Rimsha Saif

Sentiment Analysis with Deep Learning using BERT In my recent Medium article, I guide you through the practical implementation of sentiment analysis using the powerful BERT model. This hands-on tutorial walks you through each step of the process, from data preparation to model training and evaluation. Key Topics Covered: 1. Importing the Essential Libraries: Setting the stage with essential libraries for deep learning, data manipulation, and BERT integration. 2. Data Loading and Preprocessing: A comprehensive look at loading the "Emotions" Twitter dataset and applying text cleaning techniques to prepare it for BERT. 3. Train and Validation Split: Dividing the dataset into training and validation sets for optimal model training and performance evaluation. 4. BERT Tokenizer and Encoding the Data: Leveraging the BERT tokenizer to convert text into BERT-compatible tokens, enabling the model to understand the input. 5. BERT Pre-trained Model: Exploring the pre-trained BERT model specifically designed for classification tasks and its role in sentiment analysis. 6. Data Loaders: Creating efficient data loaders to streamline the process of feeding data into the BERT model. 7. Optimizer & Scheduler: Choosing the appropriate optimization algorithm and learning rate scheduler for effective model training. 8. Performance Metrics: Introducing key metrics like accuracy, precision, recall, and F1-score for evaluating the model's performance. 9. Training Loop: A step-by-step guide to training the BERT model on the sentiment analysis task. 10. Loading and Evaluating the Model: Loading the trained model and evaluating its accuracy on unseen data. Whether you're a seasoned data scientist or just beginning your journey with deep learning, this article provides a clear and practical roadmap for implementing sentiment analysis with BERT. #sentimentanalysis #deeplearning #BERT #natural_language_processing #nlp #twitter #datamining #machinelearning #AI

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Can data scientists make good algorithmic traders? Let's find out: There are 8 core skills that successful algorithmic traders have: 1. Data analysis 2. Market microstructure 3. Market knowledge 4. Technical aptitude 5. Programming 6. Mult-asset class knowledge 7. Economic Knowledge 8. Financial Modeling Data scientists already have proficiency at 4 of the key skills: Data analysis Technical aptitude Programming Economic knowledge So that leaves these financial skills: Market Microstructure Market Knowledge Multi-asset class knowledge Financial Modeling Can these skills be learned quickly? Yes. But most learners make 2 critical mistakes. They try to learn everything. They focus on theory. Instead of learning everything, a better approach is to focus on core strategies. Core strategies are simple classes of algorithmic trading that help you gain financial modeling knowledge. Examples of core strategies are: - Momentum - Portfolio - Spread Instead of focusing on the theory, learn how to apply your trading strategies. This is the best way to learn the markets. A good goal is to make mistakes fast in paper trading. Apply an algorithm, see if it works, then iterate. Are you a data scientist who wants to learn how to algorithmic trade to grow your investments? We have a live workshop on May 29th. Register here: https://lnkd.in/eX837hZe

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🚀 Exploring Machine Learning Algorithms: Polynomial Regression 📈 Today, let's know polynomial regression Polynomial Regression builds upon the principles of Simple Linear Regression but expands the model to accommodate polynomial relationships between the independent and dependent variables. 🧠 Understanding the Mathematics Behind Polynomial Regression 📊 At the heart of polynomial regression lies the idea of fitting a curve to the data points to capture non-linear relationships between the independent variable (X) and the dependent variable (Y). Instead of a straight line, we use polynomial functions to model the relationship. The equation of a polynomial regression: Y = β₀ + β₁X + β₂X² + ... + βₙXⁿ Where: Y is the dependent variable (the variable we seek to predict). X is the independent variable (the variable used for prediction). β₀ is the intercept. β₁, β₂, ..., βₙ are the coefficients of the polynomial terms. The objective of polynomial regression remains akin to multiple linear regression: to determine the best-fitting curve by minimizing the sum of squared differences between the observed and predicted values. This optimization is often achieved through methods like gradient descent or analytical techniques like Ordinary Least Squares (OLS). Once we derive the polynomial equation, we can forecast the value of Y for new data points based on the provided X values. In this instance, I've implemented a polynomial regression model using Python, demonstrating its utility in predicting complex relationships such as pay of a person based on his level of duty🔍 Dive into the code on GitHub: https://lnkd.in/dgnavusu #MachineLearningAlgorithms! #MachineLearning #DataScience #ArtificialIntelligence #Python #GitHub #DataAnalytics

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We (me and Michael Chow) gave a PyCon (2024) talk about Great Tables and the recording is 🆙 https://lnkd.in/gS6DwmSC It was truly a lot of fun talking about table-making in Python and how tables made with Great Tables could be both beautiful and functional. In the end, we hope we convinced people that using Great Tables (https://lnkd.in/gpwwys6n): - results in a reproducible reporting workflow (no need to make display tables outside of Python) - can yield beautiful tables, sure to impress most anyone - gives you a plethora of options for structuring and annotating your data for reduced time to insight So, check out the video! (And enjoy Great Tables.)

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After finishing the Machine Learning Specialization, I applied the Regresison method (Linear, Lasso and Ridge Regresison) on the Life Expectancy dataset In this project, I needed to get some insights of what affects Life Expectancy in different countries, and in order to do so: 1- Have a quick look at the correlation between features using a heatmap 2- The Status of provided countries, by creating a pie chart to express the precentage of developed to developing countries 3- Check the population of different countries by creating a box plot 4- Check the Life Expectancy range through the years 5- Get a better insight of the spreading of diseases provided in the dataset in different countries 6- Check the correlation between Life Expectancy and spreading Diseases in different Countries 7- Train the model using different Regression techniques to choose the best model with highest accuracy notebook link on GitHub: https://lnkd.in/dTrT6-pn notebook link on Kaggle: https://lnkd.in/dKqQaqWa

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🚀 Exciting News! 🚀 🔍 Delving into the world of data science competitions was an exhilarating experience for our team! We recently participated in a practice competition hosted by DrivenData, where we explored the dynamics of public health responses to infectious diseases through predictive modeling. 🦠 Our focus was on understanding vaccination patterns during the H1N1 flu pandemic of 2009. By analyzing demographic backgrounds, opinions, and health behaviors, we aimed to predict whether individuals received the H1N1 and seasonal flu vaccines. 🏆 We're thrilled to share that our team secured a rank of 1795 out of 6482 participants! 🥳. But with a score of 0.6072, there's room for improvement. We're committed to fine-tuning our models for future competitions. 🔗 If you're curious to explore our journey and learn more about our approach, feel free to check out our collaborative repository. Your feedback and support are greatly appreciated as we continue to refine our models and tackle new challenges on the horizon! 💡 #DataScience #MachineLearning #PublicHealth #DrivenData #Competition #Teamwork #Collaboration #FutureImprovements #PredictiveModeling #H1N1 #FluVaccine Team Members: Megha Gokanvi Supriya Chougale Uddhav M.

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