The A to Z Complete Guide to Data Preprocessing | Data Pre-processing in Python | Data Science
HTML-код
- Опубликовано: 7 сен 2024
- In data science, the journey from raw data to meaningful insights is possible only with careful preparation. In this video, we'll explore the landscape of data preparation, comparing the common approach with the practical approach. 🚀
Complete EDA and Data Preparation Playlist: tinyurl.com/4j3...
🔍 Common Approach: Laying the Foundation
The common approach to data preparation is like building a house using traditional methods. It involves familiar steps like missing value treatment, outlier detection and treatment, feature scaling, handling multicollinearity, and feature encoding. Each step plays a critical role in ensuring that the data is clean and ready for analysis. Not only are these steps important, their right sequence is equally important.
👉 Missing Value Treatment: Filling in the Blanks
Missing values are like gaps in a puzzle. In the common approach, we use simple techniques like mean, median, or mode imputation to fill these gaps. While these methods are quick and easy, they may not capture the true essence of the missing data.
📊 Outlier Treatment: Identifying the Odd Ones Out
Outliers can skew our analysis, much like a noisy signal disrupting a radio broadcast. The common approach involves removing or transforming these outliers to bring the data back in line with the rest of the dataset, but we also need to worry about loss of information in case we modify too much of genuine data.
📈 Feature Scaling: Bringing Balance
Features in a dataset can have varying scales, much like comparing apples to oranges. Scaling techniques like standardization or normalization are used in the common approach to bring all features to a similar scale, ensuring that no single feature dominates the analysis.
🔗 Handling Multicollinearity: Untangling the Web
Multicollinearity occurs when two or more features in a dataset are highly correlated. This can cause issues in some models. The common approach involves using techniques like variance inflation factor (VIF) to identify and mitigate multicollinearity.
🏷️ Feature Encoding: Decoding the Variables
Categorical variables need to be encoded into a numerical format for many machine learning algorithms to process them. The common approach includes methods like one-hot encoding or label encoding to achieve this.
Our follow-up video covers the practical approach to data pre-processing.
This is pure gold! Thanks for sharing the profound insights picked from the applied knowledge from the AI-ML industry.
Thank you! We are glad you liked it.
is removing the duplicates in data done before or after handling the missing values
Removing duplicates could turn out to be a bit tricky. Ideally, we should remove duplicates only if each row in the dataset has a unique identifier and that identifier itself is duplicate e.g. we know two employees can't have the same employee id, so based on this, we can remove duplicates or suggest corrections. However, two employees can have the same age, same education, same location, and same salary, as long as these are two different employees we don't want to remove duplicates. Once these points are checked and if it is found that duplicate records are just data entry errors, we can remove duplicates before removing missing values. Basically, this is hygiene, not even data preprocessing. Hope it helps!
Whene i have positive or negative infinity values ..Can I replace it with NaN an after that transfert it to normal values using median or mean strategy?
Good question. First we should find out why a value would have become infinity e.g. we might have derived a ratio variable. It could be infinity because of division by zero? Second, what are the other feature values like in such rows where some features are attaining infinity and how many such values and rows are present in the data?
You may refer to our tutorial on outlier treatment for the choice of imputation techniques.