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What advanced data cleaning techniques can you use for complex data structures?

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Data cleaning is a crucial step in data mining, especially when dealing with complex data structures such as nested, hierarchical, or relational data. These data types often require advanced techniques to identify and resolve issues such as missing values, inconsistencies, duplicates, outliers, or errors. In this article, you will learn about some of the advanced data cleaning techniques that you can use for complex data structures, and how they can improve the quality and usability of your data for data mining.

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Ali Rizvi

Data Scientist @ Turing | Machine Learning, AI, Data Analytics

1 Data Wrangling

Data wrangling is the process of transforming and reshaping complex data structures into a more suitable format for data mining. For example, you may need to flatten nested or hierarchical data into a tabular format, or join relational data from different sources or tables. Data wrangling can involve various operations such as parsing, splitting, merging, aggregating, pivoting, or filtering data. You can use tools such as Python, R, SQL, or Excel to perform data wrangling tasks, depending on the type and size of your data.

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2 Data Imputation

Data imputation is the technique of filling in missing values in your data with plausible estimates. Missing values can occur due to various reasons, such as data entry errors, sensor failures, or incomplete records. Missing values can affect the performance and accuracy of your data mining models, so you need to handle them carefully. Data imputation can use various methods, such as mean, median, mode, or regression imputation, or more advanced methods such as k-nearest neighbors, expectation-maximization, or multiple imputation. You should choose the method that best suits the nature and distribution of your data.

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Ali Rizvi

Data Scientist @ Turing | Machine Learning, AI, Data Analytics
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In many cases, I find that data imputation (or deciding to drop it) should be the done after a lot of consideration and deliberation. And this requires understanding of your data, like 1. what is it about 2. where is it coming from 3. what do you want to achieve To impute missing values, first check that does it really require any special imputation or dropping it straight away because it contained some 70% missing values? For example consider you have a column of whether an item was purchased or not. For every item purchased, you have 1. Otherwise its N/A. It is obvious that you need to put a single value in all N/As. This is a very simple example of why you should be careful before imputing or dropping a feature.

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3 Data Deduplication

Data deduplication is the technique of identifying and removing duplicate records in your data. Duplicate records can arise due to various reasons, such as data entry errors, data integration issues, or inconsistent naming conventions. Duplicate records can skew your data mining results and lead to inaccurate or misleading insights. Data deduplication can use various methods, such as exact matching, fuzzy matching, or record linkage. You should define the criteria and thresholds for identifying and resolving duplicates, and verify the results manually or with a sample.

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Ali Rizvi

Data Scientist @ Turing | Machine Learning, AI, Data Analytics
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Some of the steps I want you to make sure before you do removal of duplicates. 1. Never forget to check if your data has white spaces, spelling variations etc. In case with strings, it is specially important. 2. Make sure that whether you want to catch sample that has duplicates on all features or a subset of features. 3. Consider temporal aspects by including a time window for removing duplicates. Depending on your data, temporal aspect may also have a lot to decide whether a sample is a duplicate or not. 4. For point 1, you can also use Fuzzy matching. So, be careful that you are good with deciding whether you need an exact match or a fuzzy one. 5. I am also a fan of sorting. At times, a well sorted data gives a very good insight.

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4 Data Normalization

Data normalization is the technique of scaling or transforming your data to a common range or distribution. Data normalization can help reduce the impact of outliers, noise, or varying scales on your data mining models, and improve their performance and comparability. Data normalization can use various methods, such as min-max scaling, standardization, or log transformation. You should choose the method that best preserves the characteristics and relationships of your data.

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Ali Rizvi

Data Scientist @ Turing | Machine Learning, AI, Data Analytics
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Its important to understand the different between scaling and transformation. I'd try to explain it in the simplest words I can: Scaling is about putting your all different columns values to lie in a range without changing distribution of data!. It is usually done for some Machine Learning Algorithms like linear models and distance based models. However, its unnecessary for Tree based models. Its advantage is that it improves convergence in gradient decenet optimization Transforming is about changing the distribution of your data. Some ML algorithms assumes a particular distribution so this is why it is done. It also handles skewness and outliers

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5 Data Validation

Data validation is the technique of checking and verifying the quality and accuracy of your data after cleaning. Data validation can help you identify and correct any remaining issues or errors in your data, and ensure that your data meets the requirements and expectations of your data mining goals. Data validation can use various methods, such as descriptive statistics, data visualization, or data quality rules. You should perform data validation iteratively and systematically, and document the results and actions taken.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What advanced data cleaning techniques can you use for complex data structures?

1

2

3

4

5

6

1 Data Wrangling

2 Data Imputation

3 Data Deduplication

4 Data Normalization

5 Data Validation

6 Here’s what else to consider

Data Mining

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What advanced data cleaning techniques can you use for complex data structures?

1

2

3

4

5

6

1 Data Wrangling

2 Data Imputation

3 Data Deduplication

4 Data Normalization

5 Data Validation

6 Here’s what else to consider

Data Mining

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