You will get Fake News Detection System with AI & NLP
Project details
š° AI-Powered Fake News Detection System | NLP & LSTM
You will get a complete AI-based Fake News Detection system that accurately classifies news articles as Real or Fake using advanced Natural Language Processing (NLP) and LSTM deep learning models.
The system processes raw text, extracts meaningful patterns, and delivers reliable predictions with clear evaluation metrics. It is ideal for academic projects, research, content moderation platforms, and AI startups.
š” Key Features
ā Real vs Fake news classification
ā NLP preprocessing (tokenization, cleaning, normalization)
ā LSTM deep learning model
ā Accuracy, Precision, Recall & Confusion Matrix
ā Training & evaluation graphs
ā Custom or public dataset support
ā Clean, well-documented source code
ā Optional API / deployment support (Advanced tier)
I focus on clean implementation, accurate results, and production-ready delivery using industry best practices in Machine Learning and NLP.
You will get a complete AI-based Fake News Detection system that accurately classifies news articles as Real or Fake using advanced Natural Language Processing (NLP) and LSTM deep learning models.
The system processes raw text, extracts meaningful patterns, and delivers reliable predictions with clear evaluation metrics. It is ideal for academic projects, research, content moderation platforms, and AI startups.
š” Key Features
ā Real vs Fake news classification
ā NLP preprocessing (tokenization, cleaning, normalization)
ā LSTM deep learning model
ā Accuracy, Precision, Recall & Confusion Matrix
ā Training & evaluation graphs
ā Custom or public dataset support
ā Clean, well-documented source code
ā Optional API / deployment support (Advanced tier)
I focus on clean implementation, accurate results, and production-ready delivery using industry best practices in Machine Learning and NLP.
Machine Learning Tools
BERT, Deeplearning4j, GitHub Copilot, Google Sheets, GPT-3, Keras, MATLAB, Microsoft Power BI, MLflow, NLTK, NumPy, Open Neural Network Exchange, OpenCV, pandas, PyMC, Python, Python Scikit-Learn, PyTorch, SciPy, Sonnet, Stanford CoreNLP, Tesseract OCR, Theano, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$30
|
Standard
$60
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 6 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 0 | 3 |
Model Validation/Testing | |||
Model Documentation | - | - | |
Data Source Connectivity | - | ||
Source Code |
Frequently asked questions
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MH
Muhammad Touseef H.
Jan 25, 2026
Computer vision engineer | Objact detection,Tracking & Vedio Analytics
Excellent experience working with Mubashar. He has strong expertise in computer vision, object detection, and tracking. He fully understood my requirements, delivered accurate and well-structured results, and maintained clear communication throughout the project. Highly professional, reliable, and recommended for any AI or computer vision work.
About Mubashar
Agentic AI Engineer | LangChain, LangGraph, CrewAI | AI Agents
Islamabad, PakistanĀ - 5:14 pm local time
I specialize in ššš§š šš”šš¢š§, ššš§š šš«šš©š”, šš«šš°šš, šš®ššØššš§, and the šš©šš§šš šš šš§šš¬ ššš.
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šš®š„šš¢-šš šš§š šš²š¬ššš¦š¬: Agent networks using LangGraph and CrewAI that reason, plan, delegate tasks, and execute across complex workflows automatically
ššš šš¢š©šš„š¢š§šš¬: Retrieval systems with Pinecone, ChromaDB, and Weaviate for document intelligence, knowledge bases, and semantic search
ššš ššš«šÆšš« šššÆšš„šØš©š¦šš§š: Custom Model Context Protocol servers that connect AI agents to databases, APIs, and internal tools
ššš šš«šš”šš¬šš«ššš¢šØš§: LangChain and PydanticAI pipelines with tool calling, structured outputs, and prompt optimization
šš šš§šš¢š ššØš«š¤šš„šØš° šš®ššØš¦ššš¢šØš§: End-to-end automation for healthcare, manufacturing, real estate, and enterprise SaaS operations
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šš šš§š š š«šš¦šš°šØš«š¤š¬: LangChain Ā· LangGraph Ā· CrewAI Ā· AutoGen Ā· OpenAI Agents SDK Ā· PydanticAI Ā· Google ADK
šššššØš« ššššššš¬šš¬: Pinecone Ā· ChromaDB Ā· Weaviate Ā· Qdrant Ā· FAISS Ā· pgvector
šššš¬: GPT-4o Ā· Claude Sonnet/Opus Ā· Gemini Pro Ā· Llama Ā· Mistral
šššš¤šš§š: Python Ā· FastAPI Ā· Docker Ā· PostgreSQL Ā· MongoDB Ā· AWS
šššš š šš ššš ššššš š šš:
ā You need an autonomous agent that executes tasks end-to-end, not just answers questions
ā You are building a multi-agent system with tool use, memory, and task delegation
ā You want production infrastructure, not a prototype
ā You need clear architecture, fast delivery, and reliable communication
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Steps for completing your project
After purchasing the project, send requirements so Mubashar can start the project.
Delivery time starts when Mubashar receives requirements from you.
Mubashar works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Dataset Review
Client submits requirements and dataset (or confirms public dataset use).
Text Preprocessing & Feature Engineering
Clean, tokenize, normalize text and prepare data for training.