3- Machine learning, code for identifying the outliers in jupyter notebook
Here’s an example Python code for identifying outliers in a dataset using machine learning in Jupyter Notebook:
import pandas as pd import numpy as np from sklearn.ensemble import IsolationForest # load the dataset data = pd.read_csv("path/to/dataset.csv") # extract the columns of interest columns_of_interest = ["col1", "col2", "col3"] X = data[columns_of_interest].values # create an instance of the Isolation Forest algorithm clf = IsolationForest(n_estimators=100, max_samples="auto", contamination="auto", random_state=0) # fit the model to the data clf.fit(X) # predict the outliers in the dataset y_pred = clf.predict(X) # create a mask to identify the outliers outliers_mask = y_pred == -1 # extract the outliers from the dataset outliers = data[outliers_mask] # display the outliers print(outliers)
In this code, we first import the necessary libraries, including pandas for loading the dataset, numpy for working with arrays, and IsolationForest from scikit-learn for identifying the outliers.
We then load the dataset using pandas
read_csv function and extract the columns of interest that we want to use for outlier detection.
Next, we create an instance of the IsolationForest algorithm with some hyperparameters, including the number of estimators, maximum number of samples, and contamination rate. We fit the model to the data using the
We then use the
predict method to predict the outliers in the dataset. We create a mask to identify the outliers and extract them from the dataset. Finally, we display the outliers using the
This is just one example of how to identify outliers using machine learning in Jupyter Notebook. The specific code may vary depending on the dataset and algorithm being used.