WHO_Suicide.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "WHO Suicide.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/roshank1605A04/WHO-Suicide-Statistics/blob/master/WHO_Suicide.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "8zFSa7R16B6s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Installing the Libraries**"
      ]
    },
    {
      "metadata": {
        "id": "8fmxe-sn5v1m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7EU0hl2R6SCg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Importing the Dataset**"
      ]
    },
    {
      "metadata": {
        "id": "hVYcsGLI6RV8",
        "colab_type": "code",
        "outputId": "970aa8bc-b62b-456e-a9a2-483c777981c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('drive/My Drive/Projects/WHO Suicide/who_suicide_statistics.csv')\n",
        "\n",
        "data = data.sort_values(['year'], ascending = True)\n",
        "\n",
        "print(data.shape)\n",
        "\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(43776, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>country</th>\n",
              "      <th>year</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>suicides_no</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6204</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>1979</td>\n",
              "      <td>female</td>\n",
              "      <td>15-24 years</td>\n",
              "      <td>385.0</td>\n",
              "      <td>12448100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26470</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>1979</td>\n",
              "      <td>male</td>\n",
              "      <td>55-74 years</td>\n",
              "      <td>217.0</td>\n",
              "      <td>1040500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26471</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>1979</td>\n",
              "      <td>male</td>\n",
              "      <td>75+ years</td>\n",
              "      <td>93.0</td>\n",
              "      <td>235800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2615</th>\n",
              "      <td>Austria</td>\n",
              "      <td>1979</td>\n",
              "      <td>male</td>\n",
              "      <td>75+ years</td>\n",
              "      <td>NaN</td>\n",
              "      <td>141900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2614</th>\n",
              "      <td>Austria</td>\n",
              "      <td>1979</td>\n",
              "      <td>male</td>\n",
              "      <td>55-74 years</td>\n",
              "      <td>NaN</td>\n",
              "      <td>573800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           country  year     sex          age  suicides_no  population\n",
              "6204        Brazil  1979  female  15-24 years        385.0  12448100.0\n",
              "26470  Netherlands  1979    male  55-74 years        217.0   1040500.0\n",
              "26471  Netherlands  1979    male    75+ years         93.0    235800.0\n",
              "2615       Austria  1979    male    75+ years          NaN    141900.0\n",
              "2614       Austria  1979    male  55-74 years          NaN    573800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "metadata": {
        "id": "YI5iOY0SQlE-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Data Visualization**"
      ]
    },
    {
      "metadata": {
        "id": "IgQvIrHoQ8d7",
        "colab_type": "code",
        "outputId": "bfd9db0b-6223-479f-aa6c-5b05920ab392",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        }
      },
      "cell_type": "code",
      "source": [
        "# correlation plot\n",
        "\n",
        "f, ax = plt.subplots(figsize = (4, 3))\n",
        "\n",
        "corr = data.corr()\n",
        "sns.heatmap(corr, mask = np.zeros_like(corr, dtype = np.bool), \n",
        "            cmap = sns.diverging_palette(3, 3, as_cmap = True), square = True, ax = ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3bc0f4c630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bO1DVAqelpVU7hS4uLubixYtOC0oIl/OEG/qrNG3aFG9vb+688050Oh27d+9m4cKFzo5N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\nUkVohGJPNWkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3bc0f40f28>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "htbuYmFyRPCH",
        "colab_type": "code",
        "outputId": "3ddf7b23-aff4-4d04-bb9f-fadfb60c6d9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# renaming the columns\n",
        "\n",
        "data.rename({'sex' : 'gender', 'suicides_no' : 'suicides'}, inplace = True, axis = 1)\n",
        "\n",
        "data.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['country', 'year', 'gender', 'age', 'suicides', 'population'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "metadata": {
        "id": "xamPx3FS7Sis",
        "colab_type": "code",
        "outputId": "8def4e7e-9767-4bc6-bd78-4af4fa74834e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        }
      },
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 43776 entries, 6204 to 25858\n",
            "Data columns (total 6 columns):\n",
            "country       43776 non-null object\n",
            "year          43776 non-null int64\n",
            "gender        43776 non-null object\n",
            "age           43776 non-null object\n",
            "suicides      41520 non-null float64\n",
            "population    38316 non-null float64\n",
            "dtypes: float64(2), int64(1), object(3)\n",
            "memory usage: 2.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "i-qexeyM7UjC",
        "colab_type": "code",
        "outputId": "b0560f77-6733-43e3-fc46-d2a30545e962",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>year</th>\n",
              "      <th>suicides</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>43776.000000</td>\n",
              "      <td>41520.000000</td>\n",
              "      <td>3.831600e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1998.502467</td>\n",
              "      <td>193.315390</td>\n",
              "      <td>1.664091e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.338711</td>\n",
              "      <td>800.589926</td>\n",
              "      <td>3.647231e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1979.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.590000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1990.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.511275e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1999.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.806550e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2007.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>1.305698e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>22338.000000</td>\n",
              "      <td>4.380521e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               year      suicides    population\n",
              "count  43776.000000  41520.000000  3.831600e+04\n",
              "mean    1998.502467    193.315390  1.664091e+06\n",
              "std       10.338711    800.589926  3.647231e+06\n",
              "min     1979.000000      0.000000  2.590000e+02\n",
              "25%     1990.000000      1.000000  8.511275e+04\n",
              "50%     1999.000000     14.000000  3.806550e+05\n",
              "75%     2007.000000     91.000000  1.305698e+06\n",
              "max     2016.000000  22338.000000  4.380521e+07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "metadata": {
        "id": "NQxihaIl7WmJ",
        "colab_type": "code",
        "outputId": "74ad88cd-cb13-47b3-bedd-c4a8b31b8bc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country          0\n",
              "year             0\n",
              "gender           0\n",
              "age              0\n",
              "suicides      2256\n",
              "population    5460\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "metadata": {
        "id": "1ZCLScVb7ZW3",
        "colab_type": "code",
        "outputId": "6afe0b5f-6b37-4453-9477-b52028979f15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "data['country'].value_counts().count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "141"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "metadata": {
        "id": "xh-Mgt5X6koS",
        "colab_type": "code",
        "outputId": "c51faa5b-2f71-474a-83dd-33e33c602a59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        }
      },
      "cell_type": "code",
      "source": [
        "# visualising the different countries distribution in the dataset\n",
        "\n",
        "data['country'].value_counts(normalize = True)\n",
        "data['country'].value_counts(dropna = False).plot.bar(color = 'cyan', figsize = (24, 8))\n",
        "\n",
        "plt.title('Distribution of 141 coutries in suicides')\n",
        "plt.xlabel('country name')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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/28uTAQAAAAD0jiJ23tZqtbS1tXUet7S0pFqt9uJEAAAAAAC9q4jy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\niYnYYREREVE10axZM/z0009ISEjAjRs3IJFIMGbMGLz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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3bbbc9e470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Y4egxhbA79x9",
        "colab_type": "code",
        "outputId": "4881ae8c-c0dc-439b-c5f4-0cba08d3cdef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "data['year'].value_counts().count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "metadata": {
        "id": "0y_PwMJX7isq",
        "colab_type": "code",
        "outputId": "b92aad75-b1f7-4252-8491-9fd5223d9ba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "cell_type": "code",
      "source": [
        "# visualising the different year distribution in the dataset\n",
        "\n",
        "data['year'].value_counts(normalize = True)\n",
        "data['year'].value_counts(dropna = False,).plot.bar(color = 'magenta', figsize = (8, 6))\n",
        "\n",
        "plt.title('Distribution of suicides from the year 1985 to 2016')\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NvmnTplizZo3J8lWrVpks8/HxgY+Pj9EyS0tLzJw501zlERERqRo/yY6IiEiF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\npEJs8ERERCrEBk9ERKRCbPBEREQq9P8BjVJIYe/aBtgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3bc27f8a20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jm6_1A6BJTck",
        "colab_type": "code",
        "outputId": "0288f0a5-de28-4017-c739-653ffb5b6894",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "cell_type": "code",
      "source": [
        "# label encoding for gender\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# creating an encoder\n",
        "le = LabelEncoder()\n",
        "data['gender'] = le.fit_transform(data['gender'])\n",
        "\n",
        "data['gender'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    21888\n",
              "0    21888\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "metadata": {
        "id": "wG0QB7K3IB_8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# replacing categorical values in the age column\n",
        "\n",
        "data['age'] = data['age'].replace('5-14 years', 0)\n",
        "data['age'] = data['age'].replace('15-24 years', 1)\n",
        "data['age'] = data['age'].replace('25-34 years', 2)\n",
        "data['age'] = data['age'].replace('35-54 years', 3)\n",
        "data['age'] = data['age'].replace('55-74 years', 4)\n",
        "data['age'] = data['age'].replace('75+ years', 5)\n",
        "\n",
        "#data['age'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uHXE41lcDNF-",
        "colab_type": "code",
        "outputId": "135355ab-7942-40b9-981f-a84efadd70fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "cell_type": "code",
      "source": [
        "# suicides in different age groups\n",
        "\n",
        "x1 = data[data['age'] == 0]['suicides'].sum()\n",
        "x2 = data[data['age'] == 1]['suicides'].sum()\n",
        "x3 = data[data['age'] == 2]['suicides'].sum()\n",
        "x4 = data[data['age'] == 3]['suicides'].sum()\n",
        "x5 = data[data['age'] == 4]['suicides'].sum()\n",
        "x6 = data[data['age'] == 5]['suicides'].sum()\n",
        "\n",
        "x = pd.DataFrame([x1, x2, x3, x4, x5, x6])\n",
        "x.index = ['5-14', '15-24', '25-34', '35-54', '55-74', '75+']\n",
        "x.plot(kind = 'bar', color = 'grey')\n",
        "\n",
        "plt.title('suicides in different age groups')\n",
        "plt.xlabel('Age Group')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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crLvvvlvNmzdXaWmpatSooQsXLsjf31/Z2dmy2Wyy2WxyOBzO1+Xk5Khly5ay2Wyy2+0K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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3bc05f9ac8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5GOO-_879SLV",
        "colab_type": "code",
        "outputId": "dc35011f-bf80-4ca8-b42d-880f67c0c81c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        }
      },
      "cell_type": "code",
      "source": [
        "# visualising the gender distribution in the dataset\n",
        "\n",
        "data['gender'].value_counts(normalize = True)\n",
        "data['gender'].value_counts(dropna = False).plot.bar(color = 'black', figsize = (4, 3))\n",
        "\n",
        "plt.title('Distribution of 141 coutries in suicides')\n",
        "plt.xlabel('gender')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9++Lh4QFcuuE5IyODbt261XN16pNjPA1MWVkZTk5OaLXWJySPHDlCx44d66kq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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3bc46f4d30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "NT7dp5Q3CJpZ",
        "colab_type": "code",
        "outputId": "a33ae191-832d-4768-aad3-fc3c792784e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# total population of 141 countres over which the suicides survey is committed\n",
        "\n",
        "data['population'].sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63761315943.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "metadata": {
        "id": "CNvH3Mh4-kSr",
        "colab_type": "code",
        "outputId": "1244029b-e2cb-464f-de61-6e2daaf66939",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# Average population\n",
        "\n",
        "Avg_pop = data['population'].mean()\n",
        "print(Avg_pop)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1664091.1353742562\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wSNV1nF68WjD",
        "colab_type": "code",
        "outputId": "6676df72-f49a-427d-ed37-82e8a367020a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# total number of suicides committed in the 141 countries from 1985 to 2016\n",
        "\n",
        "data['suicides'].sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8026455.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "metadata": {
        "id": "cdveLouu_Cpk",
        "colab_type": "code",
        "outputId": "fb558619-c9a2-4daf-ac59-320525e7a611",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# Average suicide in the world\n",
        "\n",
        "Avg_sui = data['suicides'].mean()\n",
        "print(Avg_sui)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "193.3153901734104\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vYgvtkOQB3OM",
        "colab_type": "code",
        "outputId": "74e52e33-55ba-4ded-fc08-66ac57c7e39d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# Imputing the NaN values from the population column\n",
        "\n",
        "data['population'] = data['population'].fillna(data['population'].median())\n",
        "data['population'].isnull().any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "metadata": {
        "id": "AasgisFuDzI8",
        "colab_type": "code",
        "outputId": "b330ee9b-635b-42e9-90c9-ab2fdb557410",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "# Imputing the values suicides no column\n",
        "\n",
        "data['suicides'] = data['suicides'].fillna(0)\n",
        "data['suicides'].isnull().any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "metadata": {
        "id": "ab0rU71Eb_eC",
        "colab_type": "code",
        "outputId": "c987e3ea-2cc4-4520-bae6-8a2b4c2bb1f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        }
      },
      "cell_type": "code",
      "source": [
        "# rearranging the columns \n",
        "\n",
        "data = data[['country', 'year', 'gender', 'age', 'population', 'suicides']]\n",
        "data.head(0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>country</th>\n",
              "      <th>year</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>population</th>\n",
              "      <th>suicides</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [country, year, gender, age, population, suicides]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        }
      ]
    },
    {
      "metadata": {
        "id": "m0liORvUc0kl",
        "colab_type": "code",
        "outputId": "e58c8aa4-fcf5-44be-e9bb-f0eaa181ee25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        }
      },
      "cell_type": "code",
      "source": [
        "# Removing the country Column\n",
        "\n",
        "data = data.drop(['country'], axis = 1)\n",
        "data.head(0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>year</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>population</th>\n",
              "      <th>suicides</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [year, gender, age, population, suicides]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "metadata": {
        "id": "GO6yAOMo-KCf",
        "colab_type": "code",
        "outputId": "1ce39bac-d560-4bb9-9c77-6844c3fdcfb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "#splitting the data into dependent and independent variables\n",
        "\n",
        "x = data.iloc[:,:-1]\n",
        "y = data.iloc[:,-1]\n",
        "\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(43776, 4)\n",
            "(43776,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8GqxvJ7ILgNQ",
        "colab_type": "code",
        "outputId": "ee9bb698-79cf-4320-d22c-30400fa1a23f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "cell_type": "code",
      "source": [
        "# splitting the dataset into training and testing sets\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 45)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32832, 4)\n",
            "(32832,)\n",
            "(10944, 4)\n",
            "(10944,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XDZ8HsArMWNB",
        "colab_type": "code",
        "outputId": "7b48924d-4ece-403d-ae09-4787670eba58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "# min max scaling\n",
        "\n",
        "# importing the min max scaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# creating a scaler\n",
        "mm = MinMaxScaler()\n",
        "\n",
        "# scaling the independent variables\n",
        "x_train = mm.fit_transform(x_train)\n",
        "x_test = mm.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
            "  return self.partial_fit(X, y)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "YtQxJQlmdqY3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# using principal component analysis\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# creating a principal component analysis model\n",
        "#pca = PCA(n_components = None)\n",
        "\n",
        "# feeding the independent variables to the PCA model\n",
        "#x_train = pca.fit_transform(x_train)\n",
        "#x_test = pca.transform(x_test)\n",
        "\n",
        "# visualising the principal components that will explain the highest share of variance\n",
        "#explained_variance = pca.explained_variance_ratio_\n",
        "#print(explained_variance)\n",
        "\n",
        "# creating a principal component analysis model\n",
        "#pca = PCA(n_components = 1)\n",
        "\n",
        "# feeding the independent variables to the PCA model\n",
        "#x_train = pca.fit_transform(x_train)\n",
        "#x_test = pca.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7bY1_Ln_nAyk",
        "colab_type": "code",
        "outputId": "137e7e09-0b41-46d2-959f-599ce645f561",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "cell_type": "code",
      "source": [
        "# applying k means clustering\n",
        "\n",
        "# selecting the best choice for no. of clusters\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "  km = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
        "  km.fit(x_train)\n",
        "  wcss.append(km.inertia_)\n",
        "  \n",
        "plt.plot(range(1, 11), wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('no. of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BgSa6x4aRmVtKWaXT1+WIiEgbptD3gsSEKFzA/qN6dE9ERHxHoe8FfbUkr4iItAAKfS9I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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3bc705e668>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xQA-hLSZorPY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# applying kmeans with 4 clusters\n",
        "\n",
        "km = KMeans(n_clusters = 4, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
        "y_means = km.fit_predict(x_train)\n",
        "\n",
        "# visualising the clusters\n",
        "\n",
        "plt.scatter(x_train[y_means == 0, 0], x_train[y_means == 0, 1], s = 100, c = 'pink', label = 'cluster 1')\n",
        "plt.scatter(x_train[y_means == 1, 0], x_train[y_means == 1, 1], s = 100, c = 'cyan', label = 'cluster 2')\n",
        "plt.scatter(x_train[y_means == 2, 0], x_train[y_means == 2, 1], s = 100, c = 'magenta', label = 'cluster 3')\n",
        "plt.scatter(x_train[y_means == 3, 0], x_train[y_means == 3, 1], s = 100, c = 'violet', label = 'cluster 4')\n",
        "\n",
        "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], s = 100, c = 'red', label = 'centroids')\n",
        "\n",
        "plt.title('Cluster of Clients')\n",
        "plt.xlabel('cc')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mbSds_IhM8bY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "# creating the model\n",
        "model = LinearRegression()\n",
        "\n",
        "# feeding the training data into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# calculating the mean squared error\n",
        "mse = np.mean((y_test - y_pred)**2)\n",
        "print(\"MSE :\", mse)\n",
        "\n",
        "# calculating the root mean squared error\n",
        "rmse = np.sqrt(mse)\n",
        "print(\"RMSE :\", rmse)\n",
        "\n",
        "#calculating the r2 score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(\"r2_score :\", r2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "r00WQ9ecOHQW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "# creating the model\n",
        "model = SVR()\n",
        "\n",
        "# feeding the training data into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# calculating the mean squared error\n",
        "mse = np.mean((y_test - y_pred)**2)\n",
        "print(\"MSE :\", mse)\n",
        "\n",
        "# calculating the root mean squared error\n",
        "rmse = np.sqrt(mse)\n",
        "print(\"RMSE :\", rmse)\n",
        "\n",
        "#calculating the r2 score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(\"r2_score :\", r2)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VYhn0pq8OD6r",
        "colab_type": "code",
        "outputId": "6e3ebe21-361a-48ae-d10d-a2fbe36a483e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# creating the model\n",
        "model = RandomForestRegressor()\n",
        "\n",
        "# feeding the training data into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# calculating the mean squared error\n",
        "mse = np.mean((y_test - y_pred)**2)\n",
        "print(\"MSE :\", mse)\n",
        "\n",
        "# calculating the root mean squared error\n",
        "rmse = np.sqrt(mse)\n",
        "print(\"RMSE :\", rmse)\n",
        "\n",
        "#calculating the r2 score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(\"r2_score :\", r2)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "MSE : 117021.75686150225\n",
            "RMSE : 342.08442943446323\n",
            "r2_score : 0.8003564233332043\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RqZmafkhO3Jy",
        "colab_type": "code",
        "outputId": "1bbee30b-cfc4-4bd7-f09b-2f10ccf1c96f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "# creating the model\n",
        "model = DecisionTreeRegressor()\n",
        "\n",
        "# feeding the training data into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# calculating the mean squared error\n",
        "mse = np.mean((y_test - y_pred)**2)\n",
        "print(\"MSE :\", mse)\n",
        "\n",
        "# calculating the root mean squared error\n",
        "rmse = np.sqrt(mse)\n",
        "print(\"RMSE :\", rmse)\n",
        "\n",
        "#calculating the r2 score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(\"r2_score :\", r2)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE : 151779.80178841596\n",
            "RMSE : 389.5892731947531\n",
            "r2_score : 0.7410578741295126\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Hc5CF07-PJfi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "\n",
        "# creating the model\n",
        "model = AdaBoostRegressor()\n",
        "\n",
        "# feeding the training data into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# calculating the mean squared error\n",
        "mse = np.mean((y_test - y_pred)**2)\n",
        "print(\"MSE :\", mse)\n",
        "\n",
        "# calculating the root mean squared error\n",
        "rmse = np.sqrt(mse)\n",
        "print(\"RMSE :\", rmse)\n",
        "\n",
        "#calculating the r2 score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(\"r2_score :\", r2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y6Z4StyMPar3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "# creating the model\n",
        "model = MLPClassifier(hidden_layer_sizes = 100, max_iter = 50 )\n",
        "\n",
        "# feeding the training data into the model\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# predicting the test set results\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# calculating the mean squared error\n",
        "mse = np.mean((y_test - y_pred)**2)\n",
        "print(\"MSE :\", mse)\n",
        "\n",
        "# calculating the root mean squared error\n",
        "rmse = np.sqrt(mse)\n",
        "print(\"RMSE :\", rmse)\n",
        "\n",
        "#calculating the r2 score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(\"r2_score :\", r2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}