project 67 ARIMA.ipynb

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        "id": "ZkMgCidCUYEW"
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      "source": [
        "# ARIMA  =  Auto regressive integrated moving average\n",
        "\n",
        "import pandas as pd\n",
        "from statsmodels.tsa.arima.model import ARIMA"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('AB-de-Villiers-All-International-Cricket-Centuries.csv')\n",
        "data.head()"
      ],
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              "   No. Score       Against  Position  Innings  \\\n",
              "0    1   109       England         1        3   \n",
              "1    2   178   West Indies         2        2   \n",
              "2    3   114   West Indies         1        1   \n",
              "3    4   146   West Indies         1        1   \n",
              "4    5   107      Zimbabwe         4        1   \n",
              "\n",
              "                                   Venue Ground       Date Result  \n",
              "0             SuperSport Park, Centurion   Home  21-Jan-05  Drawn  \n",
              "1            Kensington Oval, Bridgetown   Away  21-Apr-05    Won  \n",
              "2   Antigua Recreation Ground, St John's   Away  29-Apr-05  Drawn  \n",
              "3  National Cricket Stadium, St George's   Away  10-Apr-07    Won  \n",
              "4             Harare Sports Club, Harare   Away  26-Aug-07    Won  "
            ],
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              "      <th></th>\n",
              "      <th>No.</th>\n",
              "      <th>Score</th>\n",
              "      <th>Against</th>\n",
              "      <th>Position</th>\n",
              "      <th>Innings</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Ground</th>\n",
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              "      <td>1</td>\n",
              "      <td>109</td>\n",
              "      <td>England</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>SuperSport Park, Centurion</td>\n",
              "      <td>Home</td>\n",
              "      <td>21-Jan-05</td>\n",
              "      <td>Drawn</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>178</td>\n",
              "      <td>West Indies</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Kensington Oval, Bridgetown</td>\n",
              "      <td>Away</td>\n",
              "      <td>21-Apr-05</td>\n",
              "      <td>Won</td>\n",
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              "      <td>West Indies</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Antigua Recreation Ground, St John's</td>\n",
              "      <td>Away</td>\n",
              "      <td>29-Apr-05</td>\n",
              "      <td>Drawn</td>\n",
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              "      <th>3</th>\n",
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              "      <td>146</td>\n",
              "      <td>West Indies</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>National Cricket Stadium, St George's</td>\n",
              "      <td>Away</td>\n",
              "      <td>10-Apr-07</td>\n",
              "      <td>Won</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>107</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Harare Sports Club, Harare</td>\n",
              "      <td>Away</td>\n",
              "      <td>26-Aug-07</td>\n",
              "      <td>Won</td>\n",
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 47,\n  \"fields\": [\n    {\n      \"column\": \"No.\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 1,\n        \"max\": 47,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          28,\n          40,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"106*\",\n          \"114*\",\n          \"174\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Against\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \" New Zealand\",\n          \" West Indies\",\n          \" Australia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Position\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Innings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Venue\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"Green Park Stadium, Kanpur\",\n          \"Senwes Park, Potchefstroom\",\n          \"SuperSport Park, Centurion,\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ground\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Home\",\n          \"Away\",\n          \"Neutral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"03-Feb-13\",\n          \"18-Jan-15\",\n          \"30-Nov-12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Result\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Won\",\n          \"No result\",\n          \"Lost\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_series_data = pd.read_csv('AB-de-Villiers-All-International-Cricket-Centuries.csv')\n",
        "\n",
        "print(time_series_data.columns)\n",
        "\n",
        "\n",
        "if 'Date' in time_series_data.columns:\n",
        "    time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])\n",
        "    time_series_data.set_index('Date', inplace=True)\n",
        "else:\n",
        "    print(\"Warning: 'Date' column not found in the dataset.\")\n",
        "    # Handle the absence of 'Date' column, e.g., by creating it or adapting your analysis.\n",
        "    # For example, if you have a column representing time, you can rename it to 'Date'\n",
        "    # time_series_data.rename(columns={'your_time_column': 'Date'}, inplace=True)\n",
        "    # time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])\n",
        "    # time_series_data.set_index('Date', inplace=True)"
      ],
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        "colab": {
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        },
        "id": "FR8Re4_PNDZQ",
        "outputId": "2d69383d-2e5c-4c20-cfbc-9865a905ea4a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['No.', 'Score', 'Against', 'Position', 'Innings', 'Venue', 'Ground',\n",
            "       'Date', 'Result'],\n",
            "      dtype='object')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-185596861c11>:7: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Score' column to numeric, handling non-numeric values\n",
        "time_series_data['Score'] = pd.to_numeric(time_series_data['Score'], errors='coerce')\n",
        "\n",
        "# Drop rows with missing 'Score' values after conversion\n",
        "time_series_data.dropna(subset=['Score'], inplace=True)\n",
        "\n",
        "model = ARIMA(time_series_data['Score'], order=(5, 1, 0))  # (p,d,q)\n",
        "model_fit = model.fit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "2kYup_hWSQl2",
        "outputId": "f57f99e2-872d-4774-e6ce-80f26c956eed"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model_fit.forecast(steps=10)\n",
        "print(predictions)"
      ],
      "metadata": {
        "id": "kEVsWCV8p1Ko",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acc52f87-71d5-419d-d40c-5a8ca1e8bdc4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26    148.614788\n",
            "27    147.562787\n",
            "28    130.597981\n",
            "29    136.637408\n",
            "30    145.742942\n",
            "31    150.924190\n",
            "32    144.657408\n",
            "33    140.668877\n",
            "34    139.283999\n",
            "35    143.259576\n",
            "Name: predicted_mean, dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
            "  return get_prediction_index(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:836: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
            "  return get_prediction_index(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Score' column to numeric, handling non-numeric values\n",
        "time_series_data['Result'] = pd.to_numeric(time_series_data['Score'], errors='coerce')\n",
        "\n",
        "# Drop rows with missing 'Score' values after conversion\n",
        "time_series_data.dropna(subset=['Result'], inplace=True)\n",
        "\n",
        "model = ARIMA(time_series_data['Result'], order=(5, 1, 0))  # (p,d,q)\n",
        "model_fit = model.fit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JJ-HfOrSWj0",
        "outputId": "995514fa-a448-40e6-ab35-74ec5eaf3aa6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model_fit.forecast(steps=10)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fGL8XlAScvz",
        "outputId": "970182f9-07e1-44e0-b0c6-4ea213aef89e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26    148.614788\n",
            "27    147.562787\n",
            "28    130.597981\n",
            "29    136.637408\n",
            "30    145.742942\n",
            "31    150.924190\n",
            "32    144.657408\n",
            "33    140.668877\n",
            "34    139.283999\n",
            "35    143.259576\n",
            "Name: predicted_mean, dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
            "  return get_prediction_index(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:836: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
            "  return get_prediction_index(\n"
          ]
        }
      ]
    }
  ]
}