{"id":20072210,"url":"https://github.com/anish202020/big-data-analysis-mini-project","last_synced_at":"2026-04-12T18:11:27.125Z","repository":{"id":262567734,"uuid":"875266220","full_name":"Anish202020/Big-Data-Analysis-Mini-Project","owner":"Anish202020","description":"The primary purpose of this code is to predict wind speed using an LSTM model. 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LSTM Wind Speed Prediction Documentation\n\u003cimg src=\"https://github.com/Anish202020/Web-Development-Data/blob/main/Banner/Banner-1/Machine%20Learning/wind-speed.png\"/\u003e\n\n[![Google Colab](https://img.shields.io/badge/Google%20Colab-F9AB00?style=for-the-badge\u0026logo=googlecolab\u0026logoColor=white)](https://colab.research.google.com/)\n[![NumPy](https://img.shields.io/badge/NumPy-013243?style=for-the-badge\u0026logo=numpy\u0026logoColor=white)](https://numpy.org/)\n[![Pandas](https://img.shields.io/badge/Pandas-150458?style=for-the-badge\u0026logo=pandas\u0026logoColor=white)](https://pandas.pydata.org/)\n[![Matplotlib](https://img.shields.io/badge/Matplotlib-003B57?style=for-the-badge\u0026logo=matplotlib\u0026logoColor=white)](https://matplotlib.org/)\n[![Keras](https://img.shields.io/badge/Keras-D00000?style=for-the-badge\u0026logo=keras\u0026logoColor=white)](https://keras.io/)\n[![TensorFlow](https://img.shields.io/badge/TensorFlow-FF6F20?style=for-the-badge\u0026logo=tensorflow\u0026logoColor=white)](https://www.tensorflow.org/)\n[![Scikit-learn](https://img.shields.io/badge/Scikit--learn-F7931E?style=for-the-badge\u0026logo=scikit-learn\u0026logoColor=white)](https://scikit-learn.org/)\n\n## Overview\n\n\u003cimg src=\"https://github.com/Anish202020/Web-Development-Data/blob/main/Logos/Website%20Logos/wind.jpg\" width=\"110\"/\u003e\n\nThis document provides an overview and explanation of the code designed to train an LSTM (Long Short-Term Memory) model to predict wind speed based on historical data.\n\n## My Project\nThis project includes a detailed report available in PDF format and Google Colab Execution.\n\n[![Download PDF](https://img.shields.io/badge/Download-PDF-blue?style=flat)](https://github.com/Anish202020/Big-Data-Analysis-Mini-Project/blob/main/Big%20Data%20Mini%20Project-2.0.pdf)\n[![Open in Google Colab](https://img.shields.io/badge/Open%20in-Google%20Colab-blue?style=flat\u0026logo=googlecolab)](https://colab.research.google.com/drive/1zSBZl_BOLwNUjKmZMQ2twBDiA8qImwV8?usp=sharing)\n\n\n## Table of Contents\n1. [Introduction](https://#introduction) \n2. [Data Requirements](https://#data-requirements) \n3. [Code Explanation](https://#code-explanation) \n    - [Data Loading and Preparation](https://#data-loading-and-preparation) \n    - [Feature Creation](https://#feature-creation) \n    - [Data Scaling](https://#data-scaling) \n    - [Model Training](https://#model-training) \n    - [Prediction and Visualization](https://#prediction-and-visualization) \n4. [Output](https://#output) \n5. [Conclusion](https://#conclusion) \n## Introduction\nThe primary purpose of this code is to predict wind speed using an LSTM model. The model is trained on historical wind speed data to forecast future values.\n\n## Data Requirements\n- **Input CSV File Structure**:\n    - **Station ID**: Unique identifier for the weather station.\n    - **Location**: Geographical location of the station.\n    - **Date**: Date of the recorded wind speed.\n    - **Wind Speed**: Recorded wind speed (in km/h or mph).\n## Code Explanation\n### Data Loading and Preparation\n- The code reads the CSV file using Pandas.\n- It extracts the wind speed values from the 4th column.\n- A plot is generated to visualize wind speed variations over time.\n### Feature Creation\n- Three input features (X1, X2, X3) are created by shifting the wind speed data.\n- Each prediction is based on the wind speeds of the previous three days.\n### Data Scaling\n- Both input features and target values are scaled to a range between 0 and 1 using MinMaxScaler.\n### Model Training\n- An LSTM model is defined using the Sequential API.\n- The model is trained on 80% of the data for 25 epochs.\n- The loss for each epoch is displayed during training.\n### Prediction and Visualization\n- The model makes predictions on the test set (20% of data).\n- Two plots are generated:\n    - **Scatter Plot**: Compares actual vs. predicted wind speed values.\n    - **Line Plot**: Shows actual and predicted wind speed over time.\n## Output\n- **Scatter Plot**: Visualizes the accuracy of predictions against actual values.\n- **Line Plot**: Illustrates how well the model captures trends and fluctuations in wind speed.\n## Conclusion\nThe final output allows for a visual assessment of the LSTM model's performance in predicting wind speeds based on historical data. A successful model will have predictions closely following actual values in the line plot and points clustering around a diagonal line in the scatter plot.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanish202020%2Fbig-data-analysis-mini-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanish202020%2Fbig-data-analysis-mini-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanish202020%2Fbig-data-analysis-mini-project/lists"}