What are the steps involved in cleaning data for ML?

Cleaning data for ML involves: 1. Inspecting & Profiling: Assess data quality, find anomalies, missing values, and inconsistencies. 2. Cleansing: Fill or remove missing values, smooth noisy data, identify and fix errors or outliers. 3. Validating: Apply rules to ensure data accuracy and consistency. 4. Standardizing: Normalize data formats to conform to standards (e.g., dates, phone numbers). 5. Deduplicating: Remove duplicate records to prevent data skew. 6. Verifying: Check for data integrity and coherence post-cleanup. 7. Transforming: Scale or convert features to suitable formats for ML algorithms (e.g., one-hot encoding for categorical variables). 8. Documenting: Record steps and decisions for reproducibility.

In the realm of machine learning data cleaning, understanding the data generation process is paramount. Before addressing issues like missing values, outliers, inconsistencies, duplicates, and errors, grasping how data is generated lays a crucial foundation. This knowledge informs the expected patterns, aiding in effective problem identification. It enables a more targeted and context-aware approach to cleaning, ensuring the resulting dataset aligns accurately with real-world phenomena. This proactive strategy enhances the reliability and performance of machine learning models by streamlining the cleaning process and maintaining fidelity to the intricacies of the data generation process.

Cleaning data for ML starts by donning the detective hat. Identify data problems like missing values, outliers, and inconsistencies. It's the investigative phase where you unveil the hidden imperfections in your dataset, setting the stage for data refinement and ensuring your ML model's success.

In order to clean data for ML, first step is to identify data problems. This is done using exploring and understanding data. We look for missing data, extreme values (outliers), multiple data type in same field, special characters instead of expected numeric value, metric's value not making sense (printer price is -ve, can be due to returns, but need to be sure of the reason). Share the findings with business SME as they are closer to the business and get their views. They can provide reasons for some of the data issues you identified and can find additional data issues.

When preparing data for machine learning, the first step is identifying data problems to address. These issues can encompass missing values, outliers, inconsistent values, duplicates, and errors. Missing values are absent data points that can introduce errors and biases. Outliers are extreme data points affecting distribution and statistics. Inconsistent values lack uniform formatting and can lead to confusion. Duplicates inflate dataset size and distort results. Errors result from inaccuracies due to human or machine mistakes, compromising data validity and reliability. Addressing these problems is crucial for accurate and reliable machine learning analysis.

Once identify the data issues, we do various data treatments to remove data issues. The treatment we do are missing value treatment, outlier treatment, processing data to have only one data type for a column etc. Also if due to some reason data is missing due to issue in data extraction or processing (at the time of merging datasets etc.), that can be fixed. I also want to highlight that besides cleaning data, it's very important to understand the business context of this data, relation of different metrics amongst one another etc. Finally, in our project plans, we should reserve significant time to understand and clean data. This is very critical.

The second step in cleaning data is to Remove or replace data problems. We can use various techniques: 1. Handling Missing Values: We can remove rows with missing values if they are a small percentage of the dataset or replace them with statistical measures such as mean, median, and mode. 2. Dealing with Outliers: We can remove extreme outliers if they are insignificant or transform the rows to reduce the impact of outliers. 3. Standardizing Inconsistent Values - We can standardize inconsistent data to ensure uniformity. 4. Handling Duplicates: We can remove duplicate records or merge them keeping a single instance. 5. Addressing Imbalanced Data - We can use techniques like oversampling or undersampling to balance class distributions.

Once the data problem is identified and tested, it is important to select the right strategy to fix (i.e. deleting, imputing, filtering, standardizing, deduplicating). I always use a sample data to test in a testing environment prior to implementing the fix in the production environment. If you decide to delete a data set, make sure it does not affect the need of that data set elsewhere. Understanding the data flow and how it is used in the data model will help you fix the root cause of the issue.

When addressing data issues for ML, removing or replacing problem data is crucial. It’s a deliberate choice: cleanse data of inaccuracies or fill in the gaps smartly. Use imputation techniques to replace missing values or prune anomalies that can mislead your model. This isn’t just housekeeping; it’s about crafting a dataset that speaks truth into your algorithms. Doing so paves the way for a model that performs with precision, reflecting the finesse of a data science expert committed to excellence.

Unsupervised learning techniques have proven to be valuable tools for imputing missing values in ML datasets. These methods are adept at capturing patterns and relationships within the data, making them suitable for handling a variety of data types and scenarios. However, it is important to carefully consider the limitations and potential biases of these methods to ensure the reliability of the imputed values.

I believe analyzing and exploring data is the most important step for a ML/ data science project. We need to identify trends, relationships between metrics, distribution of a metric etc. These patterns, relationships between metrics etc. need to be captured by the algorithm to provide appropriate metric. The models are chosen based on which model better captures data characteristics. For example if the fluctuations in a metric is significant, then it is possible that in future the actual can lie outside the range. The models which can predict outside the historical range of the response variable can do extrapolation. For ex linear regression can extrapolate, however Random forest can't. In this case we select models which do extrapolation.

Exploring and analyzing data is the compass guiding ML cleaning. Dive deep—chart the distributions, spot odd patterns, and question every outlier. This isn’t mere number-crunching; it’s a data detective’s hunt for clues. Your tools? Visualization and statistical tests. Your goal? To distill chaos into patterns a machine can learn from. It's the blend of art and science that marks a data strategist's pathway to crafting models that don't just compute but understand.

Suppose we have a dataset of customer purchase data that we have cleaned. We want to use this data to train an ML model to predict customer churn. To explore and analyze the data, we can use the following steps: Descriptive statistics: This will help us to understand the distribution of the data and identify any outliers. Visualizations: We can create visualizations of the data to identify patterns and trends. Dimensionality reduction: We can use dimensionality reduction techniques to reduce the number of variables in the data. Clustering: We can use clustering techniques to group the data into similar or homogeneous subsets. By exploring and analyzing the data, we can gain a better understanding of the data.

After cleaning the data, exploring and analyzing it is a crucial step to understand its characteristics and patterns while identifying potential issues or improvement opportunities. This involves various techniques such as descriptive statistics (mean, median, standard deviation, etc.), visualizations to show data distribution and trends, dimensionality reduction to extract relevant features, and clustering to group similar data points based on similarity measures. These methods help uncover insights and enhance data comprehension.

Descriptive Statistics: Calculate summary statistics, histograms, and visualizations to understand the data's distribution. Correlation Analysis: Examine feature correlations to identify relationships between variables. Feature Engineering: Create new features or transform existing ones to enhance model performance.

Data validation is important, else if we have incorrect input data the output will not have any use as it will not be reliable anymore. We can have data quality checks based on rules for example is price, revenue negative, do we have a region which is not available in firm's master data for geography dimension, annual revenue higher than firm's yearly revenue etc. Review data summary with business SME, as they know their business and data very well.

Data validation and consistency check involve verifying the cleaned data to ensure that it is free from errors, inconsistencies, and anomalies. This step is essential to maintain data quality and reliability throughout the data cleaning process. Here's what it entails: Identify and rectify any errors in the dataset, including typos, formatting issues, or missing values. Correcting these errors ensures that the data is accurate and reliable. Cross-check the data against domain-specific rules and constraints. For instance, if you're dealing with a dataset of customer ages, verify that there are no entries that violate logical constraints (e.g., ages greater than 1000 years) or business rules (e.g., negative account balances).

Validating and verifying data involves conducting checks to ensure data quality and accuracy. This includes cross-referencing data against reliable sources, performing sanity checks, and validating data against expected ranges or constraints. Data validation is crucial to confirm that the cleaned dataset aligns with the project's goals and domain requirements.

The best way to validate data is to have a QA engineer to validate the code while creating a user test case. Once codes are validated by the QA engineers, it is time to select the intended consumer to test the usability, map out data flow and write a user test script and let user test the data accuracy and performance accordingly.

Cross-Validation: Split the data into training and validation sets to assess model performance. Resampling: Address class imbalance by oversampling or undersampling, as needed. Data Quality Checks: Continuously validate data quality to ensure it remains consistent.

Splitting data is done into training and test to avoid over fitting of data. We build the model on training data and check if the model is performing well on test data. We also split data into multiple parts known as folds. We do training on n-1 folds and on one fold we validate the model's performance (assuming there are n folds). Objective is to check if model is able to see most of the business scenarios in training which data or lot of scenarios have got missed. If lot of scenarios have got missed, then we will stand with higher chance of some of those scenarios coming in the future, and the model will not be able to predict them, as those scenarios are not of the training dataset.

You can divide the data into chunks for training and testing, often like 80-20 or 70-30. To fine-tune, cross-validation helps by creating smaller subsets for validation. And hey, don't forget to shuffle things around randomly so your data stays unbiased and fresh before splitting.

I'd go with smaller set of training data as a starting point. Once it is fully tested and validated, you can add more data. You can split data into trainset and testset using Python lists.

While splitting train and test I prefer either time based split or strafied sampling (for imbalanced data). If data follows a trend over time, time based splitting may be meaningful. Additionally, as the data we use for future predictions will be recent data that is different from historical data, splitting train and test based on time, will give us signal about overfitting. Strafied sampling is suitable for imbalanced data. For classification models, when we have more thank 80% 0 and less than 20% 1, we should split data accordingly, to preserve the proportion. It affects the performance od model.

Data is typically divided into three main subsets: the training set, the validation set, and the test set. The purpose of each set is as follows: Training Set: This is the largest portion of the data and is used to train the machine learning model. The model learns patterns and relationships from this set. Validation Set: This set is used to fine-tune model hyperparameters and assess model performance during training. It helps in preventing overfitting by providing an independent dataset for evaluation. Test Set: The test set is used to evaluate the model's performance after it has been trained and fine-tuned. It provides an unbiased measure of the model's generalization to unseen data.

We should try to help the model as much as we can when trying to train it. Sure given a large dataset and a complex enough model, the model will probably find relationships in the data. However, larger dataset and complex model take a long time to train and run inference on. Instead, by doing simple preprocessing: like normalising the data, handling outliers and missing values, converting categorical to label or one hot encoding, we greatly simplify the model's job. This part doesn't require domain expertise. Next we could include domain expertise and perform feature engineering. Again, model doesn't have figure out these parts and we will have a much simpler model.

?? Data cleaning is pivotal for ML success. Here's a distilled guide: 1?? Spot issues: Detect missing data, outliers, and inaccuracies. 2?? Fix errors: Impute gaps and rectify anomalies. 3?? Uniform formats: Standardize data for consistency. 4?? Validate: Ensure data integrity and accuracy. 5?? Prep for ML: Split and shuffle to enhance model robustness. 6?? Tailor features: Encode and scale for optimal algorithm performance. Each step is crucial for crafting models that deliver precise insights. #DataScience #MachineLearning #AI #DataQuality

It is important to consider the context of the problem we are studying. This helps in identifying the types of anomalies we should be looking for. Once anomalies are identified, we choose the best way to deal with them remove, replace, ...transform. After the pre processing step comes splitting and looking into representative splitting of data. In case of data imbalance, random shuffling of data does not necessarily produce representative sets of data. Therefore, more systemic methods to treating the data imbalance and splitting must be tested. Feature engineering and transformation comes in the last step.

Feature Scaling: Normalize or standardize features to bring them to a similar scale. Encoding Categorical Data: Convert categorical variables into numerical representations, e.g., one-hot encoding. Dimensionality Reduction: Apply techniques like Principal Component Analysis (PCA) to reduce feature dimensions.

I think that oversampling the minority class or undersampling the majority class to balance the data should be done here. You can use methods such as Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples. Secondly, if your data includes text, you may need to perform text preprocessing, which can involve tasks like tokenization, stop-word removal, stemming, or lemmatization, and converting text into numerical representations using techniques like TF-IDF or word embeddings (Word2Vec, GloVe). Finally, we need to remember that different machine learning algorithms have specific preprocessing requirements. Decision trees or random forests may not require feature scaling, while neural networks often benefit from it.

It is important to understand the context of the data or read the documentation of the data collection process before cleaning is done. This could help inform certain decisions in the cleaning process. For example, one time during work I was supposed to assess the quality of a dataset and I had given in to the urge to dive head first into the work without properly reading the documentation, this backfired. I had incorrectly assessed the data as "complete" since there were no missing values(i.e. Nan, Na, Null etc.) observed in the data. My attention was called to the documentation by a senior and I found that the missing values were actually coded as negative numbers, each connoting a different circumstance in the dataset.

In deep learning-based mood classification from videos, start with data preprocessing: extract frames, resize, normalize, and apply OpenCV for facial landmark detection, critical for mood feature extraction. Post-detection, split the dataset into training, validation, and testing sets, ensuring uniform feature application for model reliability. Incorporate data augmentation techniques to enrich the training set, enabling greater diversity and enhancing the model’s robustness. Ensure features are uniform across all data splits to uphold the model's accuracy and dependability in mood classification. This comprehensive approach solidifies a robust dataset, setting the stage for a dependable mood classification model.

Documentation in the context of data cleaning serves several important purposes: Reproducibility: Detailed documentation ensures that the data cleaning process is transparent and reproducible. It allows you or others to understand and replicate the steps taken to transform and clean the data. Error Tracking: If errors or issues are encountered during data cleaning, documentation can help pinpoint when and where the errors occurred. This makes it easier to identify and rectify problems in the data. Data Quality Assessment: Documentation allows you to record the quality of the data at different stages of the cleaning process. You can track improvements in data quality over time and assess the impact of various cleaning procedures.

Having a well-defined data strategy and knowing your data are foundational for data cleaning in ML. Run a data quality assessment to check data duplicates, any missing values or data outliers.

Cleaning data might sound standardized, but it actually requires a good grasp of the specific domain. Take a blood pressure dataset, for example. Filling in missing values without the right know-how can lead to inaccurate or even risky health conclusions. It's like navigating a tricky terrain where expertise matters. In healthcare, especially with vital metrics like blood pressure, working closely with experts is crucial to ensure data accuracy and avoid potential health risks.