AI PROJECTS PORTFOLIO

PAST & PRESENT ACHIEVEMENTS (AI, ML, DL, NLP Projects)

1. Crop Yield Prediction - Agriculture
2. Chilli Plant Disease Classification - Agriculture 
3. Weather Classification & Prediction  - Agriculture
4. Price and Demand Forecasting for Agriculture Produce -  Agriculture 
5. Chatbot  Using LangChain
6. Speech to Text 
7. Text to Speech 
8. Ollama LLM Local Run 

Environment Used: Python 3.x, Anaconda Jupyter Notebook, Pandas, NumPy, Scikit-Learn, Keras, TensorFlow, PyTorch, XGBoost, CatBoost, LightGBM, Ollama, LangChain, Streamlit, Joblib, All Regression Algorithms, Classification Algorithms, Kaggle synthetic data(if necessary)

• Crop Yield Prediction Project: This Project aims to leverage data-driven technologies to accurately forecast agricultural output for various crops. Utilizing Machine Learning algorithms and statistical modelling, the system processes large datasets to identify trends and correlations affecting crop yield.
• Chilli Plant Disease Classification Project: This project is a computer vision based initiative designed to identify and classify diseases affecting chilli plants through Image analysis. This tool empowers farmers with real-time, accessible, and cost-effective plant health monitoring, ultimately contribute to smarter farming and sustainable agriculture.
• Weather Classification & Prediction Project: This project aims to build an intelligent system that can classify current weather conditions and accurately forecast future weather patterns using advanced machine learning and deep learning techniques.
• Price and Demand Forecasting for Agricultural Produce Project: This Project focuses on building predictive models to estimate future market prices and consumer demand for various agricultural commodities.
• Chatbot Project Using LangChain: This Project is centered around building a context aware, intelligent conversational agent that leverages the power of large language models (LLMs) and Retrieval Augmented Generation (RAG)
• Speech to Text Project: This project focuses on developing an advanced system that accurately transcribes spoken language into written text using the state of art speech recognition technologies. It empowers users with seamless voice to text solutions and drives the adoption of intelligent voice technologies.
• Text to Speech Project: This project is focused on developing an intelligent system that converts written text into natural sounding speech using cutting edge speech synthesis technologies. It bridges the gap between written and spoken communication making digital content more accessible.
• OLLAMA Local Run Project: This project is designed to explore the deployment and usage of large language models (LLMs) on local machines using the OLLAMA Framework. It focuses on configuring and optimizing these models for various tasks including text generation, summarization, question-answering, code completion and chatbot interaction.