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P01 CDFs & Percentiles.ipynb
P01 CDFs & Percentiles.ipynb
 
 
P02 Confidence Intervals Monte Carlo & Bootstrap.ipynb
P02 Confidence Intervals Monte Carlo & Bootstrap.ipynb
 
 
P03 Hypothesis Testing Z-test Normal and Bernoulli Distribution.ipynb
P03 Hypothesis Testing Z-test Normal and Bernoulli Distribution.ipynb
 
 
P04 Chi-Square Goodness of Fit Test.ipynb
P04 Chi-Square Goodness of Fit Test.ipynb
 
 
P05 Mann-Whitney & Wilcoxon & Paired t-test & Shapiro-Wilk Normality Test.ipynb
P05 Mann-Whitney & Wilcoxon & Paired t-test & Shapiro-Wilk Normality Test.ipynb
 
 
P06 ANOVA, Shapiro-Wilk, and Bartlett’s Test.ipynb
P06 ANOVA, Shapiro-Wilk, and Bartlett’s Test.ipynb
 
 
P07 Chi-Square Test of Independence with Cramér’s V, Phi (φ), and Pearson’s C.ipynb
P07 Chi-Square Test of Independence with Cramér’s V, Phi (φ), and Pearson’s C.ipynb
 
 
P08 Spearman’s Rank and Pearson Correlation Coefficients.ipynb
P08 Spearman’s Rank and Pearson Correlation Coefficients.ipynb
 
 
P09 Palmer Archipelago (Antarctica) penguin data.ipynb
P09 Palmer Archipelago (Antarctica) penguin data.ipynb
 
 
P10 Regression Assumptions Verification.ipynb
P10 Regression Assumptions Verification.ipynb
 
 
P11 Forward Selection Regression.py
P11 Forward Selection Regression.py
 
 
P12 Backward Selection Regression.py
P12 Backward Selection Regression.py
 
 
RL01 Epsilon Greedy Reinforcement.ipynb
RL01 Epsilon Greedy Reinforcement.ipynb
 
 
RL02 Comparing Different Epsilons & Bernoulli.ipynb
RL02 Comparing Different Epsilons & Bernoulli.ipynb
 
 
RL03 Initial Optimistic Values Reinforcement.ipynb
RL03 Initial Optimistic Values Reinforcement.ipynb
 
 
RL04 UCB1 Reinforcement.ipynb
RL04 UCB1 Reinforcement.ipynb
 
 
RL05 Bernoulli Thompson Sampling (Bayesian) Reinforcement.ipynb
RL05 Bernoulli Thompson Sampling (Bayesian) Reinforcement.ipynb
 
 
RL06 Gaussian Thompson Sampling (Bayesian) Reinforcement.ipynb
RL06 Gaussian Thompson Sampling (Bayesian) Reinforcement.ipynb
 
 
X01 Bayesian Linear Regression.ipynb
X01 Bayesian Linear Regression.ipynb
 
 
advertisement_clicks.csv
advertisement_clicks.csv
 
 
advertising.csv
advertising.csv
 
 
titanic_train.csv
titanic_train.csv
 
 
weight.csv
weight.csv
 
 

About

This project explores data analysis, blending core Probability Theory and Descriptive Statistics with Statistical Inference and Bayesian Machine Learning (Regression/Classification). It concludes with a comparative study of Frequentist vs. Bayesian A/B Testing.

Topics

statistics probability bayesian-methods confidence-intervals ab-testing bayesian-inference hypothesis-testing resampling-methods bayesian-linear-regression inference-statistic-analysis bayesian-naive
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