### 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:

`pythonCopy code````
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 `fit`

method.

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 `print`

function.

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.