### 2-Machine learning, code for identifying the outliers in jupyter notebook

Here’s an example Python code that can be used to identify outliers in a machine learning dataset using Jupyter Notebook:

`pythonCopy code````
import pandas as pd
import numpy as np
# Load the dataset
dataset = pd.read_csv('path/to/dataset.csv')
# Calculate Z-score
z_score = np.abs(stats.zscore(dataset))
# Set a threshold for outlier detection
threshold = 3
# Find indices of outliers
outlier_indices = np.where(z_score > threshold)
# Remove the outliers
clean_dataset = dataset.drop(outlier_indices[0], axis=0)
# Save the cleaned dataset to a new CSV file
clean_dataset.to_csv('path/to/cleaned_dataset.csv', index=False)
```

In this example code, we first import the necessary libraries such as `pandas`

and `numpy`

. We then load the dataset from a CSV file using the `pd.read_csv`

function.

Next, we calculate the Z-score of each data point in the dataset using the `np.abs(stats.zscore(dataset))`

function. The Z-score measures the distance between a data point and the mean of the dataset in units of standard deviation.

We set a threshold of 3 standard deviations for outlier detection using the `threshold`

variable. We then find the indices of the outliers in the dataset using the `np.where(z_score > threshold)`

function.

Finally, we remove the outliers from the dataset using the `drop`

method of the `pd.DataFrame`

object. The `axis=0`

parameter specifies that we want to drop rows containing outliers. We then save the cleaned dataset to a new CSV file using the `to_csv`

method of the `pd.DataFrame`

object.

This code can be run in Jupyter Notebook to easily identify and remove outliers from a machine learning dataset.