers. We then save the cleaned dataset to a new CSV file using the
to_csv method of the
This code can be run in Jupyter Notebook to easily identify and remove outliers from a machine learning dataset.