How to Visualize and Detect Outliers Using Plotly in Python?
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Condition for Detecting Outliers in Data Using Plotly and Python
Description:
Outlier detection is a critical step in data preprocessing. Outliers are data points that
significantly differ from the rest of the data and can negatively affect statistical analyses and
machine learning models. Visualizing outliers helps in identifying unusual data points and ensuring
that the analysis remains robust. Plotly is an interactive graphing library in Python that can be
used to visualize and detect outliers efficiently. This guide demonstrates how to detect outliers
using Plotly with different types of plots, along with classification metrics, and provides insights
on selecting datasets.
Step-by-Step Process
Data Collection and Preprocessing:
Choose or load a dataset that requires outlier detection. Perform basic data preprocessing such as handling missing values or scaling data if necessary.
Visualize the Data:
Use various Plotly visualization techniques to explore the dataset.
Outlier Detection:
Apply statistical methods to identify outliers (e.g., Z-score, IQR method). Visualize detected outliers using scatter plots, box plots, and heatmaps.
Evaluation:
Evaluate the detected outliers using classification metrics if the data involves a classification task.
Choosing the Right Dataset:
Understand the characteristics of the dataset, the need for outlier detection, and select accordingly.
Why Should We Choose Plotly for Outlier Detection?
Interactive Visualization:
Plotly allows interactive plots, making it easier to zoom in and out, hover over data points, and inspect the details of the data.
Flexibility:
Plotly can be used with a variety of plot types such as scatter plots, box plots, and heatmaps.
Beautiful Visualizations:
It provides visually appealing plots, making the outlier detection process more intuitive.
Integration:
Plotly integrates seamlessly with Pandas, NumPy, and other scientific libraries in Python.
Sample Source Code
# Importing necessary libraries
import pandas as pd
data = pd.read_csv("/home/soft15/soft15/nand_py/py_Exercises/python_Machine_Learning/21-11-2024/14.detect outliers using plotly /train.csv")
df = pd.DataFrame(data)
import matplotlib.pyplot as plt
import seaborn as sns
arguments = (df["Age"], df["Fare"])
sns.boxplot(arguments)
plt.show()
# Find 5 number theory (min, 25%, median, 75%, max)
minimum = min(df["Age"])
maximum = max(df["Age"])
median = df["Age"].median()
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