This artifact reproduces the core findings of "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (ICLR 2019) by Jonathan Frankle and Michael Carbin. The experiment uses a LeNet architecture trained on the MNIST dataset, applies iterative magnitude-based pruning, resets surviving weights to their original initialization, and retrains with early stopping. Each pruning iteration removes 20% of the lowest-magnitude weights and the process is repeated for 5 iterations over 5 separate trials. The results confirm that sparse subnetworks ("winning tickets") can train to comparable accuracy as the original dense model.