Handling Missing Data in the Iris Dataset: Imputation Techniques and Their Effects on Model Accuracy

This research examines the dataset from Iris to estimate the sepal lengths for iris blooms utilizing other characteristics including sepal breadth, petal duration, and petal wide. To forecast sepal dimension, linear regression approach has been developed and assessed using metrics including MSE, RMSE, MAE, and R-squared (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>). Studies show a significant relationship between both dependent as well as independent factors, allowing reliable predictions of sepal lengths. The model achieved an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 75.88%, demonstrating a strong ability to explain the variance in the dependent variable. The slightly lower Adjusted R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 75.04% highlights the model's capacity to preserve a good fit while accounting for and penalizing the inclusion of unnecessary variables that do not significantly enhance its performance.