In today’s world, Recommendation Systems play a significant role in guiding and simplifying the decision-making process for individuals and groups. However, the presence of missing data in user-item interaction matrices poses a challenge to accurately identify user preferences and provide relevant suggestions. This is particularly true for group recommendation systems that cater to multiple users. To address this challenge, we have applied four imputation techniques to individual and group recommendation models, including User-based Collaborative filtering, Matrix factorization using Singular Value Decomposition, and deep learning-based models like Autoencoders. We evaluated the effectiveness of these techniques using root mean squared error and mean absolute error metrics and observed a significant impact on the quality of recommendations. Additionally, we implemented aggregation strategies like Borda count, Additive Utilitarian, Multiplicative Utilitarian, Least Misery, and Most Pleasure for Group Recommendations. We evaluated the performance of these strategies using satisfaction score and disagreement score. Overall, our findings suggest that imputation techniques can significantly improve the quality of recommendations in both individual and group recommendation systems.