Smart Study Planner 🧠

A premium, SaaS-style dashboard built with Python and Streamlit that optimizes study schedules using a Dynamic Greedy Algorithm.

🚀 Key Features

• Dynamic Greedy Scheduling: Allocates study hours based on difficulty, urgency, and a fairness penalty to prevent subject burnout.
• SaaS Dashboard UI: Dark-themed, glassmorphic interface with color-coded metric cards and interactive intensity heatmaps.
• Resource Analytics: Donut charts for time distribution and real-time utilization metrics.
• Interactive Syllabus: Add subjects dynamically and re-generate your plan instantly.
• Exportable Plans: Download your optimized schedule as a CSV file.

📄 Project Documentation

This project includes a detailed academic report covering the algorithm, system design, and implementation.

📥 Download here:
Smart Study Planner Report

🧠 Algorithmic Thinking: The Dynamic Greedy Model

This project is built around a Dynamic Greedy Algorithm designed to solve the multi-objective optimization problem of study scheduling. Unlike a static schedule, this system recalculates priorities in real-time.

1. The Heuristic Priority Function

Each subject is evaluated using a custom heuristic that balances Complexity and Urgency:

$$PriorityScore = (Difficulty \times 3) + \left(\frac{20}{DaysLeft}\right)$$

• Difficulty (Weight 3x): Acts as the primary driver for effort allocation.
• Urgency (Reciprocal of DaysLeft): Ensures that as an exam approaches, the subject naturally climbs the priority list, regardless of its difficulty.

2. The Dynamic Progress Penalty (Fairness Constraint)

A standard "Greedy" algorithm would simply pick the hardest subject and allocate all hours to it. To prevent burnout and ensure a balanced syllabus, we implement a Progress Penalty:

$$DynamicPriority = BasePriority - (AllocatedHours \times 1.5)$$

• How it works: For every hour allocated to a subject, its priority score is temporarily reduced by 1.5 points.
• Result: This allows other subjects to "catch up" in priority, creating a distributed schedule where the student switches topics naturally throughout the day.

3. Allocation Logic (Step-by-Step)

1. Block-by-Block Allocation: Instead of allocating full days, the system works in 1-hour blocks.
2. Greedy Selection: For each block, the system identifies the subject with the highest current dynamic priority.
3. Feedback Loop: Once a block is assigned, the subject's penalty is updated, and the list is re-sorted for the next block.

4. Complexity Analysis

• Time Complexity: $O(D \cdot H \cdot N \log N)$, where $D$ is total days, $H$ is daily hours, and $N$ is the number of subjects. This ensures the dashboard remains lightning-fast even with large syllabi.
• Optimization: Uses a stable sorting approach to ensure consistency in the schedule.

📦 Installation & Usage

1. Clone the repository:

   git clone https://github.com/saatvic008/smart-study-planner.git
   cd smart-study-planner

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run app.py

🛠️ Tech Stack

• Frontend: Streamlit, Custom CSS
• Visualization: Plotly Express
• Logic: Python (Dynamic Greedy Heuristic)
• Data: Pandas