Naman-Gayakwad/Water-Issues-Prediction-Model

# Water Issues Prediction Model This repository contains a **machine learning model** designed to predict water-related issues such as **urban floods, drainage blockages, and sanitation problems** using image analysis. ## 📁 Repository Structure ``` Water-Issues-Prediction-Model/ │-- convert_model.py # Converts Keras model to TFLite format │-- infer.py # Runs inference using the trained model │-- predict_tflite.py # Runs inference using the TFLite model │ ├── Model/ │ │-- model_tf.keras # Trained Keras model │ │-- model.tflite # Converted TFLite model │ │-- sklearn_model.p # Scikit-learn trained model │ ├── Prediction Using Model/ │ │-- Sample Images/ │ │ │-- dirtylaketest.jpg │ │ │-- drainagehole.jpg │ │ │-- drainagetest.jpeg │ │ │-- flood_test.jpg │ ├── Training/ │ │-- data/ │ │ │-- train/ # Training dataset │ │ │-- val/ # Validation dataset │ │-- sklearn_trained_model.py # Sklearn training script │ │-- tflitemodel.py # Converts the trained model to TFLite ``` ## 🚀 Features - Predicts water-related issues from images. - Uses **Scikit-Learn, Keras**, and **TFLite** for efficient model deployment. - Includes both **standard and lightweight models (TFLite)** for mobile-friendly inference. - Provides scripts for training, conversion, and prediction. ## 📌 Installation To set up the environment, install the required dependencies: ```sh pip install -r requirements.txt ``` ## 📊 Model Training To train the model using **Scikit-Learn**: ```sh python Training/sklearn_trained_model.py ``` To train and convert the model to **TFLite**: ```sh python Training/tflitemodel.py ``` ## 🔄 Model Conversion To convert the Keras model to **TFLite** format: ```sh python convert_model.py ``` ## 🏗️ Running Inference Run inference using the standard model: ```sh python infer.py --image_path Sample Images/d …