Product Search Engine with Machine Learning Techniques

This product search engine leverages machine learning techniques to improve the accuracy and relevance of search results. Built using Python, the search engine is designed to match user queries to the most relevant products from a database. The core of the engine includes the following components:

Data Handling and Preprocessing:

Pandas (pd): Used for loading, processing, and handling the product data, typically stored in a structured format like CSV files. Pandas allows efficient data manipulation, such as filtering, sorting, and grouping product data.
NumPy (np): Utilized for numerical computations, ensuring efficient handling of arrays and mathematical operations during the processing of data.

Text Preprocessing:

Natural Language Toolkit (NLTK): The PorterStemmer from NLTK is used for stemming, reducing words to their root forms. This step is crucial to normalize product descriptions and search queries, ensuring that variations of words (e.g., “running” vs. “run”) are treated as equivalent.
CountVectorizer (sklearn): This is used to transform the product descriptions and search queries into a bag-of-words representation. The vectorized text data enables machine learning models to understand the relationships between different terms in the product descriptions.

Similarity Matching:

Cosine Similarity (sklearn): After vectorizing the text data, cosine similarity is employed to measure the similarity between the user’s search query and product descriptions. Cosine similarity compares the angle between the vectors (representing the query and the product descriptions), allowing the system to rank products based on relevance to the search query.
Model Persistence:

Pickle: This module is used to serialize and save the trained models and vectorizers, enabling efficient storage and loading of the machine learning components. By saving the model, the search engine can quickly respond to future search queries without retraining from scratch.

Workflow:
Data Loading: Load the product dataset using Pandas.
Text Preprocessing: Clean and preprocess the product descriptions using PorterStemmer to perform stemming, followed by vectorization using CountVectorizer.
Query Processing: When a user inputs a search query, it is preprocessed and vectorized in the same manner as the product descriptions.
Similarity Calculation: Compute cosine similarity between the query vector and the product vectors to identify and rank the most relevant products.
Result Output: Display the top-ranked products as the search result.
This system enhances the traditional product search by incorporating natural language processing and machine learning techniques, allowing for more accurate and contextually relevant search results, ultimately improving the user experience.

Key Components:

STREAMLIT: UI
The main Streamlit application script.