TimeSeriesData.ipynb

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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.pipeline import make_pipeline\n",
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "zUeIZC96FrPk"
      },
      "execution_count": null,
      "outputs": []
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    {
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      "execution_count": null,
      "metadata": {
        "id": "1e71pdaWjjQ-"
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      "source": [
        "def predict_next_5_years(df):\n",
        "    future_years=[2022, 2023, 2024, 2025, 2026, 2027]\n",
        "    # Create a DataFrame to store predictions, excluding NaN values\n",
        "    geo = df['geo'].unique()[0]\n",
        "    geo_predictions_df = pd.DataFrame({\n",
        "        'geo': [geo, geo, geo, geo, geo, geo],\n",
        "        'Year': future_years,\n",
        "    })\n",
        "    df = df.drop('geo', axis=1)\n",
        "    # Sort Data by Year\n",
        "    geo_data = df.sort_values(by='Year')\n",
        "    # Exclude rows with NaN values in the target variable\n",
        "    geo_data = geo_data.dropna(subset=geo_data.columns)\n",
        "    print(\"geo_data\")\n",
        "    if not geo_data.empty: # Check if there are samples after excluding\n",
        "      for column in geo_data.columns:\n",
        "        if column not in ['Year', 'geo']:\n",
        "          # Fit linear regression of y on t\n",
        "          pipeline = make_pipeline(\n",
        "              PolynomialFeatures(degree=1, include_bias=False),\n",
        "              LinearRegression()\n",
        "          )\n",
        "          t = geo_data['Year']\n",
        "          y_train = geo_data[column]\n",
        "          pipeline.fit(X=t.to_frame(), y=y_train)\n",
        "          future_predictions = pipeline.predict(np.array(future_years).reshape(-1, 1))\n",
        "          geo_predictions_df[column] = future_predictions.round(6)\n",
        "\n",
        "    else:  # If no samples, create a NaN row\n",
        "      geo_predictions_df[column] = [np.nan]\n",
        "\n",
        "\n",
        "    return geo_predictions_df.sort_values(by=['geo', 'Year'], ascending=[True, True])\n"
      ]
    },
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        "outputId": "f8f609a3-5cf7-4642-f4f0-72c6dd0ed1b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          geo  Year     CPI  Immigrants  Population  Housing Index       GDP  \\\n",
              "0     Austria  2011   93.35     82230.0   8391643.0          81.60  310128.7   \n",
              "1     Austria  2012   95.75     91557.0   8429991.0          87.57  318653.0   \n",
              "2     Austria  2013   97.77    101866.0   8479823.0          92.10  323910.2   \n",
              "3     Austria  2014   99.20    116262.0   8546356.0          95.33  333146.1   \n",
              "4     Austria  2015  100.00    166323.0   8642699.0         100.00  344269.2   \n",
              "..        ...   ...     ...         ...         ...            ...       ...   \n",
              "280  Slovakia  2017  100.90      7188.0   5439232.0         112.99   84669.9   \n",
              "281  Slovakia  2018  103.46      7253.0   5446771.0         121.32   89874.7   \n",
              "282  Slovakia  2019  106.33      7016.0   5454147.0         132.39   94429.7   \n",
              "283  Slovakia  2020  108.47      6775.0   5458827.0         145.06   93444.1   \n",
              "284  Slovakia  2021  111.53      5733.0   5447247.0         154.33  100255.7   \n",
              "\n",
              "     emigration  unemployment  total_deaths   Exports   Imports  \\\n",
              "0       51197.0           3.3       76479.0  127462.4  137512.5   \n",
              "1       51812.0           3.5       79436.0  129678.5  138942.4   \n",
              "2       54071.0           3.8       79526.0  131884.6  137999.8   \n",
              "3       53491.0           4.0       78252.0  134172.5  137001.2   \n",
              "4       56689.0           4.1       83073.0  137756.8  140699.2   \n",
              "..          ...           ...           ...       ...       ...   \n",
              "280      3466.0           5.4       53914.0   73790.1   72191.9   \n",
              "281      3298.0           4.3       54293.0   79136.9   78727.4   \n",
              "282      3384.0           3.8       53234.0   79962.0   80407.4   \n",
              "283      2428.0           4.4       59089.0   75586.2   73700.2   \n",
              "284      3395.0           4.5       73461.0   87868.1   87904.7   \n",
              "\n",
              "     Immigrants_pcap      GDP_pcap  Emigrants_pcap  Deaths_pcap  Exports_pcap  \\\n",
              "0           0.009799  36956.851000        0.006101     0.009114      0.015189   \n",
              "1           0.010861  37799.921732        0.006146     0.009423      0.015383   \n",
              "2           0.012013  38197.754835        0.006376     0.009378      0.015553   \n",
              "3           0.013604  38981.069827        0.006259     0.009156      0.015699   \n",
              "4           0.019244  39833.528855        0.006559     0.009612      0.015939   \n",
              "..               ...           ...             ...          ...           ...   \n",
              "280         0.001322  15566.517479        0.000637     0.009912      0.013566   \n",
              "281         0.001332  16500.546838        0.000605     0.009968      0.014529   \n",
              "282         0.001286  17313.376409        0.000620     0.009760      0.014661   \n",
              "283         0.001241  17117.981574        0.000445     0.010824      0.013847   \n",
              "284         0.001052  18404.838261        0.000623     0.013486      0.016131   \n",
              "\n",
              "     Imports_pcap  \n",
              "0        0.016387  \n",
              "1        0.016482  \n",
              "2        0.016274  \n",
              "3        0.016030  \n",
              "4        0.016280  \n",
              "..            ...  \n",
              "280      0.013272  \n",
              "281      0.014454  \n",
              "282      0.014742  \n",
              "283      0.013501  \n",
              "284      0.016137  \n",
              "\n",
              "[285 rows x 18 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>geo</th>\n",
              "      <th>Year</th>\n",
              "      <th>CPI</th>\n",
              "      <th>Immigrants</th>\n",
              "      <th>Population</th>\n",
              "      <th>Housing Index</th>\n",
              "      <th>GDP</th>\n",
              "      <th>emigration</th>\n",
              "      <th>unemployment</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>Exports</th>\n",
              "      <th>Imports</th>\n",
              "      <th>Immigrants_pcap</th>\n",
              "      <th>GDP_pcap</th>\n",
              "      <th>Emigrants_pcap</th>\n",
              "      <th>Deaths_pcap</th>\n",
              "      <th>Exports_pcap</th>\n",
              "      <th>Imports_pcap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Austria</td>\n",
              "      <td>2011</td>\n",
              "      <td>93.35</td>\n",
              "      <td>82230.0</td>\n",
              "      <td>8391643.0</td>\n",
              "      <td>81.60</td>\n",
              "      <td>310128.7</td>\n",
              "      <td>51197.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>76479.0</td>\n",
              "      <td>127462.4</td>\n",
              "      <td>137512.5</td>\n",
              "      <td>0.009799</td>\n",
              "      <td>36956.851000</td>\n",
              "      <td>0.006101</td>\n",
              "      <td>0.009114</td>\n",
              "      <td>0.015189</td>\n",
              "      <td>0.016387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Austria</td>\n",
              "      <td>2012</td>\n",
              "      <td>95.75</td>\n",
              "      <td>91557.0</td>\n",
              "      <td>8429991.0</td>\n",
              "      <td>87.57</td>\n",
              "      <td>318653.0</td>\n",
              "      <td>51812.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>79436.0</td>\n",
              "      <td>129678.5</td>\n",
              "      <td>138942.4</td>\n",
              "      <td>0.010861</td>\n",
              "      <td>37799.921732</td>\n",
              "      <td>0.006146</td>\n",
              "      <td>0.009423</td>\n",
              "      <td>0.015383</td>\n",
              "      <td>0.016482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Austria</td>\n",
              "      <td>2013</td>\n",
              "      <td>97.77</td>\n",
              "      <td>101866.0</td>\n",
              "      <td>8479823.0</td>\n",
              "      <td>92.10</td>\n",
              "      <td>323910.2</td>\n",
              "      <td>54071.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>79526.0</td>\n",
              "      <td>131884.6</td>\n",
              "      <td>137999.8</td>\n",
              "      <td>0.012013</td>\n",
              "      <td>38197.754835</td>\n",
              "      <td>0.006376</td>\n",
              "      <td>0.009378</td>\n",
              "      <td>0.015553</td>\n",
              "      <td>0.016274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Austria</td>\n",
              "      <td>2014</td>\n",
              "      <td>99.20</td>\n",
              "      <td>116262.0</td>\n",
              "      <td>8546356.0</td>\n",
              "      <td>95.33</td>\n",
              "      <td>333146.1</td>\n",
              "      <td>53491.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>78252.0</td>\n",
              "      <td>134172.5</td>\n",
              "      <td>137001.2</td>\n",
              "      <td>0.013604</td>\n",
              "      <td>38981.069827</td>\n",
              "      <td>0.006259</td>\n",
              "      <td>0.009156</td>\n",
              "      <td>0.015699</td>\n",
              "      <td>0.016030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Austria</td>\n",
              "      <td>2015</td>\n",
              "      <td>100.00</td>\n",
              "      <td>166323.0</td>\n",
              "      <td>8642699.0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>344269.2</td>\n",
              "      <td>56689.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>83073.0</td>\n",
              "      <td>137756.8</td>\n",
              "      <td>140699.2</td>\n",
              "      <td>0.019244</td>\n",
              "      <td>39833.528855</td>\n",
              "      <td>0.006559</td>\n",
              "      <td>0.009612</td>\n",
              "      <td>0.015939</td>\n",
              "      <td>0.016280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>Slovakia</td>\n",
              "      <td>2017</td>\n",
              "      <td>100.90</td>\n",
              "      <td>7188.0</td>\n",
              "      <td>5439232.0</td>\n",
              "      <td>112.99</td>\n",
              "      <td>84669.9</td>\n",
              "      <td>3466.0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>53914.0</td>\n",
              "      <td>73790.1</td>\n",
              "      <td>72191.9</td>\n",
              "      <td>0.001322</td>\n",
              "      <td>15566.517479</td>\n",
              "      <td>0.000637</td>\n",
              "      <td>0.009912</td>\n",
              "      <td>0.013566</td>\n",
              "      <td>0.013272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>Slovakia</td>\n",
              "      <td>2018</td>\n",
              "      <td>103.46</td>\n",
              "      <td>7253.0</td>\n",
              "      <td>5446771.0</td>\n",
              "      <td>121.32</td>\n",
              "      <td>89874.7</td>\n",
              "      <td>3298.0</td>\n",
              "      <td>4.3</td>\n",
              "      <td>54293.0</td>\n",
              "      <td>79136.9</td>\n",
              "      <td>78727.4</td>\n",
              "      <td>0.001332</td>\n",
              "      <td>16500.546838</td>\n",
              "      <td>0.000605</td>\n",
              "      <td>0.009968</td>\n",
              "      <td>0.014529</td>\n",
              "      <td>0.014454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>Slovakia</td>\n",
              "      <td>2019</td>\n",
              "      <td>106.33</td>\n",
              "      <td>7016.0</td>\n",
              "      <td>5454147.0</td>\n",
              "      <td>132.39</td>\n",
              "      <td>94429.7</td>\n",
              "      <td>3384.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>53234.0</td>\n",
              "      <td>79962.0</td>\n",
              "      <td>80407.4</td>\n",
              "      <td>0.001286</td>\n",
              "      <td>17313.376409</td>\n",
              "      <td>0.000620</td>\n",
              "      <td>0.009760</td>\n",
              "      <td>0.014661</td>\n",
              "      <td>0.014742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>Slovakia</td>\n",
              "      <td>2020</td>\n",
              "      <td>108.47</td>\n",
              "      <td>6775.0</td>\n",
              "      <td>5458827.0</td>\n",
              "      <td>145.06</td>\n",
              "      <td>93444.1</td>\n",
              "      <td>2428.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>59089.0</td>\n",
              "      <td>75586.2</td>\n",
              "      <td>73700.2</td>\n",
              "      <td>0.001241</td>\n",
              "      <td>17117.981574</td>\n",
              "      <td>0.000445</td>\n",
              "      <td>0.010824</td>\n",
              "      <td>0.013847</td>\n",
              "      <td>0.013501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>Slovakia</td>\n",
              "      <td>2021</td>\n",
              "      <td>111.53</td>\n",
              "      <td>5733.0</td>\n",
              "      <td>5447247.0</td>\n",
              "      <td>154.33</td>\n",
              "      <td>100255.7</td>\n",
              "      <td>3395.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>73461.0</td>\n",
              "      <td>87868.1</td>\n",
              "      <td>87904.7</td>\n",
              "      <td>0.001052</td>\n",
              "      <td>18404.838261</td>\n",
              "      <td>0.000623</td>\n",
              "      <td>0.013486</td>\n",
              "      <td>0.016131</td>\n",
              "      <td>0.016137</td>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "  }\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df_eurostat = pd.read_csv(\"https://raw.githubusercontent.com/SDuncan5/Eurostat-Data/main/eurostat_pcap_no_nans.csv\")\n",
        "df_eurostat = df_eurostat.drop(columns=[\"Unnamed: 0\"])\n",
        "df_eurostat"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_df = pd.DataFrame()\n",
        "\n",
        "for geo in df_eurostat['geo'].unique():\n",
        "    geo_data = df_eurostat[df_eurostat['geo'] == geo]\n",
        "\n",
        "    # Apply the time series prediction function\n",
        "    predicted_geo_data = predict_next_5_years(geo_data)\n",
        "\n",
        "    # Append the predicted data for the current geo to the overall DataFrame\n",
        "    predicted_df = pd.concat([predicted_df, predicted_geo_data], ignore_index=True)"
      ],
      "metadata": {
        "id": "i59hPGfQkSt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig1 = px.line(predicted_df, x=\"Year\", y=\"GDP\", color=\"geo\", markers=True, title=\"Predicted GDP\", width=800, height=800)\n",
        "fig1.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "R7vVES66_S6l",
        "outputId": "08763ef9-c26b-49ab-9dfe-a1ae4b5f8b24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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