AI Electricity Theft Detection

@sumitsthakur

I can help you build your end-to-end non-technical loss detection system. My background covers Python, Pandas, machine learning, PostgreSQL, Flask, and React, which directly matches your required stack. My approach starts with setting up the database-agnostic data processing and exploratory analysis in well-commented Jupyter notebooks. I will engineer features focusing on usage patterns, tampering evidence, and billing discrepancies, while handling class imbalance using SMOTE and cost-sensitive learning. Next, I will train and compare Random Forest, SVM, and a lightweight neural network, evaluating them using accuracy, precision, recall, F1-score, confusion matrix, and ROC curves to select the champion model. For deployment, I will wrap the model in a Flask API with single-account and batch CSV upload endpoints, paired with a React dashboard displaying theft probabilities, feature contributions, and filtering options. Finally, I will containerize the stack with a one-line Docker build and provide a clear README. Let us discuss the details so we can get started.

@PINESCRIPTERR

Hi, I can build the complete non-technical-loss detection pipeline from raw AMI and billing CSVs through PostgreSQL, reproducible model training, real-time scoring, and a React dashboard. I’d structure the Python workflow around pandas and scikit-learn, with SMOTE and cost-sensitive alternatives evaluated without data leakage, then compare Random Forest, SVM, and a lightweight neural network using accuracy, precision, recall, F1, confusion matrices, ROC/AUC and class-specific performance. The selected model would be versioned and exposed through Flask `/score` and `/batch` endpoints, with feature contributions available for dashboard explanations. I’d also include a reproducible retraining script, Docker setup, database abstraction, and notebooks that can be executed end-to-end against the supplied sample dataset. Question 1: Does the historical data already contain confirmed NTL/theft labels at account or meter level, and if so, how are those labels defined and linked to the AMI and billing records? Question 2: For the dashboard’s feature contributions, do you prefer a model-agnostic explanation such as SHAP, or should the first version expose a simpler ranked contribution/feature-importance view? Regards, Houssame.

@topcreator100

Hi There , Good afternoon! I’ve carefully checked your requirements and really interested in this job. I’m full stack node.js developer working at large-scale apps as a lead developer with U.S. and European teams. I’m offering best quality and highest performance at lowest price. I can complete your project on time and your will experience great satisfaction with me. I’m well versed in React/Redux, Angular JS, Node JS, Ruby on Rails, html/css as well as javascript and jquery. I have rich experienced in Pytorch, PostgreSQL, Python, Pandas, API Development, Machine Learning (ML), Tensorflow, Data Analysis, Flask and Data Visualization. For more information about me, please refer to my portfolios. I’m ready to discuss your project and start immediately. Looking forward to hearing you back and discussing all details.. Looking forward to serve you

@emanz03

The main risk is leakage and misleading accuracy on an imbalanced theft dataset, so I’d split by account/time first and apply SMOTE only inside training folds, while also benchmarking class-weighted models. I’d compare Random Forest, SVM and a small neural net on precision, recall, F1, ROC/AUC and confusion matrices, then package the selected model behind Flask `/score` and `/batch` endpoints with PostgreSQL-backed features and a React review dashboard. I’ve worked on Python ML pipelines with preprocessing, anomaly detection, model evaluation and production APIs. Delivery would include reproducible notebooks, retraining script, model artefacts, Docker setup and README.

@DeveloperStation

Hi, The class imbalance is the real risk here, not the model choice. On theft detection your positives are rare, so raw accuracy lies. I'd lean on cost-sensitive learning first and treat SMOTE as a comparison, then judge everything on precision-recall and the confusion matrix rather than a single lift number. Otherwise the Random Forest looks great and misses the actual thieves. I've built deterministic data ingestion pipelines and CI with Docker on the exact stack you describe, Python, Flask, React, PostgreSQL. Secure CI Pipeline & Deterministic Ingestion: 5-star contract on reproducible data workflows Since we're new, I'd structure the first milestone around the notebooks so you only release once you run them end-to-end on your sample data. One question: is the AMI data labelled with confirmed theft cases, or do we work from billing discrepancies as weak labels? Adil

@sf264

Hi there, I can develop an AI-powered electricity theft detection system using machine learning to analyze consumption patterns, identify abnormal usage, and flag potential meter tampering or energy theft. I can build a reliable solution with data preprocessing, model development, API integration, and clear documentation, tailored to your requirements. Let's discuss your dataset and detection goals so I can suggest the right approach. Best regards, Fahad.

@vishal1145

Your neural network will overfit on minority-class theft cases if you apply SMOTE before splitting train-test data, leaking synthetic samples into your validation set and inflating your F1-score by 15-20 points. This creates a model that looks great in notebooks but fails in production when real meter data arrives. Quick questions - are you planning to retrain weekly as new AMI data streams in, or is this a one-time build? And do you have labeled ground-truth theft cases, or will we need to engineer proxy labels from billing anomalies and field inspection logs? Here is the architectural approach: - PYTORCH + SMOTE: Build a TabNet model with focal loss to handle class imbalance natively, apply SMOTE only inside cross-validation folds, compare against Random Forest and SVM using stratified k-fold to prevent data leakage. - FLASK API DEVELOPMENT: Deploy two endpoints - /score for single-account inference under 200ms and /batch for CSV uploads processing 10K rows in parallel using Celery workers, return JSON with theft probability plus SHAP feature attributions. - POSTGRESQL + PANDAS: Write parameterized SQL queries using psycopg2 connection pooling, partition AMI tables by date range for query performance, store model metadata and prediction history in separate audit tables. I've built similar fraud-detection pipelines for two utility companies that reduced false positives by 40% while catching 92% of confirmed theft cases. Let's schedule a 20-minute call to walk through your labeled dataset and confirm the retraining cadence before I architect the full pipeline.

@mayr81

I can build this as a reproducible, end-to-end electricity theft detection platform, from AMI and billing CSV ingestion through model evaluation, real-time scoring, and dashboard delivery. The workflow will include PostgreSQL-ready data handling, rigorous cleaning and feature engineering, imbalance treatment through SMOTE or cost-sensitive learning, and side-by-side evaluation of Random Forest, SVM, and a lightweight neural network using precision, recall, F1, confusion matrices, ROC curves, and accuracy. The selected champion model will be packaged with versioned artefacts and a reliable retraining script. I will implement Flask endpoints for individual and CSV batch scoring, with consistent probability outputs and validation, then connect them to a focused React dashboard showing theft probability, feature contributions, and account, date, and location filters. The repository will include well-commented notebooks, clear separation between training and inference, Docker setup, and concise local deployment instructions. The implementation will be tested against your sample dataset, including notebook reproducibility, API consistency, and Chrome dashboard rendering. The result will be maintainable, extensible, and ready for future model monitoring or additional utility data sources.

@abdf2010

Hi, I am a professional web developer and I can do this project "AI Electricity Theft Detection", I have 5 years of experience in web development. I have done many projects like this. I can do this job for you. I can start right now. Please contact me. Thanks

@chiragpipal

⚠️ If you're not happy, you don’t pay. ⚠️ Hi Client, Thank you for checking my proposal and sharing the detailed project brief. I can build your end-to-end workflow using Python (pandas, scikit-learn, TensorFlow/PyTorch) with a scalable and performance-optimized design. I will deliver: • Comprehensive exploratory analysis and data cleaning in Jupyter Notebooks • Implementation of Random Forest, SVM, and lightweight neural network models • Performance evaluation with accuracy, precision, recall, F1-score, confusion matrix, and ROC curves • A Flask API for real-time scoring with /score and /batch endpoints • A React dashboard to visualize theft probability and feature contributions • Well-documented code and a one-line Docker build in the README for easy deployment You will also receive: • A retraining script for fresh data updates • Ongoing support for any queries post-delivery I am confident I can execute your vision professionally and efficiently. Looking forward to discussing the timeline and next steps. Best regards, Ch

@Azursol

Hello,! I’m excited about the opportunity to help with your project. Based on your requirements, I believe my expertise in Python, PostgreSQL aligns perfectly with your needs. How I Will Build It: I will approach your project with a structured, goal-oriented method. Using my experience in Python, Machine Learning (ML), PostgreSQL, Flask, Tensorflow, Pytorch, Data Visualization, Data Analysis, API Development, Pandas, I’ll deliver a solution that not only meets your expectations but is also scalable, efficient, and cleanly coded. I ensure seamless integration, full responsiveness, and a strong focus on performance and user experience. Why Choose Me: - 10 years of experience delivering high-quality web and software projects - Deep understanding of Python, PostgreSQL and related technologies - Strong communication and collaboration skills - A proven track record — check out my freelancer portfolio. - I’m available for a call to discuss your project in more detail - Committed to delivering results on time, every time Availability: I can start immediately and complete this task within the expected timeframe. Looking forward to working with you! Best regards, Ali Zahid Zimbabwe

@ianb65

Hi there, The core challenge is turning large AMI and billing CSV dumps into a reproducible PostgreSQL-backed workflow that handles severe class imbalance, compares reliable theft-detection models, and exposes consistent real-time scores through an API and dashboard. I have strong hands-on experience with Python and have solved similar problems involving API Development, imbalanced classification, model evaluation, and Data Visualization. My approach would be to: * Review the existing data schema, sample CSVs, notebook requirements, and current implementation. * Identify the root cause of data-quality and leakage risks, determining what can be reused versus what needs to be changed. * Implement a clean, maintainable solution using pandas, scikit-learn, Flask, PostgreSQL, and React, incorporating Machine Learning (ML) with SMOTE or cost-sensitive training. * Test edge cases, reliability, performance, security, and integration behavior, including deterministic /score and /batch probabilities. * Deploy or integrate the fix without disrupting the existing system, with reproducible notebooks, retraining scripts, Docker setup, and Chrome-ready dashboard validation. I will compare Random Forest, SVM, and a lightweight neural network using precision, recall, F1, confusion matrices, ROC curves, and lift; feature attribution will be designed consistently with the selected champion model. I prefer fixing the actual root cause rather than applying temporary patches, and I will keep

@longngoc2307

==== Hi - Truong here ==== "AI ELECTRICITY THEFT DETECTION" - you need a reproducible ML pipeline that turns messy AMI and billing data into reliable theft-risk scores and a usable dashboard. I’d keep the workflow in Python with pandas and scikit-learn, using stratified evaluation plus SMOTE or cost-sensitive learning to handle the imbalance. I’d compare Random Forest, SVM and a lightweight neural model using the requested metrics, then freeze the selected model behind Flask /score and /batch endpoints with consistent preprocessing. The main edge case is avoiding data leakage between training and validation, especially when multiple records belong to the same account. I’d also keep feature processing identical between notebook training and live API scoring so probabilities remain reproducible. Can you provide a sample AMI and billing dataset showing the available fields and the current theft/non-theft label? Looking forward to work with you.

@onkarkulkarni8

Hello, I checked your "AI Electricity Theft Detection" project description, it looks like the focus is on delivering a clean, responsive website that works well across all devices. I prefer understanding the expected layout and user experience first, then building pages that closely match the design while keeping the code organized and easy to maintain. Feel free to share the design or current website, and I'll suggest the best implementation along with a realistic timeline. Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@manishashree12

Hello,, I hope you're having a great day. I've analyzed the requirements of AI Electricity Theft Detection and I'm prepared to deliver outstanding results that align with your vision. My portfolio reflects my expertise in Python, Machine Learning (ML), PostgreSQL, Flask, Tensorflow, Pytorch, Data Visualization, Data Analysis, API Development, Pandas and Python, PostgreSQL, Machine Learning (ML), Pytorch, Tensorflow, API Development, Data Analysis, Data Visualization, Flask, Pandas. I have case studies that closely match AI Electricity Theft Detection. I believe I'm the perfect fit for AI Electricity Theft Detection. I look forward to your positive response. Regards, Manisha

@arslanshahid386

Hi, I understand you're looking for a comprehensive workflow to detect non-technical losses using AMI data, focusing on usage patterns, meter tampering, and billing discrepancies. Your data currently resides in large CSV files but needs to be migrated to PostgreSQL for better management. The main technical challenge here is effectively handling class imbalance in your dataset, which is crucial for accurate detection. My approach would involve initially assessing the dataset to determine the best method, whether SMOTE or cost-sensitive learning, to ensure that the model can accurately identify anomalies without being biased by the imbalance. Ensuring data integrity during the migration to PostgreSQL is also critical to maintain the reliability of the results. I have extensive experience with Python and API development, particularly in integrating large datasets into PostgreSQL and developing machine learning models for anomaly detection. Q1 – What specific features or attributes from the smart meter data are currently prioritized for analysis? Q2 – Are there any existing APIs or systems we need to integrate with, or is this a standalone solution? Q3 – How frequently do you anticipate the data updates, and will real-time processing be required? Looking forward to hearing from you.

@Georgeoustar

Hi there! Your project’s focus on AI-driven electricity theft detection is both timely and technically engaging. I understand you need a robust, end-to-end workflow—from Jupyter-based EDA and model development, through to a Flask API and an interactive React dashboard, all powered by PostgreSQL and Python’s leading ML libraries. With extensive experience in machine learning (sklearn, TensorFlow, PyTorch), data engineering (pandas, PostgreSQL), and building scalable APIs and dashboards, I’ve delivered similar solutions for energy analytics and anomaly detection. I’ll address class imbalance using SMOTE/cost-sensitive learning, engineer theft-related features from your smart meter data, and rigorously compare Random Forest, SVM, and neural net models with full metric reporting. Once the top model is selected, I’ll wrap it as a Flask API with /score and /batch endpoints, and deliver a React dashboard for real-time insights and filtering. Could you clarify whether meter tampering is flagged in the raw data or inferred from patterns? Also, do you have a preferred tool for feature importance visualization on the dashboard? Best Regards, George

@jeffleads

Hello. I have previous experience building end-to-end ML workflows from messy CSV/transactional data through feature engineering, imbalanced classification, model comparison, API deployment, and interactive dashboards, so I know how to turn “interesting data” into a reproducible system that actually survives testing. Two questions: Do your AMI/billing datasets already contain confirmed theft/non-technical-loss labels, and approximately how many accounts/records and features are in the largest CSV? Also, do you expect the feature contributions in the React dashboard to use SHAP or are you open to another explainability approach? Your answers will greatly help me understand the data and validation requirements; my approach will be to load and normalize the CSV data into PostgreSQL, perform leakage-safe feature engineering, compare Random Forest/SVM/neural network models with SMOTE or cost-sensitive learning, select the champion using the requested metrics, then package it into Flask /score and /batch endpoints plus an explainable React dashboard and Docker-ready repository. I can start immediately and will focus on making the entire pipeline reproducible, measurable, and practical for real-world electricity-loss investigation rather than delivering a model that only looks good inside a notebook. Let's discuss this project further Best regards, Jeff Chong.

@huzaifa306

Hi, I read the description carefully, and this is exactly the kind of work I have been doing as a machine learning engineer for the past three years. You need a complete loss detection pipeline, and I can build it from data preparation and feature engineering to model comparison, API scoring, and dashboard. I will handle the class imbalance, compare Random Forest, SVM, and a neural network using the required metrics, then deploy the selected model through Flask with a simple React dashboard. I can keep the complete solution clean, reproducible, and ready to run. Best regards, Huzaifa