Update Bonus Calculation Method to Use Min-Max Scaling

microsoft · Sep 23, 2023 · c8932c0 · c8932c0
1 parent f904ef1
commit c8932c0
Showing 1 changed file with 4 additions and 4 deletions.
diff --git a/1-Introduction/04-stats-and-probability/notebook.ipynb b/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -830,7 +830,7 @@
  "source": [
  "## Correlation and Evil Baseball Corp\n",
  "\n",
- "Correlation allows us to find relations between data sequences. In our toy example, let's pretend there is an evil baseball corporation that pays its players according to their height - the taller the player is, the more money he/she gets. Suppose there is a base salary of $1000, and an additional bonus from $0 to $100, depending on height. We will take the real players from MLB, and compute their imaginary salaries:"
+ "Correlation allows us to find relations between data sequences. In our toy example, let's pretend there is an evil baseball corporation that pays its players according to their height - the taller the player is, the more money he/she gets. Suppose there is a base salary of $1000, and an additional bonus from $0 to $100, depending on height. We will take the real players from MLB, and compute their imaginary salaries(based on Min-Max scaling):"
  ]
  },
  {
@@ -848,7 +848,7 @@
  ],
  "source": [
  "heights = df['Height']\n",
- "salaries = 1000+(heights-heights.min())/(heights.max()-heights.mean())*100\n",
+ "salaries = 1000+(heights-heights.min())/(heights.max()-heights.min())*100\n",
  "print(list(zip(heights, salaries))[:10])"
  ]
  },
@@ -935,7 +935,7 @@
  }
  ],
  "source": [
- "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.mean()))*100\n",
+ "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.min()))*100\n",
  "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
  ]
  },
@@ -960,7 +960,7 @@
  }
  ],
  "source": [
- "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.mean()))*100+np.random.random(size=len(heights))*20-10\n",
+ "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.min()))*100+np.random.random(size=len(heights))*20-10\n",
  "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
  ]
  },