Hyper-Kamiokande (Hyper-K) is a next-generation long baseline neutrino experiment. One of its primary physics goals is to measure neutrino oscillation parameters precisely, including the Dirac CP violating phase. As conventional $\nu_{\mu}$ beam generates from the J-PARC neutrino baseline contains only 1.5$\%$ of $\nu_{e}$ interaction of total, it is challenging to measure $\nu_{e}/\bar{\nu}_{e}$ scattering cross-section on nuclei. To reduce these systematic uncertainties, IWCD will be built to study neutrino interaction rates with higher precision. Simulated data comprise $\nu_{e}CC0\pi$ as the main signal with NC$\pi^{0}$ and $\nu_{\mu}CC$ are major background events. To reduce the backgrounds initially, a log-likelihood-based reconstruction algorithm to select candidate events was used. However, this method sometimes struggles to distinguish $\pi^{0}$ events properly from electron-like events. Thus, a Machine Learning-based framework has been developed and implemented to enhance the purity and efficiency of $\nu_{e}$ events.