Amaan Behlim

Next-Word-Prediction-LSTM-RNN

March 12, 2026 – Present

A deep learning project that predicts the next word in a sentence using an LSTM-based Recurrent Neural Network. The model learns language patterns from text data, converts words into sequences using a tokenizer, and predicts the most probable next word. A simple Streamlit app is included for interactive predictions.

ANN-Classification

March 9, 2026 – Present

This project implements an Artificial Neural Network (ANN) for a classification task using TensorFlow/Keras and Scikit-learn. It includes data preprocessing, model training, evaluation, and visualization. The goal is to demonstrate how neural networks learn patterns from data to make accurate predictions.

movie-recommended-system

March 3, 2026 – Present

A content-based Movie Recommendation System using TF-IDF and cosine similarity. Built with FastAPI backend and Streamlit frontend, allowing users to get similar movie suggestions instantly. Deployed as a live web app for interactive recommendations.

NLP-Prep-Lab

February 22, 2026 – Present

A structured NLP practice repository covering text preprocessing, feature engineering, classical ML, word embeddings, deep learning (RNN/LSTM), transformers, and LLM fine-tuning. Includes hands-on projects, evaluation metrics, and interview-focused notes for building strong NLP fundamentals and industry-ready skills.

Student-performance-

February 21, 2026 – Present

It predectS students performance

new

February 21, 2026 – Present

new — GitHub repository

End-to-End-ML-Poject

February 20, 2026 – Present

End-to-End-ML-Poject — GitHub repository

hierarchical-clustering

February 18, 2026 – Present

Implementation of Hierarchical Clustering using Agglomerative approach with SciPy and Scikit-learn. Includes dendrogram visualization, linkage methods comparison (single, complete, average, ward), and cluster formation analysis. Demonstrates distance metrics, mathematical intuition, and real dataset application for unsupervised learning tasks.

K-Means-Clustering

February 18, 2026 – Present

K-Means Clustering implementation using Python and Scikit-learn. This project demonstrates unsupervised learning, centroid optimization, Elbow method, silhouette score evaluation, and cluster visualization using Matplotlib. Includes clean code structure, preprocessing, and performance analysis for real-world clustering problems.

house-price-prediction-xgboost

February 14, 2026 – Present

End-to-end House Price Prediction project using XGBoost and Scikit-learn. Includes data cleaning, feature engineering, preprocessing with ColumnTransformer, model comparison, and evaluation using R2, MAE, and RMSE. Built with a structured ML workflow, proper train-test split, and production-ready pipeline design.