Missing data can disrupt machine learning workflows, but imputation can help fill in the blanks to keep your models on track. Autoencoders, a type of neural network, excel at reconstructing data by learning complex patterns, outperforming traditional methods like random forest imputation. In our experiment on housing data, autoencoders reduced errors by 3-6 times across features, proving their effectiveness in handling intricate feature relationships. This makes them a powerful tool for imputation and beyond, with applications in denoising, feature extraction, and anomaly detection.