Mapping of same hotel names in coding

 To map the same hotel names in coding, you can use various techniques depending on the programming language and the specific requirements of your project. Here are some general approaches:

1. String Comparison

You can use string comparison techniques to identify similar hotel names. This can be done using:

- *Exact Matching*: Compare hotel names exactly, character by character.

- *Fuzzy Matching*: Use algorithms like Levenshtein distance or Jaro-Winkler distance to measure the similarity between hotel names.

2. Data Preprocessing

Preprocess the hotel names by:

- *Converting to Lowercase*: Convert all hotel names to lowercase to reduce case sensitivity.

- *Removing Special Characters*: Remove special characters, punctuation, or whitespace from hotel names.

- *Tokenization*: Split hotel names into individual words or tokens.

3. Using a Dictionary or Map

Create a dictionary or map to store hotel names as keys and their corresponding mappings as values. This can help you:

- *Normalize Hotel Names*: Map different variations of hotel names to a standard name.

- *Group Similar Hotels*: Group hotels with similar names together.

4. Using Algorithms

Use algorithms like:

- *Levenshtein Distance*: Measure the distance between two strings.

- *Cosine Similarity*: Measure the cosine of the angle between two vectors in a high-dimensional space.

Example Code (Python)

Here's an example of how you can use the `fuzzywuzzy` library in Python to map similar hotel names:

from fuzzywuzzy import fuzz

from fuzzywuzzy import process

List of hotel names

hotel_names = ["Hotel ABC", "Hotel ABC Pvt Ltd", "Hotel DEF", "Hotel DEF International"]

Create a dictionary to store mappings

hotel_mappings = {}

Iterate through hotel names and find similar matches

for hotel in hotel_names:

    # Use fuzzy matching to find similar hotel names

    similar_hotels = process.extract(hotel, hotel_names, limit=2)

    

    # If a similar hotel name is found, map it to the current hotel

    if similar_hotels[1][1] > 80: # Adjust the threshold value as needed

        hotel_mappings[similar_hotels[1][0]] = hotel

print(hotel_mappings)

This code uses the `fuzzywuzzy` library to find similar hotel names and maps them to a standard name.

Use Cases

Mapping hotel names can be useful in various scenarios, such as:

- *Data Integration*: Integrating data from different sources with different hotel names.

- *Data Cleaning*: Cleaning and normalizing hotel names in a dataset.

- *Hotel Search*: Improving hotel search functionality by mapping similar hotel names.

By using these techniques, you can effectively map the same hotel names in your coding project.