Social Media Trend Predictor

This project uses Machine Learning and Genetic Algorithms to optimize posting times on social media, helping content creators and marketers identify the best day and hour to post for maximum engagement.

Problem Statement

In a world filled with digital noise, when you post on social media can be just as important as what you post.

This system analyzes past performance data and uses ML + GA to:

Predict expected engagement (likes, comments, shares)

Optimize the best day + time to post

? Technologies Used

Python

Pandas, NumPy – Data handling

Scikit-learn – Machine Learning models

Matplotlib / Seaborn – Visualization

Genetic Algorithm – Custom implementation for scheduling optimization

Jupyter Notebook – Development & analysis

How It Works

Data Preprocessing

Clean missing or irrelevant data

Feature engineering (encode days/hours/post type)

ML Model

Trained to predict engagement score

Model used: e.g., RandomForestRegressor or any suitable regression model

Genetic Algorithm

Population: Candidate posting schedules

Fitness: Predicted engagement score

Crossover & Mutation to evolve best posting time

Result

Recommended top posting times