feat: add beginner-friendly solutions and educational flashcards

Cheatsheats/plotting.md

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+| Feature Type | Target Type         | What you want to see                   | Plot to use         | What it tells you (in simple words)  | Python example                                  |
+| ------------ | ------------------- | -------------------------------------- | ------------------- | ------------------------------------ | ----------------------------------------------- |
+| Number       | Number              | Does feature increase/decrease target? | Scatter             | Shows upward/downward pattern        | `sns.scatterplot(x='age', y='salary', data=df)` |
+| Number       | Number              | Are there extreme values?              | Scatter             | Dots far away = outliers             | `sns.scatterplot(x='age', y='salary', data=df)` |
+| Number       | Number              | Is data spread normal or skewed?       | Histogram           | Shows shape of data                  | `sns.histplot(df['age'], kde=True)`             |
+| Number       | Number              | Compare many numeric features at once  | Heatmap             | Which feature relates most to target | `sns.heatmap(df.corr(), annot=True)`            |
+| Number       | Class (0/1, Yes/No) | Do classes look different?             | Box plot            | If boxes separate → feature useful   | `sns.boxplot(x='class', y='age', data=df)`      |
+| Number       | Class               | How dense values are per class         | Violin plot         | Shows distribution per class         | `sns.violinplot(x='class', y='age', data=df)`   |
+| Category     | Number              | Which category has higher target?      | Bar plot (mean)     | Shows average target per category    | `sns.barplot(x='city', y='sales', data=df)`     |
+| Category     | Number              | Which category appears most?           | Count plot          | Shows frequency                      | `sns.countplot(x='city', data=df)`              |
+| Category     | Class               | Relation between two categories        | Count plot with hue | Shows class split per category       | `sns.countplot(x='gender', hue='buy', data=df)` |
+| Many Numbers | Number/Class        | Overall relationship view              | Pairplot            | Quick scan of all relations          | `sns.pairplot(df, hue='class')`                 |

Cheatsheats/testing.md

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+| Test                     | Purpose (What it checks)                          | Data Type                           | Key Assumptions                                       | Python (scipy/statsmodels)                                         |
+| ------------------------ | ------------------------------------------------- | ----------------------------------- | ----------------------------------------------------- | ------------------------------------------------------------------ |
+| **One-sample t-test**    | Compare sample mean to a known value              | Numeric                             | Normal data                                           | `stats.ttest_1samp(x, μ)`                                          |
+| **Independent t-test**   | Compare means of 2 independent groups             | Numeric (2 groups)                  | Normal, equal variance                                | `stats.ttest_ind(a, b)`                                            |
+| **Paired t-test**        | Compare same group before vs after                | Numeric (paired)                    | Normal differences                                    | `stats.ttest_rel(a, b)`                                            |
+| **One-way ANOVA**        | Compare means of 3+ groups                        | Numeric (3+ groups)                 | Normal, equal variance                                | `stats.f_oneway(g1, g2, g3)`                                       |
+| **One-way ANOVA (OLS)**  | Check if **mean age differs across classes**      | Numeric (age) + categorical (class) | Normal residuals, equal variance, independent samples | `ols('age ~ class', data=df).fit()` + `sm.stats.anova_lm(model)`   |
+| **Two-way ANOVA**        | Effect of 2 factors on mean                       | Numeric + categorical               | Normal, equal variance                                | `statsmodels.formula.api.ols()`                                    |
+| **Mann–Whitney U**       | 2 groups, non-normal                              | Ordinal/Numeric                     | Independent                                           | `stats.mannwhitneyu(a, b)`                                         |
+| **Wilcoxon test**        | Paired, non-normal                                | Ordinal/Numeric                     | Paired                                                | `stats.wilcoxon(a, b)`                                             |
+| **Kruskal–Wallis**       | 3+ groups, non-normal                             | Ordinal/Numeric                     | Independent                                           | `stats.kruskal(g1, g2, g3)`                                        |
+| **Chi-square test**      | Relationship between categories                   | Categorical                         | Expected freq > 5                                     | `stats.chi2_contingency(table)`                                    |
+| **Fisher’s Exact**       | Small categorical samples                         | Categorical                         | 2×2 table                                             | `stats.fisher_exact(table)`                                        |
+| **Pearson correlation**  | Linear relation between 2 vars                    | Numeric                             | Normal, linear                                        | `stats.pearsonr(x, y)`                                             |
+| **Spearman correlation** | Rank-based relation                               | Ordinal/Numeric                     | Monotonic                                             | `stats.spearmanr(x, y)`                                            |
+| **Linear regression**    | Predict Y from X                                  | Numeric                             | Linearity, normal errors                              | `stats.linregress(x, y)`                                           |
+| **Logistic regression**  | Predict binary outcome                            | Numeric + categorical               | Independent                                           | `sklearn.linear_model.LogisticRegression()`                        |
+| **Shapiro-Wilk**         | Test for normality                                | Numeric                             | Random sample                                         | `stats.shapiro(x)`                                                 |
+| **Kolmogorov–Smirnov**   | Compare to a distribution                         | Numeric                             | Continuous                                            | `stats.kstest(x, 'norm')`                                          |
+| **Levene’s test**        | Test equal variance                               | Numeric                             | Independent                                           | `stats.levene(a, b)`                                               |
+| **Tukey’s HSD**          | Find **which specific groups differ** after ANOVA | Numeric + categorical (3+ groups)   | Normal data, equal variance, independent groups       | `statsmodels.stats.multicomp.pairwise_tukeyhsd(endog=y, groups=g)` |