datahabit 0.1.1

A Python library designed to analyze academic behavior and productivity patterns based on students’ task timestamps.

DataHabit

DataHabit — Academic Behavior Analyzer (OOP Python library)

Project Overview

DataHabit is a Python library designed to analyze academic behavior and productivity patterns based on students’ task timestamps. It reads submission records, detects time-based habits, and classifies users according to their working style—such as procrastinator, steady worker, or early finisher. By using data-driven insights, the library helps students and educators understand academic performance trends and improve time-management strategies.

Objectives

The main goal of DataHabit is to provide a simple yet powerful tool for analyzing and visualizing study habits through timestamp data. Specifically, it aims to:

1. Process and interpret task submission timestamps to identify behavioral patterns.
2. Classify students according to their productivity and consistency levels.
3. Generate summary reports and visualizations that reflect weekly academic activity.
4. Encourage self-reflection and better academic planning through data analytics.
5. Offer a reusable, open-source Python package that demonstrates Object-Oriented Programming (OOP) concepts applied to real-world data science.

Features

Timestamp Analyzer — classifies submissions as early, on-time, or late.

Behavior Classifier — categorizes students (e.g., Procrastinator, Consistent Worker) based on submission patterns.

Productivity Summary — computes weekly activity metrics such as average submission gaps and submission counts.

Visualizer — generates simple graphs to show time-based behavior and activity trends.

Data Cleaner / Utility — fixes missing timestamps and resolves incorrect date formats.

Deadline Predictor — estimates the next expected submission date based on historical delays.

Pattern Detector — identifies the usual submission time of day (e.g., night-owl or early-bird patterns).

Difficulty Estimator — highlights tasks with the longest delays to indicate potential difficulty levels.

Habit Score Generator — computes a combined score based on delay patterns, consistency, and task difficulty.

Report Generator — produces clean, structured summaries that can be exported as JSON or printed for review.

Installation

pip install datahabit

Example usage

from datahabit import (
    TaskData,
    DataCleaner,
    BehaviorAnalyzer
)

tasks = [
    TaskData("Quiz 1", "2024-10-01 23:59", "2024-10-01 21:30"),
    TaskData("Assignment", "2024-10-05 23:59", "2024-10-06 01:10")
]

clean = DataCleaner().clean(tasks)
analyze = BehaviorAnalyzer(clean).classify()

print(analyze)

Module descriptions

Module	Description
behavior_analyzer.py	Groups all behavior-related features, including behavior classification, pattern detection, difficulty estimation, habit scoring, and general performance analysis. Identifies trends such as procrastination, consistency, night-owl patterns, and problem tasks.
data_cleaner.py	Cleans and preprocesses raw task data by fixing missing timestamps, correcting invalid date formats, and standardizing input before analysis.
task_data.py	Stores and manages task information (deadlines, submission times, task names) and provides structured data used by all analyzers and predictors.
visualizer.py	Generates visual graphs (line charts, bar charts, timelines) to show submission patterns, productivity trends, and behavior insights.

Contributors

Name	Role / Position	Main Contribution
Dahe, Aira Grettel C.	Project Lead / GitHub Manager	Repository setup, documentation, testing
Dellosa, Karylle L.	Concept Proposer / Main Coder	Core analysis functions
Hayag, Carmel Mariane T.	Documentation Writer	README creation and narrative reports
Java, Armisty Genia L.	Visualization Developer	Graphs, plots, and visual output designs
Trillo, Rodney G.	Data Handler	Timestamp processing and data cleaning