
AI/ML Engineer | ML & Data Science Enthusiast Turning data into intelligent solutions with Python, Machine Learning, and real-world problem solving.
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Assessing your cultural and operational fit
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.
View ProjectANN-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.
View Projectmovie-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.
View ProjectNLP-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.
View ProjectEnd-to-End-ML-Poject
February 20, 2026 – Present
End-to-End-ML-Poject — GitHub repository
View Projecthierarchical-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.
View ProjectK-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.
View Projecthouse-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.
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and demonstrate a strong interest in Data Science and Machine Learning. The diversity of projects (NLP, recommendation systems, classification, clustering, regression) indicates a broad curiosity and willingness to explore different domains within the field. However, the lack of team-based projects or professional experience makes it difficult to fully assess cultural fit in a collaborative work environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. No psychometric test results or interview feedback provided.