Smart Grid Stability Prediction

Project Overview

This project focuses on predicting the stability of a grid using machine learning techniques. The primary aim is to classify whether a grid is stable or unstable based on input features derived from the dataset.

Files in the Repository

• smartgridprediction.ipynb: Jupyter notebook containing the code, analysis, and results for the project.
• smart_grid_stability_augmented.csv : Dataset used for this project
• requirements.txt : Is the pacakages that are used for the project

Key Features

• Data Preprocessing: Includes handling missing data, normalization, and feature engineering.
• Model Training: Implements various machine learning models for classification.
• Evaluation: Compares models using metrics such as accuracy, precision, recall, and F1-score.

Installation and Usage

Prerequisites

1. Python 3.8 or above
2. Required Python libraries:
   • pandas
   • numpy
   • scikit-learn
   • matplotlib
   • seaborn

Installation

1. Clone this repository:

   git clone https://github.com/saivarshith67/smart-grid-stability.git

2. Navigate to the project directory:

   cd smart-grid-stability

3. Install the required libraries:

   pip install -r requirements.txt

Running the Notebook

1. Open the Jupyter notebook:

   jupyter notebook smartgridprediction.ipynb

2. Run the cells sequentially to reproduce the analysis and results.

Dataset

Details about the dataset, including its source, features, and preprocessing steps, are provided in the notebook.

Results

• Summary of model performances and insights gained from the project.