https://github.com/madhurimarawat/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.
https://github.com/madhurimarawat/agile-sprint-and-iris-data-explorer
agile-codes agile-development agile-methodologies agile-metrics agile-planning codes complete-agile-explained data-visualization deployment documentation github-deployment iris-dataset output python readme software-engineering sprint sprint-planning streamlit-deployment streamlit-webapp
Last synced: 4 months ago
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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.
- Host: GitHub
- URL: https://github.com/madhurimarawat/agile-sprint-and-iris-data-explorer
- Owner: madhurimarawat
- License: mit
- Created: 2024-11-23T17:38:58.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-29T10:05:07.000Z (about 1 year ago)
- Last Synced: 2025-02-09T10:11:16.596Z (11 months ago)
- Topics: agile-codes, agile-development, agile-methodologies, agile-metrics, agile-planning, codes, complete-agile-explained, data-visualization, deployment, documentation, github-deployment, iris-dataset, output, python, readme, software-engineering, sprint, sprint-planning, streamlit-deployment, streamlit-webapp
- Language: Python
- Homepage: https://madhurima-agile-sprint-and-iris-data-explorer.streamlit.app/
- Size: 1.87 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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:**
---
## 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.
```
---
## **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:
```bash
pip install -r requirements.txt
```
**Usage**
Once the dependencies are installed, you can start the Streamlit app by running:
```bash
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.
---
## **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.
---
### **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.
---
### **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 |
---
### **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.
---
### **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 |
---
### **Sprint Summary**
- **Total Effort**: 35 points.
- **Average Effort per Team Member**:
- Alice: 13 points.
- Bob: 11 points.
- Charlie: 11 points.
---
### **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.
---
### **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.
---
### **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 |
---
### **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](https://streamlit.io/).
- Link GitHub repository and deploy the app.
---
## Thanks for Visiting 😄
- Drop a 🌟 if you find this repository useful.
- If you have any doubts or suggestions, feel free to reach me.
📫 How to reach me: [](https://www.linkedin.com/in/madhurima-rawat/)
- **Contribute and Discuss:** Feel free to open issues 🐛, submit pull requests 🛠️, or start discussions 💬 to help improve this repository!