{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Finding Outliers**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **30** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will work with a cleaned dataset to perform exploratory data analysis or EDA. \n",
    "You will explore the distribution of key variables and focus on identifying outliers in this lab.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will perform the following:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Analyze the distribution of key variables in the dataset.\n",
    "\n",
    "-  Identify and remove outliers using statistical methods.\n",
    "\n",
    "-  Perform relevant statistical and correlation analysis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install and import the required libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.12/site-packages (3.0.0)\n",
      "Requirement already satisfied: numpy>=1.26.0 in /opt/conda/lib/python3.12/site-packages (from pandas) (2.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.12/site-packages (3.10.8)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (4.61.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (1.4.9)\n",
      "Requirement already satisfied: numpy>=1.23 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (2.4.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (12.1.0)\n",
      "Requirement already satisfied: pyparsing>=3 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (3.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
      "Requirement already satisfied: seaborn in /opt/conda/lib/python3.12/site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.12/site-packages (from seaborn) (2.4.2)\n",
      "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.12/site-packages (from seaborn) (3.0.0)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.12/site-packages (from seaborn) (3.10.8)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.61.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (12.1.0)\n",
      "Requirement already satisfied: pyparsing>=3 in /opt/conda/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.12/site-packages (1.17.0)\n",
      "Requirement already satisfied: numpy<2.7,>=1.26.4 in /opt/conda/lib/python3.12/site-packages (from scipy) (2.4.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas\n",
    "!pip install matplotlib\n",
    "!pip install seaborn\n",
    "!pip install scipy\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import textwrap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Step 1: Load and Explore the Dataset</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset into a DataFrame and examine the structure of the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ResponseId                      MainBranch                 Age  \\\n",
      "0           1  I am a developer by profession  Under 18 years old   \n",
      "1           2  I am a developer by profession     35-44 years old   \n",
      "2           3  I am a developer by profession     45-54 years old   \n",
      "3           4           I am learning to code     18-24 years old   \n",
      "4           5  I am a developer by profession     18-24 years old   \n",
      "\n",
      "            Employment RemoteWork   Check  \\\n",
      "0  Employed, full-time     Remote  Apples   \n",
      "1  Employed, full-time     Remote  Apples   \n",
      "2  Employed, full-time     Remote  Apples   \n",
      "3   Student, full-time        NaN  Apples   \n",
      "4   Student, full-time        NaN  Apples   \n",
      "\n",
      "                                    CodingActivities  \\\n",
      "0                                              Hobby   \n",
      "1  Hobby;Contribute to open-source projects;Other...   \n",
      "2  Hobby;Contribute to open-source projects;Other...   \n",
      "3                                                NaN   \n",
      "4                                                NaN   \n",
      "\n",
      "                                             EdLevel  \\\n",
      "0                          Primary/elementary school   \n",
      "1       Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
      "2    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",
      "3  Some college/university study without earning ...   \n",
      "4  Secondary school (e.g. American high school, G...   \n",
      "\n",
      "                                           LearnCode  \\\n",
      "0                             Books / Physical media   \n",
      "1  Books / Physical media;Colleague;On the job tr...   \n",
      "2  Books / Physical media;Colleague;On the job tr...   \n",
      "3  Other online resources (e.g., videos, blogs, f...   \n",
      "4  Other online resources (e.g., videos, blogs, f...   \n",
      "\n",
      "                                     LearnCodeOnline  ... JobSatPoints_6  \\\n",
      "0                                                NaN  ...            NaN   \n",
      "1  Technical documentation;Blogs;Books;Written Tu...  ...            0.0   \n",
      "2  Technical documentation;Blogs;Books;Written Tu...  ...            NaN   \n",
      "3  Stack Overflow;How-to videos;Interactive tutorial  ...            NaN   \n",
      "4  Technical documentation;Blogs;Written Tutorial...  ...            NaN   \n",
      "\n",
      "  JobSatPoints_7 JobSatPoints_8 JobSatPoints_9 JobSatPoints_10  \\\n",
      "0            NaN            NaN            NaN             NaN   \n",
      "1            0.0            0.0            0.0             0.0   \n",
      "2            NaN            NaN            NaN             NaN   \n",
      "3            NaN            NaN            NaN             NaN   \n",
      "4            NaN            NaN            NaN             NaN   \n",
      "\n",
      "  JobSatPoints_11           SurveyLength SurveyEase ConvertedCompYearly JobSat  \n",
      "0             NaN                    NaN        NaN                 NaN    NaN  \n",
      "1             0.0                    NaN        NaN                 NaN    NaN  \n",
      "2             NaN  Appropriate in length       Easy                 NaN    NaN  \n",
      "3             NaN               Too long       Easy                 NaN    NaN  \n",
      "4             NaN              Too short       Easy                 NaN    NaN  \n",
      "\n",
      "[5 rows x 114 columns]\n",
      "Initial dataframe: (65437, 114)\n"
     ]
    }
   ],
   "source": [
    "file_url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/n01PQ9pSmiRX6520flujwQ/survey-data.csv\"\n",
    "\n",
    "#Create the dataframe\n",
    "df = pd.read_csv(file_url)\n",
    "\n",
    "#Display the top 10 records\n",
    "print(df.head())\n",
    "print('Initial dataframe:', df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Step 2: Plot the Distribution of Industry</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore how respondents are distributed across different industries.\n",
    "\n",
    "- Plot a bar chart to visualize the distribution of respondents by industry.\n",
    "\n",
    "- Highlight any notable trends.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_300/1847729545.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set(xticklabels=labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Write your code here\n",
    "pdi = df.dropna(subset=['Industry'])\n",
    "\n",
    "plt.figure(figsize=(14, 8))\n",
    "order = pdi['Industry'].value_counts().index  # the bars in descending order\n",
    "ax = sns.countplot(data=pdi, x='Industry', order=order)\n",
    "labels = [textwrap.fill(label.get_text(), width=20) for label in ax.get_xticklabels()]\n",
    "ax.set(xticklabels=labels)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Type of Industry\")\n",
    "plt.ylabel(\"Number of Respondents\")\n",
    "plt.grid(True, axis='y', linestyle='dashed')\n",
    "plt.title(\"Distribution of respondents by industry\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Most respondents work in Software development."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Step 3: Identify High Compensation Outliers</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify respondents with extremely high yearly compensation.\n",
    "\n",
    "- Calculate basic statistics (mean, median, and standard deviation) for `ConvertedCompYearly`.\n",
    "\n",
    "- Identify compensation values exceeding a defined threshold (e.g., 3 standard deviations above the mean).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       23435\n",
      "mean        86155\n",
      "std        186756\n",
      "min             1\n",
      "25%         32712\n",
      "50%         65000\n",
      "75%        107971\n",
      "max      16256603\n",
      "Name: ConvertedCompYearly, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "##Write your code here\n",
    "ihco = df.dropna(subset=['ConvertedCompYearly'])\n",
    "print(ihco.ConvertedCompYearly.describe().astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16256603. 13818022.  9000000.  6340564.  4936778.  3367716.  2584118.\n",
      "  2237846.  2153432.  2048046.  2014062.  2000000.  1900000.  1750749.\n",
      "  1750000.  1728025.  1562898.  1550391.  1500000.  1332930.  1302817.\n",
      "  1300000.  1295242.  1288874.  1273879.  1250348.  1200000.  1188001.\n",
      "  1184707.  1140481.  1074062.  1052581.  1031099.  1000000.   980887.\n",
      "   950000.   945000.   912953.   900000.   875000.   859250.   850000.\n",
      "   810000.   803285.   800000.   798000.   764327.   750000.   740517.\n",
      "   720000.   713372.   712801.   700000.   675000.   670000.   660660.\n",
      "   650000.]\n"
     ]
    }
   ],
   "source": [
    "stdev = ihco.ConvertedCompYearly.std()\n",
    "average = ihco.ConvertedCompYearly.mean()\n",
    "outliers1 = ihco[ihco['ConvertedCompYearly'] > average + 3 * stdev]['ConvertedCompYearly'].sort_values(ascending=False).unique()\n",
    "print(outliers1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Step 4: Detect Outliers in Compensation</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify outliers in the `ConvertedCompYearly` column using the IQR method.\n",
    "\n",
    "- Calculate the Interquartile Range (IQR).\n",
    "\n",
    "- Determine the upper and lower bounds for outliers.\n",
    "\n",
    "- Count and visualize outliers using a box plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers in ConvertedCompYearly: 978\n"
     ]
    }
   ],
   "source": [
    "##Write your code here\n",
    "doc = ihco.ConvertedCompYearly\n",
    "q1, q3 = doc.quantile(.25), doc.quantile(.75)\n",
    "iqr = q3 - q1\n",
    "outliers2 = doc[(doc > q3 + 1.5 * iqr) | (doc < q1 - 1.5 * iqr)].sort_values(ascending=False)\n",
    "print('Number of outliers in ConvertedCompYearly:', outliers2.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "doc.plot(kind='box', figsize=(5, 6))\n",
    "\n",
    "plt.title('ConvertedCompYearly')\n",
    "plt.ylabel('Yearly compensation, USD')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Step 5: Remove Outliers and Create a New DataFrame</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove outliers from the dataset.\n",
    "\n",
    "- Create a new DataFrame excluding rows with outliers in `ConvertedCompYearly`.\n",
    "- Validate the size of the new DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     22457.000000\n",
       "mean      71347.201897\n",
       "std       51271.396445\n",
       "min           1.000000\n",
       "25%       32000.000000\n",
       "50%       63694.000000\n",
       "75%      101281.000000\n",
       "max      220207.000000\n",
       "Name: ConvertedCompYearly, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Write your code here\n",
    "df_o = df[~df['ConvertedCompYearly'].isin(outliers2)]\n",
    "df_o['ConvertedCompYearly'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Step 6: Correlation Analysis</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the correlation between `Age` (transformed) and other numerical columns.\n",
    "\n",
    "- Map the `Age` column to approximate numeric values.\n",
    "\n",
    "- Compute correlations between `Age` and other numeric variables.\n",
    "\n",
    "- Visualize the correlation matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17., 40., 50., 21., 30., 60., nan, 66.])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age = df.Age\n",
    "Age = Age.map({'Under 18 years old': 17, '18-24 years old': 21, 'Prefer not to say': float('nan'), \n",
    "        '25-34 years old': 30, '35-44 years old': 40, '45-54 years old': 50,\n",
    "        '55-64 years old': 60, '65 years or older': 66})\n",
    "Age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>ResponseId</th>\n",
       "      <th>Age</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>WorkExp</th>\n",
       "      <th>JobSatPoints_1</th>\n",
       "      <th>JobSatPoints_4</th>\n",
       "      <th>JobSatPoints_5</th>\n",
       "      <th>JobSatPoints_6</th>\n",
       "      <th>JobSatPoints_7</th>\n",
       "      <th>JobSatPoints_8</th>\n",
       "      <th>JobSatPoints_9</th>\n",
       "      <th>JobSatPoints_10</th>\n",
       "      <th>JobSatPoints_11</th>\n",
       "      <th>ConvertedCompYearly</th>\n",
       "      <th>JobSat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ResponseId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038339</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.011955</td>\n",
       "      <td>-0.002406</td>\n",
       "      <td>-0.015738</td>\n",
       "      <td>-0.014197</td>\n",
       "      <td>-0.001908</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>-0.002968</td>\n",
       "      <td>0.001991</td>\n",
       "      <td>-0.005365</td>\n",
       "      <td>-0.005865</td>\n",
       "      <td>-0.008363</td>\n",
       "      <td>0.002425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.038339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002709</td>\n",
       "      <td>0.848165</td>\n",
       "      <td>-0.027672</td>\n",
       "      <td>-0.076214</td>\n",
       "      <td>-0.101811</td>\n",
       "      <td>-0.050353</td>\n",
       "      <td>-0.083718</td>\n",
       "      <td>-0.046519</td>\n",
       "      <td>-0.082170</td>\n",
       "      <td>-0.114871</td>\n",
       "      <td>-0.107356</td>\n",
       "      <td>0.122590</td>\n",
       "      <td>0.069514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompTotal</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.002709</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028766</td>\n",
       "      <td>-0.004891</td>\n",
       "      <td>-0.002719</td>\n",
       "      <td>-0.003033</td>\n",
       "      <td>-0.006311</td>\n",
       "      <td>-0.005893</td>\n",
       "      <td>-0.004585</td>\n",
       "      <td>-0.004494</td>\n",
       "      <td>-0.003198</td>\n",
       "      <td>-0.003045</td>\n",
       "      <td>0.080457</td>\n",
       "      <td>-0.015770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkExp</th>\n",
       "      <td>0.011955</td>\n",
       "      <td>0.848165</td>\n",
       "      <td>0.028766</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026490</td>\n",
       "      <td>-0.067241</td>\n",
       "      <td>-0.104525</td>\n",
       "      <td>-0.065352</td>\n",
       "      <td>-0.101461</td>\n",
       "      <td>-0.046958</td>\n",
       "      <td>-0.088090</td>\n",
       "      <td>-0.113707</td>\n",
       "      <td>-0.108279</td>\n",
       "      <td>0.154114</td>\n",
       "      <td>0.097322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_1</th>\n",
       "      <td>-0.002406</td>\n",
       "      <td>-0.027672</td>\n",
       "      <td>-0.004891</td>\n",
       "      <td>-0.026490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.445710</td>\n",
       "      <td>0.633765</td>\n",
       "      <td>0.618618</td>\n",
       "      <td>0.603157</td>\n",
       "      <td>0.627385</td>\n",
       "      <td>0.597527</td>\n",
       "      <td>0.585087</td>\n",
       "      <td>0.607792</td>\n",
       "      <td>0.020306</td>\n",
       "      <td>0.094068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_4</th>\n",
       "      <td>-0.015738</td>\n",
       "      <td>-0.076214</td>\n",
       "      <td>-0.002719</td>\n",
       "      <td>-0.067241</td>\n",
       "      <td>0.445710</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.526254</td>\n",
       "      <td>0.449400</td>\n",
       "      <td>0.459253</td>\n",
       "      <td>0.438763</td>\n",
       "      <td>0.472516</td>\n",
       "      <td>0.499387</td>\n",
       "      <td>0.528395</td>\n",
       "      <td>0.018197</td>\n",
       "      <td>0.029677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_5</th>\n",
       "      <td>-0.014197</td>\n",
       "      <td>-0.101811</td>\n",
       "      <td>-0.003033</td>\n",
       "      <td>-0.104525</td>\n",
       "      <td>0.633765</td>\n",
       "      <td>0.526254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.613933</td>\n",
       "      <td>0.607949</td>\n",
       "      <td>0.630353</td>\n",
       "      <td>0.619644</td>\n",
       "      <td>0.643538</td>\n",
       "      <td>0.690993</td>\n",
       "      <td>-0.023281</td>\n",
       "      <td>0.049224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_6</th>\n",
       "      <td>-0.001908</td>\n",
       "      <td>-0.050353</td>\n",
       "      <td>-0.006311</td>\n",
       "      <td>-0.065352</td>\n",
       "      <td>0.618618</td>\n",
       "      <td>0.449400</td>\n",
       "      <td>0.613933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692166</td>\n",
       "      <td>0.645094</td>\n",
       "      <td>0.632211</td>\n",
       "      <td>0.592528</td>\n",
       "      <td>0.604585</td>\n",
       "      <td>-0.008518</td>\n",
       "      <td>0.051341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_7</th>\n",
       "      <td>0.001843</td>\n",
       "      <td>-0.083718</td>\n",
       "      <td>-0.005893</td>\n",
       "      <td>-0.101461</td>\n",
       "      <td>0.603157</td>\n",
       "      <td>0.459253</td>\n",
       "      <td>0.607949</td>\n",
       "      <td>0.692166</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.698240</td>\n",
       "      <td>0.659558</td>\n",
       "      <td>0.642905</td>\n",
       "      <td>0.618444</td>\n",
       "      <td>-0.036431</td>\n",
       "      <td>0.038339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_8</th>\n",
       "      <td>-0.002968</td>\n",
       "      <td>-0.046519</td>\n",
       "      <td>-0.004585</td>\n",
       "      <td>-0.046958</td>\n",
       "      <td>0.627385</td>\n",
       "      <td>0.438763</td>\n",
       "      <td>0.630353</td>\n",
       "      <td>0.645094</td>\n",
       "      <td>0.698240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.655668</td>\n",
       "      <td>0.617320</td>\n",
       "      <td>0.625185</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>0.058258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_9</th>\n",
       "      <td>0.001991</td>\n",
       "      <td>-0.082170</td>\n",
       "      <td>-0.004494</td>\n",
       "      <td>-0.088090</td>\n",
       "      <td>0.597527</td>\n",
       "      <td>0.472516</td>\n",
       "      <td>0.619644</td>\n",
       "      <td>0.632211</td>\n",
       "      <td>0.659558</td>\n",
       "      <td>0.655668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.663628</td>\n",
       "      <td>0.650676</td>\n",
       "      <td>-0.015867</td>\n",
       "      <td>0.039173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_10</th>\n",
       "      <td>-0.005365</td>\n",
       "      <td>-0.114871</td>\n",
       "      <td>-0.003198</td>\n",
       "      <td>-0.113707</td>\n",
       "      <td>0.585087</td>\n",
       "      <td>0.499387</td>\n",
       "      <td>0.643538</td>\n",
       "      <td>0.592528</td>\n",
       "      <td>0.642905</td>\n",
       "      <td>0.617320</td>\n",
       "      <td>0.663628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.737598</td>\n",
       "      <td>-0.022228</td>\n",
       "      <td>0.045063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatPoints_11</th>\n",
       "      <td>-0.005865</td>\n",
       "      <td>-0.107356</td>\n",
       "      <td>-0.003045</td>\n",
       "      <td>-0.108279</td>\n",
       "      <td>0.607792</td>\n",
       "      <td>0.528395</td>\n",
       "      <td>0.690993</td>\n",
       "      <td>0.604585</td>\n",
       "      <td>0.618444</td>\n",
       "      <td>0.625185</td>\n",
       "      <td>0.650676</td>\n",
       "      <td>0.737598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010038</td>\n",
       "      <td>0.045093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvertedCompYearly</th>\n",
       "      <td>-0.008363</td>\n",
       "      <td>0.122590</td>\n",
       "      <td>0.080457</td>\n",
       "      <td>0.154114</td>\n",
       "      <td>0.020306</td>\n",
       "      <td>0.018197</td>\n",
       "      <td>-0.023281</td>\n",
       "      <td>-0.008518</td>\n",
       "      <td>-0.036431</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.015867</td>\n",
       "      <td>-0.022228</td>\n",
       "      <td>-0.010038</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSat</th>\n",
       "      <td>0.002425</td>\n",
       "      <td>0.069514</td>\n",
       "      <td>-0.015770</td>\n",
       "      <td>0.097322</td>\n",
       "      <td>0.094068</td>\n",
       "      <td>0.029677</td>\n",
       "      <td>0.049224</td>\n",
       "      <td>0.051341</td>\n",
       "      <td>0.038339</td>\n",
       "      <td>0.058258</td>\n",
       "      <td>0.039173</td>\n",
       "      <td>0.045063</td>\n",
       "      <td>0.045093</td>\n",
       "      <td>0.048428</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ResponseId       Age  CompTotal   WorkExp  \\\n",
       "ResponseId             1.000000 -0.038339  -0.000000  0.011955   \n",
       "Age                   -0.038339  1.000000  -0.002709  0.848165   \n",
       "CompTotal             -0.000000 -0.002709   0.000000  0.028766   \n",
       "WorkExp                0.011955  0.848165   0.028766  1.000000   \n",
       "JobSatPoints_1        -0.002406 -0.027672  -0.004891 -0.026490   \n",
       "JobSatPoints_4        -0.015738 -0.076214  -0.002719 -0.067241   \n",
       "JobSatPoints_5        -0.014197 -0.101811  -0.003033 -0.104525   \n",
       "JobSatPoints_6        -0.001908 -0.050353  -0.006311 -0.065352   \n",
       "JobSatPoints_7         0.001843 -0.083718  -0.005893 -0.101461   \n",
       "JobSatPoints_8        -0.002968 -0.046519  -0.004585 -0.046958   \n",
       "JobSatPoints_9         0.001991 -0.082170  -0.004494 -0.088090   \n",
       "JobSatPoints_10       -0.005365 -0.114871  -0.003198 -0.113707   \n",
       "JobSatPoints_11       -0.005865 -0.107356  -0.003045 -0.108279   \n",
       "ConvertedCompYearly   -0.008363  0.122590   0.080457  0.154114   \n",
       "JobSat                 0.002425  0.069514  -0.015770  0.097322   \n",
       "\n",
       "                     JobSatPoints_1  JobSatPoints_4  JobSatPoints_5  \\\n",
       "ResponseId                -0.002406       -0.015738       -0.014197   \n",
       "Age                       -0.027672       -0.076214       -0.101811   \n",
       "CompTotal                 -0.004891       -0.002719       -0.003033   \n",
       "WorkExp                   -0.026490       -0.067241       -0.104525   \n",
       "JobSatPoints_1             1.000000        0.445710        0.633765   \n",
       "JobSatPoints_4             0.445710        1.000000        0.526254   \n",
       "JobSatPoints_5             0.633765        0.526254        1.000000   \n",
       "JobSatPoints_6             0.618618        0.449400        0.613933   \n",
       "JobSatPoints_7             0.603157        0.459253        0.607949   \n",
       "JobSatPoints_8             0.627385        0.438763        0.630353   \n",
       "JobSatPoints_9             0.597527        0.472516        0.619644   \n",
       "JobSatPoints_10            0.585087        0.499387        0.643538   \n",
       "JobSatPoints_11            0.607792        0.528395        0.690993   \n",
       "ConvertedCompYearly        0.020306        0.018197       -0.023281   \n",
       "JobSat                     0.094068        0.029677        0.049224   \n",
       "\n",
       "                     JobSatPoints_6  JobSatPoints_7  JobSatPoints_8  \\\n",
       "ResponseId                -0.001908        0.001843       -0.002968   \n",
       "Age                       -0.050353       -0.083718       -0.046519   \n",
       "CompTotal                 -0.006311       -0.005893       -0.004585   \n",
       "WorkExp                   -0.065352       -0.101461       -0.046958   \n",
       "JobSatPoints_1             0.618618        0.603157        0.627385   \n",
       "JobSatPoints_4             0.449400        0.459253        0.438763   \n",
       "JobSatPoints_5             0.613933        0.607949        0.630353   \n",
       "JobSatPoints_6             1.000000        0.692166        0.645094   \n",
       "JobSatPoints_7             0.692166        1.000000        0.698240   \n",
       "JobSatPoints_8             0.645094        0.698240        1.000000   \n",
       "JobSatPoints_9             0.632211        0.659558        0.655668   \n",
       "JobSatPoints_10            0.592528        0.642905        0.617320   \n",
       "JobSatPoints_11            0.604585        0.618444        0.625185   \n",
       "ConvertedCompYearly       -0.008518       -0.036431       -0.011080   \n",
       "JobSat                     0.051341        0.038339        0.058258   \n",
       "\n",
       "                     JobSatPoints_9  JobSatPoints_10  JobSatPoints_11  \\\n",
       "ResponseId                 0.001991        -0.005365        -0.005865   \n",
       "Age                       -0.082170        -0.114871        -0.107356   \n",
       "CompTotal                 -0.004494        -0.003198        -0.003045   \n",
       "WorkExp                   -0.088090        -0.113707        -0.108279   \n",
       "JobSatPoints_1             0.597527         0.585087         0.607792   \n",
       "JobSatPoints_4             0.472516         0.499387         0.528395   \n",
       "JobSatPoints_5             0.619644         0.643538         0.690993   \n",
       "JobSatPoints_6             0.632211         0.592528         0.604585   \n",
       "JobSatPoints_7             0.659558         0.642905         0.618444   \n",
       "JobSatPoints_8             0.655668         0.617320         0.625185   \n",
       "JobSatPoints_9             1.000000         0.663628         0.650676   \n",
       "JobSatPoints_10            0.663628         1.000000         0.737598   \n",
       "JobSatPoints_11            0.650676         0.737598         1.000000   \n",
       "ConvertedCompYearly       -0.015867        -0.022228        -0.010038   \n",
       "JobSat                     0.039173         0.045063         0.045093   \n",
       "\n",
       "                     ConvertedCompYearly    JobSat  \n",
       "ResponseId                     -0.008363  0.002425  \n",
       "Age                             0.122590  0.069514  \n",
       "CompTotal                       0.080457 -0.015770  \n",
       "WorkExp                         0.154114  0.097322  \n",
       "JobSatPoints_1                  0.020306  0.094068  \n",
       "JobSatPoints_4                  0.018197  0.029677  \n",
       "JobSatPoints_5                 -0.023281  0.049224  \n",
       "JobSatPoints_6                 -0.008518  0.051341  \n",
       "JobSatPoints_7                 -0.036431  0.038339  \n",
       "JobSatPoints_8                 -0.011080  0.058258  \n",
       "JobSatPoints_9                 -0.015867  0.039173  \n",
       "JobSatPoints_10                -0.022228  0.045063  \n",
       "JobSatPoints_11                -0.010038  0.045093  \n",
       "ConvertedCompYearly             1.000000  0.048428  \n",
       "JobSat                          0.048428  1.000000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr = df.copy()\n",
    "df_corr.Age = Age\n",
    "df_corr.corr(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Summary </h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you developed essential skills in **Exploratory Data Analysis (EDA)** with a focus on outlier detection and removal. Specifically, you:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Loaded and explored the dataset to understand its structure.\n",
    "\n",
    "- Analyzed the distribution of respondents across industries.\n",
    "\n",
    "- Identified and removed high compensation outliers using statistical thresholds and the Interquartile Range (IQR) method.\n",
    "\n",
    "- Performed correlation analysis, including transforming the `Age` column into numeric values for better analysis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--\n",
    "## Change Log\n",
    "|Date (YYYY-MM-DD)|Version|Changed By|Change Description|\n",
    "|-|-|-|-|               \n",
    "|2024-10-1|1.1|Madhusudan Moole|Reviewed and updated lab|                                                                                    \n",
    "|2024-09-29|1.0|Raghul Ramesh|Created lab|\n",
    "--!>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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