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Sasza Stanczew 2025-04-09 10:28:19 +02:00
parent a74b08c68a
commit 1c35aeca30
7 changed files with 81087 additions and 25 deletions
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285
PythonAI/JupyterLab/Cwiczenie.ipynb → PythonAI/JupyterLab/Cwiczenie1.ipynb
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@ -5,12 +5,20 @@
"id": "3b43758b-7204-4cd1-b0dc-9a2ce7926ac7",
"metadata": {},
"source": [
"# Ćwiczenie"
"# Ćwiczenie 1"
]
},
{
"cell_type": "markdown",
"id": "c0538a1b-8b08-449c-87c1-8469e6e72197",
"metadata": {},
"source": [
"## Wczytaj dane"
]
},
{
"cell_type": "code",
"execution_count": 202,
"execution_count": 46,
"id": "178ae645-cad9-491c-9a26-c173eda34a00",
"metadata": {},
"outputs": [],
@ -21,7 +29,7 @@
},
{
"cell_type": "code",
"execution_count": 203,
"execution_count": 47,
"id": "540f9f69-d932-4b5a-8d70-e18a08bdcf46",
"metadata": {},
"outputs": [
@ -237,7 +245,7 @@
"[481 rows x 10 columns]"
]
},
"execution_count": 203,
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@ -250,9 +258,17 @@
"df"
]
},
{
"cell_type": "markdown",
"id": "1c107b82-f121-4bfb-8933-3b4882fb988f",
"metadata": {},
"source": [
"## Clean inclement_weather"
]
},
{
"cell_type": "code",
"execution_count": 204,
"execution_count": 48,
"id": "62e61e9e-f06e-4124-9a89-1979dc071c47",
"metadata": {},
"outputs": [
@ -480,7 +496,7 @@
"[481 rows x 11 columns]"
]
},
"execution_count": 204,
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@ -495,9 +511,17 @@
"df"
]
},
{
"cell_type": "markdown",
"id": "cf3c59c1-ae36-45bd-8b8f-de4f616a392a",
"metadata": {},
"source": [
"## Remove SNWD"
]
},
{
"cell_type": "code",
"execution_count": 205,
"execution_count": 49,
"id": "e06e7a55-1837-456b-97ef-4c58276bd7b6",
"metadata": {},
"outputs": [
@ -597,7 +621,7 @@
" <td>2018-01-05</td>\n",
" <td>?</td>\n",
" <td>0.3</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>5505.0</td>\n",
" <td>-40.0</td>\n",
" <td>NaN</td>\n",
@ -639,7 +663,7 @@
" <td>2018-12-29</td>\n",
" <td>?</td>\n",
" <td>21.3</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>5505.0</td>\n",
" <td>-40.0</td>\n",
" <td>NaN</td>\n",
@ -653,7 +677,7 @@
" <td>2018-12-30</td>\n",
" <td>?</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>5505.0</td>\n",
" <td>-40.0</td>\n",
" <td>NaN</td>\n",
@ -701,11 +725,11 @@
"1 2018-01-02 GHCND:USC00280907 0.0 0.0 -8.3 -16.1 -12.2 NaN \n",
"2 2018-01-03 GHCND:USC00280907 0.0 0.0 -4.4 -13.9 -13.3 NaN \n",
"3 2018-01-04 ? 20.6 229.0 5505.0 -40.0 NaN 19.3 \n",
"4 2018-01-05 ? 0.3 0.0 5505.0 -40.0 NaN NaN \n",
"4 2018-01-05 ? 0.3 NaN 5505.0 -40.0 NaN NaN \n",
".. ... ... ... ... ... ... ... ... \n",
"476 2018-12-28 GHCND:USC00280907 11.7 0.0 6.1 -1.7 5.0 NaN \n",
"477 2018-12-29 ? 21.3 0.0 5505.0 -40.0 NaN NaN \n",
"478 2018-12-30 ? 0.0 0.0 5505.0 -40.0 NaN NaN \n",
"477 2018-12-29 ? 21.3 NaN 5505.0 -40.0 NaN NaN \n",
"478 2018-12-30 ? 0.0 NaN 5505.0 -40.0 NaN NaN \n",
"479 2018-12-31 GHCND:USC00280907 0.0 0.0 3.3 -3.3 -2.8 NaN \n",
"480 2018-12-31 ? 0.0 0.0 5505.0 -40.0 NaN NaN \n",
"\n",
@ -725,7 +749,7 @@
"[481 rows x 11 columns]"
]
},
"execution_count": 205,
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@ -739,9 +763,17 @@
"df"
]
},
{
"cell_type": "markdown",
"id": "b0b5005b-ad90-4164-83ad-8e4030684820",
"metadata": {},
"source": [
"## Replace NaN with 0"
]
},
{
"cell_type": "code",
"execution_count": 213,
"execution_count": 50,
"id": "389f0c22-b210-45f3-9627-02c06d255a69",
"metadata": {},
"outputs": [],
@ -751,9 +783,17 @@
"df['WESF'] = df['WESF'].fillna(0)"
]
},
{
"cell_type": "markdown",
"id": "ba7a7e21-0361-49c3-a7ca-eea9c9407758",
"metadata": {},
"source": [
"## Split data into two different tables"
]
},
{
"cell_type": "code",
"execution_count": 214,
"execution_count": 51,
"id": "60362a3d-91ab-4666-bf26-afdeb3cd5c9c",
"metadata": {},
"outputs": [],
@ -765,7 +805,7 @@
},
{
"cell_type": "code",
"execution_count": 215,
"execution_count": 52,
"id": "a68dd0eb-6085-483b-99f3-a540a35515c2",
"metadata": {},
"outputs": [],
@ -781,7 +821,7 @@
},
{
"cell_type": "code",
"execution_count": 216,
"execution_count": 53,
"id": "bb4ecdab-2136-4970-a38d-6eaec8ec92ae",
"metadata": {},
"outputs": [
@ -1009,7 +1049,7 @@
"[481 rows x 11 columns]"
]
},
"execution_count": 216,
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
@ -1020,7 +1060,7 @@
},
{
"cell_type": "code",
"execution_count": 217,
"execution_count": 54,
"id": "7c8199c4-9d1b-405a-9cda-b2aef1616999",
"metadata": {},
"outputs": [
@ -1161,7 +1201,7 @@
"std 0.159067 0.0 0.159067 "
]
},
"execution_count": 217,
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
@ -1172,7 +1212,7 @@
},
{
"cell_type": "code",
"execution_count": 218,
"execution_count": 55,
"id": "7468e63c-c72b-4150-bd40-6f6e61f17af8",
"metadata": {},
"outputs": [
@ -1341,7 +1381,7 @@
"std 0.176697 "
]
},
"execution_count": 218,
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
@ -1350,10 +1390,207 @@
"df_temp.describe()"
]
},
{
"cell_type": "markdown",
"id": "ad025f7d-3200-47b1-8cc2-4a53faa6da3b",
"metadata": {},
"source": [
"## Join the tables"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "efa6a165-9988-420f-a2f0-10cbbcd5dc39",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TMAX</th>\n",
" <th>TMIN</th>\n",
" <th>TOBS</th>\n",
" <th>WESF</th>\n",
" <th>SNOW</th>\n",
" <th>PRCP</th>\n",
" <th>incl_weather_true</th>\n",
" <th>incl_weather_false</th>\n",
" <th>snow</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>157.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>16.004819</td>\n",
" <td>6.371486</td>\n",
" <td>8.712048</td>\n",
" <td>0.364968</td>\n",
" <td>4.080321</td>\n",
" <td>8.360241</td>\n",
" <td>0.048193</td>\n",
" <td>0.967871</td>\n",
" <td>0.048193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>11.000615</td>\n",
" <td>10.157809</td>\n",
" <td>9.936468</td>\n",
" <td>2.679477</td>\n",
" <td>26.325536</td>\n",
" <td>15.718945</td>\n",
" <td>0.249368</td>\n",
" <td>0.176697</td>\n",
" <td>0.249368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-11.700000</td>\n",
" <td>-17.200000</td>\n",
" <td>-16.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6.700000</td>\n",
" <td>-1.700000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>14.400000</td>\n",
" <td>5.600000</td>\n",
" <td>8.300000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.300000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>26.100000</td>\n",
" <td>15.600000</td>\n",
" <td>17.800000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.600000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>35.000000</td>\n",
" <td>23.900000</td>\n",
" <td>26.100000</td>\n",
" <td>28.700000</td>\n",
" <td>279.000000</td>\n",
" <td>79.200000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TMAX TMIN TOBS WESF SNOW PRCP \\\n",
"count 249.000000 249.000000 249.000000 157.000000 249.000000 249.000000 \n",
"mean 16.004819 6.371486 8.712048 0.364968 4.080321 8.360241 \n",
"std 11.000615 10.157809 9.936468 2.679477 26.325536 15.718945 \n",
"min -11.700000 -17.200000 -16.100000 0.000000 0.000000 0.000000 \n",
"25% 6.700000 -1.700000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 14.400000 5.600000 8.300000 0.000000 0.000000 0.300000 \n",
"75% 26.100000 15.600000 17.800000 0.000000 0.000000 8.600000 \n",
"max 35.000000 23.900000 26.100000 28.700000 279.000000 79.200000 \n",
"\n",
" incl_weather_true incl_weather_false snow \n",
"count 249.000000 249.000000 249.000000 \n",
"mean 0.048193 0.967871 0.048193 \n",
"std 0.249368 0.176697 0.249368 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 1.000000 0.000000 \n",
"50% 0.000000 1.000000 0.000000 \n",
"75% 0.000000 1.000000 0.000000 \n",
"max 2.000000 1.000000 2.000000 "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_merged = df_temp.set_index('date').join(df_snow.set_index('date'), lsuffix=\"_temp\", rsuffix=\"_snow\")\n",
"df_merged[\"SNOW\"] = df_merged.SNOW_temp.fillna(0) + df_merged.SNOW_snow.fillna(0)\n",
"df_merged[\"PRCP\"] = df_merged.PRCP_temp.fillna(0) + df_merged.PRCP_snow.fillna(0)\n",
"df_merged[\"incl_weather_true\"] = df_merged.incl_weather_true_temp.fillna(0) + df_merged.incl_weather_true_snow.fillna(0)\n",
"df_merged[\"incl_weather_false\"] = df_merged.incl_weather_false_temp.fillna(0) + df_merged.incl_weather_false_snow.fillna(0)\n",
"df_merged[\"snow\"] = df_merged.snow_temp.fillna(0) + df_merged.snow_snow.fillna(0)\n",
"\n",
"del df_merged[\"PRCP_temp\"]\n",
"del df_merged[\"PRCP_snow\"]\n",
"del df_merged[\"incl_weather_false_temp\"]\n",
"del df_merged[\"incl_weather_false_snow\"]\n",
"del df_merged[\"incl_weather_true_temp\"]\n",
"del df_merged[\"incl_weather_true_snow\"]\n",
"del df_merged[\"SNOW_temp\"]\n",
"del df_merged[\"SNOW_snow\"]\n",
"del df_merged[\"snow_temp\"]\n",
"del df_merged[\"snow_snow\"]\n",
"\n",
"\n",
"df_merged.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "efa6a165-9988-420f-a2f0-10cbbcd5dc39",
"id": "4d3b7d61-d247-49f3-a643-6fd924727cdd",
"metadata": {},
"outputs": [],
"source": []

2
PythonAI/JupyterLab/PythonAI_02.ipynb
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@ -4459,7 +4459,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
"version": "3.12.2"
}
},
"nbformat": 4,

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PythonAI/JupyterLab/PythonAI_3.ipynb Normal file
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PythonAI/JupyterLab/PythonAI_4.ipynb Normal file
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@ -0,0 +1,376 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a5fd2a1b-ac9e-474a-a04b-d583a48d3b3f",
"metadata": {},
"source": [
"# Dzień 4"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6a53b426-a1c5-485b-807c-02c3bddb5a85",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "5cdb50f3-a713-4f9d-b58d-5169529cc736",
"metadata": {},
"source": [
"## Najpopularniejsze modele uczenia maszynowego\n",
"### Regresja liniowa\n",
" - model przewiduje wartość liczbową na podstawie wartości wejściowych\n",
"### Regresja logistyczna\n",
" - mimo nazwy, służy głownie do klasyfikacji binarnej - prawdopodobieństwo przynależności do grupy\n",
" - np. w jakim stopniu prawdopodobieństwa dane objawy pasują do choroby, albo dane finansowe pasują do grupy \"dostanie kredyt\"\n",
" - \n",
"## Drzewa decyzyjne i last losowe\n",
"### Drzewo decyzyjne\n",
" - struktura przypominająca drzewo, gdzie każdy węzeł to pytanie o dane a gaęzie to odpowiedzi na to pytanie\n",
"### Las losowy\n",
" - grupa wielu drzew decyzyjnych, gdzie każde drzewo jest uczone na losowym podzbiorze cech\n",
" - końcowa decyzja jest wynikiem głosowania drzew, co poprawia dokładność\n",
"### Gradient boosting\n",
" - technika polegająca na tym, że bierzemy słabe modele (zwykle drzewa) i łączymy z innymi modelami, które koncentrują się na poprawie błędów poprzenich modeli\n",
"\n",
"## Wzmacnianie \n",
" - środowisko - przestreń, w której agent działa\n",
" - agent - autonomiczny program, który podejmuje decyzje na podstawie środowiska\n",
" - nagroda - sygnał informujący agenta o jakości akcji\n",
" - akcja - działanie podjęte przez agenta, które wpływa na środowisko\n",
"Przykład: Środowisko to plansza, duchy, owoce i tabletki, nagroda to owoce, agent to PacMan, akcja to poruszanie się.\n",
"\n",
"## Support Vector Machines - SVM \n",
" - potężne narzędzie do klasyfikacji\n",
"\n",
"## Metoda KNN\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4dbc4438-13fa-429d-894e-ffaf184f2fdb",
"metadata": {},
"outputs": [
{
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" <td>2018-12-31T00:00:00</td>\n",
" <td>WT01</td>\n",
" <td>GHCND:USW00094789</td>\n",
" <td>,,W,</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78779</th>\n",
" <td>2018-12-31T00:00:00</td>\n",
" <td>WT02</td>\n",
" <td>GHCND:USW00094789</td>\n",
" <td>,,W,</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>78780 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" date datatype station attributes value\n",
"0 2018-01-01T00:00:00 PRCP GHCND:US1CTFR0039 ,,N, 0.0\n",
"1 2018-01-01T00:00:00 PRCP GHCND:US1NJBG0015 ,,N, 0.0\n",
"2 2018-01-01T00:00:00 SNOW GHCND:US1NJBG0015 ,,N, 0.0\n",
"3 2018-01-01T00:00:00 PRCP GHCND:US1NJBG0017 ,,N, 0.0\n",
"4 2018-01-01T00:00:00 SNOW GHCND:US1NJBG0017 ,,N, 0.0\n",
"... ... ... ... ... ...\n",
"78775 2018-12-31T00:00:00 WDF5 GHCND:USW00094789 ,,W, 130.0\n",
"78776 2018-12-31T00:00:00 WSF2 GHCND:USW00094789 ,,W, 9.8\n",
"78777 2018-12-31T00:00:00 WSF5 GHCND:USW00094789 ,,W, 12.5\n",
"78778 2018-12-31T00:00:00 WT01 GHCND:USW00094789 ,,W, 1.0\n",
"78779 2018-12-31T00:00:00 WT02 GHCND:USW00094789 ,,W, 1.0\n",
"\n",
"[78780 rows x 5 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('data/nyc_weather_2018.csv')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5987fc03-1a75-45bb-b154-7fb1d8c5c1e9",
"metadata": {},
"outputs": [],
"source": [
"iris = pd.read_csv('data/iris.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1edbb5f7-577a-4460-886e-482464157465",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.colors import ListedColormap\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# To check recent matplotlib compatibility\n",
"import matplotlib\n",
"from distutils.version import LooseVersion\n",
"\n",
"\n",
"def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n",
"\n",
" # setup marker generator and color map\n",
" markers = ('o', 's', '^', 'v', '<')\n",
" colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
" cmap = ListedColormap(colors[:len(np.unique(y))])\n",
"\n",
" # plot the decision surface\n",
" x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
" x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
" xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
" np.arange(x2_min, x2_max, resolution))\n",
" lab = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
" lab = lab.reshape(xx1.shape)\n",
" plt.contourf(xx1, xx2, lab, alpha=0.3, cmap=cmap)\n",
" plt.xlim(xx1.min(), xx1.max())\n",
" plt.ylim(xx2.min(), xx2.max())\n",
"\n",
" # plot class examples\n",
" for idx, cl in enumerate(np.unique(y)):\n",
" plt.scatter(x=X[y == cl, 0],\n",
" y=X[y == cl, 1],\n",
" alpha=0.8,\n",
" c=colors[idx],\n",
" marker=markers[idx],\n",
" label=f'Class {cl}',\n",
" edgecolor='black')\n",
" # highlight test examples\n",
" if test_idx:\n",
" # plot all examples\n",
" X_test, y_test = X[test_idx, :], y[test_idx]\n",
"\n",
" plt.scatter(X_test[:, 0],\n",
" X_test[:, 1],\n",
" c='none',\n",
" edgecolor='black',\n",
" alpha=1.0,\n",
" linewidth=1,\n",
" marker='o',\n",
" s=100,\n",
" label='Test set')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0de01538-5c3f-4110-bb9d-ecdbb7f0af1b",
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'sklearn'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[32m 3\u001b[39m X_train, X_test, Y_train, Y_test = train_test_split(x,y,test_size=\u001b[32m0.3\u001b[39m, random_state=\u001b[32m1\u001b[39m, stratify=y)\n",
"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train, X_test, Y_train, Y_test = train_test_split(x,y,test_size=0.3, random_state=1, stratify=y)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "00d65b25-a305-4409-8daf-8bd4e3cd8005",
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'sklearn'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StandardScaler\n\u001b[32m 3\u001b[39m sc = StandardScaler()\n\u001b[32m 4\u001b[39m sc.fit(X_train)\n",
"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"sc = StandardScaler()\n",
"sc.fit(X_train)\n",
"X_train_std = sc.transform(X_train)\n",
"X_test_std = sc.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cecc1db-c8b7-4153-897e-9a4a216e936e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0c72b7a-b92b-4678-8bdb-3b6ca4c201ad",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "06f65b9c-bf3e-438a-a11f-8df0db4f553e",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

151
PythonAI/JupyterLab/data/iris.csv Normal file
View file

@ -0,0 +1,151 @@
sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5.0,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3.0,1.4,0.1,setosa
4.3,3.0,1.1,0.1,setosa
5.8,4.0,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1.0,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5.0,3.0,1.6,0.2,setosa
5.0,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.0,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.1,1.5,0.1,setosa
4.4,3.0,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5.0,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5.0,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3.0,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5.0,3.3,1.4,0.2,setosa
7.0,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4.0,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1.0,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5.0,2.0,3.5,1.0,versicolor
5.9,3.0,4.2,1.5,versicolor
6.0,2.2,4.0,1.0,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3.0,4.5,1.5,versicolor
5.8,2.7,4.1,1.0,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4.0,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3.0,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3.0,5.0,1.7,versicolor
6.0,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1.0,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1.0,versicolor
5.8,2.7,3.9,1.2,versicolor
6.0,2.7,5.1,1.6,versicolor
5.4,3.0,4.5,1.5,versicolor
6.0,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3.0,4.1,1.3,versicolor
5.5,2.5,4.0,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3.0,4.6,1.4,versicolor
5.8,2.6,4.0,1.2,versicolor
5.0,2.3,3.3,1.0,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3.0,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3.0,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6.0,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3.0,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3.0,5.8,2.2,virginica
7.6,3.0,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2.0,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3.0,5.5,2.1,virginica
5.7,2.5,5.0,2.0,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3.0,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6.0,2.2,5.0,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2.0,virginica
7.7,2.8,6.7,2.0,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6.0,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3.0,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3.0,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2.0,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3.0,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6.0,3.0,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3.0,5.2,2.3,virginica
6.3,2.5,5.0,1.9,virginica
6.5,3.0,5.2,2.0,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3.0,5.1,1.8,virginica
1 sepal_length sepal_width petal_length petal_width species
2 5.1 3.5 1.4 0.2 setosa
3 4.9 3.0 1.4 0.2 setosa
4 4.7 3.2 1.3 0.2 setosa
5 4.6 3.1 1.5 0.2 setosa
6 5.0 3.6 1.4 0.2 setosa
7 5.4 3.9 1.7 0.4 setosa
8 4.6 3.4 1.4 0.3 setosa
9 5.0 3.4 1.5 0.2 setosa
10 4.4 2.9 1.4 0.2 setosa
11 4.9 3.1 1.5 0.1 setosa
12 5.4 3.7 1.5 0.2 setosa
13 4.8 3.4 1.6 0.2 setosa
14 4.8 3.0 1.4 0.1 setosa
15 4.3 3.0 1.1 0.1 setosa
16 5.8 4.0 1.2 0.2 setosa
17 5.7 4.4 1.5 0.4 setosa
18 5.4 3.9 1.3 0.4 setosa
19 5.1 3.5 1.4 0.3 setosa
20 5.7 3.8 1.7 0.3 setosa
21 5.1 3.8 1.5 0.3 setosa
22 5.4 3.4 1.7 0.2 setosa
23 5.1 3.7 1.5 0.4 setosa
24 4.6 3.6 1.0 0.2 setosa
25 5.1 3.3 1.7 0.5 setosa
26 4.8 3.4 1.9 0.2 setosa
27 5.0 3.0 1.6 0.2 setosa
28 5.0 3.4 1.6 0.4 setosa
29 5.2 3.5 1.5 0.2 setosa
30 5.2 3.4 1.4 0.2 setosa
31 4.7 3.2 1.6 0.2 setosa
32 4.8 3.1 1.6 0.2 setosa
33 5.4 3.4 1.5 0.4 setosa
34 5.2 4.1 1.5 0.1 setosa
35 5.5 4.2 1.4 0.2 setosa
36 4.9 3.1 1.5 0.1 setosa
37 5.0 3.2 1.2 0.2 setosa
38 5.5 3.5 1.3 0.2 setosa
39 4.9 3.1 1.5 0.1 setosa
40 4.4 3.0 1.3 0.2 setosa
41 5.1 3.4 1.5 0.2 setosa
42 5.0 3.5 1.3 0.3 setosa
43 4.5 2.3 1.3 0.3 setosa
44 4.4 3.2 1.3 0.2 setosa
45 5.0 3.5 1.6 0.6 setosa
46 5.1 3.8 1.9 0.4 setosa
47 4.8 3.0 1.4 0.3 setosa
48 5.1 3.8 1.6 0.2 setosa
49 4.6 3.2 1.4 0.2 setosa
50 5.3 3.7 1.5 0.2 setosa
51 5.0 3.3 1.4 0.2 setosa
52 7.0 3.2 4.7 1.4 versicolor
53 6.4 3.2 4.5 1.5 versicolor
54 6.9 3.1 4.9 1.5 versicolor
55 5.5 2.3 4.0 1.3 versicolor
56 6.5 2.8 4.6 1.5 versicolor
57 5.7 2.8 4.5 1.3 versicolor
58 6.3 3.3 4.7 1.6 versicolor
59 4.9 2.4 3.3 1.0 versicolor
60 6.6 2.9 4.6 1.3 versicolor
61 5.2 2.7 3.9 1.4 versicolor
62 5.0 2.0 3.5 1.0 versicolor
63 5.9 3.0 4.2 1.5 versicolor
64 6.0 2.2 4.0 1.0 versicolor
65 6.1 2.9 4.7 1.4 versicolor
66 5.6 2.9 3.6 1.3 versicolor
67 6.7 3.1 4.4 1.4 versicolor
68 5.6 3.0 4.5 1.5 versicolor
69 5.8 2.7 4.1 1.0 versicolor
70 6.2 2.2 4.5 1.5 versicolor
71 5.6 2.5 3.9 1.1 versicolor
72 5.9 3.2 4.8 1.8 versicolor
73 6.1 2.8 4.0 1.3 versicolor
74 6.3 2.5 4.9 1.5 versicolor
75 6.1 2.8 4.7 1.2 versicolor
76 6.4 2.9 4.3 1.3 versicolor
77 6.6 3.0 4.4 1.4 versicolor
78 6.8 2.8 4.8 1.4 versicolor
79 6.7 3.0 5.0 1.7 versicolor
80 6.0 2.9 4.5 1.5 versicolor
81 5.7 2.6 3.5 1.0 versicolor
82 5.5 2.4 3.8 1.1 versicolor
83 5.5 2.4 3.7 1.0 versicolor
84 5.8 2.7 3.9 1.2 versicolor
85 6.0 2.7 5.1 1.6 versicolor
86 5.4 3.0 4.5 1.5 versicolor
87 6.0 3.4 4.5 1.6 versicolor
88 6.7 3.1 4.7 1.5 versicolor
89 6.3 2.3 4.4 1.3 versicolor
90 5.6 3.0 4.1 1.3 versicolor
91 5.5 2.5 4.0 1.3 versicolor
92 5.5 2.6 4.4 1.2 versicolor
93 6.1 3.0 4.6 1.4 versicolor
94 5.8 2.6 4.0 1.2 versicolor
95 5.0 2.3 3.3 1.0 versicolor
96 5.6 2.7 4.2 1.3 versicolor
97 5.7 3.0 4.2 1.2 versicolor
98 5.7 2.9 4.2 1.3 versicolor
99 6.2 2.9 4.3 1.3 versicolor
100 5.1 2.5 3.0 1.1 versicolor
101 5.7 2.8 4.1 1.3 versicolor
102 6.3 3.3 6.0 2.5 virginica
103 5.8 2.7 5.1 1.9 virginica
104 7.1 3.0 5.9 2.1 virginica
105 6.3 2.9 5.6 1.8 virginica
106 6.5 3.0 5.8 2.2 virginica
107 7.6 3.0 6.6 2.1 virginica
108 4.9 2.5 4.5 1.7 virginica
109 7.3 2.9 6.3 1.8 virginica
110 6.7 2.5 5.8 1.8 virginica
111 7.2 3.6 6.1 2.5 virginica
112 6.5 3.2 5.1 2.0 virginica
113 6.4 2.7 5.3 1.9 virginica
114 6.8 3.0 5.5 2.1 virginica
115 5.7 2.5 5.0 2.0 virginica
116 5.8 2.8 5.1 2.4 virginica
117 6.4 3.2 5.3 2.3 virginica
118 6.5 3.0 5.5 1.8 virginica
119 7.7 3.8 6.7 2.2 virginica
120 7.7 2.6 6.9 2.3 virginica
121 6.0 2.2 5.0 1.5 virginica
122 6.9 3.2 5.7 2.3 virginica
123 5.6 2.8 4.9 2.0 virginica
124 7.7 2.8 6.7 2.0 virginica
125 6.3 2.7 4.9 1.8 virginica
126 6.7 3.3 5.7 2.1 virginica
127 7.2 3.2 6.0 1.8 virginica
128 6.2 2.8 4.8 1.8 virginica
129 6.1 3.0 4.9 1.8 virginica
130 6.4 2.8 5.6 2.1 virginica
131 7.2 3.0 5.8 1.6 virginica
132 7.4 2.8 6.1 1.9 virginica
133 7.9 3.8 6.4 2.0 virginica
134 6.4 2.8 5.6 2.2 virginica
135 6.3 2.8 5.1 1.5 virginica
136 6.1 2.6 5.6 1.4 virginica
137 7.7 3.0 6.1 2.3 virginica
138 6.3 3.4 5.6 2.4 virginica
139 6.4 3.1 5.5 1.8 virginica
140 6.0 3.0 4.8 1.8 virginica
141 6.9 3.1 5.4 2.1 virginica
142 6.7 3.1 5.6 2.4 virginica
143 6.9 3.1 5.1 2.3 virginica
144 5.8 2.7 5.1 1.9 virginica
145 6.8 3.2 5.9 2.3 virginica
146 6.7 3.3 5.7 2.5 virginica
147 6.7 3.0 5.2 2.3 virginica
148 6.3 2.5 5.0 1.9 virginica
149 6.5 3.0 5.2 2.0 virginica
150 6.2 3.4 5.4 2.3 virginica
151 5.9 3.0 5.1 1.8 virginica

78781
PythonAI/JupyterLab/data/nyc_weather_2018.csv Normal file
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892
PythonAI/JupyterLab/data/titanic.csv Normal file
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@ -0,0 +1,892 @@
survived,ticket_class,gender,age,number_sibling_spouse,number_parent_children,passenger_fare,port_of_embarkation,age_group
0,3,male,22,1,0,7.25,S,18-59
1,1,female,38,1,0,71.2833,C,18-59
1,3,female,26,0,0,7.925,S,18-59
1,1,female,35,1,0,53.1,S,18-59
0,3,male,35,0,0,8.05,S,18-59
0,3,male,,0,0,8.4583,Q,
0,1,male,54,0,0,51.8625,S,18-59
0,3,male,2,3,1,21.075,S,0-17
1,3,female,27,0,2,11.1333,S,18-59
1,2,female,14,1,0,30.0708,C,0-17
1,3,female,4,1,1,16.7,S,0-17
1,1,female,58,0,0,26.55,S,18-59
0,3,male,20,0,0,8.05,S,18-59
0,3,male,39,1,5,31.275,S,18-59
0,3,female,14,0,0,7.8542,S,0-17
1,2,female,55,0,0,16,S,18-59
0,3,male,2,4,1,29.125,Q,0-17
1,2,male,,0,0,13,S,
0,3,female,31,1,0,18,S,18-59
1,3,female,,0,0,7.225,C,
0,2,male,35,0,0,26,S,18-59
1,2,male,34,0,0,13,S,18-59
1,3,female,15,0,0,8.0292,Q,0-17
1,1,male,28,0,0,35.5,S,18-59
0,3,female,8,3,1,21.075,S,0-17
1,3,female,38,1,5,31.3875,S,18-59
0,3,male,,0,0,7.225,C,
0,1,male,19,3,2,263,S,18-59
1,3,female,,0,0,7.8792,Q,
0,3,male,,0,0,7.8958,S,
0,1,male,40,0,0,27.7208,C,18-59
1,1,female,,1,0,146.5208,C,
1,3,female,,0,0,7.75,Q,
0,2,male,66,0,0,10.5,S,60+
0,1,male,28,1,0,82.1708,C,18-59
0,1,male,42,1,0,52,S,18-59
1,3,male,,0,0,7.2292,C,
0,3,male,21,0,0,8.05,S,18-59
0,3,female,18,2,0,18,S,18-59
1,3,female,14,1,0,11.2417,C,0-17
0,3,female,40,1,0,9.475,S,18-59
0,2,female,27,1,0,21,S,18-59
0,3,male,,0,0,7.8958,C,
1,2,female,3,1,2,41.5792,C,0-17
1,3,female,19,0,0,7.8792,Q,18-59
0,3,male,,0,0,8.05,S,
0,3,male,,1,0,15.5,Q,
1,3,female,,0,0,7.75,Q,
0,3,male,,2,0,21.6792,C,
0,3,female,18,1,0,17.8,S,18-59
0,3,male,7,4,1,39.6875,S,0-17
0,3,male,21,0,0,7.8,S,18-59
1,1,female,49,1,0,76.7292,C,18-59
1,2,female,29,1,0,26,S,18-59
0,1,male,65,0,1,61.9792,C,60+
1,1,male,,0,0,35.5,S,
1,2,female,21,0,0,10.5,S,18-59
0,3,male,28.5,0,0,7.2292,C,18-59
1,2,female,5,1,2,27.75,S,0-17
0,3,male,11,5,2,46.9,S,0-17
0,3,male,22,0,0,7.2292,C,18-59
1,1,female,38,0,0,80,,18-59
0,1,male,45,1,0,83.475,S,18-59
0,3,male,4,3,2,27.9,S,0-17
0,1,male,,0,0,27.7208,C,
1,3,male,,1,1,15.2458,C,
1,2,female,29,0,0,10.5,S,18-59
0,3,male,19,0,0,8.1583,S,18-59
1,3,female,17,4,2,7.925,S,0-17
0,3,male,26,2,0,8.6625,S,18-59
0,2,male,32,0,0,10.5,S,18-59
0,3,female,16,5,2,46.9,S,0-17
0,2,male,21,0,0,73.5,S,18-59
0,3,male,26,1,0,14.4542,C,18-59
1,3,male,32,0,0,56.4958,S,18-59
0,3,male,25,0,0,7.65,S,18-59
0,3,male,,0,0,7.8958,S,
0,3,male,,0,0,8.05,S,
1,2,male,0.83,0,2,29,S,0-17
1,3,female,30,0,0,12.475,S,18-59
0,3,male,22,0,0,9,S,18-59
1,3,male,29,0,0,9.5,S,18-59
1,3,female,,0,0,7.7875,Q,
0,1,male,28,0,0,47.1,S,18-59
1,2,female,17,0,0,10.5,S,0-17
1,3,female,33,3,0,15.85,S,18-59
0,3,male,16,1,3,34.375,S,0-17
0,3,male,,0,0,8.05,S,
1,1,female,23,3,2,263,S,18-59
0,3,male,24,0,0,8.05,S,18-59
0,3,male,29,0,0,8.05,S,18-59
0,3,male,20,0,0,7.8542,S,18-59
0,1,male,46,1,0,61.175,S,18-59
0,3,male,26,1,2,20.575,S,18-59
0,3,male,59,0,0,7.25,S,18-59
0,3,male,,0,0,8.05,S,
0,1,male,71,0,0,34.6542,C,60+
1,1,male,23,0,1,63.3583,C,18-59
1,2,female,34,0,1,23,S,18-59
0,2,male,34,1,0,26,S,18-59
0,3,female,28,0,0,7.8958,S,18-59
0,3,male,,0,0,7.8958,S,
0,1,male,21,0,1,77.2875,S,18-59
0,3,male,33,0,0,8.6542,S,18-59
0,3,male,37,2,0,7.925,S,18-59
0,3,male,28,0,0,7.8958,S,18-59
1,3,female,21,0,0,7.65,S,18-59
1,3,male,,0,0,7.775,S,
0,3,male,38,0,0,7.8958,S,18-59
1,3,female,,1,0,24.15,Q,
0,1,male,47,0,0,52,S,18-59
0,3,female,14.5,1,0,14.4542,C,0-17
0,3,male,22,0,0,8.05,S,18-59
0,3,female,20,1,0,9.825,S,18-59
0,3,female,17,0,0,14.4583,C,0-17
0,3,male,21,0,0,7.925,S,18-59
0,3,male,70.5,0,0,7.75,Q,60+
0,2,male,29,1,0,21,S,18-59
0,1,male,24,0,1,247.5208,C,18-59
0,3,female,2,4,2,31.275,S,0-17
0,2,male,21,2,0,73.5,S,18-59
0,3,male,,0,0,8.05,S,
0,2,male,32.5,1,0,30.0708,C,18-59
1,2,female,32.5,0,0,13,S,18-59
0,1,male,54,0,1,77.2875,S,18-59
1,3,male,12,1,0,11.2417,C,0-17
0,3,male,,0,0,7.75,Q,
1,3,male,24,0,0,7.1417,S,18-59
1,3,female,,1,1,22.3583,C,
0,3,male,45,0,0,6.975,S,18-59
0,3,male,33,0,0,7.8958,C,18-59
0,3,male,20,0,0,7.05,S,18-59
0,3,female,47,1,0,14.5,S,18-59
1,2,female,29,1,0,26,S,18-59
0,2,male,25,0,0,13,S,18-59
0,2,male,23,0,0,15.0458,C,18-59
1,1,female,19,0,2,26.2833,S,18-59
0,1,male,37,1,0,53.1,S,18-59
0,3,male,16,0,0,9.2167,S,0-17
0,1,male,24,0,0,79.2,C,18-59
0,3,female,,0,2,15.2458,C,
1,3,female,22,0,0,7.75,S,18-59
1,3,female,24,1,0,15.85,S,18-59
0,3,male,19,0,0,6.75,Q,18-59
0,2,male,18,0,0,11.5,S,18-59
0,2,male,19,1,1,36.75,S,18-59
1,3,male,27,0,0,7.7958,S,18-59
0,3,female,9,2,2,34.375,S,0-17
0,2,male,36.5,0,2,26,S,18-59
0,2,male,42,0,0,13,S,18-59
0,2,male,51,0,0,12.525,S,18-59
1,1,female,22,1,0,66.6,S,18-59
0,3,male,55.5,0,0,8.05,S,18-59
0,3,male,40.5,0,2,14.5,S,18-59
0,3,male,,0,0,7.3125,S,
0,1,male,51,0,1,61.3792,C,18-59
1,3,female,16,0,0,7.7333,Q,0-17
0,3,male,30,0,0,8.05,S,18-59
0,3,male,,0,0,8.6625,S,
0,3,male,,8,2,69.55,S,
0,3,male,44,0,1,16.1,S,18-59
1,2,female,40,0,0,15.75,S,18-59
0,3,male,26,0,0,7.775,S,18-59
0,3,male,17,0,0,8.6625,S,0-17
0,3,male,1,4,1,39.6875,S,0-17
1,3,male,9,0,2,20.525,S,0-17
1,1,female,,0,1,55,S,
0,3,female,45,1,4,27.9,S,18-59
0,1,male,,0,0,25.925,S,
0,3,male,28,0,0,56.4958,S,18-59
0,1,male,61,0,0,33.5,S,60+
0,3,male,4,4,1,29.125,Q,0-17
1,3,female,1,1,1,11.1333,S,0-17
0,3,male,21,0,0,7.925,S,18-59
0,1,male,56,0,0,30.6958,C,18-59
0,3,male,18,1,1,7.8542,S,18-59
0,3,male,,3,1,25.4667,S,
0,1,female,50,0,0,28.7125,C,18-59
0,2,male,30,0,0,13,S,18-59
0,3,male,36,0,0,0,S,18-59
0,3,female,,8,2,69.55,S,
0,2,male,,0,0,15.05,C,
0,3,male,9,4,2,31.3875,S,0-17
1,2,male,1,2,1,39,S,0-17
1,3,female,4,0,2,22.025,S,0-17
0,1,male,,0,0,50,S,
1,3,female,,1,0,15.5,Q,
1,1,male,45,0,0,26.55,S,18-59
0,3,male,40,1,1,15.5,Q,18-59
0,3,male,36,0,0,7.8958,S,18-59
1,2,female,32,0,0,13,S,18-59
0,2,male,19,0,0,13,S,18-59
1,3,female,19,1,0,7.8542,S,18-59
1,2,male,3,1,1,26,S,0-17
1,1,female,44,0,0,27.7208,C,18-59
1,1,female,58,0,0,146.5208,C,18-59
0,3,male,,0,0,7.75,Q,
0,3,male,42,0,1,8.4042,S,18-59
1,3,female,,0,0,7.75,Q,
0,2,female,24,0,0,13,S,18-59
0,3,male,28,0,0,9.5,S,18-59
0,3,male,,8,2,69.55,S,
0,3,male,34,0,0,6.4958,S,18-59
0,3,male,45.5,0,0,7.225,C,18-59
1,3,male,18,0,0,8.05,S,18-59
0,3,female,2,0,1,10.4625,S,0-17
0,3,male,32,1,0,15.85,S,18-59
1,3,male,26,0,0,18.7875,C,18-59
1,3,female,16,0,0,7.75,Q,0-17
1,1,male,40,0,0,31,C,18-59
0,3,male,24,0,0,7.05,S,18-59
1,2,female,35,0,0,21,S,18-59
0,3,male,22,0,0,7.25,S,18-59
0,2,male,30,0,0,13,S,18-59
0,3,male,,1,0,7.75,Q,
1,1,female,31,1,0,113.275,C,18-59
1,3,female,27,0,0,7.925,S,18-59
0,2,male,42,1,0,27,S,18-59
1,1,female,32,0,0,76.2917,C,18-59
0,2,male,30,0,0,10.5,S,18-59
1,3,male,16,0,0,8.05,S,0-17
0,2,male,27,0,0,13,S,18-59
0,3,male,51,0,0,8.05,S,18-59
0,3,male,,0,0,7.8958,S,
1,1,male,38,1,0,90,S,18-59
0,3,male,22,0,0,9.35,S,18-59
1,2,male,19,0,0,10.5,S,18-59
0,3,male,20.5,0,0,7.25,S,18-59
0,2,male,18,0,0,13,S,18-59
0,3,female,,3,1,25.4667,S,
1,1,female,35,1,0,83.475,S,18-59
0,3,male,29,0,0,7.775,S,18-59
0,2,male,59,0,0,13.5,S,18-59
1,3,female,5,4,2,31.3875,S,0-17
0,2,male,24,0,0,10.5,S,18-59
0,3,female,,0,0,7.55,S,
0,2,male,44,1,0,26,S,18-59
1,2,female,8,0,2,26.25,S,0-17
0,2,male,19,0,0,10.5,S,18-59
0,2,male,33,0,0,12.275,S,18-59
0,3,female,,1,0,14.4542,C,
1,3,female,,1,0,15.5,Q,
0,2,male,29,0,0,10.5,S,18-59
0,3,male,22,0,0,7.125,S,18-59
0,3,male,30,0,0,7.225,C,18-59
0,1,male,44,2,0,90,Q,18-59
0,3,female,25,0,0,7.775,S,18-59
1,2,female,24,0,2,14.5,S,18-59
1,1,male,37,1,1,52.5542,S,18-59
0,2,male,54,1,0,26,S,18-59
0,3,male,,0,0,7.25,S,
0,3,female,29,1,1,10.4625,S,18-59
0,1,male,62,0,0,26.55,S,60+
0,3,male,30,1,0,16.1,S,18-59
0,3,female,41,0,2,20.2125,S,18-59
1,3,female,29,0,2,15.2458,C,18-59
1,1,female,,0,0,79.2,C,
1,1,female,30,0,0,86.5,S,18-59
1,1,female,35,0,0,512.3292,C,18-59
1,2,female,50,0,1,26,S,18-59
0,3,male,,0,0,7.75,Q,
1,3,male,3,4,2,31.3875,S,0-17
0,1,male,52,1,1,79.65,S,18-59
0,1,male,40,0,0,0,S,18-59
0,3,female,,0,0,7.75,Q,
0,2,male,36,0,0,10.5,S,18-59
0,3,male,16,4,1,39.6875,S,0-17
1,3,male,25,1,0,7.775,S,18-59
1,1,female,58,0,1,153.4625,S,18-59
1,1,female,35,0,0,135.6333,S,18-59
0,1,male,,0,0,31,S,
1,3,male,25,0,0,0,S,18-59
1,2,female,41,0,1,19.5,S,18-59
0,1,male,37,0,1,29.7,C,18-59
1,3,female,,0,0,7.75,Q,
1,1,female,63,1,0,77.9583,S,60+
0,3,female,45,0,0,7.75,S,18-59
0,2,male,,0,0,0,S,
0,3,male,7,4,1,29.125,Q,0-17
1,3,female,35,1,1,20.25,S,18-59
0,3,male,65,0,0,7.75,Q,60+
0,3,male,28,0,0,7.8542,S,18-59
0,3,male,16,0,0,9.5,S,0-17
1,3,male,19,0,0,8.05,S,18-59
0,1,male,,0,0,26,S,
0,3,male,33,0,0,8.6625,C,18-59
1,3,male,30,0,0,9.5,S,18-59
0,3,male,22,0,0,7.8958,S,18-59
1,2,male,42,0,0,13,S,18-59
1,3,female,22,0,0,7.75,Q,18-59
1,1,female,26,0,0,78.85,S,18-59
1,1,female,19,1,0,91.0792,C,18-59
0,2,male,36,0,0,12.875,C,18-59
0,3,female,24,0,0,8.85,S,18-59
0,3,male,24,0,0,7.8958,S,18-59
0,1,male,,0,0,27.7208,C,
0,3,male,23.5,0,0,7.2292,C,18-59
0,1,female,2,1,2,151.55,S,0-17
1,1,male,,0,0,30.5,S,
1,1,female,50,0,1,247.5208,C,18-59
1,3,female,,0,0,7.75,Q,
1,3,male,,2,0,23.25,Q,
0,3,male,19,0,0,0,S,18-59
1,2,female,,0,0,12.35,Q,
0,3,male,,0,0,8.05,S,
1,1,male,0.92,1,2,151.55,S,0-17
1,1,female,,0,0,110.8833,C,
1,1,female,17,1,0,108.9,C,0-17
0,2,male,30,1,0,24,C,18-59
1,1,female,30,0,0,56.9292,C,18-59
1,1,female,24,0,0,83.1583,C,18-59
1,1,female,18,2,2,262.375,C,18-59
0,2,female,26,1,1,26,S,18-59
0,3,male,28,0,0,7.8958,S,18-59
0,2,male,43,1,1,26.25,S,18-59
1,3,female,26,0,0,7.8542,S,18-59
1,2,female,24,1,0,26,S,18-59
0,2,male,54,0,0,14,S,18-59
1,1,female,31,0,2,164.8667,S,18-59
1,1,female,40,1,1,134.5,C,18-59
0,3,male,22,0,0,7.25,S,18-59
0,3,male,27,0,0,7.8958,S,18-59
1,2,female,30,0,0,12.35,Q,18-59
1,2,female,22,1,1,29,S,18-59
0,3,male,,8,2,69.55,S,
1,1,female,36,0,0,135.6333,C,18-59
0,3,male,61,0,0,6.2375,S,60+
1,2,female,36,0,0,13,S,18-59
1,3,female,31,1,1,20.525,S,18-59
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1,1,male,35,0,0,26.2875,S,18-59
0,3,female,18,0,1,14.4542,C,18-59
0,3,male,25,0,0,7.7417,Q,18-59
0,3,male,26,1,0,7.8542,S,18-59
0,2,male,39,0,0,26,S,18-59
1,2,female,45,0,0,13.5,S,18-59
1,1,male,42,0,0,26.2875,S,18-59
1,1,female,22,0,0,151.55,S,18-59
1,3,male,,1,1,15.2458,C,
1,1,female,24,0,0,49.5042,C,18-59
0,1,male,,0,0,26.55,S,
1,1,male,48,1,0,52,S,18-59
0,3,male,29,0,0,9.4833,S,18-59
0,2,male,52,0,0,13,S,18-59
0,3,male,19,0,0,7.65,S,18-59
1,1,female,38,0,0,227.525,C,18-59
1,2,female,27,0,0,10.5,S,18-59
0,3,male,,0,0,15.5,Q,
0,3,male,33,0,0,7.775,S,18-59
1,2,female,6,0,1,33,S,0-17
0,3,male,17,1,0,7.0542,S,0-17
0,2,male,34,0,0,13,S,18-59
0,2,male,50,0,0,13,S,18-59
1,1,male,27,1,0,53.1,S,18-59
0,3,male,20,0,0,8.6625,S,18-59
1,2,female,30,3,0,21,S,18-59
1,3,female,,0,0,7.7375,Q,
0,2,male,25,1,0,26,S,18-59
0,3,female,25,1,0,7.925,S,18-59
1,1,female,29,0,0,211.3375,S,18-59
0,3,male,11,0,0,18.7875,C,0-17
0,2,male,,0,0,0,S,
0,2,male,23,0,0,13,S,18-59
0,2,male,23,0,0,13,S,18-59
0,3,male,28.5,0,0,16.1,S,18-59
0,3,female,48,1,3,34.375,S,18-59
1,1,male,35,0,0,512.3292,C,18-59
0,3,male,,0,0,7.8958,S,
0,3,male,,0,0,7.8958,S,
1,1,male,,0,0,30,S,
0,1,male,36,1,0,78.85,S,18-59
1,1,female,21,2,2,262.375,C,18-59
0,3,male,24,1,0,16.1,S,18-59
1,3,male,31,0,0,7.925,S,18-59
0,1,male,70,1,1,71,S,60+
0,3,male,16,1,1,20.25,S,0-17
1,2,female,30,0,0,13,S,18-59
0,1,male,19,1,0,53.1,S,18-59
0,3,male,31,0,0,7.75,Q,18-59
1,2,female,4,1,1,23,S,0-17
1,3,male,6,0,1,12.475,S,0-17
0,3,male,33,0,0,9.5,S,18-59
0,3,male,23,0,0,7.8958,S,18-59
1,2,female,48,1,2,65,S,18-59
1,2,male,0.67,1,1,14.5,S,0-17
0,3,male,28,0,0,7.7958,S,18-59
0,2,male,18,0,0,11.5,S,18-59
0,3,male,34,0,0,8.05,S,18-59
1,1,female,33,0,0,86.5,S,18-59
0,3,male,,0,0,14.5,S,
0,3,male,41,0,0,7.125,S,18-59
1,3,male,20,0,0,7.2292,C,18-59
1,1,female,36,1,2,120,S,18-59
0,3,male,16,0,0,7.775,S,0-17
1,1,female,51,1,0,77.9583,S,18-59
0,1,male,,0,0,39.6,C,
0,3,female,30.5,0,0,7.75,Q,18-59
0,3,male,,1,0,24.15,Q,
0,3,male,32,0,0,8.3625,S,18-59
0,3,male,24,0,0,9.5,S,18-59
0,3,male,48,0,0,7.8542,S,18-59
0,2,female,57,0,0,10.5,S,18-59
0,3,male,,0,0,7.225,C,
1,2,female,54,1,3,23,S,18-59
0,3,male,18,0,0,7.75,S,18-59
0,3,male,,0,0,7.75,Q,
1,3,female,5,0,0,12.475,S,0-17
0,3,male,,0,0,7.7375,Q,
1,1,female,43,0,1,211.3375,S,18-59
1,3,female,13,0,0,7.2292,C,0-17
1,1,female,17,1,0,57,S,0-17
0,1,male,29,0,0,30,S,18-59
0,3,male,,1,2,23.45,S,
0,3,male,25,0,0,7.05,S,18-59
0,3,male,25,0,0,7.25,S,18-59
1,3,female,18,0,0,7.4958,S,18-59
0,3,male,8,4,1,29.125,Q,0-17
1,3,male,1,1,2,20.575,S,0-17
0,1,male,46,0,0,79.2,C,18-59
0,3,male,,0,0,7.75,Q,
0,2,male,16,0,0,26,S,0-17
0,3,female,,8,2,69.55,S,
0,1,male,,0,0,30.6958,C,
0,3,male,25,0,0,7.8958,S,18-59
0,2,male,39,0,0,13,S,18-59
1,1,female,49,0,0,25.9292,S,18-59
1,3,female,31,0,0,8.6833,S,18-59
0,3,male,30,0,0,7.2292,C,18-59
0,3,female,30,1,1,24.15,S,18-59
0,2,male,34,0,0,13,S,18-59
1,2,female,31,1,1,26.25,S,18-59
1,1,male,11,1,2,120,S,0-17
1,3,male,0.42,0,1,8.5167,C,0-17
1,3,male,27,0,0,6.975,S,18-59
0,3,male,31,0,0,7.775,S,18-59
0,1,male,39,0,0,0,S,18-59
0,3,female,18,0,0,7.775,S,18-59
0,2,male,39,0,0,13,S,18-59
1,1,female,33,1,0,53.1,S,18-59
0,3,male,26,0,0,7.8875,S,18-59
0,3,male,39,0,0,24.15,S,18-59
0,2,male,35,0,0,10.5,S,18-59
0,3,female,6,4,2,31.275,S,0-17
0,3,male,30.5,0,0,8.05,S,18-59
0,1,male,,0,0,0,S,
0,3,female,23,0,0,7.925,S,18-59
0,2,male,31,1,1,37.0042,C,18-59
0,3,male,43,0,0,6.45,S,18-59
0,3,male,10,3,2,27.9,S,0-17
1,1,female,52,1,1,93.5,S,18-59
1,3,male,27,0,0,8.6625,S,18-59
0,1,male,38,0,0,0,S,18-59
1,3,female,27,0,1,12.475,S,18-59
0,3,male,2,4,1,39.6875,S,0-17
0,3,male,,0,0,6.95,Q,
0,3,male,,0,0,56.4958,S,
1,2,male,1,0,2,37.0042,C,0-17
1,3,male,,0,0,7.75,Q,
1,1,female,62,0,0,80,,60+
1,3,female,15,1,0,14.4542,C,0-17
1,2,male,0.83,1,1,18.75,S,0-17
0,3,male,,0,0,7.2292,C,
0,3,male,23,0,0,7.8542,S,18-59
0,3,male,18,0,0,8.3,S,18-59
1,1,female,39,1,1,83.1583,C,18-59
0,3,male,21,0,0,8.6625,S,18-59
0,3,male,,0,0,8.05,S,
1,3,male,32,0,0,56.4958,S,18-59
1,1,male,,0,0,29.7,C,
0,3,male,20,0,0,7.925,S,18-59
0,2,male,16,0,0,10.5,S,0-17
1,1,female,30,0,0,31,C,18-59
0,3,male,34.5,0,0,6.4375,C,18-59
0,3,male,17,0,0,8.6625,S,0-17
0,3,male,42,0,0,7.55,S,18-59
0,3,male,,8,2,69.55,S,
0,3,male,35,0,0,7.8958,C,18-59
0,2,male,28,0,1,33,S,18-59
1,1,female,,1,0,89.1042,C,
0,3,male,4,4,2,31.275,S,0-17
0,3,male,74,0,0,7.775,S,60+
0,3,female,9,1,1,15.2458,C,0-17
1,1,female,16,0,1,39.4,S,0-17
0,2,female,44,1,0,26,S,18-59
1,3,female,18,0,1,9.35,S,18-59
1,1,female,45,1,1,164.8667,S,18-59
1,1,male,51,0,0,26.55,S,18-59
1,3,female,24,0,3,19.2583,C,18-59
0,3,male,,0,0,7.2292,C,
0,3,male,41,2,0,14.1083,S,18-59
0,2,male,21,1,0,11.5,S,18-59
1,1,female,48,0,0,25.9292,S,18-59
0,3,female,,8,2,69.55,S,
0,2,male,24,0,0,13,S,18-59
1,2,female,42,0,0,13,S,18-59
1,2,female,27,1,0,13.8583,C,18-59
0,1,male,31,0,0,50.4958,S,18-59
0,3,male,,0,0,9.5,S,
1,3,male,4,1,1,11.1333,S,0-17
0,3,male,26,0,0,7.8958,S,18-59
1,1,female,47,1,1,52.5542,S,18-59
0,1,male,33,0,0,5,S,18-59
0,3,male,47,0,0,9,S,18-59
1,2,female,28,1,0,24,C,18-59
1,3,female,15,0,0,7.225,C,0-17
0,3,male,20,0,0,9.8458,S,18-59
0,3,male,19,0,0,7.8958,S,18-59
0,3,male,,0,0,7.8958,S,
1,1,female,56,0,1,83.1583,C,18-59
1,2,female,25,0,1,26,S,18-59
0,3,male,33,0,0,7.8958,S,18-59
0,3,female,22,0,0,10.5167,S,18-59
0,2,male,28,0,0,10.5,S,18-59
0,3,male,25,0,0,7.05,S,18-59
0,3,female,39,0,5,29.125,Q,18-59
0,2,male,27,0,0,13,S,18-59
1,1,female,19,0,0,30,S,18-59
0,3,female,,1,2,23.45,S,
1,1,male,26,0,0,30,C,18-59
0,3,male,32,0,0,7.75,Q,18-59
1 survived ticket_class gender age number_sibling_spouse number_parent_children passenger_fare port_of_embarkation age_group
2 0 3 male 22 1 0 7.25 S 18-59
3 1 1 female 38 1 0 71.2833 C 18-59
4 1 3 female 26 0 0 7.925 S 18-59
5 1 1 female 35 1 0 53.1 S 18-59
6 0 3 male 35 0 0 8.05 S 18-59
7 0 3 male 0 0 8.4583 Q
8 0 1 male 54 0 0 51.8625 S 18-59
9 0 3 male 2 3 1 21.075 S 0-17
10 1 3 female 27 0 2 11.1333 S 18-59
11 1 2 female 14 1 0 30.0708 C 0-17
12 1 3 female 4 1 1 16.7 S 0-17
13 1 1 female 58 0 0 26.55 S 18-59
14 0 3 male 20 0 0 8.05 S 18-59
15 0 3 male 39 1 5 31.275 S 18-59
16 0 3 female 14 0 0 7.8542 S 0-17
17 1 2 female 55 0 0 16 S 18-59
18 0 3 male 2 4 1 29.125 Q 0-17
19 1 2 male 0 0 13 S
20 0 3 female 31 1 0 18 S 18-59
21 1 3 female 0 0 7.225 C
22 0 2 male 35 0 0 26 S 18-59
23 1 2 male 34 0 0 13 S 18-59
24 1 3 female 15 0 0 8.0292 Q 0-17
25 1 1 male 28 0 0 35.5 S 18-59
26 0 3 female 8 3 1 21.075 S 0-17
27 1 3 female 38 1 5 31.3875 S 18-59
28 0 3 male 0 0 7.225 C
29 0 1 male 19 3 2 263 S 18-59
30 1 3 female 0 0 7.8792 Q
31 0 3 male 0 0 7.8958 S
32 0 1 male 40 0 0 27.7208 C 18-59
33 1 1 female 1 0 146.5208 C
34 1 3 female 0 0 7.75 Q
35 0 2 male 66 0 0 10.5 S 60+
36 0 1 male 28 1 0 82.1708 C 18-59
37 0 1 male 42 1 0 52 S 18-59
38 1 3 male 0 0 7.2292 C
39 0 3 male 21 0 0 8.05 S 18-59
40 0 3 female 18 2 0 18 S 18-59
41 1 3 female 14 1 0 11.2417 C 0-17
42 0 3 female 40 1 0 9.475 S 18-59
43 0 2 female 27 1 0 21 S 18-59
44 0 3 male 0 0 7.8958 C
45 1 2 female 3 1 2 41.5792 C 0-17
46 1 3 female 19 0 0 7.8792 Q 18-59
47 0 3 male 0 0 8.05 S
48 0 3 male 1 0 15.5 Q
49 1 3 female 0 0 7.75 Q
50 0 3 male 2 0 21.6792 C
51 0 3 female 18 1 0 17.8 S 18-59
52 0 3 male 7 4 1 39.6875 S 0-17
53 0 3 male 21 0 0 7.8 S 18-59
54 1 1 female 49 1 0 76.7292 C 18-59
55 1 2 female 29 1 0 26 S 18-59
56 0 1 male 65 0 1 61.9792 C 60+
57 1 1 male 0 0 35.5 S
58 1 2 female 21 0 0 10.5 S 18-59
59 0 3 male 28.5 0 0 7.2292 C 18-59
60 1 2 female 5 1 2 27.75 S 0-17
61 0 3 male 11 5 2 46.9 S 0-17
62 0 3 male 22 0 0 7.2292 C 18-59
63 1 1 female 38 0 0 80 18-59
64 0 1 male 45 1 0 83.475 S 18-59
65 0 3 male 4 3 2 27.9 S 0-17
66 0 1 male 0 0 27.7208 C
67 1 3 male 1 1 15.2458 C
68 1 2 female 29 0 0 10.5 S 18-59
69 0 3 male 19 0 0 8.1583 S 18-59
70 1 3 female 17 4 2 7.925 S 0-17
71 0 3 male 26 2 0 8.6625 S 18-59
72 0 2 male 32 0 0 10.5 S 18-59
73 0 3 female 16 5 2 46.9 S 0-17
74 0 2 male 21 0 0 73.5 S 18-59
75 0 3 male 26 1 0 14.4542 C 18-59
76 1 3 male 32 0 0 56.4958 S 18-59
77 0 3 male 25 0 0 7.65 S 18-59
78 0 3 male 0 0 7.8958 S
79 0 3 male 0 0 8.05 S
80 1 2 male 0.83 0 2 29 S 0-17
81 1 3 female 30 0 0 12.475 S 18-59
82 0 3 male 22 0 0 9 S 18-59
83 1 3 male 29 0 0 9.5 S 18-59
84 1 3 female 0 0 7.7875 Q
85 0 1 male 28 0 0 47.1 S 18-59
86 1 2 female 17 0 0 10.5 S 0-17
87 1 3 female 33 3 0 15.85 S 18-59
88 0 3 male 16 1 3 34.375 S 0-17
89 0 3 male 0 0 8.05 S
90 1 1 female 23 3 2 263 S 18-59
91 0 3 male 24 0 0 8.05 S 18-59
92 0 3 male 29 0 0 8.05 S 18-59
93 0 3 male 20 0 0 7.8542 S 18-59
94 0 1 male 46 1 0 61.175 S 18-59
95 0 3 male 26 1 2 20.575 S 18-59
96 0 3 male 59 0 0 7.25 S 18-59
97 0 3 male 0 0 8.05 S
98 0 1 male 71 0 0 34.6542 C 60+
99 1 1 male 23 0 1 63.3583 C 18-59
100 1 2 female 34 0 1 23 S 18-59
101 0 2 male 34 1 0 26 S 18-59
102 0 3 female 28 0 0 7.8958 S 18-59
103 0 3 male 0 0 7.8958 S
104 0 1 male 21 0 1 77.2875 S 18-59
105 0 3 male 33 0 0 8.6542 S 18-59
106 0 3 male 37 2 0 7.925 S 18-59
107 0 3 male 28 0 0 7.8958 S 18-59
108 1 3 female 21 0 0 7.65 S 18-59
109 1 3 male 0 0 7.775 S
110 0 3 male 38 0 0 7.8958 S 18-59
111 1 3 female 1 0 24.15 Q
112 0 1 male 47 0 0 52 S 18-59
113 0 3 female 14.5 1 0 14.4542 C 0-17
114 0 3 male 22 0 0 8.05 S 18-59
115 0 3 female 20 1 0 9.825 S 18-59
116 0 3 female 17 0 0 14.4583 C 0-17
117 0 3 male 21 0 0 7.925 S 18-59
118 0 3 male 70.5 0 0 7.75 Q 60+
119 0 2 male 29 1 0 21 S 18-59
120 0 1 male 24 0 1 247.5208 C 18-59
121 0 3 female 2 4 2 31.275 S 0-17
122 0 2 male 21 2 0 73.5 S 18-59
123 0 3 male 0 0 8.05 S
124 0 2 male 32.5 1 0 30.0708 C 18-59
125 1 2 female 32.5 0 0 13 S 18-59
126 0 1 male 54 0 1 77.2875 S 18-59
127 1 3 male 12 1 0 11.2417 C 0-17
128 0 3 male 0 0 7.75 Q
129 1 3 male 24 0 0 7.1417 S 18-59
130 1 3 female 1 1 22.3583 C
131 0 3 male 45 0 0 6.975 S 18-59
132 0 3 male 33 0 0 7.8958 C 18-59
133 0 3 male 20 0 0 7.05 S 18-59
134 0 3 female 47 1 0 14.5 S 18-59
135 1 2 female 29 1 0 26 S 18-59
136 0 2 male 25 0 0 13 S 18-59
137 0 2 male 23 0 0 15.0458 C 18-59
138 1 1 female 19 0 2 26.2833 S 18-59
139 0 1 male 37 1 0 53.1 S 18-59
140 0 3 male 16 0 0 9.2167 S 0-17
141 0 1 male 24 0 0 79.2 C 18-59
142 0 3 female 0 2 15.2458 C
143 1 3 female 22 0 0 7.75 S 18-59
144 1 3 female 24 1 0 15.85 S 18-59
145 0 3 male 19 0 0 6.75 Q 18-59
146 0 2 male 18 0 0 11.5 S 18-59
147 0 2 male 19 1 1 36.75 S 18-59
148 1 3 male 27 0 0 7.7958 S 18-59
149 0 3 female 9 2 2 34.375 S 0-17
150 0 2 male 36.5 0 2 26 S 18-59
151 0 2 male 42 0 0 13 S 18-59
152 0 2 male 51 0 0 12.525 S 18-59
153 1 1 female 22 1 0 66.6 S 18-59
154 0 3 male 55.5 0 0 8.05 S 18-59
155 0 3 male 40.5 0 2 14.5 S 18-59
156 0 3 male 0 0 7.3125 S
157 0 1 male 51 0 1 61.3792 C 18-59
158 1 3 female 16 0 0 7.7333 Q 0-17
159 0 3 male 30 0 0 8.05 S 18-59
160 0 3 male 0 0 8.6625 S
161 0 3 male 8 2 69.55 S
162 0 3 male 44 0 1 16.1 S 18-59
163 1 2 female 40 0 0 15.75 S 18-59
164 0 3 male 26 0 0 7.775 S 18-59
165 0 3 male 17 0 0 8.6625 S 0-17
166 0 3 male 1 4 1 39.6875 S 0-17
167 1 3 male 9 0 2 20.525 S 0-17
168 1 1 female 0 1 55 S
169 0 3 female 45 1 4 27.9 S 18-59
170 0 1 male 0 0 25.925 S
171 0 3 male 28 0 0 56.4958 S 18-59
172 0 1 male 61 0 0 33.5 S 60+
173 0 3 male 4 4 1 29.125 Q 0-17
174 1 3 female 1 1 1 11.1333 S 0-17
175 0 3 male 21 0 0 7.925 S 18-59
176 0 1 male 56 0 0 30.6958 C 18-59
177 0 3 male 18 1 1 7.8542 S 18-59
178 0 3 male 3 1 25.4667 S
179 0 1 female 50 0 0 28.7125 C 18-59
180 0 2 male 30 0 0 13 S 18-59
181 0 3 male 36 0 0 0 S 18-59
182 0 3 female 8 2 69.55 S
183 0 2 male 0 0 15.05 C
184 0 3 male 9 4 2 31.3875 S 0-17
185 1 2 male 1 2 1 39 S 0-17
186 1 3 female 4 0 2 22.025 S 0-17
187 0 1 male 0 0 50 S
188 1 3 female 1 0 15.5 Q
189 1 1 male 45 0 0 26.55 S 18-59
190 0 3 male 40 1 1 15.5 Q 18-59
191 0 3 male 36 0 0 7.8958 S 18-59
192 1 2 female 32 0 0 13 S 18-59
193 0 2 male 19 0 0 13 S 18-59
194 1 3 female 19 1 0 7.8542 S 18-59
195 1 2 male 3 1 1 26 S 0-17
196 1 1 female 44 0 0 27.7208 C 18-59
197 1 1 female 58 0 0 146.5208 C 18-59
198 0 3 male 0 0 7.75 Q
199 0 3 male 42 0 1 8.4042 S 18-59
200 1 3 female 0 0 7.75 Q
201 0 2 female 24 0 0 13 S 18-59
202 0 3 male 28 0 0 9.5 S 18-59
203 0 3 male 8 2 69.55 S
204 0 3 male 34 0 0 6.4958 S 18-59
205 0 3 male 45.5 0 0 7.225 C 18-59
206 1 3 male 18 0 0 8.05 S 18-59
207 0 3 female 2 0 1 10.4625 S 0-17
208 0 3 male 32 1 0 15.85 S 18-59
209 1 3 male 26 0 0 18.7875 C 18-59
210 1 3 female 16 0 0 7.75 Q 0-17
211 1 1 male 40 0 0 31 C 18-59
212 0 3 male 24 0 0 7.05 S 18-59
213 1 2 female 35 0 0 21 S 18-59
214 0 3 male 22 0 0 7.25 S 18-59
215 0 2 male 30 0 0 13 S 18-59
216 0 3 male 1 0 7.75 Q
217 1 1 female 31 1 0 113.275 C 18-59
218 1 3 female 27 0 0 7.925 S 18-59
219 0 2 male 42 1 0 27 S 18-59
220 1 1 female 32 0 0 76.2917 C 18-59
221 0 2 male 30 0 0 10.5 S 18-59
222 1 3 male 16 0 0 8.05 S 0-17
223 0 2 male 27 0 0 13 S 18-59
224 0 3 male 51 0 0 8.05 S 18-59
225 0 3 male 0 0 7.8958 S
226 1 1 male 38 1 0 90 S 18-59
227 0 3 male 22 0 0 9.35 S 18-59
228 1 2 male 19 0 0 10.5 S 18-59
229 0 3 male 20.5 0 0 7.25 S 18-59
230 0 2 male 18 0 0 13 S 18-59
231 0 3 female 3 1 25.4667 S
232 1 1 female 35 1 0 83.475 S 18-59
233 0 3 male 29 0 0 7.775 S 18-59
234 0 2 male 59 0 0 13.5 S 18-59
235 1 3 female 5 4 2 31.3875 S 0-17
236 0 2 male 24 0 0 10.5 S 18-59
237 0 3 female 0 0 7.55 S
238 0 2 male 44 1 0 26 S 18-59
239 1 2 female 8 0 2 26.25 S 0-17
240 0 2 male 19 0 0 10.5 S 18-59
241 0 2 male 33 0 0 12.275 S 18-59
242 0 3 female 1 0 14.4542 C
243 1 3 female 1 0 15.5 Q
244 0 2 male 29 0 0 10.5 S 18-59
245 0 3 male 22 0 0 7.125 S 18-59
246 0 3 male 30 0 0 7.225 C 18-59
247 0 1 male 44 2 0 90 Q 18-59
248 0 3 female 25 0 0 7.775 S 18-59
249 1 2 female 24 0 2 14.5 S 18-59
250 1 1 male 37 1 1 52.5542 S 18-59
251 0 2 male 54 1 0 26 S 18-59
252 0 3 male 0 0 7.25 S
253 0 3 female 29 1 1 10.4625 S 18-59
254 0 1 male 62 0 0 26.55 S 60+
255 0 3 male 30 1 0 16.1 S 18-59
256 0 3 female 41 0 2 20.2125 S 18-59
257 1 3 female 29 0 2 15.2458 C 18-59
258 1 1 female 0 0 79.2 C
259 1 1 female 30 0 0 86.5 S 18-59
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797 0 2 male 39 0 0 13 S 18-59
798 1 1 female 49 0 0 25.9292 S 18-59
799 1 3 female 31 0 0 8.6833 S 18-59
800 0 3 male 30 0 0 7.2292 C 18-59
801 0 3 female 30 1 1 24.15 S 18-59
802 0 2 male 34 0 0 13 S 18-59
803 1 2 female 31 1 1 26.25 S 18-59
804 1 1 male 11 1 2 120 S 0-17
805 1 3 male 0.42 0 1 8.5167 C 0-17
806 1 3 male 27 0 0 6.975 S 18-59
807 0 3 male 31 0 0 7.775 S 18-59
808 0 1 male 39 0 0 0 S 18-59
809 0 3 female 18 0 0 7.775 S 18-59
810 0 2 male 39 0 0 13 S 18-59
811 1 1 female 33 1 0 53.1 S 18-59
812 0 3 male 26 0 0 7.8875 S 18-59
813 0 3 male 39 0 0 24.15 S 18-59
814 0 2 male 35 0 0 10.5 S 18-59
815 0 3 female 6 4 2 31.275 S 0-17
816 0 3 male 30.5 0 0 8.05 S 18-59
817 0 1 male 0 0 0 S
818 0 3 female 23 0 0 7.925 S 18-59
819 0 2 male 31 1 1 37.0042 C 18-59
820 0 3 male 43 0 0 6.45 S 18-59
821 0 3 male 10 3 2 27.9 S 0-17
822 1 1 female 52 1 1 93.5 S 18-59
823 1 3 male 27 0 0 8.6625 S 18-59
824 0 1 male 38 0 0 0 S 18-59
825 1 3 female 27 0 1 12.475 S 18-59
826 0 3 male 2 4 1 39.6875 S 0-17
827 0 3 male 0 0 6.95 Q
828 0 3 male 0 0 56.4958 S
829 1 2 male 1 0 2 37.0042 C 0-17
830 1 3 male 0 0 7.75 Q
831 1 1 female 62 0 0 80 60+
832 1 3 female 15 1 0 14.4542 C 0-17
833 1 2 male 0.83 1 1 18.75 S 0-17
834 0 3 male 0 0 7.2292 C
835 0 3 male 23 0 0 7.8542 S 18-59
836 0 3 male 18 0 0 8.3 S 18-59
837 1 1 female 39 1 1 83.1583 C 18-59
838 0 3 male 21 0 0 8.6625 S 18-59
839 0 3 male 0 0 8.05 S
840 1 3 male 32 0 0 56.4958 S 18-59
841 1 1 male 0 0 29.7 C
842 0 3 male 20 0 0 7.925 S 18-59
843 0 2 male 16 0 0 10.5 S 0-17
844 1 1 female 30 0 0 31 C 18-59
845 0 3 male 34.5 0 0 6.4375 C 18-59
846 0 3 male 17 0 0 8.6625 S 0-17
847 0 3 male 42 0 0 7.55 S 18-59
848 0 3 male 8 2 69.55 S
849 0 3 male 35 0 0 7.8958 C 18-59
850 0 2 male 28 0 1 33 S 18-59
851 1 1 female 1 0 89.1042 C
852 0 3 male 4 4 2 31.275 S 0-17
853 0 3 male 74 0 0 7.775 S 60+
854 0 3 female 9 1 1 15.2458 C 0-17
855 1 1 female 16 0 1 39.4 S 0-17
856 0 2 female 44 1 0 26 S 18-59
857 1 3 female 18 0 1 9.35 S 18-59
858 1 1 female 45 1 1 164.8667 S 18-59
859 1 1 male 51 0 0 26.55 S 18-59
860 1 3 female 24 0 3 19.2583 C 18-59
861 0 3 male 0 0 7.2292 C
862 0 3 male 41 2 0 14.1083 S 18-59
863 0 2 male 21 1 0 11.5 S 18-59
864 1 1 female 48 0 0 25.9292 S 18-59
865 0 3 female 8 2 69.55 S
866 0 2 male 24 0 0 13 S 18-59
867 1 2 female 42 0 0 13 S 18-59
868 1 2 female 27 1 0 13.8583 C 18-59
869 0 1 male 31 0 0 50.4958 S 18-59
870 0 3 male 0 0 9.5 S
871 1 3 male 4 1 1 11.1333 S 0-17
872 0 3 male 26 0 0 7.8958 S 18-59
873 1 1 female 47 1 1 52.5542 S 18-59
874 0 1 male 33 0 0 5 S 18-59
875 0 3 male 47 0 0 9 S 18-59
876 1 2 female 28 1 0 24 C 18-59
877 1 3 female 15 0 0 7.225 C 0-17
878 0 3 male 20 0 0 9.8458 S 18-59
879 0 3 male 19 0 0 7.8958 S 18-59
880 0 3 male 0 0 7.8958 S
881 1 1 female 56 0 1 83.1583 C 18-59
882 1 2 female 25 0 1 26 S 18-59
883 0 3 male 33 0 0 7.8958 S 18-59
884 0 3 female 22 0 0 10.5167 S 18-59
885 0 2 male 28 0 0 10.5 S 18-59
886 0 3 male 25 0 0 7.05 S 18-59
887 0 3 female 39 0 5 29.125 Q 18-59
888 0 2 male 27 0 0 13 S 18-59
889 1 1 female 19 0 0 30 S 18-59
890 0 3 female 1 2 23.45 S
891 1 1 male 26 0 0 30 C 18-59
892 0 3 male 32 0 0 7.75 Q 18-59