Netflix Autosuggest Search Engine

By Tejas Kamble – AI/ML Developer & Researcher | tejaskamble.com

Introduction

Have you ever used the Netflix search bar and instantly seen suggestions that seem to know exactly what you’re looking for—even before you finish typing? Inspired by this, I created a Netflix Search Engine using NLP Text Suggestions — a project that bridges the power of natural language processing (NLP) with real-time search functionalities.

In this post, I’ll walk you through the codebase hosted on my GitHub: Netflix_Search_Engine_NLP_Text_suggestion, breaking down each important part, from data loading and text preprocessing to building the suggestion logic and deploying it using Flask.

📂 Project Structure

Netflix_Search_Engine_NLP_Text_suggestion/
├── app.py                  ← Flask Web App
├── netflix_titles.csv      ← Dataset of Netflix shows/movies
├── templates/
│   ├── index.html          ← Frontend UI
├── static/
│   └── style.css           ← Custom styling
├── requirements.txt        ← Python dependencies
└── README.md               ← Project overview

Dataset Overview

I used a dataset of Netflix titles (from Kaggle). It includes:

• Title: Name of the show/movie
• Description: Synopsis of the content
• Cast: Actors involved
• Genres, Date Added, Duration and more…

This dataset is essential for understanding user intent when making text suggestions.

Step-by-Step Breakdown of the Code

Loading the Dataset

df = pd.read_csv("netflix_titles.csv")
df.dropna(subset=['title'], inplace=True)

We load the dataset and ensure there are no missing values in the title column since that’s our search anchor.

Text Vectorization using TF-IDF

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(stop_words='english')
tfidf_matrix = vectorizer.fit_transform(df['title'])

• TF-IDF (Term Frequency-Inverse Document Frequency) is used to convert titles into numerical vectors.
• This helps quantify the importance of each word in the context of the entire dataset.

Cosine Similarity Search

from sklearn.metrics.pairwise import cosine_similarity

def get_recommendations(input_text):
    input_vec = vectorizer.transform([input_text])
    similarity = cosine_similarity(input_vec, tfidf_matrix)
    indices = similarity.argsort()[0][-5:][::-1]
    return df['title'].iloc[indices]

Here’s where the magic happens:

• The user input is vectorized.
• We compute cosine similarity with all titles.
• The top 5 most similar titles are returned as recommendations.

Flask Web Application

The search engine is hosted using a lightweight Flask backend.

@app.route("/", methods=["GET", "POST"])
def index():
    if request.method == "POST":
        user_input = request.form["title"]
        suggestions = get_recommendations(user_input)
        return render_template("index.html", suggestions=suggestions, query=user_input)
    return render_template("index.html")

• Accepts user input from the HTML form
• Processes it through get_recommendations()
• Displays top matching titles

Frontend – index.html

A simple yet effective UI allows users to interact with the engine.

<form method="POST">
    <input type="text" name="title" placeholder="Search for Netflix titles...">
    <button type="submit">Search</button>
</form>

If suggestions are found, they’re shown dynamically below the form.

🌐 Deployment

To run this app locally:

git clone https://github.com/tejask0512/Netflix_Search_Engine_NLP_Text_suggestion
cd Netflix_Search_Engine_NLP_Text_suggestion
pip install -r requirements.txt
python app.py

Then open http://127.0.0.1:5000 in your browser!

Key Takeaways

• TF-IDF is powerful for information retrieval tasks.
• Even a simple cosine similarity search can replicate sophisticated autocomplete behavior.
• Flask makes it easy to bring machine learning to the web.

What’s Next?

Here are a few ways I plan to extend this project:

• Use BERT or Sentence Transformers for semantic similarity.
• Add spell correction and synonym support.
• Deploy it on Render, Heroku, or HuggingFace Spaces.
• Add a recommendation engine using genres, cast similarity, or collaborative filtering.

🧑‍💻 About Me

I’m Tejas Kamble, an AI/ML Developer & Researcher passionate about building intelligent, ethical, and multilingual human-computer interaction systems. I focus on:

• AI-driven trading strategies
• NLP-based behavioral analysis
• Real-time blockchain sentiment analysis
• Deep learning for crop disease detection

Check out more of my work on my GitHub @tejask0512
🌐 Website: tejaskamble.com

💬 Feedback & Collaboration

I’d love to hear your thoughts or collaborate on cool projects!
Let’s connect: tejaskamble.com/contact