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Created July 5, 2022 14:53
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Семинарус_Прогнозирование чаевых официантам.ipynb
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"name": "Семинарус_Прогнозирование чаевых официантам.ipynb",
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{
"cell_type": "markdown",
"source": [
"## Чаевые\n",
"Данные, записанные сервером питания, выглядят следующим образом:\n",
"\n",
"1. total_bill: Общий счет в долларах, включая налоги\n",
"2. tip: Чаевые, данные официантам в долларах\n",
"3. sex: пол лица, оплачивающего счет\n",
"4. smoker: курил ли человек или нет\n",
"5. day: день недели\n",
"6. time: обед или ужин\n",
"7. size: количество человек за столом "
],
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" total_bill tip sex smoker day time size\n",
"0 16.99 1.01 Female No Sun Dinner 2\n",
"1 10.34 1.66 Male No Sun Dinner 3\n",
"2 21.01 3.50 Male No Sun Dinner 3\n",
"3 23.68 3.31 Male No Sun Dinner 2\n",
"4 24.59 3.61 Female No Sun Dinner 4"
],
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" <th></th>\n",
" <th>total_bill</th>\n",
" <th>tip</th>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
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" <th>0</th>\n",
" <td>16.99</td>\n",
" <td>1.01</td>\n",
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" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
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" <th>1</th>\n",
" <td>10.34</td>\n",
" <td>1.66</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>21.01</td>\n",
" <td>3.50</td>\n",
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" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>23.68</td>\n",
" <td>3.31</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>24.59</td>\n",
" <td>3.61</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
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]
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"\n",
"data = pd.read_csv(\"tips.csv\")\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"source": [
"## Анализ "
],
"metadata": {
"id": "oMy741scsrrb"
}
},
{
"cell_type": "code",
"source": [
"figure = px.scatter(data_frame = data,\n",
" x=\"total_bill\",\n",
" y=\"tip\",\n",
" size=\"size\",\n",
" color=\"day\",\n",
" trendline=\"ols\")\n",
"figure.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 597
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"id": "Sfy82tLKs3gI",
"outputId": "4f1147f2-7ffb-4076-bc57-0eef56168ebe"
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"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" import pandas.util.testing as tm\n"
]
},
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{
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"source": [
"figure = px.scatter(data_frame = data,\n",
" x=\"total_bill\",\n",
" y=\"tip\",\n",
" size=\"size\",\n",
" color=\"sex\",\n",
" trendline=\"ols\")\n",
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],
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"id": "DbxtE2ujs_4w",
"outputId": "83329bec-502a-499d-b8c4-023a0f34380d"
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"outputs": [
{
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},
{
"cell_type": "code",
"source": [
"figure = px.scatter(data_frame = data,\n",
" x=\"total_bill\",\n",
" y=\"tip\",\n",
" size=\"size\",\n",
" color=\"time\",\n",
" trendline=\"ols\")\n",
"figure.show()"
],
"metadata": {
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"height": 542
},
"id": "iw5owqk5tF5_",
"outputId": "6481b959-a94d-4dcc-9f22-82dff551b616"
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"execution_count": 4,
"outputs": [
{
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]
},
{
"cell_type": "code",
"source": [
"figure = px.pie(\n",
" data,\n",
" values='tip',\n",
" names='day',\n",
" hole = 0.5\n",
")\n",
"figure.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "RDEPwoJLtUG5",
"outputId": "5b4d550c-e4ab-40ef-adde-305534ba9c97"
},
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
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},
{
"cell_type": "markdown",
"source": [
"## Модель прогнозирования чаевых официанту\n"
],
"metadata": {
"id": "vh85HZ7LtuTi"
}
},
{
"cell_type": "code",
"source": [
"data[\"sex\"] = data[\"sex\"].map({\n",
" \"Female\": 0, \"Male\": 1\n",
"})\n",
"data[\"smoker\"] = data[\"smoker\"].map({\n",
" \"No\": 0, \"Yes\": 1\n",
"})\n",
"data[\"day\"] = data[\"day\"].map({\n",
" \"Thur\": 0, \"Fri\": 1, \"Sat\": 2, \"Sun\": 3\n",
"})\n",
"data[\"time\"] = data[\"time\"].map({\n",
" \"Lunch\": 0, \"Dinner\": 1\n",
"})\n",
"\n",
"data.head()"
],
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"id": "5LBkQ-9AvI3C",
"outputId": "3190cf25-8460-4bc7-d6c6-cbaad58440ef"
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"execution_count": 13,
"outputs": [
{
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"data": {
"text/plain": [
" total_bill tip sex smoker day time size\n",
"0 16.99 1.01 0 0 3 1 2\n",
"1 10.34 1.66 1 0 3 1 3\n",
"2 21.01 3.50 1 0 3 1 3\n",
"3 23.68 3.31 1 0 3 1 2\n",
"4 24.59 3.61 0 0 3 1 4"
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"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-14389b36-1524-49e7-88e6-570e08b81912 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-14389b36-1524-49e7-88e6-570e08b81912');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"x = np.array(\n",
" data[\n",
" [\"total_bill\", \"sex\", \"smoker\", \"day\", \"time\", \"size\"]\n",
" ]\n",
")\n",
"y = np.array(data[\"tip\"])\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"xtrain, xtest, ytrain, ytest = train_test_split(\n",
" x,\n",
" y,\n",
" test_size=0.2,\n",
" random_state=42\n",
")"
],
"metadata": {
"id": "H5iHRRN9t9jG"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Обучим модель"
],
"metadata": {
"id": "1_Vs9MVfuI8t"
}
},
{
"cell_type": "code",
"source": [
"from sklearn.linear_model import LinearRegression\n",
"model = LinearRegression()\n",
"model.fit(xtrain, ytrain)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DJJdSDB1uKOz",
"outputId": "f063efc2-b1d8-4e9b-a691-8152d1a9d218"
},
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression()"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"# features = [[\"total_bill\", \"sex\", \"smoker\", \"day\", \"time\", \"size\"]]\n",
"features = np.array([[24.50, 1, 0, 1, 1, 3]])\n",
"model.predict(features)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9LGzlZ_SuN5J",
"outputId": "78400c55-0dac-41b3-cf4e-168fd898133d"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([3.56927898])"
]
},
"metadata": {},
"execution_count": 17
}
]
}
]
}
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