Code.ipynb

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    "colab": {
      "name": "Code.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Prerequisities"
      ],
      "metadata": {
        "id": "SyGQcIp30x01"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --quiet shap"
      ],
      "metadata": {
        "id": "a_ogBLSd_rJ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83ace7ab-f212-42c6-f70b-c569b8c7f3c2"
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      "execution_count": null,
      "outputs": [
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          "text": [
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            "\u001b[?25h"
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    {
      "cell_type": "code",
      "source": [
        "# install prerequisities\n",
        "\n",
        "import gdown\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "import string\n",
        "import re\n",
        "import math\n",
        "import ast\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import shap\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from scipy import stats\n",
        "from collections import Counter\n",
        "from textblob import Word\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "nltk.download(\"popular\", quiet=True)\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer"
      ],
      "metadata": {
        "id": "E0UP-ZEuorSN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# make stopwords\n",
        "stopwords = stopwords.words('english')\n",
        "# make lemmatizer\n",
        "lemmatizer = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "VmvhbsuY1RU1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data from drive\n",
        "def load_data(link, index= None):\n",
        "  # Download data from gdrive\n",
        "  meta_url = 'https://drive.google.com/uc?export=download&confirm=pbef&id='\n",
        "  url = meta_url + link\n",
        "  output = 'data.csv'\n",
        "  gdown.download(url, output, quiet=True)\n",
        "  data = pd.read_csv(output, on_bad_lines='skip')\n",
        "  return data"
      ],
      "metadata": {
        "id": "Bb-JYV0VDkLP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean the employment gender data\n",
        "def employment_gender(link):\n",
        "  df = load_data(link)\n",
        "  df = df.loc[1:]\n",
        "  new_header = df.iloc[0]\n",
        "  df = df[1:]\n",
        "  df.columns = new_header.tolist()\n",
        "  df.index = np.arange(1, len(df) + 1)\n",
        "  return df"
      ],
      "metadata": {
        "id": "R9F1iHfgcQjH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Spelling correction using textblob\n",
        "def correct_word_spelling(word):\n",
        "    word = Word(word)\n",
        "    result = word.correct()\n",
        "    return result"
      ],
      "metadata": {
        "id": "9pTb3v2O2BQe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to plot time series\n",
        "def plot_timeseries(axes, x, y, color, xlabel, ylabel):\n",
        "  axes.plot(x, y, color=color)\n",
        "  axes.set_xlabel(xlabel)\n",
        "  axes.set_ylabel(ylabel, color=color)\n",
        "  axes.tick_params('y', colors=color)"
      ],
      "metadata": {
        "id": "oX92O7QFb994"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# function to clean text\n",
        "def clean_text(text, correct=True):\n",
        "  # replace some words\n",
        "  text = text.replace(\"BA/BS\",\"Bachelor Art Science\")\n",
        "  text = text.replace(\"BS\",\"Bachelor Science\")\n",
        "  text = text.replace(\"BA\",\"Bachelor Art\")\n",
        "  text = text.replace(\" and/or\", \"\")\n",
        "  # remove punctuation\n",
        "  text = \"\".join([word.lower() for word in text if word not in string.punctuation+\"’\"])\n",
        "  # remove non character text\n",
        "  tokens = re.split('\\W+', text)\n",
        "  # remove stopwords and lemmatize word\n",
        "  text = [lemmatizer.lemmatize(word) for word in tokens if word not in stopwords]\n",
        "  # spelling correction\n",
        "  if correct:\n",
        "    text = [correct_word_spelling(word) for word in text]\n",
        "  return text"
      ],
      "metadata": {
        "id": "TNCTM4MjcdNY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparation on employement indicator data"
      ],
      "metadata": {
        "id": "8RxNDsZr_M6z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Include:\n",
        "* **Preparation**\n",
        "* **Processing**\n",
        "* **Cleansing**"
      ],
      "metadata": {
        "id": "BKZFA4CWECbs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mw1J-qrydqv2"
      },
      "outputs": [],
      "source": [
        "df = load_data(\"1g_zyzr9CX5p8W8X8Wx5osVQkMY8334Lh\")\n",
        "df.drop(0, inplace=True) # Drop first columns -> id\n",
        "df['Value'] = df['Value'].astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2) # Preview"
      ],
      "metadata": {
        "colab": {
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          "height": 112
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      "execution_count": null,
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              "  Country Name Country ISO3  Year                              Indicator Name  \\\n",
              "1    Indonesia          IDN  2015  Firms with female top manager (% of firms)   \n",
              "2    Indonesia          IDN  2009  Firms with female top manager (% of firms)   \n",
              "\n",
              "   Indicator Code  Value  \n",
              "1  IC.FRM.FEMM.ZS   22.1  \n",
              "2  IC.FRM.FEMM.ZS   31.2  "
            ],
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              "      <th>1</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>IDN</td>\n",
              "      <td>2015</td>\n",
              "      <td>Firms with female top manager (% of firms)</td>\n",
              "      <td>IC.FRM.FEMM.ZS</td>\n",
              "      <td>22.1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>IDN</td>\n",
              "      <td>2009</td>\n",
              "      <td>Firms with female top manager (% of firms)</td>\n",
              "      <td>IC.FRM.FEMM.ZS</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Take the data indicator which contains gender comparation (male-female)\n",
        "df_gender = df[df[\"Indicator Name\"].str.contains(\"male\")]"
      ],
      "metadata": {
        "id": "aBHp1wzxBjet"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns 0 and 1 contained country name and id, which the number of unique is 1 -> Indonesia.\n",
        "# We can drop them and the indicator name.\n",
        "df_gender.iloc[:,0].nunique() & df_gender.iloc[:,1].nunique()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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              "1"
            ]
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          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop irrelevant columns\n",
        "df_drop = df_gender.drop(df_gender.iloc[:,[0,1,3]].columns, axis=1)"
      ],
      "metadata": {
        "id": "vNb4W31EeyIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pivotting on the indicator code and value\n",
        "pivot_df = df_drop.pivot(index='Year', columns='Indicator Code',values='Value').iloc[41:]\n",
        "pivot_df.head()"
      ],
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Indicator Code  IC.FRM.FEMM.ZS  IC.FRM.FEMO.ZS  SE.ADT.1524.LT.FE.ZS  \\\n",
              "Year                                                                   \n",
              "2001                       NaN             NaN                   NaN   \n",
              "2002                       NaN             NaN                   NaN   \n",
              "2003                       NaN             NaN                   NaN   \n",
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              "Year                                                                         \n",
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          },
          "metadata": {},
          "execution_count": 11
        }
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    {
      "cell_type": "code",
      "source": [
        "# Checking the null percentage on the data\n",
        "null_percentage = []\n",
        "for i in pivot_df.columns:\n",
        "  null_percentage.append(round(pivot_df[i].isnull().sum()*100/pivot_df.shape[0],2))\n",
        "  \n",
        "pd.Series(null_percentage).sort_values(ascending=False)[:50:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4pC_XcCnN5-",
        "outputId": "5047cbd6-2668-48e8-9505-a434580853ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60     95.24\n",
              "1      90.48\n",
              "152    85.71\n",
              "51     76.19\n",
              "95     71.43\n",
              "dtype: float64"
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          "metadata": {},
          "execution_count": 12
        }
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      "cell_type": "code",
      "source": [
        "# Determine a random threshold (in this case, we take 50%).\n",
        "# Check the columns that have more than the threshold, and drop it\n",
        "pivot_df.columns[np.array(null_percentage)<50][:10]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D10i6Ug87_Rg",
        "outputId": "ca508ecf-88b9-4f9a-87d7-f4b1e5297718"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['SE.PRE.ENRR.FE', 'SE.PRE.ENRR.MA', 'SE.PRM.CMPT.FE.ZS',\n",
              "       'SE.PRM.CMPT.MA.ZS', 'SE.PRM.ENRL.FE.ZS', 'SE.PRM.ENRR.FE',\n",
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              "      dtype='object', name='Indicator Code')"
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          "metadata": {},
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      "source": [
        "clean_df = pivot_df[pivot_df.columns[np.array(null_percentage)<50]]"
      ],
      "metadata": {
        "id": "0X5pHHFOBC8V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply iterative imputer to impute missing value\n",
        "imp_mean = IterativeImputer(random_state=0)\n",
        "clean_df = pd.DataFrame(imp_mean.fit_transform(clean_df), \n",
        "                        columns=clean_df.columns, index=clean_df.index)"
      ],
      "metadata": {
        "id": "R10dvb2h0rqZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean_df.to_excel(\"imputed_indicator.xlsx\") "
      ],
      "metadata": {
        "id": "0Xoj2E0oERhT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis on employment indicator data"
      ],
      "metadata": {
        "id": "3oYbHSotXLvP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Manual imputer on missing value from data pattern and based on iterative imputer\n",
        "imputed_df = load_data(\"1h4M6Ya9AFBzK1iXYmHEc7d7eOLjniS3Y\")\n",
        "imputed_df.loc[:,\"Year\"] = imputed_df.Year.astype(int)\n",
        "imputed_df.head(2)"
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        "colab": {
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          "height": 205
        },
        "id": "CakpOD1VC0V6",
        "outputId": "a7a70fca-d817-48e6-e48e-f8803845bd46"
      },
      "execution_count": null,
      "outputs": [
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              "   Year  SE.PRE.ENRR.FE  SE.PRE.ENRR.MA  SE.PRM.CMPT.FE.ZS  SE.PRM.CMPT.MA.ZS  \\\n",
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              "\n",
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              "1           70792759      381.111880      402.523951          10.711111   \n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>SE.PRE.ENRR.FE</th>\n",
              "      <th>SE.PRE.ENRR.MA</th>\n",
              "      <th>SE.PRM.CMPT.FE.ZS</th>\n",
              "      <th>SE.PRM.CMPT.MA.ZS</th>\n",
              "      <th>SE.PRM.ENRL.FE.ZS</th>\n",
              "      <th>SE.PRM.ENRR.FE</th>\n",
              "      <th>SE.PRM.ENRR.MA</th>\n",
              "      <th>SE.PRM.GINT.FE.ZS</th>\n",
              "      <th>SE.PRM.GINT.MA.ZS</th>\n",
              "      <th>...</th>\n",
              "      <th>SP.DYN.SMAM.MA</th>\n",
              "      <th>SG.LAW.OBHB.MR.NO</th>\n",
              "      <th>SP.DYN.TO65.FE.ZS.1</th>\n",
              "      <th>SP.DYN.TO65.MA.ZS.1</th>\n",
              "      <th>SP.POP.1564.FE.IN</th>\n",
              "      <th>SP.POP.1564.MA.IN</th>\n",
              "      <th>HD.HCI.HLOS.MA</th>\n",
              "      <th>HD.HCI.HLOS.FE</th>\n",
              "      <th>SG.DMK.ALLD.FE.ZS</th>\n",
              "      <th>SG.DMK.ALLD.MA.ZS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2001</td>\n",
              "      <td>24.882030</td>\n",
              "      <td>24.031130</td>\n",
              "      <td>94.167458</td>\n",
              "      <td>93.35778</td>\n",
              "      <td>48.62186</td>\n",
              "      <td>108.455193</td>\n",
              "      <td>111.005981</td>\n",
              "      <td>107.97810</td>\n",
              "      <td>113.91842</td>\n",
              "      <td>...</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1</td>\n",
              "      <td>70.661428</td>\n",
              "      <td>64.008986</td>\n",
              "      <td>69260465.5</td>\n",
              "      <td>69631713</td>\n",
              "      <td>380.038837</td>\n",
              "      <td>402.135946</td>\n",
              "      <td>10.327778</td>\n",
              "      <td>31.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2002</td>\n",
              "      <td>24.726709</td>\n",
              "      <td>24.092939</td>\n",
              "      <td>95.384338</td>\n",
              "      <td>94.73410</td>\n",
              "      <td>48.62113</td>\n",
              "      <td>108.698853</td>\n",
              "      <td>111.103027</td>\n",
              "      <td>111.70473</td>\n",
              "      <td>117.54551</td>\n",
              "      <td>...</td>\n",
              "      <td>25.6</td>\n",
              "      <td>1</td>\n",
              "      <td>71.278228</td>\n",
              "      <td>64.486552</td>\n",
              "      <td>70340395.0</td>\n",
              "      <td>70792759</td>\n",
              "      <td>381.111880</td>\n",
              "      <td>402.523951</td>\n",
              "      <td>10.711111</td>\n",
              "      <td>31.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 131 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set mapping from indicator code to indicator name\n",
        "# Select the feature that will be used\n",
        "code_to_desc = {\"Year\" : \"Year\",\n",
        "\"SL.EMP.TOTL.SP.FE.ZS\" : \"emp_rate_f\", \"SL.EMP.TOTL.SP.MA.ZS\" : \"emp_rate_m\",\n",
        "\n",
        "\"SL.EMP.SELF.FE.ZS\" : \"informal_emp_f\", \"SL.EMP.WORK.FE.ZS\" : \"formal_emp_f\",\n",
        "\"SL.EMP.SELF.MA.ZS\" : \"informal_emp_m\", \"SL.EMP.WORK.MA.ZS\" : \"formal_emp_m\",\n",
        "\n",
        "\"SE.PRM.NENR.FE\" : \"primary_edu_f\", \"SE.PRM.NENR.MA\" : \"primary_edu_m\",\n",
        "\"SE.SEC.NENR.FE\" : \"secondary_edu_f\", \"SE.SEC.NENR.MA\" : \"secondary_edu_m\",\n",
        "\"SE.TER.ENRR.FE\" : \"tertiary_edu_f\", \"SE.TER.ENRR.MA\" : \"tertiary_edu_m\",\n",
        "\"SE.TER.CUAT.BA.FE.ZS\" : \"bachelor_edu_f\", \"SE.TER.CUAT.BA.MA.ZS\" : \"bachelor_edu_m\",\n",
        "\"SE.TER.CUAT.MS.FE.ZS\" : \"master_edu_f\", \"SE.TER.CUAT.MS.MA.ZS\" : \"master_edu_m\",\n",
        "\n",
        "\"SL.TLF.BASC.FE.ZS\" : \"basic_edu_emp_f\", \"SL.TLF.BASC.MA.ZS\" : \"basic_edu_emp_m\", \n",
        "\"SL.TLF.INTM.FE.ZS\" : \"interm_edu_emp_f\", \"SL.TLF.INTM.MA.ZS\" : \"interm_edu_emp_m\", \n",
        "\"SL.TLF.ADVN.FE.ZS\" : \"advance_edu_emp_f\", \"SL.TLF.ADVN.MA.ZS\" : \"advance_edu_emp_m\", \n",
        "\n",
        "\"SE.ADT.LITR.FE.ZS\" : \"literacy_rate_f\", \"SE.ADT.LITR.MA.ZS\" : \"literacy_rate_m\",\n",
        "\"SP.DYN.LE00.FE.IN\" : \"life_expect_f\", \"SP.DYN.LE00.MA.IN\" : \"life_expect_m\",\n",
        "\"HD.HCI.OVRL.FE\" : \"HCI_f\", \"HD.HCI.OVRL.MA\" : \"HCI_m\",\n",
        "\"HD.HCI.HLOS.FE\" : \"HTS_f\", \"HD.HCI.HLOS.MA\" : \"HTS_m\",\n",
        "\"SP.DYN.SMAM.FE\" : \"age_marriage_f\", \"SP.DYN.SMAM.MA\" : \"age_marriage_m\",\n",
        "\"SG.DMK.ALLD.FE.ZS\" : \"decision_maker_f\", \"SG.DMK.ALLD.MA.ZS\" : \"decision_maker_m\",\n",
        "\"SP.DYN.TO65.FE.ZS\" : \"survival_f\", \"SP.DYN.TO65.MA.ZS\" : \"survival_m\",\n",
        "\"SP.POP.1564.FE.IN\" : \"population_f\", \"SP.POP.1564.MA.IN\" : \"population_m\",\n",
        "\n",
        "\"SP.DYN.AMRT.FE\" : \"mortality_rate_f\", \"SH.DYN.AIDS.FE.ZS\" : \"HIV_f\"\n",
        "}"
      ],
      "metadata": {
        "id": "AMzjPhh4G-ke"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "use_df = imputed_df[code_to_desc.keys()].drop(0)\n",
        "use_df.columns = code_to_desc.values()\n",
        "use_df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "zhX6vU6xK_S5",
        "outputId": "dfc54707-b2e8-47c7-c26c-4904192bec87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  emp_rate_f  emp_rate_m  informal_emp_f  formal_emp_f  informal_emp_m  \\\n",
              "1  2002   44.243000   79.176002       69.519997         30.48       60.689999   \n",
              "2  2003   43.830002   78.915001       70.910004         29.09       62.430000   \n",
              "\n",
              "   formal_emp_m  primary_edu_f  primary_edu_m  secondary_edu_f  ...  \\\n",
              "1     39.310001       90.86566       91.77368         50.48961  ...   \n",
              "2     37.570000       89.80657       91.44727         53.10740  ...   \n",
              "\n",
              "   age_marriage_f  age_marriage_m  decision_maker_f  decision_maker_m  \\\n",
              "1            22.0            25.6         10.711111              31.4   \n",
              "2            22.1            25.9         11.133333              31.3   \n",
              "\n",
              "   survival_f  survival_m  population_f  population_m  mortality_rate_f  HIV_f  \n",
              "1   71.278228   64.486552    70340395.0      70792759           167.236   19.0  \n",
              "2   71.895028   64.964118    71404235.0      71921499           165.531   20.3  \n",
              "\n",
              "[2 rows x 41 columns]"
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              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>emp_rate_f</th>\n",
              "      <th>emp_rate_m</th>\n",
              "      <th>informal_emp_f</th>\n",
              "      <th>formal_emp_f</th>\n",
              "      <th>informal_emp_m</th>\n",
              "      <th>formal_emp_m</th>\n",
              "      <th>primary_edu_f</th>\n",
              "      <th>primary_edu_m</th>\n",
              "      <th>secondary_edu_f</th>\n",
              "      <th>...</th>\n",
              "      <th>age_marriage_f</th>\n",
              "      <th>age_marriage_m</th>\n",
              "      <th>decision_maker_f</th>\n",
              "      <th>decision_maker_m</th>\n",
              "      <th>survival_f</th>\n",
              "      <th>survival_m</th>\n",
              "      <th>population_f</th>\n",
              "      <th>population_m</th>\n",
              "      <th>mortality_rate_f</th>\n",
              "      <th>HIV_f</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2002</td>\n",
              "      <td>44.243000</td>\n",
              "      <td>79.176002</td>\n",
              "      <td>69.519997</td>\n",
              "      <td>30.48</td>\n",
              "      <td>60.689999</td>\n",
              "      <td>39.310001</td>\n",
              "      <td>90.86566</td>\n",
              "      <td>91.77368</td>\n",
              "      <td>50.48961</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0</td>\n",
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              "      <td>70792759</td>\n",
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              "      <td>19.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2003</td>\n",
              "      <td>43.830002</td>\n",
              "      <td>78.915001</td>\n",
              "      <td>70.910004</td>\n",
              "      <td>29.09</td>\n",
              "      <td>62.430000</td>\n",
              "      <td>37.570000</td>\n",
              "      <td>89.80657</td>\n",
              "      <td>91.44727</td>\n",
              "      <td>53.10740</td>\n",
              "      <td>...</td>\n",
              "      <td>22.1</td>\n",
              "      <td>25.9</td>\n",
              "      <td>11.133333</td>\n",
              "      <td>31.3</td>\n",
              "      <td>71.895028</td>\n",
              "      <td>64.964118</td>\n",
              "      <td>71404235.0</td>\n",
              "      <td>71921499</td>\n",
              "      <td>165.531</td>\n",
              "      <td>20.3</td>\n",
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              "  </tbody>\n",
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              "<p>2 rows × 41 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the employment rate trends from male and female\n",
        "fig, ax = plt.subplots()\n",
        "plot_timeseries(ax, use_df['Year'], use_df['emp_rate_f'], 'red',\n",
        "                'Year', '% of Woman Population')\n",
        "ax2 = ax.twinx()\n",
        "plot_timeseries(ax2, use_df['Year'], use_df['emp_rate_m'], 'blue',\n",
        "                'Year', '% of Man Population')\n",
        "plt.title(\"Employment Rate Trends\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "9rC5pSEzPLtP",
        "outputId": "578b6315-12c0-4bfb-84b6-15a68f662ac6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the education trends from primary until master degree\n",
        "plt.plot(use_df.Year, use_df['primary_edu_m'], label=\"Male\")\n",
        "plt.plot(use_df.Year, use_df['primary_edu_f'], label=\"Female\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"% of School-age Population\")\n",
        "plt.title(\"Primary Education Enrollment Trends\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "rMrvUCOaXelk",
        "outputId": "dfab88a6-7180-43ef-f08d-3b829fdee2f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f80e5cc7390>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatterplot between indicator and employment rate\n",
        "plt.scatter(use_df[\"life_expect_m\"],use_df[\"emp_rate_m\"],label=\"Male\")\n",
        "plt.scatter(use_df[\"life_expect_f\"],use_df[\"emp_rate_f\"], label=\"Female\")\n",
        "plt.axvline(use_df[\"life_expect_m\"].mean(), color='blue', linestyle=\"--\", ymin=0.75, ymax=1)\n",
        "plt.text(use_df[\"life_expect_m\"].mean()-0.4,68.6,'Mean')\n",
        "plt.axvline(use_df[\"life_expect_f\"].mean(), color='orange', linestyle='--', ymin=0, ymax=0.38)\n",
        "plt.text(use_df[\"life_expect_f\"].mean()-0.4,57,'Mean')\n",
        "plt.xlabel(\"Life expectancy\")\n",
        "plt.ylabel(\"Employment rate (%)\")\n",
        "plt.title(\"Life Expectancy VS Employment Rate\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "fHjsR1vzcAt8",
        "outputId": "b79165ff-712b-4106-a167-dfd9756e3048"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f80e8819d50>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Proportion of woman formal and informal employment rate \n",
        "tmp = use_df.query(\"Year>2003\")\n",
        "plt.bar(tmp.Year, tmp['informal_emp_f'], color='r', label='Informal')\n",
        "plt.bar(tmp.Year, tmp['formal_emp_f'], bottom=tmp['informal_emp_f'], color='g',label='Formal')\n",
        "plt.axhline(50, linestyle='--')\n",
        "plt.title(\"Woman Informal & Formal Employment\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"% of Female Employment\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KqdOK2GH_dGZ",
        "outputId": "ec711c56-caeb-4b0f-d247-170fb30e67eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the employment rate grouped by education level\n",
        "plt.figure(figsize=(8,4))\n",
        "tmp = use_df[(use_df.Year%2==0) & (use_df.Year>2006)]\n",
        "bar_width = 0.4\n",
        "plt.bar(tmp.Year - bar_width , tmp['basic_edu_emp_f'], bar_width, label='Basic')\n",
        "plt.plot(tmp.Year - bar_width , tmp['basic_edu_emp_f'], color='blue', \n",
        "         marker='o', markersize=2, linestyle='--')\n",
        "plt.bar(tmp.Year, tmp['interm_edu_emp_f'], bar_width, label='Intermediate')\n",
        "plt.plot(tmp.Year, tmp['interm_edu_emp_f'], color='orange', \n",
        "         marker='o', markersize=2, linestyle='--')\n",
        "plt.bar(tmp.Year + bar_width , tmp['advance_edu_emp_f'], bar_width, label='Advance')\n",
        "plt.plot(tmp.Year + bar_width, tmp['advance_edu_emp_f'], color='green', \n",
        "         marker='o', markersize=2, linestyle='--')\n",
        "plt.ylim([35,85])\n",
        "plt.legend(loc=2)\n",
        "plt.title(\"Employement Rate with Certain Education Level\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Employment Rate (%)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ouxp_IzFBg_2",
        "outputId": "d18146ec-5c63-4152-8a95-31364283ae52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make feature and target for regression\n",
        "feature_list = ['primary_edu_f', 'secondary_edu_f', 'tertiary_edu_f', 'bachelor_edu_f',\n",
        "           'master_edu_f', 'life_expect_f', 'literacy_rate_f', 'HCI_f',\n",
        "           'HTS_f', 'age_marriage_f', 'decision_maker_f', 'survival_f',\n",
        "           'mortality_rate_f', \"HIV_f\"]\n",
        "feature = use_df[feature_list]\n",
        "target = use_df['emp_rate_f']"
      ],
      "metadata": {
        "id": "y-t6FrSX8n3c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check employment rate correlation with features\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.title(\"Employment Rate Correlation\")\n",
        "sns.heatmap(pd.concat([feature, target], axis=1).corr()[[\"emp_rate_f\"]][:-1].\\\n",
        "            sort_values(by='emp_rate_f', ascending=False), annot=True, linecolor='black', linewidths=0.01)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "4NADJBcOPxqo",
        "outputId": "9212be4f-68db-4d3d-adac-cafb7c87901b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80e3055f10>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the SHAP value from regression line\n",
        "linreg = LinearRegression().fit(feature, target)\n",
        "masker = shap.maskers.Independent(data=feature)\n",
        "explainer = shap.LinearExplainer(linreg, masker=masker)\n",
        "shap_values = explainer(feature)\n",
        "plt.title('Impact on Female Employment Rate with SHAP Value', fontsize=16)\n",
        "plt.xlabel(\"SHAP Value (model impact)\")\n",
        "shap.summary_plot(shap_values, feature, plot_type='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "81z711EM_IP5",
        "outputId": "dbd14825-a6a8-4596-d22a-edc574e0f181"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x511.2 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Investigate p value to check feature significance\n",
        "params = np.append(linreg.intercept_,linreg.coef_)\n",
        "predictions = linreg.predict(feature)\n",
        "\n",
        "newX = np.append(np.ones((len(feature),1)), feature, axis=1)\n",
        "MSE = (sum((target-predictions)**2))/(len(newX)-len(newX[0]))\n",
        "\n",
        "var_b = MSE*(np.linalg.inv(np.dot(newX.T,newX)).diagonal())\n",
        "sd_b = np.sqrt(var_b)\n",
        "ts_b = params/ sd_b\n",
        "\n",
        "p_values =[2*(1-stats.t.cdf(np.abs(i),(len(newX)-len(newX[0])))) for i in ts_b]\n",
        "\n",
        "sd_b = np.round(sd_b,3)\n",
        "ts_b = np.round(ts_b,3)\n",
        "p_values = np.round(p_values,3)\n",
        "params = np.round(params,4)\n",
        "\n",
        "myDF3 = pd.DataFrame()\n",
        "myDF3[\"Features\"],myDF3[\"Coefficients\"],myDF3[\"Standard Errors\"],myDF3[\"t values\"],myDF3[\"P_value\"] = [\n",
        "                  [\"constant\"]+feature_list,params,sd_b,ts_b,p_values]\n",
        "myDF3.drop(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "coVOTGqhRwFZ",
        "outputId": "c3dac672-c4e6-42fd-8cc9-bd7661e92d25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Features  Coefficients  Standard Errors  t values  P_value\n",
              "1      primary_edu_f        0.1408            0.352     0.400    0.728\n",
              "2    secondary_edu_f        0.7265            0.424     1.715    0.228\n",
              "3     tertiary_edu_f        0.7539            0.621     1.214    0.349\n",
              "4     bachelor_edu_f      -19.7287           14.484    -1.362    0.306\n",
              "5       master_edu_f       33.7777           15.166     2.227    0.156\n",
              "6      life_expect_f       37.7416           22.759     1.658    0.239\n",
              "7    literacy_rate_f       -2.9416            1.295    -2.272    0.151\n",
              "8              HCI_f     2777.5137         2514.218     1.105    0.384\n",
              "9              HTS_f        7.8226           12.840     0.609    0.604\n",
              "10    age_marriage_f        0.9027            2.109     0.428    0.710\n",
              "11  decision_maker_f       -1.8061            1.982    -0.911    0.458\n",
              "12        survival_f      -60.3720           36.227    -1.666    0.238\n",
              "13  mortality_rate_f       -0.7357            0.580    -1.269    0.332\n",
              "14             HIV_f        3.4761            4.493     0.774    0.520"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d26b6dfc-21ea-427d-9f3d-753911722ad0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Features</th>\n",
              "      <th>Coefficients</th>\n",
              "      <th>Standard Errors</th>\n",
              "      <th>t values</th>\n",
              "      <th>P_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>primary_edu_f</td>\n",
              "      <td>0.1408</td>\n",
              "      <td>0.352</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>secondary_edu_f</td>\n",
              "      <td>0.7265</td>\n",
              "      <td>0.424</td>\n",
              "      <td>1.715</td>\n",
              "      <td>0.228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tertiary_edu_f</td>\n",
              "      <td>0.7539</td>\n",
              "      <td>0.621</td>\n",
              "      <td>1.214</td>\n",
              "      <td>0.349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bachelor_edu_f</td>\n",
              "      <td>-19.7287</td>\n",
              "      <td>14.484</td>\n",
              "      <td>-1.362</td>\n",
              "      <td>0.306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>master_edu_f</td>\n",
              "      <td>33.7777</td>\n",
              "      <td>15.166</td>\n",
              "      <td>2.227</td>\n",
              "      <td>0.156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>life_expect_f</td>\n",
              "      <td>37.7416</td>\n",
              "      <td>22.759</td>\n",
              "      <td>1.658</td>\n",
              "      <td>0.239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>literacy_rate_f</td>\n",
              "      <td>-2.9416</td>\n",
              "      <td>1.295</td>\n",
              "      <td>-2.272</td>\n",
              "      <td>0.151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>HCI_f</td>\n",
              "      <td>2777.5137</td>\n",
              "      <td>2514.218</td>\n",
              "      <td>1.105</td>\n",
              "      <td>0.384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>HTS_f</td>\n",
              "      <td>7.8226</td>\n",
              "      <td>12.840</td>\n",
              "      <td>0.609</td>\n",
              "      <td>0.604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>age_marriage_f</td>\n",
              "      <td>0.9027</td>\n",
              "      <td>2.109</td>\n",
              "      <td>0.428</td>\n",
              "      <td>0.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>decision_maker_f</td>\n",
              "      <td>-1.8061</td>\n",
              "      <td>1.982</td>\n",
              "      <td>-0.911</td>\n",
              "      <td>0.458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>survival_f</td>\n",
              "      <td>-60.3720</td>\n",
              "      <td>36.227</td>\n",
              "      <td>-1.666</td>\n",
              "      <td>0.238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>mortality_rate_f</td>\n",
              "      <td>-0.7357</td>\n",
              "      <td>0.580</td>\n",
              "      <td>-1.269</td>\n",
              "      <td>0.332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>HIV_f</td>\n",
              "      <td>3.4761</td>\n",
              "      <td>4.493</td>\n",
              "      <td>0.774</td>\n",
              "      <td>0.520</td>\n",
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              "  "
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          "metadata": {},
          "execution_count": 27
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    {
      "cell_type": "code",
      "source": [
        "use_df.to_csv(\"gender_indicator.csv\", index=False)\n",
        "use_df.head(2)"
      ],
      "metadata": {
        "id": "mxfHXY05srcu",
        "colab": {
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          "height": 205
        },
        "outputId": "bd15222d-cc04-4857-d02d-020359bdea5a"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Year  emp_rate_f  emp_rate_m  informal_emp_f  formal_emp_f  informal_emp_m  \\\n",
              "1  2002   44.243000   79.176002       69.519997         30.48       60.689999   \n",
              "2  2003   43.830002   78.915001       70.910004         29.09       62.430000   \n",
              "\n",
              "   formal_emp_m  primary_edu_f  primary_edu_m  secondary_edu_f  ...  \\\n",
              "1     39.310001       90.86566       91.77368         50.48961  ...   \n",
              "2     37.570000       89.80657       91.44727         53.10740  ...   \n",
              "\n",
              "   age_marriage_f  age_marriage_m  decision_maker_f  decision_maker_m  \\\n",
              "1            22.0            25.6         10.711111              31.4   \n",
              "2            22.1            25.9         11.133333              31.3   \n",
              "\n",
              "   survival_f  survival_m  population_f  population_m  mortality_rate_f  HIV_f  \n",
              "1   71.278228   64.486552    70340395.0      70792759           167.236   19.0  \n",
              "2   71.895028   64.964118    71404235.0      71921499           165.531   20.3  \n",
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              "[2 rows x 41 columns]"
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "# Filter and rename the columns that we'll used for the dashboard graph\n",
        "dashboard_df = use_df[[\"Year\",'emp_rate_f', \"emp_rate_m\",\n",
        "                      'basic_edu_emp_f', 'basic_edu_emp_m', 'interm_edu_emp_f', 'interm_edu_emp_m',\n",
        "                      'advance_edu_emp_f', 'advance_edu_emp_m',\n",
        "                      \"primary_edu_f\", \"primary_edu_m\",\n",
        "                      \"secondary_edu_f\", \"secondary_edu_m\",\n",
        "                      \"tertiary_edu_f\", \"tertiary_edu_m\",\n",
        "                      \"bachelor_edu_f\", \"bachelor_edu_m\",\n",
        "                      \"master_edu_f\", \"master_edu_m\",\n",
        "                      \"informal_emp_f\", \"informal_emp_m\",\n",
        "                      \"formal_emp_f\", \"formal_emp_m\"]]\n",
        "\n",
        "dashboard_df.columns = [\"Year\",\"% of Woman Population\", \"% of Man Population\",\n",
        "                        'Female Basic', 'Male Basic', 'Female Intermediate', \n",
        "                        'Male Intermediate', \"Female Advanced\", \"Male Advanced\",\n",
        "                        \"primary_edu_f\", \"primary_edu_m\",\n",
        "                        \"secondary_edu_f\", \"secondary_edu_m\",\n",
        "                        \"tertiary_edu_f\", \"tertiary_edu_m\",\n",
        "                        \"bachelor_edu_f\", \"bachelor_edu_m\",\n",
        "                        \"master_edu_f\", \"master_edu_m\",\n",
        "                        \"Informal Female\", \"Informal Male\",\n",
        "                        \"Formal Female\", \"Formal Male\"]"
      ],
      "metadata": {
        "id": "JJUr8_Ahz1nk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dashboard_df.to_csv(\"dashboard_data.csv\", index=False)\n",
        "dashboard_df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "A-qjKX5_3Ms1",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  % of Woman Population  % of Man Population  Female Basic  Male Basic  \\\n",
              "1  2002              44.243000            79.176002     44.250000   82.220001   \n",
              "2  2003              43.830002            78.915001     46.779999   83.620003   \n",
              "\n",
              "   Female Intermediate  Male Intermediate  Female Advanced  Male Advanced  \\\n",
              "1            49.340000          85.889999        77.059998      93.080002   \n",
              "2            42.959999          81.139999        63.900002      89.790001   \n",
              "\n",
              "   primary_edu_f  ...  tertiary_edu_f  tertiary_edu_m  bachelor_edu_f  \\\n",
              "1       90.86566  ...        13.75591       15.826860             3.9   \n",
              "2       89.80657  ...        14.20265       17.788349             4.0   \n",
              "\n",
              "   bachelor_edu_m  master_edu_f  master_edu_m  Informal Female  Informal Male  \\\n",
              "1        4.683750      0.361922      0.626342        69.519997      60.689999   \n",
              "2        4.984043      0.364198      0.633304        70.910004      62.430000   \n",
              "\n",
              "   Formal Female  Formal Male  \n",
              "1          30.48    39.310001  \n",
              "2          29.09    37.570000  \n",
              "\n",
              "[2 rows x 23 columns]"
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              "      <td>29.09</td>\n",
              "      <td>37.570000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ebd098e-8ab3-4257-9bcf-6bee6ff1a8ad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparation on employment gender data"
      ],
      "metadata": {
        "id": "q9L--IZirRnQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "female_df = employment_gender(\"1U-wxQ-isFLe85m_r7_fF0guUTk4CfYWC\")\n",
        "male_df = employment_gender(\"1gm5M6P4gPaTGh_D50B99iUmQAfHjRbtg\")"
      ],
      "metadata": {
        "id": "uWd7Db8ajN1A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge female and male employment data to 1 dataframe\n",
        "emp_gender_df_raw = np.array([male_df[male_df['Country Name']=='Indonesia'].values[0],\n",
        "                          female_df[female_df['Country Name']=='Indonesia'].values[0]])\n",
        "emp_gender_df_raw = pd.DataFrame(emp_gender_df_raw.T, columns=['male','female'], index=male_df.columns).iloc[45:]\n",
        "emp_gender_df_raw.index = emp_gender_df_raw.index.astype(int)"
      ],
      "metadata": {
        "id": "Hz0latx4l7xq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emp_gender_df_raw.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "fPJlhGvUrzHZ",
        "outputId": "059bc732-7aac-42ad-854a-895f2e593418"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           male     female\n",
              "2001     80.014  46.259998\n",
              "2002  79.176002     44.243"
            ],
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              "      <th>2001</th>\n",
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              "    <tr>\n",
              "      <th>2002</th>\n",
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              "\n",
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              "          const element = document.querySelector('#df-984bb852-6057-40c9-a44e-f496450e1146');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "emp_gender_df_raw = emp_gender_df_raw.reset_index().rename(columns={\"index\":\"Year\"})\n",
        "emp_gender_df_raw.to_csv(\"employment_gender.csv\", index=False)\n",
        "emp_gender_df_raw.head(2)"
      ],
      "metadata": {
        "id": "3bqx5cUutsTq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "f1aeba47-4f83-4ceb-b0e5-08902bf555ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year       male     female\n",
              "0  2001     80.014  46.259998\n",
              "1  2002  79.176002     44.243"
            ],
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f39d6b7e-3fe5-4f24-895d-6457bbfd65e2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparation on job skills data"
      ],
      "metadata": {
        "id": "1KjcG1M1Een7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = load_data(\"1rHLCUIpP0ZcLPAcLyIUSIHvVuUT84NA0\")\n",
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "q96Zt5WQEj_v",
        "outputId": "913d891e-cef3-4e4d-8842-1560e00afd23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Company                                              Title  \\\n",
              "0  Google                       Google Cloud Program Manager   \n",
              "1  Google  Supplier Development Engineer (SDE), Cable/Con...   \n",
              "\n",
              "                       Category         Location  \\\n",
              "0            Program Management        Singapore   \n",
              "1  Manufacturing & Supply Chain  Shanghai, China   \n",
              "\n",
              "                                    Responsibilities  \\\n",
              "0  Shape, shepherd, ship, and show technical prog...   \n",
              "1  Drive cross-functional activities in the suppl...   \n",
              "\n",
              "                              Minimum Qualifications  \\\n",
              "0  BA/BS degree or equivalent practical experienc...   \n",
              "1  BS degree in an Engineering discipline or equi...   \n",
              "\n",
              "                            Preferred Qualifications  \n",
              "0  Experience in the business technology market a...  \n",
              "1  BSEE, BSME or BSIE degree.\\nExperience of usin...  "
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              "      <td>Google</td>\n",
              "      <td>Supplier Development Engineer (SDE), Cable/Con...</td>\n",
              "      <td>Manufacturing &amp; Supply Chain</td>\n",
              "      <td>Shanghai, China</td>\n",
              "      <td>Drive cross-functional activities in the suppl...</td>\n",
              "      <td>BS degree in an Engineering discipline or equi...</td>\n",
              "      <td>BSEE, BSME or BSIE degree.\\nExperience of usin...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb2df4d9-7a27-4973-93e5-eab3d33cea9f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-eb2df4d9-7a27-4973-93e5-eab3d33cea9f button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eb2df4d9-7a27-4973-93e5-eab3d33cea9f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Minimum Qualifications'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "Z06S3NWTE1yk",
        "outputId": "972777d2-c860-4b89-f132-5539c49f9522"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'BA/BS degree or equivalent practical experience.\\n3 years of experience in program and/or project management in cloud computing, enterprise software and/or marketing technologies.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Youtube company has less than 2% of the data\n",
        "df.Company.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrnLs-JKshUd",
        "outputId": "9c2b7540-db40-4b54-ad09-d03d83b528e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Google     1227\n",
              "YouTube      23\n",
              "Name: Company, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We just select google company for the data\n",
        "df = df[df[\"Company\"]==\"Google\"]"
      ],
      "metadata": {
        "id": "gbjkkf_lskn3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop irrelevant columns\n",
        "df.drop(df.iloc[:,[0,3,4]].columns, axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "tYnS2W4AFxtV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop missing value\n",
        "df.dropna(inplace=True)\n",
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-sUOMd7pC-1",
        "outputId": "77548c9e-cb58-454b-c189-5a7823c6e8c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean text on minimum and preffered qualification\n",
        "# Then merged it to one\n",
        "df['clean_minimum'] = df['Minimum Qualifications'].apply(lambda x : clean_text(x, correct=False))\n",
        "df['clean_preferred'] = df['Preferred Qualifications'].apply(lambda x : clean_text(x, correct=False))\n",
        "df['Qualification'] = df['clean_minimum'] + df['clean_preferred']\n",
        "df['temp_qualification'] = df['Qualification'].apply(lambda x: \" \".join(x))"
      ],
      "metadata": {
        "id": "cEQNU1o7pld1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop qualification that is duplicated\n",
        "df.drop_duplicates(subset='temp_qualification', inplace=True)\n",
        "# Drop unnecessary columns\n",
        "df.drop(columns=df.iloc[:,[2,3,4,5,7]].columns, inplace=True)"
      ],
      "metadata": {
        "id": "38EzEZMu93we"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nTJJbopxz96",
        "outputId": "4660a78a-d518-4e05-e5c6-267794faf786"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(820, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(\"job_skills_clean.csv\", index=False)\n",
        "df.head(2)"
      ],
      "metadata": {
        "id": "rsdM4YX5EpsC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "8605602f-71be-45f0-b851-a9c0ddf832df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "0                       Google Cloud Program Manager   \n",
              "1  Supplier Development Engineer (SDE), Cable/Con...   \n",
              "\n",
              "                       Category  \\\n",
              "0            Program Management   \n",
              "1  Manufacturing & Supply Chain   \n",
              "\n",
              "                                       Qualification  \n",
              "0  [bachelor, art, science, degree, equivalent, p...  \n",
              "1  [bachelor, science, degree, engineering, disci...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-def2ea88-767e-43b1-838d-7837a79c539f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Title</th>\n",
              "      <th>Category</th>\n",
              "      <th>Qualification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Google Cloud Program Manager</td>\n",
              "      <td>Program Management</td>\n",
              "      <td>[bachelor, art, science, degree, equivalent, p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Supplier Development Engineer (SDE), Cable/Con...</td>\n",
              "      <td>Manufacturing &amp; Supply Chain</td>\n",
              "      <td>[bachelor, science, degree, engineering, disci...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-def2ea88-767e-43b1-838d-7837a79c539f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-def2ea88-767e-43b1-838d-7837a79c539f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-def2ea88-767e-43b1-838d-7837a79c539f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recommendation System"
      ],
      "metadata": {
        "id": "foSkvyIEFq03"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = load_data(\"1zHOU1koW4j84AiQY4V4l6pASSzoMJS1x\").drop([\"Unnamed: 0\",\"Category\"], axis=1)\n",
        "df.loc[:,\"Qualification\"] = df[\"Qualification\"].apply(lambda x: ast.literal_eval(x))"
      ],
      "metadata": {
        "id": "LRELH4J1z8_i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cosine similiarity to check similiarity between 2 text\n",
        "def get_cosine(vec1, vec2):\n",
        "    vec1 = Counter(vec1)\n",
        "    vec2 = Counter(vec2)\n",
        "    intersection = set(vec1.keys()) & set(vec2.keys())\n",
        "    numerator = sum([vec1[x] * vec2[x] for x in intersection])\n",
        "\n",
        "    sum1 = sum([vec1[x] ** 2 for x in list(vec1.keys())])\n",
        "    sum2 = sum([vec2[x] ** 2 for x in list(vec2.keys())])\n",
        "    denominator = math.sqrt(sum1) * math.sqrt(sum2)\n",
        "\n",
        "    if not denominator:\n",
        "        return 0.0\n",
        "    else:\n",
        "        return float(numerator) / denominator"
      ],
      "metadata": {
        "id": "POuqVpLF6auE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Iterate every job qualification, and check the highest matching\n",
        "def check_match(df, input, amount=5):\n",
        "  score = []\n",
        "  for i in range(df.shape[0]):\n",
        "    score.append([i, get_cosine(input,df['Qualification'].iloc[i])])\n",
        "  score = np.array(score)\n",
        "  score = sorted(score, reverse=True, key=lambda x:x[1])[:amount]\n",
        "  for i in range(len(score)):\n",
        "    tmp = df.iloc[int(score[i][0])]\n",
        "    print(f\"Title: {tmp['Title']}\\nMatch: {round(score[i][1]*100,2)}%\\n\")"
      ],
      "metadata": {
        "id": "nSMT4BFN7Bz6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to calculate tf idf and check similiarity with cosine\n",
        "def tfidf_cosine(df, input, amount=5):\n",
        "  input = \" \".join(input)\n",
        "  copy_df = df.copy()\n",
        "  copy_df.loc[:,\"Qualification\"] = copy_df[\"Qualification\"].apply(lambda x: \" \".join(x))\n",
        "  copy_df.loc[len(copy_df.index)] = [\"INPUT\",input] \n",
        "  vectorizer = TfidfVectorizer(min_df=2, max_df=0.7)\n",
        "  vectorized_data = vectorizer.fit_transform(copy_df['Qualification'])\n",
        "  tfidf_df = pd.DataFrame(vectorized_data.toarray(), columns=vectorizer.get_feature_names_out())\n",
        "  tfidf_df.index = copy_df['Title']\n",
        "\n",
        "  cosine_similarity_array = cosine_similarity(tfidf_df)\n",
        "  cosine_similarity_df = pd.DataFrame(cosine_similarity_array, \n",
        "                                      index=tfidf_df.index, columns=tfidf_df.index).\\\n",
        "                                      sort_values(by='INPUT', ascending=False)\n",
        "  for i,j in cosine_similarity_df.loc[:,\"INPUT\"][1:1+amount].iteritems():\n",
        "    print(f\"Title: {i}\\nMatch: {round(j*100,2)}%\\n\")"
      ],
      "metadata": {
        "id": "QIo6ytXoOxhb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to recommend job\n",
        "def job_recommendation(input, method=0, amount=5):\n",
        "  text = clean_text(input)\n",
        "  if method:\n",
        "    tfidf_cosine(df, text, amount) # method that apply tf idf then cosine\n",
        "  else:\n",
        "    check_match(df, text, amount) # method to check text similiarity with cosine"
      ],
      "metadata": {
        "id": "y40zOxZS-hVZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"\"\n",
        "BA degree on computer science field. My interests are in processing and analyzing data.\n",
        "I Have 1 years intership experience in data science. I also have good english skills and I'm a dilligent person.\n",
        "\"\"\"\n",
        "\n",
        "job_recommendation(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIEXTckHGLcG",
        "outputId": "27f4dc87-e8d7-402e-b763-7b02b48e2507"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Title: Product Analyst, Data Science\n",
            "Match: 47.0%\n",
            "\n",
            "Title: Product Analytics Lead, Data Science\n",
            "Match: 46.43%\n",
            "\n",
            "Title: Data Analyst, Trust and Safety\n",
            "Match: 41.6%\n",
            "\n",
            "Title: Data Analyst, Product Trust and Safety\n",
            "Match: 40.41%\n",
            "\n",
            "Title: People Operations Business Intelligence Analyst\n",
            "Match: 40.0%\n",
            "\n"
          ]
        }
      ]
    }
  ]
}