11-Portfolio-World-Univeristy-Ranking

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This dataset contains the 2025 QS World University Rankings, featuring various attributes about institutions, locations, classification, research output, international presence, employment outcomes, sustainability, and overall rankings.

Possible ML Projects with This Dataset

Here are some machine learning project ideas based on this dataset:

1. Rank Prediction (Regression)

• Goal: Predict the university rank based on various features like research score, international student ratio, employment outcomes, etc.
• ML Approach: Train a regression model (Linear Regression, Random Forest, or XGBoost) to predict a university’s rank given its attributes.
• Potential Insight: Identify which factors most significantly impact rankings.

2. University Clustering (Unsupervised Learning)

• Goal: Group universities into clusters based on similarities in size, research strength, internationalization, and sustainability.
• ML Approach: Use K-Means Clustering or Hierarchical Clustering to classify universities into different tiers based on attributes.
• Potential Insight: Discover natural groupings in universities beyond just rank.

3. Feature Importance Analysis

• Goal: Identify the most important factors contributing to a university’s rank.
• ML Approach: Use techniques like SHAP values, Feature Importance from Random Forest/XGBoost, or PCAto determine which features have the most influence.
• Potential Insight: Provide data-driven insights on what makes top universities stand out.

4. Employment Score Prediction (Regression)

• Goal: Predict a university’s graduate employment score based on various factors such as research, international student ratio, sustainability, etc.
• ML Approach: Train a regression model to predict employment outcomes based on institution attributes.
• Potential Insight: Identify what makes universities more successful in producing employable graduates.

5. Internationalization Score Prediction

• Goal: Predict a university’s international student score based on location, ranking, research network, and other metrics.
• ML Approach: Regression models or Decision Trees can help understand what attracts international students.
• Potential Insight: Universities can use this information to improve international appeal.

6. Sustainability Analysis

• Goal: Find out if sustainability scores correlate with university rankings.
• ML Approach: Use Correlation Analysis, Regression, or Clustering to examine trends.
• Potential Insight: Universities can see if investing in sustainability boosts rankings.

Which Project Interests You?

Let me know which of these ideas you'd like to pursue, or if you have a specific goal in mind! 🚀

You said:

I am thinking of doing all this .. I want to link all this to specific, and demonstrate knowledge of sql, ml, pb, and data analysis... and sentiment analysis re-arrange all this and state the objective for each of the project, and how to integrate all the details above

ChatGPT said:

Comprehensive Project Plan for QS World University Rankings Analysis

Your goal is to demonstrate expertise in SQL, Machine Learning (ML), Power BI, and Data Analysis, while also incorporating Sentiment Analysis. Below is the structured breakdown of how all these elements come together.

Project Title: Data-Driven Insights into University Rankings, Employment, and Global Education Trends

Objective

This project aims to analyze QS World University Rankings using SQL for data integration, Machine Learning for predictive modeling, Power BI for visualization, and Sentiment Analysis for qualitative insights. It will uncover key drivers of university rankings, employment outcomes, sustainability impact, and internationalization trends.

Project Breakdown & Integration of Tools

Step-by-Step Execution Plan

Step 1: SQL-Based Data Integration

• Objective: Organize, clean, and structure QS ranking data.
• Tasks:
• Store and manage data in a SQL database.
• Normalize data across multiple tables (e.g., Rankings, Scores, Sustainability, Employment, Research).
• Use SQL queries for filtering, aggregating, and extracting insights.
• Key Queries:
• Rank trends over years.
• Employment vs. Research scores correlation.
• International student ratio by region.
• Tools: MySQL/PostgreSQL (Stored Procedures, Joins, Views, Indexing).

Step 2: Rank Prediction (ML Regression)

• Objective: Build a predictive model to estimate university rankings based on key factors.
• Tasks:
• Select features affecting rank (e.g., Research Score, Internationalization, Sustainability).
• Train regression models (Linear Regression, XGBoost, Random Forest).
• Evaluate model performance using RMSE, R², MAPE.
• Tools: Python (Scikit-Learn, XGBoost, Pandas, Matplotlib).

Step 3: University Clustering (Unsupervised Learning)

• Objective: Identify distinct groups of universities based on attributes like research intensity, sustainability, and internationalization.
• Tasks:
• Apply K-Means & Hierarchical Clustering.
• Use PCA to reduce dimensionality and enhance interpretability.
• Visualize clusters (e.g., Top-tier research-heavy vs. employment-focused universities).
• Tools: Python (Scikit-Learn, Seaborn, Plotly).

Step 4: Feature Importance Analysis

• Objective: Determine which factors contribute most to university rankings.
• Tasks:
• Use SHAP values for explainability.
• Perform PCA (Principal Component Analysis).
• Rank feature importance using XGBoost and Random Forest.
• Tools: Python (SHAP, PCA, Feature Importance from ML Models).

Step 5: Employment Score Prediction

• Objective: Predict the Employment Score of universities based on their ranking, research output, and other factors.
• Tasks:
• Train regression models to estimate employment scores.
• Identify which university attributes improve employment outcomes.
• Provide recommendations for universities to improve their employment score.
• Tools: Python (Scikit-Learn, XGBoost, Regression Metrics).

Step 6: Internationalization Score Prediction

• Objective: Predict how attractive a university is to international students based on key indicators.
• Tasks:
• Train ML models to estimate internationalization scores.
• Examine trends in international student growth by country/region.
• Tools: Python (Scikit-Learn, Pandas, Visualization).

Step 7: Sustainability & Rank Correlation

• Objective: Investigate if sustainability scores impact university rankings.
• Tasks:
• Use Pearson/Spearman correlation to analyze trends.
• Visualize sustainability vs. ranking using scatter plots.
• Tools: Python (Matplotlib, Seaborn, Correlation Analysis).

Step 8: Sentiment Analysis on University Reviews

• Objective: Analyze student sentiment from university reviews, forums, and news articles.
• Tasks:
• Scrape online reviews from sites like Reddit, Twitter, QS Reviews.
• Use NLP techniques to classify reviews as positive, neutral, or negative.
• Identify common themes (e.g., research quality, student life, job prospects).
• ML/NLP Approach: VADER, TextBlob, BERT, TF-IDF, Topic Modeling.
• Tools: Python (BeautifulSoup, NLTK, Scikit-Learn).

Step 9: Power BI Dashboard for QS Ranking Insights

• Objective: Present all key findings in a visually compelling dashboard.
• Tasks:
• Design an interactive dashboard showing ranking trends, employment scores, sustainability impact, and research strengths.
• Include filters for regions, countries, and ranking ranges.
• Add drill-downs for university-specific analysis.
• Tools: Power BI (DAX, Data Modeling, Interactive Visuals).

Expected Deliverables

✔ SQL database with integrated and cleaned QS ranking data

✔ Machine Learning models for rank prediction, clustering, and employment analysis

✔ Feature Importance and Sustainability Impact Insights

✔ Sentiment Analysis of student perceptions

✔ Power BI dashboard with interactive insights

How This Demonstrates Your Skills

🔹 SQL Expertise – Data integration, query optimization, and structured storage

🔹 Data Analysis – Exploratory analysis, feature engineering, and visualization

🔹 Machine Learning – Regression, clustering, explainability, and model evaluation

🔹 NLP (Sentiment Analysis) – Scraping, processing, and analyzing textual data

🔹 Power BI – Interactive reporting, storytelling, and executive dashboards

Next Steps

Would you like me to help you with:

1. Writing SQL queries for the integration process?
2. Preparing Python scripts for ML models?
3. Setting up a Power BI structure?
4. Guidance on scraping university reviews for sentiment analysis?

Let me know how you’d like to begin! 🚀