Скачать или смотреть Air Quality Analysis 2024-2025 |C6 Python Data Visualization & PM2.5 Prediction using XGBoost & SHAP

Can we predict the air we breathe? In this video, we build a complete, end-to-end Data Science project to predict PM2.5 concentration levels using the latest 2024-2025 Air Quality dataset. We don't just build a model; we compare 5 different algorithms to find the absolute best!

Bangladesh AQI Dataset 2000–2026-103 Citie |P5 AQI Prediction |Dataset Customized with Python Script .How to download Paid Research Paper ফ্রি সহজ লিগ্যাল কৌশল | Science Hub Mutual Aid Community|Part-2 .
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How to download Paid Research Paper ফ্রি সহজ লিগ্যাল কৌশল | Science Hub Mutual Aid Community|Part-2 .
Details Look Here P1: • 1.How to download Paid Research Paper ফ্রি...
Part2: • 2.How to download Paid Research Paper ফ্রি... .Hey! I’m Md. Shofikul Islam.I’m interested in learning every day and sharing knowledge with others.
Follow me and stay connected through the links below. Thank you for your continuous support 💙 .
My Facebook ID link: / mdshofikul.islam.165033

My Youtube Channel Link: / @md.shofikul-islamtech

My Facebook page Link:https://www.facebook.com/profile.php?...

My github Link: https://github.com/shofikul2024

My Instagram Link:  / mdshofikul4.  .

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My Email: islamshofi942@gmail.com , shofikulislam454565@gmail.com

[Key Features of This Project]

Real-World Data: Analysis of 10,000+ records from the 2024-2025 AQI dataset.

Comparison Study: We test Linear Regression, Ridge, Lasso, Random Forest, and XGBoost.

Explainable AI (XAI): Using SHAP analysis to see exactly which features (like Temperature or Humidity) drive pollution levels.

Publication-Ready: Includes 17 custom-generated, high-resolution (300 DPI) visualizations and full statistical validation (VIF, OLS p-values).

[What You’ll Learn]

Preprocessing: Handling missing values with Median Imputation & Feature Scaling.

EDA: Creating Correlation Heatmaps and checking for Multicollinearity with VIF.

Modeling: Hyperparameter tuning for XGBoost and Random Forest.

Evaluation: Interpreting R-squared, MAE, and RMSE for research-grade results.

Diagnostics: Residual analysis and Learning Curves to ensure no overfitting.

[Tech Stack Used]

Language: Python 3.x

Libraries: Pandas, Scikit-Learn, XGBoost, Seaborn, Matplotlib

Explainability: SHAP (SHapley Additive exPlanations)

Stats: Statsmodels & SciPy