diff --git a/SL/aufgaben/workshop3/devenv.nix b/SL/aufgaben/workshop3/devenv.nix index a489fc9..d6838db 100644 --- a/SL/aufgaben/workshop3/devenv.nix +++ b/SL/aufgaben/workshop3/devenv.nix @@ -24,5 +24,6 @@ packages = [ pkgs.graphviz pkgs.zsh + pkgs.zlib ]; } diff --git a/SL/aufgaben/workshop3/src/ws2_feature_engineering.ipynb b/SL/aufgaben/workshop3/src/ws2_feature_engineering.ipynb deleted file mode 100644 index 17ab6e2..0000000 --- a/SL/aufgaben/workshop3/src/ws2_feature_engineering.ipynb +++ /dev/null @@ -1,2023 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ebe6815d", - "metadata": {}, - "source": [ - "# Workshop 2 – Feature Engineering & Datenaufbereitung\n", - "\n", - "Untersuchung des Melbourne Housing Datasets aus ML-Perspektive.\n", - "\n", - "**Punkte:**\n", - "1. Ungeeignete Variablen identifizieren\n", - "2. Missing Values – Strategien pro Variable\n", - "3. Nicht-numerische Variablen – Encoding-Strategien\n", - "4. Anomalien entdecken\n", - "5. Empfehlungen formulieren" - ] - }, - { - "cell_type": "markdown", - "id": "da07c895", - "metadata": {}, - "source": [ - "## 1. Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "c70c47e0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "# plot inline\n", - "%matplotlib inline\n", - "\n", - "# Display-Optionen\n", - "pd.set_option('display.max_columns', None)\n", - "pd.set_option('display.width', 200)\n", - "pd.set_option('display.max_rows', 50)\n", - "\n", - "# Plot-Style\n", - "sns.set_theme(style='whitegrid')\n", - "plt.rcParams['figure.figsize'] = (10, 5)\n", - "\n", - "DATA_PATH = '../data/melb_data.csv'" - ] - }, - { - "cell_type": "markdown", - "id": "9465aa14", - "metadata": {}, - "source": [ - "## 2. Daten laden & erste Sichtung" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9a54f1a6", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape: (18396, 22)\n" - ] - }, - { - "data": { - "text/html": [ - "
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Unnamed: 0SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltCouncilAreaLattitudeLongtitudeRegionnamePropertycount
01Abbotsford85 Turner St2h1480000.0SBiggin3/12/20162.53067.02.01.01.0202.0NaNNaNYarra-37.7996144.9984Northern Metropolitan4019.0
12Abbotsford25 Bloomburg St2h1035000.0SBiggin4/02/20162.53067.02.01.00.0156.079.01900.0Yarra-37.8079144.9934Northern Metropolitan4019.0
24Abbotsford5 Charles St3h1465000.0SPBiggin4/03/20172.53067.03.02.00.0134.0150.01900.0Yarra-37.8093144.9944Northern Metropolitan4019.0
35Abbotsford40 Federation La3h850000.0PIBiggin4/03/20172.53067.03.02.01.094.0NaNNaNYarra-37.7969144.9969Northern Metropolitan4019.0
46Abbotsford55a Park St4h1600000.0VBNelson4/06/20162.53067.03.01.02.0120.0142.02014.0Yarra-37.8072144.9941Northern Metropolitan4019.0
\n", - "
" - ], - "text/plain": [ - " Unnamed: 0 Suburb Address Rooms Type Price Method SellerG Date Distance Postcode Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt CouncilArea Lattitude \\\n", - "0 1 Abbotsford 85 Turner St 2 h 1480000.0 S Biggin 3/12/2016 2.5 3067.0 2.0 1.0 1.0 202.0 NaN NaN Yarra -37.7996 \n", - "1 2 Abbotsford 25 Bloomburg St 2 h 1035000.0 S Biggin 4/02/2016 2.5 3067.0 2.0 1.0 0.0 156.0 79.0 1900.0 Yarra -37.8079 \n", - "2 4 Abbotsford 5 Charles St 3 h 1465000.0 SP Biggin 4/03/2017 2.5 3067.0 3.0 2.0 0.0 134.0 150.0 1900.0 Yarra -37.8093 \n", - "3 5 Abbotsford 40 Federation La 3 h 850000.0 PI Biggin 4/03/2017 2.5 3067.0 3.0 2.0 1.0 94.0 NaN NaN Yarra -37.7969 \n", - "4 6 Abbotsford 55a Park St 4 h 1600000.0 VB Nelson 4/06/2016 2.5 3067.0 3.0 1.0 2.0 120.0 142.0 2014.0 Yarra -37.8072 \n", - "\n", - " Longtitude Regionname Propertycount \n", - "0 144.9984 Northern Metropolitan 4019.0 \n", - "1 144.9934 Northern Metropolitan 4019.0 \n", - "2 144.9944 Northern Metropolitan 4019.0 \n", - "3 144.9969 Northern Metropolitan 4019.0 \n", - "4 144.9941 Northern Metropolitan 4019.0 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv(DATA_PATH)\n", - "print(f'Shape: {df.shape}')\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8f52d4e6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 18396 entries, 0 to 18395\n", - "Data columns (total 22 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Unnamed: 0 18396 non-null int64 \n", - " 1 Suburb 18396 non-null object \n", - " 2 Address 18396 non-null object \n", - " 3 Rooms 18396 non-null int64 \n", - " 4 Type 18396 non-null object \n", - " 5 Price 18396 non-null float64\n", - " 6 Method 18396 non-null object \n", - " 7 SellerG 18396 non-null object \n", - " 8 Date 18396 non-null object \n", - " 9 Distance 18395 non-null float64\n", - " 10 Postcode 18395 non-null float64\n", - " 11 Bedroom2 14927 non-null float64\n", - " 12 Bathroom 14925 non-null float64\n", - " 13 Car 14820 non-null float64\n", - " 14 Landsize 13603 non-null float64\n", - " 15 BuildingArea 7762 non-null float64\n", - " 16 YearBuilt 8958 non-null float64\n", - " 17 CouncilArea 12233 non-null object \n", - " 18 Lattitude 15064 non-null float64\n", - " 19 Longtitude 15064 non-null float64\n", - " 20 Regionname 18395 non-null object \n", - " 21 Propertycount 18395 non-null float64\n", - "dtypes: float64(12), int64(2), object(8)\n", - "memory usage: 3.1+ MB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "284ec9d1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countuniquetopfreqmeanstdmin25%50%75%max
Unnamed: 018396.0NaNNaNNaN11826.7870736800.7104481.05936.7511820.517734.2523546.0
Suburb18396330Reservoir541NaNNaNNaNNaNNaNNaNNaN
Address183961813416 Smith St3NaNNaNNaNNaNNaNNaNNaN
Rooms18396.0NaNNaNNaN2.935040.9582021.02.03.03.012.0
Type183963h12095NaNNaNNaNNaNNaNNaNNaN
Price18396.0NaNNaNNaN1056697.460915641921.6667185000.0633000.0880000.01302000.09000000.0
Method183965S12034NaNNaNNaNNaNNaNNaNNaN
SellerG18396305Nelson2002NaNNaNNaNNaNNaNNaNNaN
Date183965827/05/2017610NaNNaNNaNNaNNaNNaNNaN
Distance18395.0NaNNaNNaN10.3899866.009050.06.39.713.348.1
Postcode18395.0NaNNaNNaN3107.14014795.0009953000.03046.03085.03149.03978.0
Bedroom214927.0NaNNaNNaN2.9130430.9646410.02.03.03.020.0
Bathroom14925.0NaNNaNNaN1.5384920.6893110.01.01.02.08.0
Car14820.0NaNNaNNaN1.615520.9559160.01.02.02.010.0
Landsize13603.0NaNNaNNaN558.1163713987.3265860.0176.5440.0651.0433014.0
BuildingArea7762.0NaNNaNNaN151.220219519.1885960.093.0126.0174.044515.0
YearBuilt8958.0NaNNaNNaN1965.87999637.0132611196.01950.01970.02000.02018.0
CouncilArea1223333Moreland1163NaNNaNNaNNaNNaNNaNNaN
Lattitude15064.0NaNNaNNaN-37.8098490.081152-38.18255-37.8581-37.803625-37.75627-37.40853
Longtitude15064.0NaNNaNNaN144.9963380.106375144.43181144.931193145.00092145.06145.52635
Regionname183958Southern Metropolitan6343NaNNaNNaNNaNNaNNaNNaN
Propertycount18395.0NaNNaNNaN7517.9752654488.416599249.04294.06567.010331.021650.0
\n", - "
" - ], - "text/plain": [ - " count unique top freq mean std min 25% 50% 75% max\n", - "Unnamed: 0 18396.0 NaN NaN NaN 11826.787073 6800.710448 1.0 5936.75 11820.5 17734.25 23546.0\n", - "Suburb 18396 330 Reservoir 541 NaN NaN NaN NaN NaN NaN NaN\n", - "Address 18396 18134 16 Smith St 3 NaN NaN NaN NaN NaN NaN NaN\n", - "Rooms 18396.0 NaN NaN NaN 2.93504 0.958202 1.0 2.0 3.0 3.0 12.0\n", - "Type 18396 3 h 12095 NaN NaN NaN NaN NaN NaN NaN\n", - "Price 18396.0 NaN NaN NaN 1056697.460915 641921.66671 85000.0 633000.0 880000.0 1302000.0 9000000.0\n", - "Method 18396 5 S 12034 NaN NaN NaN NaN NaN NaN NaN\n", - "SellerG 18396 305 Nelson 2002 NaN NaN NaN NaN NaN NaN NaN\n", - "Date 18396 58 27/05/2017 610 NaN NaN NaN NaN NaN NaN NaN\n", - "Distance 18395.0 NaN NaN NaN 10.389986 6.00905 0.0 6.3 9.7 13.3 48.1\n", - "Postcode 18395.0 NaN NaN NaN 3107.140147 95.000995 3000.0 3046.0 3085.0 3149.0 3978.0\n", - "Bedroom2 14927.0 NaN NaN NaN 2.913043 0.964641 0.0 2.0 3.0 3.0 20.0\n", - "Bathroom 14925.0 NaN NaN NaN 1.538492 0.689311 0.0 1.0 1.0 2.0 8.0\n", - "Car 14820.0 NaN NaN NaN 1.61552 0.955916 0.0 1.0 2.0 2.0 10.0\n", - "Landsize 13603.0 NaN NaN NaN 558.116371 3987.326586 0.0 176.5 440.0 651.0 433014.0\n", - "BuildingArea 7762.0 NaN NaN NaN 151.220219 519.188596 0.0 93.0 126.0 174.0 44515.0\n", - "YearBuilt 8958.0 NaN NaN NaN 1965.879996 37.013261 1196.0 1950.0 1970.0 2000.0 2018.0\n", - "CouncilArea 12233 33 Moreland 1163 NaN NaN NaN NaN NaN NaN NaN\n", - "Lattitude 15064.0 NaN NaN NaN -37.809849 0.081152 -38.18255 -37.8581 -37.803625 -37.75627 -37.40853\n", - "Longtitude 15064.0 NaN NaN NaN 144.996338 0.106375 144.43181 144.931193 145.00092 145.06 145.52635\n", - "Regionname 18395 8 Southern Metropolitan 6343 NaN NaN NaN NaN NaN NaN NaN\n", - "Propertycount 18395.0 NaN NaN NaN 7517.975265 4488.416599 249.0 4294.0 6567.0 10331.0 21650.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe(include='all').transpose()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8dc59d74", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Unnamed: 0 18396\n", - "Address 18134\n", - "Longtitude 8168\n", - "Lattitude 7518\n", - "Price 2470\n", - "Landsize 1449\n", - "BuildingArea 613\n", - "Suburb 330\n", - "Propertycount 324\n", - "SellerG 305\n", - "Distance 210\n", - "Postcode 205\n", - "YearBuilt 144\n", - "Date 58\n", - "CouncilArea 33\n", - "Bedroom2 12\n", - "Car 11\n", - "Rooms 11\n", - "Bathroom 9\n", - "Regionname 8\n", - "Method 5\n", - "Type 3\n", - "dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Anzahl unique Werte pro Spalte – wichtig für Encoding-Entscheidungen\n", - "df.nunique().sort_values(ascending=False)" - ] - }, - { - "cell_type": "markdown", - "id": "7262acc9", - "metadata": {}, - "source": [ - "## 3. Profiling-Report\n", - "\n", - "`ydata-profiling` generiert einen umfassenden HTML-Report.\n", - "Bei ~13k Zeilen dauert das ein paar Sekunden." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "90d4c058", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_168904/2895230826.py:1: DeprecationWarning: \n", - " `import ydata_profiling` is deprecated and will not receive more updates. \n", - " Please install fg-data-profiling via `pip install fg-data-profiling` and use `import data_profiling` instead.\n", - " \n", - " from ydata_profiling import ProfileReport\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2c48579dacb347a39be9fc92c2918fe1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Summarize dataset: 0%| | 0/5 [00:00" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from ydata_profiling import ProfileReport\n", - "\n", - "profile = ProfileReport(df, title='Melbourne Housing – Profiling', minimal=False)\n", - "# Inline anzeigen:\n", - "profile.to_notebook_iframe()\n", - "# Alternativ als Datei speichern:\n", - "# profile.to_file('../reports/melb_profiling.html')" - ] - }, - { - "cell_type": "markdown", - "id": "4f5b8d45", - "metadata": {}, - "source": [ - "## 4. Ungeeignete Variablen\n", - "\n", - "**Frage:** Welche Variablen sind von vornherein nicht für ML brauchbar?\n", - "\n", - "**Typische Kandidaten:**\n", - "- IDs / Index-Spalten (keine Information)\n", - "- Freitextfelder mit sehr hoher Kardinalität (≈ n unique)\n", - "- Spalten mit nur einem Wert (keine Varianz)\n", - "- Stark redundante Spalten (z.B. wenn `Bedroom2` ≈ `Rooms`)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "aab43134", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Suburb unique: 330 / 18396 (1.8%)\n", - "Address unique: 18134 / 18396 (98.6%)\n", - "Type unique: 3 / 18396 (0.0%)\n", - "Method unique: 5 / 18396 (0.0%)\n", - "SellerG unique: 305 / 18396 (1.7%)\n", - "Date unique: 58 / 18396 (0.3%)\n", - "CouncilArea unique: 33 / 18396 (0.2%)\n", - "Regionname unique: 8 / 18396 (0.0%)\n" - ] - } - ], - "source": [ - "# Kardinalitäts-Check für object-Spalten\n", - "cat_cols = df.select_dtypes(include='object').columns\n", - "for col in cat_cols:\n", - " print(f'{col:15s} unique: {df[col].nunique():6d} / {len(df)} ({df[col].nunique()/len(df)*100:.1f}%)')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "78315e42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "np.float64(0.7742444009567298)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Redundanz-Check: Rooms vs Bedroom2\n", - "(df['Rooms'] == df['Bedroom2']).mean() # Anteil identischer Werte" - ] - }, - { - "cell_type": "markdown", - "id": "ea0fa2b9", - "metadata": {}, - "source": [ - "**Beobachtungen:**\n", - "\n", - "- Bedroom2 löschen, Rooms behalten (oder umgekehrt)\n", - " - Begründung: Multikollinearität, identische Information" - ] - }, - { - "cell_type": "markdown", - "id": "c8497e33", - "metadata": {}, - "source": [ - "## 5. Missing Values\n", - "\n", - "**Strategien:**\n", - "\n", - "| Strategie | Wann |\n", - "|---|---|\n", - "| Zeile droppen | wenige Missings, MCAR |\n", - "| Spalte droppen | >50% fehlen, wenig Info |\n", - "| Mean/Median imputieren | numerisch |\n", - "| Mode imputieren | kategorial |\n", - "| Konstanter Wert | \"fehlt\" hat Bedeutung |\n", - "| KNN/Iterative | komplexere Muster |\n", - "| Indikator-Spalte | \"fehlt\" ist Information |" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "37efa106", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countpct
BuildingArea1063457.806045
YearBuilt943851.304631
CouncilArea616333.501848
Landsize479326.054577
Car357619.439008
Bathroom347118.868232
Bedroom2346918.857360
Lattitude333218.112633
Longtitude333218.112633
Distance10.005436
Propertycount10.005436
Postcode10.005436
Regionname10.005436
\n", - "
" - ], - "text/plain": [ - " count pct\n", - "BuildingArea 10634 57.806045\n", - "YearBuilt 9438 51.304631\n", - "CouncilArea 6163 33.501848\n", - "Landsize 4793 26.054577\n", - "Car 3576 19.439008\n", - "Bathroom 3471 18.868232\n", - "Bedroom2 3469 18.857360\n", - "Lattitude 3332 18.112633\n", - "Longtitude 3332 18.112633\n", - "Distance 1 0.005436\n", - "Propertycount 1 0.005436\n", - "Postcode 1 0.005436\n", - "Regionname 1 0.005436" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Missing Values – absolut und prozentual\n", - "missing = pd.DataFrame({\n", - " 'count': df.isna().sum(),\n", - " 'pct': df.isna().mean() * 100\n", - "}).sort_values('pct', ascending=False)\n", - "missing[missing['count'] > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5fc4f4e3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualisierung: Missing-Pattern\n", - "fig, ax = plt.subplots(figsize=(12, 4))\n", - "sns.heatmap(df.isna().T, cbar=False, cmap='viridis', ax=ax)\n", - "ax.set_title('Missing Values Pattern')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6c427b29", - "metadata": {}, - "source": [ - "**Empfehlungen pro Spalte:**\n", - "\n", - "| Spalte | Missing % | Strategie | Begründung |\n", - "|---|---|---|---|\n", - "| _BuildingArea_ | _?_ | _?_ | _?_ |\n", - "| _YearBuilt_ | _?_ | _?_ | _?_ |\n", - "| _..._ | | | |" - ] - }, - { - "cell_type": "markdown", - "id": "8882c19a", - "metadata": {}, - "source": [ - "## 6. Kategoriale Variablen – Encoding\n", - "\n", - "**Strategien:**\n", - "\n", - "| Strategie | Wann |\n", - "|---|---|\n", - "| Label-Encoding | ordinal mit Reihenfolge |\n", - "| One-Hot | nominal, wenige Kategorien (<~15) |\n", - "| Target-Encoding | hohe Kardinalität, Vorsicht Leakage |\n", - "| Frequency-Encoding | hohe Kardinalität, einfach |\n", - "| Drop | unbrauchbar (z.B. Address) |" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "89e9b5a7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Suburb (330 unique) – Top 5 ===\n", - "Suburb\n", - "Reservoir 541\n", - "Bentleigh East 368\n", - "Richmond 333\n", - "Preston 312\n", - "Brunswick 286\n", - "Name: count, dtype: int64\n", - "\n", - "=== Address (18134 unique) – Top 5 ===\n", - "Address\n", - "16 Smith St 3\n", - "2 Bruce St 3\n", - "36 Aberfeldie St 3\n", - "5 Margaret St 3\n", - "14 Arthur St 3\n", - "Name: count, dtype: int64\n", - "\n", - "=== Type (3 unique) ===\n", - "Type\n", - "h 12095\n", - "u 4296\n", - "t 2005\n", - "Name: count, dtype: int64\n", - "\n", - "=== Method (5 unique) ===\n", - "Method\n", - "S 12034\n", - "SP 2349\n", - "PI 2189\n", - "VB 1696\n", - "SA 128\n", - "Name: count, dtype: int64\n", - "\n", - "=== SellerG (305 unique) – Top 5 ===\n", - "SellerG\n", - "Nelson 2002\n", - "Jellis 1759\n", - "hockingstuart 1580\n", - "Barry 1390\n", - "Ray 1032\n", - "Name: count, dtype: int64\n", - "\n", - "=== Date (58 unique) – Top 5 ===\n", - "Date\n", - "27/05/2017 610\n", - "23/09/2017 591\n", - "16/09/2017 546\n", - "3/06/2017 525\n", - "26/08/2017 523\n", - "Name: count, dtype: int64\n", - "\n", - "=== CouncilArea (33 unique) – Top 5 ===\n", - "CouncilArea\n", - "Moreland 1163\n", - "Boroondara 1160\n", - "Moonee Valley 999\n", - "Darebin 934\n", - "Glen Eira 848\n", - "Name: count, dtype: int64\n", - "\n", - "=== Regionname (8 unique) ===\n", - "Regionname\n", - "Southern Metropolitan 6343\n", - "Northern Metropolitan 5307\n", - "Western Metropolitan 3887\n", - "Eastern Metropolitan 1995\n", - "South-Eastern Metropolitan 680\n", - "Eastern Victoria 78\n", - "Northern Victoria 63\n", - "Western Victoria 42\n", - "Name: count, dtype: int64\n" - ] - } - ], - "source": [ - "# Kategorien-Verteilung der object-Spalten\n", - "for col in cat_cols:\n", - " n = df[col].nunique()\n", - " if n <= 15:\n", - " print(f'\\n=== {col} ({n} unique) ===')\n", - " print(df[col].value_counts())\n", - " else:\n", - " print(f'\\n=== {col} ({n} unique) – Top 5 ===')\n", - " print(df[col].value_counts().head())" - ] - }, - { - "cell_type": "markdown", - "id": "c4de543c", - "metadata": {}, - "source": [ - "**Empfehlungen pro Spalte:**\n", - "\n", - "| Spalte | Kardinalität | Strategie | Begründung |\n", - "|---|---|---|---|\n", - "| _Type_ | _3_ | _One-Hot_ | _wenige Kategorien_ |\n", - "| _..._ | | | |" - ] - }, - { - "cell_type": "markdown", - "id": "ee6cfe9a", - "metadata": {}, - "source": [ - "## 7. Anomalien & Outlier\n", - "\n", - "**Was suchen:**\n", - "- numerische Outlier (Boxplot, IQR, z-Score)\n", - "- inkonsistente Werte (z.B. `BuildingArea > Landsize`)\n", - "- unmögliche Werte (negativer Preis, Baujahr 1196)\n", - "- Duplikate" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "5eccb0f7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vollständige Duplikate: 0\n" - ] - } - ], - "source": [ - "# Duplikate\n", - "print(f'Vollständige Duplikate: {df.duplicated().sum()}')" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "a8e6b3d5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Boxplots numerischer Variablen\n", - "num_cols = df.select_dtypes(include=[np.number]).columns.drop('Unnamed: 0', errors='ignore')\n", - "\n", - "n = len(num_cols)\n", - "nrows = (n + 2) // 3\n", - "fig, axes = plt.subplots(nrows=nrows, ncols=3, figsize=(15, 4*nrows))\n", - "for ax, col in zip(axes.flat, num_cols):\n", - " sns.boxplot(x=df[col], ax=ax)\n", - " ax.set_title(col)\n", - "for ax in axes.flat[n:]:\n", - " ax.axis('off')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "864d7618", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BuildingArea > Landsize 1325\n", - "BuildingArea == 0 24\n", - "Landsize == 0 1942\n", - "YearBuilt < 1800 1\n", - "YearBuilt > 2018 0\n", - "Koordinaten ausserhalb Melbourne: 0\n" - ] - }, - { - "data": { - "text/plain": [ - "BuildingArea\n", - "120.0 119\n", - "100.0 96\n", - "110.0 96\n", - "130.0 90\n", - "115.0 87\n", - "150.0 82\n", - "112.0 74\n", - "140.0 72\n", - "90.0 72\n", - "104.0 70\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Sanity-Checks\n", - "checks = {\n", - " 'BuildingArea > Landsize': ((df['BuildingArea'] > df['Landsize']) & df['Landsize'].notna()).sum(),\n", - " 'BuildingArea == 0': (df['BuildingArea'] == 0).sum(),\n", - " 'Landsize == 0': (df['Landsize'] == 0).sum(),\n", - " 'YearBuilt < 1800': (df['YearBuilt'] < 1800).sum(),\n", - " 'YearBuilt > 2018': (df['YearBuilt'] > 2018).sum(),\n", - "}\n", - "for name, count in checks.items():\n", - " print(f'{name:30s} {count}')\n", - " \n", - "# Gleiche Adresse mehrfach verkauft?\n", - "df[df.duplicated(subset=['Address'], keep=False)].sort_values('Address').head(20)\n", - "\n", - "# Melbourne liegt ca. bei Lat -37 bis -38, Lon 144 bis 146\n", - "df[['Lattitude', 'Longtitude']].describe()\n", - "weird = df[(df['Lattitude'] > -37) | (df['Lattitude'] < -39) |\n", - " (df['Longtitude'] < 144) | (df['Longtitude'] > 146)]\n", - "print(f'Koordinaten ausserhalb Melbourne: {len(weird)}')\n", - "\n", - "# spikes\n", - "df['Landsize'].value_counts().head(10)\n", - "df['BuildingArea'].value_counts().head(10)" - ] - }, - { - "cell_type": "markdown", - "id": "fa91da02", - "metadata": {}, - "source": [ - "**Gefundene Anomalien:**\n", - "\n", - "- BuildingArea > Landsize: 1325 Zeilen\n", - " → erklärbar für Apartments/Mehrstöcker, bei Houses verdächtig\n", - "- BuildingArea == 0: 24 Zeilen → als NaN markieren\n", - "- Landsize == 0: 1942 Zeilen → plausibel für Units, sonst NaN\n", - "- YearBuilt < 1800: 1 Zeile → wahrscheinlich Tippfehler, droppen oder korrigieren\n", - "- Price stark right-skewed → Log-Transform empfohlen\n", - "- Keine vollständigen Duplikate, aber Adressen tauchen mehrfach auf\n", - " (legitime Wiederverkäufe)" - ] - }, - { - "cell_type": "markdown", - "id": "54c5cd65", - "metadata": {}, - "source": [ - "## 8. Empfehlungen für Datenaufbereitung\n", - "\n", - "**Drop:**\n", - "- _Spalten, die wegfallen_\n", - "\n", - "**Imputation:**\n", - "- _Spalte → Strategie_\n", - "\n", - "**Encoding:**\n", - "- _Spalte → Strategie_\n", - "\n", - "**Outlier-Behandlung:**\n", - "- _Strategie (Cap, Drop, Log-Transform...)_\n", - "\n", - "**Weitere Hinweise:**\n", - "- _z.B. Date in datetime konvertieren_\n", - "- _Lat/Long als Paar verwenden_" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}