Ignoring Data Cleaning
In data science, data cleaning means making the data look tidy. Processing with cleaned data provides accurate results and avoids underfitting. Ignoring data cleaning makes our results unreliable and leads us wrong. It also makes our analysis confusing. We get data from various sources like web scraping, third parties, surveys, etc. The collected data comes in all shapes and sizes. Data cleaning is the process of finding mistakes and fixing missing parts.
Causes of Ignoring Data Cleaning
Example: Sales Data with Duplicate Entries -Let’s consider the analysis of sales data of a product. We may notice duplicate entries of some data due to technical issues during data collection. Ignoring data cleaning makes us proceed with sales analysis with duplicates. Consequently, the sales analysis shows high numbers, making the product seem more popular than it is.
Key Aspects of Data cleaning
This step involves careful analysis of our data for mistakes like inaccuracies and typing errors. It is like proofreading a document to ensure the information is correct.
- Handling Missing values: Sometimes, the data might be incomplete. The data will be with blanks where information should be. The data cleaning step decides which data should be filled in this gap. It can be either filling it with appropriate data or dropping it responsibly.
- Standardizing Formats: Data comes in different formats. If analysis is performed with different data formats, it leads to inconsistent results. Hence, to ensure consistency, all data should follow the same structure and also the same measurement units. This consistency makes the analysis easier.
- Dealing with outliers: Outliers are the data points that don’t fit in the data range. The data cleaning process will either convert the outlier to fit into the data range or remove it.
Practical Tips
- Develop a systematic approach to data cleaning that includes the creation of reusable functions for common things.
- Use libraries such as Pandas and Scikit-leran for efficient data cleaning and preprocessing .
6 Common Mistakes to Avoid in Data Science Code
As we know Data Science is a powerful field that extracts meaningful insights from vast data. It is our job to discover hidden secrets from the available data. Well, that is what data science is. In this world, we use computers to solve problems and bring out hidden insights. When we enter into such a big journey, there are certain things we should watch out for. Those who like playing with data know the tricky part of understanding the data and the possibility of making mistakes during the data processing.
How can I avoid mistakes in my Data Science Code?
How can I write my Data Science code more efficiently?
To answer all your questions, In this article, you get to know Six common mistakes to avoid in data science code in detail.
Table of Content
- Ignoring Data Cleaning
- Neglecting Exploratory Data Analysis
- Ignoring Feature Scaling
- Using default Hyperparameters
- Overfitting the Model
- Not documenting the code
- Conclusion