Shakeel Ahmed
This channel is dedicated to exploring various statistical methods for data analysis using a range of statistical software. Through example-based explanations, it demonstrates practical implementations of statistical techniques in R, accompanied by clear interpretations of the results. Additionally, the channel offers valuable insights and tips on conducting research, writing impactful projects, and successfully publishing studies.
Random Forest Explainability with SHAP in Python | Beeswarm, Waterfall & Feature Importance
Getting SHAP Values | Use of Waterfall & Beeswarm Plots | Linear Regression & GAM Models in Python
Using ChatGPT for R Programming: How AI Helps and Where It Fails?: Explanation with Examples
Week 2: Basic Unit Level Models in Small Area Estimation | BLUP Derivation Explained
PCA in Python Step by Step | Dimensionality Reduction & Feature Extraction Explained
Week 1: When and Why to Use Small Area Estimation (SAE)? | Need of SAE in Pakistan
K-Means Clustering in Python | Classification of Objects with Unsupervised Learning
Quantile Regression in Python | Robust Modeling of Health Outcomes with Interpretation
Gamma Regression Step by Step in Python | Modeling Non-Normal Outcomes with Interpretation
Elastic Net Regression in Python Step-by-Step | Prediction, Variable Selection & Model Evaluation
Lasso Regression Analysis in Python | Prediction, Variable Selection & Model Evaluation
Ridge Regression in Python | Prediction, Variable Selection & Model Evaluation.
Naïve Bayes Classifier for Predicting Health Outcomes in Python | Evaluation, Visualization
Predicting Health Outcomes with GAM in Python | Evaluation, Visualization & Interpretation
Logistic Model Tree (LMT) for Predicting Health Outcomes| Interpretation, Evaluation, Visualization
XGBoost for Predicting Health Outcomes in Python: Interpretation, Evaluation, & Visualization
AdaBoost Algorithm for Predicting Health Outcomes in Python | Steps, Evaluation & Interpretation
Gradient Boosting Algorithm for Predicting Health Outcomes in Python | Step-by-Step Tutorial
Neural Networks for Predicting Health Outcomes in Python | Steps, Evaluation & Interpretation
K-Nearest Neighborhood Method for Predicting Health outcomes in Python with Interpretation
SVM Model for Health Outcome Prediction in Python| Preprocessing , Accuracy, AUC & Interpretation
Predicting Health Outcomes with Random Forest in Python | Interpretation, Visualization, Evaluation
CART in Python | Predicting Categorical Outcomes with Step-by-Step Interpretation & Visualization
How to Upload a YouTube Video Using Generative AI for Title, Description & Tags | Step-by-Step Guide
Poisson Regression in Python Explained: Count Data Modeling, Assumptions, Implementation
Data Science: Logistic Regression in Python: Classification, Confusion Matrix, ROC, F1 Score
Logistic Regression in Python: Assumptions, Implementation & Adjusted Odds Ratio Explained
Multiple Linear Regression in Python | Interpretation & Model Selection Using R-squared
Data Science: Simple Linear Regression for Prediction in Python | Train-Test Split, Model Evaluation
Simple Linear Regression in Python | Assumptions, Interpretation & Diagnostics Explained