TY - GEN
T1 - Handling Missing Data in the Iris Dataset
T2 - 2025 IEEE International Global Conference in Emerging Technology, GINOTECH 2025
AU - Agrawal, Vikash K.
AU - Bogireddy, Srinivas Rao
AU - Murari, Haritha
AU - Patil, Lalit N.
AU - Panwar, Vikas S.
AU - Patil, Yashraj M.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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 (R2). Studies show a significant relationship between both dependent as well as independent factors, allowing reliable predictions of sepal lengths. The model achieved an R2 score of 75.88%, demonstrating a strong ability to explain the variance in the dependent variable. The slightly lower Adjusted R2 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.
AB - 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 (R2). Studies show a significant relationship between both dependent as well as independent factors, allowing reliable predictions of sepal lengths. The model achieved an R2 score of 75.88%, demonstrating a strong ability to explain the variance in the dependent variable. The slightly lower Adjusted R2 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.
UR - https://www.scopus.com/pages/publications/105013618613
UR - https://www.scopus.com/pages/publications/105013618613#tab=citedBy
U2 - 10.1109/GINOTECH63460.2025.11076709
DO - 10.1109/GINOTECH63460.2025.11076709
M3 - Conference contribution
AN - SCOPUS:105013618613
T3 - 2025 Global Conference in Emerging Technology, GINOTECH 2025
BT - 2025 Global Conference in Emerging Technology, GINOTECH 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 May 2025 through 11 May 2025
ER -