(2) Reformat markdown cells

2.Clustering/clustering.ipynb

@@ -873,25 +873,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "***Many features have relationships with each other***\n",
-    "- `child_mort` clearly increases when `income`, `gdpp`, and `exports` decreases\n",
+    "> ***Many features have relationships with each other***\n",
+    "> - `child_mort` clearly increases when `income`, `gdpp`, and `exports` decreases\n",
+    "> - Rise in `inflation` also leads to high `child_mort` cases\n",
+    "> - Rise in `exports` clearly increases `gdpp`, `income`, and `imports`\n",
+    "> - Spending on `health` has a small rise in `life_expec` and also decreases the `child_mort`\n",
+    "> - `income`, and `gdpp` display a very high 0.9 correlation value\n",
+    "> - High `income` has lead to higher `life_expec` but decreases the `total_fer`\n",
+    "> - As expected high `inflation` has a negative effect on the financial features\n",
+    "> - High `inflation` displays a high `total_fer` and `child_mort`\n",
+    "> - According to the data, higher `life_expec` displays a low `total_fer`\n",
+    "> - Higher `gdpp` has lead more spending on `health`\n",
     "\n",
-    "- Rise in `inflation` also leads to high `child_mort` cases\n",
-    "- Rise in `exports` clearly increases `gdpp`, `income`, and `imports`\n",
-    "- Spending on `health` has a small rise in `life_expec` and also decreases the `child_mort`\n",
-    "- `income`, and `gdpp` display a very high 0.9 correlation value\n",
-    "- High `income` has lead to higher `life_expec` but decreases the `total_fer`\n",
-    "- As expected high `inflation` has a negative effect on the financial features\n",
-    "- High `inflation` displays a high `total_fer` and `child_mort`\n",
-    "- According to the data, higher `life_expec` displays a low `total_fer`\n",
-    "- Higher `gdpp` has lead more spending on `health`\n",
-    "\n",
-    "***We can clearly see that some features are essentially from the same category and they have the same reaction to other features of different category***\n",
-    "\n",
-    "The 3 categories of the features are:\n",
-    "1. **Health**: `child_mort`, `health`, `life_expec`, `total_fer`\n",
-    "1. **Trade**: `imports`, `exports`\n",
-    "1. **Finance**: `income`, `inflation`, `gdpp`"
+    "> ***We can clearly see that some features are essentially from the same category and they have the same reaction to other features of different category***\n",
+    "> The 3 categories of the features are:\n",
+    "> 1. **Health**: `child_mort`, `health`, `life_expec`, `total_fer`\n",
+    "> 1. **Trade**: `imports`, `exports`\n",
+    "> 1. **Finance**: `income`, `inflation`, `gdpp`"
    ]
   },
   {