Agile-Sprint-and-Iris-Data-Explorer

Streamlit app that combines agile sprint planning with data visualization of the Iris dataset. It helps analyze task distribution and explore key data insights interactively.

Streamlit App Snapshots:

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Directory Structure

📂 Agile-Sprint-and-Iris-Data-Explorer
├── 📁 Codes
│   ├── 📄 requirements.txt             # 📜 Contains necessary dependencies for running the Streamlit app.
│   ├── 📄 Streamlit_app.py             # 🚀 Main script for the Streamlit web app, enabling interactive Iris dataset visualization.
│   ├── 📄 Iris_Dataset_Analysis.py     # 📊 Python script for core analysis, preprocessing, and visualizing the Iris dataset.
│
├── 📁 Documentation Files
│   ├── 📄 Agile_Sprint_Planning.md     # 📋 Sprint planning documentation with timeline and completed tasks.
│   ├── 📄 Agile_Sprint_Planning.pdf    # 📄 Formatted PDF summarizing project outputs and features for sharing.
│
├── 📁 Output
│   ├── 📄 Experiment 8 Output.docx     # 📝 Word document detailing experiment results.
│   ├── 📄 Experiment 8 Output.pdf      # 📑 PDF version of the experiment results for easy distribution.
│
├── 📄 README.md                        # 📘 Project overview, setup instructions, and feature details.
├── 📄 LICENSE.md                       # 📜 License information for using and modifying the project.

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Installation and Usage

To run the Streamlit app, you need to install the required dependencies. You can install the necessary libraries by running the following command:

pip install -r requirements.txt

Usage

Once the dependencies are installed, you can start the Streamlit app by running:

streamlit run Streamlit_app.py

This will launch the application in your default web browser, where you can interact with the dataset and explore different visualizations.

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Steps in the Project

1. Project Overview: Agile Sprint Planning

Goal:

• Conduct a simulated sprint planning session using Agile principles.
• Break down user stories into tasks, estimate their effort, and allocate them among team members (Alice, Bob, Charlie).
• Visualize and analyze sprint data using Streamlit.

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2. Example Agile Sprint Planning Workflow

User Stories Example:

Dataset and sprint task list tailored specifically to the described project, focusing on tasks like data collection, pipeline creation, deployment, and bug fixing.

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Dataset Example for Agile Sprint Planning

Story ID	Description	Priority	Effort	Assigned To
1	Collect dataset and clean for processing.	Medium	4	Alice
2	Create a pipeline for visualizing sprint data.	High	6	Bob
3	Deploy Streamlit app to cloud hosting.	High	8	Charlie
4	Fix app crash caused by missing input validation.	High	9	Alice
5	Implement effort estimation slider for tasks.	Medium	5	Bob
6	Add team assignment logic for user stories.	Low	3	Charlie

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Steps for Task Breakdown and Assignment

1. Task Identification: Break the project into actionable tasks (e.g., collect data, fix bugs, deploy app).
2. Effort Estimation: Use points to estimate the effort needed for each task (1 = low, 10 = high).
3. Prioritization: Assign priority levels (High, Medium, Low) based on project needs.
4. Team Assignment: Allocate tasks evenly among team members to balance workload.

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Generated Sprint Plan

Task	Points (Effort)	Priority	Assigned To
Data collection and preprocessing	4	Medium	Alice
Build sprint visualization pipeline	6	High	Bob
Deploy app to Streamlit cloud	8	High	Charlie
Fix crash on invalid user input	9	High	Alice
Add effort slider for estimation	5	Medium	Bob
Add team assignment functionality	3	Low	Charlie

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Sprint Summary

• Total Effort: 35 points.
• Average Effort per Team Member:
  • Alice: 13 points.
  • Bob: 11 points.
  • Charlie: 11 points.

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Effort Distribution Visualization

• A bar chart showing points distributed across Alice, Bob, and Charlie.
• Ensures balance and highlights potential overloads.

This setup uses Agile principles to organize, estimate, and allocate tasks efficiently, making the project manageable and transparent.

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4. Code for Agile Sprint Planning Simulation

The integration of the Agile Sprint Planning and dataset visualization in Streamlit includes:

1. Effort Estimation:

   • Use the slider widget to estimate effort for each user story (range: 1–10). Effort is based on complexity and priority.

2. Task Allocation:

   • Assign stories to team members using the dropdown menu. The system ensures that tasks are distributed evenly among Alice, Bob, and Charlie.

3. Visualization:

   • Bar chart showing effort distribution among team members, helping to identify if workloads are balanced.

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5. Explanation of Agile Sprint Process

Step	Code/Action	Output
Console Analysis	Simple Python script to analyze Iris dataset with pandas and visualize with seaborn.	Dataset overview, summary statistics, pair plot
Streamlit Integration	Modified script to display dataset, stats, and plot interactively in Streamlit.	Interactive Streamlit app
Deployment	Set up GitHub repo, add requirements.txt, deploy to Streamlit Cloud.	Deployed app link

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6. Deployment Pipeline

Steps:

1. Set Up GitHub Repository: Push code and sample datasets to GitHub.

2. Create requirements.txt:

   streamlit
   pandas
   matplotlib
   

3. Deploy on Streamlit Cloud:

   • Log in to Streamlit Cloud.
   • Link GitHub repository and deploy the app.

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