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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ysS5rgTMWpwL" | |
| }, | |
| "source": [ | |
| "# **Capstone project: Providing data-driven suggestions for HR**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "pZNZpq0EotHt" | |
| }, | |
| "source": [ | |
| "## Description and deliverables\n", | |
| "\n", | |
| "This capstone project is an opportunity for you to analyze a dataset and build predictive models that can provide insights to the Human Resources (HR) department of a large consulting firm.\n", | |
| "\n", | |
| "Upon completion, you will have two artifacts that you would be able to present to future employers. One is a brief one-page summary of this project that you would present to external stakeholders as the data professional in Salifort Motors. The other is a complete code notebook provided here. Please consider your prior course work and select one way to achieve this given project question. Either use a regression model or machine learning model to predict whether or not an employee will leave the company. The exemplar following this actiivty shows both approaches, but you only need to do one.\n", | |
| "\n", | |
| "In your deliverables, you will include the model evaluation (and interpretation if applicable), a data visualization(s) of your choice that is directly related to the question you ask, ethical considerations, and the resources you used to troubleshoot and find answers or solutions.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# **PACE stages**\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "kh258TrMicdM" | |
| }, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "psz51YkZVwtN" | |
| }, | |
| "source": [ | |
| "## **Pace: Plan**\n", | |
| "\n", | |
| "Consider the questions in your PACE Strategy Document to reflect on the Plan stage.\n", | |
| "\n", | |
| "In this stage, consider the following:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "gLEEr6peWcF7" | |
| }, | |
| "source": [ | |
| "### Understand the business scenario and problem\n", | |
| "\n", | |
| "The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They refer to you as a data analytics professional and ask you to provide data-driven suggestions based on your understanding of the data. They have the following question: what’s likely to make the employee leave the company?\n", | |
| "\n", | |
| "Your goals in this project are to analyze the data collected by the HR department and to build a model that predicts whether or not an employee will leave the company.\n", | |
| "\n", | |
| "If you can predict employees likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "lnRdR6eacUkK" | |
| }, | |
| "source": [ | |
| "### Familiarize yourself with the HR dataset\n", | |
| "\n", | |
| "The dataset that you'll be using in this lab contains 15,000 rows and 10 columns for the variables listed below. \n", | |
| "\n", | |
| "**Note:** you don't need to download any data to complete this lab. For more information about the data, refer to its source on [Kaggle](https://www.kaggle.com/datasets/mfaisalqureshi/hr-analytics-and-job-prediction?select=HR_comma_sep.csv).\n", | |
| "\n", | |
| "Variable |Description |\n", | |
| "-----|-----|\n", | |
| "satisfaction_level|Employee-reported job satisfaction level [0–1]|\n", | |
| "last_evaluation|Score of employee's last performance review [0–1]|\n", | |
| "number_project|Number of projects employee contributes to|\n", | |
| "average_monthly_hours|Average number of hours employee worked per month|\n", | |
| "time_spend_company|How long the employee has been with the company (years)\n", | |
| "Work_accident|Whether or not the employee experienced an accident while at work\n", | |
| "left|Whether or not the employee left the company\n", | |
| "promotion_last_5years|Whether or not the employee was promoted in the last 5 years\n", | |
| "Department|The employee's department\n", | |
| "salary|The employee's salary (U.S. dollars)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PoC0_CTLaK2p" | |
| }, | |
| "source": [ | |
| "💭\n", | |
| "### Reflect on these questions as you complete the plan stage.\n", | |
| "\n", | |
| "* Who are your stakeholders for this project?\n", | |
| "- What are you trying to solve or accomplish?\n", | |
| "- What are your initial observations when you explore the data?\n", | |
| "- What resources do you find yourself using as you complete this stage? (Make sure to include the links.)\n", | |
| "- Do you have any ethical considerations in this stage?\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "EAfj2mxlHyO3" | |
| }, | |
| "source": [ | |
| "[Double-click to enter your responses here.]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Dfnvw1HZsfbd" | |
| }, | |
| "source": [ | |
| "## Step 1. Imports\n", | |
| "\n", | |
| "* Import packages\n", | |
| "* Load dataset\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "51UAXIOLC_8P" | |
| }, | |
| "source": [ | |
| "### Import packages" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "id": "hVWGpX9As4e1" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Import packages\n", | |
| "\n", | |
| "# For data manipulation\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "# For data visualization\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import seaborn as sns\n", | |
| "\n", | |
| "# For displaying all of the columns in dataframes\n", | |
| "pd.set_option('display.max_columns', None)\n", | |
| "\n", | |
| "# For data modeling\n", | |
| "from xgboost import XGBClassifier\n", | |
| "from xgboost import XGBRegressor\n", | |
| "from xgboost import plot_importance\n", | |
| "\n", | |
| "from sklearn.linear_model import LogisticRegression\n", | |
| "from sklearn.tree import DecisionTreeClassifier\n", | |
| "from sklearn.ensemble import RandomForestClassifier\n", | |
| "\n", | |
| "# For metrics and helpful functions\n", | |
| "from sklearn.model_selection import GridSearchCV, train_test_split\n", | |
| "from sklearn.metrics import accuracy_score, precision_score, recall_score,\\\n", | |
| "f1_score, confusion_matrix, ConfusionMatrixDisplay, classification_report\n", | |
| "from sklearn.metrics import roc_auc_score, roc_curve\n", | |
| "from sklearn.tree import plot_tree\n", | |
| "\n", | |
| "# For saving models\n", | |
| "import pickle" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "zM2P9yLWDIjN" | |
| }, | |
| "source": [ | |
| "### Load dataset\n", | |
| "\n", | |
| "`Pandas` is used to read a dataset called **`HR_capstone_dataset.csv`.** As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "id": "Bs0cJR5BDPgQ" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>satisfaction_level</th>\n", | |
| " <th>last_evaluation</th>\n", | |
| " <th>number_project</th>\n", | |
| " <th>average_montly_hours</th>\n", | |
| " <th>time_spend_company</th>\n", | |
| " <th>Work_accident</th>\n", | |
| " <th>left</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " <th>Department</th>\n", | |
| " <th>salary</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.38</td>\n", | |
| " <td>0.53</td>\n", | |
| " <td>2</td>\n", | |
| " <td>157</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.80</td>\n", | |
| " <td>0.86</td>\n", | |
| " <td>5</td>\n", | |
| " <td>262</td>\n", | |
| " <td>6</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>medium</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.11</td>\n", | |
| " <td>0.88</td>\n", | |
| " <td>7</td>\n", | |
| " <td>272</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>medium</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.72</td>\n", | |
| " <td>0.87</td>\n", | |
| " <td>5</td>\n", | |
| " <td>223</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.37</td>\n", | |
| " <td>0.52</td>\n", | |
| " <td>2</td>\n", | |
| " <td>159</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " satisfaction_level last_evaluation number_project average_montly_hours \\\n", | |
| "0 0.38 0.53 2 157 \n", | |
| "1 0.80 0.86 5 262 \n", | |
| "2 0.11 0.88 7 272 \n", | |
| "3 0.72 0.87 5 223 \n", | |
| "4 0.37 0.52 2 159 \n", | |
| "\n", | |
| " time_spend_company Work_accident left promotion_last_5years Department \\\n", | |
| "0 3 0 1 0 sales \n", | |
| "1 6 0 1 0 sales \n", | |
| "2 4 0 1 0 sales \n", | |
| "3 5 0 1 0 sales \n", | |
| "4 3 0 1 0 sales \n", | |
| "\n", | |
| " salary \n", | |
| "0 low \n", | |
| "1 medium \n", | |
| "2 medium \n", | |
| "3 low \n", | |
| "4 low " | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# RUN THIS CELL TO IMPORT YOUR DATA. \n", | |
| "\n", | |
| "# Load dataset into a dataframe\n", | |
| "df0 = pd.read_csv(\"HR_capstone_dataset.csv\")\n", | |
| "\n", | |
| "\n", | |
| "# Display first few rows of the dataframe\n", | |
| "df0.head()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "wF_LLorPs5G_" | |
| }, | |
| "source": [ | |
| "## Step 2. Data Exploration (Initial EDA and data cleaning)\n", | |
| "\n", | |
| "- Understand your variables\n", | |
| "- Clean your dataset (missing data, redundant data, outliers)\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "3LF6h1v9FYz2" | |
| }, | |
| "source": [ | |
| "### Gather basic information about the data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "id": "6XbfdPoKurMf" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "RangeIndex: 14999 entries, 0 to 14998\n", | |
| "Data columns (total 10 columns):\n", | |
| " # Column Non-Null Count Dtype \n", | |
| "--- ------ -------------- ----- \n", | |
| " 0 satisfaction_level 14999 non-null float64\n", | |
| " 1 last_evaluation 14999 non-null float64\n", | |
| " 2 number_project 14999 non-null int64 \n", | |
| " 3 average_montly_hours 14999 non-null int64 \n", | |
| " 4 time_spend_company 14999 non-null int64 \n", | |
| " 5 Work_accident 14999 non-null int64 \n", | |
| " 6 left 14999 non-null int64 \n", | |
| " 7 promotion_last_5years 14999 non-null int64 \n", | |
| " 8 Department 14999 non-null object \n", | |
| " 9 salary 14999 non-null object \n", | |
| "dtypes: float64(2), int64(6), object(2)\n", | |
| "memory usage: 1.1+ MB\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Gather basic information about the data\n", | |
| "df0.info()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "6JMl_rQ1Fgte" | |
| }, | |
| "source": [ | |
| "### Gather descriptive statistics about the data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "id": "_5VRL-kzE8y1" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>satisfaction_level</th>\n", | |
| " <th>last_evaluation</th>\n", | |
| " <th>number_project</th>\n", | |
| " <th>average_montly_hours</th>\n", | |
| " <th>time_spend_company</th>\n", | |
| " <th>Work_accident</th>\n", | |
| " <th>left</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>14999.000000</td>\n", | |
| " <td>14999.000000</td>\n", | |
| " <td>14999.000000</td>\n", | |
| " <td>14999.000000</td>\n", | |
| " <td>14999.000000</td>\n", | |
| " <td>14999.000000</td>\n", | |
| " <td>14999.000000</td>\n", | |
| " <td>14999.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>0.612834</td>\n", | |
| " <td>0.716102</td>\n", | |
| " <td>3.803054</td>\n", | |
| " <td>201.050337</td>\n", | |
| " <td>3.498233</td>\n", | |
| " <td>0.144610</td>\n", | |
| " <td>0.238083</td>\n", | |
| " <td>0.021268</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>0.248631</td>\n", | |
| " <td>0.171169</td>\n", | |
| " <td>1.232592</td>\n", | |
| " <td>49.943099</td>\n", | |
| " <td>1.460136</td>\n", | |
| " <td>0.351719</td>\n", | |
| " <td>0.425924</td>\n", | |
| " <td>0.144281</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0.090000</td>\n", | |
| " <td>0.360000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>96.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>0.440000</td>\n", | |
| " <td>0.560000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>156.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>0.640000</td>\n", | |
| " <td>0.720000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>200.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>0.820000</td>\n", | |
| " <td>0.870000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>245.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>310.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " satisfaction_level last_evaluation number_project \\\n", | |
| "count 14999.000000 14999.000000 14999.000000 \n", | |
| "mean 0.612834 0.716102 3.803054 \n", | |
| "std 0.248631 0.171169 1.232592 \n", | |
| "min 0.090000 0.360000 2.000000 \n", | |
| "25% 0.440000 0.560000 3.000000 \n", | |
| "50% 0.640000 0.720000 4.000000 \n", | |
| "75% 0.820000 0.870000 5.000000 \n", | |
| "max 1.000000 1.000000 7.000000 \n", | |
| "\n", | |
| " average_montly_hours time_spend_company Work_accident left \\\n", | |
| "count 14999.000000 14999.000000 14999.000000 14999.000000 \n", | |
| "mean 201.050337 3.498233 0.144610 0.238083 \n", | |
| "std 49.943099 1.460136 0.351719 0.425924 \n", | |
| "min 96.000000 2.000000 0.000000 0.000000 \n", | |
| "25% 156.000000 3.000000 0.000000 0.000000 \n", | |
| "50% 200.000000 3.000000 0.000000 0.000000 \n", | |
| "75% 245.000000 4.000000 0.000000 0.000000 \n", | |
| "max 310.000000 10.000000 1.000000 1.000000 \n", | |
| "\n", | |
| " promotion_last_5years \n", | |
| "count 14999.000000 \n", | |
| "mean 0.021268 \n", | |
| "std 0.144281 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 " | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Gather descriptive statistics about the data\n", | |
| "df0.describe()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QR7eFNU0FklJ" | |
| }, | |
| "source": [ | |
| "### Rename columns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "_TtE0JLPJyLF" | |
| }, | |
| "source": [ | |
| "As a data cleaning step, rename the columns as needed. Standardize the column names so that they are all in `snake_case`, correct any column names that are misspelled, and make column names more concise as needed." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "id": "kEn21u2bqrEI" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n", | |
| " 'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\n", | |
| " 'promotion_last_5years', 'Department', 'salary'],\n", | |
| " dtype='object')" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Display all column names\n", | |
| "df0.columns\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "id": "npUQA8jMFJQD" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Index(['satisfaction_level', 'last_evaluation', 'no_projects',\n", | |
| " 'avg_monthly_hours', 'tenure', 'work_accidents', 'left',\n", | |
| " 'promotion_last_5years', 'department', 'salary'],\n", | |
| " dtype='object')" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Rename columns as needed\n", | |
| "df0 = df0.rename(columns={'number_project': 'no_projects',\n", | |
| " 'Work_accident': 'work_accidents',\n", | |
| " 'average_montly_hours': 'avg_monthly_hours',\n", | |
| " 'time_spend_company': 'tenure',\n", | |
| " 'Department': 'department'})\n", | |
| "\n", | |
| "\n", | |
| "# Display all column names after the update\n", | |
| "df0.columns\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e-G2QlQxBq__" | |
| }, | |
| "source": [ | |
| "### Check missing values" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "GeiUUqeaBt-I" | |
| }, | |
| "source": [ | |
| "Check for any missing values in the data." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "id": "EN9MvN0GByVV" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "satisfaction_level 0\n", | |
| "last_evaluation 0\n", | |
| "no_projects 0\n", | |
| "avg_monthly_hours 0\n", | |
| "tenure 0\n", | |
| "work_accidents 0\n", | |
| "left 0\n", | |
| "promotion_last_5years 0\n", | |
| "department 0\n", | |
| "salary 0\n", | |
| "dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check for missing values\n", | |
| "df0.isnull().sum()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "hBvrijItKQI9" | |
| }, | |
| "source": [ | |
| "### Check duplicates" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Q7ystBsdsGaL" | |
| }, | |
| "source": [ | |
| "Check for any duplicate entries in the data." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "id": "CFFLc5AOZ7-x" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "3008" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check for duplicates\n", | |
| "df0.duplicated().sum()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "id": "ZHGlDbKAcBLM" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>satisfaction_level</th>\n", | |
| " <th>last_evaluation</th>\n", | |
| " <th>no_projects</th>\n", | |
| " <th>avg_monthly_hours</th>\n", | |
| " <th>tenure</th>\n", | |
| " <th>work_accidents</th>\n", | |
| " <th>left</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " <th>department</th>\n", | |
| " <th>salary</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>396</th>\n", | |
| " <td>0.46</td>\n", | |
| " <td>0.57</td>\n", | |
| " <td>2</td>\n", | |
| " <td>139</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>866</th>\n", | |
| " <td>0.41</td>\n", | |
| " <td>0.46</td>\n", | |
| " <td>2</td>\n", | |
| " <td>128</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>accounting</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1317</th>\n", | |
| " <td>0.37</td>\n", | |
| " <td>0.51</td>\n", | |
| " <td>2</td>\n", | |
| " <td>127</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>medium</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1368</th>\n", | |
| " <td>0.41</td>\n", | |
| " <td>0.52</td>\n", | |
| " <td>2</td>\n", | |
| " <td>132</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>RandD</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1461</th>\n", | |
| " <td>0.42</td>\n", | |
| " <td>0.53</td>\n", | |
| " <td>2</td>\n", | |
| " <td>142</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " satisfaction_level last_evaluation no_projects avg_monthly_hours \\\n", | |
| "396 0.46 0.57 2 139 \n", | |
| "866 0.41 0.46 2 128 \n", | |
| "1317 0.37 0.51 2 127 \n", | |
| "1368 0.41 0.52 2 132 \n", | |
| "1461 0.42 0.53 2 142 \n", | |
| "\n", | |
| " tenure work_accidents left promotion_last_5years department salary \n", | |
| "396 3 0 1 0 sales low \n", | |
| "866 3 0 1 0 accounting low \n", | |
| "1317 3 0 1 0 sales medium \n", | |
| "1368 3 0 1 0 RandD low \n", | |
| "1461 3 0 1 0 sales low " | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Inspect some rows containing duplicates as needed\n", | |
| "mask = df0.duplicated()\n", | |
| "df0[mask].head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "id": "wCr34Rppdjay" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>satisfaction_level</th>\n", | |
| " <th>last_evaluation</th>\n", | |
| " <th>no_projects</th>\n", | |
| " <th>avg_monthly_hours</th>\n", | |
| " <th>tenure</th>\n", | |
| " <th>work_accidents</th>\n", | |
| " <th>left</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " <th>department</th>\n", | |
| " <th>salary</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.38</td>\n", | |
| " <td>0.53</td>\n", | |
| " <td>2</td>\n", | |
| " <td>157</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.80</td>\n", | |
| " <td>0.86</td>\n", | |
| " <td>5</td>\n", | |
| " <td>262</td>\n", | |
| " <td>6</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>medium</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.11</td>\n", | |
| " <td>0.88</td>\n", | |
| " <td>7</td>\n", | |
| " <td>272</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>medium</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.72</td>\n", | |
| " <td>0.87</td>\n", | |
| " <td>5</td>\n", | |
| " <td>223</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.37</td>\n", | |
| " <td>0.52</td>\n", | |
| " <td>2</td>\n", | |
| " <td>159</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>sales</td>\n", | |
| " <td>low</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " satisfaction_level last_evaluation no_projects avg_monthly_hours \\\n", | |
| "0 0.38 0.53 2 157 \n", | |
| "1 0.80 0.86 5 262 \n", | |
| "2 0.11 0.88 7 272 \n", | |
| "3 0.72 0.87 5 223 \n", | |
| "4 0.37 0.52 2 159 \n", | |
| "\n", | |
| " tenure work_accidents left promotion_last_5years department salary \n", | |
| "0 3 0 1 0 sales low \n", | |
| "1 6 0 1 0 sales medium \n", | |
| "2 4 0 1 0 sales medium \n", | |
| "3 5 0 1 0 sales low \n", | |
| "4 3 0 1 0 sales low " | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Drop duplicates and save resulting dataframe in a new variable as needed\n", | |
| "df = df0.drop_duplicates(keep='first').reset_index(drop=True)\n", | |
| "\n", | |
| "\n", | |
| "# Display first few rows of new dataframe as needed\n", | |
| "df.head()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "4knHIoTIFu83" | |
| }, | |
| "source": [ | |
| "### Check outliers" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "EaVx2fk8GC_m" | |
| }, | |
| "source": [ | |
| "Check for outliers in the data." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "id": "pilaGYgh4LHM" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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DredQ2WnAY62HUMkPZebEWndsiHhoY4uIhUN9rFsbl09bJJUYD0klxkNdmGs9gEbP1zwklbjzkFRiPCSVGA9JJcZDUonxkFTyv4ABI72vDN48AAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 288x432 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Create a boxplot to visualize distribution of `tenure` and detect any outliers\n", | |
| "plt.figure(figsize=(4,6))\n", | |
| "sns.boxplot(data=df, y='tenure', color='#6b9080')\n", | |
| "plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "id": "ohctgiHyFykI" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "IQR: 1.0\n", | |
| "Upper Limit: 5.5\n", | |
| "Lower Limit: 1.5\n", | |
| "Number of outliers: 824\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Determine the number of rows containing outliers\n", | |
| "\n", | |
| "q25 = df['tenure'].quantile(0.25)\n", | |
| "q75 = df['tenure'].quantile(0.75)\n", | |
| "median = df['tenure'].median()\n", | |
| "\n", | |
| "iqr = q75 - q25\n", | |
| "\n", | |
| "upper_limit = q75 + (iqr*1.5)\n", | |
| "lower_limit = q25 - (iqr*1.5)\n", | |
| "print('IQR:', iqr)\n", | |
| "print('Upper Limit:', upper_limit)\n", | |
| "print('Lower Limit:', lower_limit)\n", | |
| "\n", | |
| "outliers = df[(df['tenure'] > upper_limit) | (df['tenure'] < lower_limit)]\n", | |
| "print('Number of outliers:', len(outliers)) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "eP_rPN31Kmx0" | |
| }, | |
| "source": [ | |
| "Certain types of models are more sensitive to outliers than others. When you get to the stage of building your model, consider whether to remove outliers, based on the type of model you decide to use." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "mA7Mz_SnI8km" | |
| }, | |
| "source": [ | |
| "# pAce: Analyze Stage\n", | |
| "- Perform EDA (analyze relationships between variables)\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Gmk6oBT3jgbA" | |
| }, | |
| "source": [ | |
| "💭\n", | |
| "### Reflect on these questions as you complete the analyze stage.\n", | |
| "\n", | |
| "- What did you observe about the relationships between variables?\n", | |
| "- What do you observe about the distributions in the data?\n", | |
| "- What transformations did you make with your data? Why did you chose to make those decisions?\n", | |
| "- What are some purposes of EDA before constructing a predictive model?\n", | |
| "- What resources do you find yourself using as you complete this stage? (Make sure to include the links.)\n", | |
| "- Do you have any ethical considerations in this stage?\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "KDcWrk57kao2" | |
| }, | |
| "source": [ | |
| "## Step 2. Data Exploration (Continue EDA)\n", | |
| "\n", | |
| "Begin by understanding how many employees left and what percentage of all employees this figure represents." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "0 10000\n", | |
| "1 1991\n", | |
| "Name: left, dtype: int64\n", | |
| "0 83.39588\n", | |
| "1 16.60412\n", | |
| "Name: left, dtype: float64\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Get numbers of people who left vs. stayed\n", | |
| "print(df['left'].value_counts()) \n", | |
| "\n", | |
| "# Get percentages of people who left vs. stayed\n", | |
| "print(df['left'].value_counts(normalize=True)*100) \n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "DmVMzXPSuYk1" | |
| }, | |
| "source": [ | |
| "### Data visualizations" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "_hw4qkZzBr-W" | |
| }, | |
| "source": [ | |
| "Now, examine variables that you're interested in, and create plots to visualize relationships between variables in the data." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "id": "Qf0VbjX8-DBQ" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Create a plot as needed\n", | |
| "\n", | |
| "sns.histplot(data=df, x='salary', multiple='dodge', hue='left')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "id": "F8HlhjMy9X3A" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Create a plot as needed\n", | |
| "sns.histplot(data=df, x='avg_monthly_hours', bins=range(80,301,20), multiple='dodge', hue='left')\n", | |
| "plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "id": "NUyBruMee-EI" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 576x432 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Create a plot as needed\n", | |
| "\n", | |
| "\n", | |
| "plt.figure(figsize=(8,6))\n", | |
| "sns.histplot(data=df, x='tenure', multiple='dodge', shrink=6, hue='left')\n", | |
| "plt.show()\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": { | |
| "id": "3v1uJR5y3MEy" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Create a plot as needed\n", | |
| "plt.figure(figsize=(10,5))\n", | |
| "sns.boxplot(data=df, x='no_projects', y='avg_monthly_hours', hue='left')\n", | |
| "plt.legend(title='left', loc=(1.02,0.8))\n", | |
| "plt.show()\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "id": "UCVs81NILbhn" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x360 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Create a plot as needed\n", | |
| "plt.figure(figsize=(10,5))\n", | |
| "sns.boxplot(data=df, x='tenure', y='avg_monthly_hours', hue='left')\n", | |
| "plt.legend(title='left', loc=(1.02,0.8))\n", | |
| "plt.show()\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": { | |
| "id": "cGitCvzvdbjF" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1122.38x360 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "\n", | |
| "f = sns.FacetGrid(df, col='salary', height=5, hue='left', col_wrap=3)\n", | |
| "f.map_dataframe(sns.histplot, x='avg_monthly_hours', multiple='dodge', binwidth=10)\n", | |
| "f.add_legend()\n", | |
| "plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1122.38x360 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "\n", | |
| "f = sns.FacetGrid(df, col='salary', hue='left', height=5)\n", | |
| "f.map_dataframe(sns.scatterplot, x='avg_monthly_hours', y='satisfaction_level')\n", | |
| "f.add_legend()\n", | |
| "plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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/fn3+5Rtrc8ejTp52KcAAHXnNxVm//sHTLmPF0mOBA9FjAVYvIzAAAACAwRNgAAAAAIMnwAAAAAAGT4ABAAAADJ5JPAEAYJXwqGrgQIb+qGojMAAAAIDBW3AERlW9L0nvb3t3n7KsFQEAABPhUdXAgQz9UdWLuYXktydeBQAAAMABLBhgdPdH9ryuqiOTPLy7r51oVQAAAACzLHoOjKp6epIrkmwbLz+uqi6aVGEAAAAAeyxlEs9XJzk+yZeSpLuvSHLM8pcEAAAAsK+lBBg7u/vLE6sEAAAAYD8WM4nnHldV1XOSrKmqY5OcmeRvJ1MWAAAAwF5LGYGxOcmjk3wjyR8k+XKSl0yiKAAAAIDZljIC43u6+xVJXjGpYgAAAADms5QRGL9bVddU1Wur6tETqwgAAABgjkUHGN39lCRPTnJLknOrantVvXJShQEAAADssZQRGOnuf+nus5K8MMkVSV41kaoAAAAAZll0gFFV31tVr66qq5O8KaMnkBw9scoAAAAAxpYyiefvJXlXkhO7++YJ1QMAAABwJ4sOMLr7h6vqyCQPn2A9AAAAAHeylFtInp7RvBfbxsuPq6qLJlUYAAAAwB5LmcTz1UmOT/KlJOnuK5Ics/wlAQAAAOxrKQHGzu7+8sQqAQAAANiPpQQYV1XVc5Ksqapjq+rsjJ5EckBVtamqrq2qmap6+X72eXJVXVFVV1fVR5ZQEwAAALAKLCXA2Jzk0Um+kdHTSP4tyUsOdEBVrUny5iRPTXJckmdX1XFz9rl/krckOaW7H53kp5dQEwAAALAKLOUpJLcnecX4Z7GOTzLT3dclSVVdmOTUJJ+atc9zkrynu/95fJ0vLuH8AAAAwCqwYIBRVe9L0vvb3t2nHODw9Uk+N2v5xiQ/NGef705yeFX9VZL7JHljd5+/UF0AAADA6rGYERi/fRDnr3nWzQ1D1ib5wSQ/keTIJB+rqr/r7n/c50RVZyQ5I0ke/vCHH0RJAMylxwJMjh4LsDwWDDC6e1GTalbVu7v7GXNW35jkYbOWj05y8zz73NrdX0vytaq6NMljk+wTYHT3uUnOTZKNGzfud0QIAEunxwJMjh4LsDyWMonnQh45z7rLkhxbVY+oqnskeVaSi+bs86dJnlhVa6vqnhndYvLpZawLAAAAWOEWPYnnItwpTe7unVX14iQfTLImyXndfXVVvXC8/Zzu/nRVbUtyZZLdSd7R3VctY10AAADACrecAca8uvviJBfPWXfOnOXXJ3n9pGsBAAAAVqblvIVkvgk7AQAAAA7acgYYv7aM5wIAAAD4tkXfQlJV/yHJq5N85/i4StLd/ciMXnxoEgUCAAAALGUOjP+d5L8l+XiSXZMpBwAAAODOlhJgfLm7/2xilQAAAADsx1ICjL+sqtcneU+Sb+xZ2d3/sOxVAQAAAMyylADjh8Z/bpy1rpP8+PKVAwAAAHBniw4wuvspkywEAAAAYH8W/RjVqrpfVf1uVV0+/vmdqrrfJIsDAAAASJYQYCQ5L8lXkvzM+OffkvzeJIoCAAAAmG0pc2B8V3c/Y9byb1TVFctdEAAAAMBcSxmBcUdVPWHPQlX9hyR3LH9JAAAAAPtaygiM/5pk63jei0pyW5LnTaIoAAAAgNmW8hSSK5I8tqruO17+t4lVtQKsuf22HHnNxdMugwE47Ouj/yvsPuK+U66EoVhz+21JHjztMlY0PZY99Fjm0mMBVq8FA4yq+i/d/ftV9Stz1idJuvt3J1TbYG3YsGHaJTAgMzNfSZJseKRfptjjwfrEQfB3x2x6LHemxwKsVosZgXGv8Z/3mWdbL2MtK8bmzZunXQIDsmXLliTJG9/4xilXAncPeiyz6bEAwB4LBhjd/bbxy0u6+29mbxtP5AkAAKwQbtNjD7fpMdfQb9NbyiSeZyf5gUWsAwAABsjtN8zmNj3ubNi36S1mDowfSfKjSY6aMw/GfZOsmVRhAADA8nKbHrO5TY+VZjEjMO6R5N7jfWfPg/FvSZ45iaIAAAAAZlvMHBgfSfKRqnpnd3/2ENQEAAAAsI/DlrDvO6rq/nsWquoBVfXBCdQEAAAAsI+lBBgP6u4v7Vno7n9N8h3LXxIAAADAvpYSYOyuqofvWaiq70zSy18SAAAAwL6W8hjVVyT566r6yHj5SUnOWP6SAAAAAPa16ACju7dV1Q8k+eEkleS/dfetE6sMAAAAYGwpIzCSZFeSLyY5IslxVZXuvnT5ywIAAADYa9EBRlX9YpItSY5OckVGIzE+luTHJ1MaAAAAwMhSJvHckuTxST7b3U9J8v1JbplIVQAAAACzLCXA+Hp3fz1Jqurfdfc1Sb5nMmUBAAAA7LWUOTBurKr7J3lvkg9X1b8muXkyZQEAAADstWCAUVWP6O7ru/s/jVe9uqr+Msn9kmybaHUAAAAAWdwtJH+cJFX153tWdPdHuvui7v7mxCoDAAAAGFvMLSSHVdV/T/LdVfUrczd29+8uf1kAAAAAey1mBMazknw9o7DjPvP8AAAAAEzUgiMwuvvaJL9VVVd2958dgpoAAAAA9rGUx6h+d1Xdt0b+d1X9Q1WdNLHKAAAAAMaWEmD8fHf/W5KTkhyV5PlJ/r+JVAUAAAAwy1ICjBr/eXKS3+vuT85aBwAAADAxSwkwPl5VH8oowPhgVd0nye7JlAUAAACw12Ieo7rHLyR5XJLruvv2qlqX0W0kAAAAABO1YIBRVY/q7msyCi+S5JFV7hwBAAAADp3FjMD4lSRnJPmdebZ1kh9f1ooAAAAA5lgwwOjuM8Yvn9rdX5+9raqOmEhVAAAAALMsZRLPv13kOgAAAIBltZg5MP6fJOuTHFlV35+9j069b5J7TrA2AAAAgCSLmwPjJ5M8L8nRSX531vqvJPn1CdQEAAAAsI/FzIGxNcnWqnpGd7/7ENQEAAAAsI/FjMBIknT3u6vqaUkeneSIWetfM4nCAAAAAPZY9CSeVXVOkp9NsjmjeTB+Osl3TqguAAAAgG9bylNIfrS7T0vyr939G0l+JMnDJlMWAAAAwF5LCTC+Pv7z9qp6aJKdSR6x/CUBAAAA7GvRc2AkeV9V3T/J65P8Q5JO8vaJVAUAAAAwy1ICjGuS7BpP5nlckh9I8t7JlAUAAACw11JuIfl/u/srVfWEJCcmeWeSt06kKgAAAIBZlhJg7Br/+bQk53T3nya5x/KXBAAAALCvpQQYN1XV25L8TJKLq+rfLfF4AAAAgLtkKQHEzyT5YJJN3f2lJA9M8tKFDqqqTVV1bVXNVNXLD7Df46tqV1U9cwk1AQAAAKvAoifx7O7bk7xn1vLnk3z+QMdU1Zokb85ozowbk1xWVRd196fm2e+3MgpIAAAAAPYx6VtAjk8y093Xdfc3k1yY5NR59tuc5N1JvjjhegAAAIAVaNIBxvokn5u1fON43bdV1fok/ynJOQc6UVWdUVWXV9Xlt9xyy7IXCrCa6bEAk6PHAiyPSQcYNc+6nrP8v5L8WnfvmmffvQd1n9vdG7t741FHHbVsBQKgxwJMkh4LsDwWPQfGXXRjkofNWj46yc1z9tmY5MKqSpIHJTm5qnZ293snXBsAAACwQkw6wLgsybFV9YgkNyV5VpLnzN6hux+x53VVvTPJ+4UXAAAAwGwTDTC6e2dVvTijp4usSXJed19dVS8cbz/gvBcAAAAAyeRHYKS7L05y8Zx18wYX3f28SdcDAAAArDyTnsQTAAAA4KAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8CYeYFTVpqq6tqpmqurl82x/blVdOf7526p67KRrAgAAAFaWiQYYVbUmyZuTPDXJcUmeXVXHzdnt+iQ/1t2PSfLaJOdOsiYAAABg5Zn0CIzjk8x093Xd/c0kFyY5dfYO3f233f2v48W/S3L0hGsCAAAAVphJBxjrk3xu1vKN43X78wtJ/my+DVV1RlVdXlWX33LLLctYIgB6LMDk6LEAy2PSAUbNs67n3bHqKRkFGL823/buPre7N3b3xqOOOmoZSwRAjwWYHD0WYHmsnfD5b0zysFnLRye5ee5OVfWYJO9I8tTu3jHhmgAAAIAVZtIjMC5LcmxVPaKq7pHkWUkumr1DVT08yXuS/Fx3/+OE6wEAAABWoImOwOjunVX14iQfTLImyXndfXVVvXC8/Zwkr0qyLslbqipJdnb3xknWBQAAAKwsk76FJN19cZKL56w7Z9brX0zyi5OuAwAAAFi5Jn0LCQAAAMBBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZv7bQLYOU6++yzMzMzM+0ypm7P38GWLVumXMkwbNiwIZs3b552GbDi6bEjeuxe+issHz12RI/dS49dGQQYcJCOPPLIaZcAcLelxwJMjh7LSlPdPe0almzjxo19+eWXT7sMgKGru3KQHguwKHoswOTM22PNgQEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAg1fdPe0alqyqbkny2WnXAbM8KMmt0y4C5ri1uzct9SA9lgHSYxkiPZa7Cz2WIZq3x67IAAOGpqou7+6N064D4O5IjwWYHD2WlcQtJAAAAMDgCTAAAACAwRNgwPI4d9oFANyN6bEAk6PHsmKYAwMAAAAYPCMwAAAAgMETYAAAAACDJ8AAAAAABk+AAQAAAAyeAAMAAAAYPAEGAAAAMHgCDAAAAGDwBBgAAADA4AkwAAAAgMETYHDIVdVXF7HPmVX16aq6oKp+qqqOW6Zrn1dVX6yqqw6wz5Or6p3LcK13VtUzD/Y8y1DHgn/fwHDpmStXVf1VVW0cv764qu4/7ZqApdOHD52qOmZ/77WqXlNVJyxw/Kur6lcnUx1DIMBgqF6U5OTufm6Sn0qyLB8CSd6ZZNMynWtZVdXaadcArFh65sB198nd/aVp1wFMjD48Yd39qu6+5FBek+ERYDBVVfXSqrqsqq6sqt8YrzsnySOTXFRVr0hySpLXV9UVVfVdB3O97r40yW13sdZ7VdUHquqTVXVVVf3seP2rxu/hqqo6t6pqnmPn3Wf87dz/qKqPJHlFVV1fVYePt923qm7Ys3ywauT14xq2z6r/LVV1yvj1n1TVeePXv1BVr1uOawPLQ8+cbM8cf/N3TVW9Y3ztC6rqhKr6m6r6TFUdP+u9nTeu8RNVdep4/ZFVdeH43+cPkxw569w3VNWD5n67WFW/WlWvnvX+3lBVl46/yX18Vb1nfG39GAZAHz4kv7uuqaq3V9XVVfWhqjpyfP5vjxCpqpPH/fqvq+qsqnr/rOOPG9d5XVWdeZC1MDAr6tsL7l6q6qQkxyY5Pkll1PSf1N0vrKpNSbf6kqoAAB/iSURBVJ7S3bdW1bFJ3t/dfzzPOZ6b5KXznH6mu5d7CNymJDd399PG177feP2buvs143X/J8l/TPK+OcceaJ/7d/ePjbcdk+RpSd6b5FlJ3t3d35p9ooN4z/85yeOSPDbJg5JcVlWXJrk0yROTXJRkfZKHjPd/QpILD3A+4BDSMw9Zz9yQ5KeTnJHksiTPyagfnpLk1zP6ZvUVSf6iu3++RreF/H1VXZLkl5Lc3t2PqarHJPmHA/4Nze+b3f2kqtqS5E+T/GBG//HyT1X1hu7ecRfOCSwDffiQ9eFjkzy7u19QVX+U5BlJfn/W+Y5I8rYkT+ru66vqXXOOf1SSpyS5T5Jrq+qtc2ti5RJgME0njX8+MV6+d0YN69LFnqC7L0hywfKXNq/tSX67qn4row+lj47XP6WqXpbknkkemOTq3PlD4ED7/OGs/d6R5GUZfQg8P8kL5hZxEO/5CUne1d27knxhnJw/PslHk7ykRvdqfirJA6rqIUl+JInUGoZDzxyZdM+8vru3J0lVXZ3kz7u7q2p7kmPG+5yU5JTae5/1EUkenuRJSc4aX/fKqrpyCdfd46Lxn9uTXN3dnx/Xcl2ShyURYMD06MMjh6IPXzF+/fHs7b17PCrJdd19/Xj5XRmFznt8oLu/keQbVfXFJA9OcuMSrs+ACTCYpkrym939trt8gkOYYnf3P1bVDyY5OclvVtWHkvzPJG9JsrG7PzceBnzEnBqPWGCfr826xt/UaHjxjyVZ0913msToIN7znYYHjq95U1U9IKOU/tKMPqR+JslXu/srBzgfcGjpmSOT7pnfmPV696zl3dn7e1MleUZ3XzvnWknS85xztp3Z9xbeI+Zsn329ubX4vQ2mSx8eOZR9eFdm3Y6355TzvuH9H6933o34x2SaPpjktVV1QXd/tarWJ/lWd39xzn5fyWgI2J0cyhS7qh6a5Lbu/v0azUb9vOxt5rdW1b2TPDPJ3OGCi9lntvMzSpJfO9/Gg3jPlyb5paramlFI8aTs/TD5WJKXJPnxJOvG9R2oRuDQ0zPnN6meeSAfTLK5qjaPR2d8f3d/IqM++9wkf1lV35fkMfMc+4Uk31FV65J8NaNh2duWuT5gMvTh+R3qPnxNkkdW1THdfUOSn13GczNwJvFkarr7Q0n+IMnHxkNz/zjzN/sLk7y0RhOlHdRESON75D6W5Huq6saq+oUlHP7vM7rP+YqM7n9+3XhG+bdnNETvvRndL72PxewzxwVJHpDRB8Fy+pMkVyb5ZJK/SPKy7v6X8baPJlnb3TMZ3bP9wPE6YCD0zP2aVM88kNcmOTzJlTWakHPPL+1vTXLv8a0jL0vy93MPHN+H/Zok/zfJ+zP6RRxYAfTh/Tqkfbi778joqS/bquqvMwqGv3wors30VfdCIx1hdamqJyd5Xnc/b0rXf2aSU7v756ZxfYCl0DMBpms19uGquvd4FEwleXOSz3T3Gw7V9Zket5DAgFTV2UmemtG9igAcgJ4JMF1T7MMvqKrTk9wjo0lV7/K8JKwsRmDAHOPHQT2uu9875VIABk/PBJgufZjVRIABAAAADJ5JPAEAAIDBW5FzYGzatKm3bfPEMYAFLPSc9HnpsQCLoscCTM68PXZFjsC49dZbp10CwN2WHgswOXoswF23IgMMAAAAYHURYAAAAACDJ8AAAAAABk+AAQAAAAyeAAMAAAAYPAEGAAAAMHgCDAAAAGDwBBgAAADA4AkwAAAAgMETYAAAAACDN9EAo6rOq6ovVtVV+9leVXVWVc1U1ZVV9QOTrAcAAABYmSY9AuOdSTYdYPtTkxw7/jkjyVsnXA8AAACwAk00wOjuS5PcdoBdTk1yfo/8XZL7V9VDJlkTAAAAsPJMew6M9Uk+N2v5xvG6O6mqM6rq8qq6/JZbbjkkxQGsFnoswOTosQDLY9oBRs2zrufbsbvP7e6N3b3xqKOOmnBZAKuLHgswOXoswPKYdoBxY5KHzVo+OsnNU6oFAAAAGKhpBxgXJTlt/DSSH07y5e7+/JRrAgAAAAZm7SRPXlXvSvLkJA+qqhuT/PckhydJd5+T5OIkJyeZSXJ7kudPsh4AAABgZZpogNHdz15geyf55UnWAAAAAKx8076FBAAAAGBBAgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOAJMAAAAIDBE2AAAAAAgyfAAAAAAAZPgAEAAAAMngADAAAAGDwBBgAAADB4AgwAAABg8AQYAAAAwOCtnXYBrFxnn312ZmZmpl3G1N10001JkvXr10+5kmHYsGFDNm/ePO0yYMXTY0f02L30VwBWOwEGHKQ77rhj2iUA3G3psQDAHgIM7jLfAo1s2bIlSfLGN75xypUAdyd67IgeCwDsYQ4MAACAVWhmZiZPe9rT3LLIiiHAAAAAWIVe97rX5Wtf+1pe97rXTbsUWBQBBgAAwCozMzOTG264IUlyww03GIXBiiDAAAAAWGXmjrowCoOVQIABAACwyuwZfbG/ZRgiAQYAAMAqc8wxxxxwGYZIgAEAALDKvPKVrzzgMgyRAAMAAGCV2bBhw7dHXRxzzDHZsGHDdAuCRRBgAAAArEKvfOUrc6973cvoC1aMtdMuAAAAgENvw4YN+cAHPjDtMmDRjMAAAABYhXbs2JEzzzwzO3bsmHYpsCgCDAAAgFVo69at2b59e84///xplwKLIsAAAABYZXbs2JFt27alu7Nt2zajMFgRBBgAAACrzNatW7N79+4kya5du4zCYEUQYAAAAKwyl1xySXbu3Jkk2blzZz784Q9PuSJYmAADAABglTnhhBOydu3ooZRr167NiSeeOOWKYGECDAAAgFXm9NNPz2GHjf5zcM2aNTnttNOmXBEsTIABAACwyqxbty6bNm1KVWXTpk1Zt27dtEuCBa2ddgEAAAAceqeffnpuuOEGoy9YMQQYAAAAq9C6dety1llnTbsMWDS3kAAAAACDJ8AAAAAABk+AAQAAAAyeAAMAAGAV2rFjR84888zs2LFj2qXAoggwAAAAVqGtW7dm+/btOf/886ddCiyKAAMAAGCV2bFjR7Zt25buzrZt24zCYEUQYAAAAKwyW7duze7du5Mku3btMgqDFUGAAQAAsMpccskl2blzZ5Jk586d+fCHPzzlimBhAgwAAIBV5oQTTkhVJUmqKieeeOKUK4KFCTAAAABWmVNOOSXdnSTp7jz96U+fckWwMAEGAADAKnPRRRftMwLjfe9735QrgoUJMAAAAFaZSy65ZJ8RGObAYCUQYAAAAKwyJ5xwQtauXZskWbt2rTkwWBEEGAAAAKvM6aefnsMOG/3n4GGHHZbTTjttyhXBwiYeYFTVpqq6tqpmqurl82y/X1W9r6o+WVVXV9XzJ10TAADAarZu3bo89KEPTZI89KEPzbp166ZcESxsogFGVa1J8uYkT01yXJJnV9Vxc3b75SSf6u7HJnlykt+pqntMsi4AAIDVbMeOHbnpppuSJDfffHN27Ngx5YpgYZMegXF8kpnuvq67v5nkwiSnztmnk9ynRlPg3jvJbUl2TrguAACAVWvr1q3ZvXt3kmTXrl05//zzp1wRLGzSAcb6JJ+btXzjeN1sb0ryvUluTrI9yZbu3j33RFV1RlVdXlWX33LLLZOqF2BV0mMBJkePZYguueSS7Nq1K8kowPAUElaCSQcYNc+6nrP8k0muSPLQJI9L8qaquu+dDuo+t7s3dvfGo446avkrBVjF9FiAydFjGaInPOEJ+yw/8YlPnFIlsHiTDjBuTPKwWctHZzTSYrbnJ3lPj8wkuT7JoyZcFwAAwKo1uoMfVpZJBxiXJTm2qh4xnpjzWUkumrPPPyf5iSSpqgcn+Z4k1024LgAAgFXrox/96AGXYYgmGmB0984kL07ywSSfTvJH3X11Vb2wql443u21SX60qrYn+fMkv9bdt06yLgAAgNXshBNOyNq1a5Mka9euzYknnjjlimBhayd9ge6+OMnFc9adM+v1zUlOmnQdAAAAjJx++unZtm1bkmTNmjU57bTTplwRLGzSt5AAAAAwMOvWrcumTZtSVdm0aVPWrVs37ZJgQRMfgQEAAMDwnH766bnhhhuMvmDFEGAAAACsQuvWrctZZ5017TJg0dxCAgAAsArt2LEjZ555Znbs2DHtUmBRBBgAAACr0NatW7N9+/acf/750y4FFkWAAQAAsMrs2LEj27ZtS3dn27ZtRmGwIggwAAAAVpmtW7dm9+7dSZJdu3YZhcGKIMAAAABYZS655JLs3LkzSbJz5858+MMfnnJFsDABBgAAwCpzwgknZO3a0UMp165dmxNPPHHKFcHCPEYVAABYVc4+++zMzMxMu4yp+ta3vvXtERi7du3KZz7zmWzZsmXKVU3Phg0bsnnz5mmXwQKMwAAAAFhlDj/88G+PwHjgAx+Yww8/fMoVwcKMwAAAAFYV37SPvOhFL8pnP/vZnHvuuVm3bt20y4EFGYEBAACwCh1++OHZsGGD8IIVQ4ABAAAADJ4AAwAAABg8AQYAAAAweCbxBGBQPNqO2fb8b2E1P9qPO/O4Q4DVSYABwKDMzMzkiqs+nV33fOC0S2EADvtmJ0k+ft0XplwJQ7Hm9tumXQIAUyLAAGBwdt3zgbnjUSdPuwxggI685uJplwDAlAgw7gLDm5nN8GbmY3gzAAAsLwHGXWB4M7MZ3sxchjcDAMDyE2DcRYY3A/tjeDMAACw/j1EFAAAABk+AAQAAAAyeAAMAAAAYPAEGAAAAMHgCDAAAAGDwBBgAAADA4AkwAAAAgMETYAAAAACDJ8AAAAAABk+AAQAAAAyeAAMAAAAYPAEGAAAAMHgCDAAAAGDwBBgAAADA4AkwAAAAgMFbu9AOVfW+JL2/7d19yrJWBAAAADDHggFGkt+eeBUAAAAAB7BggNHdH9nzuqqOTPLw7r52olUBAAAAzLLoOTCq6ulJrkiybbz8uKq6aFKFAQAAAOyxlEk8X53k+CRfSpLuviLJMctfEgAAAMC+lhJg7OzuL0+sEgAAAID9WMwknntcVVXPSbKmqo5NcmaSv51MWQAAAAB7LWUExuYkj07yjSR/kOT/b+9eYy09yzIA3w9ToUOlFKTWOGWY1g1iScTDyEk8gKKljRICKuIhqKTBhHGUxECkYtX5IUEb64jWilCMif1hG6wyUA/RGkWTFlKRwkB2apFpQVrb0qbT0rR9/LH3btfs7plZq11f17e7rytZWes7rJU7k8mbzD3v975fSfLLQ4QCAAAAmDTLDIxv7u53JnnnUGEAAAAANjLLDIwLq+pgVf12Vb1gsEQAAAAA60xdYHT3K5J8f5JbklxSVf9VVecPFQwAAABgzSwzMNLdX+ruP0jyliTXJXnXIKkAAAAAJkxdYFTVt1TVBVV1fZI/zMoOJKcPlgwAAABg1SyLeH4gyV8meVV33zxQHgAAAIBHmLrA6O6XVNX2JDsHzAMAAADwCLM8QvIjWVn34qOrx99WVVcOFQwAAABgzSyLeF6Q5EVJ7kiS7r4uya75RwIAAAA40iwFxv3d/ZXBkgAAAAAcxSyLeH6qqt6YZFtVPTfJL2VlJxIAAACAQc0yA2NPkhck+WpWdiO5M8kvH+9LVXV2VX22qpar6h1Huef7q+q6qrq+qq6eIRMAAACwBcyyC8nhJO9cfU2lqrYleW+SVyU5lOSaqrqyuz89cc8pSf4oydnd/T9V9fXT/j4AAACwNRy3wKiqv0nSR7ve3T96jK+/KMlyd9+w+luXJXlNkk9P3PPGJFd09/+s/t6Xp8gNAAAAbCHTzMD43cfw+zuSfGHi+FCSF6+753lJvqaq/jnJ05Jc1N1/vv6Hquq8JOclyc6dOx9DJADWM8YCDMcYCzAfxy0wunuqNSmq6vLuft360xv95AYZvjPJDyTZnuTfq+o/uvtz63JckuSSJNm9e/dRZ4QAMDtjLMBwjLEA8zHLLiTHc+YG5w4lefbE8elJbt7gnlu7++4kd1fVvyR5YZLPBQAAACCz7UJyPBu1ydckeW5VnVFVT07yhiRXrrvnr5N8T1WdUFVPzcojJp+ZYy4AAABgk5vnDIxH6O77q+qtSa5Ksi3J+7v7+qp6y+r1i7v7M1X10SSfTPJgkvd196eGzAUAAABsLvMsMDZa7yLdfSDJgXXnLl53/J4k75ljFgAAAOAJZJ6PkLx9jr8FAAAA8JCpZ2BU1XcnuSDJc1a/V0m6u8/Myoe/GyIgAAAAwCyPkPxZkl9J8vEkDwwTBwAAAOCRZikwvtLdHxksCQAAAMBRzFJg/FNVvSfJFUm+unayuz8x91QAAAAAE2YpMF68+r574lwneeX84gAAAAA80tQFRne/YsggAAAAAEcz9TaqVfX0qrqwqq5dff1eVT19yHAAAAAAyQwFRpL3J7kryY+vvu5M8oEhQgEAAABMmmUNjG/q7tdNHP9mVV0370AAAAAA680yA+Oeqnr52kFVfXeSe+YfCQAAAOBIs8zA+MUkH1xd96KS3JbkTUOEAgAAAJg0yy4k1yV5YVWdvHp852CpAAAAACYct8Coqp/u7r+oqretO58k6e4LB8oGAAAAkGS6GRgnrb4/bYNrPccsAAAAABs6boHR3X+y+vEfuvvfJq+tLuQJAAAAMKhZdiHZP+U5AAAAgLmaZg2MlyZ5WZJT162DcXKSbUMFAwAAAFgzzRoYT07ytav3Tq6DcWeS1w8RCgAAAGDSNGtgXJ3k6qq6tLs//zhkAgAAADjCLGtgvK+qTlk7qKpnVNVVA2QCAAAAOMIsBcazuvuOtYPuvj3J188/EgAAAMCRZikwHqyqnWsHVfWcJD3/SAAAAABHmmYRzzXvTPKvVXX16vH3Jjlv/pEAAIAh7N+/P8vLy4uOwUis/V3Yu3fvgpMwJktLS9mzZ8+iY2xo6gKjuz9aVd+R5CVJKsmvdPetgyUDAADmanl5Odd96jN54KnPXHQURuBJ961MqP/4Df+74CSMxbbDty06wjHNMgMjSR5I8uUkJyY5q6rS3f8y/1gAAMAQHnjqM3PP889ZdAxghLYfPLDoCMc0dYFRVW9OsjfJ6Umuy8pMjH9P8sphogEAAACsmGURz71JvivJ57v7FUm+Pcktg6QCAAAAmDBLgXFvd9+bJFX1lO4+mOSbh4kFAAAA8LBZ1sA4VFWnJPlQkr+vqtuT3DxMLAAAAICHHbfAqKozuvu/u/u1q6cuqKp/SvL0JB8dNB0AAABApnuE5K+SpKr+ce1Ed1/d3Vd2932DJQMAAABYNc0jJE+qqt9I8ryqetv6i9194fxjAQAAADxsmhkYb0hyb1bKjqdt8AIAAAAY1HFnYHT3Z5O8u6o+2d0feRwyAQAAABxhlm1Un1dVJ9eKP6uqT1TVDw2WDAAAAGDVLAXGz3f3nUl+KMmpSX4uye8MkgoAAABgwiwFRq2+n5PkA939nxPnAAAAAAYzS4Hx8ar6u6wUGFdV1dOSPDhMLAAAAICHTbON6ppfSPJtSW7o7sNV9XVZeYwEAAAAYFDHLTCq6vndfTAr5UWSnFnlyREAAADg8TPNDIy3JTkvye9tcK2TvHKuiQAAAADWOW6B0d3nrX58dXffO3mtqk4cJBUAAADAhFkW8fzYlOcAAAAA5mqaNTC+IcmOJNur6tvz8NapJyd56oDZAAAAAJJMtwbGDyd5U5LTk1w4cf6uJL82QCYAAACAI0yzBsYHk3ywql7X3Zc/DpkAAAAAjjDNDIwkSXdfXlXnJnlBkhMnzv/WEMEAAAAA1ky9iGdVXZzkJ5Lsyco6GD+W5DkD5QIAAAB4yCy7kLysu382ye3d/ZtJXprk2cPEAgAAAHjYLAXGvavvh6vqG5Pcn+SM+UcCAAAAONLUa2Ak+ZuqOiXJe5J8Ikkn+dNBUgEAAABMmKXAOJjkgdXFPM9K8h1JPjRMLAAAYN5uuummbDv8lWw/eGDRUYAR2nb4/3LTTfcvOsZRzfIIya93911V9fIkr0pyaZI/HiQVAAAAwIRZZmA8sPp+bpKLu/uvq+qC+UcCAACGsGPHjnzpqyfknuefs+gowAhtP3ggO3actugYRzVLgXFTVf1Jkh9M8u6qekpmm8HxhGHqHXAsY596N3bGWOBYjLEAW9csBcSPJ7kqydndfUeSZyb51eN9qarOrqrPVtVyVb3jGPd9V1U9UFWvnyETAAAAsAVMPQOjuw8nuWLi+ItJvnis71TVtiTvzcqaGYeSXFNVV3b3pze4791ZKUhGz9Q74FjGPvVu7IyxwLEYYwG2rqEfAXlRkuXuvqG770tyWZLXbHDfniSXJ/nywHkAAACATWjoAmNHki9MHB9aPfeQqtqR5LVJLj7WD1XVeVV1bVVde8stt8w9KMBWZowFGI4xFmA+hi4waoNzve7495O8vbsf2ODeh7/UfUl37+7u3aeeeurcAgJgjAUYkjEWYD5m2YXk0TiU5NkTx6cnuXndPbuTXFZVSfKsJOdU1f3d/aGBswEAAACbxNAFxjVJnltVZyS5Kckbkrxx8obuPmPtc1VdmuRvlRcAAADApEELjO6+v6rempXdRbYleX93X19Vb1m9fsx1LwAAAACS4WdgpLsPJDmw7tyGxUV3v2noPAAAAMDmM/QingAAAACPmQIDAAAAGD0FBgAAADB6CgwAAABg9BQYAAAAwOgpMAAAAIDRU2AAAAAAo6fAAAAAAEZPgQEAAACMngIDAAAAGL0TFh0AAAB4/Gw7fFu2Hzyw6BiMwJPuvTNJ8uCJJy84CWOx7fBtSU5bdIyjUmAAAMAWsbS0tOgIjMjy8l1JkqUzx/sPVh5vp416nFBgAADAFrFnz55FR2BE9u7dmyS56KKLFpwEpmMNDAAAAGD0FBgAAADA6CkwAAAAgNFTYAAAAACjp8AAAAAARk+BAQAAAIyeAgMAAAAYPQUGAAAAMHoKDAAAAGD0FBgAAADA6CkwAAAAgNFTYAAAAACjp8AAAAAARk+BAQAAAIyeAgMAAAAYPQUGAAAAMHoKDAAAAGD0FBgAAADA6CkwAAAAgNFTYAAAAACjp8AAAAAARk+BAQAAAIyeAgMAAAAYPQUGAAAAMHoKDAAAAGD0FBgAAADA6CkwAAAAgNFTYAAAAACjp8AAAAAARk+BAQAAAIyeAgMAAAAYPQUGAAAAMHoKDAAAAGD0FBgAAADA6CkwAAAAgNFTYAAAAACjp8AAAAAARk+BAQAAAIyeAgMAAAAYPQUGAAAAMHoKDAAAAGD0FBgAAADA6CkwAAAAgNEbvMCoqrOr6rNVtVxV79jg+k9V1SdXXx+rqhcOnQkAAADYXAYtMKpqW5L3Jnl1krOS/GRVnbXutv9O8n3d/a1JfjvJJUNmAgAAADafoWdgvCjJcnff0N33JbksyWsmb+juj3X37auH/5Hk9IEzAQAAAJvM0AXGjiRfmDg+tHruaH4hyUcGTQQAAABsOicM/Pu1wbne8MaqV2SlwHj5Ua6fl+S8JNm5c+e88gEQYyzAkIyxAPMx9AyMQ0mePXF8epKb199UVd+a5H1JXtPd/7fRD3X3Jd29u7t3n3rqqYOEBdiqjLEAwzHGAszH0AXGNUmeW1VnVNWTk7whyZWTN1TVziRXJPmZ7v7cwHkAAACATWjQR0i6+/6qemuSq5JsS/L+7r6+qt6yev3iJO9K8nVJ/qiqkuT+7t49ZC4AAABgcxl6DYx094EkB9adu3ji85uTvHnoHAAAAMDmNfQjJAAAAACPmQIDAAAAGD0FBgAAADB6CgwAAABg9BQYAAAAwOgpMAAAAIDRU2AAAAAAo6fAAAAAAEZPgQEAAACMngIDAAAAGL0TFh0AAADg8bR///4sLy8vOsbCrf0Z7N27d8FJFm9paSl79uxZdAyOQ4EBAACwBW3fvn3REWAmCgwAAGBL8T/tsDlZAwMAAAAYPQUGAAAAMHoKDAAAAGD0rIHxKG07fFu2Hzyw6BiMwJPuvTNJ8uCJJy84CWOx7fBtSU5bdAwAAHhCUWA8CktLS4uOwIgsL9+VJFk60z9YWXOacQIAAOZMgfEoWLWYSWv7Zl900UULTgIAAPDEZQ0MAAAAYPQUGAAAAFvQ8vJyzj333CwvLy86CkxFgQEAALAF7du3L3fffXf27du36CgwFQUGAADAFrO8vJwbb7wxSXLjjTeahcGmYBFPAEbHVtWssVU169mqGuZj/ayLffv25dJLL11MGJiSAgOAUbEFLZNsVc0j2aoa5mFt9sXRjmGMFBgAjIqtqplkq2qAYZxyyim54447Hjp+xjOescA0MB1rYAAAAGwxk+VFktx+++0LSgLTU2AAAAAAo6fAAAAAAEbPGhg8avv377fdUvLQn8Hac9pb3dLSkjUMYA6MsSuMsQ8zvgLzZA0MNiMFBjxG27dvX3QEgCcsYyzAMKyBwWakwOBR879AAMMxxgIwpF27dh2xdequXbsWlgWmZQ0MAACALeb8888/5jGMkQIDAABgi1laWnpo1sWuXbuytLS02EAwBQUGAADAFnT++efnpJNOMvuCTcMaGAAAAFvQ0tJSPvzhDy86BkzNDAwAAABg9BQYAAAAwOgpMAAAAIDRU2AAAAAAo6fAAAAAAEZPgQEAAACMngIDAAAAGD0FBgAAADB6CgwAAABg9Kq7F51hZlV1S5LPLzoHTHhWklsXHQLWubW7z571S8ZYRsgYyxgZY3miMMYyRhuOsZuywICxqapru3v3onMAPBEZYwGGY4xlM/EICQAAADB6CgwAAABg9BQYMB+XLDoAwBOYMRZgOMZYNg1rYAAAAACjZwYGAAAAMHoKDAAAAGD0FBgAAADA6CkwAAAAgNFTYAAAAACj9//xzjsaQ+QrFgAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 1080x720 with 6 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "\n", | |
| "f = sns.FacetGrid(df, col='salary', row='left', height=5)\n", | |
| "f.map_dataframe(sns.boxplot, y='satisfaction_level')\n", | |
| "\n", | |
| "plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "DeTmNVlAANLd" | |
| }, | |
| "source": [ | |
| "### Insights" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "sQT8YqymD-yL" | |
| }, | |
| "source": [ | |
| "What insights can you gather from the plots you created to visualize the data? \n", | |
| "\n", | |
| "- Most of employees who left have low satisfaction levels. employees with more tenure tend to not leave, employees who work more than 55 hours are more likely to leave regardless of their salary, employees with high salaries are less likely to leave, all employees who worked on 7 projects left, maybe they didn't receive the desired promotion or salary." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Lca9c8XON8lc" | |
| }, | |
| "source": [ | |
| "# paCe: Construct Stage\n", | |
| "- Determine which models are most appropriate\n", | |
| "- Construct the model\n", | |
| "- Confirm model assumptions\n", | |
| "- Evaluate model results to determine how well your model fits the data\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Bf2BU6B1QmTg" | |
| }, | |
| "source": [ | |
| "🔎\n", | |
| "## Recall model assumptions\n", | |
| "\n", | |
| "**Logistic Regression model assumptions**\n", | |
| "- Outcome variable is categorical\n", | |
| "- Observations are independent of each other\n", | |
| "- No severe multicollinearity among X variables\n", | |
| "- No extreme outliers\n", | |
| "- Linear relationship between each X variable and the logit of the outcome variable\n", | |
| "- Sufficiently large sample size\n", | |
| "\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "F3v7IVv2_ai5" | |
| }, | |
| "source": [ | |
| "💭\n", | |
| "### Reflect on these questions as you complete the constructing stage.\n", | |
| "\n", | |
| "- Do you notice anything odd?\n", | |
| "- Which independent variables did you choose for the model and why?\n", | |
| "- Are each of the assumptions met?\n", | |
| "- How well does your model fit the data?\n", | |
| "- Can you improve it? Is there anything you would change about the model?\n", | |
| "- What resources do you find yourself using as you complete this stage? (Make sure to include the links.)\n", | |
| "- Do you have any ethical considerations in this stage?\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "moS7voiMOW67" | |
| }, | |
| "source": [ | |
| "[Double-click to enter your responses here.]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ZDG9v-NCS69j" | |
| }, | |
| "source": [ | |
| "## Step 3. Model Building, Step 4. Results and Evaluation\n", | |
| "- Fit a model that predicts the outcome variable using two or more independent variables\n", | |
| "- Check model assumptions\n", | |
| "- Evaluate the model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QxCXjdpSaB8A" | |
| }, | |
| "source": [ | |
| "### Identify the type of prediction task." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "YcCoHhnKcEov" | |
| }, | |
| "source": [ | |
| "[Double-click to enter your responses here.]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "yeUlo3W0ais-" | |
| }, | |
| "source": [ | |
| "### Identify the types of models most appropriate for this task." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "A0Mo6f9tOchC" | |
| }, | |
| "source": [ | |
| "[Double-click to enter your responses here.]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "OrW2oXy4OfD1" | |
| }, | |
| "source": [ | |
| "### Modeling\n", | |
| "\n", | |
| "Add as many cells as you need to conduct the modeling process." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": { | |
| "id": "UePZZyi_Okdz" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>satisfaction_level</th>\n", | |
| " <th>last_evaluation</th>\n", | |
| " <th>no_projects</th>\n", | |
| " <th>avg_monthly_hours</th>\n", | |
| " <th>tenure</th>\n", | |
| " <th>work_accidents</th>\n", | |
| " <th>left</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " <th>salary</th>\n", | |
| " <th>department_IT</th>\n", | |
| " <th>department_RandD</th>\n", | |
| " <th>department_accounting</th>\n", | |
| " <th>department_hr</th>\n", | |
| " <th>department_management</th>\n", | |
| " <th>department_marketing</th>\n", | |
| " <th>department_product_mng</th>\n", | |
| " <th>department_sales</th>\n", | |
| " <th>department_support</th>\n", | |
| " <th>department_technical</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.38</td>\n", | |
| " <td>0.53</td>\n", | |
| " <td>2</td>\n", | |
| " <td>157</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.80</td>\n", | |
| " <td>0.86</td>\n", | |
| " <td>5</td>\n", | |
| " <td>262</td>\n", | |
| " <td>6</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.11</td>\n", | |
| " <td>0.88</td>\n", | |
| " <td>7</td>\n", | |
| " <td>272</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.72</td>\n", | |
| " <td>0.87</td>\n", | |
| " <td>5</td>\n", | |
| " <td>223</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.37</td>\n", | |
| " <td>0.52</td>\n", | |
| " <td>2</td>\n", | |
| " <td>159</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " satisfaction_level last_evaluation no_projects avg_monthly_hours \\\n", | |
| "0 0.38 0.53 2 157 \n", | |
| "1 0.80 0.86 5 262 \n", | |
| "2 0.11 0.88 7 272 \n", | |
| "3 0.72 0.87 5 223 \n", | |
| "4 0.37 0.52 2 159 \n", | |
| "\n", | |
| " tenure work_accidents left promotion_last_5years salary department_IT \\\n", | |
| "0 3 0 1 0 0 0 \n", | |
| "1 6 0 1 0 1 0 \n", | |
| "2 4 0 1 0 1 0 \n", | |
| "3 5 0 1 0 0 0 \n", | |
| "4 3 0 1 0 0 0 \n", | |
| "\n", | |
| " department_RandD department_accounting department_hr \\\n", | |
| "0 0 0 0 \n", | |
| "1 0 0 0 \n", | |
| "2 0 0 0 \n", | |
| "3 0 0 0 \n", | |
| "4 0 0 0 \n", | |
| "\n", | |
| " department_management department_marketing department_product_mng \\\n", | |
| "0 0 0 0 \n", | |
| "1 0 0 0 \n", | |
| "2 0 0 0 \n", | |
| "3 0 0 0 \n", | |
| "4 0 0 0 \n", | |
| "\n", | |
| " department_sales department_support department_technical \n", | |
| "0 1 0 0 \n", | |
| "1 1 0 0 \n", | |
| "2 1 0 0 \n", | |
| "3 1 0 0 \n", | |
| "4 1 0 0 " | |
| ] | |
| }, | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_enc = df.copy()\n", | |
| "\n", | |
| "# Encode the `salary` column as an ordinal numeric category\n", | |
| "df_enc['salary'] = (\n", | |
| " df_enc['salary'].astype('category')\n", | |
| " .cat.set_categories(['low', 'medium', 'high'])\n", | |
| " .cat.codes\n", | |
| ")\n", | |
| "\n", | |
| "# Dummy encode the `department` column\n", | |
| "df_enc = pd.get_dummies(df_enc, drop_first=False)\n", | |
| "\n", | |
| "# Display the new dataframe\n", | |
| "df_enc.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 576x432 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(8, 6))\n", | |
| "sns.heatmap(df_enc[['satisfaction_level', 'last_evaluation', 'no_projects', 'avg_monthly_hours', 'tenure']]\n", | |
| " .corr(), annot=True, cmap=\"crest\")\n", | |
| "plt.title('Heatmap of the dataset')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "pd.crosstab(df['department'], df['left']).plot(kind ='bar',color='br')\n", | |
| "plt.title('Counts of employees who left versus stayed across department')\n", | |
| "plt.ylabel('Employee count')\n", | |
| "plt.xlabel('Department')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>satisfaction_level</th>\n", | |
| " <th>last_evaluation</th>\n", | |
| " <th>no_projects</th>\n", | |
| " <th>avg_monthly_hours</th>\n", | |
| " <th>tenure</th>\n", | |
| " <th>work_accidents</th>\n", | |
| " <th>left</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " <th>salary</th>\n", | |
| " <th>department_IT</th>\n", | |
| " <th>department_RandD</th>\n", | |
| " <th>department_accounting</th>\n", | |
| " <th>department_hr</th>\n", | |
| " <th>department_management</th>\n", | |
| " <th>department_marketing</th>\n", | |
| " <th>department_product_mng</th>\n", | |
| " <th>department_sales</th>\n", | |
| " <th>department_support</th>\n", | |
| " <th>department_technical</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.38</td>\n", | |
| " <td>0.53</td>\n", | |
| " <td>2</td>\n", | |
| " <td>157</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.11</td>\n", | |
| " <td>0.88</td>\n", | |
| " <td>7</td>\n", | |
| " <td>272</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.72</td>\n", | |
| " <td>0.87</td>\n", | |
| " <td>5</td>\n", | |
| " <td>223</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.37</td>\n", | |
| " <td>0.52</td>\n", | |
| " <td>2</td>\n", | |
| " <td>159</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>0.41</td>\n", | |
| " <td>0.50</td>\n", | |
| " <td>2</td>\n", | |
| " <td>153</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " satisfaction_level last_evaluation no_projects avg_monthly_hours \\\n", | |
| "0 0.38 0.53 2 157 \n", | |
| "2 0.11 0.88 7 272 \n", | |
| "3 0.72 0.87 5 223 \n", | |
| "4 0.37 0.52 2 159 \n", | |
| "5 0.41 0.50 2 153 \n", | |
| "\n", | |
| " tenure work_accidents left promotion_last_5years salary department_IT \\\n", | |
| "0 3 0 1 0 0 0 \n", | |
| "2 4 0 1 0 1 0 \n", | |
| "3 5 0 1 0 0 0 \n", | |
| "4 3 0 1 0 0 0 \n", | |
| "5 3 0 1 0 0 0 \n", | |
| "\n", | |
| " department_RandD department_accounting department_hr \\\n", | |
| "0 0 0 0 \n", | |
| "2 0 0 0 \n", | |
| "3 0 0 0 \n", | |
| "4 0 0 0 \n", | |
| "5 0 0 0 \n", | |
| "\n", | |
| " department_management department_marketing department_product_mng \\\n", | |
| "0 0 0 0 \n", | |
| "2 0 0 0 \n", | |
| "3 0 0 0 \n", | |
| "4 0 0 0 \n", | |
| "5 0 0 0 \n", | |
| "\n", | |
| " department_sales department_support department_technical \n", | |
| "0 1 0 0 \n", | |
| "2 1 0 0 \n", | |
| "3 1 0 0 \n", | |
| "4 1 0 0 \n", | |
| "5 1 0 0 " | |
| ] | |
| }, | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Select rows without outliers in `tenure` and save resulting dataframe in a new variable\n", | |
| "df_logreg = df_enc[(df_enc['tenure'] >= lower_limit) & (df_enc['tenure'] <= upper_limit)]\n", | |
| "\n", | |
| "# Display first few rows of new dataframe\n", | |
| "df_logreg.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "y = df_logreg['left']\n", | |
| "X = df_logreg.drop('left', axis=1)\n", | |
| "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=42)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Construct a logistic regression model and fit it to the training dataset\n", | |
| "log_clf = LogisticRegression(random_state=42, max_iter=500).fit(X_train, y_train)\n", | |
| "# Use the logistic regression model to get predictions on the test set\n", | |
| "y_pred = log_clf.predict(X_test)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Compute values for confusion matrix\n", | |
| "log_cm = confusion_matrix(y_test, y_pred, labels=log_clf.classes_)\n", | |
| "\n", | |
| "# Create display of confusion matrix\n", | |
| "log_disp = ConfusionMatrixDisplay(confusion_matrix=log_cm, \n", | |
| " display_labels=log_clf.classes_)\n", | |
| "\n", | |
| "# Plot confusion matrix\n", | |
| "log_disp.plot(values_format='')\n", | |
| "\n", | |
| "# Display plot\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0 0.831468\n", | |
| "1 0.168532\n", | |
| "Name: left, dtype: float64" | |
| ] | |
| }, | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_logreg['left'].value_counts(normalize=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " precision recall f1-score support\n", | |
| "\n", | |
| "Predicted would not leave 0.86 0.93 0.90 2321\n", | |
| " Predicted would leave 0.44 0.26 0.33 471\n", | |
| "\n", | |
| " accuracy 0.82 2792\n", | |
| " macro avg 0.65 0.60 0.61 2792\n", | |
| " weighted avg 0.79 0.82 0.80 2792\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Create classification report for logistic regression model\n", | |
| "target_names = ['Predicted would not leave', 'Predicted would leave']\n", | |
| "print(classification_report(y_test, y_pred, target_names=target_names))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0 1\n", | |
| "1 1\n", | |
| "2 1\n", | |
| "3 1\n", | |
| "4 1\n", | |
| "Name: left, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 32, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Isolate the outcome variable\n", | |
| "y = df_enc['left']\n", | |
| "\n", | |
| "# Display the first few rows of `y`\n", | |
| "y.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>satisfaction_level</th>\n", | |
| " <th>last_evaluation</th>\n", | |
| " <th>no_projects</th>\n", | |
| " <th>avg_monthly_hours</th>\n", | |
| " <th>tenure</th>\n", | |
| " <th>work_accidents</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " <th>salary</th>\n", | |
| " <th>department_IT</th>\n", | |
| " <th>department_RandD</th>\n", | |
| " <th>department_accounting</th>\n", | |
| " <th>department_hr</th>\n", | |
| " <th>department_management</th>\n", | |
| " <th>department_marketing</th>\n", | |
| " <th>department_product_mng</th>\n", | |
| " <th>department_sales</th>\n", | |
| " <th>department_support</th>\n", | |
| " <th>department_technical</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.38</td>\n", | |
| " <td>0.53</td>\n", | |
| " <td>2</td>\n", | |
| " <td>157</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.80</td>\n", | |
| " <td>0.86</td>\n", | |
| " <td>5</td>\n", | |
| " <td>262</td>\n", | |
| " <td>6</td>\n", | |
| " <td>0</td>\n", | |
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| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.11</td>\n", | |
| " <td>0.88</td>\n", | |
| " <td>7</td>\n", | |
| " <td>272</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.72</td>\n", | |
| " <td>0.87</td>\n", | |
| " <td>5</td>\n", | |
| " <td>223</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.37</td>\n", | |
| " <td>0.52</td>\n", | |
| " <td>2</td>\n", | |
| " <td>159</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
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| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " satisfaction_level last_evaluation no_projects avg_monthly_hours \\\n", | |
| "0 0.38 0.53 2 157 \n", | |
| "1 0.80 0.86 5 262 \n", | |
| "2 0.11 0.88 7 272 \n", | |
| "3 0.72 0.87 5 223 \n", | |
| "4 0.37 0.52 2 159 \n", | |
| "\n", | |
| " tenure work_accidents promotion_last_5years salary department_IT \\\n", | |
| "0 3 0 0 0 0 \n", | |
| "1 6 0 0 1 0 \n", | |
| "2 4 0 0 1 0 \n", | |
| "3 5 0 0 0 0 \n", | |
| "4 3 0 0 0 0 \n", | |
| "\n", | |
| " department_RandD department_accounting department_hr \\\n", | |
| "0 0 0 0 \n", | |
| "1 0 0 0 \n", | |
| "2 0 0 0 \n", | |
| "3 0 0 0 \n", | |
| "4 0 0 0 \n", | |
| "\n", | |
| " department_management department_marketing department_product_mng \\\n", | |
| "0 0 0 0 \n", | |
| "1 0 0 0 \n", | |
| "2 0 0 0 \n", | |
| "3 0 0 0 \n", | |
| "4 0 0 0 \n", | |
| "\n", | |
| " department_sales department_support department_technical \n", | |
| "0 1 0 0 \n", | |
| "1 1 0 0 \n", | |
| "2 1 0 0 \n", | |
| "3 1 0 0 \n", | |
| "4 1 0 0 " | |
| ] | |
| }, | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Select the features\n", | |
| "X = df_enc.drop('left', axis=1)\n", | |
| "\n", | |
| "# Display the first few rows of `X`\n", | |
| "X.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Split the data\n", | |
| "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=0)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 35, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Instantiate model\n", | |
| "tree = DecisionTreeClassifier(random_state=0)\n", | |
| "\n", | |
| "# Assign a dictionary of hyperparameters to search over\n", | |
| "cv_params = {'max_depth':[4, 6, 8, None],\n", | |
| " 'min_samples_leaf': [2, 5, 1],\n", | |
| " 'min_samples_split': [2, 4, 6]\n", | |
| " }\n", | |
| "\n", | |
| "# Assign a dictionary of scoring metrics to capture\n", | |
| "scoring = {'accuracy', 'precision', 'recall', 'f1', 'roc_auc'}\n", | |
| "\n", | |
| "# Instantiate GridSearch\n", | |
| "tree1 = GridSearchCV(tree, cv_params, scoring=scoring, cv=4, refit='roc_auc')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 2.78 s, sys: 0 ns, total: 2.78 s\n", | |
| "Wall time: 2.78 s\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "GridSearchCV(cv=4, error_score=nan,\n", | |
| " estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n", | |
| " criterion='gini', max_depth=None,\n", | |
| " max_features=None,\n", | |
| " max_leaf_nodes=None,\n", | |
| " min_impurity_decrease=0.0,\n", | |
| " min_impurity_split=None,\n", | |
| " min_samples_leaf=1,\n", | |
| " min_samples_split=2,\n", | |
| " min_weight_fraction_leaf=0.0,\n", | |
| " presort='deprecated',\n", | |
| " random_state=0, splitter='best'),\n", | |
| " iid='deprecated', n_jobs=None,\n", | |
| " param_grid={'max_depth': [4, 6, 8, None],\n", | |
| " 'min_samples_leaf': [2, 5, 1],\n", | |
| " 'min_samples_split': [2, 4, 6]},\n", | |
| " pre_dispatch='2*n_jobs', refit='roc_auc', return_train_score=False,\n", | |
| " scoring={'precision', 'f1', 'roc_auc', 'accuracy', 'recall'},\n", | |
| " verbose=0)" | |
| ] | |
| }, | |
| "execution_count": 36, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "%%time\n", | |
| "tree1.fit(X_train, y_train)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'max_depth': 4, 'min_samples_leaf': 5, 'min_samples_split': 2}" | |
| ] | |
| }, | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check best parameters\n", | |
| "tree1.best_params_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 38, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.969819392792457" | |
| ] | |
| }, | |
| "execution_count": 38, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check best AUC score on CV\n", | |
| "tree1.best_score_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def make_results(model_name:str, model_object, metric:str):\n", | |
| " '''\n", | |
| " Arguments:\n", | |
| " model_name (string): what you want the model to be called in the output table\n", | |
| " model_object: a fit GridSearchCV object\n", | |
| " metric (string): precision, recall, f1, accuracy, or auc\n", | |
| " \n", | |
| " Returns a pandas df with the F1, recall, precision, accuracy, and auc scores\n", | |
| " for the model with the best mean 'metric' score across all validation folds. \n", | |
| " '''\n", | |
| "\n", | |
| " # Create dictionary that maps input metric to actual metric name in GridSearchCV\n", | |
| " metric_dict = {'auc': 'mean_test_roc_auc',\n", | |
| " 'precision': 'mean_test_precision',\n", | |
| " 'recall': 'mean_test_recall',\n", | |
| " 'f1': 'mean_test_f1',\n", | |
| " 'accuracy': 'mean_test_accuracy'\n", | |
| " }\n", | |
| "\n", | |
| " # Get all the results from the CV and put them in a df\n", | |
| " cv_results = pd.DataFrame(model_object.cv_results_)\n", | |
| "\n", | |
| " # Isolate the row of the df with the max(metric) score\n", | |
| " best_estimator_results = cv_results.iloc[cv_results[metric_dict[metric]].idxmax(), :]\n", | |
| "\n", | |
| " # Extract Accuracy, precision, recall, and f1 score from that row\n", | |
| " auc = best_estimator_results.mean_test_roc_auc\n", | |
| " f1 = best_estimator_results.mean_test_f1\n", | |
| " recall = best_estimator_results.mean_test_recall\n", | |
| " precision = best_estimator_results.mean_test_precision\n", | |
| " accuracy = best_estimator_results.mean_test_accuracy\n", | |
| " \n", | |
| " # Create table of results\n", | |
| " table = pd.DataFrame()\n", | |
| " table = pd.DataFrame({'model': [model_name],\n", | |
| " 'precision': [precision],\n", | |
| " 'recall': [recall],\n", | |
| " 'F1': [f1],\n", | |
| " 'accuracy': [accuracy],\n", | |
| " 'auc': [auc]\n", | |
| " })\n", | |
| " \n", | |
| " return table" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>model</th>\n", | |
| " <th>precision</th>\n", | |
| " <th>recall</th>\n", | |
| " <th>F1</th>\n", | |
| " <th>accuracy</th>\n", | |
| " <th>auc</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>decision tree cv</td>\n", | |
| " <td>0.914552</td>\n", | |
| " <td>0.916949</td>\n", | |
| " <td>0.915707</td>\n", | |
| " <td>0.971978</td>\n", | |
| " <td>0.969819</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " model precision recall F1 accuracy auc\n", | |
| "0 decision tree cv 0.914552 0.916949 0.915707 0.971978 0.969819" | |
| ] | |
| }, | |
| "execution_count": 40, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Get all CV scores\n", | |
| "tree1_cv_results = make_results('decision tree cv', tree1, 'auc')\n", | |
| "tree1_cv_results" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 42, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Instantiate model\n", | |
| "rf = RandomForestClassifier(random_state=0)\n", | |
| "\n", | |
| "# Assign a dictionary of hyperparameters to search over\n", | |
| "cv_params = {'max_depth': [3,5, None], \n", | |
| " 'max_features': [1.0],\n", | |
| " 'max_samples': [0.7, 1.0],\n", | |
| " 'min_samples_leaf': [1,2,3],\n", | |
| " 'min_samples_split': [2,3,4],\n", | |
| " 'n_estimators': [300, 500],\n", | |
| " } \n", | |
| "\n", | |
| "# Assign a dictionary of scoring metrics to capture\n", | |
| "scoring = {'accuracy', 'precision', 'recall', 'f1', 'roc_auc'}\n", | |
| "\n", | |
| "# Instantiate GridSearch\n", | |
| "rf1 = GridSearchCV(rf, cv_params, scoring=scoring, cv=4, refit='roc_auc')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 43, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 9min 8s, sys: 0 ns, total: 9min 8s\n", | |
| "Wall time: 9min 8s\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "GridSearchCV(cv=4, error_score=nan,\n", | |
| " estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n", | |
| " class_weight=None,\n", | |
| " criterion='gini', max_depth=None,\n", | |
| " max_features='auto',\n", | |
| " max_leaf_nodes=None,\n", | |
| " max_samples=None,\n", | |
| " min_impurity_decrease=0.0,\n", | |
| " min_impurity_split=None,\n", | |
| " min_samples_leaf=1,\n", | |
| " min_samples_split=2,\n", | |
| " min_weight_fraction_leaf=0.0,\n", | |
| " n_estimators=100, n_jobs=None,...\n", | |
| " verbose=0, warm_start=False),\n", | |
| " iid='deprecated', n_jobs=None,\n", | |
| " param_grid={'max_depth': [3, 5, None], 'max_features': [1.0],\n", | |
| " 'max_samples': [0.7, 1.0],\n", | |
| " 'min_samples_leaf': [1, 2, 3],\n", | |
| " 'min_samples_split': [2, 3, 4],\n", | |
| " 'n_estimators': [300, 500]},\n", | |
| " pre_dispatch='2*n_jobs', refit='roc_auc', return_train_score=False,\n", | |
| " scoring={'precision', 'f1', 'roc_auc', 'accuracy', 'recall'},\n", | |
| " verbose=0)" | |
| ] | |
| }, | |
| "execution_count": 43, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "%%time\n", | |
| "rf1.fit(X_train, y_train) # --> Wall time: ~10min" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 47, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Define a path to the folder where you want to save the model\n", | |
| "path = '/home/jovyan/work/'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 48, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def write_pickle(path, model_object, save_as:str):\n", | |
| " '''\n", | |
| " In: \n", | |
| " path: path of folder where you want to save the pickle\n", | |
| " model_object: a model you want to pickle\n", | |
| " save_as: filename for how you want to save the model\n", | |
| "\n", | |
| " Out: A call to pickle the model in the folder indicated\n", | |
| " ''' \n", | |
| "\n", | |
| " with open(path + save_as + '.pickle', 'wb') as to_write:\n", | |
| " pickle.dump(model_object, to_write)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 49, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def read_pickle(path, saved_model_name:str):\n", | |
| " '''\n", | |
| " In: \n", | |
| " path: path to folder where you want to read from\n", | |
| " saved_model_name: filename of pickled model you want to read in\n", | |
| "\n", | |
| " Out: \n", | |
| " model: the pickled model \n", | |
| " '''\n", | |
| " with open(path + saved_model_name + '.pickle', 'rb') as to_read:\n", | |
| " model = pickle.load(to_read)\n", | |
| "\n", | |
| " return model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 50, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Write pickle\n", | |
| "write_pickle(path, rf1, 'hr_rf1')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 51, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Read pickle\n", | |
| "rf1 = read_pickle(path, 'hr_rf1')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 52, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.9804250949807172" | |
| ] | |
| }, | |
| "execution_count": 52, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check best AUC score on CV\n", | |
| "rf1.best_score_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'max_depth': 5,\n", | |
| " 'max_features': 1.0,\n", | |
| " 'max_samples': 0.7,\n", | |
| " 'min_samples_leaf': 1,\n", | |
| " 'min_samples_split': 4,\n", | |
| " 'n_estimators': 500}" | |
| ] | |
| }, | |
| "execution_count": 53, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check best params\n", | |
| "rf1.best_params_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 54, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " model precision recall F1 accuracy auc\n", | |
| "0 decision tree cv 0.914552 0.916949 0.915707 0.971978 0.969819\n", | |
| " model precision recall F1 accuracy auc\n", | |
| "0 random forest cv 0.950023 0.915614 0.932467 0.977983 0.980425\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Get all CV scores\n", | |
| "rf1_cv_results = make_results('random forest cv', rf1, 'auc')\n", | |
| "print(tree1_cv_results)\n", | |
| "print(rf1_cv_results)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 55, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def get_scores(model_name:str, model, X_test_data, y_test_data):\n", | |
| " '''\n", | |
| " Generate a table of test scores.\n", | |
| "\n", | |
| " In: \n", | |
| " model_name (string): How you want your model to be named in the output table\n", | |
| " model: A fit GridSearchCV object\n", | |
| " X_test_data: numpy array of X_test data\n", | |
| " y_test_data: numpy array of y_test data\n", | |
| "\n", | |
| " Out: pandas df of precision, recall, f1, accuracy, and AUC scores for your model\n", | |
| " '''\n", | |
| "\n", | |
| " preds = model.best_estimator_.predict(X_test_data)\n", | |
| "\n", | |
| " auc = roc_auc_score(y_test_data, preds)\n", | |
| " accuracy = accuracy_score(y_test_data, preds)\n", | |
| " precision = precision_score(y_test_data, preds)\n", | |
| " recall = recall_score(y_test_data, preds)\n", | |
| " f1 = f1_score(y_test_data, preds)\n", | |
| "\n", | |
| " table = pd.DataFrame({'model': [model_name],\n", | |
| " 'precision': [precision], \n", | |
| " 'recall': [recall],\n", | |
| " 'f1': [f1],\n", | |
| " 'accuracy': [accuracy],\n", | |
| " 'AUC': [auc]\n", | |
| " })\n", | |
| " \n", | |
| " return table" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 56, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>model</th>\n", | |
| " <th>precision</th>\n", | |
| " <th>recall</th>\n", | |
| " <th>f1</th>\n", | |
| " <th>accuracy</th>\n", | |
| " <th>AUC</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>random forest1 test</td>\n", | |
| " <td>0.964211</td>\n", | |
| " <td>0.919679</td>\n", | |
| " <td>0.941418</td>\n", | |
| " <td>0.980987</td>\n", | |
| " <td>0.956439</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " model precision recall f1 accuracy AUC\n", | |
| "0 random forest1 test 0.964211 0.919679 0.941418 0.980987 0.956439" | |
| ] | |
| }, | |
| "execution_count": 56, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Get predictions on test data\n", | |
| "rf1_test_scores = get_scores('random forest1 test', rf1, X_test, y_test)\n", | |
| "rf1_test_scores" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 57, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>last_evaluation</th>\n", | |
| " <th>no_projects</th>\n", | |
| " <th>avg_monthly_hours</th>\n", | |
| " <th>tenure</th>\n", | |
| " <th>work_accidents</th>\n", | |
| " <th>left</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " <th>salary</th>\n", | |
| " <th>department_IT</th>\n", | |
| " <th>department_RandD</th>\n", | |
| " <th>department_accounting</th>\n", | |
| " <th>department_hr</th>\n", | |
| " <th>department_management</th>\n", | |
| " <th>department_marketing</th>\n", | |
| " <th>department_product_mng</th>\n", | |
| " <th>department_sales</th>\n", | |
| " <th>department_support</th>\n", | |
| " <th>department_technical</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.53</td>\n", | |
| " <td>2</td>\n", | |
| " <td>157</td>\n", | |
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| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.86</td>\n", | |
| " <td>5</td>\n", | |
| " <td>262</td>\n", | |
| " <td>6</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.88</td>\n", | |
| " <td>7</td>\n", | |
| " <td>272</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.87</td>\n", | |
| " <td>5</td>\n", | |
| " <td>223</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.52</td>\n", | |
| " <td>2</td>\n", | |
| " <td>159</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " last_evaluation no_projects avg_monthly_hours tenure work_accidents \\\n", | |
| "0 0.53 2 157 3 0 \n", | |
| "1 0.86 5 262 6 0 \n", | |
| "2 0.88 7 272 4 0 \n", | |
| "3 0.87 5 223 5 0 \n", | |
| "4 0.52 2 159 3 0 \n", | |
| "\n", | |
| " left promotion_last_5years salary department_IT department_RandD \\\n", | |
| "0 1 0 0 0 0 \n", | |
| "1 1 0 1 0 0 \n", | |
| "2 1 0 1 0 0 \n", | |
| "3 1 0 0 0 0 \n", | |
| "4 1 0 0 0 0 \n", | |
| "\n", | |
| " department_accounting department_hr department_management \\\n", | |
| "0 0 0 0 \n", | |
| "1 0 0 0 \n", | |
| "2 0 0 0 \n", | |
| "3 0 0 0 \n", | |
| "4 0 0 0 \n", | |
| "\n", | |
| " department_marketing department_product_mng department_sales \\\n", | |
| "0 0 0 1 \n", | |
| "1 0 0 1 \n", | |
| "2 0 0 1 \n", | |
| "3 0 0 1 \n", | |
| "4 0 0 1 \n", | |
| "\n", | |
| " department_support department_technical \n", | |
| "0 0 0 \n", | |
| "1 0 0 \n", | |
| "2 0 0 \n", | |
| "3 0 0 \n", | |
| "4 0 0 " | |
| ] | |
| }, | |
| "execution_count": 57, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Drop `satisfaction_level` and save resulting dataframe in new variable\n", | |
| "df2 = df_enc.drop('satisfaction_level', axis=1)\n", | |
| "\n", | |
| "# Display first few rows of new dataframe\n", | |
| "df2.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 59, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Max hours: 310\n", | |
| "Min hours: 96\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Create `overworked` column. For now, it's identical to average monthly hours.\n", | |
| "df2['overworked'] = df2['avg_monthly_hours']\n", | |
| "\n", | |
| "# Inspect max and min average monthly hours values\n", | |
| "print('Max hours:', df2['overworked'].max())\n", | |
| "print('Min hours:', df2['overworked'].min())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 60, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0 0\n", | |
| "1 1\n", | |
| "2 1\n", | |
| "3 1\n", | |
| "4 0\n", | |
| "Name: overworked, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 60, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Define `overworked` as working > 175 hrs/week\n", | |
| "df2['overworked'] = (df2['overworked'] > 175).astype(int)\n", | |
| "\n", | |
| "# Display first few rows of new column\n", | |
| "df2['overworked'].head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 61, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
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| " .dataframe tbody tr th {\n", | |
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| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>last_evaluation</th>\n", | |
| " <th>no_projects</th>\n", | |
| " <th>tenure</th>\n", | |
| " <th>work_accidents</th>\n", | |
| " <th>left</th>\n", | |
| " <th>promotion_last_5years</th>\n", | |
| " <th>salary</th>\n", | |
| " <th>department_IT</th>\n", | |
| " <th>department_RandD</th>\n", | |
| " <th>department_accounting</th>\n", | |
| " <th>department_hr</th>\n", | |
| " <th>department_management</th>\n", | |
| " <th>department_marketing</th>\n", | |
| " <th>department_product_mng</th>\n", | |
| " <th>department_sales</th>\n", | |
| " <th>department_support</th>\n", | |
| " <th>department_technical</th>\n", | |
| " <th>overworked</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.53</td>\n", | |
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| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.86</td>\n", | |
| " <td>5</td>\n", | |
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| " <td>0</td>\n", | |
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| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.88</td>\n", | |
| " <td>7</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.87</td>\n", | |
| " <td>5</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
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| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0.52</td>\n", | |
| " <td>2</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
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| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " last_evaluation no_projects tenure work_accidents left \\\n", | |
| "0 0.53 2 3 0 1 \n", | |
| "1 0.86 5 6 0 1 \n", | |
| "2 0.88 7 4 0 1 \n", | |
| "3 0.87 5 5 0 1 \n", | |
| "4 0.52 2 3 0 1 \n", | |
| "\n", | |
| " promotion_last_5years salary department_IT department_RandD \\\n", | |
| "0 0 0 0 0 \n", | |
| "1 0 1 0 0 \n", | |
| "2 0 1 0 0 \n", | |
| "3 0 0 0 0 \n", | |
| "4 0 0 0 0 \n", | |
| "\n", | |
| " department_accounting department_hr department_management \\\n", | |
| "0 0 0 0 \n", | |
| "1 0 0 0 \n", | |
| "2 0 0 0 \n", | |
| "3 0 0 0 \n", | |
| "4 0 0 0 \n", | |
| "\n", | |
| " department_marketing department_product_mng department_sales \\\n", | |
| "0 0 0 1 \n", | |
| "1 0 0 1 \n", | |
| "2 0 0 1 \n", | |
| "3 0 0 1 \n", | |
| "4 0 0 1 \n", | |
| "\n", | |
| " department_support department_technical overworked \n", | |
| "0 0 0 0 \n", | |
| "1 0 0 1 \n", | |
| "2 0 0 1 \n", | |
| "3 0 0 1 \n", | |
| "4 0 0 0 " | |
| ] | |
| }, | |
| "execution_count": 61, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Drop the `average_monthly_hours` column\n", | |
| "df2 = df2.drop('avg_monthly_hours', axis=1)\n", | |
| "\n", | |
| "# Display first few rows of resulting dataframe\n", | |
| "df2.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 62, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Isolate the outcome variable\n", | |
| "y = df2['left']\n", | |
| "\n", | |
| "# Select the features\n", | |
| "X = df2.drop('left', axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 63, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Create test data\n", | |
| "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=0)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 64, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Instantiate model\n", | |
| "tree = DecisionTreeClassifier(random_state=0)\n", | |
| "\n", | |
| "# Assign a dictionary of hyperparameters to search over\n", | |
| "cv_params = {'max_depth':[4, 6, 8, None],\n", | |
| " 'min_samples_leaf': [2, 5, 1],\n", | |
| " 'min_samples_split': [2, 4, 6]\n", | |
| " }\n", | |
| "\n", | |
| "# Assign a dictionary of scoring metrics to capture\n", | |
| "scoring = {'accuracy', 'precision', 'recall', 'f1', 'roc_auc'}\n", | |
| "\n", | |
| "# Instantiate GridSearch\n", | |
| "tree2 = GridSearchCV(tree, cv_params, scoring=scoring, cv=4, refit='roc_auc')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 65, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 2.23 s, sys: 0 ns, total: 2.23 s\n", | |
| "Wall time: 2.23 s\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "GridSearchCV(cv=4, error_score=nan,\n", | |
| " estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n", | |
| " criterion='gini', max_depth=None,\n", | |
| " max_features=None,\n", | |
| " max_leaf_nodes=None,\n", | |
| " min_impurity_decrease=0.0,\n", | |
| " min_impurity_split=None,\n", | |
| " min_samples_leaf=1,\n", | |
| " min_samples_split=2,\n", | |
| " min_weight_fraction_leaf=0.0,\n", | |
| " presort='deprecated',\n", | |
| " random_state=0, splitter='best'),\n", | |
| " iid='deprecated', n_jobs=None,\n", | |
| " param_grid={'max_depth': [4, 6, 8, None],\n", | |
| " 'min_samples_leaf': [2, 5, 1],\n", | |
| " 'min_samples_split': [2, 4, 6]},\n", | |
| " pre_dispatch='2*n_jobs', refit='roc_auc', return_train_score=False,\n", | |
| " scoring={'precision', 'f1', 'roc_auc', 'accuracy', 'recall'},\n", | |
| " verbose=0)" | |
| ] | |
| }, | |
| "execution_count": 65, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "%%time\n", | |
| "tree2.fit(X_train, y_train)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 66, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'max_depth': 6, 'min_samples_leaf': 2, 'min_samples_split': 6}" | |
| ] | |
| }, | |
| "execution_count": 66, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check best params\n", | |
| "tree2.best_params_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 67, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.9586752505340426" | |
| ] | |
| }, | |
| "execution_count": 67, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check best AUC score on CV\n", | |
| "tree2.best_score_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 68, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " model precision recall F1 accuracy auc\n", | |
| "0 decision tree cv 0.914552 0.916949 0.915707 0.971978 0.969819\n", | |
| " model precision recall F1 accuracy auc\n", | |
| "0 decision tree2 cv 0.856693 0.903553 0.878882 0.958523 0.958675\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Get all CV scores\n", | |
| "tree2_cv_results = make_results('decision tree2 cv', tree2, 'auc')\n", | |
| "print(tree1_cv_results)\n", | |
| "print(tree2_cv_results)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 69, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Instantiate model\n", | |
| "rf = RandomForestClassifier(random_state=0)\n", | |
| "\n", | |
| "# Assign a dictionary of hyperparameters to search over\n", | |
| "cv_params = {'max_depth': [3,5, None], \n", | |
| " 'max_features': [1.0],\n", | |
| " 'max_samples': [0.7, 1.0],\n", | |
| " 'min_samples_leaf': [1,2,3],\n", | |
| " 'min_samples_split': [2,3,4],\n", | |
| " 'n_estimators': [300, 500],\n", | |
| " } \n", | |
| "\n", | |
| "# Assign a dictionary of scoring metrics to capture\n", | |
| "scoring = {'accuracy', 'precision', 'recall', 'f1', 'roc_auc'}\n", | |
| "\n", | |
| "# Instantiate GridSearch\n", | |
| "rf2 = GridSearchCV(rf, cv_params, scoring=scoring, cv=4, refit='roc_auc')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 70, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "CPU times: user 6min 51s, sys: 0 ns, total: 6min 51s\n", | |
| "Wall time: 6min 51s\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "GridSearchCV(cv=4, error_score=nan,\n", | |
| " estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n", | |
| " class_weight=None,\n", | |
| " criterion='gini', max_depth=None,\n", | |
| " max_features='auto',\n", | |
| " max_leaf_nodes=None,\n", | |
| " max_samples=None,\n", | |
| " min_impurity_decrease=0.0,\n", | |
| " min_impurity_split=None,\n", | |
| " min_samples_leaf=1,\n", | |
| " min_samples_split=2,\n", | |
| " min_weight_fraction_leaf=0.0,\n", | |
| " n_estimators=100, n_jobs=None,...\n", | |
| " verbose=0, warm_start=False),\n", | |
| " iid='deprecated', n_jobs=None,\n", | |
| " param_grid={'max_depth': [3, 5, None], 'max_features': [1.0],\n", | |
| " 'max_samples': [0.7, 1.0],\n", | |
| " 'min_samples_leaf': [1, 2, 3],\n", | |
| " 'min_samples_split': [2, 3, 4],\n", | |
| " 'n_estimators': [300, 500]},\n", | |
| " pre_dispatch='2*n_jobs', refit='roc_auc', return_train_score=False,\n", | |
| " scoring={'precision', 'f1', 'roc_auc', 'accuracy', 'recall'},\n", | |
| " verbose=0)" | |
| ] | |
| }, | |
| "execution_count": 70, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "%%time\n", | |
| "rf2.fit(X_train, y_train) # --> Wall time: 7min 5s" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 71, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Write pickle\n", | |
| "write_pickle(path, rf2, 'hr_rf2')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 72, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Read in pickle\n", | |
| "rf2 = read_pickle(path, 'hr_rf2')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 73, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'max_depth': 5,\n", | |
| " 'max_features': 1.0,\n", | |
| " 'max_samples': 0.7,\n", | |
| " 'min_samples_leaf': 2,\n", | |
| " 'min_samples_split': 2,\n", | |
| " 'n_estimators': 300}" | |
| ] | |
| }, | |
| "execution_count": 73, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check best params\n", | |
| "rf2.best_params_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 74, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.9648100662833985" | |
| ] | |
| }, | |
| "execution_count": 74, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Check best AUC score on CV\n", | |
| "rf2.best_score_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 75, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " model precision recall F1 accuracy auc\n", | |
| "0 decision tree2 cv 0.856693 0.903553 0.878882 0.958523 0.958675\n", | |
| " model precision recall F1 accuracy auc\n", | |
| "0 random forest2 cv 0.866758 0.878754 0.872407 0.957411 0.96481\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Get all CV scores\n", | |
| "rf2_cv_results = make_results('random forest2 cv', rf2, 'auc')\n", | |
| "print(tree2_cv_results)\n", | |
| "print(rf2_cv_results)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 76, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .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>model</th>\n", | |
| " <th>precision</th>\n", | |
| " <th>recall</th>\n", | |
| " <th>f1</th>\n", | |
| " <th>accuracy</th>\n", | |
| " <th>AUC</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>random forest2 test</td>\n", | |
| " <td>0.870406</td>\n", | |
| " <td>0.903614</td>\n", | |
| " <td>0.8867</td>\n", | |
| " <td>0.961641</td>\n", | |
| " <td>0.938407</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " model precision recall f1 accuracy AUC\n", | |
| "0 random forest2 test 0.870406 0.903614 0.8867 0.961641 0.938407" | |
| ] | |
| }, | |
| "execution_count": 76, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Get predictions on test data\n", | |
| "rf2_test_scores = get_scores('random forest2 test', rf2, X_test, y_test)\n", | |
| "rf2_test_scores" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 77, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Generate array of values for confusion matrix\n", | |
| "preds = rf2.best_estimator_.predict(X_test)\n", | |
| "cm = confusion_matrix(y_test, preds, labels=rf2.classes_)\n", | |
| "\n", | |
| "# Plot confusion matrix\n", | |
| "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n", | |
| " display_labels=rf2.classes_)\n", | |
| "disp.plot(values_format='');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 83, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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