🏢 Apartment Rent Sorting System

This project is a C++ program that reads apartment data from a CSV file and sorts it using different sorting algorithms.

It focuses on comparing the performance of:

• Quick Sort
• Heap Sort

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📂 Project Description

The system processes a dataset containing apartment listings in Kuala Lumpur & Selangor and sorts them based on:

• Monthly Rent
• Apartment Size

The program also measures the execution time of each sorting algorithm for comparison.

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⚙️ Features

• Read and clean CSV dataset

• Handle missing and inconsistent data

• Sort data using:

  • Quick Sort
  • Heap Sort

• Output sorted results into new CSV files

• Measure sorting performance (time taken)

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🧱 Data Structure

Each apartment is stored using a struct:

struct Apartment {
    string ads_id;
    string prop_name;
    string completion_year;
    int monthly_rent;
    string location;
    string property_type;
    int rooms;
    int parking;
    int bathroom;
    int size;
    string furnished;
};

The program stores up to 19,991 records.

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📊 Sorting Algorithms Used

1. Quick Sort

• Average time complexity: O(n log n)
• Fast in practice
• Uses divide-and-conquer approach

2. Heap Sort

• Time complexity: O(n log n)
• Consistent performance
• Uses binary heap structure

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📁 Output Files

After running the program, the following files will be generated:

• HeapSort(monthly_rent)-Mudah-Apartment-KL-Selangor.csv
• HeapSort(Size)-Mudah-Apartment-KL-Selangor.csv
• QuickSort(Monthly_Rent)-Mudah-Apartment-KL-Selangor.csv
• QuickSort(Size)-Mudah-Apartment-KL-Selangor.csv

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🚀 How to Run

1. Make sure you have a C++ compiler (e.g. g++)

2. Place the dataset file in the same folder:

   mudah-apartment-kl-selangor.csv
   

3. Compile the program:

   g++ RentSystem.cpp -o RentSystem

4. Run the program:

   ./RentSystem

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📝 Notes

• Missing values are handled by replacing with default values (e.g., 0 or "N/A")
• Strings like "RM" and "per month" are cleaned before processing
• Sorting is done on copies of the dataset to preserve original data

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