We need Data to create a machine-learning model.
Acquiring Data or Data Acquisition is the first and one of the most crucial steps in any data analysis or machine learning project. It involves gathering, collecting and measuring information from various sources to draw meaningful insights. We used this data for analysis, modelling, and decision-making. The quality and relevance of the acquired data directly impact the analysis outcomes.
Importance of Data Acquisition
Good data acquisition is essential for reliable insights and predictions because it lays the foundation for data analysis and machine learning. Poor data acquisition can lead to incorrect conclusions and flawed decision-making.
For data acquisition:
Manual labelling: The label is the output provided with the data to make it understandable to the machine. Data is collected and manually labelled on the other side.
From observing human behaviours: Human behaviour is that we perform certain activities in many places. When we visit a website, our actions are recorded in a log. The log includes our searches, the time of our visit, whether we purchase on a commerce website, the purchase amount, the items purchased, and where the purchase was made.
| User ID | Time | Price ($) | Purchased |
|---|---|---|---|
| 4783 | 21 Jan 8:15:20 | 7.95 | yes |
| 3893 | 3 Mar 11:30:15 | 10 | yes |
| 7453 | 11 Jun 14:15:5 | 9.5 | no |
| 931 | 2 Aug 20:30:55 | 12.9 | yes |
From observing behaviours of Machines: Data can be obtained from the machine, and logs can be created for any industrial machine to monitor their performance on various variables.
Download from websites / partnerships: Thanks to the open internet, you can find so many datasets available for free online.
- Computer vision or Image datasets.
- Self-driving car datasets.
- Speech recognition datasets
- Medical imaging datasets.
Surveys and Questionnaires are familiar methods for collecting data directly from individuals. They can be conducted online, via phone, or in person. This method is valid for gathering specific information from a targeted audience.
Web Scraping involves extracting data from websites. This method is valid for collecting large amounts of data from online sources. However, it’s necessary to ensure that web scraping complies with legal and ethical guidelines.
APIs (Application Programming Interfaces) allow you to access data from various online services and databases. Many organizations provide APIs to share their data with developers and researchers.
Databases are structured data collections that can be easily accessed, managed, and updated. Data can be acquired from internal databases within an organization or from external databases available for public use.
Sensors and Internet of Things (IoT) devices collect data from the physical world. This method is commonly used in industries like manufacturing, healthcare, and smart cities.
Challenges in Acquiring Data
- Data Quality: Ensuring the data accuracy, completeness, and consistency is a primary challenge. Poor quality data can lead to incorrect analysis and decisions.
- Data Privacy and Security: Collecting data sometimes involves handling sensitive information. It’s crucial to comply with data privacy laws and ensure data is securely stored and processed.
- Data Integration: Combining data from different sources can be challenging due to differences in formats, structures, and quality. Effective data integration is necessary for comprehensive analysis.