Here we will see the steps involved in identifying the outlier value in a dataframe. Follow the steps below: import pandas as pd import numpy as np def outliers(data, threshold=1.5): “”” outlier “”” q1 , q3 = np.percentile(data, [25, 75]) iqr = q3 – q1 lower_bound = q1 – (threshold * iqr) upper_bound = q3 + (threshold * iqr) return […]
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”, […]