How can you improve your ML model's performance with better data cleaning skills?

I will go with the saying - 'Garbage in, garbage out!'. This basically implies that no matter how advanced your modeling techniques are, the results will be irrelevant unless the right data is used in the first place. Some of the aspects of data cleaning I always check before jumping into the modeling phase include: 1. Imputing missing values with the appropriate business logic. 2. Handling outliers. 3. Removing redundant/duplicate data. 4. Format adjustments of select features. 5. Encoding categorical variables. 6. Feature engineering (standardizations, normalizations) 7. Identifying any manual data ingestion tasks that can be automated by data engineers. 8. Validating the data against some predefined logics to complete sanity checks.

Quality of ML model depends on the quality of your own data. The quality of data is improved vastly by data cleaning process . The data cleaning process improves the quality of model predictions which helps business outcome , revenue and reliability . The data cleaning avoid the following 1. Overfitting of model 2. Model under fitting 3. Model bias 4. Model poor performance 5. More Resource consumption 6. More data processing time & effort

By following these techniques, you can improve your ML model’s performance with better data-cleaning skills: 1) Identify and remove duplicates 2) Handle missing values 3)Standardize the data: 4)Remove outliers 5)Feature engineering

Data cleaning is by the far the most important step in the machine learning pipeline. By ensuring that the data we feed into our ML model is clean , of good quality and free of bias as well as it does not contain missing values that may significantly impact the accuracy of our model we can guarantee the reliability of model performance when we validate it on unseen data , many models tend to perform rather well on training data but once we test it on unseen data they fail to globalize what they’ve learned during training so again data scientists must spend time cleaning the data and ensuring that it’s accurate, fair , free of bias and consistent consequently the outcomes can be more reliable and effective

Data cleaning ensures that the data used for training is pristine, free from biases, and consistent. Clean data not only leads to immediate improvements in training accuracy but also guarantees the model's reliability and effectiveness in real-world applications, where it must make accurate predictions on unseen data.

To identify these issues, you can employ various methods: Data Profiling: This process involves summarizing and exploring the data using statistics and visualizations to gain insights into its characteristics and potential issues. Data Validation: Here, you check the data against predefined rules and constraints to identify discrepancies and violations. Data Auditing: This process involves detecting and reporting data quality issues using automated or manual techniques, making it possible to address the identified problems effectively.

There are some common methods to spot data quality issues : 1. Use Descriptive Statistics 2. Visual Inspection 3. Data Profiling 4. Domain Knowledge 5. Data Completeness 6. Consistency Checks 7. Duplicate data 8. Timelines 9. Data Accuracy 10. Validations Regularly performing these checks and maintaining a vigilant eye on the data can help in identifying and addressing data quality issues before they impact the integrity of your analysis.

Look for missing or incomplete data. This is a frequent problem with data quality that can be brought on by a number of things, including human error, computer bugs, or corrupted data. Recognize any duplicate data. It can be challenging to obtain an accurate picture of your data and can distort your results if you have duplicate data. Verify your data for irregularities. Inconsistencies in your data's format or values may fall under this category. Check your data for outliers. Data points known as outliers diverge dramatically from the rest of the data. They may also distort your findings and make it challenging to obtain a true picture of your data. Check your data for mistakes. Typographical errors, wrong

Data quality issues can manifest in various ways: - Data points with incomplete or null entries. - Data points that significantly deviate from the majority of the data. - Variations in data formats, units, or naming conventions. - Repeated entries that may skew results. - Typos, incorrect values, or anomalies. Exploratory data analysis, visualizations, and summary statistics can help identify these issues.

Start data cleaning by determining what is wrong with your data. Look for the following: Are there rows with empty values? Entire columns with no data? Which data is missing and why? How is data distributed? Remember, visualizations are your friends. Plot outliers. Check distributions to see which groups or ranges are more heavily represented in your dataset. Keep an eye out for the weird: are there impossible values? Like “date of birth: male”, “address: -1234”. Is your data consistent? Why are the same product names written in uppercase and other times in camelCase? Wear your detective hat and jot down everything interesting, surprising or even weird.

Firstly, fill the missing values with a dummy value and fit a tree based model. Check the decision rules / interpret the model. You may find valuable information/pattern which can be used as a new feature. Later, you can try other approaches such as deletion or imputation. A better option is multiple imputation which accounts for uncertainty. Multiple imputation performs multiple rounds of imputation analyzed together, addressing issues like reduced sample size from deletion or added bias from simple imputation. Another technique is predictive mean matching which imputes values closest to the predicted mean while respecting distributions. The best approach depends on the nature and amount of missing data, and impacts of bias vs variability.

Missing data can be valuable or problematic, depending on the context. If some people don’t answer a survey question about their favorite color, we can keep that as a separate category, like “No Response” This way, we don’t ignore their choice, which might matter. If we have data on people’s income, and many values are missing, we can’t just fill them randomly. That could give us wrong results. We need to think if there’s a reason for the missing data, like maybe rich people didn’t want to share their income. Then, we should use our knowledge or ask experts how to handle the missing information in a good way. So, it’s not just about filling gaps; it’s about knowing when to keep, when to guess, and when to use missing data as information.

The choice of method depends on the specific dataset, the extent of missingness, and the nature of the problem. Each approach has its advantages and limitations, and careful consideration is necessary to ensure that handling missing values does not introduce bias or errors into the analysis.

Missing data is very risky for our ML model, and the way we treat it influence the performance of our ML model. There are several ways to handle missing values, via mean, median, or the most common entry in the feature , i.e., the mode. Which one to choose depends on the problem statement, and the data we have. Also, we cannot delete the null cells if they are > 5% of the total dataset in number. If they are <5%, we can remove those cells and go on with our analysis and model building. Otherwise, treating them with correct value is important. In categorical features, the decision depends on how imbalanced the dataset is and other factors influencing the category.

Often people end up deleting missing value records or let's say they simply end up imputing it with mean values but that should not be the go to approach. We need to understand what type of missing values we are dealing with . Is it a Missing value at Random case or "Missing value completely at Random" or there's an underlying pattern to the missing values? If there's stochasticity involved in the missing values then we can surely go ahead with imputation (probably median imputation can work better than mean imputation in this numeric feature case). However we can also use KNN Imputer or Regression/Classification techniques to filll the missing values in the dataset.

Case Study: Detecting Credit Card Fraud Background and Challenge: Build and implement a machine learning model to detect anomalous or fraudulent transactions in real-time to prevent financial losses. Approach: - Data Exploration; - Data Preprocessing; - Feature Selection; - Model Selection; - Training and Evaluation; - Hyperparameter Tuning: - Real-Time Detection; - Monitoring and Maintenance; ? Outcome: By implementing this machine learning solution, the financial institution can efficiently detect credit card fraud in real-time, thereby safeguarding their customers from unauthorized transactions.

Dealing with outliers and anomalies is crucial in machine learning to prevent them from adversely affecting model performance. Here's a summary of the key steps involved: Detection: Identify outliers and anomalies using statistical tests, clustering algorithms, or specialized anomaly detection models. This step helps pinpoint data points that deviate significantly from the majority. Handling: After detection, you have several options for handling outliers: Removal: Remove the outliers from the dataset to reduce noise and improve model accuracy. However, be cautious, as this may result in the loss of valuable information.

You can consider three approaches: Remove Outliers: Outliers can skew averages and distort statistics. Remove them by eliminating data points in the upper and lower X-percentile of your dataset. Segment Outliers: Group outliers separately from the rest of the data. This is valuable for focused analysis. Ex: identifying high-paying customers who spend significantly more than the average can inform targeted marketing and sales strategies. Use Statistical Methods: Keep outliers in your analysis but employ statistical methods. Weighted means, which give more weight to the "normal" part of the distribution, and trimmed means are common approaches for analyzing datasets with outliers while mitigating their negative impact.

The identification of outliers may be accomplished by the use of statistical methodologies or machine learning algorithms. The choice of whether to eliminate, modify, or retain outliers should be made based on their influence on the particular situation at hand.

Handling outliers and anomalies in your data is crucial for robust ML model performance: 1. Detection: Use statistical tests, clustering algorithms, or dedicated anomaly detection models to identify outliers. This step is vital for understanding the scope of the issue. 2. Removal: Consider removing outliers to reduce noise and enhance accuracy. However, be cautious, as this may result in valuable information loss. 3. Transformation: Apply scaling, normalization, or standardization techniques to mitigate outlier effects on data distribution and scale. This can help maintain data integrity while minimizing outlier impact. Select the most suitable approach to ensure a balanced treatment of outliers and anomalies.

Tips on how to select relevant features: 1.EDA: Identify patterns, correlations, and trends that can guide your feature selection. 2.Utilize Random Forest to rank features based on their contribution to the predictive power of the model. 3.Use statistical tests (chi-squared, ANOVA) to assess the relationship between individual features and the target variable. 4.Employ Recursive Feature Elimination, a backward selection method. 5.Feature engineering can help uncover more informative representations of the data and enhance model performance. 6.Automated Feature Selection Algorithms: Consider using recursive feature selection, forward selection, or stepwise selection. 7.Regularization Techniques 8.Feature Importance from Neural Networks

Problem Understanding: Comprehend the problem and its context to identify potentially influential features. Exploratory Data Analysis (EDA): Investigate data, recognizing initial features that may impact the outcome. Correlation Analysis: Use correlation matrices to gauge relationships between features and the target variable. Model-Based Selection: Employ models like Random Forest to compute feature importance scores and pick the most relevant ones. Univariate Selection: Apply statistical tests (e.g., chi-squared) to select features with the most significant impact on the target. Regularization (LASSO): Implement L1 regularization to shrink less relevant features, leaving the most impactful ones.

Methods: There are different approaches to feature selection: Filter Methods: Features are selected based on their statistical properties, such as correlation, variance, or information gain. These methods are fast and model-independent but may overlook feature interactions. Wrapper Methods: Features are selected based on their impact on the model's performance metrics (e.g., accuracy, precision, recall). Wrapper methods are model-specific and optimized but can be computationally intensive and prone to overfitting.

Feature selection involves choosing the most relevant variables to include in your model. Techniques include: - Identifying highly correlated features and retaining only one of them. - Using methods like tree-based models to rank features by importance. - Applying techniques like Principal Component Analysis (PCA) to reduce the number of features. Feature selection helps reduce model complexity and enhances generalization.

There are certain techniques to find relevant features from our dataset for our ML model. All the following points are to be applied on data with no null values. 1. Check for correlation among different features. Now, take the features with correlation > 0.8 and use PCA on them to create one single feature. 2. Check for feature importance by plotting the feature importance graph from randomforest. Check for most relevant features and use them, while removing the features with negligible influence on the prediction. This way, we will have reduced features but all the important information secured with us due to PCA.

Data cleaning is a crucial but often overlooked step in machine learning. It involves preparing data by removing errors, inconsistencies, outliers, missing values, and irrelevant features. Proper data cleaning can significantly enhance an ML model's performance by reducing noise, improving accuracy, and enhancing interpretability. In this collaborative article, AI and LinkedIn community experts discuss the importance of data cleaning, how to identify data quality issues, handle missing values, deal with outliers and anomalies, select relevant features, and validate and monitor data. Data cleaning ensures that ML models learn from accurate, consistent, and reliable data, ultimately leading to more reliable results and decisions.

Use open-source tools like Evidently and deepchecks to monitor data quality in production. These generate rich reports to help you detect data drift and other anomalies.

Take control of your data pipelines and perform data quality checks and comparisons with previous states at every step of the pipeline. Understanding how the variables' distributions changes, the correlations, and their impact on the model metrics and business results are key to deliver RoI in AI.

Data validation involves ensuring data accuracy, consistency, and reliability through statistical analysis, visualization, and cross-validation. Monitoring encompasses ongoing checks, automated alerts, and tracking data quality metrics to detect changes or anomalies in real-time or over periods. Using appropriate tools and following best practices ensures robust data validation and monitoring processes.

Whether automation is your cup of tea or not, remember the main steps when cleaning data: ? Identify the problematic data ? Clean the data ? Remove, encode, fill in any missing data ? Remove outliers or analyze them separately ? Purge contaminated data and correct leaking pipelines ? Standardize inconsistent data ? Check if your data makes sense (is valid) ? Deduplicate multiple records of the same dataForesee and prevent type issues (string issues, DateTime issues) ? Remove engineering errors (aka structural errors) ? Rinse and repeat

Adding my five cents here to the all said: poor data can introduce bias that has the potential to greatly impact the results of business decisions relying on the model.

ML model's quality is a direct reflection of the quality of the data it's trained on. The points mentioned above regarding data cleansing are indeed valuable and align with best practices. There are a couple of additional considerations to enhance the data preparation process. One such consideration is the normalization of data.Features come in varying scales and units, ensuring a harmonized scale is crucial. Techniques such as log transformations, one-hot encoding, or leveraging ensemble methods can be employed to achieve this. Another aspect of data refinement involves addressing class imbalance. Effectively handling imbalanced data ensures that the model can provide accurate predictions for both minority and majority classes.

Some other things you can consider include class balance and imbalance, the uniformity of the dataset, and your use case. Of course the data you have matters, but it's not just about the data, it's about how you decide to leverage it. You can transform your data as you need for your use case. For example, if you have to measure the change of a variable over time, it may be better to transform the data to give you the change values. On the other hand, if you were to measure the value of a variable at discrete points it time, it would be easier to work with those values in time rather than their deltas.

Here are some additional tips for improving data-cleaning skills: 1. Use data visualization tools: Data visualization tools can be used to identify outliers and patterns in the data that would be difficult to spot manually. ex: powerbi, tableau etc. 2. Use automated data cleaning tools: There are a number of automated data cleaning tools available that can help you save time and effort. However, it is important to carefully review the results of any automated data cleaning process to ensure that the data is clean and accurate. 3. Collaborate with other data scientists: Collaborating with other data scientists is a great way to learn new data-cleaning techniques and to get feedback on your work.