Wikit

📚 Wikit for the cricket fans, by cricket fans

📝

The project is a dynamic app that connects sports fans with similar interests through intelligent clustering algorithms. Its main objective is to create a vibrant community by facilitating discussions and interactions among fans who share a passion for the same sports team.

Using state-of-the-art unsupervised machine learning techniques, the app employs scikit-learn's powerful K-means clustering model. This allows the app to analyze user profiles, preferences, and engagement patterns to group fans into clusters based on their shared interests, team affiliations, and other relevant factors. By matching fans with the most compatible groups, the app fosters engaging conversations, enables the exchange of ideas and opinions, and creates a sense of belonging among users.

The app provides a dedicated chat room for each fan cluster, where users can actively participate in discussions, share news, cheer for their team, and express their enthusiasm. By connecting fans who have similar viewpoints, the app enhances the overall user experience and promotes meaningful connections within the sports community.

Additionally, the app offers a range of supplementary features to enhance the user's engagement. It provides comprehensive analytics, data visualizations, and performance graphs that highlight the team's progress and achievements over time. Users can also access targeted advertisements, enabling them to explore and purchase merchandise and tickets associated with their favorite team.

With its focus on intelligent clustering and fostering engaging discussions, this app provides a platform for sports fans to connect, share their passion, and create a vibrant community centered around their beloved sports team.

✨ List of key features:

• An app that connects fans of the same team together in a chat room.
• Utilizes an existing AI matchmaking algorithm to connect fans with the most similar profiles.
• Includes a page to display team support, analytics, data, and a graph of improvement over time.
• Offers merchandise and ticket advertisements for the supported team.
• Provides three tiers: Free, Basic, and Premium.
• Generates revenue through targeted advertisements, premium and pro tier subscriptions, and tournament sponsorships.

🔧 Technologies used in this project:

• Backend: Python, Flask
• Machine Learning: scikit-learn for unsupervised K-means clustering model
• Frontend: Tailwind CSS
• Database: Firebase with Firestore

Prerequisites:

• Node.js
• npm package manager installed

Step 1: Clone the repository

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git clone https://github.com/aarushaggarwal-theone/wikit

Step 2: Open Host directory

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cd frontend

Step 3: Install dependencies

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npm i

Step 4: Set up Firebase and Firestore

• Create a Firebase project and enable Firestore database.
• Obtain the Firebase configuration credentials and add them to a .env file in the following format

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apiKey=YOUR_API_KEY
authDomain=YOUR_AUthDOMAIN
databaseURL=YOUR_DB_URL
projectId=YOUR_PROJECT_ID
storageBucket=YOUR_STORAGE_BUCKET
messagingSenderId=YOUR_MESSAGE_SENDER_ID
appId=YOUR_APP_ID
measurementId=YOUR_MEASUREMENT_ID

Step 5: Run the Next App at https://localhost:3000

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npm run start

Step 6: Access the app Open your web browser and visit http://localhost:3000 to access the app locally.

Note: If the default port 3000 is already in use, you can specify a different port by modifying the package.json file.

That's it! You have successfully set up and run the project locally on your machine. You can now explore the app's features and interact with the chat room, leveraging the power of the intelligent clustering algorithm to connect with like-minded sports fans.