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nouvelle tantative
2023-05-15 16:40:08 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 32,
"id": "38b06b0d-0dea-4af3-805b-b89e702b7342",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"url = \"https://pixees.fr/informatiquelycee/n_site/asset/titanic.csv\""
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "740c50e6-d0c1-4884-a850-9efeac8ae0f8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as plt\n",
"titanic = pd.io.parsers.read_csv(url)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "1a9d6995-a967-4d87-9ea0-e1676b02b3a6",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "8d952731-6adc-4501-ba7f-56470e052b39",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 PassengerId 891 non-null int64 \n",
" 1 Survived 891 non-null int64 \n",
" 2 Pclass 891 non-null int64 \n",
" 3 Name 891 non-null object \n",
" 4 Sex 891 non-null object \n",
" 5 Age 714 non-null float64\n",
" 6 SibSp 891 non-null int64 \n",
" 7 Parch 891 non-null int64 \n",
" 8 Ticket 891 non-null object \n",
" 9 Fare 891 non-null float64\n",
" 10 Cabin 204 non-null object \n",
" 11 Embarked 889 non-null object \n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.7+ KB\n"
]
}
],
"source": [
"titanic.info()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "f1f8916a-b816-435e-a774-6d4922625ba9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"titanic = titanic.drop(['Name', 'PassengerId', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "855b71ca-0050-4805-9994-473cae0ef160",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age\n",
"886 0 2 male 27.0\n",
"887 1 1 female 19.0\n",
"888 0 3 female NaN\n",
"889 1 1 male 26.0\n",
"890 0 3 male 32.0"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.tail()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "a344b6de-0adc-41f3-aff0-d0fc5c8a6122",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"titanic = titanic.dropna(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "e4ca7224-7526-4f5f-8ea5-0ef1163fcc88",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" .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>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>714.000000</td>\n",
" <td>714.000000</td>\n",
" <td>714.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.406162</td>\n",
" <td>2.236695</td>\n",
" <td>29.699118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.491460</td>\n",
" <td>0.838250</td>\n",
" <td>14.526497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>20.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Age\n",
"count 714.000000 714.000000 714.000000\n",
"mean 0.406162 2.236695 29.699118\n",
"std 0.491460 0.838250 14.526497\n",
"min 0.000000 1.000000 0.420000\n",
"25% 0.000000 1.000000 20.125000\n",
"50% 0.000000 2.000000 28.000000\n",
"75% 1.000000 3.000000 38.000000\n",
"max 1.000000 3.000000 80.000000"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.describe()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "708acc2d-35d6-4a4e-8a61-286293ee4749",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"3 355\n",
"1 186\n",
"2 173\n",
"Name: Pclass, dtype: int64"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic['Pclass'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "1634faa8-5376-425c-80f2-e5a888dc4f49",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"titanic['Pclass'].value_counts().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "189efdcd-be75-4bc1-9ec0-8068be847fcf",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
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},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"titanic['Age'].hist()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "0230f489-2426-4d7e-a690-a707342ac750",
"metadata": {
"tags": []
},
"outputs": [
{
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"text/plain": [
" Survived Pclass Age\n",
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"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.groupby(['Sex']).mean()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "32e3ce02-e5f0-421c-add0-a00d769c4564",
"metadata": {
"tags": []
},
"outputs": [
{
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"text/plain": [
" Survived Pclass Sex Age\n",
"0 0 3 0 22.0\n",
"1 1 1 1 38.0\n",
"2 1 3 1 26.0\n",
"3 1 1 1 35.0\n",
"4 0 3 0 35.0"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic['Sex'].replace(['male', 'female'], [0, 1], inplace = True)\n",
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "6c09f3e9-2d6b-47b4-9686-334f36555576",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "0db39046-27f6-46d2-8a1e-cff9bbee08cb",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model = KNeighborsClassifier()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "df2c5596-8b3f-4aa4-a275-f2fec2aded01",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"Y = titanic['Survived']"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "bb50e382-b20b-4867-8135-36138a2af422",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"X = titanic.drop('Survived',axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "d4cc3886-9ac9-4069-b531-57cf9aa869c3",
"metadata": {
"tags": []
},
"outputs": [
{
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"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
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"source": [
"Y"
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{
"cell_type": "code",
"execution_count": 55,
"id": "9c97a8c2-ebd9-4426-a0e3-9539b1319daa",
"metadata": {
"tags": []
},
"outputs": [
{
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" Pclass Sex Age\n",
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"4 3 0 35.0\n",
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"execution_count": 55,
"metadata": {},
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"X"
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{
"cell_type": "code",
"execution_count": 56,
"id": "87e4489f-b1d6-4763-9357-299d4aa5eded",
"metadata": {
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},
"outputs": [
{
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},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "0ea5aae9-6238-4814-860f-3c2263834ca0",
"metadata": {
"tags": []
},
"outputs": [
{
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" 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
" 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0,\n",
" 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
" 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
" 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
" 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
" 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
" 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n",
" 1, 0, 1, 0, 0, 0, 0, 1, 1, 0])"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "5764d5e1-5973-473c-9656-7d59e91ff9ab",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def survie(model, pclass=3, Sex=0, Age=26):\n",
" X = np.array([pclass, Sex, Age]).reshape(1, 3)\n",
" print(model.predict_proba(X))"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "21c34fa6-30a6-4ae6-8a09-e090d0ab72ec",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.6 0.4]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/mamba/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
" warnings.warn(\n"
]
}
],
"source": [
"survie(model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "756dd72b-5055-4517-b884-3fe4ea4bdbb2",
"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": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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