Exploratory Data Analysis is a data analytics process to understand the data in depth and learn the different data characteristics, often with visual means. This allows you to get a better feel of your data and find useful patterns in it.
It is crucial to understand it in depth before you perform data analysis and run your data through an algorithm. You need to know the patterns in your data and determine which variables are important and which do not play a significant role in the output. Further, some variables may have correlations with other variables. You also need to recognize errors in your data.
All of this can be done with Exploratory Data Analysis. It helps you gather insights and make better sense of the data, and removes irregularities and unnecessary values from data.
Helps you prepare your dataset for analysis.
Allows a machine learning model to predict our dataset better.
Gives you more accurate results.
It also helps us to choose a better machine learning model.
Steps Involved in Exploratory Data Analysis
** 1. Understand the Problem **
Before starting the exploratory data analysis (EDA), it is essential to understand the problem you are trying to solve. What is the research question or business problem you are trying to answer? What are the goals of the analysis? Understanding the context of the data will help you frame the analysis and guide your EDA efforts.
2. Data Collection
Data collection is an essential part of exploratory data analysis. It refers to the process of finding and loading data into our system. Good, reliable data can be found on various public sites or bought from private organizations. Some reliable sites for data collection are Kaggle, Github, Machine Learning Repository, etc.
example
Let’s explore steps of Exploratory data analysis in detail using customer churn analysis based on the customers behaviour on the website or app data.
We will classify what kind of customers are likely to sign up for the paid subscription of a website. After analyzing and classifying the dataset, we will be able to do the targeting-based marketing or recommendation to the customers who are likely to sign up for the paid subscription plan.
Import the Libraries:
import pandas as pd
import numpy as np
import re
import string
import matplotlib.pyplot as plt
import seaborn as sn
from dateutil import parser
import warnings
warnings.filterwarnings('ignore')
Data is stored in csv file format, hence we are importing it using pd.read_csv
data = pd.read_csv('app_data.csv')
How many entries (Rows) and attributes(Columns) are present in the data? What is the shape of the data?
data.shape
(50000, 12)
.shape method returns number of rows by number of columns in the dataset. So, in our dataset we have 50000 rows and 12 columns.
Display the first 5 entries of the data.
data.head()
.head() method gives the first 5 rows of the dataset. It is useful for seeing some example values for each variable.
What are the different features available in the data?
data.columns
.columns method returns all the columns in the dataset.
Display the distribution of Numerical Variables.
data.describe()
.describe() method summarizes the count, mean, standard deviation, min, and max for numeric variables. It helps to understand the skewness in the data.
3. Data Cleaning
Data cleaning refers to the process of removing unwanted variables and values from your dataset and getting rid of any irregularities in it. Such anomalies can disproportionately skew the data and hence adversely affect the results. Some steps that can be done to clean data are:
Missing Data
Irregular Data (Outliers)
Unnecessary Data — Repetitive Data, Duplicates and more
Inconsistent Data — Capitalization, Addresses and more
4. Explore the Data
Once you have cleaned the data, the next step is to explore the data. Exploratory data analysis involves examining the data to identify patterns, relationships, and trends. There are several ways to explore the data:
a. Descriptive Statistics: Descriptive statistics summarize the data's main characteristics, such as mean, median, mode, standard deviation, and variance.
import math
import statistics
import numpy as np
import scipy.stats
import pandas as pd
These are all the packages you’ll need for Python statistics calculations. Usually, you won’t use Python’s built-in math package, but it’ll be useful in this tutorial. Later, you’ll import matplotlib.pyplot for data visualization.
b. Data Visualization: Data visualization is a powerful way to explore the data. You can create charts, graphs, and plots to visualize the data's distribution, relationships, and patterns.
c. Statistical Tests: Statistical tests can help you test hypotheses and identify significant differences between groups.
5. Identify Outliers
An outlier is a data point that differs significantly from the majority of the data taken from a sample or population. There are many possible causes of outliers, but here are a few to start you off:
Natural variation in data
Change in the behavior of the observed system
Errors in data collection
Data collection errors are a particularly prominent cause of outliers. For example, the limitations of measurement instruments or procedures can mean that the correct data is simply not obtainable. Other errors can be caused by miscalculations, data contamination, human error, and more.
There isn’t a precise mathematical definition of outliers. You have to rely on experience, knowledge about the subject of interest, and common sense to determine if a data point is an outlier and how to handle it.
6. Identify Patterns and Relationships
Once you have explored the data, you can identify patterns and relationships between variables. Correlation analysis can help you identify the relationship between two variables, and regression analysis can help you predict the outcome variable based on the predictor variables.
7. Iterate
Iterative data exploration is an essential aspect of the exploratory data analysis (EDA) process. EDA is an iterative process that involves repeatedly looking at data from different angles and perspectives to gain a deeper understanding of its properties and relationships.
In the initial stages of EDA, you may start with a general overview of the data to understand its size, shape, and structure. Once you have a basic understanding of the data, you may start to explore specific aspects, such as relationships between variables, distributions, or outliers.
As you uncover new information, you may need to go back and revisit earlier steps of the process, updating or refining your analysis. This iterative approach allows you to build a more complete and nuanced understanding of the data, and can help you identify patterns, trends, or anomalies that may be missed with a single pass through the data.
Overall, iterative data exploration is a critical part of the EDA process, allowing you to explore data from different angles, uncover hidden relationships, and gain a deeper understanding of its properties and patterns.
8. Reporting
After completing an exploratory data analysis (EDA), it's important to communicate your findings to others. One way to do this is by creating a report that summarizes your EDA process, the insights gained, and any recommendations or conclusions that can be drawn from the data.
Here are some steps you can follow to create a report after an EDA:
Start with an introduction: Begin by providing some context about the data and the purpose of the analysis. This could include a brief overview of the data source, the problem you're trying to solve, or the goals of the analysis.
Describe your EDA process: Explain the methods you used to explore the data, such as summary statistics, visualizations, or hypothesis testing. Provide details on the data cleaning and preparation steps you took, as well as any challenges or limitations you encountered.
Present your findings: Summarize the key insights you gained from the analysis. This could include trends, patterns, correlations, outliers, or other noteworthy observations. Use visualizations, such as charts, graphs, or tables, to help illustrate your findings.
Draw conclusions: Based on your findings, draw conclusions about the data and the problem you're trying to solve. Identify any relationships, trends, or patterns that are significant, and provide context for why they matter. Be sure to acknowledge any limitations or uncertainties in your analysis.
Make recommendations: Based on your conclusions, provide recommendations for next steps or actions that could be taken based on the insights gained from the EDA. This could include further analysis, data collection, or changes to business processes.
Conclude with a summary: Provide a brief summary of the key points of your report, highlighting the most important findings and recommendations.
Overall, the goal of the report is to provide a clear, concise, and accurate summary of the EDA process and its results. It should be tailored to the intended audience, using language and visuals that are accessible and easy to understand.
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