Exploratory Data Analysis EDA In Python

Exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.

What is Exploratory Data Analysis used for?

Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.

The goals of Exploratory Data Analysis?

Exploratory data analysis (EDA) involves using graphics and visualizations to explore and analyze a data set. The goal is to explore, investigate and learn, as opposed to confirming statistical hypotheses.

Importance of EDA

EDA helps in identify errors in data sets. Gives a better understanding of the data set. It helps detect outliers or anomalous events. Helps understand data set variables and the relationship among them.

What are the components of EDA?

There are four basic parts of every EDA tool. The pattern editor, component editor, schematic layout, and PCB layout program. The names of the modules might vary from program to program, and they might be used differently, but they all exist in one form or another.

Skills needed for exploratory data analysis.

This includes practical expertise, such as knowing how to scrape and store data. It also requires more nuanced problem-solving abilities, such as how to analyze data and draw conclusions from it. As a statistical approach, exploratory data analysis (or EDA) is vital for learning more about a new dataset.

Difference between data visualization and exploratory data analysis.

Exploratory data analysis is a way to better understand your data which helps in further Data preprocessing. And data visualization is key, making the exploratory data analysis process streamline and easily analyzing data using wonderful plots and charts.

Methods used in Exploratory Data Analysis.

The main methods of analysis used are histogram, stem and leaf plot, and box plots. Fairly simple and easy to create some of the most valuable types includes two charts you can generate when doing EDA are Histograms and Scatter plots.

A histogram allows us to see the distribution of a particular variable.

A scatter plot allows us to see a relationship between two or more variables.

A Simple Example of EDA By Pairplot image:

Some of the most common plots used for Exploratory Data Analysis are as under.

1. Histograms.

2. Scatter plots.

3. Pair plots.

4. Box plots.

5. Violin plots.

6. Distribution Plots.

What are the Steps inoled while doing EDA in numerical data?

Steps for EDA

1, Look at the whole data overview. train.head()

2. Look at missing data.

3. Split the dataset into numerical and categorical(String).

4. Look at data distribution for numerical columns.

5. Look at Data Characteristics of text variables.

Difference between EDA and IDA.

EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.

Example of EDA

EDA on Abalone Dataset has been performed. This Dataset has 9 features with 1 Dependent and 8 Independent Features. You can Download the pdf of the EDA just by clicking on the download button of the embedded pdf in your PC as well as Mobile

Note: For Downloading on Mobile Phones, just click on the 'Open' Button. The pdf file will be automatically downloaded. You just have to wait for a few seconds to see the download option available on your mobile screen.