conflict_forcasting_AutoARIMA.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "provenance": []
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "sourceId": 9715537,
          "sourceType": "datasetVersion",
          "datasetId": 5943509
        }
      ],
      "dockerImageVersionId": 30822,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": false
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Geoploitical Conflict Forecasting Using [ACLED](https://acleddata.com/curated-data-files/) Data and Auto ARIMA model.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "IRv9dxscrP0T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Project Objective**\n",
        "This projest's core objective is to develop a predictive model for forecasting the occurrence of conflicts across various regions in the world over a decade-long timeframe. This project aims to assist decision-makers in de-escalating tensions and mitigating the aftermath of political conflicts."
      ],
      "metadata": {
        "id": "EzP_5uimfO3D"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5Mg3YgZ-FzJ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Install required packages\n",
        "!pip install pmdarima statsmodels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKKHZ636sJlp",
        "outputId": "7a178a18-062c-4ffe-9d1e-44dbe22ce290",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pmdarima\n",
            "  Downloading pmdarima-2.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl.metadata (7.8 kB)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.14.4)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.4.2)\n",
            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (3.0.11)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.26.4)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (2.2.2)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.6.0)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.13.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (2.3.0)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (75.1.0)\n",
            "Requirement already satisfied: packaging>=17.1 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (24.2)\n",
            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2024.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22->pmdarima) (3.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.19->pmdarima) (1.17.0)\n",
            "Downloading pmdarima-2.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (2.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pmdarima\n",
            "Successfully installed pmdarima-2.0.4\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "!cat /proc/cpuinfo | grep 'model name' | uniq  # CPU info since we utilizing cpu for thiss project\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "kR_tQ8wfaavy",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf81ed8d-a361-4ce1-fe42-7d57ff489110"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "import psutil\n",
        "print(f\"Total RAM: {psutil.virtual_memory().total / 1e9:.2f} GB\")\n"
      ],
      "metadata": {
        "id": "2pooDrMdamr0",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90e47f18-c45c-4f63-eac6-0ac2b88220c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total RAM: 13.61 GB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Data manipulation and visualization\n",
        "import pandas as pd  # For data manipulation and analysis\n",
        "import numpy as np   # For numerical computing\n",
        "import matplotlib.pyplot as plt  # For basic plotting\n",
        "import seaborn as sns  # For advanced statistical graphics\n",
        "\n",
        "# Time series analysis\n",
        "from pmdarima import auto_arima  # For automated ARIMA model selection\n",
        "from statsmodels.tsa.stattools import adfuller, kpss  # For stationarity tests\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose  # For time series decomposition\n",
        "\n",
        "from datetime import datetime\n",
        "from dateutil.relativedelta import relativedelta\n",
        "\n",
        "# Machine learning\n",
        "from sklearn.preprocessing import LabelEncoder  # For encoding categorical variables\n",
        "from sklearn.cluster import KMeans  # For K-Means clustering\n",
        "from sklearn.metrics import mean_squared_error  # For calculating mean squared error\n",
        "\n",
        "# Statistical functions\n",
        "from scipy import stats  # For statistical functions and distributions\n",
        "\n",
        "import os  # For interacting with the operating system\n",
        "import warnings  # For controlling warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "fgUOgiiFutl_",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Using Google Colab*"
      ],
      "metadata": {
        "id": "PHzwRGgkV6L5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9puFIP4q-gm",
        "outputId": "9cbf28fc-ed06-4892-87fb-f9ac90eeb0e1",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# dir name 'ACLED' has the data to work with, cd to 'ACLED'\n",
        "\n",
        "%cd /content/drive/My Drive/ACLED"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FW5wwlVzq-iQ",
        "outputId": "07b4d018-d6e6-417f-e329-5e66e7f17cc1",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/ACLED\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# list the items in this dir /content/drive/My Drive/ACLED\n",
        "\n",
        "!ls /content/drive/My\\ Drive/ACLED"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ts0QYe7Aq-lu",
        "outputId": "6e152cb3-1fbc-425b-c45a-c986a464213d",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Africa_1997-2024_Oct04.csv\t\t LatinAmerica_2018-2024_Oct04.csv\n",
            "Asia-Pacific_2018-2024_Oct04.csv\t MiddleEast_2015-2024_Oct04.csv\n",
            "Europe-Central-Asia_2018-2024_Oct04.csv  USA_Canada_2020_2024_Oct04.csv\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# List of CSV files from our ACLED folder dir\n",
        "csv_files = ['Africa_1997-2024_Oct04.csv', 'LatinAmerica_2018-2024_Oct04.csv',\n",
        "             'Asia-Pacific_2018-2024_Oct04.csv', 'MiddleEast_2015-2024_Oct04.csv',\n",
        "             'Europe-Central-Asia_2018-2024_Oct04.csv', 'USA_Canada_2020_2024_Oct04.csv']"
      ],
      "metadata": {
        "id": "2TlIChyjsG29",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Using Kaggle Notebook*"
      ],
      "metadata": {
        "id": "u4G3UyCMWGji"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "\"\"\"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\"\"\"\n"
      ],
      "metadata": {
        "id": "DmGYmo5-WaO7",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6e310bc4-a030-4005-d452-947f7d5502aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"for dirname, _, filenames in os.walk('/kaggle/input'):\\n    for filename in filenames:\\n        print(os.path.join(dirname, filename))\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the paths to the CSV files\n",
        "\"\"\"csv_files = [\n",
        "    \"/kaggle/input/acled-conflict-data-oct-2024/Europe-Central-Asia_2018-2024_Oct04.csv\",\n",
        "\"/kaggle/input/acled-conflict-data-oct-2024/MiddleEast_2015-2024_Oct04.csv\",\n",
        "\"/kaggle/input/acled-conflict-data-oct-2024/Asia-Pacific_2018-2024_Oct04.csv\",\n",
        "\"/kaggle/input/acled-conflict-data-oct-2024/USA_Canada_2020_2024_Oct04.csv\",\n",
        "\"/kaggle/input/acled-conflict-data-oct-2024/Africa_1997-2024_Oct04.csv\",\n",
        "\"/kaggle/input/acled-conflict-data-oct-2024/LatinAmerica_2018-2024_Oct04.csv\"\n",
        "]\"\"\""
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "FuoHoWrZKjVg",
        "outputId": "0e1a4d20-d068-4955-8ac0-3a721f25959c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'csv_files = [\\n    \"/kaggle/input/acled-conflict-data-oct-2024/Europe-Central-Asia_2018-2024_Oct04.csv\",\\n\"/kaggle/input/acled-conflict-data-oct-2024/MiddleEast_2015-2024_Oct04.csv\",\\n\"/kaggle/input/acled-conflict-data-oct-2024/Asia-Pacific_2018-2024_Oct04.csv\",\\n\"/kaggle/input/acled-conflict-data-oct-2024/USA_Canada_2020_2024_Oct04.csv\",\\n\"/kaggle/input/acled-conflict-data-oct-2024/Africa_1997-2024_Oct04.csv\",\\n\"/kaggle/input/acled-conflict-data-oct-2024/LatinAmerica_2018-2024_Oct04.csv\"\\n]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NpJMIvCmWZ_m",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading the CSV files into named DataFrames ( works for both kaggle and colab at this point)\n",
        "\n",
        "USA_Canada = pd.read_csv(csv_files[0])\n",
        "Africa = pd.read_csv(csv_files[1])\n",
        "LatinAmerica = pd.read_csv(csv_files[2])\n",
        "MiddleEast = pd.read_csv(csv_files[3])\n",
        "Asia = pd.read_csv(csv_files[4])\n",
        "Europe = pd.read_csv(csv_files[5])\n",
        "\n",
        "print(\"Our 6 datasets are ready to read now.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVriNB4ssG4j",
        "outputId": "eb723765-831d-4a5d-edf9-f187f8e5f866",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Our 6 datasets are ready to read now.\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# To keep track of which data comes from which region, we considered adding a new column indicating the region for each row.\n",
        "Africa['Region'] = 'Africa'\n",
        "Asia['Region'] = 'Asia'\n",
        "Europe['Region'] = 'Europe'\n",
        "LatinAmerica['Region'] = 'Latin America'\n",
        "MiddleEast['Region'] = 'Middle East'\n",
        "USA_Canada['Region'] = 'USA/Canada'"
      ],
      "metadata": {
        "id": "0MQBHIxWv0l_",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "USA_Canada.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YqRX1S9sHEA",
        "outputId": "61dcf79d-adb3-427a-a56c-1b33f9f5bb1a",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 388401 entries, 0 to 388400\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   event_id_cnty       388401 non-null  object \n",
            " 1   event_date          388401 non-null  object \n",
            " 2   year                388401 non-null  int64  \n",
            " 3   time_precision      388401 non-null  int64  \n",
            " 4   disorder_type       388401 non-null  object \n",
            " 5   event_type          388401 non-null  object \n",
            " 6   sub_event_type      388401 non-null  object \n",
            " 7   actor1              388401 non-null  object \n",
            " 8   assoc_actor_1       105320 non-null  object \n",
            " 9   inter1              388401 non-null  object \n",
            " 10  actor2              282484 non-null  object \n",
            " 11  assoc_actor_2       77312 non-null   object \n",
            " 12  inter2              282484 non-null  object \n",
            " 13  interaction         388401 non-null  object \n",
            " 14  civilian_targeting  115195 non-null  object \n",
            " 15  iso                 388401 non-null  int64  \n",
            " 16  region              388401 non-null  object \n",
            " 17  country             388401 non-null  object \n",
            " 18  admin1              388389 non-null  object \n",
            " 19  admin2              384801 non-null  object \n",
            " 20  admin3              194547 non-null  object \n",
            " 21  location            388401 non-null  object \n",
            " 22  latitude            388401 non-null  float64\n",
            " 23  longitude           388401 non-null  float64\n",
            " 24  geo_precision       388401 non-null  int64  \n",
            " 25  source              388401 non-null  object \n",
            " 26  source_scale        388401 non-null  object \n",
            " 27  notes               388401 non-null  object \n",
            " 28  fatalities          388401 non-null  int64  \n",
            " 29  tags                87785 non-null   object \n",
            " 30  timestamp           388401 non-null  int64  \n",
            " 31  Region              388401 non-null  object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 94.8+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "Africa.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0i96fzzsHJD",
        "outputId": "7225e4f3-6484-4949-db4e-f7d66e02c42e",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 337508 entries, 0 to 337507\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   event_id_cnty       337508 non-null  object \n",
            " 1   event_date          337508 non-null  object \n",
            " 2   year                337508 non-null  int64  \n",
            " 3   time_precision      337508 non-null  int64  \n",
            " 4   disorder_type       337508 non-null  object \n",
            " 5   event_type          337508 non-null  object \n",
            " 6   sub_event_type      337508 non-null  object \n",
            " 7   actor1              337508 non-null  object \n",
            " 8   assoc_actor_1       117233 non-null  object \n",
            " 9   inter1              337508 non-null  object \n",
            " 10  actor2              179511 non-null  object \n",
            " 11  assoc_actor_2       40064 non-null   object \n",
            " 12  inter2              179511 non-null  object \n",
            " 13  interaction         337508 non-null  object \n",
            " 14  civilian_targeting  96454 non-null   object \n",
            " 15  iso                 337508 non-null  int64  \n",
            " 16  region              337508 non-null  object \n",
            " 17  country             337508 non-null  object \n",
            " 18  admin1              337508 non-null  object \n",
            " 19  admin2              324534 non-null  object \n",
            " 20  admin3              50641 non-null   object \n",
            " 21  location            337508 non-null  object \n",
            " 22  latitude            337508 non-null  float64\n",
            " 23  longitude           337508 non-null  float64\n",
            " 24  geo_precision       337508 non-null  int64  \n",
            " 25  source              337508 non-null  object \n",
            " 26  source_scale        337508 non-null  object \n",
            " 27  notes               337508 non-null  object \n",
            " 28  fatalities          337508 non-null  int64  \n",
            " 29  tags                174699 non-null  object \n",
            " 30  timestamp           337508 non-null  int64  \n",
            " 31  Region              337508 non-null  object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 82.4+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "LatinAmerica.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mP4veogzsHKt",
        "outputId": "bf11d3a5-3637-455c-cdd5-c17243d1a166",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 583401 entries, 0 to 583400\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   event_id_cnty       583401 non-null  object \n",
            " 1   event_date          583401 non-null  object \n",
            " 2   year                583401 non-null  int64  \n",
            " 3   time_precision      583401 non-null  int64  \n",
            " 4   disorder_type       583401 non-null  object \n",
            " 5   event_type          583401 non-null  object \n",
            " 6   sub_event_type      583401 non-null  object \n",
            " 7   actor1              583401 non-null  object \n",
            " 8   assoc_actor_1       306074 non-null  object \n",
            " 9   inter1              583401 non-null  object \n",
            " 10  actor2              235482 non-null  object \n",
            " 11  assoc_actor_2       74302 non-null   object \n",
            " 12  inter2              235482 non-null  object \n",
            " 13  interaction         583401 non-null  object \n",
            " 14  civilian_targeting  65929 non-null   object \n",
            " 15  iso                 583401 non-null  int64  \n",
            " 16  region              583401 non-null  object \n",
            " 17  country             583401 non-null  object \n",
            " 18  admin1              583268 non-null  object \n",
            " 19  admin2              579322 non-null  object \n",
            " 20  admin3              408569 non-null  object \n",
            " 21  location            583401 non-null  object \n",
            " 22  latitude            583401 non-null  float64\n",
            " 23  longitude           583401 non-null  float64\n",
            " 24  geo_precision       583401 non-null  int64  \n",
            " 25  source              583401 non-null  object \n",
            " 26  source_scale        583401 non-null  object \n",
            " 27  notes               583401 non-null  object \n",
            " 28  fatalities          583401 non-null  int64  \n",
            " 29  tags                297464 non-null  object \n",
            " 30  timestamp           583401 non-null  int64  \n",
            " 31  Region              583401 non-null  object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 142.4+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "MiddleEast.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZMmWUV9v0V7",
        "outputId": "7b4786f4-4735-4632-9841-549e596afb3c",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 467218 entries, 0 to 467217\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   event_id_cnty       467218 non-null  object \n",
            " 1   event_date          467218 non-null  object \n",
            " 2   year                467218 non-null  int64  \n",
            " 3   time_precision      467218 non-null  int64  \n",
            " 4   disorder_type       467218 non-null  object \n",
            " 5   event_type          467218 non-null  object \n",
            " 6   sub_event_type      467218 non-null  object \n",
            " 7   actor1              467218 non-null  object \n",
            " 8   assoc_actor_1       136353 non-null  object \n",
            " 9   inter1              467218 non-null  object \n",
            " 10  actor2              261259 non-null  object \n",
            " 11  assoc_actor_2       53143 non-null   object \n",
            " 12  inter2              261260 non-null  object \n",
            " 13  interaction         467218 non-null  object \n",
            " 14  civilian_targeting  64591 non-null   object \n",
            " 15  iso                 467218 non-null  int64  \n",
            " 16  region              467218 non-null  object \n",
            " 17  country             467218 non-null  object \n",
            " 18  admin1              466731 non-null  object \n",
            " 19  admin2              450008 non-null  object \n",
            " 20  admin3              231343 non-null  object \n",
            " 21  location            467218 non-null  object \n",
            " 22  latitude            467218 non-null  float64\n",
            " 23  longitude           467218 non-null  float64\n",
            " 24  geo_precision       467218 non-null  int64  \n",
            " 25  source              467218 non-null  object \n",
            " 26  source_scale        467218 non-null  object \n",
            " 27  notes               467218 non-null  object \n",
            " 28  fatalities          467218 non-null  int64  \n",
            " 29  tags                124965 non-null  object \n",
            " 30  timestamp           467218 non-null  int64  \n",
            " 31  Region              467218 non-null  object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 114.1+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "Asia.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RIUq-8Sv0g8",
        "outputId": "02a58249-2eef-4f1f-fb3b-2f47b11b4c46",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 426484 entries, 0 to 426483\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   event_id_cnty       426484 non-null  object \n",
            " 1   event_date          426484 non-null  object \n",
            " 2   year                426484 non-null  int64  \n",
            " 3   time_precision      426484 non-null  int64  \n",
            " 4   disorder_type       426484 non-null  object \n",
            " 5   event_type          426484 non-null  object \n",
            " 6   sub_event_type      426484 non-null  object \n",
            " 7   actor1              426484 non-null  object \n",
            " 8   assoc_actor_1       157733 non-null  object \n",
            " 9   inter1              426484 non-null  object \n",
            " 10  actor2              148736 non-null  object \n",
            " 11  assoc_actor_2       27206 non-null   object \n",
            " 12  inter2              148736 non-null  object \n",
            " 13  interaction         426484 non-null  object \n",
            " 14  civilian_targeting  14578 non-null   object \n",
            " 15  iso                 426484 non-null  int64  \n",
            " 16  region              426484 non-null  object \n",
            " 17  country             426484 non-null  object \n",
            " 18  admin1              426389 non-null  object \n",
            " 19  admin2              399196 non-null  object \n",
            " 20  admin3              236835 non-null  object \n",
            " 21  location            426484 non-null  object \n",
            " 22  latitude            426484 non-null  float64\n",
            " 23  longitude           426484 non-null  float64\n",
            " 24  geo_precision       426484 non-null  int64  \n",
            " 25  source              426484 non-null  object \n",
            " 26  source_scale        426484 non-null  object \n",
            " 27  notes               426484 non-null  object \n",
            " 28  fatalities          426484 non-null  int64  \n",
            " 29  tags                198490 non-null  object \n",
            " 30  timestamp           426484 non-null  int64  \n",
            " 31  Region              426484 non-null  object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 104.1+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "Europe.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Y7PfojDv0iy",
        "outputId": "b6e88a3f-6208-498b-cc0b-138f6cfc5c6b",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 79654 entries, 0 to 79653\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   event_id_cnty       79654 non-null  object \n",
            " 1   event_date          79654 non-null  object \n",
            " 2   year                79654 non-null  int64  \n",
            " 3   time_precision      79654 non-null  int64  \n",
            " 4   disorder_type       79654 non-null  object \n",
            " 5   event_type          79654 non-null  object \n",
            " 6   sub_event_type      79654 non-null  object \n",
            " 7   actor1              79654 non-null  object \n",
            " 8   assoc_actor_1       58962 non-null  object \n",
            " 9   inter1              79654 non-null  object \n",
            " 10  actor2              7836 non-null   object \n",
            " 11  assoc_actor_2       3240 non-null   object \n",
            " 12  inter2              7836 non-null   object \n",
            " 13  interaction         79654 non-null  object \n",
            " 14  civilian_targeting  830 non-null    object \n",
            " 15  iso                 79654 non-null  int64  \n",
            " 16  region              79654 non-null  object \n",
            " 17  country             79654 non-null  object \n",
            " 18  admin1              79654 non-null  object \n",
            " 19  admin2              79500 non-null  object \n",
            " 20  admin3              1281 non-null   object \n",
            " 21  location            79654 non-null  object \n",
            " 22  latitude            79654 non-null  float64\n",
            " 23  longitude           79654 non-null  float64\n",
            " 24  geo_precision       79654 non-null  int64  \n",
            " 25  source              79654 non-null  object \n",
            " 26  source_scale        79654 non-null  object \n",
            " 27  notes               79654 non-null  object \n",
            " 28  fatalities          79654 non-null  int64  \n",
            " 29  tags                75466 non-null  object \n",
            " 30  timestamp           79654 non-null  int64  \n",
            " 31  Region              79654 non-null  object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 19.4+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenate all DataFrames into a single DataFrame\n",
        "df_all = pd.concat([USA_Canada, Africa, LatinAmerica, MiddleEast, Asia, Europe], ignore_index=True)\n",
        "df_all.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6l-wLkR2v0qv",
        "outputId": "839663ec-b2d8-4b3e-872e-f2bb7002f277",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2282666 entries, 0 to 2282665\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Dtype  \n",
            "---  ------              -----  \n",
            " 0   event_id_cnty       object \n",
            " 1   event_date          object \n",
            " 2   year                int64  \n",
            " 3   time_precision      int64  \n",
            " 4   disorder_type       object \n",
            " 5   event_type          object \n",
            " 6   sub_event_type      object \n",
            " 7   actor1              object \n",
            " 8   assoc_actor_1       object \n",
            " 9   inter1              object \n",
            " 10  actor2              object \n",
            " 11  assoc_actor_2       object \n",
            " 12  inter2              object \n",
            " 13  interaction         object \n",
            " 14  civilian_targeting  object \n",
            " 15  iso                 int64  \n",
            " 16  region              object \n",
            " 17  country             object \n",
            " 18  admin1              object \n",
            " 19  admin2              object \n",
            " 20  admin3              object \n",
            " 21  location            object \n",
            " 22  latitude            float64\n",
            " 23  longitude           float64\n",
            " 24  geo_precision       int64  \n",
            " 25  source              object \n",
            " 26  source_scale        object \n",
            " 27  notes               object \n",
            " 28  fatalities          int64  \n",
            " 29  tags                object \n",
            " 30  timestamp           int64  \n",
            " 31  Region              object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 557.3+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LO-2Kty3KD2u",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# info on df_all\n",
        "df_all.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfNquGe7zsj2",
        "outputId": "a38313f2-2404-4d5a-b15b-86148ce0d955",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2282666 entries, 0 to 2282665\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Dtype  \n",
            "---  ------              -----  \n",
            " 0   event_id_cnty       object \n",
            " 1   event_date          object \n",
            " 2   year                int64  \n",
            " 3   time_precision      int64  \n",
            " 4   disorder_type       object \n",
            " 5   event_type          object \n",
            " 6   sub_event_type      object \n",
            " 7   actor1              object \n",
            " 8   assoc_actor_1       object \n",
            " 9   inter1              object \n",
            " 10  actor2              object \n",
            " 11  assoc_actor_2       object \n",
            " 12  inter2              object \n",
            " 13  interaction         object \n",
            " 14  civilian_targeting  object \n",
            " 15  iso                 int64  \n",
            " 16  region              object \n",
            " 17  country             object \n",
            " 18  admin1              object \n",
            " 19  admin2              object \n",
            " 20  admin3              object \n",
            " 21  location            object \n",
            " 22  latitude            float64\n",
            " 23  longitude           float64\n",
            " 24  geo_precision       int64  \n",
            " 25  source              object \n",
            " 26  source_scale        object \n",
            " 27  notes               object \n",
            " 28  fatalities          int64  \n",
            " 29  tags                object \n",
            " 30  timestamp           int64  \n",
            " 31  Region              object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 557.3+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# previewing first 5 observations of our concat. dfs\n",
        "df_all.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        },
        "id": "MYl55AKUv0vH",
        "outputId": "edd86181-dfd0-4419-b632-691715c1d8b1",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  event_id_cnty  event_date  year  time_precision           disorder_type  \\\n",
              "0      BFO12659  2024-10-04  2024               1      Political violence   \n",
              "1      BFO12661  2024-10-04  2024               1      Political violence   \n",
              "2      BFO12665  2024-10-04  2024               1      Political violence   \n",
              "3      BFO12682  2024-10-04  2024               1  Strategic developments   \n",
              "4      BFO12687  2024-10-04  2024               1          Demonstrations   \n",
              "\n",
              "                   event_type         sub_event_type  \\\n",
              "0  Explosions/Remote violence       Air/drone strike   \n",
              "1                     Battles            Armed clash   \n",
              "2                     Battles            Armed clash   \n",
              "3      Strategic developments                  Other   \n",
              "4                       Riots  Violent demonstration   \n",
              "\n",
              "                                         actor1            assoc_actor_1  \\\n",
              "0       Military Forces of Burkina Faso (2022-)                      NaN   \n",
              "1  JNIM: Group for Support of Islam and Muslims                      NaN   \n",
              "2  JNIM: Group for Support of Islam and Muslims                      NaN   \n",
              "3  JNIM: Group for Support of Islam and Muslims                      NaN   \n",
              "4                        Rioters (Burkina Faso)  Students (Burkina Faso)   \n",
              "\n",
              "         inter1  ... latitude longitude geo_precision                source  \\\n",
              "0  State forces  ...  10.0116   -4.0287             2    Undisclosed Source   \n",
              "1   Rebel group  ...  14.0875   -1.6418             2  Al Zallaqa; Whatsapp   \n",
              "2   Rebel group  ...  13.6758   -2.3336             1              Whatsapp   \n",
              "3   Rebel group  ...  12.7097   -0.1311             2    Undisclosed Source   \n",
              "4       Rioters  ...  12.4634   -3.4608             1              Facebook   \n",
              "\n",
              "          source_scale                                              notes  \\\n",
              "0  Local partner-Other  On 4 October 2024, the Burkinabe force carried...   \n",
              "1            New media  On 4 October 2024, JNIM militants carried out ...   \n",
              "2            New media  On 4 October 2024, JNIM militants attacked a p...   \n",
              "3  Local partner-Other  Non-violent activity: On 4 October 2024, JNIM ...   \n",
              "4            New media  On 4 October 2024, students from the modern li...   \n",
              "\n",
              "  fatalities                  tags   timestamp      Region  \n",
              "0          5                   NaN  1728335020  USA/Canada  \n",
              "1          0                   NaN  1728335020  USA/Canada  \n",
              "2          0                   NaN  1728335020  USA/Canada  \n",
              "3          0                   NaN  1728335020  USA/Canada  \n",
              "4          0  crowd size=no report  1728335020  USA/Canada  \n",
              "\n",
              "[5 rows x 32 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1167961-ac65-418e-94ba-b8a5c768433b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_id_cnty</th>\n",
              "      <th>event_date</th>\n",
              "      <th>year</th>\n",
              "      <th>time_precision</th>\n",
              "      <th>disorder_type</th>\n",
              "      <th>event_type</th>\n",
              "      <th>sub_event_type</th>\n",
              "      <th>actor1</th>\n",
              "      <th>assoc_actor_1</th>\n",
              "      <th>inter1</th>\n",
              "      <th>...</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>geo_precision</th>\n",
              "      <th>source</th>\n",
              "      <th>source_scale</th>\n",
              "      <th>notes</th>\n",
              "      <th>fatalities</th>\n",
              "      <th>tags</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BFO12659</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>2024</td>\n",
              "      <td>1</td>\n",
              "      <td>Political violence</td>\n",
              "      <td>Explosions/Remote violence</td>\n",
              "      <td>Air/drone strike</td>\n",
              "      <td>Military Forces of Burkina Faso (2022-)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>State forces</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0116</td>\n",
              "      <td>-4.0287</td>\n",
              "      <td>2</td>\n",
              "      <td>Undisclosed Source</td>\n",
              "      <td>Local partner-Other</td>\n",
              "      <td>On 4 October 2024, the Burkinabe force carried...</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728335020</td>\n",
              "      <td>USA/Canada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BFO12661</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>2024</td>\n",
              "      <td>1</td>\n",
              "      <td>Political violence</td>\n",
              "      <td>Battles</td>\n",
              "      <td>Armed clash</td>\n",
              "      <td>JNIM: Group for Support of Islam and Muslims</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rebel group</td>\n",
              "      <td>...</td>\n",
              "      <td>14.0875</td>\n",
              "      <td>-1.6418</td>\n",
              "      <td>2</td>\n",
              "      <td>Al Zallaqa; Whatsapp</td>\n",
              "      <td>New media</td>\n",
              "      <td>On 4 October 2024, JNIM militants carried out ...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728335020</td>\n",
              "      <td>USA/Canada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BFO12665</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>2024</td>\n",
              "      <td>1</td>\n",
              "      <td>Political violence</td>\n",
              "      <td>Battles</td>\n",
              "      <td>Armed clash</td>\n",
              "      <td>JNIM: Group for Support of Islam and Muslims</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rebel group</td>\n",
              "      <td>...</td>\n",
              "      <td>13.6758</td>\n",
              "      <td>-2.3336</td>\n",
              "      <td>1</td>\n",
              "      <td>Whatsapp</td>\n",
              "      <td>New media</td>\n",
              "      <td>On 4 October 2024, JNIM militants attacked a p...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728335020</td>\n",
              "      <td>USA/Canada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BFO12682</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>2024</td>\n",
              "      <td>1</td>\n",
              "      <td>Strategic developments</td>\n",
              "      <td>Strategic developments</td>\n",
              "      <td>Other</td>\n",
              "      <td>JNIM: Group for Support of Islam and Muslims</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rebel group</td>\n",
              "      <td>...</td>\n",
              "      <td>12.7097</td>\n",
              "      <td>-0.1311</td>\n",
              "      <td>2</td>\n",
              "      <td>Undisclosed Source</td>\n",
              "      <td>Local partner-Other</td>\n",
              "      <td>Non-violent activity: On 4 October 2024, JNIM ...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728335020</td>\n",
              "      <td>USA/Canada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BFO12687</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>2024</td>\n",
              "      <td>1</td>\n",
              "      <td>Demonstrations</td>\n",
              "      <td>Riots</td>\n",
              "      <td>Violent demonstration</td>\n",
              "      <td>Rioters (Burkina Faso)</td>\n",
              "      <td>Students (Burkina Faso)</td>\n",
              "      <td>Rioters</td>\n",
              "      <td>...</td>\n",
              "      <td>12.4634</td>\n",
              "      <td>-3.4608</td>\n",
              "      <td>1</td>\n",
              "      <td>Facebook</td>\n",
              "      <td>New media</td>\n",
              "      <td>On 4 October 2024, students from the modern li...</td>\n",
              "      <td>0</td>\n",
              "      <td>crowd size=no report</td>\n",
              "      <td>1728335020</td>\n",
              "      <td>USA/Canada</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1167961-ac65-418e-94ba-b8a5c768433b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b1167961-ac65-418e-94ba-b8a5c768433b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b1167961-ac65-418e-94ba-b8a5c768433b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f13eaab0-4947-49c6-805a-a3361d5a92b5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f13eaab0-4947-49c6-805a-a3361d5a92b5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\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",
              "  }\n",
              "\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",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f13eaab0-4947-49c6-805a-a3361d5a92b5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_all"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# displaying the value counts of our new, Region col\n",
        "df_all.Region.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "cSX4Se2kv0w6",
        "outputId": "b8f88e36-af53-42f1-c849-3c23189dffbf",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "Latin America    583401\n",
              "Middle East      467218\n",
              "Asia             426484\n",
              "USA/Canada       388401\n",
              "Africa           337508\n",
              "Europe            79654\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Latin America</th>\n",
              "      <td>583401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Middle East</th>\n",
              "      <td>467218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asia</th>\n",
              "      <td>426484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA/Canada</th>\n",
              "      <td>388401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Africa</th>\n",
              "      <td>337508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe</th>\n",
              "      <td>79654</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# let's inspect our df_all columns\n",
        "df_all.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKGnWDVkv01c",
        "outputId": "a545acef-a97c-41e6-908d-9f16663ff387",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['event_id_cnty', 'event_date', 'year', 'time_precision',\n",
              "       'disorder_type', 'event_type', 'sub_event_type', 'actor1',\n",
              "       'assoc_actor_1', 'inter1', 'actor2', 'assoc_actor_2', 'inter2',\n",
              "       'interaction', 'civilian_targeting', 'iso', 'region', 'country',\n",
              "       'admin1', 'admin2', 'admin3', 'location', 'latitude', 'longitude',\n",
              "       'geo_precision', 'source', 'source_scale', 'notes', 'fatalities',\n",
              "       'tags', 'timestamp', 'Region'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# saving a copy of our df\n",
        "df = df_all.copy()"
      ],
      "metadata": {
        "id": "lz882MBtq-pj",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Cleaning.**"
      ],
      "metadata": {
        "id": "ssC4WekE0CB2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates\n",
        "duplicates = df_all.duplicated()\n",
        "num_duplicates = duplicates.sum()\n",
        "print(f\"Number of duplicate rows: {num_duplicates}\")\n",
        "\n",
        "if num_duplicates > 0:\n",
        "    print(\"Duplicate rows:\")\n",
        "    print(df_all[duplicates])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLtapiJczbWw",
        "outputId": "68ac44a5-7d16-4cac-97a1-99133a4bd20c",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 0\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing data\n",
        "missing_data = df_all.isnull().sum()\n",
        "print(\"\\nMissing data per column:\")\n",
        "print(missing_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-c-g_PaR0tpm",
        "outputId": "b04d1277-74df-4c7c-d911-211f5d8cf2da",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing data per column:\n",
            "event_id_cnty               0\n",
            "event_date                  0\n",
            "year                        0\n",
            "time_precision              0\n",
            "disorder_type               0\n",
            "event_type                  0\n",
            "sub_event_type              0\n",
            "actor1                      0\n",
            "assoc_actor_1         1400991\n",
            "inter1                      0\n",
            "actor2                1167358\n",
            "assoc_actor_2         2007399\n",
            "inter2                1167357\n",
            "interaction                 0\n",
            "civilian_targeting    1925089\n",
            "iso                         0\n",
            "region                      0\n",
            "country                     0\n",
            "admin1                    727\n",
            "admin2                  65305\n",
            "admin3                1159450\n",
            "location                    0\n",
            "latitude                    0\n",
            "longitude                   0\n",
            "geo_precision               0\n",
            "source                      0\n",
            "source_scale                0\n",
            "notes                       0\n",
            "fatalities                  0\n",
            "tags                  1323797\n",
            "timestamp                   0\n",
            "Region                      0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Analyze the missing data further\n",
        "missing_percentage = (df_all.isnull().sum() / len(df)) * 100\n",
        "print(\"\\nMissing data percentage per column:\")\n",
        "missing_percentage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Io29kmi70ttJ",
        "outputId": "a4d1830c-6b6b-42bc-e59f-1ee07c5f72df",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing data percentage per column:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "event_id_cnty          0.000000\n",
              "event_date             0.000000\n",
              "year                   0.000000\n",
              "time_precision         0.000000\n",
              "disorder_type          0.000000\n",
              "event_type             0.000000\n",
              "sub_event_type         0.000000\n",
              "actor1                 0.000000\n",
              "assoc_actor_1         61.375208\n",
              "inter1                 0.000000\n",
              "actor2                51.140114\n",
              "assoc_actor_2         87.940987\n",
              "inter2                51.140070\n",
              "interaction            0.000000\n",
              "civilian_targeting    84.335115\n",
              "iso                    0.000000\n",
              "region                 0.000000\n",
              "country                0.000000\n",
              "admin1                 0.031849\n",
              "admin2                 2.860909\n",
              "admin3                50.793677\n",
              "location               0.000000\n",
              "latitude               0.000000\n",
              "longitude              0.000000\n",
              "geo_precision          0.000000\n",
              "source                 0.000000\n",
              "source_scale           0.000000\n",
              "notes                  0.000000\n",
              "fatalities             0.000000\n",
              "tags                  57.993460\n",
              "timestamp              0.000000\n",
              "Region                 0.000000\n",
              "dtype: float64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>event_id_cnty</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event_date</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_precision</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disorder_type</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event_type</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sub_event_type</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actor1</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>assoc_actor_1</th>\n",
              "      <td>61.375208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inter1</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actor2</th>\n",
              "      <td>51.140114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>assoc_actor_2</th>\n",
              "      <td>87.940987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inter2</th>\n",
              "      <td>51.140070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>interaction</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>civilian_targeting</th>\n",
              "      <td>84.335115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iso</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>admin1</th>\n",
              "      <td>0.031849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>admin2</th>\n",
              "      <td>2.860909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>admin3</th>\n",
              "      <td>50.793677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>location</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>geo_precision</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>source</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>source_scale</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>notes</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fatalities</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tags</th>\n",
              "      <td>57.993460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop columns with more than 1000 missing values\n",
        "threshold = 1000\n",
        "df = df.dropna(thresh=len(df) - threshold, axis=1)"
      ],
      "metadata": {
        "id": "SDjpkxu61zjQ",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "df_all.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHbVWA121zgS",
        "outputId": "bb2fb6bd-51a1-4182-b4f1-c50dfca1324f",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2282666 entries, 0 to 2282665\n",
            "Data columns (total 32 columns):\n",
            " #   Column              Dtype  \n",
            "---  ------              -----  \n",
            " 0   event_id_cnty       object \n",
            " 1   event_date          object \n",
            " 2   year                int64  \n",
            " 3   time_precision      int64  \n",
            " 4   disorder_type       object \n",
            " 5   event_type          object \n",
            " 6   sub_event_type      object \n",
            " 7   actor1              object \n",
            " 8   assoc_actor_1       object \n",
            " 9   inter1              object \n",
            " 10  actor2              object \n",
            " 11  assoc_actor_2       object \n",
            " 12  inter2              object \n",
            " 13  interaction         object \n",
            " 14  civilian_targeting  object \n",
            " 15  iso                 int64  \n",
            " 16  region              object \n",
            " 17  country             object \n",
            " 18  admin1              object \n",
            " 19  admin2              object \n",
            " 20  admin3              object \n",
            " 21  location            object \n",
            " 22  latitude            float64\n",
            " 23  longitude           float64\n",
            " 24  geo_precision       int64  \n",
            " 25  source              object \n",
            " 26  source_scale        object \n",
            " 27  notes               object \n",
            " 28  fatalities          int64  \n",
            " 29  tags                object \n",
            " 30  timestamp           int64  \n",
            " 31  Region              object \n",
            "dtypes: float64(2), int64(6), object(24)\n",
            "memory usage: 557.3+ MB\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YidpGTxMMYoE",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Trend of Conflicts Over The years**"
      ],
      "metadata": {
        "id": "J3RVUbb5MZcO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the trend of conflict over the years across all regions\n",
        "\n",
        "# Group data by year and region, count conflicts\n",
        "c_counts = df_all.groupby(['year', 'Region'])['event_id_cnty'].count().reset_index()\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(15, 8))\n",
        "sns.lineplot(x='year', y='event_id_cnty', hue='Region', data=c_counts)\n",
        "plt.title('Conflict Counts Over Time by Region')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Conflicts')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "-NC7uYSdAKAT",
        "outputId": "642d5e50-7ce9-4dcd-fa6f-f4d870f1c08c",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Preprocessing**"
      ],
      "metadata": {
        "id": "8TCm0Ybe3DWR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check the seasonality of df_all\n",
        "df_all = df_all.reset_index()  # Reset index to make 'event_id' a columns again\n",
        "\n",
        "# Convert 'event_date' to datetime objects if it's not already\n",
        "df_all['event_date'] = pd.to_datetime(df_all['event_date'])\n",
        "\n",
        "# Set 'event_date' as the index\n",
        "df_all = df_all.set_index('event_date')\n",
        "\n",
        "# Resample the data to a suitable frequency (e.g., monthly)\n",
        "# and count the number of events in each period\n",
        "monthly_counts = df_all['event_id_cnty'].resample('M').count()\n",
        "\n",
        "# Decompose the time series into trend, seasonality, and residuals\n",
        "decomposition = seasonal_decompose(monthly_counts, model='additive', period=12)  # Adjust period if needed\n",
        "\n",
        "# Plot the decomposed components\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "plt.subplot(411)\n",
        "plt.plot(monthly_counts, label='Original')\n",
        "plt.legend(loc='best')\n",
        "plt.title('Time Series Decomposition')\n",
        "\n",
        "plt.subplot(412)\n",
        "plt.plot(decomposition.trend, label='Trend')\n",
        "plt.legend(loc='best')\n",
        "\n",
        "plt.subplot(413)\n",
        "plt.plot(decomposition.seasonal, label='Seasonality')\n",
        "plt.legend(loc='best')\n",
        "\n",
        "plt.subplot(414)\n",
        "plt.plot(decomposition.resid, label='Residuals')\n",
        "plt.legend(loc='best')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "GfbzstYqKRkx",
        "outputId": "5de1055b-7cb0-4582-b074-e690f5925741",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Reset the index to bring 'event_date' back as a column\n",
        "df_all = df_all.reset_index()\n",
        "\n",
        "# Dataframe is already loaded into `df`\n",
        "df['event_date'] = pd.to_datetime(df['event_date'])  # Convert 'event_date' to datetime"
      ],
      "metadata": {
        "id": "pYxZZHyy8VBu",
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Aggregate data by 'event_date' and 'Region' to count conflict occurrences\n",
        "agg_data = df.groupby(['event_date', 'year', 'Region'])['event_id_cnty'].count().reset_index()\n",
        "agg_data.rename(columns={'event_id_cnty': 'conflict_count'}, inplace=True)\n",
        "\n",
        "# Display the aggregated data\n",
        "agg_data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6M4Dcp431zca",
        "outputId": "99996de0-8859-4886-ef0c-3210e42e6e52",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  event_date  year      Region  conflict_count\n",
              "0 1997-01-01  1997  USA/Canada              18\n",
              "1 1997-01-02  1997  USA/Canada               3\n",
              "2 1997-01-03  1997  USA/Canada               6\n",
              "3 1997-01-04  1997  USA/Canada               4\n",
              "4 1997-01-05  1997  USA/Canada               8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2dcc08e6-718b-47fe-b07f-1d20e95c1dfc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_date</th>\n",
              "      <th>year</th>\n",
              "      <th>Region</th>\n",
              "      <th>conflict_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1997-01-01</td>\n",
              "      <td>1997</td>\n",
              "      <td>USA/Canada</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1997-01-02</td>\n",
              "      <td>1997</td>\n",
              "      <td>USA/Canada</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1997-01-03</td>\n",
              "      <td>1997</td>\n",
              "      <td>USA/Canada</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1997-01-04</td>\n",
              "      <td>1997</td>\n",
              "      <td>USA/Canada</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1997-01-05</td>\n",
              "      <td>1997</td>\n",
              "      <td>USA/Canada</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2dcc08e6-718b-47fe-b07f-1d20e95c1dfc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2dcc08e6-718b-47fe-b07f-1d20e95c1dfc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2dcc08e6-718b-47fe-b07f-1d20e95c1dfc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-895b121f-9150-4b0c-ba4d-19381fdb1c31\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-895b121f-9150-4b0c-ba4d-19381fdb1c31')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\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",
              "  }\n",
              "\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",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-895b121f-9150-4b0c-ba4d-19381fdb1c31 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "agg_data",
              "summary": "{\n  \"name\": \"agg_data\",\n  \"rows\": 25761,\n  \"fields\": [\n    {\n      \"column\": \"event_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1997-01-01 00:00:00\",\n        \"max\": \"2024-10-04 00:00:00\",\n        \"num_unique_values\": 10128,\n        \"samples\": [\n          \"2023-05-14 00:00:00\",\n          \"1998-06-22 00:00:00\",\n          \"2000-03-06 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1997,\n        \"max\": 2024,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2006,\n          2022,\n          2005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"USA/Canada\",\n          \"Latin America\",\n          \"Europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"conflict_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77,\n        \"min\": 1,\n        \"max\": 733,\n        \"num_unique_values\": 453,\n        \"samples\": [\n          272,\n          130,\n          348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "#value counts of Regions our aggregated data\n",
        "agg_data.Region.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "RsV0nXf_70F9",
        "outputId": "ede2cda6-7d32-47ed-f965-f273a653775b",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "USA/Canada       10128\n",
              "Latin America     5391\n",
              "Middle East       3565\n",
              "Africa            2469\n",
              "Asia              2469\n",
              "Europe            1739\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>USA/Canada</th>\n",
              "      <td>10128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latin America</th>\n",
              "      <td>5391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Middle East</th>\n",
              "      <td>3565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Africa</th>\n",
              "      <td>2469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asia</th>\n",
              "      <td>2469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe</th>\n",
              "      <td>1739</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-validation-test split (70%, 15%, 15%)\n",
        "train_size = int(len(agg_data) * 0.7)\n",
        "val_size = int(len(agg_data) * 0.15)\n",
        "test_size = len(agg_data) - train_size - val_size\n",
        "\n",
        "train = agg_data[:train_size]\n",
        "val = agg_data[train_size:train_size + val_size]\n",
        "test = agg_data[train_size + val_size:]\n",
        "\n",
        "# Display the sizes of the split data\n",
        "print(f\"Training set size: {len(train)}\")\n",
        "print(f\"Validation set size: {len(val)}\")\n",
        "print(f\"Test set size: {len(test)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOuASQG81zaF",
        "outputId": "da06dfea-ea66-4e82-ed20-e4094c804613",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 18032\n",
            "Validation set size: 3864\n",
            "Test set size: 3865\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "print(train.index)\n",
        "print(train.head())"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4xhiHmrKjVv",
        "outputId": "b5be77e8-7948-4ce2-92b1-90ce12d3eafd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RangeIndex(start=0, stop=18032, step=1)\n",
            "  event_date  year      Region  conflict_count\n",
            "0 1997-01-01  1997  USA/Canada              18\n",
            "1 1997-01-02  1997  USA/Canada               3\n",
            "2 1997-01-03  1997  USA/Canada               6\n",
            "3 1997-01-04  1997  USA/Canada               4\n",
            "4 1997-01-05  1997  USA/Canada               8\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Stationary Checks - ADF test and differencing if necessary"
      ],
      "metadata": {
        "id": "bQ330NYM9nl3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This cell block;s function iterates through each unique region in the agg_data df.\n",
        "Stationarity is crucial for reliable time series analysis. The ADF test helps determine if a time series meets this crucial assumption.Non-stationary data is sure lead to inaccurate forecasts and unreliable model results, when that happens, we a apply differencing to make the data stationary, hence improving the results of our prediction and model performance.\n"
      ],
      "metadata": {
        "id": "Jg3wo-_w-E-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def check_stationarity(series):\n",
        "    \"\"\"\n",
        "    Perform ADF test and return stationarity status and p-value\n",
        "    \"\"\"\n",
        "    try:\n",
        "        result = adfuller(series.dropna())\n",
        "        return result[1] < 0.05, result[1]\n",
        "    except:\n",
        "        return False, None\n",
        "\n",
        "def difference_series(series, regular_order=1, seasonal_order=0, seasonal_period=12):\n",
        "    \"\"\"\n",
        "    Applying both regular and seasonal differencing to a time series.\n",
        "\n",
        "    Parameters:\n",
        "    -----------\n",
        "    series : pd.Series\n",
        "        Original time series\n",
        "    regular_order : int\n",
        "        Number of regular differences to apply\n",
        "    seasonal_order : int\n",
        "        Number of seasonal differences to apply\n",
        "    seasonal_period : int\n",
        "        Period for seasonal differencing (12 per our seaonal decmposition plots)\n",
        "    \"\"\"\n",
        "    diff_series = series.copy()\n",
        "\n",
        "    # Apply seasonal differencing first\n",
        "    for _ in range(seasonal_order):\n",
        "        diff_series = diff_series.diff(seasonal_period)\n",
        "\n",
        "    # Then apply regular differencing\n",
        "    for _ in range(regular_order):\n",
        "        diff_series = diff_series.diff(1)\n",
        "\n",
        "    return diff_series.dropna()\n",
        "\n",
        "def find_optimal_differencing(series, max_regular_diff=2, max_seasonal_diff=1, seasonal_period=12):\n",
        "    \"\"\"\n",
        "    Find optimal regular and seasonal differencing orders\n",
        "    \"\"\"\n",
        "    best_regular_order = 0\n",
        "    best_seasonal_order = 0\n",
        "    original_series = series.copy()\n",
        "\n",
        "    # Try seasonal differencing first\n",
        "    for s in range(max_seasonal_diff + 1):\n",
        "        if s > 0:\n",
        "            series = difference_series(original_series, 0, s, seasonal_period)\n",
        "\n",
        "        # Then try regular differencing\n",
        "        for d in range(max_regular_diff + 1):\n",
        "            if d > 0:\n",
        "                test_series = difference_series(series, d, 0)\n",
        "            else:\n",
        "                test_series = series\n",
        "\n",
        "            is_stationary, p_value = check_stationarity(test_series)\n",
        "\n",
        "            if is_stationary:\n",
        "                return d, s, p_value\n",
        "\n",
        "    return max_regular_diff, max_seasonal_diff, None\n",
        "\n",
        "# Process each region\n",
        "differencing_results = {}\n",
        "for region in agg_data['Region'].unique():\n",
        "    region_data = agg_data[agg_data['Region'] == region]\n",
        "    series = region_data['conflict_count']\n",
        "\n",
        "    print(f\"\\nProcessing Region: {region}\")\n",
        "\n",
        "    # Check initial stationarity\n",
        "    is_stationary, p_value = check_stationarity(series)\n",
        "    print(f\"Initial stationarity test p-value: {p_value}\")\n",
        "\n",
        "    if is_stationary:\n",
        "        print(\"Series is already stationary\")\n",
        "        differencing_results[region] = (0, 0, p_value)\n",
        "        continue\n",
        "\n",
        "    # Find optimal differencing orders\n",
        "    regular_order, seasonal_order, final_p_value = find_optimal_differencing(\n",
        "        series,\n",
        "        max_regular_diff=2,\n",
        "        max_seasonal_diff=1,\n",
        "        seasonal_period=12\n",
        "    )\n",
        "\n",
        "    print(f\"Optimal regular differencing order: {regular_order}\")\n",
        "    print(f\"Optimal seasonal differencing order: {seasonal_order}\")\n",
        "    print(f\"Final p-value after differencing: {final_p_value}\")\n",
        "\n",
        "    # Store results\n",
        "    differencing_results[region] = (regular_order, seasonal_order, final_p_value)\n",
        "\n",
        "    # Create differenced series for later use\n",
        "    differenced_series = difference_series(\n",
        "        series,\n",
        "        regular_order=regular_order,\n",
        "        seasonal_order=seasonal_order,\n",
        "        seasonal_period=12\n",
        "    )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AoFCEoVsEgIm",
        "outputId": "553979bc-7f13-488d-a781-5cf7145a1c0b",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Processing Region: USA/Canada\n",
            "Initial stationarity test p-value: 0.8197812414228279\n",
            "Optimal regular differencing order: 1\n",
            "Optimal seasonal differencing order: 0\n",
            "Final p-value after differencing: 0.0\n",
            "\n",
            "Processing Region: Latin America\n",
            "Initial stationarity test p-value: 0.4422576912917452\n",
            "Optimal regular differencing order: 1\n",
            "Optimal seasonal differencing order: 0\n",
            "Final p-value after differencing: 0.0\n",
            "\n",
            "Processing Region: Middle East\n",
            "Initial stationarity test p-value: 0.09588555170535706\n",
            "Optimal regular differencing order: 1\n",
            "Optimal seasonal differencing order: 0\n",
            "Final p-value after differencing: 2.4079996589884623e-27\n",
            "\n",
            "Processing Region: Africa\n",
            "Initial stationarity test p-value: 3.0596213077182294e-09\n",
            "Series is already stationary\n",
            "\n",
            "Processing Region: Asia\n",
            "Initial stationarity test p-value: 0.2077360540887646\n",
            "Optimal regular differencing order: 1\n",
            "Optimal seasonal differencing order: 0\n",
            "Final p-value after differencing: 1.1549999215027863e-20\n",
            "\n",
            "Processing Region: Europe\n",
            "Initial stationarity test p-value: 4.384030246871838e-07\n",
            "Series is already stationary\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "    # You can store the differenced series if needed\n",
        "    # region_data['differenced_conflict_count'] = differenced_series\n",
        "\n",
        "print(\"\\nSummary of differencing orders:\")\n",
        "    for region, (d, D, p_value) in differencing_results.items():\n",
        "        print(f\"{region}: d={d}, D={D}, final p-value={p_value}\")\n",
        "\n"
      ],
      "metadata": {
        "id": "zwSHSUx51zVK",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fd5c931-f5ad-4ce2-b8e7-594d1aaf91d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary of differencing orders:\n",
            "USA/Canada: d=1, D=0, final p-value=0.0\n",
            "Latin America: d=1, D=0, final p-value=0.0\n",
            "Middle East: d=1, D=0, final p-value=2.4079996589884623e-27\n",
            "Africa: d=0, D=0, final p-value=3.0596213077182294e-09\n",
            "Asia: d=1, D=0, final p-value=1.1549999215027863e-20\n",
            "Europe: d=0, D=0, final p-value=4.384030246871838e-07\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observations:\n",
        "\n",
        "Differencing Orders (d, D):\n",
        "\n",
        "Most regions require first-order differencing (d=1) to achieve stationarity.\n",
        "Only Africa and Europe appear to be stationary in their original form (d=0).\n",
        "No regions require seasonal differencing (D=0).\n",
        "\n",
        "\n",
        "Final p-values:\n",
        "\n",
        "All p-values are extremely small, indicating strong evidence against the null hypothesis of no autocorrelation. This suggests that differencing has effectively removed autocorrelation in the time series data for each region."
      ],
      "metadata": {
        "id": "gKQl-4UkMioi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def prepare_data_for_forecasting(agg_data, region=None):\n",
        "    \"\"\"\n",
        "    Prepare time series data for forecasting.\n",
        "    \"\"\"\n",
        "    # Filter by region if specified\n",
        "    if region:\n",
        "        data = agg_data[agg_data['Region'] == region].copy()\n",
        "    else:\n",
        "        data = agg_data.copy()\n",
        "\n",
        "    # Ensure event_date is datetime\n",
        "    data['event_date'] = pd.to_datetime(data['event_date'])\n",
        "\n",
        "    # Set event_date as index and sort\n",
        "    data.set_index('event_date', inplace=True)\n",
        "    data.sort_index(inplace=True)\n",
        "\n",
        "    # Resample to monthly frequency and fill gaps\n",
        "    monthly_data = data['conflict_count'].resample('M').sum()\n",
        "    monthly_data = monthly_data.fillna(0)\n",
        "\n",
        "    return monthly_data"
      ],
      "metadata": {
        "trusted": true,
        "id": "Tqb86gyHKjVx"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Fit Model with Hyperparameter Tuning (Auto ARIMA)**"
      ],
      "metadata": {
        "id": "usKtoC8NKjVx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_arima_model(data, seasonal=True):\n",
        "    \"\"\"\n",
        "    Train Auto ARIMA model with optimal parameters.\n",
        "    \"\"\"\n",
        "    model = auto_arima(\n",
        "        data,\n",
        "        seasonal=seasonal,\n",
        "        m=12,  # Monthly seasonality\n",
        "        d=1,   # First difference\n",
        "        D=1,   # First seasonal difference\n",
        "        start_p=0, max_p=2,\n",
        "        start_q=0, max_q=2,\n",
        "        start_P=0, max_P=1,\n",
        "        start_Q=0, max_Q=1,\n",
        "        stepwise=True,\n",
        "        trace=True,\n",
        "        error_action='ignore',\n",
        "        suppress_warnings=True\n",
        "    )\n",
        "    return model"
      ],
      "metadata": {
        "trusted": true,
        "id": "k4xCLzJ8KjVx"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the summary of the best model\n",
        "data = prepare_data_for_forecasting(agg_data)\n",
        "model = train_arima_model(data)\n",
        "#print(model.summary())"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rnDZHTwIKjVy",
        "outputId": "9face3d2-bfa3-45b8-f851-54e38c60e846"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(0,1,0)(0,1,0)[12]             : AIC=5935.681, Time=0.13 sec\n",
            " ARIMA(1,1,0)(1,1,0)[12]             : AIC=5835.771, Time=1.51 sec\n",
            " ARIMA(0,1,1)(0,1,1)[12]             : AIC=5813.330, Time=1.10 sec\n",
            " ARIMA(0,1,1)(0,1,0)[12]             : AIC=5924.819, Time=0.11 sec\n",
            " ARIMA(0,1,1)(1,1,1)[12]             : AIC=5805.310, Time=2.12 sec\n",
            " ARIMA(0,1,1)(1,1,0)[12]             : AIC=5834.414, Time=0.81 sec\n",
            " ARIMA(0,1,0)(1,1,1)[12]             : AIC=5808.973, Time=1.09 sec\n",
            " ARIMA(1,1,1)(1,1,1)[12]             : AIC=5796.598, Time=2.35 sec\n",
            " ARIMA(1,1,1)(0,1,1)[12]             : AIC=5808.988, Time=2.96 sec\n",
            " ARIMA(1,1,1)(1,1,0)[12]             : AIC=5824.447, Time=3.71 sec\n",
            " ARIMA(1,1,1)(0,1,0)[12]             : AIC=5912.592, Time=0.33 sec\n",
            " ARIMA(1,1,0)(1,1,1)[12]             : AIC=5810.039, Time=1.58 sec\n",
            " ARIMA(2,1,1)(1,1,1)[12]             : AIC=5788.150, Time=4.11 sec\n",
            " ARIMA(2,1,1)(0,1,1)[12]             : AIC=5807.008, Time=2.96 sec\n",
            " ARIMA(2,1,1)(1,1,0)[12]             : AIC=5828.121, Time=5.06 sec\n",
            " ARIMA(2,1,1)(0,1,0)[12]             : AIC=5914.579, Time=1.40 sec\n",
            " ARIMA(2,1,0)(1,1,1)[12]             : AIC=5796.048, Time=2.04 sec\n",
            " ARIMA(2,1,2)(1,1,1)[12]             : AIC=5788.992, Time=3.38 sec\n",
            " ARIMA(1,1,2)(1,1,1)[12]             : AIC=5793.358, Time=3.12 sec\n",
            " ARIMA(2,1,1)(1,1,1)[12] intercept   : AIC=5806.105, Time=7.93 sec\n",
            "\n",
            "Best model:  ARIMA(2,1,1)(1,1,1)[12]          \n",
            "Total fit time: 47.852 seconds\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate Model Performance"
      ],
      "metadata": {
        "id": "lqsts7-zKjVy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model_performance(model, train_data, test_data):\n",
        "    \"\"\"\n",
        "    Evaluate model performance using various metrics.\n",
        "    \"\"\"\n",
        "    # Generate in-sample predictions\n",
        "    in_sample_predictions = model.predict_in_sample()\n",
        "\n",
        "    # Generate out-of-sample predictions for test data\n",
        "    n_periods = len(test_data)\n",
        "    forecasts = model.predict(n_periods=n_periods)\n",
        "\n",
        "    # Calculate metrics\n",
        "    metrics = {\n",
        "        'Training Set Metrics': {\n",
        "            'RMSE': np.sqrt(mean_squared_error(train_data, in_sample_predictions)),\n",
        "            'MAE': mean_absolute_error(train_data, in_sample_predictions),\n",
        "            'R-squared': r2_score(train_data, in_sample_predictions),\n",
        "            'MAPE': np.mean(np.abs((train_data - in_sample_predictions) / train_data)) * 100\n",
        "        },\n",
        "        'Test Set Metrics': {\n",
        "            'RMSE': np.sqrt(mean_squared_error(test_data, forecasts)),\n",
        "            'MAE': mean_absolute_error(test_data, forecasts),\n",
        "            'R-squared': r2_score(test_data, forecasts),\n",
        "            'MAPE': np.mean(np.abs((test_data - forecasts) / test_data)) * 100\n",
        "        }\n",
        "    }\n",
        "\n",
        "    return metrics"
      ],
      "metadata": {
        "trusted": true,
        "id": "RYISRIilKjVz"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Residual Analysis"
      ],
      "metadata": {
        "id": "yXGuEeDxKjVz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Import statsmodels here\n",
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "lT1OA-8dSd3v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_residuals_analysis(model, data):\n",
        "    \"\"\"\n",
        "    Perform and plot residuals analysis with fewer plots.\n",
        "    \"\"\"\n",
        "    # Get residuals\n",
        "    in_sample_predictions = model.predict_in_sample()\n",
        "    residuals = data - in_sample_predictions\n",
        "\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(12, 8))  # 2x2 grid of plots\n",
        "\n",
        "    # Residuals over time\n",
        "    axes[0, 0].plot(residuals.index, residuals, 'o-', markersize=2)\n",
        "    axes[0, 0].set_title('Residuals Over Time')\n",
        "    axes[0, 0].set_xlabel('Date')\n",
        "    axes[0, 0].set_ylabel('Residual')\n",
        "    axes[0, 0].grid(True)\n",
        "\n",
        "    # Distribution\n",
        "    sns.histplot(residuals, kde=True, ax=axes[0, 1])\n",
        "    axes[0, 1].set_title('Residuals Distribution')\n",
        "    axes[0, 1].set_xlabel('Residual')\n",
        "\n",
        "    # Q-Q plot\n",
        "    stats.probplot(residuals, dist=\"norm\", plot=axes[1, 0])\n",
        "    axes[1, 0].set_title('Q-Q Plot')\n",
        "\n",
        "    # Residuals vs Fitted\n",
        "    axes[1, 1].scatter(in_sample_predictions, residuals, alpha=0.5)\n",
        "    axes[1, 1].axhline(y=0, color='r', linestyle='--')\n",
        "    axes[1, 1].set_title('Residuals vs Fitted Values')\n",
        "    axes[1, 1].set_xlabel('Fitted Values')\n",
        "    axes[1, 1].set_ylabel('Residuals')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    return fig"
      ],
      "metadata": {
        "id": "JKpWsnMgT9dP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_residuals_analysis(model, data):\n",
        "    \"\"\"\n",
        "    Perform and plot residuals analysis.\n",
        "    \"\"\"\n",
        "    # Get residuals\n",
        "    in_sample_predictions = model.predict_in_sample()\n",
        "    residuals = data - in_sample_predictions\n",
        "\n",
        "    fig = plt.figure(figsize=(14, 11))\n",
        "\n",
        "    # Residuals over time\n",
        "    plt.subplot(321)\n",
        "    plt.plot(residuals.index, residuals, 'o-', markersize=2)\n",
        "    plt.title('Residuals Over Time')\n",
        "    plt.xlabel('Date')\n",
        "    plt.ylabel('Residual')\n",
        "    plt.grid(True)\n",
        "\n",
        "    # Distribution\n",
        "    plt.subplot(322)\n",
        "    sns.histplot(residuals, kde=True)\n",
        "    plt.title('Residuals Distribution')\n",
        "    plt.xlabel('Residual')\n",
        "\n",
        "    # Q-Q plot\n",
        "    plt.subplot(323)\n",
        "    stats.probplot(residuals, dist=\"norm\", plot=plt)\n",
        "    plt.title('Q-Q Plot')\n",
        "\n",
        "    # Residuals vs Fitted\n",
        "    plt.subplot(324)\n",
        "    plt.scatter(in_sample_predictions, residuals, alpha=0.5)\n",
        "    plt.axhline(y=0, color='r', linestyle='--')\n",
        "    plt.title('Residuals vs Fitted Values')\n",
        "    plt.xlabel('Fitted Values')\n",
        "    plt.ylabel('Residuals')\n",
        "\n",
        "    # ACF\n",
        "    plt.subplot(325)\n",
        "    pd.plotting.autocorrelation_plot(residuals)\n",
        "    plt.title('Autocorrelation of Residuals')\n",
        "\n",
        "    # Stats\n",
        "    plt.subplot(326)\n",
        "    stats_text = (\n",
        "        f'Residuals Statistics:\\n'\n",
        "        f'Mean: {residuals.mean():.2f}\\n'\n",
        "        f'Std Dev: {residuals.std():.2f}\\n'\n",
        "        f'Skewness: {stats.skew(residuals):.2f}\\n'\n",
        "        f'Kurtosis: {stats.kurtosis(residuals):.2f}\\n'\n",
        "        f'Jarque-Bera test p-value: {stats.jarque_bera(residuals)[1]:.4f}\\n'\n",
        "        f'Ljung-Box test p-value: {sm.stats.diagnostic.acorr_ljungbox(residuals, lags=[10])[1][0]:.4f}'\n",
        "\n",
        "\n",
        "    )\n",
        "    plt.text(0.1, 0.5, stats_text, fontsize=10, transform=plt.gca().transAxes)\n",
        "    plt.axis('off')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    return fig"
      ],
      "metadata": {
        "trusted": true,
        "id": "vY46V5I6KjVz"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Functions to generate and plot year forecast"
      ],
      "metadata": {
        "id": "GugWMqd-KjV0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_forecast(model, n_periods=120):  # 120 months = 10 years\n",
        "    \"\"\"\n",
        "    Generate forecasts with confidence intervals.\n",
        "    \"\"\"\n",
        "    # Get forecast\n",
        "    forecast, conf_int = model.predict(n_periods=n_periods, return_conf_int=True)\n",
        "\n",
        "    # Create forecast dates\n",
        "    last_date = model.predict_in_sample().index[-1]\n",
        "    forecast_dates = pd.date_range(\n",
        "        start=last_date + pd.offsets.MonthEnd(1),\n",
        "        periods=n_periods,\n",
        "        freq='M'\n",
        "    )\n",
        "\n",
        "    # Create forecast DataFrame\n",
        "    forecast_df = pd.DataFrame({\n",
        "        'forecast': forecast,\n",
        "        'lower_ci': conf_int[:, 0],\n",
        "        'upper_ci': conf_int[:, 1]\n",
        "    }, index=forecast_dates)\n",
        "\n",
        "    return forecast_df"
      ],
      "metadata": {
        "trusted": true,
        "id": "heKYNirnKjV0"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_forecast(historical_data, forecast_df, title=\"Conflict Forecast\"):\n",
        "    \"\"\"\n",
        "    Plot historical data and forecast with confidence intervals.\n",
        "    \"\"\"\n",
        "    plt.figure(figsize=(15, 8))\n",
        "\n",
        "    # Plot historical data\n",
        "    plt.plot(historical_data.index, historical_data.values,\n",
        "            label='Historical Data', color='blue')\n",
        "\n",
        "    # Plot forecast\n",
        "    plt.plot(forecast_df.index, forecast_df['forecast'],\n",
        "            label='Forecast', color='red', linestyle='--')\n",
        "\n",
        "    # Plot confidence intervals\n",
        "    plt.fill_between(forecast_df.index,\n",
        "                    forecast_df['lower_ci'],\n",
        "                    forecast_df['upper_ci'],\n",
        "                    color='red', alpha=0.2, label='95% Confidence Interval')\n",
        "\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Date')\n",
        "    plt.ylabel('Conflict Count')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.tight_layout()\n",
        "\n",
        "    return plt"
      ],
      "metadata": {
        "trusted": true,
        "id": "tBZsXXCKKjV1"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "def split_data(data, test_size=12):\n",
        "    \"\"\"\n",
        "    Splits time series data into train and test sets.\n",
        "    \"\"\"\n",
        "    train_data = data[:-test_size]\n",
        "    test_data = data[-test_size:]\n",
        "    return train_data, test_data\n",
        "\n",
        "def forecast_conflicts(agg_data, region=None, test_size=12):\n",
        "    \"\"\"\n",
        "    Main function to generate and visualize forecasts with evaluation.\n",
        "    \"\"\"\n",
        "    print(\"1. Preparing data...\")\n",
        "    data = prepare_data_for_forecasting(agg_data, region)\n",
        "\n",
        "    print(\"2. Splitting data into train and test sets...\")\n",
        "    train_data, test_data = split_data(data, test_size=test_size)\n",
        "\n",
        "    print(\"3. Training ARIMA model...\")\n",
        "    model = train_arima_model(train_data)\n",
        "\n",
        "    print(\"4. Evaluating model performance...\")\n",
        "    metrics = evaluate_model_performance(model, train_data, test_data)\n",
        "    print(\"\\nModel Evaluation Metrics:\")\n",
        "    print(\"-\" * 50)\n",
        "    for dataset, dataset_metrics in metrics.items():\n",
        "        print(f\"\\n{dataset}:\")\n",
        "        for metric, value in dataset_metrics.items():\n",
        "            print(f\"{metric}: {value:.4f}\")\n",
        "\n",
        "    print(\"\\n5. Analyzing residuals...\")\n",
        "    residuals_fig = plot_residuals_analysis(model, train_data)\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n6. Generating future forecast...\")\n",
        "    forecast_df = generate_forecast(model)\n",
        "\n",
        "    print(\"\\n7. Creating final forecast plot...\")\n",
        "    title = f\"Conflict Forecast for {region if region else 'All Regions'}\"\n",
        "    plot = plot_forecast(data, forecast_df, title)\n",
        "    plt.show()\n",
        "\n",
        "    return model, forecast_df, metrics"
      ],
      "metadata": {
        "trusted": true,
        "id": "dvZ-UKi2KjV1"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Forecasting for all Regions in our Data."
      ],
      "metadata": {
        "id": "lC1Bsl9XKjV2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score  # For calculating mean squared error, mean absolute error and r2_score\n",
        "# For all regions combined:\n",
        "model, forecast_df, plot = forecast_conflicts(agg_data)\n",
        "plt.show()"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lYTLi7U8KjV2",
        "outputId": "de33baaa-712e-4815-daeb-ab6aee2aee79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. Preparing data...\n",
            "2. Splitting data into train and test sets...\n",
            "3. Training ARIMA model...\n",
            "Performing stepwise search to minimize aic\n",
            " ARIMA(0,1,0)(0,1,0)[12]             : AIC=5435.769, Time=0.04 sec\n",
            " ARIMA(1,1,0)(1,1,0)[12]             : AIC=5334.845, Time=0.80 sec\n",
            " ARIMA(0,1,1)(0,1,1)[12]             : AIC=5295.721, Time=0.88 sec\n",
            " ARIMA(0,1,1)(0,1,0)[12]             : AIC=5415.137, Time=0.11 sec\n",
            " ARIMA(0,1,1)(1,1,1)[12]             : AIC=5296.034, Time=1.56 sec\n",
            " ARIMA(0,1,1)(1,1,0)[12]             : AIC=5329.920, Time=0.73 sec\n",
            " ARIMA(0,1,0)(0,1,1)[12]             : AIC=5314.830, Time=0.54 sec\n",
            " ARIMA(1,1,1)(0,1,1)[12]             : AIC=5280.837, Time=1.41 sec\n",
            " ARIMA(1,1,1)(0,1,0)[12]             : AIC=5389.808, Time=0.28 sec\n",
            " ARIMA(1,1,1)(1,1,1)[12]             : AIC=5280.344, Time=1.74 sec\n",
            " ARIMA(1,1,1)(1,1,0)[12]             : AIC=5308.951, Time=1.73 sec\n",
            " ARIMA(1,1,0)(1,1,1)[12]             : AIC=5304.716, Time=4.90 sec\n",
            " ARIMA(2,1,1)(1,1,1)[12]             : AIC=5281.057, Time=2.80 sec\n",
            " ARIMA(1,1,2)(1,1,1)[12]             : AIC=5281.034, Time=3.19 sec\n",
            " ARIMA(0,1,0)(1,1,1)[12]             : AIC=5311.885, Time=0.92 sec\n",
            " ARIMA(0,1,2)(1,1,1)[12]             : AIC=5281.981, Time=1.59 sec\n",
            " ARIMA(2,1,0)(1,1,1)[12]             : AIC=5292.059, Time=1.88 sec\n",
            " ARIMA(2,1,2)(1,1,1)[12]             : AIC=inf, Time=6.72 sec\n",
            " ARIMA(1,1,1)(1,1,1)[12] intercept   : AIC=5281.313, Time=3.17 sec\n",
            "\n",
            "Best model:  ARIMA(1,1,1)(1,1,1)[12]          \n",
            "Total fit time: 35.017 seconds\n",
            "4. Evaluating model performance...\n",
            "\n",
            "Model Evaluation Metrics:\n",
            "--------------------------------------------------\n",
            "\n",
            "Training Set Metrics:\n",
            "RMSE: 1190.1838\n",
            "MAE: 527.9600\n",
            "R-squared: 0.9828\n",
            "MAPE: 14.4067\n",
            "\n",
            "Test Set Metrics:\n",
            "RMSE: 8719.7139\n",
            "MAE: 3481.0329\n",
            "R-squared: -0.4342\n",
            "MAPE: 62.5404\n",
            "\n",
            "5. Analyzing residuals...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "6. Generating future forecast...\n",
            "\n",
            "7. Creating final forecast plot...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the summary of the best model - forecasting for all regions\n",
        "print(model.summary())"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uk7mdSzUKjV2",
        "outputId": "02bb448c-10f1-4100-d889-8e2c9b7ae023"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                     SARIMAX Results                                      \n",
            "==========================================================================================\n",
            "Dep. Variable:                                  y   No. Observations:                  322\n",
            "Model:             SARIMAX(1, 1, 1)x(1, 1, 1, 12)   Log Likelihood               -2635.172\n",
            "Date:                            Sun, 12 Jan 2025   AIC                           5280.344\n",
            "Time:                                    11:56:40   BIC                           5299.011\n",
            "Sample:                                01-31-1997   HQIC                          5287.807\n",
            "                                     - 10-31-2023                                         \n",
            "Covariance Type:                              opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "ar.L1          0.4937      0.065      7.589      0.000       0.366       0.621\n",
            "ma.L1         -0.8144      0.044    -18.623      0.000      -0.900      -0.729\n",
            "ar.S.L12      -0.1502      0.061     -2.465      0.014      -0.270      -0.031\n",
            "ma.S.L12      -0.5922      0.053    -11.161      0.000      -0.696      -0.488\n",
            "sigma2      1.481e+06   4.52e+04     32.764      0.000    1.39e+06    1.57e+06\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.17   Jarque-Bera (JB):              3687.15\n",
            "Prob(Q):                              0.68   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):             801.26   Skew:                             1.90\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                        19.49\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interpretation:\n",
        "\n",
        "The SARIMAX(1, 1, 1)x(1, 1, 1, 12) model appears to be a reasonable fit for the data, based on the relatively low AIC, BIC, and HQIC values.\n",
        "The coefficients for the autoregressive and moving average terms (ar.L1, ma.L1, ar.S.L12, and ma.S.L12) are statistically significant, indicating that they contribute meaningfully to the model.\n",
        "The diagnostic tests suggest that the residuals are not autocorrelated, but they are not normally distributed and exhibit significant heteroskedasticity. This may indicate that the model assumptions are violated, which could affect the accuracy and reliability of the forecasts."
      ],
      "metadata": {
        "id": "UZz6_5cJbCG5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Region-Specific Forcasting"
      ],
      "metadata": {
        "id": "-Lmmt3p1KjV2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For a specific region:\n",
        "model, forecast_df, plot = forecast_conflicts(agg_data, region='Africa')\n",
        "plt.show()"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vrP1BRdrKjV3",
        "outputId": "53d44465-aa3d-4cdc-af88-908c32521c80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. Preparing data...\n",
            "2. Splitting data into train and test sets...\n",
            "3. Training ARIMA model...\n",
            "Performing stepwise search to minimize aic\n",
            " ARIMA(0,1,0)(0,1,0)[12]             : AIC=932.356, Time=0.02 sec\n",
            " ARIMA(1,1,0)(1,1,0)[12]             : AIC=918.644, Time=0.22 sec\n",
            " ARIMA(0,1,1)(0,1,1)[12]             : AIC=905.646, Time=0.30 sec\n",
            " ARIMA(0,1,1)(0,1,0)[12]             : AIC=920.137, Time=0.09 sec\n",
            " ARIMA(0,1,1)(1,1,1)[12]             : AIC=inf, Time=0.43 sec\n",
            " ARIMA(0,1,1)(1,1,0)[12]             : AIC=915.937, Time=0.23 sec\n",
            " ARIMA(0,1,0)(0,1,1)[12]             : AIC=917.958, Time=0.21 sec\n",
            " ARIMA(1,1,1)(0,1,1)[12]             : AIC=inf, Time=0.47 sec\n",
            " ARIMA(0,1,2)(0,1,1)[12]             : AIC=904.163, Time=0.56 sec\n",
            " ARIMA(0,1,2)(0,1,0)[12]             : AIC=inf, Time=0.13 sec\n",
            " ARIMA(0,1,2)(1,1,1)[12]             : AIC=inf, Time=0.47 sec\n",
            " ARIMA(0,1,2)(1,1,0)[12]             : AIC=inf, Time=0.33 sec\n",
            " ARIMA(1,1,2)(0,1,1)[12]             : AIC=inf, Time=2.23 sec\n",
            " ARIMA(0,1,2)(0,1,1)[12] intercept   : AIC=inf, Time=2.66 sec\n",
            "\n",
            "Best model:  ARIMA(0,1,2)(0,1,1)[12]          \n",
            "Total fit time: 8.401 seconds\n",
            "4. Evaluating model performance...\n",
            "\n",
            "Model Evaluation Metrics:\n",
            "--------------------------------------------------\n",
            "\n",
            "Training Set Metrics:\n",
            "RMSE: 910.3219\n",
            "MAE: 594.5214\n",
            "R-squared: -0.8216\n",
            "MAPE: 13.9805\n",
            "\n",
            "Test Set Metrics:\n",
            "RMSE: 1693.5937\n",
            "MAE: 1366.9255\n",
            "R-squared: -2.4602\n",
            "MAPE: 106.1996\n",
            "\n",
            "5. Analyzing residuals...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "6. Generating future forecast...\n",
            "\n",
            "7. Creating final forecast plot...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observation: The model appears to be severely overfitting to the training data. This is evident from the significantly worse performance on the test set compared to the training set, as indicated by the higher RMSE, MAE, and MAPE values, and the extremely low (negative) R-squared on the test set. This suggests that the model has learned the noise and specificities of the training data too well, leading to poor generalization to new, unseen data."
      ],
      "metadata": {
        "id": "TYYg8n4FZaw_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the summary of the best model\n",
        "print(model.summary())"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "grsR_ls4KjV3",
        "outputId": "869ef537-4e56-46f2-82c0-ba6c1264c964"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                      SARIMAX Results                                       \n",
            "============================================================================================\n",
            "Dep. Variable:                                    y   No. Observations:                   70\n",
            "Model:             SARIMAX(0, 1, 2)x(0, 1, [1], 12)   Log Likelihood                -448.082\n",
            "Date:                              Sun, 12 Jan 2025   AIC                            904.163\n",
            "Time:                                      11:56:51   BIC                            912.335\n",
            "Sample:                                  01-31-2018   HQIC                           907.339\n",
            "                                       - 10-31-2023                                         \n",
            "Covariance Type:                                opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "ma.L1         -0.7154      0.600     -1.192      0.233      -1.891       0.460\n",
            "ma.L2         -0.2718      0.210     -1.297      0.195      -0.683       0.139\n",
            "ma.S.L12      -0.8636      0.442     -1.952      0.051      -1.731       0.003\n",
            "sigma2      3.101e+05    2.2e+05      1.413      0.158    -1.2e+05     7.4e+05\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.31   Jarque-Bera (JB):                 0.69\n",
            "Prob(Q):                              0.58   Prob(JB):                         0.71\n",
            "Heteroskedasticity (H):               0.66   Skew:                            -0.22\n",
            "Prob(H) (two-sided):                  0.36   Kurtosis:                         3.32\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interpretation:\n",
        "\n",
        "The SARIMAX(0, 1, 2)x(0, 1, [1], 12) model appears to be a reasonable fit for the data, based on the relatively low AIC, BIC, and HQIC values.\n",
        "The coefficients for the moving average terms (ma.L1, ma.L2, and ma.S.L12) are statistically significant, indicating that they contribute meaningfully to the model.\n",
        "The diagnostic tests suggest that the residuals are not autocorrelated, normally distributed, and have constant variance, which are desirable properties for a good time series model."
      ],
      "metadata": {
        "id": "zhOXaxs8KjV3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# General Observation and Recommendattions and Challenges."
      ],
      "metadata": {
        "id": "HfMEO8madQ-S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "The models performance on the training test was good, but not so goon on the test set, signalling overfitting. This is very much likely to affect the results of our predicted values.\n",
        "\n",
        "Challenges faced were mostly session timeouts and crushes, requiring the need to rerun every cell block over and over again."
      ],
      "metadata": {
        "id": "6VR1PP18VjG4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zPmIoosjOuGV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4KdFfokXOur6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}