Estás metido hasta las rodillas en un proyecto de minería de datos. ¿Cómo se afrontan los retos de la falta de datos?

Before imputation, Here are a few suggestions for intelligently filling null values: - For categorical data, the missing value may represent a different class of its own, so we can treat as a new class of the column. - For numerical data, non-existence can be an information (e.g. for count columns, null values can be considered as 0). Alternatively, you may fill it with the average based on a specific dimension, like a customer_id. (Sample and population averages should be avoided).

A step-by-step approach is best to handle missing data. Start by checking whether the missing data is random or systemic, which may be due to recording errors. Then see whether the proportion of missing data in a specific variable is greater than 40% of the total number of unique records, if so those variables are best left out of the analysis. An imputation method can be carried out for variables whose missing percentage is less than 40%. If the missing percentage of data is less than 20%, a mean imputation (if the data follows a normal distribution) or more commonly a median imputation can be carried out. if the data is missing somewhere between 20% and 40% a regression or a KNN-based imputation strategy might prove effective.

When dealing with missing data in a data mining project, its impact can be significant. Missing data can lead to biased results and reduce the accuracy of your models. To mitigate this, use techniques such as imputation, data augmentation, or deletion of incomplete records. It's essential to analyze the pattern and reason for missingness to apply the most appropriate method. Ensuring data quality is crucial for reliable outcomes.

Asking the following questions are very valuable when working with the missing data. - How much of the data is missing, is it sparse? - Is the missing data random or systematic? - How does it affect my data statistically? - Do they have to be avoided or can they be given garbage values without impacting statistics of the data? - Are these points measurable? - Can the missing values be estimated using other datapoints? - Could they possibly be unwanted outliers? - How does the visualization look for these data points?

Start with impact analysis of missing based on identified business requirements in context to financials and other objectives. Basis on this impact analysis, either a approach can be taken to ignore but to ensure / plan to fix this or develop measure to fill these with relevant corresponding data by adopting various models and ensuring that outcome is not impacted from reporting and intelligence perspective.

Data imputation is the recovery measure uner this situation. For numerical value, it is better to replace with median value as median is independent of extreme values. On the other hand, mode is the helpline for categorical values. In my opinion, the median or mode to be selected on the basis of the cluster median or cluster mode after data segragation for the simlsr target values.

Data imputation is the process of filling in missing data within a dataset. When facing missing data in a data mining project, you can use techniques such as mean/mode/median substitution, regression imputation, or more sophisticated methods like k-nearest neighbors (KNN) and machine learning models. These methods help maintain the dataset's integrity and enable more accurate analysis. However, it's crucial to understand the nature of the missing data and choose an appropriate method to avoid introducing biases. Properly handling missing data can significantly improve the quality of your data mining results.

Following the impact assessment, data imputation becomes a critical task. There are several techniques available for this purpose. Simple methods like mean, median, or mode imputation replace missing values with central tendencies but can introduce bias if the data is not normally distributed. More sophisticated techniques, such as K-Nearest Neighbors (KNN) imputation, estimate and fill in missing values using the nearest neighbors, thereby preserving relationships within the data. Regression imputation predicts missing values using regression models based on other available data, while multiple imputation creates several imputed datasets and averages the results to account for uncertainty.

Depending on the statistical measures of the overall data, some estimations, assumptions, and data description; we can identify what form of Data Imputation can be done. Often we choose either central tendancies as basic as average, or perform predictive analysis, deriving predictive results to fill up the empty spaces. This does tend to bias the results. Which is why, data imputation needs to be dealt in such a way, where it does not impact the overall data to significant changes.

Métodos estatísticos: Utilize métodos como a média, mediana ou moda para preencher os valores faltantes. Esses métodos são simples, mas podem introduzir viés se não forem usados corretamente. Modelos preditivos: Utilize modelos como regressão, K-Nearest Neighbors (KNN) ou algoritmos baseados em árvore para prever os valores faltantes com base em outros dados disponíveis. Técnicas avançadas: Considere técnicas como imputação múltipla ou métodos baseados em redes neurais para dados complexos ou de alta dimensionalidade.

When handling missing data in a data mining project, algorithms like k-Nearest Neighbors (k-NN) and Multiple Imputation by Chained Equations (MICE) can help. k-NN fills missing values based on the nearest data points, while MICE creates multiple datasets with imputed values and combines results. Simple imputation methods, such as mean or median replacement, are also useful but might introduce bias. For robust results, it's crucial to analyze the pattern of missing data first. Choosing the right algorithm depends on the dataset's characteristics and the project's goals.

Using methods such as predictive modeling, auto-encoders, MLE technique, multiple imputation (often can be used for hybrid models) and so on are effective to clear up empty data. d-Trees and Random Forests are some other algorithms which contribute nicely on this spot. We avoid choosing algorithms when we know it can severely impact the bias in the data.

Certain algorithms inherently handle missing data more effectively, which can reduce the need for extensive imputation. For instance, decision trees can handle gaps by using the available data to make splits. Random forests, an ensemble of decision trees, can also manage missing values effectively. The Expectation-Maximization (EM) algorithm iteratively estimates missing values and updates model parameters, making it useful for probabilistic data handling. Additionally, some advanced neural network architectures and ensemble methods incorporate missing data handling directly into the model training process.

Árvores de decisão e florestas aleatórias: Esses algoritmos podem lidar bem com dados faltantes, pois podem construir árvores que não dependem de todas as variáveis. Algoritmos baseados em matriz: Métodos como Singular Value Decomposition (SVD) ou técnicas de fatoração de matriz podem ser usados para imputar valores faltantes em conjuntos de dados estruturados como matrizes.

When dealing with missing data in feature engineering, you can use techniques like imputation to fill in missing values with mean, median, or mode, ensuring data consistency. Alternatively, more advanced methods like k-nearest neighbors (KNN) or regression imputation can be employed for better accuracy. Sometimes, missing data itself can be informative, so adding an indicator variable might be useful. You can also consider data augmentation or using algorithms that handle missing data natively. Finally, ensure thorough data validation to maintain the integrity of your dataset.

Criação de novas variáveis: Crie variáveis que indiquem a presença ou ausência de dados para ajudar os modelos a capturar a falta de dados como uma informação relevante. Transformações: Utilize transformações de variáveis para reduzir a sensibilidade dos modelos à falta de dados.

I agree totally. Sometimes we focus so much on the missingness that we forget about model overfitting. A good model speaks more volume that a bad one, and besides there are several methods you can use to either replace or remove the missing values.

To avoid overfitting while handling missing data in a data mining project: Use techniques like cross-validation to evaluate model performance. Regularize your model using methods like L1 or L2 regularization. Impute missing data with strategies such as mean, median, or model-based imputation. Simplify the model by reducing the number of features. Ensure the training and validation datasets are representative of the problem space.

To mitigate overfitting, data analysts can employ several strategies. Regularization techniques, such as Lasso or Ridge regression, impose constraints on the model's complexity, thereby preventing it from fitting the noise in the data too closely. Cross-validation is another crucial tool that helps assess how well the model generalizes by partitioning the data into training and validation sets multiple times, validating the model on different subsets.

Regularização: Use técnicas de regularização como Lasso, Ridge ou Elastic Net para penalizar a complexidade do modelo e evitar o ajuste excessivo aos dados de treino. Validação cruzada: Utilize métodos de validação cruzada para garantir que o modelo generalize bem para novos dados.

Coleta de dados adicionais: Se possível, colete mais dados ou dados complementares para preencher as lacunas existentes. Parcerias e fontes externas: Considere utilizar dados de fontes externas ou parcerias com outras organizações para enriquecer seu conjunto de dados. Documentação: Mantenha uma documentação clara e detalhada de todas as etapas de imputação e das decisões tomadas durante o processo para garantir a reproducibilidade e transparência do projeto. Ao combinar essas abordagens, é possível mitigar muitos dos desafios associados à falta de dados e obter resultados mais robustos e confiáveis em projetos de mineração de dados.