{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "7a83c7a6-a09a-4b61-8191-4570d5159657", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "id": "64fd2fde-9c86-4a90-a878-6798856dd3bc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
089203Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
189313Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
289402Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
389503Wirz, Mr. Albertmale27.0003151548.6625NaNS
489613Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 892 0 3 \n", "1 893 1 3 \n", "2 894 0 2 \n", "3 895 0 3 \n", "4 896 1 3 \n", "\n", " Name Sex Age SibSp Parch \\\n", "0 Kelly, Mr. James male 34.5 0 0 \n", "1 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 \n", "2 Myles, Mr. Thomas Francis male 62.0 0 0 \n", "3 Wirz, Mr. Albert male 27.0 0 0 \n", "4 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 \n", "\n", " Ticket Fare Cabin Embarked \n", "0 330911 7.8292 NaN Q \n", "1 363272 7.0000 NaN S \n", "2 240276 9.6875 NaN Q \n", "3 315154 8.6625 NaN S \n", "4 3101298 12.2875 NaN S " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('tested_titanic.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "adaebed8-8101-4ddb-9a5f-a87a3872a0b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId 418\n", "Survived 2\n", "Pclass 3\n", "Name 418\n", "Sex 2\n", "Age 79\n", "SibSp 7\n", "Parch 8\n", "Ticket 363\n", "Fare 169\n", "Cabin 76\n", "Embarked 3\n", "dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.nunique()" ] }, { "cell_type": "code", "execution_count": 4, "id": "b8270628-33f9-426a-bd5e-0b208f0894b5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(418, 12)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 5, "id": "678c1af7-96e5-45d3-b3ca-e6b4512ef1e7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count418.000000418.000000418.000000332.000000418.000000418.000000417.000000
mean1100.5000000.3636362.26555030.2725900.4473680.39234435.627188
std120.8104580.4816220.84183814.1812090.8967600.98142955.907576
min892.0000000.0000001.0000000.1700000.0000000.0000000.000000
25%996.2500000.0000001.00000021.0000000.0000000.0000007.895800
50%1100.5000000.0000003.00000027.0000000.0000000.00000014.454200
75%1204.7500001.0000003.00000039.0000001.0000000.00000031.500000
max1309.0000001.0000003.00000076.0000008.0000009.000000512.329200
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" ], "text/plain": [ " PassengerId Survived Pclass Age SibSp \\\n", "count 418.000000 418.000000 418.000000 332.000000 418.000000 \n", "mean 1100.500000 0.363636 2.265550 30.272590 0.447368 \n", "std 120.810458 0.481622 0.841838 14.181209 0.896760 \n", "min 892.000000 0.000000 1.000000 0.170000 0.000000 \n", "25% 996.250000 0.000000 1.000000 21.000000 0.000000 \n", "50% 1100.500000 0.000000 3.000000 27.000000 0.000000 \n", "75% 1204.750000 1.000000 3.000000 39.000000 1.000000 \n", "max 1309.000000 1.000000 3.000000 76.000000 8.000000 \n", "\n", " Parch Fare \n", "count 418.000000 417.000000 \n", "mean 0.392344 35.627188 \n", "std 0.981429 55.907576 \n", "min 0.000000 0.000000 \n", "25% 0.000000 7.895800 \n", "50% 0.000000 14.454200 \n", "75% 0.000000 31.500000 \n", "max 9.000000 512.329200 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 6, "id": "050d4f9b-b668-47a8-bed1-bc332f245f71", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "86" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Age'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 7, "id": "08ac6118-4624-4460-ada2-ab56779f8575", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Survived\n", "0 266\n", "1 152\n", "Name: count, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Survived'].value_counts()" ] }, { "cell_type": "code", "execution_count": 8, "id": "d412d27f-a778-4cf2-a74e-070374f00d2e", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.int64' object has no attribute 'startswith'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m sns\u001b[38;5;241m.\u001b[39mcountplot(data \u001b[38;5;241m=\u001b[39m df, x \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSurvived\u001b[39m\u001b[38;5;124m'\u001b[39m], hue \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPclass\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:2955\u001b[0m, in \u001b[0;36mcountplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m 2952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2953\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2955\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m 2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1587\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m 1585\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[0;32m 1586\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[1;32m-> 1587\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m 1588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 1589\u001b[0m ax\u001b[38;5;241m.\u001b[39minvert_yaxis()\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:767\u001b[0m, in \u001b[0;36m_CategoricalPlotter.annotate_axes\u001b[1;34m(self, ax)\u001b[0m\n\u001b[0;32m 764\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylim(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m.5\u001b[39m, auto\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 767\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend(loc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbest\u001b[39m\u001b[38;5;124m\"\u001b[39m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_title)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:322\u001b[0m, in \u001b[0;36mAxes.legend\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 204\u001b[0m \u001b[38;5;129m@_docstring\u001b[39m\u001b[38;5;241m.\u001b[39mdedent_interpd\n\u001b[0;32m 205\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlegend\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 206\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;124;03m Place a legend on the Axes.\u001b[39;00m\n\u001b[0;32m 208\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[38;5;124;03m .. plot:: gallery/text_labels_and_annotations/legend.py\u001b[39;00m\n\u001b[0;32m 321\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 322\u001b[0m handles, labels, kwargs \u001b[38;5;241m=\u001b[39m mlegend\u001b[38;5;241m.\u001b[39m_parse_legend_args([\u001b[38;5;28mself\u001b[39m], \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 323\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend_ \u001b[38;5;241m=\u001b[39m mlegend\u001b[38;5;241m.\u001b[39mLegend(\u001b[38;5;28mself\u001b[39m, handles, labels, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 324\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend_\u001b[38;5;241m.\u001b[39m_remove_method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_remove_legend\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\legend.py:1361\u001b[0m, in \u001b[0;36m_parse_legend_args\u001b[1;34m(axs, handles, labels, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1357\u001b[0m handles \u001b[38;5;241m=\u001b[39m [handle \u001b[38;5;28;01mfor\u001b[39;00m handle, label\n\u001b[0;32m 1358\u001b[0m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(_get_legend_handles(axs, handlers), labels)]\n\u001b[0;32m 1360\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# 0 args: automatically detect labels and handles.\u001b[39;00m\n\u001b[1;32m-> 1361\u001b[0m handles, labels \u001b[38;5;241m=\u001b[39m _get_legend_handles_labels(axs, handlers)\n\u001b[0;32m 1362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m handles:\n\u001b[0;32m 1363\u001b[0m log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[0;32m 1364\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo artists with labels found to put in legend. Note that \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1365\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martists whose label start with an underscore are ignored \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1366\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhen legend() is called with no argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\legend.py:1291\u001b[0m, in \u001b[0;36m_get_legend_handles_labels\u001b[1;34m(axs, legend_handler_map)\u001b[0m\n\u001b[0;32m 1289\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handle \u001b[38;5;129;01min\u001b[39;00m _get_legend_handles(axs, legend_handler_map):\n\u001b[0;32m 1290\u001b[0m label \u001b[38;5;241m=\u001b[39m handle\u001b[38;5;241m.\u001b[39mget_label()\n\u001b[1;32m-> 1291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m label \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m label\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m 1292\u001b[0m handles\u001b[38;5;241m.\u001b[39mappend(handle)\n\u001b[0;32m 1293\u001b[0m labels\u001b[38;5;241m.\u001b[39mappend(label)\n", "\u001b[1;31mAttributeError\u001b[0m: 'numpy.int64' object has no attribute 'startswith'" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(data = df, x = df['Survived'], hue = df['Pclass'])" ] }, { "cell_type": "code", "execution_count": 9, "id": "ccbd38c8-0f97-4931-8306-6aada604e7a6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 male\n", "1 female\n", "2 male\n", "3 male\n", "4 female\n", " ... \n", "413 male\n", "414 female\n", "415 male\n", "416 male\n", "417 male\n", "Name: Sex, Length: 418, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Sex']" ] }, { "cell_type": "code", "execution_count": 10, "id": "2cfe231d-a6e3-48e5-abd8-8be762f89c45", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.int64' object has no attribute 'startswith'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m sns\u001b[38;5;241m.\u001b[39mcountplot( x \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSex\u001b[39m\u001b[38;5;124m'\u001b[39m], hue \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSurvived\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:2955\u001b[0m, in \u001b[0;36mcountplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m 2952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2953\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2955\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m 2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1587\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m 1585\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[0;32m 1586\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[1;32m-> 1587\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m 1588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 1589\u001b[0m ax\u001b[38;5;241m.\u001b[39minvert_yaxis()\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:767\u001b[0m, in \u001b[0;36m_CategoricalPlotter.annotate_axes\u001b[1;34m(self, ax)\u001b[0m\n\u001b[0;32m 764\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylim(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m.5\u001b[39m, auto\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 767\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend(loc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbest\u001b[39m\u001b[38;5;124m\"\u001b[39m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_title)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:322\u001b[0m, in \u001b[0;36mAxes.legend\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 204\u001b[0m \u001b[38;5;129m@_docstring\u001b[39m\u001b[38;5;241m.\u001b[39mdedent_interpd\n\u001b[0;32m 205\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlegend\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 206\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;124;03m Place a legend on the Axes.\u001b[39;00m\n\u001b[0;32m 208\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[38;5;124;03m .. plot:: gallery/text_labels_and_annotations/legend.py\u001b[39;00m\n\u001b[0;32m 321\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 322\u001b[0m handles, labels, kwargs \u001b[38;5;241m=\u001b[39m mlegend\u001b[38;5;241m.\u001b[39m_parse_legend_args([\u001b[38;5;28mself\u001b[39m], \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 323\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend_ \u001b[38;5;241m=\u001b[39m mlegend\u001b[38;5;241m.\u001b[39mLegend(\u001b[38;5;28mself\u001b[39m, handles, labels, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 324\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend_\u001b[38;5;241m.\u001b[39m_remove_method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_remove_legend\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\legend.py:1361\u001b[0m, in \u001b[0;36m_parse_legend_args\u001b[1;34m(axs, handles, labels, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1357\u001b[0m handles \u001b[38;5;241m=\u001b[39m [handle \u001b[38;5;28;01mfor\u001b[39;00m handle, label\n\u001b[0;32m 1358\u001b[0m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(_get_legend_handles(axs, handlers), labels)]\n\u001b[0;32m 1360\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# 0 args: automatically detect labels and handles.\u001b[39;00m\n\u001b[1;32m-> 1361\u001b[0m handles, labels \u001b[38;5;241m=\u001b[39m _get_legend_handles_labels(axs, handlers)\n\u001b[0;32m 1362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m handles:\n\u001b[0;32m 1363\u001b[0m log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[0;32m 1364\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo artists with labels found to put in legend. Note that \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1365\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martists whose label start with an underscore are ignored \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1366\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhen legend() is called with no argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\legend.py:1291\u001b[0m, in \u001b[0;36m_get_legend_handles_labels\u001b[1;34m(axs, legend_handler_map)\u001b[0m\n\u001b[0;32m 1289\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handle \u001b[38;5;129;01min\u001b[39;00m _get_legend_handles(axs, legend_handler_map):\n\u001b[0;32m 1290\u001b[0m label \u001b[38;5;241m=\u001b[39m handle\u001b[38;5;241m.\u001b[39mget_label()\n\u001b[1;32m-> 1291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m label \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m label\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m 1292\u001b[0m handles\u001b[38;5;241m.\u001b[39mappend(handle)\n\u001b[0;32m 1293\u001b[0m labels\u001b[38;5;241m.\u001b[39mappend(label)\n", "\u001b[1;31mAttributeError\u001b[0m: 'numpy.int64' object has no attribute 'startswith'" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot( x = df['Sex'], hue = df['Survived'])" ] }, { "cell_type": "code", "execution_count": 11, "id": "aae40950-0366-4af8-a425-a16aa00388b8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
089203Kelly, Mr. James134.5003309117.8292NaNQ
189313Wilkes, Mrs. James (Ellen Needs)047.0103632727.0000NaNS
289402Myles, Mr. Thomas Francis162.0002402769.6875NaNQ
389503Wirz, Mr. Albert127.0003151548.6625NaNS
489613Hirvonen, Mrs. Alexander (Helga E Lindqvist)022.011310129812.2875NaNS
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 892 0 3 \n", "1 893 1 3 \n", "2 894 0 2 \n", "3 895 0 3 \n", "4 896 1 3 \n", "\n", " Name Sex Age SibSp Parch \\\n", "0 Kelly, Mr. James 1 34.5 0 0 \n", "1 Wilkes, Mrs. James (Ellen Needs) 0 47.0 1 0 \n", "2 Myles, Mr. Thomas Francis 1 62.0 0 0 \n", "3 Wirz, Mr. Albert 1 27.0 0 0 \n", "4 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 0 22.0 1 1 \n", "\n", " Ticket Fare Cabin Embarked \n", "0 330911 7.8292 NaN Q \n", "1 363272 7.0000 NaN S \n", "2 240276 9.6875 NaN Q \n", "3 315154 8.6625 NaN S \n", "4 3101298 12.2875 NaN S " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "encoder = LabelEncoder()\n", "\n", "df['Sex'] = encoder.fit_transform(df['Sex'])\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 12, "id": "08700cc7-1083-4ce0-b68b-818ce5fe2cd2", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.int64' object has no attribute 'startswith'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[12], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m sns\u001b[38;5;241m.\u001b[39mcountplot(x \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSex\u001b[39m\u001b[38;5;124m'\u001b[39m], hue \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSurvived\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:2955\u001b[0m, in \u001b[0;36mcountplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m 2952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2953\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2955\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m 2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1587\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m 1585\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[0;32m 1586\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[1;32m-> 1587\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m 1588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 1589\u001b[0m ax\u001b[38;5;241m.\u001b[39minvert_yaxis()\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:767\u001b[0m, in \u001b[0;36m_CategoricalPlotter.annotate_axes\u001b[1;34m(self, ax)\u001b[0m\n\u001b[0;32m 764\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylim(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m.5\u001b[39m, auto\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 767\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend(loc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbest\u001b[39m\u001b[38;5;124m\"\u001b[39m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_title)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:322\u001b[0m, in \u001b[0;36mAxes.legend\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 204\u001b[0m \u001b[38;5;129m@_docstring\u001b[39m\u001b[38;5;241m.\u001b[39mdedent_interpd\n\u001b[0;32m 205\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlegend\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 206\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;124;03m Place a legend on the Axes.\u001b[39;00m\n\u001b[0;32m 208\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[38;5;124;03m .. plot:: gallery/text_labels_and_annotations/legend.py\u001b[39;00m\n\u001b[0;32m 321\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 322\u001b[0m handles, labels, kwargs \u001b[38;5;241m=\u001b[39m mlegend\u001b[38;5;241m.\u001b[39m_parse_legend_args([\u001b[38;5;28mself\u001b[39m], \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 323\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend_ \u001b[38;5;241m=\u001b[39m mlegend\u001b[38;5;241m.\u001b[39mLegend(\u001b[38;5;28mself\u001b[39m, handles, labels, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 324\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend_\u001b[38;5;241m.\u001b[39m_remove_method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_remove_legend\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\legend.py:1361\u001b[0m, in \u001b[0;36m_parse_legend_args\u001b[1;34m(axs, handles, labels, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1357\u001b[0m handles \u001b[38;5;241m=\u001b[39m [handle \u001b[38;5;28;01mfor\u001b[39;00m handle, label\n\u001b[0;32m 1358\u001b[0m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(_get_legend_handles(axs, handlers), labels)]\n\u001b[0;32m 1360\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# 0 args: automatically detect labels and handles.\u001b[39;00m\n\u001b[1;32m-> 1361\u001b[0m handles, labels \u001b[38;5;241m=\u001b[39m _get_legend_handles_labels(axs, handlers)\n\u001b[0;32m 1362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m handles:\n\u001b[0;32m 1363\u001b[0m log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[0;32m 1364\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo artists with labels found to put in legend. Note that \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1365\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martists whose label start with an underscore are ignored \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1366\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhen legend() is called with no argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\legend.py:1291\u001b[0m, in \u001b[0;36m_get_legend_handles_labels\u001b[1;34m(axs, legend_handler_map)\u001b[0m\n\u001b[0;32m 1289\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handle \u001b[38;5;129;01min\u001b[39;00m _get_legend_handles(axs, legend_handler_map):\n\u001b[0;32m 1290\u001b[0m label \u001b[38;5;241m=\u001b[39m handle\u001b[38;5;241m.\u001b[39mget_label()\n\u001b[1;32m-> 1291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m label \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m label\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m 1292\u001b[0m handles\u001b[38;5;241m.\u001b[39mappend(handle)\n\u001b[0;32m 1293\u001b[0m labels\u001b[38;5;241m.\u001b[39mappend(label)\n", "\u001b[1;31mAttributeError\u001b[0m: 'numpy.int64' object has no attribute 'startswith'" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x = df['Sex'], hue = df['Survived'])" ] }, { "cell_type": "code", "execution_count": 13, "id": "62034042-620d-498b-a6bc-4c8f38790f2f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "86" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Age'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 14, "id": "0c1a7522-38a4-4b63-be9a-18ba2a0712e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(df['Age'])" ] }, { "cell_type": "code", "execution_count": 15, "id": "3134dd1a-552e-4431-b57f-2a92b63776e5", "metadata": {}, "outputs": [], "source": [ "df['Age'].fillna(df.Age.median(), inplace = True)" ] }, { "cell_type": "code", "execution_count": 16, "id": "9e087e12-0eb0-4895-8dea-21514c88ad2f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Age'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 17, "id": "bdd8727f-485f-4b6f-9fe0-eb2cfc4a4cc7", "metadata": {}, "outputs": [], "source": [ "X = df[['Pclass', 'Sex', 'Age']] \n", "y = df['Survived']" ] }, { "cell_type": "code", "execution_count": 18, "id": "4f268d49-2101-4136-871e-077d3d4f6439", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.33)" ] }, { "cell_type": "code", "execution_count": 19, "id": "29e69f91-781b-4c16-8d08-35f20b9a91d4", "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "classifier = LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 20, "id": "2033dc05-8a34-4be4-a40a-0c14d09d0038", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LogisticRegression()
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" ], "text/plain": [ "LogisticRegression()" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 21, "id": "c0aac3da-59ee-425e-8d7f-4db279a689f1", "metadata": {}, "outputs": [], "source": [ "y_pred = classifier.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 22, "id": "992b8a97-8594-4ac0-bd8d-963c374b883a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n", " 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0,\n", " 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1,\n", " 1, 0, 0, 0, 0, 1], dtype=int64)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred" ] }, { "cell_type": "code", "execution_count": 23, "id": "db2322a7-7416-47a1-8695-04b6716104ef", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the accuracy score is 1.0\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 93\n", " 1 1.00 1.00 1.00 45\n", "\n", " accuracy 1.00 138\n", " macro avg 1.00 1.00 1.00 138\n", "weighted avg 1.00 1.00 1.00 138\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report, accuracy_score\n", "print(\"the accuracy score is \", accuracy_score(y_test, y_pred))\n", "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 24, "id": "f8e87d14-9540-499e-b22b-6c70cc4dcb08", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You are dead\n" ] } ], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "# female = 0 male = 1\n", "# df[['Pclass', 'Sex', 'Age']]\n", "# result = classifier.predict([[3,0,22]]) # survived\n", "# result = classifier.predict([[3,1,22]]) # died\n", "# result = classifier.predict([[2,0,22]]) # survived\n", "# result = classifier.predict([[2,1,22]]) # died\n", "# result = classifier.predict([[1,0,22]]) # survived\n", "# result = classifier.predict([[1,1,22]]) # died\n", "# result = classifier.predict([[3,0,62]]) # survived\n", "# result = classifier.predict([[3,1,62]]) # died\n", "# result = classifier.predict([[2,0,62]]) # died\n", "# result = classifier.predict([[2,1,62]]) # died\n", "# result = classifier.predict([[1,0,62]]) # died\n", "result = classifier.predict([[1,1,62]]) # died\n", "\n", "if(result == 0):\n", " print(\"You are dead\")\n", "else:\n", " print(\"Congratulations! You Survived\")" ] }, { "cell_type": "code", "execution_count": null, "id": "31208f51-fbb2-4d45-8431-b3d8f2cb3822", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }