Stock Price Prediction with Neural Networks in Python with Deployment скачать в хорошем качестве

Stock Price Prediction with Neural Networks in Python with Deployment

Want to predict stock prices using machine learning and neural networks? In this in-depth tutorial, we’ll show you how to build a powerful Stock Price Prediction model in Python and deploy it as an interactive web app using Streamlit. Learn to harness the power of data and AI to make predictions on stock market trends! 📌 What You’ll Learn in This Video: • Data preprocessing and feature selection for financial datasets. • Building a stock price prediction model with machine learning and neural networks. • Using LSTM (Long Short-Term Memory) for time-series forecasting. • Evaluating model accuracy with metrics like MSE. • Deploying the model as a user-friendly web app using Streamlit. 📊 Topics Covered: • Exploratory Data Analysis (EDA) on stock market data. • Feature engineering for time-series data. • Implementing machine learning algorithms and deep learning models. • Deploying your stock price prediction system using Streamlit for real-time forecasting. 🚀 Tools and Libraries Used: • Python • Pandas, NumPy • Scikit-learn • TensorFlow/Keras (for neural networks) • Matplotlib • Streamlit 💡 Who Is This Video For? This tutorial is designed for data scientists, and finance enthusiasts who want to learn how to build stock market prediction systems and deploy them as web apps for real-world applications. 🔗 Links Mentioned in the Video: • Code Repository: https://github.com/TensorTitans01/Sto... 👉 Subscribe for More Machine Learning Tutorials! If you’re passionate about machine learning, deep learning, and practical projects, subscribe to Tensor Titans. Stay ahead in the AI world with weekly tutorials and tips! 🔔 Turn On Notifications to get updates on our latest uploads. 💬 Have Questions? Feel free to leave a comment, and we’ll get back to you! #StockPricePrediction #NeuralNetworks #MachineLearning #Python #Streamlit #TensorTitans