Interactive NLP-based AI system designed to manage cinema bookings and provide a seamless user experience.
I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding,to optimize performance. conducted extensive hyperparameter tuning and evaluation, enhancing accuracy and in AI-driven conversational systems.
Full-featured information retrieval system that indexes and enables searching through the CACM (Communications of the ACM) corpus. Live at https://cacm.ianmaloba.com or https://codepen.io/ianmaloba/full/mydReKQ
Build a Web App called AI-Powered Recipe Recommender App
In this notebook we analyze and classify news articles using machine learning techniques, including Logistic Regression, Naive Bayes, Support Vector Machines, and Random Forests. Explore text vectorization and NLP for accurate news categorization.
Market trends and investment insights
Data Science Project for detecting the Fake News using NLP and 6 Machine Learning Models i.e, Decision Tree, Random Forest, AdaBoost, Perceptron, Logistic Regression, SGDClassifier
"This repository consists of **Acne Detection using YOLO** for identifying acne from facial images and **Machine Learning-based Product Recommendation** for suggesting suitable skincare products based on acne severity and skin type."
Prediction of airline passenger referrals using Logistic Regression, GridSearchCV, and TF-IDF vectorization with Python, Pandas, Scikit-learn, and Excel.
AniVerse is an anime recommender system which recommends user based on explicit feedback from user.
The purpose of this project is to build a machine learning model to classify SMS messages as either "spam" or "ham" (not spam). Using TF-IDF vectorization and LinearSVC, it reads an SMS dataset, transforms text data into numerical features, and trains a model to distinguish between spam and ham. The "SMSSpamCollection" dataset has labeled messages.
Repository for the course Essentials in Text and Speech Processing Fall 2024
Predicting the topic of news articles
Amazon product review sentiment analysis using Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB) multiclass as classifier models, Synthetic Minority Oversampling Technique (SMOTE) as feature oversampler, and TF-IDF vectorization as feature, Synthetic Minority Oversampling Technique (SMOTE) as oversampler, and k-fold CV.
Codebase ideation (for better understanding in Django way) for LLM without using pre-trained models, with custom embeddings (TF-IDF or Word2Vec), FAISS for vector storage.
Tool for processing, categorizing, and searching through PDF documents and images using unsupervised K-means clustering and OCR.
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