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:

pythonCopy codeimport 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.