{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import time\n", "import datetime\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sb" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\Admin\\\\Documents\\\\machine learning\\\\uncleandata.csv'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m DataTrain \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC:\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mAdmin\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mDocuments\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mmachine learning\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124muncleandata.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n", "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n", "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[0;32m 1881\u001b[0m f,\n\u001b[0;32m 1882\u001b[0m mode,\n\u001b[0;32m 1883\u001b[0m encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m 1884\u001b[0m compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m 1885\u001b[0m memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m 1886\u001b[0m is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[0;32m 1887\u001b[0m errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 1888\u001b[0m storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m 1889\u001b[0m )\n\u001b[0;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \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[0;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m 874\u001b[0m handle,\n\u001b[0;32m 875\u001b[0m ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m 876\u001b[0m encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[0;32m 877\u001b[0m errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m 878\u001b[0m newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 879\u001b[0m )\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\Admin\\\\Documents\\\\machine learning\\\\uncleandata.csv'" ] } ], "source": [ "DataTrain = pd.read_csv(\"C:\\\\Users\\\\Admin\\\\Documents\\\\machine learning\\\\uncleandata.csv\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "11" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Rows, Columns = DataTrain.shape\n", "Columns" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15099" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Rows" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 15099 entries, 0 to 15098\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 satisfaction_level 15099 non-null float64\n", " 1 last_evaluation 15099 non-null float64\n", " 2 number_project 15099 non-null int64 \n", " 3 average_montly_hours 14728 non-null float64\n", " 4 time_spend_company 14948 non-null float64\n", " 5 work_accident 15099 non-null int64 \n", " 6 left 15099 non-null object \n", " 7 promotion_last_5years 15099 non-null int64 \n", " 8 is_smoker 239 non-null object \n", " 9 department 15099 non-null object \n", " 10 salary 15099 non-null object \n", "dtypes: float64(4), int64(3), object(4)\n", "memory usage: 1.3+ MB\n" ] } ], "source": [ "DataTrain.info() " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2840" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#DataTrain.duplicated()\n", "DataTrain.duplicated().sum()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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satisfaction_levellast_evaluationnumber_projectaverage_montly_hourstime_spend_companywork_accidentleftpromotion_last_5yearsis_smokerdepartmentsalary
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\n", "
" ], "text/plain": [ " satisfaction_level last_evaluation number_project average_montly_hours \\\n", "0 0.38 0.53 2 157.0 \n", "1 0.80 0.86 5 262.0 \n", "2 0.11 0.88 7 272.0 \n", "3 0.72 0.87 5 223.0 \n", "4 0.37 0.52 2 NaN \n", "\n", " time_spend_company work_accident left promotion_last_5years is_smoker \\\n", "0 3.0 0 yes 0 NaN \n", "1 6.0 0 yes 0 yes \n", "2 4.0 0 yes 0 NaN \n", "3 5.0 0 yes 0 NaN \n", "4 NaN 0 yes 0 no \n", "\n", " department salary \n", "0 sales low \n", "1 sales medium \n", "2 sales medium \n", "3 sales low \n", "4 sales low " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DataTrain.drop_duplicates(inplace=True)\n", "DataTrain.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]],\n", " dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DataTrain.hist(column='time_spend_company')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DataTrain.hist(column='work_accident')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]],\n", " dtype=object)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DataTrain.hist(column='average_montly_hours')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DataTrain.hist(column='number_project')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DataTrain.hist(column='last_evaluation')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]],\n", " dtype=object)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sb.histplot(DataTrain[\"department\"])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sb.histplot(DataTrain[\"is_smoker\"])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ], "text/plain": [ " satisfaction_level last_evaluation number_project \\\n", "1473 0.09 0.96 6 \n", "7313 0.31 0.42 2 \n", "6164 0.72 0.55 4 \n", "8943 0.36 0.66 4 \n", "9054 0.64 0.77 3 \n", "... ... ... ... \n", "6688 0.52 0.95 3 \n", "4788 0.93 0.74 2 \n", "7748 0.62 0.65 3 \n", "7479 0.65 0.97 3 \n", "10852 0.84 0.54 3 \n", "\n", " average_montly_hours time_spend_company work_accident \\\n", "1473 245.00 4.0 0 \n", "7313 169.00 5.0 0 \n", "6164 145.00 3.0 0 \n", "8943 97.00 2.0 0 \n", "9054 249.00 2.0 1 \n", "... ... ... ... \n", "6688 171.00 3.0 1 \n", "4788 169.00 4.0 0 \n", "7748 249.00 3.0 0 \n", "7479 198.00 3.0 0 \n", "10852 200.51 3.0 0 \n", "\n", " promotion_last_5years department salary \n", "1473 0 sales medium \n", "7313 0 IT low \n", "6164 0 technical low \n", "8943 0 sales high \n", "9054 0 support low \n", "... ... ... ... \n", "6688 0 support low \n", "4788 0 support low \n", "7748 0 technical low \n", "7479 0 RandD low \n", "10852 0 sales low \n", "\n", "[9807 rows x 9 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datatrain = DataTrain.drop(columns='left')\n", "DataTrainSet,DataTestSet = train_test_split(datatrain,test_size=0.2)\n", "DataTrainSet" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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satisfaction_levellast_evaluationnumber_projectaverage_montly_hourstime_spend_companywork_accidentpromotion_last_5yearsdepartmentsalary
92011.000.894152.03.000technicallow
47380.680.993263.03.011marketingmedium
116520.740.675216.03.000saleslow
49710.630.744155.02.000salesmedium
24070.690.634217.03.000technicalmedium
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" ], "text/plain": [ " satisfaction_level last_evaluation number_project \\\n", "9201 1.00 0.89 4 \n", "4738 0.68 0.99 3 \n", "11652 0.74 0.67 5 \n", "4971 0.63 0.74 4 \n", "2407 0.69 0.63 4 \n", "... ... ... ... \n", "4920 0.96 0.71 3 \n", "8713 0.81 0.50 3 \n", "5251 0.50 0.50 4 \n", "7905 0.54 0.62 2 \n", "10408 1.00 0.88 4 \n", "\n", " average_montly_hours time_spend_company work_accident \\\n", "9201 152.0 3.0 0 \n", "4738 263.0 3.0 1 \n", "11652 216.0 3.0 0 \n", "4971 155.0 2.0 0 \n", "2407 217.0 3.0 0 \n", "... ... ... ... \n", "4920 170.0 3.0 0 \n", "8713 198.0 3.0 0 \n", "5251 267.0 3.0 0 \n", "7905 141.0 2.0 0 \n", "10408 252.0 4.0 0 \n", "\n", " promotion_last_5years department salary \n", "9201 0 technical low \n", "4738 1 marketing medium \n", "11652 0 sales low \n", "4971 0 sales medium \n", "2407 0 technical medium \n", "... ... ... ... \n", "4920 0 technical low \n", "8713 0 sales low \n", "5251 0 IT medium \n", "7905 0 technical medium \n", "10408 0 accounting low \n", "\n", "[9807 rows x 9 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TrainingSet,TestingSet = train_test_split(datatrain,test_size=0.2,stratify=DataTrain['left'])\n", "TrainingSet" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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satisfaction_levellast_evaluationnumber_projectaverage_montly_hourstime_spend_companywork_accidentpromotion_last_5yearsdepartmentsalary
3550.410.572136.03.000supportlow
70900.490.572213.03.010product_mnglow
107440.150.813191.05.000managementmedium
19120.370.572158.03.000technicallow
100100.850.665189.03.000salesmedium
..............................
52670.280.795202.05.000accountinglow
50000.351.006186.02.000technicallow
38960.560.683149.02.000managementmedium
4130.450.542142.03.000hrmedium
63690.910.814139.02.000technicalhigh
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2452 rows × 9 columns

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" ], "text/plain": [ " satisfaction_level last_evaluation number_project \\\n", "355 0.41 0.57 2 \n", "7090 0.49 0.57 2 \n", "10744 0.15 0.81 3 \n", "1912 0.37 0.57 2 \n", "10010 0.85 0.66 5 \n", "... ... ... ... \n", "5267 0.28 0.79 5 \n", "5000 0.35 1.00 6 \n", "3896 0.56 0.68 3 \n", "413 0.45 0.54 2 \n", "6369 0.91 0.81 4 \n", "\n", " average_montly_hours time_spend_company work_accident \\\n", "355 136.0 3.0 0 \n", "7090 213.0 3.0 1 \n", "10744 191.0 5.0 0 \n", "1912 158.0 3.0 0 \n", "10010 189.0 3.0 0 \n", "... ... ... ... \n", "5267 202.0 5.0 0 \n", "5000 186.0 2.0 0 \n", "3896 149.0 2.0 0 \n", "413 142.0 3.0 0 \n", "6369 139.0 2.0 0 \n", "\n", " promotion_last_5years department salary \n", "355 0 support low \n", "7090 0 product_mng low \n", "10744 0 management medium \n", "1912 0 technical low \n", "10010 0 sales medium \n", "... ... ... ... \n", "5267 0 accounting low \n", "5000 0 technical low \n", "3896 0 management medium \n", "413 0 hr medium \n", "6369 0 technical high \n", "\n", "[2452 rows x 9 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TestingSet" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }