Atomic Habits Tracker

Personal analytics project that tracks and visualizes daily habits. Inspired by James Clear's "Atomic Habits" book - wanted to see if small habits actually compound over time.

What It Does

Tracks 9 daily habits across 5 areas:

Career & Learning - Daily learning time and activities
Personal Development - Reading progress and books completed
Health & Fitness - Workouts, steps, water intake, sleep
Finance - Income and expense tracking
Relationships - Family check-ins and friend contacts

Python scripts generate realistic habit data and create an Excel dashboard with charts, statistics, and a calendar heatmap showing 34 days of tracking.

Features

Automated Excel dashboard generation
Calendar heatmap showing daily performance
Statistical analysis and completion rates
29 tracked variables across 9 core habits
Realistic data patterns (weekend vs weekday differences)

Tech Stack

Python 3.8+
Pandas - Data manipulation
NumPy - Numerical operations
XlsxWriter - Excel file generation

Quick Start

Install dependencies:

pip install -r requirements.txt

Generate data:

python src/generate_data.py

Create dashboard:

python src/create_excel_dashboard_v2.py

Open Atomic_Habits_Dashboard_v2.xlsx to see the visualizations.

What's in the Dataset

34 days of habit tracking (Jan 1 - Feb 3, 2026) with 29 variables:

Date/Time: Date, day of week, week number, weekend flag

Learning: Completion status, minutes spent, activity type (reading, courses, videos, projects)

Reading: Pages read, current book, books finished

Finance: Weekly income ($337/week), daily expenses

Health: Workout completion, cardio, daily steps, water intake (glasses), sleep hours

Relationships: Family check-ins, friend contacts

Metrics: Daily habits completed, total habits, completion percentage

See DATA_DICTIONARY.md for complete variable definitions.

Results

From 34 days of tracking:

76% average completion rate across all habits
Weekend performance 15% lower than weekdays
Learning most consistent habit at 91% completion
4 books completed in the tracking period
53+ hours invested in learning activities
Strong correlation between learning and reading habits

The calendar heatmap revealed clear momentum patterns and helped identify when I was building streaks versus slipping.

Screenshots

Dashboard Overview

Calendar Heatmap

Dashboard Contents

The Excel file includes:

Summary Sheet - Key statistics and performance metrics
Calendar Heatmap - Color-coded daily performance grid
Data Sheet - Complete dataset table
Charts Sheet - Trend lines, bar charts, and visualizations

Project Structure

atomic-habits-tracker/
├── src/
│   ├── generate_data.py              # Creates habit dataset
│   └── create_excel_dashboard_v2.py  # Generates Excel dashboard
├── data/
│   └── habits_data.csv               # Sample data output
├── images/
│   ├── dashboard.png                 # Dashboard screenshot
│   └── calendar_heatmap.png          # Heatmap visualization
├── README.md                         # Project overview
├── DATA_DICTIONARY.md                # Variable definitions
├── PROJECT_WALKTHROUGH.md            # Detailed guide for interviewers
├── requirements.txt                  # Python dependencies
├── index.html                        # GitHub Pages landing page
└── LICENSE                           # MIT License

Use Cases

This project demonstrates:

End-to-end data pipeline (generation, analysis, visualization)
Automated report creation
Excel automation using Python
Statistical analysis and pattern recognition
Real-world application of data analytics

Similar techniques apply to customer behavior analysis, product usage tracking, marketing analytics, and business reporting.

Future Enhancements

Potential additions:

Web dashboard using Streamlit or Dash
Real-time data input interface
Machine learning predictions for habit adherence
Statistical significance testing
Database backend (SQLite/PostgreSQL)
Time series forecasting
Mobile app integration

License

MIT License - see LICENSE file for details.

About

This is a personal project built to practice data analysis skills and explore whether habit tracking actually helps with consistency. The data is synthetically generated but based on realistic behavioral patterns.

Want more context? Check out PROJECT_WALKTHROUGH.md for detailed explanations and technical deep dives.

aymanmomin/atomic-habits-tracker