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1. Learn Data Science Step by Step
Create a beginner-friendly 8-week roadmap to learn data science with weekly goals, tools, and mini projects.
2. Simplify Any ML Concept
Explain logistic regression in simple words with real-life analogy, math intuition, and Python example.
3. Data Cleaning Assistant
Here’s my messy dataset: [describe or paste sample]. Suggest step-by-step data cleaning and preprocessing steps in Pandas.
4. Model Performance Debugger
My model has high training accuracy but low test accuracy. Suggest possible reasons and how to fix overfitting.
5. Visualize Like a Pro
Show me 5 types of data visualizations in Python using matplotlib/seaborn and when to use each.
6. Ask Me Data Science Interview Questions
Ask me 5 entry-level data science interview questions one by one. Evaluate my answers and suggest improvements.
7. End to End ML Project Guide
Guide me through an end-to-end machine learning project with a real dataset (e.g., Titanic, Iris). Include data loading, EDA, modeling, and evaluation
8. Learn with Code + Explanation
Give me a simple linear regression model in Python with detailed explanation of each line of code
9. Algorithm Comparer
Compare Random Forest vs XGBoost — use cases, differences, advantages, and when to use each.
10. Auto Data Insights
Here’s my dataset: [describe it]. What key insights or trends can I extract? Suggest visualizations and metrics.
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