Case-Complete-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "59e0394e-4e69-4b80-a31c-88b5ea1fe38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b7ecc59-cc8e-4b98-9d12-718c3daa7f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Today we will cover the missing value\n",
    "#1: drop the values---->mean remove item\n",
    "#2: impute the missing values have two type univariate and bivariate missing.\n",
    "#in bivariate we using knn algo to handle missing value and  iterative imputer algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ec65f5d-d427-4f9a-a31d-6bbc09bed159",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Today we learn how to remove missing values from the data.its is also called compete case analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "74ebf4f2-6004-4483-9573-82cf875fa732",
   "metadata": {},
   "outputs": [],
   "source": [
    "#IN CASE COMPLETE ANALYSIS if we found some missing value in some colum then we use to droop the complete row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "62ee5bcd-2c5f-44b6-bf16-99b1c18879f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#When we use CCA(Complete case analysis)\n",
    "#when our data is missing at random values so we use to drop the values if we found consqutive at the first or at thevery last in the data then we use\n",
    "#to impute that data.missing completely at random.( MCAR).if the data is missing under 5% then we use CCA compete case analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6eba2d5b-7167-4f9e-9c55-98e1b534f28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   enrollee_id      city  city_development_index gender  \\\n",
       "0         8949  city_103                   0.920   Male   \n",
       "1        29725   city_40                   0.776   Male   \n",
       "2        11561   city_21                   0.624    NaN   \n",
       "\n",
       "       relevent_experience enrolled_university education_level  \\\n",
       "0  Has relevent experience       no_enrollment        Graduate   \n",
       "1   No relevent experience       no_enrollment        Graduate   \n",
       "2   No relevent experience    Full time course        Graduate   \n",
       "\n",
       "  major_discipline  experience company_size company_type  training_hours  \\\n",
       "0             STEM        20.0          NaN          NaN            36.0   \n",
       "1             STEM        15.0        50-99      Pvt Ltd            47.0   \n",
       "2             STEM         5.0          NaN          NaN            83.0   \n",
       "\n",
       "   target  \n",
       "0     1.0  \n",
       "1     0.0  \n",
       "2     0.0  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"data_science_job.csv\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ebde275c-dc6e-4947-ba96-58591059649b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "enrollee_id                0.000000\n",
       "city                       0.000000\n",
       "city_development_index     2.500261\n",
       "gender                    23.530640\n",
       "relevent_experience        0.000000\n",
       "enrolled_university        2.014824\n",
       "education_level            2.401086\n",
       "major_discipline          14.683161\n",
       "experience                 0.339284\n",
       "company_size              30.994885\n",
       "company_type              32.049274\n",
       "training_hours             3.998330\n",
       "target                     0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check how much the data is missing in the columns \n",
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "cc82a0f0-2195-46f6-9190-24bec5955d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19158, 13)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dec2a4b0-44d5-4c23-9429-3bd7ebd835f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will apply the CCA/drop values on that columns where missing data is less or to 5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f795241b-dcba-4ce3-8b57-5487ca578e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "dd=df[[\"training_hours\",\"education_level\",\"enrolled_university\",\"city_development_index\",\"experience\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "7fc4961f-e25e-4eb1-b7dc-2f678250adad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>training_hours</th>\n",
       "      <th>education_level</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9026</th>\n",
       "      <td>11.0</td>\n",
       "      <td>Masters</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>0.920</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11899</th>\n",
       "      <td>70.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>0.920</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14425</th>\n",
       "      <td>113.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>0.735</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17795</th>\n",
       "      <td>64.0</td>\n",
       "      <td>Masters</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>0.903</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15897</th>\n",
       "      <td>42.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>0.920</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       training_hours education_level enrolled_university  \\\n",
       "9026             11.0         Masters       no_enrollment   \n",
       "11899            70.0     High School       no_enrollment   \n",
       "14425           113.0        Graduate       no_enrollment   \n",
       "17795            64.0         Masters       no_enrollment   \n",
       "15897            42.0     High School    Full time course   \n",
       "\n",
       "       city_development_index  experience  \n",
       "9026                    0.920        20.0  \n",
       "11899                   0.920         6.0  \n",
       "14425                   0.735         2.0  \n",
       "17795                   0.903        15.0  \n",
       "15897                   0.920         5.0  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "6606b103-fda5-4510-881a-af33bb377441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='training_hours', ylabel='Count'>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df[\"training_hours\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "c39423ab-0119-4ecd-ad6a-68d8b48fb09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='training_hours', ylabel='Density'>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(dd[\"training_hours\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "13ebaebb-b392-4cd1-8eea-74f962010924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='training_hours', ylabel='Count'>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=dd[\"training_hours\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d1cbb5f5-a6c8-4ec0-8e8f-fd07bb0c4b80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19158 entries, 0 to 19157\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   training_hours          18392 non-null  float64\n",
      " 1   education_level         18698 non-null  object \n",
      " 2   enrolled_university     18772 non-null  object \n",
      " 3   city_development_index  18679 non-null  float64\n",
      " 4   experience              19093 non-null  float64\n",
      "dtypes: float64(3), object(2)\n",
      "memory usage: 748.5+ KB\n"
     ]
    }
   ],
   "source": [
    "dd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "f5f5751a-bad5-406d-a76b-7984be9317a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19158, 5)\n"
     ]
    }
   ],
   "source": [
    "len(dd.dropna())/len(dd)\n",
    "#Before dropin the rows the data is:\n",
    "print(dd.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "b72eecfa-f54b-48fc-bb8a-f5772ed4ff44",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Temp\\ipykernel_6740\\1858763369.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data=dd.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "data=dd.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "94be67d6-503f-4de2-bef3-0194555a27f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17182, 5)\n"
     ]
    }
   ],
   "source": [
    "#After droping the rows the new data is \n",
    "print(dd.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5227af33-6222-4eab-995c-34b94ed894cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='training_hours', ylabel='Count'>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=dd[\"training_hours\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "398bb862-2b8d-4dd7-9bf6-dfb32363afea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='training_hours', ylabel='Density'>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=dd[\"training_hours\"]) #its applying on the new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "4f078418-0b6f-4668-a71d-6b9c818de62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city_development_index', ylabel='Count'>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=dd[\"city_development_index\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ea98c5ff-8f8a-4c29-83df-c23d68083402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city_development_index', ylabel='Density'>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=dd[\"city_development_index\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a368e80-2634-4462-9efa-5802e8958757",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}