Social Media Engagement Prediction

machine learning pipeline

to predict engagement on social media posts using structured data. This project demonstrates data cleaning, feature preparation, preprocessing pipelines, model training, and evaluation in scikit-learn.

Stack: Python, scikit-learn, pandas, Matplotlib, Seaborn.

View on GitHub

Key Contributions

• Dataset: Social media posts dataset with features such as platform, post type, post time, likes, comments, shares, and sentiment.
• Exploratory Data Analysis (EDA): Inspected distributions, correlations, and engagement patterns by platform and post type.

Data Preprocessing

• Scaled numeric features (likes, comments, shares) with StandardScaler.
• Encoded categorical features (platform, post_type, post_day, sentiment_score) using OneHotEncoder.

Model Training

• Tested multiple models, including Logistic Regression and Decision Tree Classifier.
• Evaluated with accuracy scores on test data.

Skills Demonstrated

• Machine Learning (classification pipelines)
• Feature scaling, encoding, and preprocessing with ColumnTransformer
• Pipeline design & evaluation in scikit-learn
• EDA and visualization with Pandas, Matplotlib, Seaborn
• Structured documentation of methodology & reasoning

Links

• Rendered ipynb Notebook PDF