Mohit Rathod

About

Data Scientist | AI & Automation Enthusiast | Freelancer

I am a Data Scientist with a strong foundation in data analysis, machine learning, and AI technologies. I have a Bachelor's degree in Chemistry from Bhakt Kavi Narsinh Mehta University, which gave me a strong base in analytical thinking and problem-solving. My professional journey began in the chemical industry, where I worked as a Quality Control Officer.

Along the way, I discovered a strong interest in data science, which led me to transition into this field.

I completed a comprehensive course in Data Science and Generative AI. Through hands-on practice and continuous learning, I have built solid expertise in data cleaning, exploratory data analysis, machine learning, deep learning, natural language processing, Generative AI, and AI agent frameworks. I am proficient in tools like Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, LangChain, and Streamlit.

Currently, I am entering the freelancing space with services that include:

Excel Data Entry, Cleaning & Formatting
Data Cleaning & Preprocessing (Pandas, NumPy, Excel)
Data Visualization (Matplotlib, Seaborn, Excel)
Machine Learning & Deep Learning Models (Scikit-Learn, TensorFlow, PyTorch)
Natural Language Processing: Sentiment Analysis, Text Classification
Generative AI Solutions and LangChain-based AI Agent Development

I am also actively learning how to build AI automation solutions for businesses. This is part of my bigger vision - to build my own AI Automation Agency, Insightforge.ai, focused on helping companies automate tasks and make smarter decisions using AI.

I am passionate about solving meaningful problems, continuously expanding my capabilities, and contributing value through data-driven solutions.

View My Resume

Projects

J.A.R.V.I.S.

Jarvis is a versatile voice assistant activated via the wake word 'Jarvis,' the Win+J hotkey, or a dedicated button. It streamlines task automation and phone operations through ADB (Android Debug Bridge) while leveraging Hugging Face's deepseek r1 model for natural language understanding and HuggingChat for conversational interactions. Designed to be modular and highly customizable, it allows seamless adaptation and feature expansion.

AI Code Assistant

AI Code Assistant is an intelligent AI tool built with Streamlit that leverages the power of local LLMs via Ollama and LangChain to assist with end-to-end coding tasks. It supports code generation, explanation, debugging, completion, and review across multiple programming languages. Powered by the qwen2.5-coder:3b model, the app provides fast, reliable and context-aware assistance. Designed for both productivity and learning, AI Code Assistant offers an interactive interface that streamlines complex coding workflows with just a few clicks.

AI Resume Analyzer - Resume Screening Tool

This project is an AI-powered resume analysis tool designed to provide users with detailed feedback and actionable suggestions to improve their resumes. Leveraging the Llama 3.2 model through Ollama, the application supports PDF and TXT resume formats and offers job role-specific recommendations along with an overall resume rating. Built with a user-friendly Streamlit interface, it enables users to upload their resumes, optionally specify the job role, and receive AI-driven insights to enhance their chances in the job market. The project integrates advanced AI processing using LangChain and is structured for easy deployment and use.

AI Web Scraper - Web Scraping with AI

AI Web Scraper is a smart, LLM-powered web scraping tool built with Selenium, BeautifulSoup, Ollama and Streamlit. It uses stealth techniques (random user agents, headless browsing, and scrolling) to mimic human behavior and avoid detection while scraping websites. The cleaned content is processed in chunks and passed to the Mistral LLM via Ollama, which intelligently extracts user-specified information from scraped data. The interactive Streamlit interface allows users to input URLs and custom queries, making data extraction from any webpage intuitive, precise and user-driven.

InsightForge - AI Blog Generator

Insightforge AI Blog Generator is my first large-scale AI agent project, designed to simplify content creation with just a single topic input. It uses Google's Gemini model and CrewAI agents to research, write, optimize, and polish full-length blog posts — along with generating platform-ready social media content including hashtags, best posting times, and engagement tips. The app runs on a user-friendly Streamlit interface and outputs clean, structured results. It's fully modular and will continue to evolve with new improvements and features.

Query My PDF - PDF Q&A Chatbot

An intelligent chatbot capable of answering questions from any uploaded PDF using Retrieval-Augmented Generation (RAG). Built with LangChain and powered by Google's Gemini LLM and Generative AI Embeddings, it semantically processes and retrieves the most relevant content using FAISS and MultiQueryRetriever with MMR for diverse context. Users can upload and preview PDFs, ask questions, and receive accurate, context-aware answers through a clean Streamlit interface. This project demonstrates my ability to integrate LLMs, vector databases, and retrieval chains to create practical AI-powered tools.

AI SQL Agent - An SQL query generator

AI SQL Agent - An SQL query generator is an intelligent application that allows users to query MySQL databases using natural language. Built with Google Gemini LLM and LangChain, it translates plain English into accurate SQL queries, executes them, and returns results as downloadable Pandas DataFrames. The app features a clean Streamlit UI, supports complex schema-aware queries, and ensures robust database interaction via SQLAlchemy. This project showcases the seamless integration of LLMs with real-world data infrastructure for intuitive data access.

YouTube Video Q&A Chatbot

The YouTube Video Q&A Chatbot is a Streamlit-based web app that lets users paste a YouTube video URL to fetch its transcript and interact with a chatbot about the video's content. Built on Langchain's Retrieval-Augmented Generation (RAG) architecture, the app uses transcript extraction, text splitting, vector embeddings, and FAISS for efficient context retrieval. It employs Google Gemini AI to generate context-aware answers based on the video transcript, ensuring accurate responses. The app features a user-friendly interface with video display, details, and an interactive chat for seamless Q&A.

InsightForge - Research AI Agent

Insightforge Research Assistant is my first AI agent project, marking a key milestone in my data science journey. Built using Langchain, Google Gemini 1.5 Pro, and Streamlit, it automates online research, summarizes insights, and saves them in a structured format — a simple yet powerful tool designed for students, researchers, and curious minds.
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Voice Assistant

This Voice Assistant simplifies tasks through voice commands and real-time interaction. Built with Flask, it can answer questions, stream music from YouTube, provide live weather updates via the Open-Meteo API, set reminders with notifications, and display conversations through an interactive chat-based UI. With voice input and output, it offers a seamless and efficient user experience.

Insightforge Chat App

Insightforge Chat App is an AI-powered chat application that allows users to interact with a conversational AI model. This application provides concise answers to user queries and offers detailed explanations when requested. Built using Groq and the Llama3-70B-8192 model, it delivers powerful, intelligent responses.
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AskMyDoc - Document Question Answer

AskMyDoc AI is an AI-powered document Q&A system that allows users to upload TXT, PDF, or DOCX files and ask questions based on their content. Built with Streamlit and powered by a pretrained RoBERTa model, it efficiently extracts text and provides accurate answers in real time. This project showcases the power of NLP in document understanding, making it a useful tool for research, academics, and professionals.

Movie Recommender App

The Movie Recommender System is a content-based engine that suggests similar movies based on user input. It processes metadata like genres, keywords, cast, and crew from the TMDB 5000 Movies dataset using Pandas and NumPy. Movie attributes are transformed into text-based "tags," vectorized with CountVectorizer, and similarity scores are computed using cosine similarity. A Streamlit web app allows users to search for a movie and get five recommendations with posters fetched via the TMDB API.
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Audio Classification

The Real-Time Audio Intent Classification project captures live voice input, converts speech to text using Hugging Face Whisper, and classifies user intent with a pre-trained NLP model. By leveraging Few-Shot Learning, the system accurately identifies intents like Shopping, Customer Support, or Billing without requiring additional fine-tuning. With features such as automatic silence detection, real-time processing, and seamless intent categorization, this project demonstrates the power of speech recognition and natural language understanding for creating interactive, voice-driven applications.

Name Entity Recognition (NER)

This project is a simple yet powerful Named Entity Recognition (NER) app built with Streamlit and spaCy. It provides an intuitive interface to explore Natural Language Processing (NLP) concepts. The app features Word Tokenization, where users can input text to see it broken into tokens, and Named Entity Recognition, which highlights entities like names, organizations, and locations using interactive visuals powered by spacy-streamlit. It's a practical tool for beginners and enthusiasts to understand and experiment with NLP in a user-friendly way.
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Duplicate Question Pair Classifier

This project detects duplicate question pairs using NLP techniques. It preprocesses the input questions, extracts features like common words and token similarities, and uses a Random Forest classifier to predict if two questions are duplicates. The app, built with Streamlit, provides an interactive interface for real-time predictions.
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Cat-Dog Classifier Using VGG

Developed a Cats vs Dogs Classification Model using a fine-tuned VGG16 architecture with transfer learning. Trained on the Kaggle dataset, the model achieved a validation accuracy of 95.68%. Leveraged data augmentation and early stopping to enhance performance and prevent overfitting. Integrated the trained model into a Flask web app for real-time image classification, showcasing expertise in deep learning and AI-driven solutions.

Number Plate Detection

This project is a real-time license plate detection system built using the Haar Cascade Classifier in OpenCV. The application captures video input from a webcam, detects license plates in real-time, and allows users to save the detected plate region of interest (ROI) by pressing the s key.

Trading Guide App

This Project showcases a comprehensive web-based application designed to assist investors in making informed stock market decisions. The app offers three core features: Stock Analysis, Stock Prediction, and CAPM Return Calculation. Users can analyze historical stock data, predict future stock prices using advanced forecasting models, and calculate the expected return on their investments using the Capital Asset Pricing Model (CAPM). The project integrates data from Yahoo Finance and uses machine learning techniques to deliver valuable insights, empowering users to optimize their investment strategies effectively.
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Sentiment Analyzer

SentimentAnalyzer is a simple yet powerful sentiment analysis tool built using Hugging Face's DistilBERT model and Streamlit. In this project, I explored how to use pre-trained models from Hugging Face and integrated them with a user-friendly interface to predict the sentiment of text. This project marks my first step into generative AI, where I learned not only how to use powerful AI models but also how to deploy them as interactive web apps. Although simple in design, I gained valuable insights into the process of working with machine learning models and building real-world AI applications.
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Myntra Review Scrapper

I developed a review scraper for Myntra that collects product reviews using web scraping techniques, leveraging BeautifulSoup and Selenium. The data is stored in MongoDB, and I created a streamlined interface using Streamlit to visualize and perform basic analysis on the reviews. This project automates the review-fetching process and provides key insights from user feedback in an efficient manner.

Corona Dashboard

Created a user-friendly Corona Dashboard that provides detailed state-wise and monthly COVID-19 statistics. The dashboard features interactive visuals for confirmed cases, recoveries, and deaths, offering a clear and concise view of the pandemic's impact across different regions.

Nifty_50 Dashboard

This Nifty50 dashboard provides comprehensive insights into Nifty 50 stocks, including descriptions, quarterly results, profit-loss statements, shareholding patterns, stock price charts, and candlestick charts. The data spans from March 2022 to March 2023, offering a detailed view of the financial performance and market trends of these stocks.
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IPL Dashboard

This dashboard offers extensive information on IPL matches played between 2008 and 2022, providing a detailed overview of match data and performance trends over the years.
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Amazon Web Scrapping

A sophisticated web scraper to gather detailed product reviews from Amazon. The scraper efficiently extracts information such as review titles, ratings, authors and full review text.
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YouTube Scraper

Discover the latest YouTube videos with ease using our YouTube Scraper. This advanced web application dynamically fetches real-time data from YouTube, providing you with up-to-date video titles, view counts, posting dates, and thumbnails.
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INDIA General Election 2024

This dashboard provides an in-depth analysis of the 2024 General Election in India, focusing on: Party Ad Spend: 'Tracks financial investments in ads by various political parties', Voter Turnout Analysis: 'Examines voter participation across different states and election phases', Electorate Data: 'Presents information on total registered voters and the percentage of votes cast'
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DecisionTree Classifier

This Decision Tree Classifier project is an interactive web application designed to generate and visualize datasets, adjust hyperparameters, and run a decision tree model for classification. Users can create custom datasets by specifying the number of samples and clusters per class, with the results displayed in a scatter plot. The application allows fine-tuning of various hyperparameters, enabling users to explore the impact of different settings on model performance. Additionally, users can visualize the decision tree and access an accuracy report to evaluate the model's effectiveness.
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Probability Distributions

This interactive platform provides comprehensive details and functionalities for understanding statistical distributions such as Bernoulli, Binomial, Poisson, Normal, and more. Adjust distribution parameters dynamically and generate distribution plot to observe how changes impact distribution shapes and characteristics.
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Twitter Sentiment Analysis

This project performs sentiment analysis on tweets using a Multinomial Naive Bayes classifier and CountVectorizer. The dataset includes tweets categorized as positive, negative, or neutral. The Multinomial Naive Bayes model predicts tweet sentiment based on word distributions, while the CountVectorizer converts text into numerical features. Pickle is used to save and load the model and vectorizer for deployment. This project demonstrates the end-to-end process of text preprocessing, model training, and deployment.
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Contact

Phone:
8200257844
Email:
mohitrathod723@gmail.com
Address:
Vadodara, Gujarat, India