https://github.com/seekai-786/electricity_theft_detection

https://github.com/seekai-786/electricity_theft_detection

Last synced: 7 days ago
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• Host: GitHub
• URL: https://github.com/seekai-786/electricity_theft_detection
• Owner: SeekAI-786
• License: apache-2.0
• Created: 2024-12-25T15:02:45.000Z (11 days ago)
• Default Branch: main
• Last Pushed: 2024-12-25T16:05:04.000Z (11 days ago)
• Last Synced: 2024-12-25T16:18:43.939Z (11 days ago)
• Language: Python
• Size: 3.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Electricity Theft Detection System

## Purpose

Electricity theft is a major issue in regions like Karachi, where unauthorized consumption of electricity leads to significant losses for utility companies. This project provides a solution for detecting electricity theft using machine learning models. By analyzing various factors such as electricity usage, voltage fluctuations, and historical data, the system predicts the likelihood of theft.

## Model Overview

The machine learning model used in this project is a **Random Forest Classifier**. This model was chosen due to its ability to handle complex data with multiple features and its robustness in classification tasks.

### Key Techniques Used:

1. **SMOTE (Synthetic Minority Over-sampling Technique)**: Used to handle class imbalance in the dataset. It generates synthetic samples for the underrepresented class (theft) to improve model performance.

   

2. **Grid Search for Hyperparameter Tuning**: The model was fine-tuned using `GridSearchCV`, which optimizes the hyperparameters of the Random Forest classifier to find the best-performing configuration. Key hyperparameters tuned include:

   - `n_estimators`: The number of trees in the forest (100, 200, 300).

   - `max_depth`: The maximum depth of the trees (None, 10, 20).

   - `min_samples_split`: The minimum number of samples required to split an internal node (2, 5, 10).

### Model Pipeline:

The model is part of a pipeline that includes:

- **Standard Scaling**: Scales input features to standardize the dataset.

- **Random Forest Classifier**: A robust classification model to predict the likelihood of electricity theft.

## How to Run the Streamlit App

1. **Install Dependencies**:

   Before running the Streamlit app, make sure to install all necessary dependencies. You can do this by running the following command:

   ```bash

   pip install -r requirements.txt

2. **Run the Streamlit App**:

Once the dependencies are installed, you can run the Streamlit app using the following command:

```bash

streamlit run app.py

```

This will launch the app in your web browser, typically at http://localhost:8501.