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AI & Machine Learning v1.0.0 Intermediate

Software Behaviour Predictor

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Updated 5 hours ago

Software issue tracking systems collect large volumes of text-based bug reports and user complaints

Technologies & Skills

HTML CSS Python Django MySQL (via WAMP Server) Machine Learning: LSTM (Keras TensorFlow) Deployment: Localhost using WAMP Server Tools & Libraries: pandas numpy scikit-learn TensorFlow / Keras Django REST Framework
INR 3,999
INR 4,999 20% OFF

Limited time offer

What's Included

Complete Source Code
Documentation
Project Report
Presentation Slides
External Download Link

Support & Customization

Support: None
Custom modifications not available
File Size 1.87 MB
Last Updated Jul 01, 2026

Resource Links

Purchase this project to unlock source and premium resources. Document/report remain secure preview-based on this page.

🎯 Project Title

Predictive Modeling of Software Issue Types using LSTM


📚 Technologies Used

Frontend: HTML, CSS, Bootstrap

Backend: Python, Django (Web Framework)

Database: MySQL (via WAMP Server)

Machine Learning: LSTM (Keras, TensorFlow)

Deployment: Localhost using WAMP Server

Tools & Libraries:

pandas, numpy

scikit-learn

TensorFlow / Keras

Django REST Framework

🔍 Problem Statement

Software issue tracking systems collect large volumes of text-based bug reports and user complaints. Manually labeling and prioritizing them is time-consuming and error-prone.


This project automates the classification of issue types such as:


Enhancement

Bug

Documentation

Feature Request

Query

✅ Key Features

Multi-label text classification using LSTM

Web-based interface to input and predict issue type

Accuracy and probability display for each class

User-friendly interface

Integrated with MySQL using Django models

Trained on real-world dataset

🏗️ System Architecture

User Input (Text Report)

Preprocessing

LSTM Model Prediction

Display Predicted Labels

Store & Display History (Optional)

Future Enhancements


Known Issues


Installation

How to Run (Local Setup)

1. Clone the Repository

git clone https://github.com/your-username/software-predictor.git cd software-predictor


Setup Python Environment pip install -r requirements.txt


Setup Database Start WAMP Server


Create a new MySQL database: predictive_modeling_of_software_behavior


Run Django migrations:


python manage.py makemigrations python manage.py migrate


Start Server python manage.py runserver Visit: http://127.0.0.1:8000/ to use the application.

Usage

Clone the Repository

git clone https://github.com/your-username/software-predictor.git cd software-predictor


Setup Python Environment pip install -r requirements.txt


Setup Database Start WAMP Server


Create a new MySQL database: predictive_modeling_of_software_behavior


Run Django migrations:


python manage.py makemigrations python manage.py migrate


Start Server python manage.py runserver Visit: http://127.0.0.1:8000/ to use the application.

System Requirements

Minimum Hardware Requirements

  • Processor: Intel Core i5 (7th Gen) or AMD Ryzen 5
  • RAM: 8 GB DDR4 (Necessary for running Django, MySQL, and ML models concurrently)
  • Storage: 20 GB available HDD/SSD space
  • GPU: Not required (Standard integrated graphics are sufficient for basic ML inference)

Recommended Hardware Requirements

  • Processor: Intel Core i7 (10th Gen) or AMD Ryzen 7 (For faster ML model training)
  • RAM: 16 GB DDR4 (Smooth multitasking between IDE, Server, and Data Processing)
  • Storage: 50 GB available SSD space (Speeds up MySQL queries and dataset loading)

Software Requirements

  • Operating System: Windows 10/11, macOS, or Linux (Ubuntu 20.04+)
  • Environment: Python 3.8 to 3.11 (Matching typical Django + ML library compatibility)
  • Database Server: WAMP Server 3.x (with MySQL 5.7+ or MariaDB)
  • Web Framework: Django 4.x or 5.x
  • Version Control: Git (For cloning the repository)
  • Supported Browsers: Google Chrome, Mozilla Firefox, or Microsoft Edge

Python Dependencies (Included in requirements.txt)

  • Core Framework: django
  • Database Connector: mysqlclient or pymysql
  • Data Processing: numpy, pandas
  • Machine Learning: scikit-learn (and optionally scipy, joblib for saving models)
  • Visualization: matplotlib, seaborn


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