0.2.0 Product Requirements

local-data-platform/MVP-RequirementDoc.md

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+
+# MVP Product Requirement Doc
+**No** |**Key Updates** | **Date Updated** | **Updated by**
+-- | -- | -- | -- |
+1 | First Draft Completed |  28 Nov 2024 | Arpita Dhir |  
+2 | Team Review | 28 Nov 2024 | Tushar and Mrtunjay |  
+
+## Objective
+
+This outlines the requirements for Local Data Platform, a web-based solution aimed at helping businesses make data-driven decisions. 
+
+The platform will allow users to:
+
+1. Search for User insights
+2. Predict User Behaviour wrt Business
+3. Upload Business data for analysis
+4. Download User Insights as CSV files
+
+### **Problem Statement**
+
+Businesses often struggle to make informed decisions due to a lack of actionable insights. Access to predictive analytics tailored to their specific datasets remains limited, expensive, or overly complex.
+
+### **Who are we solving for?**
+
+- **Small to Medium-Sized Enterprises (SMEs)**: Businesses with limited access to dedicated data science teams or tools.
+- **Decision-Makers**: Business owners, managers, or analysts seeking clear, actionable insights from their data.
+
+### **Reasons it may not work?**
+1. Users might hesitate to upload sensitive business data
+2. Insights might be too complex or irrelevant for the target audience
+3. Issues with data ingestion, prediction accuracy, or CSV export functionality
+4. Low adoption due to insufficient marketing
+
+
+## **Phases**
+
+### MVP Mission
+1. Allow users to Log in/Sign up
+2. Users should have business insights dashboard
+3. They can search insights 
+4. They can manage insights i.e add and delete
+6. Allows users to upload data to LDP 
+7. Enables downloading of actionable insights in CSV format.
+
+## **Success Metrics**
+
+**Adoption Metrics**
+- Number of registered businesses [Aim: 10]
+- Percentage of users actively uploading data [Aim: 75%]
+- Cost to conversion [Aim: 2$]
+
+**Engagement Metrics**
+- Time spent on the platform [Aim: 2 hr]
+- Number of insights searched per user per hour [Aim: 5+]
+- Number of insights downloaded per user per hour [Aim: 5+]
+
+**Customer Satisfaction**
+- User retention rates [Aim: 50%]
+- User Churn rates [Aim: 20%]
+- NPS Feedback [70]
+
+
+Phase | Task | Timeline | Owner | Deliverables
+-- | -- | -- | -- | --
+Planning | Finalize PRD | 24 Nov 24 | Tushar | Approved Doc
+Design | Create the handoff ready website design  | 20 Dec 24 | Arpita | High-fidelity designs
+Frontend Dev | Build website on Framer | 24 Dec 24 | Arpita | Local Website
+Backend Dev | API and package integration | 24 Dec 24 | Tushar | APIs and backend services
+Integration | Combine frontend and backend | 28 Dec 24 | Tushar | Fully functional Staging Website 
+Testing | QA and bug fixing | 28 Dec 24 | Tushar | Bug-free release-ready product
+Marketing | Social posts to engage till Launch | 15 Dec 24 - 15 Jan 25 | Arpita | Social Posts
+Deployment | Launch MVP | 15 Jan 25 | Tushar | Live platform
+
+## Specifications 
+### **Product Design**
+- **Key Actions**
+    - Authentication & Onboarding
+    - Upload local data/ Ingest data
+    - Search for insights
+    - Download button for CSV exports.
+    - Landing page with USP
+    - Dashboard page 
+        - showcasing insights
+        - data upload/download options
+        - pinned data insights
+    - **Frontend**: Framer
+    - Responsive Design
+
+### **Backend Requirements**
+- **Framework**: Python
+- **Key Actions**:
+    - Data ingestion APIs for parsing and validating uploaded files.
+    - Prediction model powered by LDP
+    - RESTful APIs for frontend-backend communication.
+    - Export functionality to create CSV files for download.
+- **Database**: 
+
+### **Testing**
+- **Unit Testing**: Validate frontend components and backend APIs.
+- **Integration Testing**: Ensure seamless interaction between frontend and backend.
+- **User Acceptance Testing (UAT)**: Validate product functionality with beta users.

partner.py

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+"""
+Product Requirements Doc
+
+Product? 
+- Users are people who want to find and learn new recipes 
+- Recipe is an explanation of how a dish is made. It has:
+ 	- ingredients: quantity, ingredient name 
+  	- process
+   	- prep time: minutes
+    	- cook time: minutes
+     	- difficulty: easy, intermediate, hard
+      	- course: starter, main course, dessert, beverage
+       	- Price: Rupees
+- Offline Dinners
+	- Register: Must be signed in to attend event 
+ 	- Dinner Name
+  	- Cuisine
+   	- Chef Name
+    	- Menu: All dishes available for dinner
+ 	- Price: Rupees
+"""
+from local_data_platform import helloworld
+from local_data_platform.entity import User, Product
+from local_data_platform.transaction import Transaction 
+
+
+
+'''
+	Product? Uber 
+	User? Rider & Driver
+	Handshake?
+	_Booking_
+		*Event_id*
+		Rider :+1 Uber
+		Uber :? Driver
+		Driver :+1 Uber
+		Uber :+1 Rider
+'''
+class Booking(Transaction):
+
+	def __init__(self, product: Uber, provider: Driver, consumer: Rider):
+		self.product = product
+		self.provider = provider
+		self.consumer = consumer
+
+	def get():
+		pass
+
+	def put():
+		pass
+class Uber(Product):
+
+
+class Rider(User):
+
+
+class Driver(User):
+
+
+helloworld()