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

Here’s an example Python code for identifying outliers in a machine learning dataset using Jupyter Notebook:

In this example code, we first load the dataset into a pandas dataframe using the pd.read_csv() function.

We then define a function called detect_outliers that takes in a dataset and uses the z-score method to detect outliers. The function first calculates the mean and standard deviation of the data, and then sets a threshold for detecting outliers as three times the standard deviation.

import pandas as pd
import numpy as np

# load the dataset
df = pd.read_csv('path/to/dataset.csv')

# define a function to detect outliers
def detect_outliers(data):
    # calculate the mean and standard deviation of the data
    mean = np.mean(data)
    std = np.std(data)
    # set the threshold for detecting outliers
    threshold = 3 * std
    # identify outliers using the z-score method
    z_scores = [(x - mean) / std for x in data]
    outliers = np.where(np.abs(z_scores) > threshold)
    return outliers[0]

# apply the detect_outliers function to each column of the dataset
outliers = {}
for column in df.columns:
    column_outliers = detect_outliers(df[column])
    outliers[column] = column_outliers

# print the outliers for each column
for column, column_outliers in outliers.items():
    print('Outliers in column {}: {}'.format(column, column_outliers))

The function then calculates the z-scores for each data point in the dataset, and identifies outliers as any data point with an absolute z-score greater than the threshold.

Next, we apply the detect_outliers function to each column of the dataset using a for loop, and store the outliers for each column in a dictionary called outliers.

Finally, we print the outliers for each column by iterating through the outliers dictionary using another for loop.

This code can help you identify outliers in your machine learning dataset, which is an important step in data preprocessing and cleaning.