renamed directory, created mad method python file in tools

2025-12-10 19:29:18 +01:00
parent 2ee8b96b22
commit c7295f310c
2 changed files with 23 additions and 0 deletions
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e790b157",
+   "metadata": {},
+   "source": [
+    "Im folgenden wird auf die Daten das MAD Outlier removal angewendet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bd7c061",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "\n",
+    "def mad_outlier_removal(df, columns, threshold=3.5):\n",
+    "    \"\"\"\n",
+    "    Entfernt Ausreißer basierend auf Median Absolute Deviation (MAD).\n",
+    "    threshold: typischer Wert ist 3.5 (entspricht robustem Z-Score Cutoff).\n",
+    "    \"\"\"\n",
+    "    df_clean = df.copy()\n",
+    "    for col in columns:\n",
+    "        median = df_clean[col].median()\n",
+    "        mad = np.median(np.abs(df_clean[col] - median))\n",
+    "        if mad == 0:\n",
+    "            continue  # keine Streuung, keine Ausreißer\n",
+    "        robust_z = 0.6745 * (df_clean[col] - median) / mad\n",
+    "        mask = np.abs(robust_z) <= threshold\n",
+    "        df_clean = df_clean[mask]\n",
+    "    return df_clean"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}