Identify Dataset Imbalance And Perform Data Preprocessing

Identify dataset imbalance and perform data preprocessing steps

Read the employee_attrition dataset and verify if the dataset is balanced or imbalanced by counting the number of "Yes" and "No" responses in the target variable "Attrition." Categorize the dataset accordingly. Then, identify the numerical and categorical input variables, excluding the target variable. Next, find and remove numerical variables with zero variance and categorical variables with low (single-level) or excessively high (above 200 levels) variance. Standardize the numerical variables using z-score scaling, encode categorical variables through dummy encoding, and convert the target variable "Attrition" into binary form. Finally, balance the dataset by equalizing the counts of the target classes, split it into training and testing sets, train classifiers (KNN and Random Forest), and evaluate their accuracy.

Paper For Above instruction

The initial step in analyzing the employee attrition dataset involves reading the dataset into a DataFrame, which facilitates data manipulation and analysis. Once loaded, the critical task is to assess whether the dataset is balanced or imbalanced concerning the target variable, "Attrition." This is achieved by counting the number of instances labeled as 'Yes' and 'No.' An imbalanced dataset, where one class significantly outnumbers the other, can bias model training, leading to poor generalization. To identify imbalance, one can use the pandas value_counts() method on "Attrition" and compare the counts of 'Yes' and 'No.' If the counts are equal, the dataset is balanced; otherwise, it is imbalanced, warranting techniques such as oversampling, undersampling, or weighting to mitigate bias.

Subsequently, the identification of feature variables is crucial for effective modeling. The features are categorized into numerical and categorical based on their data types. Numerical variables typically include age, salary, and years at the company, whereas categorical variables represent nominal data like department, gender, and job role. It's vital to exclude the target variable "Attrition" from these feature lists. This segregation enables tailored preprocessing steps such as scaling for numerical data and encoding for categorical data.

One of the common issues in working with raw datasets is the presence of features with zero variance. Such features do not vary across samples and thus contribute no informational value. Standard deviation serves as the metric for variance; variables with a standard deviation of zero should be removed from the dataset to improve model efficiency and reduce noise. This involves calculating the standard deviation for each numerical feature and dropping those with zero values.

Similarly, categorical variables that contain only a single level (category) across all observations are uninformative and can be dropped. To identify these, one can compute the number of unique levels per categorical feature. Variables with only one unique value should be excluded. Conversely, variables with very high cardinality—more than 200 levels—can also pose problems such as overfitting and increased computational complexity. These high-cardinality features should be dropped to streamline the analysis.

Feature scaling through standardization ensures that variables contribute equally to model training, especially for distance-based algorithms like K-Nearest Neighbors. Standardization involves subtracting the mean and dividing by the standard deviation for each numerical feature, resulting in features with mean zero and variance one. This process enhances the convergence of many algorithms and can improve predictive performance.

Categorical variables need to be transformed into numerical format for most machine learning algorithms. Dummy encoding, which involves creating binary indicator variables for each category level, is a common approach. For example, a variable "JobRole" with levels such as "Manager," "Engineer," and "Technician" would be transformed into separate binary variables. Importantly, the target variable "Attrition" must be encoded into binary form, where 'Yes' maps to 1 and 'No' maps to 0, to serve as the output variable for classification tasks.

Data balancing aims to address class imbalance issues. Techniques such as oversampling the minority class (e.g., duplicating 'Yes' instances) or undersampling the majority class (e.g., reducing 'No' instances) can equalize class distributions. Achieving balanced classes can lead to improved model sensitivity and overall accuracy. After balancing, the dataset is split into training and testing subsets, typically maintaining a 70-30 split, using tools like sklearn's train_test_split function.

The modeling phase involves training classifiers such as K-Nearest Neighbors (KNN) with k=3 and Random Forest with 100 estimators. Models are trained on the training data, then used to predict outcomes on the test data. Model accuracy is evaluated by comparing predicted labels to true labels, providing a performance metric that guides model selection and tuning. Validation ensures that the models generalize well to unseen data, contributing to robust predictive analytics in employee attrition modeling.

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