Exploratory-Data-Analysis-using-Data-Visualization-Techniques.

Exploratory Data Analysis (EDA) is a key step in the data analysis process. Data visualization techniques bring Data to life, by making it accessible and understandable-and especially to people not adept to the tech world and help to understand the characteristics of your dataset.

Data visualizations, either through shapes, graphs, patterns or colors, provide a better way in which we can perceive information.

Visualization techniques using python.

Histograms- Visualize the distribution of a numerical variable, illustrate the distribution of data, making it easy to spot trends and outliers.

See below example.

import matplotlib.pyplot as plt

import numpy as np

data = np.random.randn(1000)### use your dataset

plt.hist(data, bins=20, color='green', edgecolor='black')

plt.title('Histogram Example')

plt.xlabel('Value')

plt.ylabel('Frequency')

plt.show()

Box Plots- This technique Show the summary statistics of a numerical variable, including the median, quartiles, and outlier, allowing you to grasp data variability from a glance.

*Below is an example. *

import matplotlib.pyplot as plt

import numpy as np

np.random.seed(2)

dataSet = np.random.normal(100, 25, 200)

print(dataSet)

figure = plt.figure(figsize =(10, 8))

plt.boxplot(dataSet)

plt.show()

** Bar charts- ** Bar charts are an excellent way for comparing categorical data. enabling you to easily identify which categories are most prevalent and identify trends or animalities.
Below is an example.

import matplotlib.pyplot as plt

categories = [' A', 'B', ' C',' D']

values = [10, 20, 15 ,5]

plt.bar(categories, values, color='skyblue')

plt.title('Bar Chart Example')

plt.xlabel('Category')

plt.ylabel('Value')

plt.show()

Scatter Plots:
Visualize the relationship between two numerical variables.
Below is an example.

import matplotlib.pyplot as plt

import numpy as np

x=5

y=30

plt.scatter(x, y, color="purple", marker="o")

plt.title(" Scatter Plot") ###Insert your title

plt.xlabel("X variable") ###the x variable

plt.ylabel("Y variable") ##y-axis label

plt.show()

Line and making each point with a circle.

See example below.

import matplotlib.pyplot as plt

import numpy as np


ypoints = np.array([3, 8, 1, 10])


plt.plot(ypoints, marker = 'o')

plt.show()

Summary

Exploratory data analysis is an iterative process, and the choice of visualization techniques depends on your data and objectives. You can combine multiple techniques to gain a deeper understanding of your data and uncover important patterns and insights.