worldpopulation_manya.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "BhyGHrIrnuSM"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error,r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "lZ7vWB9UohWn",
        "outputId": "7c863143-89c3-44f2-a7c0-99a6cfa9fe7d"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "\n",
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              "        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>Country</th>\n",
              "      <th>Population 2024</th>\n",
              "      <th>Population 2023</th>\n",
              "      <th>Area (km2)</th>\n",
              "      <th>Density (/km2)</th>\n",
              "      <th>Growth Rate</th>\n",
              "      <th>World %</th>\n",
              "      <th>World Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>1441719852</td>\n",
              "      <td>1428627663</td>\n",
              "      <td>3M</td>\n",
              "      <td>485.0</td>\n",
              "      <td>0.0092</td>\n",
              "      <td>0.1801</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>China</td>\n",
              "      <td>1425178782</td>\n",
              "      <td>1425671352</td>\n",
              "      <td>9.4M</td>\n",
              "      <td>151.0</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.1780</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>United States</td>\n",
              "      <td>341814420</td>\n",
              "      <td>339996563</td>\n",
              "      <td>9.1M</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0053</td>\n",
              "      <td>0.0427</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>279798049</td>\n",
              "      <td>277534122</td>\n",
              "      <td>1.9M</td>\n",
              "      <td>149.0</td>\n",
              "      <td>0.0082</td>\n",
              "      <td>0.0350</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>245209815</td>\n",
              "      <td>240485658</td>\n",
              "      <td>770.9K</td>\n",
              "      <td>318.0</td>\n",
              "      <td>0.0196</td>\n",
              "      <td>0.0306</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Country  Population 2024  Population 2023 Area (km2)  Density (/km2)  \\\n",
              "0          India       1441719852       1428627663         3M           485.0   \n",
              "1          China       1425178782       1425671352       9.4M           151.0   \n",
              "2  United States        341814420        339996563       9.1M            37.0   \n",
              "3      Indonesia        279798049        277534122       1.9M           149.0   \n",
              "4       Pakistan        245209815        240485658     770.9K           318.0   \n",
              "\n",
              "   Growth Rate  World %  World Rank  \n",
              "0       0.0092   0.1801           1  \n",
              "1      -0.0003   0.1780           2  \n",
              "2       0.0053   0.0427           3  \n",
              "3       0.0082   0.0350           4  \n",
              "4       0.0196   0.0306           5  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=pd.read_csv('World Population by country 2024.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "s2QUwF_B3uoe",
        "outputId": "4061107e-be8d-46e6-d373-a5b7ac8f7a12"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Population 2024</th>\n",
              "      <th>Population 2023</th>\n",
              "      <th>Area (km2)</th>\n",
              "      <th>Density (/km2)</th>\n",
              "      <th>Growth Rate</th>\n",
              "      <th>World %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>1441719852</td>\n",
              "      <td>1428627663</td>\n",
              "      <td>3M</td>\n",
              "      <td>485.0</td>\n",
              "      <td>0.0092</td>\n",
              "      <td>0.1801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>China</td>\n",
              "      <td>1425178782</td>\n",
              "      <td>1425671352</td>\n",
              "      <td>9.4M</td>\n",
              "      <td>151.0</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.1780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>United States</td>\n",
              "      <td>341814420</td>\n",
              "      <td>339996563</td>\n",
              "      <td>9.1M</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0053</td>\n",
              "      <td>0.0427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>279798049</td>\n",
              "      <td>277534122</td>\n",
              "      <td>1.9M</td>\n",
              "      <td>149.0</td>\n",
              "      <td>0.0082</td>\n",
              "      <td>0.0350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>245209815</td>\n",
              "      <td>240485658</td>\n",
              "      <td>770.9K</td>\n",
              "      <td>318.0</td>\n",
              "      <td>0.0196</td>\n",
              "      <td>0.0306</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Country  Population 2024  Population 2023 Area (km2)  Density (/km2)  \\\n",
              "0          India       1441719852       1428627663         3M           485.0   \n",
              "1          China       1425178782       1425671352       9.4M           151.0   \n",
              "2  United States        341814420        339996563       9.1M            37.0   \n",
              "3      Indonesia        279798049        277534122       1.9M           149.0   \n",
              "4       Pakistan        245209815        240485658     770.9K           318.0   \n",
              "\n",
              "   Growth Rate  World %  \n",
              "0       0.0092   0.1801  \n",
              "1      -0.0003   0.1780  \n",
              "2       0.0053   0.0427  \n",
              "3       0.0082   0.0350  \n",
              "4       0.0196   0.0306  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=df.drop(['World Rank'],axis=1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lOgWPqG57Zs",
        "outputId": "cce394b9-e134-40d0-ca85-2d60beec9edd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 234 entries, 0 to 233\n",
            "Data columns (total 7 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Country          234 non-null    object \n",
            " 1   Population 2024  234 non-null    int64  \n",
            " 2   Population 2023  234 non-null    int64  \n",
            " 3   Area (km2)       234 non-null    object \n",
            " 4   Density (/km2)   234 non-null    float64\n",
            " 5   Growth Rate      234 non-null    float64\n",
            " 6   World %          228 non-null    float64\n",
            "dtypes: float64(3), int64(2), object(2)\n",
            "memory usage: 12.9+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45d2AJLl6F88",
        "outputId": "ad11fdf1-d4b8-46a4-f21f-868e5b534342"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Country', 'Population 2024', 'Population 2023', 'Area (km2)',\n",
              "       'Density (/km2)', 'Growth Rate', 'World %'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mxhwjzmh6Kpv",
        "outputId": "0506a446-38e9-4da5-9812-3161127a47ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Country            0\n",
              "Population 2024    0\n",
              "Population 2023    0\n",
              "Area (km2)         0\n",
              "Density (/km2)     0\n",
              "Growth Rate        0\n",
              "World %            6\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PV6yAVbM-5OQ"
      },
      "source": [
        "##handling missing values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NrCl4_PO6l1T",
        "outputId": "8bd4c757-2b3c-49dc-e280-8b3b2583abc1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Country            0\n",
              "Population 2024    0\n",
              "Population 2023    0\n",
              "Area (km2)         0\n",
              "Density (/km2)     0\n",
              "Growth Rate        0\n",
              "World %            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.fillna(0,inplace=True)\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "L1EvUffwUKvJ",
        "outputId": "162b84e3-efe6-47aa-a418-5230f08f98f9"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Population 2024</th>\n",
              "      <th>Population 2023</th>\n",
              "      <th>Area (km2)</th>\n",
              "      <th>Density (/km2)</th>\n",
              "      <th>Growth Rate</th>\n",
              "      <th>World %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>1441719852</td>\n",
              "      <td>1428627663</td>\n",
              "      <td>3M</td>\n",
              "      <td>485.0</td>\n",
              "      <td>0.0092</td>\n",
              "      <td>0.1801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>China</td>\n",
              "      <td>1425178782</td>\n",
              "      <td>1425671352</td>\n",
              "      <td>9.4M</td>\n",
              "      <td>151.0</td>\n",
              "      <td>-0.0003</td>\n",
              "      <td>0.1780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>United States</td>\n",
              "      <td>341814420</td>\n",
              "      <td>339996563</td>\n",
              "      <td>9.1M</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0053</td>\n",
              "      <td>0.0427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>279798049</td>\n",
              "      <td>277534122</td>\n",
              "      <td>1.9M</td>\n",
              "      <td>149.0</td>\n",
              "      <td>0.0082</td>\n",
              "      <td>0.0350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>245209815</td>\n",
              "      <td>240485658</td>\n",
              "      <td>770.9K</td>\n",
              "      <td>318.0</td>\n",
              "      <td>0.0196</td>\n",
              "      <td>0.0306</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Country  Population 2024  Population 2023 Area (km2)  Density (/km2)  \\\n",
              "0          India       1441719852       1428627663         3M           485.0   \n",
              "1          China       1425178782       1425671352       9.4M           151.0   \n",
              "2  United States        341814420        339996563       9.1M            37.0   \n",
              "3      Indonesia        279798049        277534122       1.9M           149.0   \n",
              "4       Pakistan        245209815        240485658     770.9K           318.0   \n",
              "\n",
              "   Growth Rate  World %  \n",
              "0       0.0092   0.1801  \n",
              "1      -0.0003   0.1780  \n",
              "2       0.0053   0.0427  \n",
              "3       0.0082   0.0350  \n",
              "4       0.0196   0.0306  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCjBK-ptRMf4"
      },
      "source": [
        "##Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "5bp3nJFDJv5s",
        "outputId": "cbab5893-5a4f-400a-ad2d-d01dcacdf175"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# converting area to type float\n",
        "def convert_area(area):\n",
        "    if isinstance(area, str):\n",
        "        if area == '< 1':\n",
        "            return 0.5\n",
        "        if 'M' in area:\n",
        "            return float(area.replace('M', '')) * 1000000\n",
        "        elif 'K' in area:\n",
        "            return float(area.replace('K', '')) * 1000\n",
        "    return float(area)\n",
        "df['Area (km2)'] = df['Area (km2)'].apply(convert_area)\n",
        "df[\"pop change\"] = df[\"Population 2024\"]- df[\"Population 2023\"]\n",
        "x = df[['Population 2023', 'Area (km2)', 'Density (/km2)', 'Growth Rate', 'World %', 'pop change']]\n",
        "y = df['Population 2024']\n",
        "#Plotting Population 2024 vs count\n",
        "fig, axs = plt.subplots(2, 2, figsize=(15, 10))\n",
        "sns.histplot(df['Population 2023'], kde=True, ax=axs[0, 0])\n",
        "axs[0, 0].set_title('Distribution of Population 2023')\n",
        "#Plotting Area vs count\n",
        "sns.histplot(df['Area (km2)'], kde=True, ax=axs[0, 1])\n",
        "axs[0, 1].set_title('Distribution of Area (km²)')\n",
        "#Plotting Density 2024 vs count\n",
        "sns.histplot(df['Density (/km2)'], kde=True, ax=axs[1, 0])\n",
        "axs[1, 0].set_title('Distribution of Density (/km²)')\n",
        "#Plotting Growth rate vs count\n",
        "sns.histplot(df['Growth Rate'], kde=True, ax=axs[1, 1])\n",
        "axs[1, 1].set_title('Distribution of Growth Rate')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "KUv3FwKxYY3W",
        "outputId": "e99195cb-88b1-4dd1-9428-738415fe0ac0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Country', ylabel='Population 2024'>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(x='Country', y='Population 2024',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "YmOSvPcBHQee",
        "outputId": "7521952e-8f2d-47df-fc9f-d49219bedf54"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#correlation matrix using numeric columns\n",
        "numeric_cols = df.select_dtypes(include=[np.number])\n",
        "correlation_matrix = numeric_cols.corr()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "koZ3UUA4XNI3"
      },
      "source": [
        "##Pre-Processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpeiimnTRVI0"
      },
      "source": [
        "##Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4p-PBPFBRafq",
        "outputId": "153ce8b1-dbb7-4931-cdc4-2194dc55ced2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model training using linear regression\n",
            "REG SCORE\n",
            "1.0\n",
            "MEAN SQUARED ERROR\n",
            "3.166481161783017e-16\n",
            "R2 SCORE\n",
            "1.0\n"
          ]
        }
      ],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    x, y, test_size=0.30, random_state=42)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "x_scaled=scaler.fit_transform(x_train)\n",
        "x_scaled_test=scaler.transform(x_test)\n",
        "print(\"Model training using linear regression\")\n",
        "regressor=LinearRegression()\n",
        "reg=regressor.fit(x_scaled,y_train)\n",
        "print(\"REG SCORE\")\n",
        "print(reg.score(x_scaled_test,y_test))\n",
        "y_pred1=reg.predict(x_scaled_test)\n",
        "print(\"MEAN SQUARED ERROR\")\n",
        "print(mean_squared_error(y_test,y_pred1))\n",
        "print(\"R2 SCORE\")\n",
        "print(r2_score(y_test,y_pred1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pi1_-n0xVNQU",
        "outputId": "b3de3c67-2f82-4c87-829a-9cfd9f4090a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model training using DECISION TREE REGRESSOR\n",
            "Model Training using decision tree\n",
            "-2276.846316994171\n",
            "MEAN SQUARED ERROR\n",
            "1.9843014787873736e+18\n",
            "R2 SCORE\n",
            "-2276.846316994171\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "d:\\Applications\\Anaconda\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names\n",
            "  warnings.warn(\n",
            "d:\\Applications\\Anaconda\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "print(\"Model training using DECISION TREE REGRESSOR\")\n",
        "clf = DecisionTreeRegressor()\n",
        "clf.fit(x_scaled,y_train)\n",
        "print(\"Model Training using decision tree\")\n",
        "print(clf.score(x_test,y_test))\n",
        "y_pred2=clf.predict(x_test)\n",
        "print(\"MEAN SQUARED ERROR\")\n",
        "print(mean_squared_error(y_test,y_pred2))\n",
        "print(\"R2 SCORE\")\n",
        "print(r2_score(y_test,y_pred2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnL5QnwSexE4",
        "outputId": "82299669-842a-4001-a9a9-b64340b1e8fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model training using RANDOM FOREST REGRESSOR\n",
            "0.9968409752916403\n",
            "MEAN SQUARED ERROR\n",
            "2751922881520.716\n",
            "R2 SCORE\n",
            "0.9968409752916403\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "print(\"Model training using RANDOM FOREST REGRESSOR\")\n",
        "clf=RandomForestRegressor()\n",
        "clf.fit(x_train,y_train)\n",
        "print(clf.score(x_test,y_test))\n",
        "y_pred3=clf.predict(x_test)\n",
        "print(\"MEAN SQUARED ERROR\")\n",
        "print(mean_squared_error(y_test,y_pred3))\n",
        "print(\"R2 SCORE\")\n",
        "print(r2_score(y_test,y_pred3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f836bqAsgv74",
        "outputId": "87587c07-8b2b-4abe-d1b5-2113dde563b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model training using SVR\n",
            "-0.12031250769683388\n",
            "MEAN SQUARED ERROR\n",
            "975938433221571.6\n",
            "R2 SCORE\n",
            "-0.12031250769683388\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVR\n",
        "print(\"Model training using SVR\")\n",
        "clf=SVR()\n",
        "clf.fit(x_train,y_train)\n",
        "print(clf.score(x_test,y_test))\n",
        "y_pred4=clf.predict(x_test)\n",
        "print(\"MEAN SQUARED ERROR\")\n",
        "print(mean_squared_error(y_test,y_pred4))\n",
        "print(\"R2 SCORE\")\n",
        "print(r2_score(y_test,y_pred4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "CD00IXplJ4jV"
      },
      "outputs": [],
      "source": [
        "# Linear regression has least mean squared error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aa9QjZ6ajQ1b",
        "outputId": "c1588ab1-8ba8-42fc-9179-a26d39b91e91"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "<class 'pandas.core.series.Series'>\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "print(type(y_pred1))\n",
        "print(type(y_test))\n",
        "print(type(x_test))\n",
        "print(type(x_scaled))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rR74hNTbIxop",
        "outputId": "d4df26c7-21ec-40b4-a083-c4a600e974ac"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Population 2023</th>\n",
              "      <th>Area (km2)</th>\n",
              "      <th>Density (/km2)</th>\n",
              "      <th>Growth Rate</th>\n",
              "      <th>World %</th>\n",
              "      <th>pop change</th>\n",
              "      <th>y_test</th>\n",
              "      <th>y_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18092026</td>\n",
              "      <td>107200.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>0.0147</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>266404</td>\n",
              "      <td>18358430</td>\n",
              "      <td>18358430.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>63544</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1013.0</td>\n",
              "      <td>0.0038</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>244</td>\n",
              "      <td>63788</td>\n",
              "      <td>63788.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>335995</td>\n",
              "      <td>374.0</td>\n",
              "      <td>925.0</td>\n",
              "      <td>0.0298</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>10001</td>\n",
              "      <td>345996</td>\n",
              "      <td>345996.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>126527060</td>\n",
              "      <td>1100000.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0.0252</td>\n",
              "      <td>0.0162</td>\n",
              "      <td>3192659</td>\n",
              "      <td>129719719</td>\n",
              "      <td>129719719.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4468087</td>\n",
              "      <td>74200.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0134</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>59874</td>\n",
              "      <td>4527961</td>\n",
              "      <td>4527961.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Population 2023  Area (km2)  Density (/km2)  Growth Rate  World %  \\\n",
              "0         18092026    107200.0           171.0       0.0147   0.0023   \n",
              "1            63544        63.0          1013.0       0.0038   0.0000   \n",
              "2           335995       374.0           925.0       0.0298   0.0000   \n",
              "3        126527060   1100000.0           115.0       0.0252   0.0162   \n",
              "4          4468087     74200.0            61.0       0.0134   0.0006   \n",
              "\n",
              "   pop change     y_test       y_pred  \n",
              "0      266404   18358430   18358430.0  \n",
              "1         244      63788      63788.0  \n",
              "2       10001     345996     345996.0  \n",
              "3     3192659  129719719  129719719.0  \n",
              "4       59874    4527961    4527961.0  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred1=pd.Series(y_pred1,index=y_test.index,name='y_pred')\n",
        "df_new=pd.concat([x_test,y_test.rename('y_test'),y_pred1],axis=1)\n",
        "df_new.reset_index(drop=True,inplace=True)\n",
        "df_new.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "iwJ58av5NXNs"
      },
      "outputs": [],
      "source": [
        "df_new.to_csv('World Population 2024 REPORT',header=True,index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WBvnJGkuXLxu"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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      "codemirror_mode": {
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