ADM Homework 4 - MovieLens Dataset Usage for recommendation system

---

Homework Overview

This repository contains a single file: main.ipynb, which includes the entire implementation for Homework 4 of the ADM Course. The project is divided into two major tasks:

1. MovieLens Dataset Clustering and Analysis
2. Dataset link: https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset?select=rating.csv
3. Game Strategy Optimization for Arya and Mario

---

1. MovieLens Dataset Clustering and Analysis

Objective:

• Perform feature engineering, dimensionality reduction, and clustering on the MovieLens dataset.
• Use clustering algorithms such as:
  • K-means
  • K-means++
  • Gaussian Mixture Model (GMM)
  • HDBSCAN
• Evaluate and compare clustering results using metrics such as:
  • Silhouette Score
  • Davies-Bouldin Index (DBI)

---

Key Sections Implemented:

• Data Preprocessing and Feature Engineering
• Dimensionality Reduction (PCA)
• Clustering Implementation (from scratch and libraries)
• Optimal Cluster Selection using Elbow and Silhouette Methods
• Visualization of Clustering Results
• Comparison of Algorithms Based on Evaluation Metrics

---

2. Game Strategy Optimization (Arya vs Mario)

Objective:

• Implement an optimal game strategy using:
  • Recursive Algorithm (Exponential Time)
  • Memoization with Dynamic Programming (Polynomial Time)
  • Minimax Algorithm with Alpha-Beta Pruning (Suggested by LLM)

---

Key Sections Implemented:

• Recursive Game Strategy: Find optimal moves for Arya and Mario.
• Memoization Optimization: Reduce time complexity from O(2^N) to O(N²).
• Minimax with Alpha-Beta Pruning: Use a game-theoretic approach to improve efficiency.
• Comparative Analysis: Compare runtime, scores, and performance of all three methods.

---

How to Use:

1. Clone the Repository:

   git clone https://github.com/Mujtaba240/ADM-HW4.git