Rahul Ghosh Email & Phone Number

I have a Bachelor's degree in Computer Science Engineering from Terna Engineering College, where I learned the fundamentals of programming, databases, and web development. I have also completed multiple online courses and certifications to enhance my skills in SQL, Python, Machine Learning ,Natural Language Processing ,Flask ,Excel, PowerBI . I am a keen observer, a critical thinker, and a team player who can manage competing priorities and perform under pressure. My goal is to leverage my skills and knowledge to solve real-world problems and create value through data.📈 Expertise:My journey in data science has equipped me with expertise in:Data Science Machine LearningNatural Language ProcessingDatabase ManagementData WranglingData VisualizationStatistical AnalysisAI Applications🌟 Achievements:I take pride in:1. Sentiment Analysis On Tripadvisor Dataset https://github.com/rahul4151/Sentiment-AnalysisI conducted sentiment analysis using the VADER and Sentiment Intensity Analyzer tool on a dataset of TripAdvisor reviews. Leveraging Python and NLP techniques, I analyzed the sentiment intensity of the reviews and generated Wordcloud for the same2.Chatbots  https://rahulghoshchatbot.pythonanywhere.com/Developed a chatbot using cosine similarity and n-gram vectorization, utilizing bi- and trigram models for improved context and accuracy. The chatbot processes user input, compares it with predefined responses using cosine similarity, and delivers contextually relevant replies and Deployed Using Flask3. Plagiarism Detection Between 2 PDF  https://github.com/rahul4151/PlagiarismDeveloped a plagiarism detection tool using Streamlit, allowing users to upload two PDF files for comparison. The software utilizes cosine similarity and Count Vectorizer to analyze the textual content of the documents. Upon comparison, it computes the cosine similarity score to determine the degree of similarity between the documents4. Spam or Ham Detection System for Email Messages  https://github.com/rahul4151/project_spam_detectionDeveloped solution employing Multinomial Naive Bayes to distinguish emails. Leveraged Scikit-learn to preprocess and train the model Utilized Count Vectorizer to transform textual data into numerical features. 🤝 Let's Connect:I'm excited to connect with fellow data enthusiasts, professionals, and thought leaders. Whether you're interested in collaborating on projects, sharing insights, or simply exchanging ideas, feel free to reach out. Let's join forces to turn data into actionable wisdom!