Added the code of Greedy Algorithms

[KunjMaheshwari]

🚀 Greedy Algorithms in Java This PR introduces a set of Greedy Algorithms implemented in Java, designed to solve optimization problems by making locally optimal choices at each step. The following algorithms have been implemented, with a focus on clarity, efficiency, and scalability:

Activity Selection Problem: Determines the maximum number of activities that can be performed without overlapping, using a sorted approach. Fractional Knapsack Problem: Implements the fractional knapsack solution by maximizing the value-to-weight ratio. Huffman Encoding: Constructs an optimal prefix code using greedy techniques to minimize the average encoding length. Job Sequencing Problem: Finds the optimal job scheduling to maximize profit when each job has a deadline. Minimum Coins Problem: Provides a solution to determine the minimum number of coins required to make a specific amount of change.

📝 Key Highlights: Efficient Algorithms: Time complexity has been minimized where possible, leveraging sorting techniques and heap-based data structures for performance. Modular Code: Each algorithm is implemented as an independent module for ease of testing and integration. Extensive Documentation: Inline comments and Javadoc annotations have been added to explain the logic, assumptions, and edge case handling for each algorithm. Test Cases: Comprehensive test cases included to validate different scenarios, from basic functionality to edge cases.

💡 Why Greedy Algorithms? Greedy algorithms are a powerful tool for solving optimization problems, where the local optimal solutions lead to a global optimum. This PR adds foundational algorithms that can be extended and modified for more complex use cases in various domains like scheduling, compression, and resource allocation.

[KunjMaheshwari]

Please can you add labels in this pull request also. Thankyou so much for accepting this. @vishal8113