8 Best Visualizations To Consider For Your Data Science Projects!

Visualizations are a significant aspect of any data science project.

In Data Science, having a clear approach and orientation to solve the particular problem statement is crucial. Luckily, the tools required for Data Science offer the users and developers an excellent way to create to visualize the data and datasets to build effective models.

Thanks to visualization, most complex problems can be broken down into simpler elements for data scientists to figure out the optimal model architectures and solutions to complicated tasks. Hence, visualizations play a vital role in the successful completion of every major Data Science project. Without the use of visualization, it is nearly impossible to gauge the data patterns of a difficult task.

In this article, we will understand some of the basic features of data visualizations and try to interpret the benefits of exploratory data analysis while solving any kind of task. We will discuss a few essential libraries for visualization purposes. Then, we will have a detailed discussion on the eight best types of visualization techniques every Data Scientist should know about. Finally, we will conclude with a real-time example of interpreting these visualizations.

Understanding EDA:

In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It is an essential aspect of Data Science that you should consider while working on any kind of task. When you visualize the data, you gain an intuitive understanding, and your brain can perceive the various notions of their working standards. You also get several ideas and feedback on how you can work on these datasets.

Exploratory Data Analysis in Data Science is a great approach for developers to perform a detailed analysis on the available or collected datasets with the help of visualization techniques to obtain effective and efficient solutions. The exploratory data analysis step is considered to be the most important step to gain a procedural understanding of the further implementation of the data that is to occur in the future progress of the project.

While developing your projects or Machine Learning models, it is highly recommended that you take the exploratory data analysis step very seriously as it will help to reduce the workload and effort required for the particular task. If you know the essential elements to consider for the development of the project, it becomes easier for you to interpret, analyze, and develop.

Libraries:

Matplotlib.pyplot and seaborn are the two best library modules for visualization and performing exploratory data analysis tasks. These allow you to plot many graphical structures that are going to be extremely helpful for analyzing your data. These two libraries are some of the best approaches to solve almost any kind of visualization task of the available data in a Data Science project. They provide numerous applications and modules to solve multiple tasks.

Plotly is another great visualization tool that can be utilized by all data scientists for gaining high-quality visualizations. Plotly allows the users to get a glance at the 3-D visualizations to have a 3-Dimensional view of the overall dataset. This method allows the user to interpret the problem in a much more concise manner and ultimately develop amazing models.

The Tensorboard tool available in the Tensorflow deep learning framework is a fantastic way to the visualizations of the overall performance of deep learning model architectures that you have built. Using these graphs produced by Tensorboard, the developers can easily understand the interpretations of the train and validation data. They can figure out the over-fitting or under-fitting mechanisms to find alternative solutions.

8 Best Visualizations You Must Know As A Data Scientist:

Let us explore eight visualization techniques that every data scientist or Data Science enthusiast should totally know about. The eight visualization techniques described in this section will include an image as well as a small sample code block for a better understanding of these visualization methodologies. Let us begin exploring each of these concepts.

1. Bar Graph:

matplotlib.pyplot.bar()

The bar graph is one of the best visualization techniques that are available in the matplotlib library for users to utilize. The graphs are often used for the comparison of the data elements present in the datasets.

These comparisons provide a clear approach for the data scientists to interpret the approach to choose for attempting to solve the particular problem. It is also extremely useful to understand the type of data and figure out if the data is balanced or unbalanced.

2. Scatter Plot:

matplotlib.pyplot.scatter()

The scatter plot is another great visualization technique all the practitioners of Data Science must be accustomed to. These plots are mainly used for two purposes. These two reasons can be described in a simplistic manner.

The first reason is similar to the bar graphs where these scatter plots can be utilized to determine the exact comparisons of the datasets provided to the user. They can also be employed to determine the different values as well as the differ able parameters in the distribution trends of the data elements.

3. Pie Chart:

import matplotlib
matplotlib.axes.Axes.pie
matplotlib.pyplot.pie

The pie chart is a brilliant way to visualize the data elements in your dataset. This visualization technique is deployed in Data Science projects to figure out your calculations and computations in percentages. This technique is especially useful for determining a percentage approach on your datasets.

The pie chart can also be utilized for the purpose of comparison of the numerous distributions of the data and data elements. However, it is important to notice that the slices produced in the pie chart have variable spaces. Otherwise, this could make the visualization complex because all the percentages would be closer to each other than expected.

4. Histograms:

matplotlib.pyplot.hist()

Histograms are another amazing technique present in the vast tool kit of the matplotlib library. These histogram visualizations are best utilized for the plotting of frequency distributions. Histograms are used to generate accurate images and graphs of a list or an array in Python. This application is especially for Data Science projects.

The applications of histograms include the development of visualizations for problems related to time series analysis and another business forecasting. If you have a long wide stable frequency pattern or an array of data elements, utilize this opportunity to benefit from the histogram visualization technique available in the matplotlib library.

5. Heatmaps:

The above figure is a representation for the Heatmap for the various hyperparameters for a problem related to decision trees.

import seaborn as sns
sns.heatmap()

Heatmaps are one of the most useful visualization technique for the computation of complex problems related to hyperparameter tuning in the procedural building and analysis of machine learning algorithms like decision trees, random forests, K-nearest neighbors (KNN), and other algorithms that require the use of figuring out the precise hyperparameter values.

These heatmaps visualization often use both the matplotlib and the seaborn library for the interpretation of the accurate values to determine the best values for the particular task. The heatmaps produce multi-color interpretations that help to gain a more detailed understanding of the data elements.

6. Box Blots:

matplotlib.pyplot.boxplot()

According to Wikipedia, in descriptive statistics, a box plot or boxplot is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending from the boxes indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram.

The box plots are a great visualization technique that can be employed to solve most of the complex problems while analyzing some of the datasets. The lower and upper quartile are useful for indicating the below and above ranges for the precise values.

7. Violin Plots:

matplotlib.axes.Axes.violinplot

The next visualization technique that we will discuss in this article is the Violin plots. These plots are highly similar to the previously mentioned box plots. While a box plot only shows summary statistics such as mean/median and interquartile ranges, the violin plot shows the full distribution of the data. This makes Violin plots extremely useful for solving unique problems with higher ranges on the available datasets.

8. 3-D Plots:

3-Dimensional plots are a fantastic way for the users to gain an intuitive understanding of the numerous data elements present in the working example of the project. I would highly recommend checking out this visualization technique closely.

Using a 3-D view will help the developers to gain a detailed understanding of how our structure looks like and what methods can they employ to achieve the most fruitful results. You can operate the overall view of the visualization to suit your preference and understand the data more closely.

Quick Example For 3 Plots:

For this simple quick example, I will be implementing a custom fruits dataset that will allow us to have five fruits along with their respective quantities. Using these figures and values obtained, we will visualize this available data to create three visualizations, namely bar graph, scatter plot, and pie chart. Before we begin this process, let us import the essential matplotlib library that we will utilize for the computation of this task.

import matplotlib.pyplot as plt

Once we have imported the matplotlib library, we will focus on the preparation of our dataset. We will use five fruits, namely apple, mango, grapes, strawberry, and oranges, respectively interconnected with their overall quantities. We will utilize the below code block to append some data to our respective lists that serve a particular purpose. Check out the complete guide to dictionaries in Python from the link provided below.

Mastering Dictionaries And Sets In Python!

Fruits = {"Apple": 30, "Mango": 15, "Grapes": 20, "Strawberry": 10, "Oranges": 25}

classes = []
counts = []
total = 0

for i, j in Fruits.items():
    classes.append(i)
    counts.append(j)
    total += j

Once we have finished the procedure of creating our dataset successfully, let us look at our first visualization technique to analyze the fruits dataset. The below code block provides the user the entire code to build the bar graph for the fruits dataset and accomplish the accurate graph for the visualization procedure.

plt.bar(classes, counts, width=0.5)
plt.title("Bar Graph of Fruits")
plt.xlabel("Classes")
plt.ylabel("Counts")

The image above is an accurate representation of how the bar graph visualization on the fruits dataset looks with the x-axis containing the classes, which are the fruits. The y-axis contains the quantity or the total counts for each of the fruits that are visualized. The below code block provided is how you can construct the scatter plot for the fruits dataset that we created earlier.

plt.scatter(classes, counts)
plt.plot(classes, counts, '-o')
plt.title("Scatter Plot of Fruits")
plt.xlabel("Classes")
plt.ylabel("Counts")
plt.show()

The image above is an accurate representation of how the Scatter plot visualization technique on the fruits dataset looks with the x-axis containing the classes, which are the fruits. The y-axis contains the quantity or the total counts for each of the fruits that are visualized. The below code block provided is how you can construct the pie chart for the fruits dataset that we created earlier.

plt.pie(counts, labels = classes, autopct='%2.1f%%',
        shadow=True, startangle=90)
plt.title("Pie Chart of Fruits")
plt.xlabel("Classes")
plt.ylabel("Counts")
plt.show()

The above visualization contains the pie chart technique, which includes various classes along with their respective percentages of occurrences. I would highly recommend the interested viewers to try out more visualization methods and techniques on their own with numerous problem statements to analyze and understand the working procedure of these structures developed in a more concise manner.

Conclusion:

We have figured out some essential and useful visualization techniques that will be helpful for data scientists to approach a wide variety of Data Science problems. These eight visualization techniques should be your primary focus in order to obtain a strong insight and figure out the best approach to solve the particular problem.

Visualization is one of the most significant steps in Data Science that must be explored within the second or third stage of building the machine learning or deep learning model. The visualization stage in Data Science usually comes after the collection or the pre-processing of data sections in a project. Hence, we are ensured to be provided with a detailed visual approach on the best features to focus on for development purposes.

Visualization allows developers to carefully examine multiple scenarios and plan their approach to solving the project accordingly. Focusing on the improvement and advancements of your visualization techniques will not only improve the Productivity of solving your projects but also significantly increase the speed of finding the best approaches and optimal solutions. Hence, make it a habit to dwell on exploring so that the other steps become much more efficient.

If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.

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