User Story

As a marketing team member at Legoland devlopment, I want to analyze the impact of post length on user engagement, so that I can optimize the organization’s social media strategy to increase user interaction by 20%.

Phase	Task	Details	Tools/Tech
Phase 1: Set Up the Project	1. Define Objectives and Requirements	- Create an API to predict user engagement based on the length of a social media post. - Target engagement metrics like likes, retweets, shares. - Integrate with social media API (e.g., Twitter).	- Define engagement metrics - Plan data structure
	2. Choose a Social Media Platform	- Choose Twitter for ease of access. - Set up developer access to get the API keys for authentication.	- Twitter Developer API
	3. Set Up Project Environment	- Install required libraries. - Set up a virtual environment for dependencies (e.g., pipenv or venv).	- Python - Tweepy - Pandas
Phase 2: Data Collection	1. Fetch Data from Social Media API	- Use Tweepy to fetch recent posts from Palomar Health’s Twitter account. - Pull data such as text, likes, retweets, comments, and timestamp.	- Tweepy - Pandas
	2. Store Data in Structured Format	- Store the fetched data in a Pandas DataFrame for easy manipulation. - Clean and preprocess data to remove noise (e.g., bots, irrelevant posts).	- Pandas - CSV (or Database for long-term storage)
Phase 3: Data Preprocessing	1. Clean Data	- Remove unnecessary data (bot posts, spam, duplicates). - Filter posts based on engagement metrics (e.g., focus on posts with significant engagement).	- Pandas
	2. Format Data for ML Analysis	- Extract relevant features: post length, likes, retweets, comments. - Standardize data types (e.g., numeric for engagement, categorical for content type).	- Pandas
Phase 4: Machine Learning Model	1. Select a Model	- Use a simple machine learning model like Linear Regression or Decision Tree Regression. - Predict engagement based on post length.	- Scikit-learn
	2. Train the Model	- Split the dataset into training and test sets. - Train the model on the training data and evaluate performance using the test data. - Test various algorithms to find the best-performing model.	- Scikit-learn - Train/Test Split
	3. Evaluate the Model	- Evaluate model performance using metrics like Mean Absolute Error (MAE) or R-squared. - Fine-tune hyperparameters to improve model accuracy.	- Scikit-learn - Cross-validation
Phase 5: API Development	1. Set Up Flask/FastAPI Server	- Create an API using Flask or FastAPI. - Build API endpoints to input post data and return engagement predictions based on post length.	- Flask - FastAPI
	2. Integrate Model into API	- Use the trained model in the backend. - Call the model from the API to return predictions based on the length of the social media post.	- Flask/FastAPI - Pickle for model serialization
Phase 6: Data Insights and Visualization	1. Create Visualizations of Engagement	- Use data visualization tools (e.g., Matplotlib, Seaborn) to visualize the relationship between post length and user engagement. - Present insights such as trends, engagement patterns, etc.	- Matplotlib - Seaborn
	2. Create a Dashboard	- Build a dashboard to showcase engagement trends, predictive model results, and insights. - Display how post length impacts engagement over time with interactive visualizations.	- Dash - Plotly
Phase 7: Testing and Deployment	1. Test API and Model	- Test the API with various input data to ensure it works correctly. - Evaluate the model’s performance in real-time by using new posts.	- Unit Testing - Postman (API Testing)
	2. Deploy the API	- Deploy the API to a cloud server or platform (e.g., Heroku, AWS Lambda). - Ensure the API is accessible and functions in a production environment.	- Heroku - AWS

User Story

Structured Plan for Social Media Post Length Analysis API