https://github.com/suraj-sedai/lstm-deep-learning
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.
https://github.com/suraj-sedai/lstm-deep-learning
lstm-neural-networks lstm-sentiment-analysis numpy pandas python sklearn tensorflow
Last synced: 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/suraj-sedai/lstm-deep-learning
- Owner: Suraj-Sedai
- Created: 2025-02-06T03:40:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-07T05:23:42.000Z (over 1 year ago)
- Last Synced: 2025-02-07T05:23:45.194Z (over 1 year ago)
- Topics: lstm-neural-networks, lstm-sentiment-analysis, numpy, pandas, python, sklearn, tensorflow
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
### **📊 LSTM Sequence Prediction with TensorFlow**
---
## **🔥 Overview**
This 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**.
---
## **📌 Project Structure**
```
LSTM-Deep-Learning
│── script/
│ ├── Basic/
│ │ ├── level1.py # Basic Linear Prediction (No LSTM)
│ │ ├── level2.py # Data Preprocessing for LSTM
│ │ ├── level3.py # Basic LSTM Model
│ │ ├── level4.py # Multi-Step Prediction
│ │ ├── level5.py # Using More Past Data
│ │ ├── LSTM_model.py # General LSTM Implementation
│ │
│ ├── Advanced/
│ │ ├── many-to-one.py # Predicting one future value using multiple past values
│ │ ├── many-to-many.py # Predicting a full sequence
│ │ ├── multi-step-prediction.py # Multi-step LSTM forecasting
│ │
│ ├── Application/
│ │ ├── Sales_prediction.py # Predicting sales & customer behavior
│ │ ├── Stock_price_prediction.py # Stock price forecasting with LSTM
│ │
│── README.md # Documentation
│── .dist/ # Distribution folder
```
---
## **📌 Levels Covered**
### **✅ Basic (Step-by-Step Learning)**
📌 **Level 1: Linear Prediction (No LSTM)**
- Uses a **basic neural network (`Dense`)** for simple regression.
📌 **Level 2: Data Preprocessing for LSTM**
- Converts **sequential data** into a format suitable for LSTM models.
📌 **Level 3: Basic LSTM Model**
- Introduces `LSTM` layers to learn sequence patterns.
📌 **Level 4: Multi-Step Prediction**
- Predicts **multiple future values** instead of just one.
📌 **Level 5: Using More Past Data**
- Uses **multiple past values** for better predictions.
---
### **✅ Advanced LSTM Techniques**
📌 **Many-to-One Prediction**
- Uses **multiple past time steps** to predict **one future value**.
📌 **Many-to-Many Prediction**
- Predicts an **entire sequence** of future values.
📌 **Multi-Step Prediction**
- Predicts **multiple future points in time**, useful for time series forecasting.
---
### **✅ Real-World Applications**
📌 **Sales Prediction**
- Predicts **future sales trends** based on historical data.
📌 **Stock Price Forecasting**
- Uses **Yahoo Finance stock data** to predict market movements.
---
## **🛠 Installation & Requirements**
### **1️⃣ Clone the Repository**
```bash
git clone https://github.com/Suraj-Sedai/LSTM-Deep-Learning.git
```
### **2️⃣ Install Dependencies**
```bash
pip install tensorflow numpy pandas matplotlib scikit-learn yfinance
```
---
## **🚀 How to Run**
### **1️⃣ Running Basic Level Scripts**
To run the basic sequence prediction models:
```bash
python script/Basic/level1.py
```
```bash
python script/Basic/level2.py
```
### **2️⃣ Running Advanced Level Scripts**
For more complex sequence predictions:
```bash
python script/Advanced/many-to-one.py
```
```bash
python script/Advanced/many-to-many.py
```
### **3️⃣ Running Real-World Applications**
```bash
python script/Application/Sales_prediction.py
```
```bash
python script/Application/Stock_price_prediction.py
```
---
## **🎯 Next Steps**
📌 Improve **real-world applications** (e.g., **weather forecasting**, **Chat Bot**).
📌 **Fine-tune LSTM models** for better accuracy.
📌 Implement **Bidirectional LSTMs** for NLP applications.
---
## **📩 Contact & Contribution**
Want to contribute? Feel free to submit a **pull request** or open an **issue**!
For any questions, reach out to **surajsedai05@gmail.com**.