Artificial Intelligence is a very critical aspect of technology that has come to stay, with the transformative power and disruptive power of AI it is very important to look into how it functions in a broader view and perspective. Dataset is the foundational bases of every AI systems and models, which could serve has a structured or unstructured information for AI system or models. The ability for AI system to learn pattern, relationships, and decision rules has made it a system that is heavily dependent on data or information. That is the more reason why it is very important for this data and information to be of good quality, diverse and balanced because of their ability to affect the outcome of any AI model or system.
In the AI channel, datasets go through numerous essential phases. Which includes data collections, data cleaning, data preprocessing, data transformation, and partitioning into training, validation, and testing sets. Proper dataset handling guarantees the AI models ability to be applied in different kind of scenarios is guaranteed such that it is free of bias, and makes accurate predictions I every application.
Dataset could be in several formats or could take several shapes and for which includes image data, text data, video data and a time series data. All depending on the use cases or applications. For instance medical dataset which includes medical images, medical equipment sensor data can be used has dataset to enable AI systems and models for adequate disease diagnosis. And for every AI application in any field there are datasets available for AI models to adequately learn from. AI models and systems are only has good has the dataset they are train on and from.
Dataset visualization methods
1. Histograms
Histograms are one of the tools that are commonly used to visualize a given dataset it is basically used to explore the distribution of numerical features in a given dataset.
- They group continuous values into bins (intervals) and count how many values fall into each bin.
- The x-axis represents the range of values (e.g., age, income, temperature).
- The y-axis represents the frequency (count) or density of data points in each bin.
The shape of the histogram shows whether the distribution is normal (bell curve), skewed, uniform, bimodal, etc.
A. Normal Distribution (Bell Curve)
Normal histogram distribution occurs in dataset that most data points are clustered around the mean (average) and in this case the probability decreases symmetrically as you move away from the mean. Example of such distribution occurs in exams scores of a class or course, human height of a group of people and much more.
B. Skewed Distribution
- Right-Skewed (Positive Skew): Right-Skewed distribution occurs in a dataset that most values are small/low, but a few very large values stretch the tail to the right. Example of such distribution occurs in Income distribution where most people earn modest salaries, but a few billionaires push the curve to the right.
- Left-Skewed (Negative Skew): Left-Skewed distribution occurs in a dataset that most values are large, but a few very small ones stretch the tail to the left. Example of such distribution Retirement age where most people retire late (60–75), but a few retire very early, pulling the distribution left.
C. Uniform Distribution
Uniform histogram distribution occurs in dataset that all values are equally likely across the range of value and there is No obvious “peak” or concentration of values. Example of such is rolling a fair dice (1–6 all equally likely).
D. Bimodal Distribution
Bimodal distribution occurs in dataset that has two peaks (modes), often because it combines two different groups. Example of such distribution are exam results with two distinct groups: students who studied vs. those who didn’t.
