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AIM-PIbd-32-Kaznacheeva-E-K/lab_4/Lab4.ipynb
kaznacheeva 5bfab95a94 комит
2024-11-23 12:21:27 +04:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Начало лабораторной работы**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n",
" 'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n",
" 'remote_ratio', 'company_location', 'company_size'],\n",
" dtype='object')\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
"print(df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" .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>work_year</th>\n",
" <th>experience_level</th>\n",
" <th>employment_type</th>\n",
" <th>job_title</th>\n",
" <th>salary</th>\n",
" <th>salary_currency</th>\n",
" <th>salary_in_usd</th>\n",
" <th>employee_residence</th>\n",
" <th>remote_ratio</th>\n",
" <th>company_location</th>\n",
" <th>company_size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Principal Data Scientist</td>\n",
" <td>80000</td>\n",
" <td>EUR</td>\n",
" <td>85847</td>\n",
" <td>ES</td>\n",
" <td>100</td>\n",
" <td>ES</td>\n",
" <td>L</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2023</td>\n",
" <td>MI</td>\n",
" <td>CT</td>\n",
" <td>ML Engineer</td>\n",
" <td>30000</td>\n",
" <td>USD</td>\n",
" <td>30000</td>\n",
" <td>US</td>\n",
" <td>100</td>\n",
" <td>US</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2023</td>\n",
" <td>MI</td>\n",
" <td>CT</td>\n",
" <td>ML Engineer</td>\n",
" <td>25500</td>\n",
" <td>USD</td>\n",
" <td>25500</td>\n",
" <td>US</td>\n",
" <td>100</td>\n",
" <td>US</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Scientist</td>\n",
" <td>175000</td>\n",
" <td>USD</td>\n",
" <td>175000</td>\n",
" <td>CA</td>\n",
" <td>100</td>\n",
" <td>CA</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Scientist</td>\n",
" <td>120000</td>\n",
" <td>USD</td>\n",
" <td>120000</td>\n",
" <td>CA</td>\n",
" <td>100</td>\n",
" <td>CA</td>\n",
" <td>M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" work_year experience_level employment_type job_title \\\n",
"0 2023 SE FT Principal Data Scientist \n",
"1 2023 MI CT ML Engineer \n",
"2 2023 MI CT ML Engineer \n",
"3 2023 SE FT Data Scientist \n",
"4 2023 SE FT Data Scientist \n",
"\n",
" salary salary_currency salary_in_usd employee_residence remote_ratio \\\n",
"0 80000 EUR 85847 ES 100 \n",
"1 30000 USD 30000 US 100 \n",
"2 25500 USD 25500 US 100 \n",
"3 175000 USD 175000 CA 100 \n",
"4 120000 USD 120000 CA 100 \n",
"\n",
" company_location company_size \n",
"0 ES L \n",
"1 US S \n",
"2 US S \n",
"3 CA M \n",
"4 CA M "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\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>work_year</th>\n",
" <th>salary</th>\n",
" <th>salary_in_usd</th>\n",
" <th>remote_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3755.000000</td>\n",
" <td>3.755000e+03</td>\n",
" <td>3755.000000</td>\n",
" <td>3755.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2022.373635</td>\n",
" <td>1.906956e+05</td>\n",
" <td>137570.389880</td>\n",
" <td>46.271638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.691448</td>\n",
" <td>6.716765e+05</td>\n",
" <td>63055.625278</td>\n",
" <td>48.589050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2020.000000</td>\n",
" <td>6.000000e+03</td>\n",
" <td>5132.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2022.000000</td>\n",
" <td>1.000000e+05</td>\n",
" <td>95000.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2022.000000</td>\n",
" <td>1.380000e+05</td>\n",
" <td>135000.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2023.000000</td>\n",
" <td>1.800000e+05</td>\n",
" <td>175000.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2023.000000</td>\n",
" <td>3.040000e+07</td>\n",
" <td>450000.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" work_year salary salary_in_usd remote_ratio\n",
"count 3755.000000 3.755000e+03 3755.000000 3755.000000\n",
"mean 2022.373635 1.906956e+05 137570.389880 46.271638\n",
"std 0.691448 6.716765e+05 63055.625278 48.589050\n",
"min 2020.000000 6.000000e+03 5132.000000 0.000000\n",
"25% 2022.000000 1.000000e+05 95000.000000 0.000000\n",
"50% 2022.000000 1.380000e+05 135000.000000 0.000000\n",
"75% 2023.000000 1.800000e+05 175000.000000 100.000000\n",
"max 2023.000000 3.040000e+07 450000.000000 100.000000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"work_year 0\n",
"experience_level 0\n",
"employment_type 0\n",
"job_title 0\n",
"salary 0\n",
"salary_currency 0\n",
"salary_in_usd 0\n",
"employee_residence 0\n",
"remote_ratio 0\n",
"company_location 0\n",
"company_size 0\n",
"dtype: int64\n",
"work_year False\n",
"experience_level False\n",
"employment_type False\n",
"job_title False\n",
"salary False\n",
"salary_currency False\n",
"salary_in_usd False\n",
"employee_residence False\n",
"remote_ratio False\n",
"company_location False\n",
"company_size False\n",
"dtype: bool\n"
]
}
],
"source": [
"# Процент пропущенных значений признаков\n",
"for i in df.columns:\n",
" null_rate = df[i].isnull().sum() / len(df) * 100\n",
" if null_rate > 0:\n",
" print(f'{i} Процент пустых значений: %{null_rate:.2f}')\n",
"\n",
"print(df.isnull().sum())\n",
"\n",
"print(df.isnull().any())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Классификация"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'X_train'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>work_year</th>\n",
" <th>experience_level</th>\n",
" <th>employment_type</th>\n",
" <th>job_title</th>\n",
" <th>salary</th>\n",
" <th>salary_currency</th>\n",
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" <th>employee_residence</th>\n",
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" <th>company_size</th>\n",
" <th>above_median_salary</th>\n",
" <th>salary_category</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>1809</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Engineer</td>\n",
" <td>182000</td>\n",
" <td>USD</td>\n",
" <td>182000</td>\n",
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" <th>1082</th>\n",
" <td>2023</td>\n",
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" <th>1686</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>BI Developer</td>\n",
" <td>140000</td>\n",
" <td>USD</td>\n",
" <td>140000</td>\n",
" <td>US</td>\n",
" <td>100</td>\n",
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" <td>M</td>\n",
" <td>1</td>\n",
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" <th>1600</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Scientist</td>\n",
" <td>140000</td>\n",
" <td>USD</td>\n",
" <td>140000</td>\n",
" <td>US</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>1376</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Engineer</td>\n",
" <td>226700</td>\n",
" <td>USD</td>\n",
" <td>226700</td>\n",
" <td>US</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2706</th>\n",
" <td>2022</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Engineer</td>\n",
" <td>160000</td>\n",
" <td>USD</td>\n",
" <td>160000</td>\n",
" <td>US</td>\n",
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" <th>928</th>\n",
" <td>2023</td>\n",
" <td>MI</td>\n",
" <td>FT</td>\n",
" <td>Data Engineer</td>\n",
" <td>200000</td>\n",
" <td>USD</td>\n",
" <td>200000</td>\n",
" <td>US</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>2023</td>\n",
" <td>MI</td>\n",
" <td>FT</td>\n",
" <td>Data Engineer</td>\n",
" <td>140000</td>\n",
" <td>USD</td>\n",
" <td>140000</td>\n",
" <td>US</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>716</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Scientist</td>\n",
" <td>297300</td>\n",
" <td>USD</td>\n",
" <td>297300</td>\n",
" <td>US</td>\n",
" <td>100</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1299</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Engineer</td>\n",
" <td>133832</td>\n",
" <td>USD</td>\n",
" <td>133832</td>\n",
" <td>US</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3004 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" work_year experience_level employment_type job_title \\\n",
"1809 2023 SE FT Data Engineer \n",
"1082 2023 SE FT Machine Learning Engineer \n",
"1686 2023 SE FT BI Developer \n",
"1600 2023 SE FT Data Scientist \n",
"1376 2023 SE FT Data Engineer \n",
"... ... ... ... ... \n",
"2706 2022 SE FT Data Engineer \n",
"928 2023 MI FT Data Engineer \n",
"564 2023 MI FT Data Engineer \n",
"716 2023 SE FT Data Scientist \n",
"1299 2023 SE FT Data Engineer \n",
"\n",
" salary salary_currency salary_in_usd employee_residence remote_ratio \\\n",
"1809 182000 USD 182000 US 100 \n",
"1082 126000 USD 126000 US 0 \n",
"1686 140000 USD 140000 US 100 \n",
"1600 140000 USD 140000 US 0 \n",
"1376 226700 USD 226700 US 0 \n",
"... ... ... ... ... ... \n",
"2706 160000 USD 160000 US 100 \n",
"928 200000 USD 200000 US 0 \n",
"564 140000 USD 140000 US 0 \n",
"716 297300 USD 297300 US 100 \n",
"1299 133832 USD 133832 US 0 \n",
"\n",
" company_location company_size above_median_salary salary_category \n",
"1809 US M 1 1 \n",
"1082 US M 0 1 \n",
"1686 US M 1 1 \n",
"1600 US M 1 1 \n",
"1376 US M 1 2 \n",
"... ... ... ... ... \n",
"2706 US M 1 1 \n",
"928 US M 1 1 \n",
"564 US M 1 1 \n",
"716 US M 1 2 \n",
"1299 US M 0 1 \n",
"\n",
"[3004 rows x 13 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"'y_train'"
]
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"text/plain": [
" above_median_salary\n",
"1809 1\n",
"1082 0\n",
"1686 1\n",
"1600 1\n",
"1376 1\n",
"... ...\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>work_year</th>\n",
" <th>experience_level</th>\n",
" <th>employment_type</th>\n",
" <th>job_title</th>\n",
" <th>salary</th>\n",
" <th>salary_currency</th>\n",
" <th>salary_in_usd</th>\n",
" <th>employee_residence</th>\n",
" <th>remote_ratio</th>\n",
" <th>company_location</th>\n",
" <th>company_size</th>\n",
" <th>above_median_salary</th>\n",
" <th>salary_category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3459</th>\n",
" <td>2022</td>\n",
" <td>MI</td>\n",
" <td>FT</td>\n",
" <td>Research Scientist</td>\n",
" <td>59000</td>\n",
" <td>EUR</td>\n",
" <td>61989</td>\n",
" <td>AT</td>\n",
" <td>0</td>\n",
" <td>AT</td>\n",
" <td>L</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3724</th>\n",
" <td>2021</td>\n",
" <td>EN</td>\n",
" <td>FT</td>\n",
" <td>Business Data Analyst</td>\n",
" <td>50000</td>\n",
" <td>EUR</td>\n",
" <td>59102</td>\n",
" <td>LU</td>\n",
" <td>100</td>\n",
" <td>LU</td>\n",
" <td>L</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1795</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Engineer</td>\n",
" <td>180000</td>\n",
" <td>USD</td>\n",
" <td>180000</td>\n",
" <td>US</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3535</th>\n",
" <td>2021</td>\n",
" <td>MI</td>\n",
" <td>FT</td>\n",
" <td>Data Scientist</td>\n",
" <td>50000</td>\n",
" <td>USD</td>\n",
" <td>50000</td>\n",
" <td>NG</td>\n",
" <td>100</td>\n",
" <td>NG</td>\n",
" <td>L</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3255</th>\n",
" <td>2022</td>\n",
" <td>MI</td>\n",
" <td>FT</td>\n",
" <td>Data Analyst</td>\n",
" <td>106260</td>\n",
" <td>USD</td>\n",
" <td>106260</td>\n",
" <td>US</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1943</th>\n",
" <td>2022</td>\n",
" <td>MI</td>\n",
" <td>FT</td>\n",
" <td>Data Engineer</td>\n",
" <td>120000</td>\n",
" <td>USD</td>\n",
" <td>120000</td>\n",
" <td>US</td>\n",
" <td>100</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>573</th>\n",
" <td>2023</td>\n",
" <td>EN</td>\n",
" <td>FT</td>\n",
" <td>Autonomous Vehicle Technician</td>\n",
" <td>7000</td>\n",
" <td>USD</td>\n",
" <td>7000</td>\n",
" <td>GH</td>\n",
" <td>0</td>\n",
" <td>GH</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3013</th>\n",
" <td>2022</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Machine Learning Engineer</td>\n",
" <td>129300</td>\n",
" <td>USD</td>\n",
" <td>129300</td>\n",
" <td>US</td>\n",
" <td>0</td>\n",
" <td>US</td>\n",
" <td>M</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>327</th>\n",
" <td>2023</td>\n",
" <td>EN</td>\n",
" <td>FT</td>\n",
" <td>Data Scientist</td>\n",
" <td>70000</td>\n",
" <td>CAD</td>\n",
" <td>51753</td>\n",
" <td>CA</td>\n",
" <td>100</td>\n",
" <td>CA</td>\n",
" <td>L</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1565</th>\n",
" <td>2023</td>\n",
" <td>SE</td>\n",
" <td>FT</td>\n",
" <td>Data Analyst</td>\n",
" <td>48000</td>\n",
" <td>EUR</td>\n",
" <td>51508</td>\n",
" <td>ES</td>\n",
" <td>0</td>\n",
" <td>ES</td>\n",
" <td>M</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>751 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" work_year experience_level employment_type \\\n",
"3459 2022 MI FT \n",
"3724 2021 EN FT \n",
"1795 2023 SE FT \n",
"3535 2021 MI FT \n",
"3255 2022 MI FT \n",
"... ... ... ... \n",
"1943 2022 MI FT \n",
"573 2023 EN FT \n",
"3013 2022 SE FT \n",
"327 2023 EN FT \n",
"1565 2023 SE FT \n",
"\n",
" job_title salary salary_currency salary_in_usd \\\n",
"3459 Research Scientist 59000 EUR 61989 \n",
"3724 Business Data Analyst 50000 EUR 59102 \n",
"1795 Data Engineer 180000 USD 180000 \n",
"3535 Data Scientist 50000 USD 50000 \n",
"3255 Data Analyst 106260 USD 106260 \n",
"... ... ... ... ... \n",
"1943 Data Engineer 120000 USD 120000 \n",
"573 Autonomous Vehicle Technician 7000 USD 7000 \n",
"3013 Machine Learning Engineer 129300 USD 129300 \n",
"327 Data Scientist 70000 CAD 51753 \n",
"1565 Data Analyst 48000 EUR 51508 \n",
"\n",
" employee_residence remote_ratio company_location company_size \\\n",
"3459 AT 0 AT L \n",
"3724 LU 100 LU L \n",
"1795 US 0 US M \n",
"3535 NG 100 NG L \n",
"3255 US 0 US M \n",
"... ... ... ... ... \n",
"1943 US 100 US M \n",
"573 GH 0 GH S \n",
"3013 US 0 US M \n",
"327 CA 100 CA L \n",
"1565 ES 0 ES M \n",
"\n",
" above_median_salary salary_category \n",
"3459 0 0 \n",
"3724 0 0 \n",
"1795 1 1 \n",
"3535 0 0 \n",
"3255 0 1 \n",
"... ... ... \n",
"1943 0 1 \n",
"573 0 0 \n",
"3013 0 1 \n",
"327 0 0 \n",
"1565 0 0 \n",
"\n",
"[751 rows x 13 columns]"
]
},
"metadata": {},
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},
{
"data": {
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"'y_test'"
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"text/plain": [
" above_median_salary\n",
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"... ...\n",
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"text": [
"work_year int64\n",
"experience_level object\n",
"employment_type object\n",
"job_title object\n",
"salary int64\n",
"salary_currency object\n",
"salary_in_usd int64\n",
"employee_residence object\n",
"remote_ratio int64\n",
"company_location object\n",
"company_size object\n",
"above_median_salary int64\n",
"salary_category category\n",
"dtype: object\n"
]
},
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from typing import Tuple\n",
"import pandas as pd\n",
"from pandas import DataFrame\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
"\n",
"# Создание целевого признака\n",
"median_salary = df['salary_in_usd'].median()\n",
"df['above_median_salary'] = np.where(df['salary_in_usd'] > median_salary, 1, 0)\n",
"\n",
"# Разделение на признаки и целевую переменную\n",
"X = df.drop(columns=['salary_in_usd', 'above_median_salary'])\n",
"y = df['above_median_salary']\n",
"\n",
"# Примерная категоризация\n",
"df['salary_category'] = pd.cut(df['salary_in_usd'], bins=[0, 100000, 200000, np.inf], labels=[0, 1, 2])\n",
"\n",
"# Выбор признаков и целевых переменных\n",
"X = df.drop(columns=['salary_in_usd', 'salary_category'])\n",
"\n",
"def split_stratified_into_train_val_test(\n",
" df_input,\n",
" stratify_colname=\"y\",\n",
" frac_train=0.6,\n",
" frac_val=0.15,\n",
" frac_test=0.25,\n",
" random_state=None,\n",
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
" \n",
" if frac_train + frac_val + frac_test != 1.0:\n",
" raise ValueError(\n",
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
" % (frac_train, frac_val, frac_test)\n",
" )\n",
" \n",
" if stratify_colname not in df_input.columns:\n",
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
" X = df_input # Contains all columns.\n",
" y = df_input[\n",
" [stratify_colname]\n",
" ] # Dataframe of just the column on which to stratify.\n",
" \n",
" # Split original dataframe into train and temp dataframes.\n",
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
" )\n",
"\n",
" if frac_val <= 0:\n",
" assert len(df_input) == len(df_train) + len(df_temp)\n",
" return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
" # Split the temp dataframe into val and test dataframes.\n",
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
"\n",
" df_val, df_test, y_val, y_test = train_test_split(\n",
" df_temp,\n",
" y_temp,\n",
" stratify=y_temp,\n",
" test_size=relative_frac_test,\n",
" random_state=random_state,\n",
" )\n",
"\n",
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
" return df_train, df_val, df_test, y_train, y_val, y_test\n",
"\n",
"X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
" df, stratify_colname=\"above_median_salary\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=42\n",
")\n",
"\n",
"display(\"X_train\", X_train)\n",
"display(\"y_train\", y_train)\n",
"\n",
"display(\"X_test\", X_test)\n",
"display(\"y_test\", y_test)\n",
"\n",
"# Проверка преобразования\n",
"print(df.dtypes)\n",
"\n",
"# Визуализация распределения зарплат\n",
"plt.figure(figsize=(10, 6))\n",
"sns.histplot(df['salary_in_usd'], bins=50, kde=True)\n",
"plt.title('Распределение зарплат')\n",
"plt.xlabel('Зарплата (USD)')\n",
"plt.ylabel('Частота')\n",
"plt.show()\n",
"\n",
"# Визуализация зависимости между зарплатой и уровнем опыта\n",
"plt.figure(figsize=(10, 6))\n",
"sns.boxplot(x='experience_level', y='salary_in_usd', data=df)\n",
"plt.title('Зависимость зарплаты от уровня опыта')\n",
"plt.xlabel('Уровень опыта')\n",
"plt.ylabel('Зарплата (USD)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Теперь перейдем к делению на выборки и созданию ориентира"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер обучающей выборки: (3004, 10)\n",
"Размер тестовой выборки: (751, 10)\n",
"Baseline Accuracy: 0.5126498002663116\n",
"Baseline F1 Score: 0.3474826991241725\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score, f1_score\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
"\n",
"# Создание целевого признака\n",
"median_salary = df['salary_in_usd'].median()\n",
"df['above_median_salary'] = np.where(df['salary_in_usd'] > median_salary, 1, 0)\n",
"\n",
"# Разделение на признаки и целевую переменную\n",
"features = ['work_year', 'experience_level', 'employment_type', 'job_title', 'salary', 'salary_currency', 'remote_ratio', 'employee_residence', 'company_location', 'company_size']\n",
"target = 'above_median_salary'\n",
"\n",
"# Разделение данных на тренировочный и тестовый наборы\n",
"X_train, X_test, y_train, y_test = train_test_split(df[features], df[target], test_size=0.2, random_state=42, stratify=df[target])\n",
"\n",
"print(\"Размер обучающей выборки:\", X_train.shape)\n",
"print(\"Размер тестовой выборки:\", X_test.shape)\n",
"\n",
"# Создание ориентира (baseline)\n",
"baseline_threshold = y_train.mean()\n",
"baseline_predictions = [1 if pred > baseline_threshold else 0 for pred in [baseline_threshold] * len(y_test)]\n",
"\n",
"# Вычисление метрик для ориентира\n",
"baseline_accuracy = accuracy_score(y_test, baseline_predictions)\n",
"baseline_f1 = f1_score(y_test, baseline_predictions, average='weighted')\n",
"\n",
"print('Baseline Accuracy:', baseline_accuracy)\n",
"print('Baseline F1 Score:', baseline_f1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создание конвейера и обучение моделей"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Logistic Regression\n",
"Accuracy: 0.7523\n",
"F1 Score: 0.7609\n",
"----------------------------------------\n",
"Model: Decision Tree\n",
"Accuracy: 0.9960\n",
"F1 Score: 0.9959\n",
"----------------------------------------\n",
"Model: Gradient Boosting\n",
"Accuracy: 0.9947\n",
"F1 Score: 0.9945\n",
"----------------------------------------\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.metrics import accuracy_score, f1_score\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
"\n",
"# Создание целевого признака\n",
"median_salary = df['salary_in_usd'].median()\n",
"df['above_median_salary'] = np.where(df['salary_in_usd'] > median_salary, 1, 0)\n",
"\n",
"# Разделение на признаки и целевую переменную\n",
"X = df.drop(columns=['salary_in_usd', 'above_median_salary'])\n",
"y = df['above_median_salary']\n",
"\n",
"# Разделение данных на тренировочный и тестовый наборы\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
"\n",
"# Определение столбцов\n",
"numeric_columns = [\"work_year\", \"salary\", \"remote_ratio\"]\n",
"cat_columns = [\"experience_level\", \"employment_type\", \"job_title\", \"salary_currency\", \"employee_residence\", \"company_location\", \"company_size\"]\n",
"\n",
"# Предобработка данных\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numeric_columns),\n",
" ('cat', OneHotEncoder(handle_unknown='ignore'), cat_columns)])\n",
"\n",
"# Создание конвейеров для моделей\n",
"pipeline_logistic_regression = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('classifier', LogisticRegression(random_state=42))])\n",
"\n",
"pipeline_decision_tree = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('classifier', DecisionTreeClassifier(random_state=42))])\n",
"\n",
"pipeline_gradient_boosting = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('classifier', GradientBoostingClassifier(random_state=42))])\n",
"\n",
"# Список конвейеров \n",
"pipelines = [\n",
" ('Logistic Regression', pipeline_logistic_regression),\n",
" ('Decision Tree', pipeline_decision_tree),\n",
" ('Gradient Boosting', pipeline_gradient_boosting)\n",
"]\n",
"\n",
"# Обучение моделей и вывод результатов\n",
"for name, pipeline in pipelines:\n",
" pipeline.fit(X_train, y_train)\n",
" y_pred = pipeline.predict(X_test)\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" f1 = f1_score(y_test, y_pred)\n",
" print(f\"Model: {name}\")\n",
" print(f\"Accuracy: {accuracy:.4f}\")\n",
" print(f\"F1 Score: {f1:.4f}\")\n",
" print(\"-\" * 40)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Оценка качества моделей"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Logistic Regression\n",
"Accuracy: 0.7523302263648469\n",
"F1 Score: 0.7517841210039291\n",
"\n",
"Model: Decision Tree\n",
"Accuracy: 0.996005326231691\n",
"F1 Score: 0.9960048583691977\n",
"\n",
"Model: Gradient Boosting\n",
"Accuracy: 0.9946737683089214\n",
"F1 Score: 0.9946728986768623\n",
"\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score, f1_score\n",
"\n",
"for name, pipeline in pipelines:\n",
" y_pred = pipeline.predict(X_test)\n",
" print(f\"Model: {name}\")\n",
" print('Accuracy:', accuracy_score(y_test, y_pred))\n",
" print('F1 Score:', f1_score(y_test, y_pred, average='weighted'))\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Регрессия\n",
"Цель: Разработать модель регрессии, которая будет предсказывать зарплату (salary_in_usd) на основе демографических данных, типа работы и других факторов."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер данных до удаления выбросов: (3755, 11)\n",
"Размер данных после удаления выбросов: (3708, 11)\n"
]
}
],
"source": [
"import pandas as pd\n",
"from scipy import stats\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
"\n",
"# Определение числовых признаков\n",
"numeric_features = ['work_year', 'salary', 'salary_in_usd', 'remote_ratio']\n",
"\n",
"# Вычисление z-оценок для числовых признаков\n",
"z_scores = stats.zscore(df[numeric_features])\n",
"\n",
"# Определение порога для удаления выбросов\n",
"threshold = 3\n",
"\n",
"# Удаление выбросов\n",
"df_cleaned = df[(z_scores < threshold).all(axis=1)]\n",
"\n",
"print(\"Размер данных до удаления выбросов:\", df.shape)\n",
"print(\"Размер данных после удаления выбросов:\", df_cleaned.shape)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер обучающей выборки: (2966, 9)\n",
"Размер тестовой выборки: (742, 9)\n",
"Baseline MAE: 48988.97819674187\n",
"Baseline MSE: 3791583837.2779293\n",
"Baseline R²: -0.005051587587466155\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
"\n",
"# Определение признаков и целевой переменной\n",
"features = ['work_year', 'experience_level', 'employment_type', 'job_title', 'salary_currency', 'remote_ratio', 'employee_residence', 'company_location', 'company_size']\n",
"target = 'salary_in_usd'\n",
"\n",
"# Разделение данных на тренировочный и тестовый наборы\n",
"X_train, X_test, y_train, y_test = train_test_split(df_cleaned[features], df_cleaned[target], test_size=0.2, random_state=42)\n",
"\n",
"print(\"Размер обучающей выборки:\", X_train.shape)\n",
"print(\"Размер тестовой выборки:\", X_test.shape)\n",
"\n",
"# Создание ориентира (baseline)\n",
"baseline_predictions = [y_train.mean()] * len(y_test)\n",
"\n",
"# Вычисление метрик для ориентира\n",
"print('Baseline MAE:', mean_absolute_error(y_test, baseline_predictions))\n",
"print('Baseline MSE:', mean_squared_error(y_test, baseline_predictions))\n",
"print('Baseline R²:', r2_score(y_test, baseline_predictions))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер данных до удаления выбросов: (3755, 11)\n",
"Размер данных после удаления выбросов: (3733, 11)\n",
"Размер обучающей выборки: (2986, 9)\n",
"Размер тестовой выборки: (747, 9)\n",
"Baseline MAE: 47593.92288600708\n",
"Baseline MSE: 3680965527.9964128\n",
"Baseline R²: -0.0016576422593919116\n",
"Model: Linear Regression trained.\n",
"Model: Decision Tree trained.\n",
"Model: Gradient Boosting trained.\n",
"Model: Linear Regression\n",
"MAE: 36617.65439873256\n",
"MSE: 2194684192.4416404\n",
"R²: 0.4027865306031213\n",
"\n",
"Model: Decision Tree\n",
"MAE: 36516.71804922624\n",
"MSE: 2246643776.062331\n",
"R²: 0.38864738324451775\n",
"\n",
"Model: Gradient Boosting\n",
"MAE: 35842.80843437428\n",
"MSE: 2125285552.2470944\n",
"R²: 0.42167116230764956\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"from scipy import stats\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.ensemble import GradientBoostingRegressor\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
"\n",
"# Определение числовых признаков\n",
"numeric_features = ['work_year', 'salary_in_usd', 'remote_ratio']\n",
"\n",
"# Вычисление z-оценок для числовых признаков\n",
"z_scores = stats.zscore(df[numeric_features])\n",
"\n",
"# Определение порога для удаления выбросов\n",
"threshold = 3\n",
"\n",
"# Удаление выбросов\n",
"df_cleaned = df[(z_scores < threshold).all(axis=1)]\n",
"\n",
"print(\"Размер данных до удаления выбросов:\", df.shape)\n",
"print(\"Размер данных после удаления выбросов:\", df_cleaned.shape)\n",
"\n",
"# Разделение на выборки и создание ориентира\n",
"features = ['work_year', 'experience_level', 'employment_type', 'job_title', 'salary_currency', 'remote_ratio', 'employee_residence', 'company_location', 'company_size']\n",
"target = 'salary_in_usd'\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(df_cleaned[features], df_cleaned[target], test_size=0.2, random_state=42)\n",
"\n",
"print(\"Размер обучающей выборки:\", X_train.shape)\n",
"print(\"Размер тестовой выборки:\", X_test.shape)\n",
"\n",
"# Создание ориентира (baseline)\n",
"baseline_predictions = [y_train.mean()] * len(y_test)\n",
"\n",
"print('Baseline MAE:', mean_absolute_error(y_test, baseline_predictions))\n",
"print('Baseline MSE:', mean_squared_error(y_test, baseline_predictions))\n",
"print('Baseline R²:', r2_score(y_test, baseline_predictions))\n",
"\n",
"# Создание конвейера и обучение моделей\n",
"categorical_features = ['experience_level', 'employment_type', 'job_title', 'salary_currency', 'employee_residence', 'company_location', 'company_size']\n",
"numeric_features = ['work_year', 'remote_ratio']\n",
"\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numeric_features),\n",
" ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)])\n",
"\n",
"pipeline_linear_regression = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('regressor', LinearRegression())])\n",
"\n",
"pipeline_decision_tree = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('regressor', DecisionTreeRegressor(random_state=42))])\n",
"\n",
"pipeline_gradient_boosting = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('regressor', GradientBoostingRegressor(random_state=42))])\n",
"\n",
"pipelines = [\n",
" ('Linear Regression', pipeline_linear_regression),\n",
" ('Decision Tree', pipeline_decision_tree),\n",
" ('Gradient Boosting', pipeline_gradient_boosting)\n",
"]\n",
"\n",
"for name, pipeline in pipelines:\n",
" pipeline.fit(X_train, y_train)\n",
" print(f\"Model: {name} trained.\")\n",
"\n",
"# Оценка качества моделей\n",
"for name, pipeline in pipelines:\n",
" y_pred = pipeline.predict(X_test)\n",
" print(f\"Model: {name}\")\n",
" print('MAE:', mean_absolute_error(y_test, y_pred))\n",
" print('MSE:', mean_squared_error(y_test, y_pred))\n",
" print('R²:', r2_score(y_test, y_pred))\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Linear Regression\n",
"MAE: 36617.65439873256\n",
"MSE: 2194684192.4416404\n",
"R²: 0.4027865306031213\n",
"\n",
"Model: Decision Tree\n",
"MAE: 36516.71804922624\n",
"MSE: 2246643776.062331\n",
"R²: 0.38864738324451775\n",
"\n",
"Model: Gradient Boosting\n",
"MAE: 35842.80843437428\n",
"MSE: 2125285552.2470944\n",
"R²: 0.42167116230764956\n",
"\n"
]
}
],
"source": [
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
"\n",
"for name, pipeline in pipelines:\n",
" y_pred = pipeline.predict(X_test)\n",
" print(f\"Model: {name}\")\n",
" print('MAE:', mean_absolute_error(y_test, y_pred))\n",
" print('MSE:', mean_squared_error(y_test, y_pred))\n",
" print('R²:', r2_score(y_test, y_pred))\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Пропущенные значения:\n",
" work_year 0\n",
"experience_level 0\n",
"employment_type 0\n",
"job_title 0\n",
"salary 0\n",
"salary_currency 0\n",
"salary_in_usd 0\n",
"employee_residence 0\n",
"remote_ratio 0\n",
"company_location 0\n",
"company_size 0\n",
"dtype: int64\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"from scipy.stats import uniform, randint\n",
"from sklearn.model_selection import RandomizedSearchCV\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
"\n",
"# Проверка на пропущенные значения\n",
"print(\"Пропущенные значения:\\n\", df.isnull().sum())\n",
"\n",
"# Удаление строк с пропущенными значениями\n",
"df = df.dropna()\n",
"\n",
"# Выбор признаков и целевой переменной\n",
"features = ['work_year', 'experience_level', 'employment_type', 'job_title', 'employee_residence', 'remote_ratio', 'company_location', 'company_size']\n",
"target = 'salary_in_usd'\n",
"\n",
"# Определение категориальных и числовых признаков\n",
"categorical_features = ['experience_level', 'employment_type', 'job_title', 'employee_residence', 'company_location', 'company_size']\n",
"numeric_features = ['work_year', 'remote_ratio']\n",
"\n",
"# Создание пайплайна для обработки данных\n",
"categorical_transformer = Pipeline(steps=[\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
"])\n",
"\n",
"numeric_transformer = Pipeline(steps=[\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', numeric_transformer, numeric_features),\n",
" ('cat', categorical_transformer, categorical_features)\n",
" ])\n",
"\n",
"# Преобразование данных\n",
"X = preprocessor.fit_transform(df[features])\n",
"y = df[target]\n",
"\n",
"# Разделение данных на обучающую и тестовую выборки\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 4 is smaller than n_iter=10. Running 4 iterations. For exhaustive searches, use GridSearchCV.\n",
" warnings.warn(\n",
"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n",
"6 fits failed out of a total of 12.\n",
"The score on these train-test partitions for these parameters will be set to nan.\n",
"If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
"\n",
"Below are more details about the failures:\n",
"--------------------------------------------------------------------------------\n",
"6 fits failed with the following error:\n",
"Traceback (most recent call last):\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
" return fit_method(estimator, *args, **kwargs)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\pipeline.py\", line 473, in fit\n",
" self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
" return fit_method(estimator, *args, **kwargs)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_base.py\", line 609, in fit\n",
" X, y = self._validate_data(\n",
" ^^^^^^^^^^^^^^^^^^^^\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n",
" X, y = check_X_y(X, y, **check_params)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1301, in check_X_y\n",
" X = check_array(\n",
" ^^^^^^^^^^^^\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 971, in check_array\n",
" array = _ensure_sparse_format(\n",
" ^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 595, in _ensure_sparse_format\n",
" raise TypeError(\n",
"TypeError: Sparse data was passed for X, but dense data is required. Use '.toarray()' to convert to a dense numpy array.\n",
"\n",
" warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [ nan 0.37308723 nan 0.37316524]\n",
" warnings.warn(\n",
"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14908\\2948510432.py:70: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
" axes[i].set_xticklabels(params.keys(), rotation=45, ha=\"right\") #Поворачиваем подписи на оси х\n",
"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14908\\2948510432.py:70: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
" axes[i].set_xticklabels(params.keys(), rotation=45, ha=\"right\") #Поворачиваем подписи на оси х\n",
"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14908\\2948510432.py:70: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
" axes[i].set_xticklabels(params.keys(), rotation=45, ha=\"right\") #Поворачиваем подписи на оси х\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x1500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
"from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
"from scipy.stats import uniform, randint\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
"\n",
"# ... (ваш код предобработки данных, как в предыдущем примере) ...\n",
"\n",
"# Определение распределений для гиперпараметров\n",
"param_distributions = {\n",
" 'Linear Regression': {\n",
" 'regressor__fit_intercept': [True, False],\n",
" 'regressor__positive': [True, False]\n",
" },\n",
" 'Random Forest': {\n",
" 'regressor__n_estimators': randint(50, 200),\n",
" 'regressor__max_depth': [None, 10, 20],\n",
" 'regressor__min_samples_split': randint(2, 11),\n",
" 'regressor__min_samples_leaf': randint(1, 5),\n",
" 'regressor__bootstrap': [True, False]\n",
" },\n",
" 'Gradient Boosting': {\n",
" 'regressor__n_estimators': randint(50, 200),\n",
" 'regressor__learning_rate': uniform(0.01, 0.49), # uniform distribution for learning rate\n",
" 'regressor__max_depth': [3, 5, 7],\n",
" 'regressor__min_samples_split': randint(2, 11),\n",
" 'regressor__min_samples_leaf': randint(1, 5),\n",
" 'regressor__subsample': uniform(0.5, 0.5) # uniform distribution for subsample\n",
"\n",
" }\n",
"}\n",
"\n",
"# Словарь для хранения лучших моделей и их гиперпараметров\n",
"best_models = {}\n",
"\n",
"# Цикл для обучения и настройки гиперпараметров каждой модели\n",
"for model_name, model_params in param_distributions.items():\n",
" if model_name == 'Linear Regression':\n",
" model = LinearRegression()\n",
" elif model_name == 'Random Forest':\n",
" model = RandomForestRegressor(random_state=42)\n",
" elif model_name == 'Gradient Boosting':\n",
" model = GradientBoostingRegressor(random_state=42)\n",
" else:\n",
" continue #Обработка неизвестных моделей\n",
"\n",
" pipeline = Pipeline([('regressor', model)])\n",
" random_search = RandomizedSearchCV(pipeline, param_distributions=model_params, n_iter=10, cv=3, n_jobs=-1, random_state=42)\n",
" random_search.fit(X_train, y_train)\n",
" best_models[model_name] = random_search.best_params_\n",
"\n",
"\n",
"# Визуализация лучших гиперпараметров\n",
"\n",
"fig, axes = plt.subplots(len(best_models), 1, figsize=(10, 5 * len(best_models)))\n",
"if len(best_models) == 1:\n",
" axes = [axes] # обработка случая с одной моделью\n",
"\n",
"for i, (model_name, params) in enumerate(best_models.items()):\n",
" axes[i].bar(params.keys(), params.values())\n",
" axes[i].set_title(f\"Лучшие гиперпараметры для {model_name}\")\n",
" axes[i].set_xticklabels(params.keys(), rotation=45, ha=\"right\") #Поворачиваем подписи на оси х\n",
" axes[i].tick_params(axis='x', which='major', labelsize=8) # Размер шрифта подписей оси х\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "aimenv",
"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": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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