Disease Monitoring AI: Social Media Prototype
Budget: $30 – $250 USD
Prototype for AI-Based Disease Monitoring Using Social Media Data
Project Overview:
We are looking for a freelancer to develop a simple prototype for a project that monitors disease-related trends using social media data (e.g., Twitter). This is a proof-of-concept project intended to explore feasibility, and we plan to expand it into a larger project once we get confidance.
The main goal is to extract and analyze basic trends using natural language processing (NLP) and machine learning, along with simple visualizations.
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Scope of Work:
1. Data Collection:
Collect a small dataset of tweets or public health data using the Twitter API or any other publicly available source.
Focus on disease-related keywords (e.g., “flu,” “dengue,” or “outbreak”).
2. Data Preprocessing:
Perform basic cleaning of the collected data (e.g., remove duplicates, tokenize text).
No need for extensive geospatial tagging or advanced processing—keep it simple.
3. Basic Analysis:
Use simple NLP techniques (e.g., sentiment analysis or word frequency analysis) to identify key trends.
Build a basic machine learning model (e.g., linear regression or decision tree) to predict disease trends based on tweet activity.
4. Visualization:
Create basic visualizations like bar charts or line graphs to show trends.
Use tools like Matplotlib or Seaborn for simplicity.
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Deliverables:
1. A small-scale prototype pipeline for data collection, analysis, and visualization.
2. Basic machine learning model implementation.
3. Complete source code and brief documentation (no advanced details needed).
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Required Skills:
Python programming (Pandas, NumPy, Matplotlib). Or other if uou can.
Experience with basic NLP (e.g., text preprocessing, sentiment analysis).
Familiarity with APIs (e.g., Twitter API).
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Budget and Timeline:
Budget: usd 80-120
Time: 10 days
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Note to Applicants:
This is a prototype project only, and we aim to keep it simple and functional. After successfully completing this prototype, we plan to post a much larger project for full-scale development.
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Project Overview:
We are looking for a freelancer to develop a simple prototype for a project that monitors disease-related trends using social media data (e.g., Twitter). This is a proof-of-concept project intended to explore feasibility, and we plan to expand it into a larger project once we get confidance.
The main goal is to extract and analyze basic trends using natural language processing (NLP) and machine learning, along with simple visualizations.
---
Scope of Work:
1. Data Collection:
Collect a small dataset of tweets or public health data using the Twitter API or any other publicly available source.
Focus on disease-related keywords (e.g., “flu,” “dengue,” or “outbreak”).
2. Data Preprocessing:
Perform basic cleaning of the collected data (e.g., remove duplicates, tokenize text).
No need for extensive geospatial tagging or advanced processing—keep it simple.
3. Basic Analysis:
Use simple NLP techniques (e.g., sentiment analysis or word frequency analysis) to identify key trends.
Build a basic machine learning model (e.g., linear regression or decision tree) to predict disease trends based on tweet activity.
4. Visualization:
Create basic visualizations like bar charts or line graphs to show trends.
Use tools like Matplotlib or Seaborn for simplicity.
---
Deliverables:
1. A small-scale prototype pipeline for data collection, analysis, and visualization.
2. Basic machine learning model implementation.
3. Complete source code and brief documentation (no advanced details needed).
---
Required Skills:
Python programming (Pandas, NumPy, Matplotlib). Or other if uou can.
Experience with basic NLP (e.g., text preprocessing, sentiment analysis).
Familiarity with APIs (e.g., Twitter API).
---
Budget and Timeline:
Budget: usd 80-120
Time: 10 days
---
Note to Applicants:
This is a prototype project only, and we aim to keep it simple and functional. After successfully completing this prototype, we plan to post a much larger project for full-scale development.
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Related categories:
Business, Accounting, Human Resources & Legal
Python
Machine Learning (ML)
Data Mining
Data Science