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Fastag Fraud Detection System
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# Dataset | ||
## Overview | ||
This folder contains the dataset used for the Fast Tag Fraud Detection project. | ||
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- **File**: `dataset.csv` | ||
- **Description**: This CSV file includes Fastag transaction data, detailing various attributes such as vehicle type, toll booth location, transaction amount, etc. | ||
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If the dataset is too large to upload here, please refer to the [link](https://www.kaggle.com/datasets/krishujeniya/fastag-frauds-records) to download it. |
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Fastag Fraud Detection System/Dataset/dataset.csv
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Fastag Fraud Detection System/Images/Explanatory Analysis:/corelation.png
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Fastag Fraud Detection System/Images/Explanatory Analysis:/corelation_graph.png
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... Fraud Detection System/Images/Explanatory Analysis:/featuer transformation.png
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Fastag Fraud Detection System/Images/Explanatory Analysis:/feature_graph.png
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Fastag Fraud Detection System/Images/Explanatory Analysis:/feature_implement.png
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Fastag Fraud Detection System/Images/Explanatory Analysis:/feature_shap.png
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# Images | ||
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## Overview | ||
This folder contains images generated during different stages of the project: | ||
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## Structure | ||
- `.eda` : Visualizations from exploratory data analysis. | ||
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- `.explanatory_analysis`: Detailed charts explaining relationships and patterns. | ||
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- `.feature_engineering`: Visuals created during feature engineering. | ||
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- `.ml_deployment`: Images related to the model deployment. | ||
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- `.ml_evaluation`: Actual vs predicted graphs and other evaluation metrics. | ||
- `.Actual_vs_Predicted`: Comparison between actual and predicted values. | ||
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Fastag Fraud Detection System/Images/eda/Screenshot from 2024-06-15 12-41-19.png
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Fastag Fraud Detection System/Images/eda/amount_distribution of fraud.png
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...raud Detection System/Images/eda/checkformissing and drop nulland nonunique.png
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Fastag Fraud Detection System/Images/feature/define feature and encode.png
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... Detection System/Images/feature/definex,y..column transfer and train split.png
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...g Fraud Detection System/Images/feature/feature transfer for model training.png
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...tem/Images/ml evaluation/actual vs predicted/actual vs fraud for each model.png
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# Model | ||
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This folder contains the Jupyter notebook and associated files for the project. | ||
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## Contents | ||
- **File**: `projectfile.ipynb` | ||
- Contains all the steps of data exploration, feature engineering, model development, and evaluation. | ||
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- **File**: `dataset.csv` | ||
- The dataset used within the notebook for analysis and model training. | ||
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- **File**: `model.pkl` | ||
- The trained model saved in a pickle file format. | ||
- Use this model for deployment or further analysis. | ||
- Load the model using the following code: | ||
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## Instructions | ||
1. Open the `projectfile.ipynb` to view the step-by-step workflow. | ||
2. Install the necessary dependencies listed in the `requirements.txt` file. |
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