Unemployment_Analysis.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import sklearn.metrics\n",
        "from matplotlib import pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "Dataset link: https://drive.google.com/file/d/1SASbfSXeRFSqcHwa-AKC8wgAAy2ncLUQ/view?usp=sharing\n",
        "The dataset contains unemployment figures of 235 countries from 1991 to 2021. The task is to perform EDA and apply prediction algorithms on the dataset."
      ],
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        "unempdf=pd.read_csv(\"unemployment_analysis.csv\")"
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      "execution_count": null,
      "outputs": []
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        "print(unempdf.head())"
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          "text": [
            "                  Country Name Country Code        1991        1992  \\\n",
            "0  Africa Eastern and Southern          AFE    7.8 lacs   7.84 lacs   \n",
            "1                  Afghanistan          AFG  10.65 lacs  10.82 lacs   \n",
            "2   Africa Western and Central          AFW   4.42 lacs   4.53 lacs   \n",
            "3                       Angola          AGO   4.21 lacs   4.21 lacs   \n",
            "4                      Albania          ALB  10.31 lacs  30.01 lacs   \n",
            "\n",
            "         1993        1994        1995        1996        1997        1998  \\\n",
            "0   7.85 lacs   7.84 lacs   7.83 lacs   7.84 lacs   7.86 lacs   7.81 lacs   \n",
            "1  10.72 lacs  10.73 lacs  11.18 lacs  10.96 lacs  10.78 lacs   10.8 lacs   \n",
            "2   4.55 lacs   4.54 lacs   4.53 lacs   4.57 lacs    4.6 lacs   4.66 lacs   \n",
            "3   4.23 lacs   4.16 lacs   4.11 lacs    4.1 lacs   4.09 lacs   4.07 lacs   \n",
            "4  25.26 lacs  20.84 lacs  14.61 lacs  13.93 lacs  16.88 lacs  20.05 lacs   \n",
            "\n",
            "   ...        2012        2013        2014        2015        2016  \\\n",
            "0  ...   6.56 lacs   6.45 lacs   6.41 lacs   6.49 lacs   6.61 lacs   \n",
            "1  ...  11.34 lacs  11.19 lacs  11.14 lacs  11.13 lacs  11.16 lacs   \n",
            "2  ...   4.64 lacs   4.41 lacs   4.69 lacs   4.63 lacs   5.57 lacs   \n",
            "3  ...   7.35 lacs   7.37 lacs   7.37 lacs   7.39 lacs   7.41 lacs   \n",
            "4  ...  13.38 lacs  15.87 lacs  18.05 lacs  17.19 lacs  15.42 lacs   \n",
            "\n",
            "         2017        2018        2019        2020        2021  \n",
            "0   6.71 lacs   6.73 lacs   6.91 lacs   7.56 lacs   8.11 lacs  \n",
            "1  11.18 lacs  11.15 lacs  11.22 lacs  11.71 lacs  13.28 lacs  \n",
            "2   6.02 lacs   6.04 lacs   6.06 lacs   6.77 lacs   6.84 lacs  \n",
            "3   7.41 lacs   7.42 lacs   7.42 lacs   8.33 lacs   8.53 lacs  \n",
            "4  13.62 lacs   12.3 lacs  11.47 lacs  13.33 lacs  11.82 lacs  \n",
            "\n",
            "[5 rows x 33 columns]\n"
          ]
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              "             count unique                          top freq\n",
              "Country Name   235    235  Africa Eastern and Southern    1\n",
              "Country Code   235    235                          AFE    1\n",
              "1991           235    216                     1.9 lacs    4\n",
              "1992           235    207                    4.99 lacs    3\n",
              "1993           235    210                     6.6 lacs    4\n",
              "1994           235    214                    6.59 lacs    3\n",
              "1995           235    213                    5.63 lacs    3\n",
              "1996           235    217                     6.6 lacs    3\n",
              "1997           235    217                    6.63 lacs    3\n",
              "1998           235    212                     2.9 lacs    3\n",
              "1999           235    214                    6.16 lacs    3\n",
              "2000           234    218                    6.67 lacs    3\n",
              "2001           235    216                    6.65 lacs    3\n",
              "2002           235    218                    6.77 lacs    3\n",
              "2003           235    219                    5.93 lacs    4\n",
              "2004           235    212                    6.35 lacs    4\n",
              "2005           235    214                    6.22 lacs    3\n",
              "2006           235    219                    3.86 lacs    3\n",
              "2007           235    217                    5.95 lacs    3\n",
              "2008           235    211                    5.86 lacs    4\n",
              "2009           235    214                    5.86 lacs    4\n",
              "2010           235    216                       6 lacs    3\n",
              "2011           235    216                    4.79 lacs    3\n",
              "2012           235    212                    5.85 lacs    3\n",
              "2013           235    216                    5.71 lacs    3\n",
              "2014           235    215                   10.85 lacs    3\n",
              "2015           234    216                    5.82 lacs    3\n",
              "2016           235    219                     5.9 lacs    3\n",
              "2017           235    206                    6.47 lacs    3\n",
              "2018           235    217                    6.48 lacs    3\n",
              "2019           235    208                    4.45 lacs    4\n",
              "2020           235    218                    7.28 lacs    3\n",
              "2021           235    217                    7.66 lacs    3"
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Country Name</th>\n",
              "      <td>235</td>\n",
              "      <td>235</td>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country Code</th>\n",
              "      <td>235</td>\n",
              "      <td>235</td>\n",
              "      <td>AFE</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>235</td>\n",
              "      <td>216</td>\n",
              "      <td>1.9 lacs</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>235</td>\n",
              "      <td>207</td>\n",
              "      <td>4.99 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>235</td>\n",
              "      <td>210</td>\n",
              "      <td>6.6 lacs</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>235</td>\n",
              "      <td>214</td>\n",
              "      <td>6.59 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>235</td>\n",
              "      <td>213</td>\n",
              "      <td>5.63 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>235</td>\n",
              "      <td>217</td>\n",
              "      <td>6.6 lacs</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>235</td>\n",
              "      <td>217</td>\n",
              "      <td>6.63 lacs</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>235</td>\n",
              "      <td>212</td>\n",
              "      <td>2.9 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>235</td>\n",
              "      <td>214</td>\n",
              "      <td>6.16 lacs</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>234</td>\n",
              "      <td>218</td>\n",
              "      <td>6.67 lacs</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>235</td>\n",
              "      <td>216</td>\n",
              "      <td>6.65 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>235</td>\n",
              "      <td>218</td>\n",
              "      <td>6.77 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>235</td>\n",
              "      <td>219</td>\n",
              "      <td>5.93 lacs</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>235</td>\n",
              "      <td>212</td>\n",
              "      <td>6.35 lacs</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>235</td>\n",
              "      <td>214</td>\n",
              "      <td>6.22 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>235</td>\n",
              "      <td>219</td>\n",
              "      <td>3.86 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>235</td>\n",
              "      <td>217</td>\n",
              "      <td>5.95 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>235</td>\n",
              "      <td>211</td>\n",
              "      <td>5.86 lacs</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>235</td>\n",
              "      <td>214</td>\n",
              "      <td>5.86 lacs</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>235</td>\n",
              "      <td>216</td>\n",
              "      <td>6 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>235</td>\n",
              "      <td>216</td>\n",
              "      <td>4.79 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>235</td>\n",
              "      <td>212</td>\n",
              "      <td>5.85 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>235</td>\n",
              "      <td>216</td>\n",
              "      <td>5.71 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>235</td>\n",
              "      <td>215</td>\n",
              "      <td>10.85 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>234</td>\n",
              "      <td>216</td>\n",
              "      <td>5.82 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>235</td>\n",
              "      <td>219</td>\n",
              "      <td>5.9 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>235</td>\n",
              "      <td>206</td>\n",
              "      <td>6.47 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>235</td>\n",
              "      <td>217</td>\n",
              "      <td>6.48 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>235</td>\n",
              "      <td>208</td>\n",
              "      <td>4.45 lacs</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>235</td>\n",
              "      <td>218</td>\n",
              "      <td>7.28 lacs</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>235</td>\n",
              "      <td>217</td>\n",
              "      <td>7.66 lacs</td>\n",
              "      <td>3</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "## EDA and Data Cleaning"
      ],
      "metadata": {
        "id": "YMGkwrnPjTgW"
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    },
    {
      "cell_type": "code",
      "source": [
        "years=unempdf.filter(regex='[1-2][0-9]{3}',axis=1)\n",
        "for i in years:\n",
        "  if i == '1991':\n",
        "    years[i] = years[i].squeeze()\n",
        "  years[i]=pd.DataFrame(years[i].replace('[^\\d.]+', '')).astype(float)\n",
        "\n",
        "# print(years)\n",
        "years = years.T.drop_duplicates().T\n",
        "newdf = [unempdf.iloc[:,0:2],years]\n",
        "unempdf = pd.concat(newdf,axis=1,join='inner')\n",
        "print(unempdf.head())"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  Country Name Country Code   1991   1992   1993   1994  \\\n",
            "0  Africa Eastern and Southern          AFE   7.80   7.84   7.85   7.84   \n",
            "1                  Afghanistan          AFG  10.65  10.82  10.72  10.73   \n",
            "2   Africa Western and Central          AFW   4.42   4.53   4.55   4.54   \n",
            "3                       Angola          AGO   4.21   4.21   4.23   4.16   \n",
            "4                      Albania          ALB  10.31  30.01  25.26  20.84   \n",
            "\n",
            "    1995   1996   1997   1998  ...   2012   2013   2014   2015   2016   2017  \\\n",
            "0   7.83   7.84   7.86   7.81  ...   6.56   6.45   6.41   6.49   6.61   6.71   \n",
            "1  11.18  10.96  10.78  10.80  ...  11.34  11.19  11.14  11.13  11.16  11.18   \n",
            "2   4.53   4.57   4.60   4.66  ...   4.64   4.41   4.69   4.63   5.57   6.02   \n",
            "3   4.11   4.10   4.09   4.07  ...   7.35   7.37   7.37   7.39   7.41   7.41   \n",
            "4  14.61  13.93  16.88  20.05  ...  13.38  15.87  18.05  17.19  15.42  13.62   \n",
            "\n",
            "    2018   2019   2020   2021  \n",
            "0   6.73   6.91   7.56   8.11  \n",
            "1  11.15  11.22  11.71  13.28  \n",
            "2   6.04   6.06   6.77   6.84  \n",
            "3   7.42   7.42   8.33   8.53  \n",
            "4  12.30  11.47  13.33  11.82  \n",
            "\n",
            "[5 rows x 33 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(unempdf.dtypes)"
      ],
      "metadata": {
        "id": "5dzFqMDkcmAS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "600dfe1c-abee-4751-ba34-fccfd60104a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Country Name     object\n",
            "Country Code     object\n",
            "1991            float64\n",
            "1992            float64\n",
            "1993            float64\n",
            "1994            float64\n",
            "1995            float64\n",
            "1996            float64\n",
            "1997            float64\n",
            "1998            float64\n",
            "1999            float64\n",
            "2000            float64\n",
            "2001            float64\n",
            "2002            float64\n",
            "2003            float64\n",
            "2004            float64\n",
            "2005            float64\n",
            "2006            float64\n",
            "2007            float64\n",
            "2008            float64\n",
            "2009            float64\n",
            "2010            float64\n",
            "2011            float64\n",
            "2012            float64\n",
            "2013            float64\n",
            "2014            float64\n",
            "2015            float64\n",
            "2016            float64\n",
            "2017            float64\n",
            "2018            float64\n",
            "2019            float64\n",
            "2020            float64\n",
            "2021            float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# unempdf['Country Name'].unique()\n",
        "di = unempdf.set_index('Country Name').T.to_dict('list');\n",
        "for i in di.keys():\n",
        "  print(i,di[i][0])"
      ],
      "metadata": {
        "id": "esnq7-Apc9ue",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ff55a77-125f-4039-8e6c-c1babea8b1a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Africa Eastern and Southern AFE\n",
            "Afghanistan AFG\n",
            "Africa Western and Central AFW\n",
            "Angola AGO\n",
            "Albania ALB\n",
            "Arab World ARB\n",
            "United Arab Emirates ARE\n",
            "Argentina ARG\n",
            "Armenia ARM\n",
            "Australia AUS\n",
            "Austria AUT\n",
            "Azerbaijan AZE\n",
            "Burundi BDI\n",
            "Belgium BEL\n",
            "Benin BEN\n",
            "Burkina Faso BFA\n",
            "Bangladesh BGD\n",
            "Bulgaria BGR\n",
            "Bahrain BHR\n",
            "Bahamas, The BHS\n",
            "Bosnia and Herzegovina BIH\n",
            "Belarus BLR\n",
            "Belize BLZ\n",
            "Bolivia BOL\n",
            "Brazil BRA\n",
            "Barbados BRB\n",
            "Brunei Darussalam BRN\n",
            "Bhutan BTN\n",
            "Botswana BWA\n",
            "Central African Republic CAF\n",
            "Canada CAN\n",
            "Central Europe and the Baltics CEB\n",
            "Switzerland CHE\n",
            "Channel Islands CHI\n",
            "Chile CHL\n",
            "China CHN\n",
            "Cote d'Ivoire CIV\n",
            "Cameroon CMR\n",
            "Congo, Dem. Rep. COD\n",
            "Congo, Rep. COG\n",
            "Colombia COL\n",
            "Comoros COM\n",
            "Cabo Verde CPV\n",
            "Costa Rica CRI\n",
            "Caribbean small states CSS\n",
            "Cuba CUB\n",
            "Cyprus CYP\n",
            "Czech Republic CZE\n",
            "Germany DEU\n",
            "Djibouti DJI\n",
            "Denmark DNK\n",
            "Dominican Republic DOM\n",
            "Algeria DZA\n",
            "East Asia & Pacific (excluding high income) EAP\n",
            "Early-demographic dividend EAR\n",
            "East Asia & Pacific EAS\n",
            "Europe & Central Asia (excluding high income) ECA\n",
            "Europe & Central Asia ECS\n",
            "Ecuador ECU\n",
            "Egypt, Arab Rep. EGY\n",
            "Euro area EMU\n",
            "Eritrea ERI\n",
            "Spain ESP\n",
            "Estonia EST\n",
            "Ethiopia ETH\n",
            "European Union EUU\n",
            "Fragile and conflict affected situations FCS\n",
            "Finland FIN\n",
            "Fiji FJI\n",
            "France FRA\n",
            "Gabon GAB\n",
            "United Kingdom GBR\n",
            "Georgia GEO\n",
            "Ghana GHA\n",
            "Guinea GIN\n",
            "Gambia, The GMB\n",
            "Guinea-Bissau GNB\n",
            "Equatorial Guinea GNQ\n",
            "Greece GRC\n",
            "Guatemala GTM\n",
            "Guam GUM\n",
            "Guyana GUY\n",
            "High income HIC\n",
            "Hong Kong SAR, China HKG\n",
            "Honduras HND\n",
            "Heavily indebted poor countries (HIPC) HPC\n",
            "Croatia HRV\n",
            "Haiti HTI\n",
            "Hungary HUN\n",
            "IBRD only IBD\n",
            "IDA & IBRD total IBT\n",
            "IDA total IDA\n",
            "IDA blend IDB\n",
            "Indonesia IDN\n",
            "IDA only IDX\n",
            "India IND\n",
            "Ireland IRL\n",
            "Iran, Islamic Rep. IRN\n",
            "Iraq IRQ\n",
            "Iceland ISL\n",
            "Israel ISR\n",
            "Italy ITA\n",
            "Jamaica JAM\n",
            "Jordan JOR\n",
            "Japan JPN\n",
            "Kazakhstan KAZ\n",
            "Kenya KEN\n",
            "Kyrgyz Republic KGZ\n",
            "Cambodia KHM\n",
            "Korea, Rep. KOR\n",
            "Kuwait KWT\n",
            "Latin America & Caribbean (excluding high income) LAC\n",
            "Lao PDR LAO\n",
            "Lebanon LBN\n",
            "Liberia LBR\n",
            "Libya LBY\n",
            "St. Lucia LCA\n",
            "Latin America & Caribbean LCN\n",
            "Least developed countries: UN classification LDC\n",
            "Low income LIC\n",
            "Sri Lanka LKA\n",
            "Lower middle income LMC\n",
            "Low & middle income LMY\n",
            "Lesotho LSO\n",
            "Late-demographic dividend LTE\n",
            "Lithuania LTU\n",
            "Luxembourg LUX\n",
            "Latvia LVA\n",
            "Macao SAR, China MAC\n",
            "Morocco MAR\n",
            "Moldova MDA\n",
            "Madagascar MDG\n",
            "Maldives MDV\n",
            "Middle East & North Africa MEA\n",
            "Mexico MEX\n",
            "Middle income MIC\n",
            "North Macedonia MKD\n",
            "Mali MLI\n",
            "Malta MLT\n",
            "Myanmar MMR\n",
            "Middle East & North Africa (excluding high income) MNA\n",
            "Montenegro MNE\n",
            "Mongolia MNG\n",
            "Mozambique MOZ\n",
            "Mauritania MRT\n",
            "Mauritius MUS\n",
            "Malawi MWI\n",
            "Malaysia MYS\n",
            "North America NAC\n",
            "Namibia NAM\n",
            "New Caledonia NCL\n",
            "Niger NER\n",
            "Nigeria NGA\n",
            "Nicaragua NIC\n",
            "Netherlands NLD\n",
            "Norway NOR\n",
            "Nepal NPL\n",
            "New Zealand NZL\n",
            "OECD members OED\n",
            "Oman OMN\n",
            "Other small states OSS\n",
            "Pakistan PAK\n",
            "Panama PAN\n",
            "Peru PER\n",
            "Philippines PHL\n",
            "Papua New Guinea PNG\n",
            "Poland POL\n",
            "Pre-demographic dividend PRE\n",
            "Puerto Rico PRI\n",
            "Korea, Dem. People's Rep. PRK\n",
            "Portugal PRT\n",
            "Paraguay PRY\n",
            "West Bank and Gaza PSE\n",
            "Pacific island small states PSS\n",
            "Post-demographic dividend PST\n",
            "French Polynesia PYF\n",
            "Qatar QAT\n",
            "Romania ROU\n",
            "Russian Federation RUS\n",
            "Rwanda RWA\n",
            "South Asia SAS\n",
            "Saudi Arabia SAU\n",
            "Sudan SDN\n",
            "Senegal SEN\n",
            "Singapore SGP\n",
            "Solomon Islands SLB\n",
            "Sierra Leone SLE\n",
            "El Salvador SLV\n",
            "Somalia SOM\n",
            "Serbia SRB\n",
            "Sub-Saharan Africa (excluding high income) SSA\n",
            "South Sudan SSD\n",
            "Sub-Saharan Africa SSF\n",
            "Small states SST\n",
            "Sao Tome and Principe STP\n",
            "Suriname SUR\n",
            "Slovak Republic SVK\n",
            "Slovenia SVN\n",
            "Sweden SWE\n",
            "Eswatini SWZ\n",
            "Syrian Arab Republic SYR\n",
            "Chad TCD\n",
            "East Asia & Pacific (IDA & IBRD countries) TEA\n",
            "Europe & Central Asia (IDA & IBRD countries) TEC\n",
            "Togo TGO\n",
            "Thailand THA\n",
            "Tajikistan TJK\n",
            "Turkmenistan TKM\n",
            "Latin America & the Caribbean (IDA & IBRD countries) TLA\n",
            "Timor-Leste TLS\n",
            "Middle East & North Africa (IDA & IBRD countries) TMN\n",
            "Tonga TON\n",
            "South Asia (IDA & IBRD) TSA\n",
            "Sub-Saharan Africa (IDA & IBRD countries) TSS\n",
            "Trinidad and Tobago TTO\n",
            "Tunisia TUN\n",
            "Turkiye TUR\n",
            "Tanzania TZA\n",
            "Uganda UGA\n",
            "Ukraine UKR\n",
            "Upper middle income UMC\n",
            "Uruguay URY\n",
            "United States USA\n",
            "Uzbekistan UZB\n",
            "St. Vincent and the Grenadines VCT\n",
            "Venezuela, RB VEN\n",
            "Virgin Islands (U.S.) VIR\n",
            "Vietnam VNM\n",
            "Vanuatu VUT\n",
            "World WLD\n",
            "Samoa WSM\n",
            "Yemen, Rep. YEM\n",
            "South Africa ZAF\n",
            "Zambia ZMB\n",
            "Zimbabwe ZWE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "und = pd.DataFrame(years.to_dict())\n",
        "und['Total unemployment'] = und.sum(axis=1)\n",
        "# Total unemployment of all countries\n",
        "print(und['Total unemployment'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YC4VnE4xEfDF",
        "outputId": "45705ed0-4512-47d6-a6a4-a6d2d583cf26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      224.57\n",
            "1      345.38\n",
            "2      153.27\n",
            "3      169.02\n",
            "4      505.86\n",
            "        ...  \n",
            "230    180.98\n",
            "231    365.60\n",
            "232    875.21\n",
            "233    407.96\n",
            "234    160.50\n",
            "Name: Total unemployment, Length: 235, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def data_cleaning():\n",
        "  unempdf.isna().sum(axis=1)\n",
        "  print(unempdf[unempdf.isnull().any(axis=1)])\n",
        "  unempd = unempdf.fillna(unempdf.mean())\n",
        "  print(unempd.isnull().sum())\n",
        "data_cleaning()"
      ],
      "metadata": {
        "id": "4yfwc_xIdTz1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34e50f0c-be16-4a1c-e49f-e1317fb5af45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Country Name Country Code       1991       1992       1993       1994  \\\n",
            "136  North Macedonia          MKD  24.5 lacs  26.3 lacs  27.7 lacs    30 lacs   \n",
            "228          Vanuatu          VUT  1.68 lacs  1.69 lacs  1.73 lacs  1.59 lacs   \n",
            "\n",
            "          1995       1996       1997       1998  ...        2012       2013  \\\n",
            "136  35.6 lacs  38.8 lacs    36 lacs  34.5 lacs  ...  31.02 lacs    29 lacs   \n",
            "228  1.74 lacs  1.72 lacs  1.68 lacs  1.76 lacs  ...   1.85 lacs  1.86 lacs   \n",
            "\n",
            "           2014       2015        2016        2017        2018        2019  \\\n",
            "136  28.03 lacs        NaN  23.72 lacs  22.38 lacs  20.74 lacs  17.26 lacs   \n",
            "228   1.81 lacs  1.86 lacs   1.78 lacs   1.75 lacs   1.81 lacs    1.8 lacs   \n",
            "\n",
            "          2020       2021  \n",
            "136  17.2 lacs  16.2 lacs  \n",
            "228  2.12 lacs  2.18 lacs  \n",
            "\n",
            "[2 rows x 33 columns]\n",
            "Country Name    0\n",
            "Country Code    0\n",
            "1991            0\n",
            "1992            0\n",
            "1993            0\n",
            "1994            0\n",
            "1995            0\n",
            "1996            0\n",
            "1997            0\n",
            "1998            0\n",
            "1999            0\n",
            "2000            1\n",
            "2001            0\n",
            "2002            0\n",
            "2003            0\n",
            "2004            0\n",
            "2005            0\n",
            "2006            0\n",
            "2007            0\n",
            "2008            0\n",
            "2009            0\n",
            "2010            0\n",
            "2011            0\n",
            "2012            0\n",
            "2013            0\n",
            "2014            0\n",
            "2015            1\n",
            "2016            0\n",
            "2017            0\n",
            "2018            0\n",
            "2019            0\n",
            "2020            0\n",
            "2021            0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A3FtMtSzge9S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f6bbdc2-3a2b-4574-9af9-20c0fc95bd5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [Country Name, Country Code, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021]\n",
            "Index: []\n",
            "\n",
            "[0 rows x 33 columns]\n",
            "Country Name    0\n",
            "Country Code    0\n",
            "1991            0\n",
            "1992            0\n",
            "1993            0\n",
            "1994            0\n",
            "1995            0\n",
            "1996            0\n",
            "1997            0\n",
            "1998            0\n",
            "1999            0\n",
            "2000            0\n",
            "2001            0\n",
            "2002            0\n",
            "2003            0\n",
            "2004            0\n",
            "2005            0\n",
            "2006            0\n",
            "2007            0\n",
            "2008            0\n",
            "2009            0\n",
            "2010            0\n",
            "2011            0\n",
            "2012            0\n",
            "2013            0\n",
            "2014            0\n",
            "2015            0\n",
            "2016            0\n",
            "2017            0\n",
            "2018            0\n",
            "2019            0\n",
            "2020            0\n",
            "2021            0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# print(df)\n",
        "# df=benin.iloc[:,2:]\n",
        "# outliers=[]\n",
        "# def detect_outlier(data_1):\n",
        "    \n",
        "#     threshold=np.median(data_1)\n",
        "#     mean_1 = np.mean(data_1)\n",
        "#     std_1 =np.std(data_1)\n",
        "    \n",
        "#     for y in data_1:\n",
        "#         z_score= (y - mean_1)/std_1 \n",
        "#         # print(z_score,threshold)\n",
        "#         if np.abs(z_score) > threshold:\n",
        "#             outliers.append(y)\n",
        "#     return outliers\n",
        "# outlier_datapoints = detect_outlier(df)\n",
        "# print(outlier_datapoints)"
      ],
      "metadata": {
        "id": "P47-tugWyVdT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "benin=(unempdf[unempdf['Country Name']=='Benin']).T.drop(['Country Name','Country Code']).T\n",
        "bah=unempdf[unempdf['Country Name']=='Bahrain'].T.drop(['Country Name','Country Code']).T\n",
        "df=benin.values.tolist()[0]\n",
        "df2=bah.values.tolist()[0]\n",
        "sns.boxplot(df)"
      ],
      "metadata": {
        "id": "MoiFxdvJkEvQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "d5b87440-9465-419b-ea7a-932991872f31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 154
        },
        {
          "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": [
        "sns.boxplot(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "CzLsE0mg47ZY",
        "outputId": "0f0aa814-8ca9-43de-a69c-04dacf3d567c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 155
        },
        {
          "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": [
        "# Function to find outliers using IQR\n",
        "def find_outliers_IQR(df):\n",
        "\n",
        "   q1=df.quantile(0.25)\n",
        "\n",
        "   q3=df.quantile(0.75)\n",
        "\n",
        "   IQR=q3-q1\n",
        "   \n",
        "   outliers = df[((df<(q1-1.5*IQR)) | (df>(q3+1.5*IQR)))]\n",
        "\n",
        "   return outliers\n",
        "# For Benin\n",
        "outliers = find_outliers_IQR(pd.DataFrame(df))\n",
        "\n",
        "print(\"number of outliers: \"+ str(outliers.notna().sum()))\n",
        "\n",
        "print(\"max outlier value: \"+ str(outliers.max()))\n",
        "\n",
        "print(\"min outlier value: \"+ str(outliers.min()))\n",
        "print(outliers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGOSrC9_5kth",
        "outputId": "16fcd21d-aa19-4dca-be89-c20423b0c45a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of outliers: 0    1\n",
            "dtype: int64\n",
            "max outlier value: 0    2.65\n",
            "dtype: float64\n",
            "min outlier value: 0    2.65\n",
            "dtype: float64\n",
            "       0\n",
            "0    NaN\n",
            "1    NaN\n",
            "2    NaN\n",
            "3    NaN\n",
            "4    NaN\n",
            "5    NaN\n",
            "6    NaN\n",
            "7    NaN\n",
            "8    NaN\n",
            "9    NaN\n",
            "10   NaN\n",
            "11   NaN\n",
            "12   NaN\n",
            "13   NaN\n",
            "14   NaN\n",
            "15   NaN\n",
            "16   NaN\n",
            "17   NaN\n",
            "18   NaN\n",
            "19   NaN\n",
            "20  2.65\n",
            "21   NaN\n",
            "22   NaN\n",
            "23   NaN\n",
            "24   NaN\n",
            "25   NaN\n",
            "26   NaN\n",
            "27   NaN\n",
            "28   NaN\n",
            "29   NaN\n",
            "30   NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For Bahrain\n",
        "outliers2 = find_outliers_IQR(pd.DataFrame(df2))\n",
        "\n",
        "print(\"number of outliers:\",outliers2.notna().sum())\n",
        "\n",
        "print(\"max outlier value: \"+ str(outliers2.max()))\n",
        "\n",
        "print(\"min outlier value: \"+ str(outliers2.min()))\n",
        "print(outliers2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZmOATo5913W",
        "outputId": "5563e09f-4ea9-49c7-c531-5a29bfd2e386"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of outliers: 0    4\n",
            "dtype: int64\n",
            "max outlier value: 0    111.2\n",
            "dtype: float64\n",
            "min outlier value: 0    0.00011\n",
            "dtype: float64\n",
            "            0\n",
            "0         NaN\n",
            "1         NaN\n",
            "2         NaN\n",
            "3         NaN\n",
            "4         NaN\n",
            "5         NaN\n",
            "6         NaN\n",
            "7         NaN\n",
            "8         NaN\n",
            "9         NaN\n",
            "10  111.20000\n",
            "11        NaN\n",
            "12        NaN\n",
            "13        NaN\n",
            "14        NaN\n",
            "15    0.00011\n",
            "16        NaN\n",
            "17        NaN\n",
            "18        NaN\n",
            "19        NaN\n",
            "20        NaN\n",
            "21        NaN\n",
            "22        NaN\n",
            "23        NaN\n",
            "24        NaN\n",
            "25        NaN\n",
            "26        NaN\n",
            "27        NaN\n",
            "28        NaN\n",
            "29    1.78000\n",
            "30    1.87000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import mean\n",
        "for i in range(len(df)):\n",
        "  if df[i] in outliers.values:\n",
        "    df[i] = mean(df)\n",
        "print(df)\n",
        "outliers = find_outliers_IQR(pd.DataFrame(df))\n",
        "print(\"number of outliers: \"+ str(outliers.notna().sum()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_m5Hyrh9_zs",
        "outputId": "e6b11e4f-a3bd-4d95-f3cb-b610ff50eaac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.49, 1.51, 1.41, 1.36, 1.25, 1.18, 1.1, 1.02, 0.94, 0.85, 0.77, 0.69, 0.74, 0.77, 8.2e-06, 0.86, 0.89, 0.94, 1.0, 1.04, 1.320000264516129, 2.47, 2.29, 2.13, 2.01, 1.83, 1.64, 1.47, 1.47, 1.58, 1.57]\n",
            "number of outliers: 0    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(df2)):\n",
        "  if df2[i] in outliers2.values:\n",
        "    df2[i] = mean(df2)\n",
        "print(df2)\n",
        "outliers2 = find_outliers_IQR(pd.DataFrame(df2))\n",
        "print(\"number of outliers: \"+ str(outliers2.notna().sum()))\n",
        "\n",
        "for i in range(len(df2)):\n",
        "  if df2[i] in outliers2.values:\n",
        "    df2[i] = mean(df2)\n",
        "print(df2)\n",
        "outliers2 = find_outliers_IQR(pd.DataFrame(df2))\n",
        "print(\"number of outliers: \"+ str(outliers2.notna().sum()))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-5uEP8IBPIa",
        "outputId": "e6c5e7df-cb11-4048-d66e-b53eb0fa74bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.01, 1.05, 1.01, 1.11, 1.08, 1.08, 1.09, 1.08, 1.09, 1.08, 4.68064870967742, 1.11, 1.09, 1.09, 1.09, 1.2445406035379813, 1.09, 1.11, 1.14, 1.13, 1.18, 1.2, 1.18, 1.19, 1.2, 1.19, 1.18, 1.2, 1.2, 1.284683526232755, 1.2687055754660697]\n",
            "number of outliers: 0    1\n",
            "dtype: int64\n",
            "[1.01, 1.05, 1.01, 1.11, 1.08, 1.08, 1.09, 1.08, 1.09, 1.08, 1.2493089811262654, 1.11, 1.09, 1.09, 1.09, 1.2445406035379813, 1.09, 1.11, 1.14, 1.13, 1.18, 1.2, 1.18, 1.19, 1.2, 1.19, 1.18, 1.2, 1.2, 1.284683526232755, 1.2687055754660697]\n",
            "number of outliers: 0    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unempdf2 = pd.melt(unempdf,id_vars=['Country Name','Country Code'],var_name='Year',value_name='No. of unemployed')\n",
        "print(unempdf2.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eiMfpVTQC31V",
        "outputId": "1e4e9e31-5413-4da5-8c13-b058b33f69a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  Country Name Country Code  Year  No. of unemployed\n",
            "0  Africa Eastern and Southern          AFE  1991               7.80\n",
            "1                  Afghanistan          AFG  1991              10.65\n",
            "2   Africa Western and Central          AFW  1991               4.42\n",
            "3                       Angola          AGO  1991               4.21\n",
            "4                      Albania          ALB  1991              10.31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pivot = unempdf2.groupby(['Country Name','Country Code','Year']).sum().reset_index();\n",
        "print(df_pivot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHBkeZ4dJedE",
        "outputId": "d4c29626-bfc3-4162-91bc-1485c65e4af7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Country Name Country Code  Year  No. of unemployed\n",
            "0     Afghanistan          AFG  1991              10.65\n",
            "1     Afghanistan          AFG  1992              10.82\n",
            "2     Afghanistan          AFG  1993              10.72\n",
            "3     Afghanistan          AFG  1994              10.73\n",
            "4     Afghanistan          AFG  1995              11.18\n",
            "...           ...          ...   ...                ...\n",
            "7280     Zimbabwe          ZWE  2017               4.78\n",
            "7281     Zimbabwe          ZWE  2018               4.80\n",
            "7282     Zimbabwe          ZWE  2019               4.83\n",
            "7283     Zimbabwe          ZWE  2020               5.35\n",
            "7284     Zimbabwe          ZWE  2021               5.17\n",
            "\n",
            "[7285 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pivot.rename(columns={'Country Name':'Country_name','Country Code':'Country_code'},inplace=True)\n",
        "print(df_pivot.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FmaM-JKTvpJ",
        "outputId": "18fffd3f-3406-4124-dec2-e04f0efdc620"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Country_name Country_code  Year  No. of unemployed\n",
            "0  Afghanistan          AFG  1991              10.65\n",
            "1  Afghanistan          AFG  1992              10.82\n",
            "2  Afghanistan          AFG  1993              10.72\n",
            "3  Afghanistan          AFG  1994              10.73\n",
            "4  Afghanistan          AFG  1995              11.18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def descriptive_stats(country_code):\n",
        "  print(\"\\nStatistics for country with code\",country_code)\n",
        "  # Mean, median, mode, standard deviation of \n",
        "  unemp = (df_pivot.loc[df_pivot['Country_code']==country_code])['No. of unemployed']\n",
        "  print(\"Mean:\",unemp.mean())\n",
        "  print(\"Median:\",unemp.median())\n",
        "  print(\"Mode:\",unemp.mode())\n",
        "  print(\"Standard deviation:\",unemp.std())\n",
        "\n",
        "  # Max and min unemployment\n",
        "  matches=df_pivot.loc[df_pivot['No. of unemployed']==unemp.max()]\n",
        "  print(\"Maximum:\\n\",matches.loc[matches['Country_code'] == country_code]['Year'])\n",
        "  matches=df_pivot.loc[df_pivot['No. of unemployed']==unemp.min()]\n",
        "  print(\"Minimum:\\n\",matches.loc[matches['Country_code'] == country_code]['Year'])\n",
        "\n",
        "# 5 countries with maximum unemployment in 2021 were\n",
        "print(\"Top 5 countries with max unemployment in 2021\")\n",
        "unemp2021 = df_pivot.loc[df_pivot['Year']=='2021']\n",
        "top5=unemp2021.nlargest(5,columns='No. of unemployed')\n",
        "print(top5)\n",
        "\n",
        "# Top 3 countries with unemployment greater than 2021 \n",
        "print(\"Top 3 countries with unemployment greater than 5 lakhs in 2021\")\n",
        "print(unemp2021.loc[unemp2021['No. of unemployed']>5].nlargest(3,columns='No. of unemployed'))\n",
        "\n",
        "# Percentage change in unemployment\n",
        "print(\"Percentage change in unemployment\")\n",
        "# pct = top5['No. of unemployed'].pct_change()\n",
        "top5rec = pd.DataFrame()\n",
        "for i in top5['Country_name']:\n",
        "  top5rec = top5rec.append(df_pivot.loc[df_pivot['Country_name']==i])\n",
        "# print(top5rec.groupby(['Country_name','Country_code']).first())\n",
        "# print(top5rec['Country_name'].unique())\n",
        "pct = top5rec.groupby(['Country_name','Country_code'])['No. of unemployed'].pct_change().mul(100).round(2).shift(-1)\n",
        "print(pct)\n",
        "descriptive_stats('AFG')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZNfAAWgVKvm",
        "outputId": "15219a24-bf10-4d22-83d8-6be73c2b14fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 5 countries with max unemployment in 2021\n",
            "            Country_name Country_code  Year  No. of unemployed\n",
            "5982        South Africa          ZAF  2021              33.56\n",
            "1611            Djibouti          DJI  2021              28.39\n",
            "1983            Eswatini          SWZ  2021              25.76\n",
            "7160  West Bank and Gaza          PSE  2021              24.90\n",
            "743             Botswana          BWA  2021              24.72\n",
            "Top 3 countries with unemployment greater than 5 lakhs in 2021\n",
            "      Country_name Country_code  Year  No. of unemployed\n",
            "5982  South Africa          ZAF  2021              33.56\n",
            "1611      Djibouti          DJI  2021              28.39\n",
            "1983      Eswatini          SWZ  2021              25.76\n",
            "Percentage change in unemployment\n",
            "5952     0.10\n",
            "5953    -0.20\n",
            "5954    -0.10\n",
            "5955     0.00\n",
            "5956    -0.07\n",
            "        ...  \n",
            "739      2.32\n",
            "740      2.45\n",
            "741     10.26\n",
            "742     -0.84\n",
            "743       NaN\n",
            "Name: No. of unemployed, Length: 155, dtype: float64\n",
            "\n",
            "Statistics for country with code AFG\n",
            "Mean: 11.141290322580645\n",
            "Median: 11.14\n",
            "Mode: 0    10.81\n",
            "Name: No. of unemployed, dtype: float64\n",
            "Standard deviation: 0.4619721631980571\n",
            "Maximum:\n",
            " 30    2021\n",
            "Name: Year, dtype: object\n",
            "Minimum:\n",
            " 0    1991\n",
            "Name: Year, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "descriptive_stats('BGR')"
      ],
      "metadata": {
        "id": "ykC5ItIiocdG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c569eab0-70e9-4e10-9432-3caba8b8431c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Statistics for country with code BGR\n",
            "Mean: 10.92193548387097\n",
            "Median: 11.26\n",
            "Mode: 0    11.1\n",
            "Name: No. of unemployed, dtype: float64\n",
            "Standard deviation: 4.064550339504431\n",
            "Maximum:\n",
            " 816    2001\n",
            "Name: Year, dtype: object\n",
            "Minimum:\n",
            " 834    2019\n",
            "Name: Year, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Minimum employment for country with code BGR was in 2019"
      ],
      "metadata": {
        "id": "fD0Q3a7QECcO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pct = df_pivot.groupby(['Country_name','Country_code'])['No. of unemployed'].pct_change()\n",
        "lis = [df_pivot.iloc[:,0:2],pct]\n",
        "undf2=pd.concat(lis,axis=1,join='inner')\n",
        "undf2.rename(columns = {'No. of unemployed':'% change in unemp. compared to prev year'}, inplace = True)\n",
        "print(undf2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RUnqFUcmEGek",
        "outputId": "7f188ea3-66b0-4fd7-a6bc-b40c4347fe83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Country_name Country_code  % change in unemp. compared to prev year\n",
            "0     Afghanistan          AFG                                       NaN\n",
            "1     Afghanistan          AFG                                  0.015962\n",
            "2     Afghanistan          AFG                                 -0.009242\n",
            "3     Afghanistan          AFG                                  0.000933\n",
            "4     Afghanistan          AFG                                  0.041938\n",
            "...           ...          ...                                       ...\n",
            "7280     Zimbabwe          ZWE                                 -0.002088\n",
            "7281     Zimbabwe          ZWE                                  0.004184\n",
            "7282     Zimbabwe          ZWE                                  0.006250\n",
            "7283     Zimbabwe          ZWE                                  0.107660\n",
            "7284     Zimbabwe          ZWE                                 -0.033645\n",
            "\n",
            "[7285 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "japan=df_pivot[df_pivot['Country_name']=='Japan']\n",
        "print(japan)\n",
        "descriptive_stats('JPN')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkoYyUFCkY-w",
        "outputId": "081d1e46-ac2d-49fa-f6d0-ec1187a370b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Country_name Country_code  Year  No. of unemployed\n",
            "3286        Japan          JPN  1991               2.10\n",
            "3287        Japan          JPN  1992               2.20\n",
            "3288        Japan          JPN  1993               2.50\n",
            "3289        Japan          JPN  1994               2.90\n",
            "3290        Japan          JPN  1995               3.20\n",
            "3291        Japan          JPN  1996               3.40\n",
            "3292        Japan          JPN  1997               3.40\n",
            "3293        Japan          JPN  1998               4.10\n",
            "3294        Japan          JPN  1999               4.70\n",
            "3295        Japan          JPN  2000               4.75\n",
            "3296        Japan          JPN  2001               5.02\n",
            "3297        Japan          JPN  2002               5.39\n",
            "3298        Japan          JPN  2003               5.25\n",
            "3299        Japan          JPN  2004               4.73\n",
            "3300        Japan          JPN  2005               4.45\n",
            "3301        Japan          JPN  2006               4.19\n",
            "3302        Japan          JPN  2007               3.89\n",
            "3303        Japan          JPN  2008               4.00\n",
            "3304        Japan          JPN  2009               5.07\n",
            "3305        Japan          JPN  2010               5.10\n",
            "3306        Japan          JPN  2011               4.55\n",
            "3307        Japan          JPN  2012               4.36\n",
            "3308        Japan          JPN  2013               4.00\n",
            "3309        Japan          JPN  2014               3.60\n",
            "3310        Japan          JPN  2015               3.40\n",
            "3311        Japan          JPN  2016               3.10\n",
            "3312        Japan          JPN  2017               2.80\n",
            "3313        Japan          JPN  2018               2.40\n",
            "3314        Japan          JPN  2019               2.40\n",
            "3315        Japan          JPN  2020               2.80\n",
            "3316        Japan          JPN  2021               2.80\n",
            "\n",
            "Statistics for country with code JPN\n",
            "Mean: 3.759677419354839\n",
            "Median: 3.89\n",
            "Mode: 0    2.8\n",
            "1    3.4\n",
            "Name: No. of unemployed, dtype: float64\n",
            "Standard deviation: 0.9969569829267718\n",
            "Maximum:\n",
            " 3297    2002\n",
            "Name: Year, dtype: object\n",
            "Minimum:\n",
            " 3286    1991\n",
            "Name: Year, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing Japan's unemployment rate in 1991 with next year since previous year data is not available"
      ],
      "metadata": {
        "id": "Dnpx0IKPl3C5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pctnextyr = japan['No. of unemployed'].pct_change().mul(100).round(2).shift(-1)\n",
        "print(pctnextyr.iloc[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAuHgP12l2kV",
        "outputId": "ec7597d6-612c-41a5-da92-9fac445bc892"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.76\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unemployment rate of 1991 was 4.76% greater than when compared to 1992\n",
        "(since % change = 1992_val-1991_val/1991_val)"
      ],
      "metadata": {
        "id": "70-uD5LonTf0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(undf2[undf2['Country_code']=='MDA']['% change in unemp. compared to prev year'].min())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDYCT2qKnrZJ",
        "outputId": "eb95d78e-a9cf-4111-a8c2-6e41d3bf60c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.3130081300813008\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plots"
      ],
      "metadata": {
        "id": "I903M4GUtX3X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top10unemp=unemp2021.nlargest(10,columns='No. of unemployed')\n",
        "top10unemp.plot.bar(x='Country_name', y=\"No. of unemployed\", rot=70, title=\"No. of unemployed - Year 2021\");\n",
        "plt.xlabel(\"Country name\")\n",
        "plt.ylabel(\"No. of unemployed (in lakhs)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "cxlqVEG-ta_Z",
        "outputId": "07700c9b-f8bd-40ff-f347-3ef57e750882"
      },
      "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": [
        "top5rec['% change'] = top5rec[top5rec['Year']>'1998'][top5rec['Year']<'2020']['No. of unemployed'].pct_change()\n",
        "for i in top5rec.dropna()['Country_name'].unique():\n",
        "  top5rec[top5rec['Country_name']==i].plot.line(x='Year',y='% change',label=i)\n",
        "  plt.xlabel(\"Years between 2000 and 2019\")\n",
        "  plt.ylabel(\"% change in unemployment\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "G6b0TVTTunep",
        "outputId": "209228bf-059e-4335-a247-1c8ffe0fa9f4"
      },
      "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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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": "markdown",
      "source": [
        "Below for prediction plot"
      ],
      "metadata": {
        "id": "hu91YIjJyJT5"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uOvr4SalyHgF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wsm=df_pivot[df_pivot['Country_code']=='WSM']\n",
        "# print(wsm.iloc[::5,:])\n",
        "wsm.iloc[::5,:].plot.line(x='Year',y='No. of unemployed')\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"No. of unemployed (in lakhs)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "4gpSe8G6yIJw",
        "outputId": "71ef0ae4-8291-409e-910a-6b1d1376ab21"
      },
      "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": "markdown",
      "source": [
        "The above graphs on 2021's 5 highest unemployed countries show that most of them are stabilizing at rate while South Africa and Djibouti's are still increasing percent change of unemployment."
      ],
      "metadata": {
        "id": "WQc4EjDOzNNM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ML prediction"
      ],
      "metadata": {
        "id": "q5b0AgU92syT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima_model import ARMA\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "ttULuP81y_Ot"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# years['MA_3'] = years.iloc[:,1].rolling(window=3).mean()\n",
        "# print(years)\n",
        "def sma(data, n):\n",
        "  sma = data.rolling(window = n).mean()\n",
        "  return pd.DataFrame(sma)\n",
        "\n",
        "def predict_future(no_yr):\n",
        "  for i in range(no_yr):\n",
        "    years[f'sma_3_{i}'] = sma(years.iloc[:-(i+1),1], 3)\n",
        "  print(years.tail())\n",
        "\n",
        "predict_future(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RD2YXUYt3Idi",
        "outputId": "90765196-230b-471a-c989-7fe84fdc0cbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      1991   1992   1993   1994   1995   1996   1997   1998   1999   2000  \\\n",
            "230   2.10   2.38   2.63   3.04   3.19   3.47   3.90   4.18   4.48   4.66   \n",
            "231   8.32   8.31   8.35   8.34   8.96   9.59  10.20  10.81  11.46  11.56   \n",
            "232  29.95  29.98  29.92  29.89  29.89  29.87  29.91  29.95  29.91  29.88   \n",
            "233  18.90  19.37  19.70  18.43  16.81  15.30  13.64  12.00  12.44  12.93   \n",
            "234   4.94   4.99   4.97   4.96   5.63   6.25   6.93   6.46   6.00   5.69   \n",
            "\n",
            "     ...   2014   2015   2016   2017   2018   2019   2020   2021    sma_3_0  \\\n",
            "230  ...   8.72   8.50   8.31   8.58   8.69   8.41   9.15   9.84   3.010000   \n",
            "231  ...  13.47  13.77  13.43  13.30  13.15  13.06  13.39  13.57   5.216667   \n",
            "232  ...  24.89  25.15  26.54  27.04  26.91  28.47  29.22  33.56  13.556667   \n",
            "233  ...   9.36  10.13  10.87  11.63  12.01  12.52  12.85  13.03  19.220000   \n",
            "234  ...   4.77   4.78   4.79   4.78   4.80   4.83   5.35   5.17        NaN   \n",
            "\n",
            "       sma_3_1  \n",
            "230   3.010000  \n",
            "231   5.216667  \n",
            "232  13.556667  \n",
            "233        NaN  \n",
            "234        NaN  \n",
            "\n",
            "[5 rows x 33 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(years.iloc[:,1], label = 'Years', linewidth = 5, alpha = 0.3)\n",
        "plt.plot(years['sma_3_0'], label = 'Year 2022')\n",
        "plt.plot(years['sma_3_1'], label = 'Year 2023')\n",
        "plt.title('Simple Moving Averages - 3 months')\n",
        "plt.legend(loc = 'upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "hmnb3nT45vsx",
        "outputId": "fe3fd4e0-8d37-463c-b5e6-e27aa519c376"
      },
      "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": [
        "print(\"Minimum in 2022:\",years['sma_3_0'].min(),\"\\nMaximum in 2022:\",years['sma_3_0'].max())\n",
        "print(\"Minimum in 2023:\",years['sma_3_1'].min(),\"\\nMaximum in 2023:\",years['sma_3_1'].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "segJ2nrrHjwQ",
        "outputId": "669264ac-a0dd-4a29-8a19-dd77584d291a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum in 2022: 1.3566666666666656 \n",
            "Maximum in 2022: 19.22\n",
            "Minimum in 2023: 1.3566666666666656 \n",
            "Maximum in 2023: 15.446666666666665\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "msk = (years.index < len(years)-30)\n",
        "df_train = years[msk].copy()\n",
        "df_test = years[~msk].copy()\n",
        "model = ARIMA(df_train, order=(2,1,0))\n",
        "model_fit = model.fit()\n",
        "print(model_fit.summary())"
      ],
      "metadata": {
        "id": "CzBOOXGZJSdL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}