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Schedule Interview NowWorking at Softaims has been an experience that continues to shape my perspective on what it means to build quality software. I’ve learned that technology alone doesn’t solve problems—understanding people, processes, and context is what truly drives innovation. Every project begins with a question: what value are we creating, and how can we make it lasting? This mindset has helped me develop systems that are both adaptable and reliable, designed to evolve as business needs change. I take a thoughtful approach to problem-solving. Instead of rushing toward quick fixes, I prioritize clarity, sustainability, and collaboration. Every decision in development carries long-term implications, and I strive to make those decisions with care and intention. This philosophy allows me to contribute to projects that are not only functional, but also aligned with the values and goals of the people who use them. Softaims has also given me the opportunity to work with diverse teams and clients, exposing me to different perspectives and problem domains. I’ve come to appreciate the balance between technical excellence and human-centered design. What drives me most is seeing our solutions empower businesses and individuals to operate more efficiently, make better decisions, and achieve meaningful outcomes. Every challenge here is a chance to learn something new—about technology, teamwork, or the way people interact with digital systems. As I continue to grow with Softaims, my focus remains on delivering solutions that are innovative, responsible, and enduring.
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This project develops a Q&A semantic search system using LangChain and ChromaDB. LangChain processes user queries, converting them into vector embeddings. ChromaDB stores these embeddings, allowing for efficient semantic search and retrieval. When a query is made, LangChain converts it into a vector, which is compared against stored data in ChromaDB to find relevant responses based on meaning, not just keywords. The system dynamically stores new data and retrieves contextually accurate answers quickly.
Machine Learning Project Tools: TPOT, pandas, numpy, scikit-learn Process: Installed required libraries. Imported dataset from Google Drive. Preprocessed data: removed columns with >80% NaN values, calculated means for certain columns, and dropped unnecessary columns. Cleaned data: filled NaN values with medians, converted string values to appropriate formats. Engineered features using SelectKBest and RandomForestClassifier. Explored data: checked NaN percentages and unique values. Objective: Prepare dataset for ML model training and evaluation.
Project Description: Building a Question-Answer Chatbot with Pinecone, Langchain, and ChatGPT-4 Introduction: The aim of this project is to develop a robust question-answer chatbot utilizing cutting-edge technologies such as Pinecone for efficient data storage, Langchain for language processing, and ChatGPT-4 from OpenAI for generating responses. The project involves extracting data from PDF documents, storing it in Pinecone for quick retrieval, and creating a user-friendly front end using Streamlit for seamless interaction. Project Objectives: Develop a pipeline for extracting textual data from PDF documents. Utilize Pinecone for storing and indexing the extracted data to enable fast retrieval. Implement Langchain for preprocessing and language understanding tasks such as tokenization, stemming, and semantic analysis. Integrate ChatGPT-4 model for generating natural and contextually relevant responses to user queries. Design and develop a user interface using Streamlit to facilitate user interaction with the chatbot. Implement functionality to store user interactions and feedback directly from the chat interface. Methodology: Data Extraction: PDF parsing libraries will be employed to extract textual data from PDF documents. The extracted text will be cleaned and prepared for further processing. Data Storage with Pinecone: Pinecone, a scalable vector database, will be used to store the preprocessed text data efficiently. This enables fast retrieval of relevant information during chatbot interactions. Language Processing with Langchain: Langchain will be utilized for various language processing tasks including tokenization, entity recognition, and semantic analysis. This step is crucial for understanding user queries and formulating appropriate responses. ChatGPT-4 Integration: ChatGPT-4, a state-of-the-art conversational AI model, will be integrated into the system to generate human-like responses to user queries. The model will be fine-tuned using the preprocessed data to ensure relevance and coherence in responses. Frontend Development with Streamlit: Streamlit, a popular Python library for building interactive web applications, will be used to create a user-friendly front end for the chatbot. The interface will allow users to input queries and view responses in real-time. Data Logging and Feedback Mechanism: Functionality will be implemented to log user interactions and feedback directly from the chat interface. This data can be used for further training and improvement of the chatbot. Expected Outcome: The project aims to deliver a fully functional question-answer chatbot capable of understanding user queries, retrieving relevant information from stored data, and generating natural responses in real-time. The integration of Pinecone, Langchain, and ChatGPT-4 will ensure high performance, scalability, and accuracy in the chatbot's interactions. Additionally, the user-friendly interface developed using Streamlit will enhance the overall user experience. Conclusion: By leveraging state-of-the-art technologies and innovative approaches, this project seeks to push the boundaries of conversational AI and provide users with a seamless and efficient means of accessing information. Through effective data storage, language processing, and response generation mechanisms, the chatbot aims to cater to a wide range of user queries and provide valuable insights in a conversational manner.
Project Description: Multilingual Text-to-Speech Translation Model Overview: I have successfully developed a robust Text-to-Speech (TTS) translation model utilizing cutting-edge technology, specifically employing OpenAI generative AI capabilities. This project allows seamless conversion of written text into speech in a multitude of languages. The implementation was carried out using Python, leveraging the power of OpenAI generative models, and the development environment was Jupyter Notebook. Key Features: Multilingual Capabilities: The core functionality of the project lies in its ability to translate text into various languages, providing a versatile solution for users with diverse linguistic needs. Generative AI-Powered: The model incorporates state-of-the-art generative AI from OpenAI, ensuring high-quality and contextually relevant speech synthesis. This leads to a more natural and human-like output, enhancing the overall user experience. Python Implementation: The entire project is coded in Python, taking advantage of its extensive libraries and frameworks for machine learning and natural language processing. Python's flexibility and readability contribute to the project's maintainability and ease of further development. Jupyter Notebook Environment: The development process took place within the Jupyter Notebook environment, providing an interactive and collaborative platform. This choice enhances transparency in code execution and facilitates seamless sharing of the project with other developers or stakeholders. Technical Stack: Programming Language: Python Machine Learning Framework: OpenAI Generative AI Development Environment: Jupyter Notebook Use Cases: The Text-to-Speech translation model has broad applications across various industries, including but not limited to: Language Learning Platforms: Integration with language learning apps for pronunciation practice. Accessibility Solutions: Enabling visually impaired individuals to consume written content. Multilingual Content Creation: Facilitating the creation of audio content in different languages for diverse audiences. Future Developments: Ongoing efforts will focus on continuous improvement, expanding language support, and refining the model's accuracy. Additionally, user feedback and emerging technologies will be considered for future enhancements. This project stands as a testament to the intersection of artificial intelligence, language processing, and user-centric design, providing a valuable tool for bridging language gaps and enhancing accessibility to information globally.
Project Overview: Emotion Classification and Sentiment Analysis Prediction System In a dynamic landscape where understanding user sentiments is paramount, our team undertook a cutting-edge project in collaboration with a discerning client. Leveraging advanced machine learning techniques and the power of natural language processing, we developed a robust Emotion Classification and Sentiment Analysis Prediction System. Technological Framework: The system was meticulously crafted using Python, a versatile programming language, and capitalized on essential libraries including NumPy and Pandas for efficient data manipulation and analysis. The backbone of the model was formed by implementing word2vec techniques, enabling the system to comprehend intricate semantic relationships within textual data. Machine Learning Methodology: Employing logistic regression, a powerful and interpretable machine learning algorithm, our system learned to predict sentiments with remarkable accuracy. Logistic regression, known for its simplicity and efficiency, allowed us to create a model that not only excelled in performance but also provided valuable insights into the factors influencing sentiment. Feature Engineering and Word Embeddings: The utilization of word2vec, a state-of-the-art word embedding technique, facilitated the transformation of words into meaningful numerical vectors. This not only enhanced the model's understanding of contextual relationships but also contributed to the overall precision of sentiment predictions. Data Handling and Analysis: Numpy and Pandas played pivotal roles in managing and processing the vast datasets involved in training the model. These libraries streamlined data preprocessing, ensuring that the model was trained on clean and relevant information, leading to a more accurate sentiment analysis. Client Impact: The resultant Emotion Classification and Sentiment Analysis Prediction System has empowered our client to gain deep insights into the sentiments expressed in textual data. Whether it be customer reviews, social media comments, or any form of text-based communication, our system provides an invaluable tool for understanding and responding to user sentiments effectively.
Bachelor's degree in Information Technology
2016-01-01-2020-01-01
Master's degree in Data Science
2024-01-01-2026-01-01