Medicine Review Classification Model Using XGBoost Classifier |Machine Learning Project| Inttrvu.ai

Building a medicine review classification model using XGBoost (Extreme Gradient Boosting) classifier can be a great approach. XGBoost is known for its efficiency and effectiveness in handling structured data, making it suitable for this task. In this video, Mr. Rohit Mande (Founder of Inttrvu.ai) will build classification model of medicine review using XG Boost classifier Here's a step-by-step guide on how you can proceed: 1. Data Collection and Preprocessing: * Gather a dataset of medicine reviews. You can find such datasets on platforms like Kaggle or through web scraping from medicine review websites. * Preprocess the data by cleaning text, removing special characters, converting text to lowercase, tokenizing, removing stop words, and stemming or lemmatizing the words. 2. Feature Extraction: * Use techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings (like Word2Vec or GloVe) to convert text data into numerical features. * You may also consider other features like review length, sentiment scores, etc. 3. Split Data: * Split the dataset into training and testing sets to evaluate the model's performance. 4. Train XGBoost Model: * Initialize and train an XGBoost classifier on the training data. * Tune hyperparameters like learning rate, maximum depth, and number of estimators using techniques like grid search or random search to optimize performance. 5. Evaluate Model: * Evaluate the trained model on the testing data using metrics like accuracy, precision, recall, F1-score, and confusion matrix. 6. Improve Model (if necessary): * If the model performance is not satisfactory, consider adjusting hyperparameters, feature engineering, or using more advanced techniques like ensemble methods. 7. Deployment: * Once satisfied with the model's performance, deploy it in a suitable environment where it can be accessed for classifying new medicine reviews. Additional Tips: Consider techniques like cross-validation to ensure robustness of your model. Handle class imbalance if present in your dataset using techniques like oversampling, undersampling, or using class weights. Monitor the model's performance over time and retrain it periodically with new data to maintain its accuracy. By following these steps, you can build an effective medicine review classification model using the XGBoost classifier Whether you're a beginner or an experienced practitioner, this tutorial equips you with the knowledge and skills to create a robust medicine review classifier using XGBoost. Don't miss out on this essential guide to leveraging machine learning for analyzing medicine reviews. Watch now and start building your own classifier today! If you find this tutorial helpful, don't forget to like, share, and subscribe for more insightful content on machine learning and data science. Video Timestamps 00:00 Introduction 00:42 Importing Modules 01:15 Read & Explore Data 04:11 Text Data Cleaning 11:14 TFIDF Feature Extraction From Cleaned Data 15:10 Prepare Data For Model Training 16:07 Fit & Evaluate The Model 19:19 Get Feature Importance From XG Boost About Us: Rohit Mande is Founder and CEO of inttrvu.ai. He has 10+ years of professional experience as a Data Scientist. In his previous role as 'Chief Data Scientist at Barclays' he was leading a team of Data Scientists. He has done his Masters from Technical University of Darmstadt, Germany in 2013-2015. He is also having published patent applications listed on Google Patents. He is passionate about helping people in transitioning to Data Science role. Website : https://inttrvu.ai/ Instagram :https://www.instagram.com/inttrvu.ai/ LinkedIn :https://www.linkedin.com/in/rohit-mande-15a3a154/ Mail: [email protected] Contact Number: +91 7756043707 Address: Sr.No.19, Office no. 307, Acharya House, Plot No.24, 12/1, Bavdhan, Pune, Maharashtra 411021 #datascience #machinelearning #datascientist #machinelearningproject #inttrvu #datasciencecareer