Browse our collection of AI & Machine Learning final-year projects crafted using Python, TensorFlow, Scikit-learn, and cutting-edge algorithms. These projects solve real-world problems with smart predictions, automation, and data analysis—perfect for showcasing your technical skills in AI, ML, and data science.

AI_CONTEXT_START
This project requires Python 3.8 or higher. Follow these steps to run it locally:

1. Clone the repository (access granted upon purchase).
2. Navigate to the project directory:
   cd house-price-prediction-ai
3. Install required packages:
   pip install -r requirements.txt
4. Ensure the trained model (model.pkl) and scaler (scaler.pkl) are in the 'models' folder.
5. Run the Flask app:
   python app.py
6. Open your browser and go to http://localhost:5000

For troubleshooting:
- Make sure all dependencies are installed.
- Check file paths in app.py if model loading fails.
- Use virtual environment to avoid conflicts.

You can also deploy it on Render/Heroku by adding a Procfile and Gunicorn.
AI_CONTEXT_END

<h2>Overview</h2>
  <p>
    The <strong>House Price Prediction AI</strong> is a full-stack web application that leverages <strong>machine learning (ML)</strong> to predict residential property prices based on multiple input features like lot area, year built, quality, garage type, and more. Built using <strong>Python, Flask, and scikit-learn</strong>, this project is perfect for students looking for <strong>unique final year projects</strong> that stand out in placements and academics.
  </p>

  <h2>Key Features</h2>
  <ul>
    <li><strong>Advanced ML Model:</strong> Trained using <strong>Random Forest Regressor</strong> and <strong>Gradient Boosting</strong> for high accuracy.</li>
    <li><strong>Interactive Web Interface:</strong> Built with HTML, CSS, and JavaScript for seamless user experience.</li>
    <li><strong>Real-Time Predictions:</strong> Instant price estimation after form submission.</li>
    <li><strong>Data Visualization:</strong> Integrated <strong>Plotly.js</strong> charts showing feature importance and price trends.</li>
    <li><strong>Responsive Design:</strong> Works flawlessly on mobile, tablet, and desktop.</li>
    <li><strong>Model Persistence:</strong> Pre-trained model saved using <strong>joblib</strong> for fast deployment.</li>
    <li><strong>User-Friendly Input Form:</strong> Sliders and dropdowns for easy data entry.</li>
  </ul>

  <h2>Applications & Use Cases</h2>
  <p>
    This project is ideal for:
  </p>
  <ul>
    <li><strong>Final year college projects</strong> in Computer Science, IT, or Data Science.</li>
    <li>Real estate startups looking for price estimation tools.</li>
    <li>Learning end-to-end <strong>machine learning pipeline</strong> development.</li>
    <li>Understanding model deployment using <strong>Flask API</strong>.</li>
    <li>Gaining hands-on experience in <strong>data preprocessing, feature engineering, and model evaluation</strong>.</li>
  </ul>

  <h2>Technology Stack</h2>
  <p>
    <strong>Backend:</strong> Python, Flask<br>
    <strong>Machine Learning:</strong> scikit-learn, pandas, numpy, joblib<br>
    <strong>Frontend:</strong> HTML5, CSS3, JavaScript, Plotly.js<br>
    <strong>UI/UX:</strong> Flexbox, Grid, Font Awesome Icons<br>
    <strong>Deployment:</strong> Local Server (Flask), Ready for Cloud (Heroku, Render)
  </p>

  <h2>What You Get?</h2>
  <p>
    When you purchase this project from <strong>CodeAJ Marketplace</strong>, you can choose from:
  </p>
  <ul>
    <li>Complete Source Code (₹99 onwards)</li>
    <li>Project Setup Assistance (+₹199)</li>
    <li>Detailed Source Code Explanation (+₹299)</li>
    <li>Ready-to-Submit Project Report (PPT + PDF) (+₹399)</li>
  </ul>
  <p style="font-style: italic;">
    Combine options and get a <strong>complete final year project package</strong> — presentation, code, report, and support!
  </p>

Build your career with this unique final year Python project — House Price Prediction using ML, Flask & Data Visualization. Get source code, setup guide, explanation & report. Only at CodeAJ Marketplace.

House Price Prediction AI | Best Python Project for Final Year Students | CodeAJ Marketplace

A cutting-edge Python ML project for house price prediction using Flask & scikit-learn — ideal for final year students. Get source code, setup guide, explanation, and report starting at just ₹99!

Clone the repository: git clone [repository-url] && cd collage-AI-chatbot
Create virtual environment: python -m venv venv && source venv/bin/activate (venv\Scripts\activate on Windows)
Install dependencies: pip install -r requirements.txt
Set up .env file with GEMINI_API_KEY=your_api_key_here
Run migrations: python manage.py migrate
Populate initial data: python manage.py populate_data
Start server: python manage.py runserver
Access at http://localhost:8000

<div class="project-description">
  <h2>Overview</h2>
  <p>
    The <strong>College AI Assistant</strong> is an intelligent, NLP-powered chatbot designed to streamline information access in educational institutions. Whether it's course details, exam schedules, library hours, or admission processes, this chatbot delivers instant, accurate responses — reducing administrative load and enhancing student experience.
  </p>

  <h3>Key Features</h3>
  <ul>
    <li><strong>Multi-Layered Response Engine:</strong> Combines quick replies, keyword matching, category-based logic, semantic search, and Google Gemini AI for intelligent, context-aware answers.</li>
    <li><strong>Real-Time Chat Interface:</strong> Clean, responsive UI with message history, session tracking, and interactive quick-reply buttons for common queries.</li>
    <li><strong>AI-Powered Intelligence:</strong> Leverages Google's <strong>Gemini API</strong> and NLP techniques (TF-IDF + Cosine Similarity) to understand natural language and deliver human-like responses.</li>
    <li><strong>Comprehensive Knowledge Base:</strong> Covers 10+ college domains including facilities, faculty, clubs, timetables, events, and contact information.</li>
    <li><strong>Scalable & Customizable:</strong> Easily extendable to include new departments, courses, or campuses. Ideal for universities, colleges, and coaching centers.</li>
  </ul>

  <h3>Applications</h3>
  <p>
    This AI assistant is perfect for:
  </p>
  <ul>
    <li>University websites and portals</li>
    <li>Student onboarding and orientation</li>
    <li>Admission inquiry automation</li>
    <li>24/7 support for FAQs</li>
    <li>Reducing workload on academic staff</li>
    <li>Improving digital engagement on campus</li>
  </ul>

  <h3>Technology Stack</h3>
  <p>
    Built with modern, scalable technologies for performance and maintainability:
  </p>
  <ul>
    <li><strong>Backend:</strong> Django (Python)</li>
    <li><strong>Frontend:</strong> HTML5, CSS3, JavaScript (Vanilla JS)</li>
    <li><strong>Database:</strong> SQLite (can be upgraded to PostgreSQL/MySQL)</li>
    <li><strong>AI & NLP:</strong> Google Gemini API, scikit-learn, TF-IDF Vectorization, Cosine Similarity</li>
    <li><strong>Security:</strong> Environment variables, session management, CSRF protection</li>
  </ul>

  <h3>Why Choose This Project?</h3>
  <p>
    - <strong>Ready-to-Deploy:</strong> Comes with full setup guide and data population command.<br>
    - <strong>Low Cost, High Value:</strong> Source code starts at just ₹99!<br>
    - <strong>Customization Options:</strong> Add your college branding, update content, or integrate with existing portals.<br>
    - <strong>Educational Use:</strong> Perfect for CS/IT students, developers, and institutions building smart campus solutions.
  </p>

 
</div>

Get the College AI Assistant source code (₹99+). A smart Django-based chatbot with Google Gemini AI, NLP, and semantic search for universities. Includes setup, explanation & report options.

College AI Assistant | Django & Gemini AI Chatbot for Colleges – CodeAj Marketplace

A smart, AI-powered college chatbot built with Django and Google Gemini API that answers student queries about courses, timetables, events, admissions, and more — instantly and accurately.

# Step 1: Clone the Repository
git clone https://github.com/yourusername/crime-data-visualization.git   
cd crime-data-visualization

# Step 2: Create and Activate Virtual Environment
python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/Mac:
source venv/bin/activate

# Step 3: Install Dependencies
pip install -r requirements.txt

# Step 4: Run the Application
python app.py

# Step 5: Access the App
Open your browser and go to http://localhost:5000

<h2>Overview</h2>
<p>Discover the future of crime analytics with our <strong>Crime Data Visualization & Prediction Tool</strong> – a web-based platform designed to help users analyze historical crime data, visualize patterns, and predict future outcomes using machine learning models.</p>

<h3>Why This Tool?</h3>
<p>With increasing urbanization and crime rates, understanding crime patterns becomes crucial for effective governance and law enforcement. Our tool offers a user-friendly interface to explore and analyze crime statistics across 29 major Indian cities, making it ideal for researchers, students, and public safety officials.</p>

<h3>Key Features</h3>
<ul>
    <li><strong>Interactive Dashboard:</strong> View real-time crime statistics including total cases, solved cases, top crime types, and city-wise distribution.</li>
    <li><strong>Crime Visualization:</strong> Explore interactive charts for crime trends, victim demographics, weapon usage, and more using Plotly.js for dynamic visualizations.</li>
    <li><strong>Predictive Analytics:</strong> Predict case resolution outcomes using a trained machine learning model with confidence scores based on input parameters like time, location, crime type, and victim details.</li>
    <li><strong>Custom Filters:</strong> Filter data by time, geography, and crime type to drill down into specific trends and patterns.</li>
    <li><strong>Exportable Insights:</strong> Export visualizations and reports for further analysis or presentations.</li>
</ul>

<h3>Applications</h3>
<ul>
    <li>Law enforcement agencies for crime forecasting</li>
    <li>Academic research in criminology and data science</li>
    <li>Public policy development and urban planning</li>
    <li>Student projects in machine learning and data visualization</li>
</ul>

<h3>Technology Stack</h3>
<p>Built using a robust combination of Python, Flask, Plotly, and scikit-learn, this tool ensures high performance and accuracy in data processing and visualization. The frontend is powered by HTML, CSS, and JavaScript, ensuring a responsive and modern user interface.</p>

<h3>How It Works</h3>
<p>The system ingests historical crime data from Indian cities, processes it using Pandas, trains a classification model using scikit-learn, and deploys it via Flask API. Users can input case details to get real-time predictions and visualize trends through interactive charts.</p>

<h3>Why Choose This Project?</h3>
<ul>
    <li>Ready-to-deploy web application</li>
    <li>ML-powered predictions for real-world applications</li>
    <li>Beautiful, responsive UI with interactive visualizations</li>
    <li>Well-documented codebase and installation guide</li>
    <li>Perfect for academic and professional use</li>
</ul>

<h3>Conclusion</h3>
<p>The <strong>Crime Data Visualization & Prediction Tool</strong> is a powerful solution for understanding and forecasting crime patterns in India. Whether you're a student, researcher, or policy maker, this tool provides actionable insights through data-driven visualizations and predictive analytics.</p>

A web-based tool for analyzing and predicting crime trends in major Indian cities using machine learning and interactive data visualization. Ideal for researchers, students, and law enforcement agencies.

Crime Data Visualization & Prediction Tool – ML-Powered Crime Analytics for Indian Cities

A powerful web application that leverages machine learning and data visualization to analyze crime trends and predict case outcomes in major Indian cities. Ideal for law enforcement, researchers, and students.

A web-based tool for analyzing and predicting crime trends in major Indian cities using machine learning and interactive data visualization.

Crime Data Visualization & Prediction Tool

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Place the following files in root directory:
random_forest_model.joblib
naive_bayes_model.joblib
Symptom-severity.csv

# Run the app
python app.py

# Access at:
http://localhost:5000

<section class="project-description">
  <h2>Overview</h2>
  <p>The <strong>Disease Prediction Web Application</strong> is a powerful tool designed to help users identify potential illnesses based on selected symptoms. Utilizing advanced machine learning algorithms like Random Forest and Naive Bayes, this app delivers accurate predictions with probability scores, enabling informed decision-making.</p>

  <h3>Key Features</h3>
  <ul>
    <li><strong>Interactive Symptom Selection:</strong> Search and choose from an extensive list of symptoms with real-time filtering.</li>
    <li><strong>Severity Weighted Predictions:</strong> Symptoms are weighted by severity to enhance prediction accuracy.</li>
    <li><strong>Top 3 Disease Predictions:</strong> Both models provide top 3 possible diseases along with confidence percentages.</li>
    <li><strong>Mobile Responsive Design:</strong> Works seamlessly across desktops, tablets, and smartphones.</li>
    <li><strong>Real-Time Filtering:</strong> Instant symptom search improves usability and speed.</li>
  </ul>

  <h3>Applications</h3>
  <p>This application can be used in various scenarios including:</p>
  <ul>
    <li>Educational platforms for teaching AI/ML concepts.</li>
    <li>Preliminary diagnosis tools for healthcare startups.</li>
    <li>Personal health awareness applications.</li>
    <li>Research projects in the field of medical AI.</li>
  </ul>

  <h3>Technology Stack</h3>
  <p>Built using modern technologies, the backend runs on Flask while the frontend uses HTML, CSS Grid, and JavaScript for dynamic interaction. Two trained models ensure high performance and reliability.</p>

  <h3>Why Choose This Project?</h3>
  <p>Whether you're a developer looking to explore machine learning or a student working on a capstone project, this disease prediction system offers a perfect blend of functionality, simplicity, and educational value. It's ready to deploy, fully customizable, and comes with detailed documentation.</p>
</section>

Predict diseases based on symptoms using Machine Learning. Explore this AI-powered web app built with Python Flask, Random Forest & Naive Bayes models.

AI Disease Prediction Web Application Using ML Models | Final year Projects

A responsive web application that predicts diseases using symptoms selected by users with Machine Learning models (Random Forest & Naive Bayes) . Accurate, fast, and mobile-friendly.

A web-based system that predicts diseases based on user-selected symptoms using Machine Learning models like Random Forest and Naive Bayes.

AI Disease Prediction Web Application

1. Clone the repository:
git clone <repository-url>
cd student-performance-prediction

2. Create a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:
pip install -r requirements.txt

4. Run the application:
python app.py

5. Access the app at:
http://localhost:5000

<h2>Overview</h2>
<p>The <strong>Student Performance Prediction Web Application</strong> is a powerful tool built using Python, Flask, and TensorFlow to help students, educators, and institutions make data-driven decisions about academic progress. By analyzing input parameters like study hours, attendance, and past grades, this app provides accurate predictions of final scores using a trained neural network model.</p>

<h2>Key Features</h2>
<ul>
  <li><strong>Real-Time Predictions:</strong> Get instant academic performance forecasts based on user inputs.</li>
  <li><strong>Interactive UI:</strong> Modern and responsive interface built with HTML, CSS, and JavaScript.</li>
  <li><strong>Data Visualization:</strong> Visualize performance metrics using Plotly charts for better understanding.</li>
  <li><strong>Printable Results:</strong> Generate and print detailed analysis reports for future reference.</li>
  <li><strong>Performance Categorization:</strong> Classify predicted outcomes into categories such as "Excellent", "Good", "Average", or "Needs Improvement".</li>
  <li><strong>Recommendation Engine:</strong> Get personalized suggestions for improving academic performance.</li>
</ul>

<h2>Applications</h2>
<ul>
  <li>Educational institutions can use it for early intervention strategies.</li>
  <li>Students can track their progress and identify areas needing attention.</li>
  <li>Teachers can assess student performance trends and offer targeted support.</li>
  <li>Researchers can explore how different factors affect academic success.</li>
</ul>

<h2>Technology Stack</h2>
<p>This project is powered by:</p>
<ul>
  <li>Backend: Flask (Python)</li>
  <li>Frontend: HTML5, CSS3, JavaScript</li>
  <li>Machine Learning: TensorFlow & Keras</li>
  <li>Visualization: Plotly.js</li>
  <li>Model Deployment: Local H5 file</li>
</ul>

<h2>Why Choose This Project?</h2>
<p>This web application is not only an excellent example of machine learning in education but also a practical solution for improving academic outcomes. With easy installation, intuitive UI, and detailed analytics, it's perfect for both beginners and advanced developers looking to build impactful ML-based tools.</p>

Predict student performance accurately using a machine learning model. Enter study hours, attendance, and prior grades to get real-time predictions and detailed analysis. Powered by Flask and TensorFlow.

Student Performance Prediction Web App | CodeAJ Marketplace

A machine learning-powered web application that predicts student academic performance using neural networks. Enter study hours, attendance, and prior grades to get real-time score predictions and actionable insights.

CodeAJ

A machine learning-powered web application that predicts student academic performance using neural networks. Enter study hours, attendance, and prior grades to get real-time score predictions.

Student Performance Prediction Web App

To install and run LeafScan AI:\n\n1. Extract the repository:\nExtract the download Zip file\ncd leaf-diseases-detect\n\n2. Create a virtual environment:\npython -m venv venv\nsource venv/bin/activate  # For Linux/Mac\nvenv\\Scripts\\activate     # For Windows\n\n3. Install dependencies:\npip install -r requirements.txt\n\n4. Run the application:\npython app.py\n\nAccess at: http://localhost:5000

<section class='project-details'><h2>Project Overview</h2><p><strong>LeafScan AI</strong> is a state-of-the-art <strong>plant disease detection system</strong> designed for agricultural professionals, researchers, and farming enthusiasts. Built using <strong>Flask</strong>, modern web technologies, and powered by <strong>deep learning models</strong>, this tool helps users identify and classify <strong>33 different types of leaf diseases</strong> across <strong>9 plant species</strong>.</p><h3>🔍 Key Features</h3><ul><li><strong>Advanced Disease Detection:</strong> Leverages deep learning and transfer learning for accurate results.</li><li><strong>High Accuracy:</strong> Achieves up to 96% classification accuracy for plant diseases.</li><li><strong>Real-time Analysis:</strong> Instant detection with confidence scores displayed on the UI.</li><li><strong>Responsive Design:</strong> Works seamlessly on both desktop and mobile devices.</li><li><strong>Drag & Drop Interface:</strong> Easy image upload system for quick diagnosis.</li><li><strong>Detailed Results:</strong> Clear visualization of predictions including confidence percentages.</li></ul><h3>🌱 Supported Plants</h3><p>LeafScan AI supports the following plant species:</p><ul><li>Apple</li><li>Cherry</li><li>Corn</li><li>Grape</li><li>Peach</li><li>Pepper</li><li>Potato</li><li>Strawberry</li><li>Tomato</li></ul><h3>⚡ Technology Stack</h3><p>This powerful application is built using a robust combination of backend and frontend technologies:</p><ul><li><strong>Backend:</strong> Flask (Python)</li><li><strong>Frontend:</strong> HTML5, CSS3, JavaScript</li><li><strong>AI/ML:</strong> TensorFlow, Keras</li><li><strong>Image Processing:</strong> OpenCV</li><li><strong>UI Framework:</strong> Bootstrap 5</li><li><strong>Animations:</strong> AOS (Animate On Scroll)</li></ul><h3>🧰 Installation Guide</h3><p>To install and run LeafScan AI locally, follow these steps:</p><pre>1. Extract the repository:<br>Extract the downloaded zip file<br>cd leaf-diseases-detect<br><br>2. Create a virtual environment (recommended):<br>python -m venv venv<br>source venv/bin/activate  # For Linux/Mac<br>venv\\Scripts\\activate     # For Windows<br><br>3. Install required dependencies:<br>pip install -r requirements.txt</pre><h3>🚀 Usage Instructions</h3><pre>1. Start the Flask application:<br>python app.py<br><br>2. Navigate to http://localhost:5000 in your browser.<br><br>3. Upload an image of a leaf using the drag & drop or file browser interface.<br><br>4. Wait for the AI model to analyze the image and display the result with confidence score.</pre><h3>🧠 Model Details</h3><p>The core of LeafScan AI is a pre-trained deep learning model optimized for real-time inference. It has been trained on a comprehensive dataset of plant leaf images and provides:</p><ul><li>Support for 33 leaf diseases</li><li>Confidence scores for each prediction</li><li>Fast and accurate results</li></ul><h3>🔗 API Documentation</h3><p>LeafScan AI also offers a RESTful API endpoint for integration into other systems:</p><h4>POST /predict</h4><p><strong>Request Format:</strong></p><ul><li>Method: POST</li><li>Content-Type: multipart/form-data</li><li>Body: file (image file)</li></ul><p><strong>Response Format:</strong></p><pre>{<br>    \"success\": true,<br>    \"prediction\": \"Disease Name\",<br>    \"confidence\": 95.5,<br>    \"image_path\": \"path/to/image\"<br>}</pre><h3>📷 Best Practices for Image Upload</h3><p>To ensure optimal detection performance, follow these best practices when uploading images:</p><ul><li>Use clear, well-lit images</li><li>Focus on a single leaf</li><li>Ensure the leaf fills most of the frame</li><li>Use supported file formats (JPG, JPEG, PNG)</li><li>Keep file size under 16MB</li></ul></section>

LeafScan AI detects 33 leaf diseases across 9 plants with 96% accuracy. Built with Flask, TensorFlow, and OpenCV. Get source code starting at ₹99.

LeafScan AI – Plant Disease Detection System Using Deep Learning

LeafScan AI uses deep learning to detect 33 types of leaf diseases across 9 plant species with up to 96% accuracy. Ideal for agricultural professionals and farmers.

An advanced plant disease detection system that identifies 33 types of leaf diseases across 9 plant species using deep learning and transfer learning techniques.

AI/ML Projects

Smart House Price Prediction AI – Final Year Python Project with ML & Flask

College AI Assistant – Smart Chatbot for Universities & Colleges | Powered by Django & Google Gemini AI

Crime Data Visualization & Prediction Tool – AI-Powered Crime Analytics for Indian Cities

AI-Powered Disease Prediction Web App Using Machine Learning

Student Performance Prediction Web App Using Machine Learning

LeafScan AI - Plant Disease Detection System Using Deep Learning