Implemented Data Visualization Projects
Repo for all the projects ( vertical post)…
Welcome back peeps.
Since we are now focusing on our goals for 2023 — new vertical series than horizontal ( means you will find all the contents of the series in one post and projects in second than developing/extending it to new posts every time). So, keep checking this post every day to see new projects.
Prerequisite to these projects —
Complete 60 days of Data Science and Machine Learning before starting this series ( link below) —
Projects Videos —
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Let’s dive in!
Data visualization is the process of creating graphical representations of data in order to better understand and communicate the information it contains. It can help to identify patterns, trends, and insights in the data that may not be immediately apparent from looking at raw numbers.
There are many different types of charts and visualization techniques that can be used to represent data, including:
- Bar charts: Used to compare values across different categories. They can be vertical (column chart) or horizontal (bar chart)
- Line charts: Used to show trends over time or across different categories.
- Pie charts: Used to show the proportion of different categories in a whole.
- Scatter plots: Used to show the relationship between two or more variables.
- Histograms: Used to show the distribution of a single variable.
- Heat maps: Used to show the density of data points in a two-dimensional space.
- Box plots: Used to show the distribution and variability of a set of data.
- Tree maps: used to show the hierarchical structure of data and the relative size of different categories.
- Network diagrams: used to show relationships between entities and how they are connected.
- Word Clouds: used to show the frequency of words in a text, the size of the word represents the frequency.
Choosing the right chart or visualization technique depends on the type of data you have and the message you want to convey. It’s important to use the appropriate visualization to effectively communicate the insights from the data.
This post will house all the Data Visualization projects related to the topics below-
Data Visualization
Data Understanding
Data Manipulation
Python, Pandas and Numpy
Charts in Data Visualization
Which chart to use and when?
Data visualizations using Matplotlib and Seaborn
Data Visualizations using Plotly
Data Visualizations using Bokeh
Data Visualizations in R
Dynamic Charts
How to best represent your data?
Categorical and Numerical Features
Missing Value Analysis
Fill the missing Values
Unique Value Analysis
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Correlation Analysis
Data Profiling
Feature Engineering
GroupBy Features
Categorical and Numerical Features
Missing Value Analysis
Fill the missing Values
Unique Value Analysis
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Correlation Analysis
Linear Regression
Data Profiling
Feature Engineering
Sort Values
Categorical and Numerical Features
Correlation Coefficients
Day 23: Data Analytics Project 9
Linear Regression
Data Profiling
Correlation Coefficients
Spearman’s ρ
Pearson’s r
Kendall’s τ
Cramér’s V (φc)
Phik (φk)
Standardization
Encoding
Linear Regression
Categorical and Numerical Features
Missing Value Analysis
Unique Value Analysis
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Summary Functions
Indexing
Grouping
Sorting
Data Profiling
Categorical and Numerical Features
Missing Value Analysis
Unique Value Analysis
Data Visualization
Correlation Coefficients
Power BI
Tableau
Tableau Main Charts
Performance Charts
Regression
Linear Regression
Multi Linear Regression
Polynomial Regression
Regression
Support Vector Regression
Decision Tree Regression
Random Forest Regression
Classification
Naive Bayes
Random Forest
Missing Value Analysis
Unique Value Analysis
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