1-Machine learning, code for identifying the outliers in jupyter notebook
Certainly, here’s an example Python code for identifying outliers in a dataset using machine learning in a Jupyter notebook:
import pandas as pd import numpy as np from sklearn.ensemble import IsolationForest # load the dataset into a pandas dataframe data = pd.read_csv('dataset.csv') # specify the column(s) to be used for outlier detection X = data[['column1', 'column2']] # create an IsolationForest instance with contamination value # representing the percentage of outliers in the data clf = IsolationForest(contamination=0.1) # fit the model to the data clf.fit(X) # predict the outliers in the data outliers = clf.predict(X) # add a new column to the dataframe to store the outlier predictions data['outlier'] = pd.Series(outliers) # print the rows containing outliers print(data[data['outlier'] == -1])
In this example code, we first import the necessary libraries, including pandas, numpy, and IsolationForest from scikit-learn.
We load the dataset into a pandas dataframe using the
read_csv function. We then specify the column(s) to be used for outlier detection by creating a new dataframe
X containing only those columns.
Next, we create an instance of the IsolationForest algorithm and specify the
contamination parameter, which represents the percentage of outliers in the data.
We fit the model to the data using the
fit method and then use the
predict method to generate predictions for each row in the dataframe.
We add a new column to the dataframe called ‘outlier’, which stores the outlier predictions for each row. Finally, we print the rows containing outliers by filtering the dataframe for rows where the ‘outlier’ column is equal to -1.
This approach uses machine learning to identify outliers in a dataset, which can be helpful in identifying data quality issues or anomalies in the data.