Personalized Study Planner

A web application that creates tailored study plans based on user goals (e.g., "Prepare for CAT 2025") and feedback (e.g., "I’m struggling with calculus"). It uses NLP to interpret user input, A* to optimize the study schedule, Hill Climbing to adjust task priorities, and Min-Max to balance study and leisure activities.

---

Table of Contents

• Features
• How It Works
  • User Goals & Feedback
  • Core Algorithms
• Getting Started
  • Prerequisites
  • Installation
  • Running the App
• Usage
• Architecture & Design
  • Main Modules Overview
• API Reference
• Contributing
• License

---

Features

• Personalized Study Plans: Input your goals and topics you struggle with; get a customized, actionable study schedule.
• Adaptive Scheduling: Plans are updated in real-time based on your feedback.
• Intelligent Algorithms: Utilizes advanced algorithms to maximize productivity and balance leisure.
  • Natural Language Processing (NLP) for understanding user input.
  • A* Search for optimizing study timeline.
  • Hill Climbing for adjusting task priorities.
  • Min-Max for balancing study and leisure.
• Goal Tracking & Analytics: Visual progress tracker and detailed analytics.
• User-Friendly Interface: Designed for ease of use with interactive dashboards.

---

How It Works

User Goals & Feedback

• Goal Setting:
  Enter your exam (e.g., CAT 2025), subjects, and preferred study style.
• Feedback Integration:
  Specify topics where you feel weak (e.g., calculus, vocabulary). System adapts schedule accordingly.
• Continuous Adaptation:
  As you give daily/weekly feedback, your plan and priorities are adjusted using intelligent algorithms.

---

Core Algorithms

• Natural Language Processing (NLP):
  Parses your written goals and feedback, identifies key subjects and pain points.

• A Search Algorithm:*
  Finds the most efficient path toward your target exam date, scheduling necessary topics while considering difficulty and available time.

• Hill Climbing:
  Adjusts the order and priority of topics based on your ongoing feedback to maximize learning effectiveness.

• Min-Max Strategy:
  Balances study load and leisure time, preventing burnout and promoting holistic success.

---

Getting Started

Prerequisites

• Python 3.8+
• pip package manager

Installation

git clone https://github.com/rohit03-cmd/Personalized-Study-Planner.git
cd Personalized-Study-Planner
pip install -r requirements.txt

Running the App

python app.py

Or use the provided runner/launch script as documented.

---

Usage

1. Register/Login: Create a user profile.
2. Enter Goals: Specify exam/year (e.g., "CAT 2025"), subjects, time available.
3. Provide Feedback: Update the app with topics you find difficult.
4. Receive Study Plan: View your personalized schedule, daily tasks, and progress.
5. Track Progress: Update completion; system adapts plan based on new feedback.

---

Architecture & Design

Main Modules Overview

• nlp/: NLP utilities for parsing user input
• planner/: Core planning algorithms (A*, Hill Climb, Min-Max)
• api/: RESTful API endpoints for frontend/backend communication
• models/: Data models (Users, Plans, Tasks)
• frontend/: (Optional, if included) User interface code

---

API Reference

• POST /plan – Create study plan
• POST /feedback – Submit user feedback (struggling topics, mood)
• GET /progress – Retrieve current progress stats
• (Expand with endpoints as project matures)

---

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature-name)
3. Commit your changes
4. Open a Pull Request
5. Ensure code passes all tests and style checks

---

License

Distributed under the MIT License. See LICENSE for more information.

---

Acknowledgments

• Algorithms inspired by AI planning techniques.
• Thanks to the Python communities for libraries and inspiration.