DEV Community: Alen Kurian

🎬 Movie Recommendation System using Machine Learning

Alen Kurian — Fri, 20 Mar 2026 15:48:37 +0000

Recently, I built a movie recommendation system as part of my learning in Artificial Intelligence and Machine Learning.

The idea is simple — when a user enters a movie name, the system suggests similar movies.

💡 Why I Built This

While learning machine learning, I wanted to build something practical instead of just studying theory.

Recommendation systems are used in platforms like Netflix and YouTube, so I thought it would be a good project to understand how they work.

🚀 What the Project Does

The system takes a movie name as input and recommends similar movies based on:

overview (story)
genres
keywords
cast
director

🧠 How It Works (Simple)

Combine all movie details into one text
Convert text into numbers using TF-IDF
Find similarity between movies using cosine similarity
Return top 5 similar movies

🛠 Tech Used

Python
scikit-learn
pandas
Flask
HTML, CSS, JavaScript

🔄 How It Works

User enters a movie name
Request goes to Flask backend
Model finds similar movies
Results are shown on screen

🧪 What I Learned

basics of recommendation systems
NLP and text processing
TF-IDF and similarity
how to connect ML with a web app

🔗 GitHub

https://github.com/alenkurian-ml/Content-Based-Movie-Recommendation

📌 Final Thoughts

This is a simple project, but it helped me understand how recommendation systems actually work.

I’m still learning and building projects step by step.

Thanks for reading 🙂

Building a Spam Classifier using Machine Learning and Flask

Alen Kurian — Thu, 19 Mar 2026 14:20:19 +0000

Recently, I built a small project where I created a spam classifier that can detect whether a message is spam or not.

This is part of my learning journey in Artificial Intelligence and Machine Learning, and I wanted to build something practical instead of just reading theory.

💡 Why I Built This

While learning machine learning, I understood that just watching tutorials is not enough. The best way to learn is by building projects.

Spam detection is something we see in real life (emails, messages, etc.), so I thought it would be a good project to start with.

🚀 What This Project Does

The application takes a text message as input and tells whether it is:

Spam

Not Spam

Example:

Input:

You have won a free iPhone!

Output:

Spam (Confidence: 99%)
🧠 How It Works (Simple Explanation)

I used a basic machine learning pipeline:

Clean the text (remove unwanted words and symbols)

Convert text into numbers using TF-IDF

Train a Naive Bayes model

Use the trained model to predict new inputs

🛠 Tech Stack

Python

scikit-learn

pandas

nltk

Flask

HTML, CSS, JavaScript

⚙️ Project Structure

The project is divided into different parts:

preprocessing.py → cleans the text

train.py → trains the model

predict.py → predicts from command line

app.py → Flask backend

index.html → frontend

🌐 How the Web App Works

User enters a message

Frontend sends request to Flask

Backend processes the text

Model predicts the result

Result is shown instantly

🧪 What I Learned

From this project, I understood:

how NLP works at a basic level

how machine learning models are trained

how to connect ML with a web application

how to create a simple API using Flask

This was my first time building a full project from start to end, and it helped me understand things much better.

🔗 GitHub

You can check out the project here:

https://github.com/alenkurian-ml/spam-classifier

📌 Final Thoughts

This is a small project, but for me it was an important step in learning AI.

I’m still learning and building projects step by step. I plan to explore more advanced topics and improve my skills.

If you are also learning machine learning, I would suggest starting with small projects like this.

Thanks for reading 🙂

Building a Simple Sentiment Analysis Model with Python

Alen Kurian — Mon, 16 Mar 2026 12:37:50 +0000

Recently I built a small sentiment analysis project as part of my learning journey in Artificial Intelligence and Machine Learning.

The goal of this project was simple: create a model that can analyze text and predict whether the sentiment is positive or negative.

Why I Built This Project

While learning machine learning concepts, I realized that the best way to understand them is by building small practical projects. Sentiment analysis is a common beginner NLP project and a great way to understand how text data can be processed by machine learning models.

What the Project Does

The program takes a piece of text as input and predicts the sentiment.

Example:

Input:
"I love this product"

Output:
Positive sentiment

Technologies Used

Python

scikit-learn

Natural Language Processing (NLP)

Project Structure

train.py – trains the machine learning model
predict.py – predicts sentiment from text input
app.py – runs the application
vectorizer.pkl – saved text vectorizer
sentiment_model.pkl – trained model

What I Learned

This project helped me understand:

how text data is processed

how machine learning models are trained

how predictions are made from new input data

GitHub Repository

You can check out the project here:

https://github.com/alenkurian-ml/sentimentanalysisbasic

Final Thoughts

This is a small step in my journey of learning Artificial Intelligence and Machine Learning. I plan to continue building more projects and improving my understanding of AI concepts.

My Journey Into Artificial Intelligence: Why I Chose This Path

Alen Kurian — Mon, 16 Mar 2026 09:28:25 +0000

Hello everyone 👋

My name is Alen Kurian, and I’m currently exploring the world of Artificial Intelligence and Machine Learning. I’m still at the beginning of this journey, but it’s a path that excites me every day.

This article is not about achievements or expertise. It’s simply about why I chose this field and what motivates me to keep learning.

Where My Interest in Technology Started

Like many students in India, my interest in technology started with simple curiosity. During my school days, computers always fascinated me. At first, it was just about using them — browsing the internet, trying different software, and exploring how things worked.

Over time, that curiosity slowly grew stronger. I began to wonder about things beyond just using technology.

Questions like:

How do machines understand data?

How do recommendation systems suggest what we should watch or buy?

How can a program recognize an image or understand text?

These questions eventually introduced me to the world of Artificial Intelligence and Machine Learning.

Why Artificial Intelligence?

Artificial Intelligence feels like one of the most exciting areas in technology today.

The idea that machines can learn patterns from data and make decisions is fascinating. From healthcare and education to transportation and business, AI has the potential to solve many real-world problems.

What attracted me most is that AI is not only about writing code. It brings together multiple areas like:

Mathematics

Logic

Data

Problem solving

When these elements work together, we can build systems that behave intelligently.

My Current Learning Journey

Right now, I’m focusing on learning the fundamentals of machine learning and experimenting with small projects.

I believe the best way to learn programming and AI is by building things, even if the projects are simple at first.

Some of the things I’m currently focusing on include:

Understanding machine learning concepts

Building small AI projects using Python

Participating in hackathons and coding challenges

Sharing my learning journey online

You can find my projects on GitHub, where I’m gradually building my portfolio and documenting what I learn.

Why I Decided to Share My Journey Publicly

Many developers wait until they feel “ready” before sharing their work. I used to think the same way.

But over time I realized something important — learning itself is worth sharing.

By writing about my journey and sharing my projects, I hope to:

document my progress

connect with other developers

encourage beginners who are also starting their journey

Looking Ahead

AI is a vast field, and there is always something new to learn. The technology is evolving rapidly, which makes the journey both challenging and exciting.

My long-term goal is to grow as an AI developer and researcher, building intelligent systems that can solve meaningful real-world problems.

For now, I’m focused on learning step by step, building projects, and staying curious about the possibilities of technology.

Final Thoughts

Every developer starts somewhere.

This article is simply the beginning of my journey into Artificial Intelligence. I’m excited to see where this path will take me.

If you are also learning AI or programming, feel free to connect with me on LinkedIn or explore my projects on GitHub.

Let’s keep learning and building together 🚀