{"id":25131778,"url":"https://github.com/suraj-sedai/lstm-deep-learning","last_synced_at":"2026-04-11T06:04:07.069Z","repository":{"id":276063030,"uuid":"928094062","full_name":"Suraj-Sedai/LSTM-Deep-Learning","owner":"Suraj-Sedai","description":"A step-by-step guide to mastering sequence prediction using TensorFlow and LSTM. This repository covers everything from basic linear predictions to advanced multi-step forecasting, many-to-many LSTM models, and real-world applications like stock price forecasting and sales prediction.","archived":false,"fork":false,"pushed_at":"2025-02-07T05:23:42.000Z","size":12,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-07T05:23:45.194Z","etag":null,"topics":["lstm-neural-networks","lstm-sentiment-analysis","numpy","pandas","python","sklearn","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Suraj-Sedai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-06T03:40:32.000Z","updated_at":"2025-02-07T05:19:05.000Z","dependencies_parsed_at":"2025-02-07T05:23:51.917Z","dependency_job_id":"1cdfabef-668a-4ca3-8c1e-536b6de42c07","html_url":"https://github.com/Suraj-Sedai/LSTM-Deep-Learning","commit_stats":null,"previous_names":["suraj-sedai/prediction_ml","suraj-sedai/lstm-deep-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Suraj-Sedai%2FLSTM-Deep-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Suraj-Sedai%2FLSTM-Deep-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Suraj-Sedai%2FLSTM-Deep-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Suraj-Sedai%2FLSTM-Deep-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Suraj-Sedai","download_url":"https://codeload.github.com/Suraj-Sedai/LSTM-Deep-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246911498,"owners_count":20853657,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["lstm-neural-networks","lstm-sentiment-analysis","numpy","pandas","python","sklearn","tensorflow"],"created_at":"2025-02-08T14:14:29.805Z","updated_at":"2025-12-30T23:10:01.597Z","avatar_url":"https://github.com/Suraj-Sedai.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"### **📊 LSTM Sequence Prediction with TensorFlow**  \n\n---\n\n## **🔥 Overview**  \nThis repository provides a **structured learning guide** for **LSTM-based sequence prediction** using **TensorFlow**. It is divided into different levels, from **basic sequence learning** to **real-world applications** such as **stock price forecasting and sales prediction**.  \n\n---\n\n## **📌 Project Structure**  \n```\nLSTM-Deep-Learning\n│── script/\n│   ├── Basic/\n│   │   ├── level1.py               # Basic Linear Prediction (No LSTM)\n│   │   ├── level2.py               # Data Preprocessing for LSTM\n│   │   ├── level3.py               # Basic LSTM Model\n│   │   ├── level4.py               # Multi-Step Prediction\n│   │   ├── level5.py               # Using More Past Data\n│   │   ├── LSTM_model.py           # General LSTM Implementation\n│   │\n│   ├── Advanced/\n│   │   ├── many-to-one.py          # Predicting one future value using multiple past values\n│   │   ├── many-to-many.py         # Predicting a full sequence\n│   │   ├── multi-step-prediction.py  # Multi-step LSTM forecasting\n│   │\n│   ├── Application/\n│   │   ├── Sales_prediction.py     # Predicting sales \u0026 customer behavior\n│   │   ├── Stock_price_prediction.py  # Stock price forecasting with LSTM\n│   │\n│── README.md  # Documentation\n│── .dist/     # Distribution folder\n```\n\n---\n\n## **📌 Levels Covered**  \n\n### **✅ Basic (Step-by-Step Learning)**  \n📌 **Level 1: Linear Prediction (No LSTM)**  \n   - Uses a **basic neural network (`Dense`)** for simple regression.  \n📌 **Level 2: Data Preprocessing for LSTM**  \n   - Converts **sequential data** into a format suitable for LSTM models.  \n📌 **Level 3: Basic LSTM Model**  \n   - Introduces `LSTM` layers to learn sequence patterns.  \n📌 **Level 4: Multi-Step Prediction**  \n   - Predicts **multiple future values** instead of just one.  \n📌 **Level 5: Using More Past Data**  \n   - Uses **multiple past values** for better predictions.  \n\n---\n\n### **✅ Advanced LSTM Techniques**  \n📌 **Many-to-One Prediction**  \n   - Uses **multiple past time steps** to predict **one future value**.  \n📌 **Many-to-Many Prediction**  \n   - Predicts an **entire sequence** of future values.  \n📌 **Multi-Step Prediction**  \n   - Predicts **multiple future points in time**, useful for time series forecasting.  \n\n---\n\n### **✅ Real-World Applications**  \n📌 **Sales Prediction**  \n   - Predicts **future sales trends** based on historical data.  \n📌 **Stock Price Forecasting**  \n   - Uses **Yahoo Finance stock data** to predict market movements.  \n\n---\n\n## **🛠 Installation \u0026 Requirements**  \n\n### **1️⃣ Clone the Repository**  \n```bash\ngit clone https://github.com/Suraj-Sedai/LSTM-Deep-Learning.git\n```\n\n### **2️⃣ Install Dependencies**  \n```bash\npip install tensorflow numpy pandas matplotlib scikit-learn yfinance\n```\n\n---\n\n## **🚀 How to Run**  \n\n### **1️⃣ Running Basic Level Scripts**  \nTo run the basic sequence prediction models:  \n```bash\npython script/Basic/level1.py\n```\n```bash\npython script/Basic/level2.py\n```\n\n### **2️⃣ Running Advanced Level Scripts**  \nFor more complex sequence predictions:  \n```bash\npython script/Advanced/many-to-one.py\n```\n```bash\npython script/Advanced/many-to-many.py\n```\n\n### **3️⃣ Running Real-World Applications**  \n```bash\npython script/Application/Sales_prediction.py\n```\n```bash\npython script/Application/Stock_price_prediction.py\n```\n\n---\n\n## **🎯 Next Steps**  \n📌 Improve **real-world applications** (e.g., **weather forecasting**, **Chat Bot**).  \n📌 **Fine-tune LSTM models** for better accuracy.  \n📌 Implement **Bidirectional LSTMs** for NLP applications.  \n\n---\n\n## **📩 Contact \u0026 Contribution**  \nWant to contribute? Feel free to submit a **pull request** or open an **issue**!  \nFor any questions, reach out to **surajsedai05@gmail.com**.  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsuraj-sedai%2Flstm-deep-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsuraj-sedai%2Flstm-deep-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsuraj-sedai%2Flstm-deep-learning/lists"}