Rishabh Jain’s Post

The code is on Spam sms detection by using the NLP and Machine learning model. In this code i have build model that can classify SMS messages as spam or legitimate. Use techniques like TF-IDF or word embeddings with classifiers like Naive Bayes, Logistic Regression, or Support Vector Machines to identify spam messages. #Encryptix #internship #datascience

🎉 I'm thrilled to share my first project from my internship at Brainwave Matrix Solutions, where I embarked on an incredible journey into the world of Machine Learning. Here's a snapshot of what I achieved: 🚀 Data Gathering: I sourced datasets from Kaggle containing both real and fake news articles, labeling them meticulously with 'REAL' and 'FAKE' tags. This essential step set the stage for our analysis. 🔧 Data Preprocessing: Leveraging NLTK, I cleaned and preprocessed the text data by removing punctuation, converting text to lowercase, and eliminating extra spaces. This ensured the data was ready for further analysis. 📊 Feature Extraction: Using TF-IDF (Term Frequency-Inverse Document Frequency) Vectorizer, I transformed the cleaned text into numerical features. I also included n-grams to capture more context from the text data, a crucial step for the machine learning model. 📈 Model Training: I split the dataset into training and testing subsets and trained a RandomForestClassifier. The model showed impressive performance, achieving an accuracy of 98.94% on the test set. 🔍 Evaluation and Testing: The trained model and the TF-IDF vectorizer were saved for future use. I tested the model on new text samples to ensure its reliability, confirming its ability to accurately identify fake news. The model demonstrated remarkable accuracy in distinguishing real news from fake news. 📁 Project Outcome: By implementing n-grams and A RandomForestClassifier, I ensured a unique approach compared to traditional methods. This project not only deepened my understanding of Machine Learning but also honed my skills in Natural Language Processing and model evaluation. A huge thank you to Brainwave Matrix Solutions for this incredible learning opportunity! I am excited to continue exploring and growing in the field of AI and Machine Learning. #BrainWaveMatrixSolutions #Internship #MachineLearning #DataScience #FakeNewsDetection #NLP #RandomForest #AI #Python #DataAnalysis #KaggleDataset #PassionateLearner #LearningEveryday #TFIDF #TextMining #FeatureEngineering

Thrilled to announce the completion of Task 1 in my internship at Codsoft! 🌟 I've developed a rule-based chatbot that responds intelligently to user inputs using if-else statements and pattern matching techniques. This project has given me a solid foundation in natural language processing and conversation flow. Excited to continue exploring the realms of AI! https://lnkd.in/dziPAJ2i CodSoft #ChatbotDevelopment #InternshipMilestones #Codsoft #AI #Python #ML #DL

🚀 Exciting Milestone! 🚀 I’m thrilled to share that I’ve successfully completed my first task for ProDigy Infotech! 🎉 As part of my Generative AI internship, I had the opportunity to fine-tune the GPT-2 model to generate contextually relevant and coherent text based on custom datasets. This task was both challenging and rewarding, pushing me to explore advanced text generation techniques using models like GPT-2. 🤖 Here’s a quick breakdown of what I accomplished: ✅ Set up the environment with necessary libraries (transformers, torch, etc.) ✅ Preprocessed and tokenized a custom dataset for training ✅ Fine-tuned GPT-2, experimenting with hyperparameters for optimal performance ✅ Tested different text generation methods (Greedy Search, Beam Search, Top-K Sampling, and Top-p Sampling) ✅ Evaluated model performance through perplexity scores and qualitative assessment This project gave me hands-on experience with generative models and a deeper understanding of how AI can be used for creative tasks like text generation. 💡 GitHub: https://lnkd.in/dwDVvvUD Looking forward to tackling the next challenges and learning more along the way! 💪 #GenerativeAI #GPT2 #MachineLearning #AI #ProDigyInfotech #TextGeneration #AIInternship #LearningJourney #AIResearch

🚀 Exciting Update: Completed Next Internship Task in Sentiment Analysis! Innomatics Research Labs 📊 I'm thrilled to announce the successful completion of my latest internship task, focusing on sentiment analysis of customer reviews. 🌟 This project delved into the intricacies of text pre-processing and various NLP concepts, including stop words, lemmatization, stemming, bag of words, TF-IDF, BERT, and Word2Vec. The highlight was building a robust machine learning model to predict customer sentiment, coupled with hyperparameter tuning to ensure optimal accuracy. 🎯 Despite facing the challenge of an unbalanced dataset, I leveraged advanced techniques like SMOTE and other statistical methods to achieve a better prediction outcome. What's more, I developed a Flask backend interface to enable real-time user input and seamlessly applied the entire ML pipeline. The cherry on top? Deploying the model on Amazon EC2 for accessibility and scalability. 🚀 I extend my heartfelt gratitude to Kanav Bansal for generously sharing their knowledge and guiding us through this project. 🙏 Feel free to check out the deployed model :- http://3.92.161.98:5000/ Explore the GitHub repository for a deeper dive into the project. https://lnkd.in/gScn7TT5 Your feedback and insights are always appreciated! 🌟 #SentimentAnalysis #NLP #MachineLearning #Flask #AmazonEC2 #InternshipJourney

🚀 Delighted to unveil the culmination of my journey at Innomatics Research Labs Research Lab, where I spearheaded the development of an advanced search engine algorithm aimed at enhancing the relevance of video subtitles. 🎯 Project Highlights: 📌 Objective: Crafting a Semantic Search Engine harnessing NLP techniques to elevate subtitle retrieval precision in response to user queries. 📌 Core Logic: ⿡ Data Preprocessing: Rigorously cleaned and vectorized subtitles to ensure unparalleled accuracy. ⿢ Semantic Vectorization: Leveraged state-of-the-art BERT-based SentenceTransformers for embeddings, capturing intricate semantic nuances. ⿣ Document Chunking: Conquered the challenge of embedding large documents by skillfully segmenting them into manageable chunks with meticulously designed overlapping windows. 📌 Outcome: Seamlessly implemented cosine similarity calculations to gauge document relevance to user queries, guaranteeing precise and pertinent search results. 📌 Contribution: Akanksha Wagh A profound gratitude to the visionary Kanav Bansal Sir for his unparalleled guidance, and to Raghu Ram Aduri and the entire team at Innomatics Research Labs for their unwavering support and encouragement throughout this enriching internship journey. 🔗 Dive into the project on GitHub:https: https://lnkd.in/gmQ82W2w #SearchEngine #NLP #MachineLearning #SemanticSearch #InnomaticsResearchLab #Internship #Gratitude 

I successfully completed my AI internship task of creating a sentiment analyzer. The project involved developing a web application with a Flask backend and a React frontend, utilizing the VADER sentiment analysis tool. This experience enhanced my skills in natural language processing and integrating pre-trained models into practical applications. #PRASUNET #ARTIFICIALINTELLIGENCE

Exciting News.... I have completed a summer internship with CodeAI x Microsoft Reskill and was asked to complete four tasks. The below mentioned project is the first...where I performed a code on Sentiment Analysis on Customer Feedback Using NLP......Also this was all possible with the extreme help from the team of CodeAI KJSIT , Reskilll, Azure Developer Community Project 1/4:- Sentiment Analysis on Customer Feedback Using NLP

Second task at CodeAlpha ‘s ML internship. This project focuses on Twitter sentiment analysis using machine learning and NLP techniques. It involves preprocessing tweets, converting text data into numerical form using TF-IDF Vectorizer and training Logistic Regression to classify tweets as positive or negative. The trained model is saved for predictions. Here’s a github repository link: https://lnkd.in/d_a5SVVi

Hello Connections! Successfully completed my internship task 3 of building a simple chatbot using the NLTK library in Python! This project was a fantastic learning experience, diving deep into the basics of natural language processing. From tokenization and stemming to crafting meaningful responses, I explored how NLTK helps bridge the gap between human language and machine understanding. 1.The chatbot is capable of tokenizing and processing user input 2.Responding intelligently using context-based logic 3.Incorporating basic NLP techniques like stemming and lemmatization This task taught me the importance of understanding user intent and crafting responses that feel engaging. While the chatbot is simple, it marks a foundational step toward more advanced conversational AI systems. This accomplishment has further fueled my passion for NLP and AI. Can't wait to work on more complex projects! #chatbot #python #pythonprogramming #nltklib #AI #machinetranslation #internship #TechIndiaItSolutions Github URL: https://lnkd.in/gRdGJjM5