TimeSeries_SP500_Predict.ipynb

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        "Time-Series Project: Predicting The Stock Market With Machine Learning And Python\n",
        "\n"
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      "source": [
        "import yfinance as yf"
      ]
    },
    {
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      "source": [
        "sp500 = yf.Ticker(\"^GSPC\")"
      ],
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        "id": "5fWvOEyaFA-f"
      },
      "execution_count": null,
      "outputs": []
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    {
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      "source": [
        "sp500 = sp500.history(period=\"max\")"
      ],
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        "id": "8GKpNTZcFi1b"
      },
      "execution_count": null,
      "outputs": []
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        "sp500"
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              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
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              "\n",
              "                               Volume  Dividends  Stock Splits  \n",
              "Date                                                            \n",
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              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0b44e616-6057-4ddf-bc10-b039cdf45e68 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sp500');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 24191,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1927-12-30 00:00:00-05:00\",\n        \"max\": \"2024-04-19 00:00:00-04:00\",\n        \"num_unique_values\": 24191,\n        \"samples\": [\n          \"2023-07-27 00:00:00-04:00\",\n          \"1958-02-19 00:00:00-05:00\",\n          \"2013-04-30 00:00:00-04:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1014.3741302391886,\n        \"min\": 0.0,\n        \"max\": 5257.97021484375,\n        \"num_unique_values\": 13559,\n        \"samples\": [\n          1314.449951171875,\n          1380.5799560546875,\n          330.1400146484375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1008.8432306152081,\n        \"min\": 4.400000095367432,\n        \"max\": 5264.85009765625,\n        \"num_unique_values\": 16672,\n        \"samples\": [\n          1467.93994140625,\n          1852.6500244140625,\n          4422.18017578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 997.3243294129372,\n        \"min\": 4.400000095367432,\n        \"max\": 5245.81982421875,\n        \"num_unique_values\": 16670,\n        \"samples\": [\n          2441.419921875,\n          1409.1099853515625,\n          843.2100219726562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1003.4496630965386,\n        \"min\": 4.400000095367432,\n        \"max\": 5254.35009765625,\n        \"num_unique_values\": 16670,\n        \"samples\": [\n          2444.239990234375,\n          1410.43994140625,\n          843.4299926757812\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1612241667,\n        \"min\": 0,\n        \"max\": 11456230000,\n        \"num_unique_values\": 12918,\n        \"samples\": [\n          1304400000,\n          3332510000,\n          194590000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dividends\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Splits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FziY5tdyGUgC",
        "outputId": "743180ec-309f-4a15-b905-4a66b1cd5305"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['1927-12-30 00:00:00-05:00', '1928-01-03 00:00:00-05:00',\n",
              "               '1928-01-04 00:00:00-05:00', '1928-01-05 00:00:00-05:00',\n",
              "               '1928-01-06 00:00:00-05:00', '1928-01-09 00:00:00-05:00',\n",
              "               '1928-01-10 00:00:00-05:00', '1928-01-11 00:00:00-05:00',\n",
              "               '1928-01-12 00:00:00-05:00', '1928-01-13 00:00:00-05:00',\n",
              "               ...\n",
              "               '2024-04-08 00:00:00-04:00', '2024-04-09 00:00:00-04:00',\n",
              "               '2024-04-10 00:00:00-04:00', '2024-04-11 00:00:00-04:00',\n",
              "               '2024-04-12 00:00:00-04:00', '2024-04-15 00:00:00-04:00',\n",
              "               '2024-04-16 00:00:00-04:00', '2024-04-17 00:00:00-04:00',\n",
              "               '2024-04-18 00:00:00-04:00', '2024-04-19 00:00:00-04:00'],\n",
              "              dtype='datetime64[ns, America/New_York]', name='Date', length=24191, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500.plot.line(y=\"Close\", use_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "mDiqsTKCGpZX",
        "outputId": "5586f567-c8cd-483c-afd1-26f467282dd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del sp500[\"Dividends\"]\n",
        "del sp500[\"Stock Splits\"]"
      ],
      "metadata": {
        "id": "MrH-QSY-G90i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500[\"Tomorrow\"] = sp500[\"Close\"].shift(-1)"
      ],
      "metadata": {
        "id": "vMZh9ZdTHNVd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "Bsu_XpTAHpPs",
        "outputId": "fe0722e8-9834-41eb-9eee-719c9ef33fd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1927-12-30 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "1928-01-03 00:00:00-05:00    17.760000    17.760000    17.760000    17.760000   \n",
              "1928-01-04 00:00:00-05:00    17.719999    17.719999    17.719999    17.719999   \n",
              "1928-01-05 00:00:00-05:00    17.549999    17.549999    17.549999    17.549999   \n",
              "1928-01-06 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "...                                ...          ...          ...          ...   \n",
              "2024-04-15 00:00:00-04:00  5149.669922  5168.430176  5052.470215  5061.819824   \n",
              "2024-04-16 00:00:00-04:00  5064.589844  5079.839844  5039.830078  5051.410156   \n",
              "2024-04-17 00:00:00-04:00  5068.970215  5077.959961  5007.250000  5022.209961   \n",
              "2024-04-18 00:00:00-04:00  5031.520020  5056.660156  5001.890137  5011.120117   \n",
              "2024-04-19 00:00:00-04:00  5005.439941  5019.020020  4953.560059  4967.229980   \n",
              "\n",
              "                               Volume     Tomorrow  \n",
              "Date                                                \n",
              "1927-12-30 00:00:00-05:00           0    17.760000  \n",
              "1928-01-03 00:00:00-05:00           0    17.719999  \n",
              "1928-01-04 00:00:00-05:00           0    17.549999  \n",
              "1928-01-05 00:00:00-05:00           0    17.660000  \n",
              "1928-01-06 00:00:00-05:00           0    17.500000  \n",
              "...                               ...          ...  \n",
              "2024-04-15 00:00:00-04:00  3950210000  5051.410156  \n",
              "2024-04-16 00:00:00-04:00  4006200000  5022.209961  \n",
              "2024-04-17 00:00:00-04:00  3596130000  5011.120117  \n",
              "2024-04-18 00:00:00-04:00  3619760000  4967.229980  \n",
              "2024-04-19 00:00:00-04:00  2733302000          NaN  \n",
              "\n",
              "[24191 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a592bfa2-1e28-45a1-86d2-4fd768aa1c87\" 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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Tomorrow</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1927-12-30 00:00:00-05:00</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.760000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-03 00:00:00-05:00</th>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.719999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-04 00:00:00-05:00</th>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>0</td>\n",
              "      <td>17.549999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-05 00:00:00-05:00</th>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>0</td>\n",
              "      <td>17.660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-06 00:00:00-05:00</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-15 00:00:00-04:00</th>\n",
              "      <td>5149.669922</td>\n",
              "      <td>5168.430176</td>\n",
              "      <td>5052.470215</td>\n",
              "      <td>5061.819824</td>\n",
              "      <td>3950210000</td>\n",
              "      <td>5051.410156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-16 00:00:00-04:00</th>\n",
              "      <td>5064.589844</td>\n",
              "      <td>5079.839844</td>\n",
              "      <td>5039.830078</td>\n",
              "      <td>5051.410156</td>\n",
              "      <td>4006200000</td>\n",
              "      <td>5022.209961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-17 00:00:00-04:00</th>\n",
              "      <td>5068.970215</td>\n",
              "      <td>5077.959961</td>\n",
              "      <td>5007.250000</td>\n",
              "      <td>5022.209961</td>\n",
              "      <td>3596130000</td>\n",
              "      <td>5011.120117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-18 00:00:00-04:00</th>\n",
              "      <td>5031.520020</td>\n",
              "      <td>5056.660156</td>\n",
              "      <td>5001.890137</td>\n",
              "      <td>5011.120117</td>\n",
              "      <td>3619760000</td>\n",
              "      <td>4967.229980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-19 00:00:00-04:00</th>\n",
              "      <td>5005.439941</td>\n",
              "      <td>5019.020020</td>\n",
              "      <td>4953.560059</td>\n",
              "      <td>4967.229980</td>\n",
              "      <td>2733302000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24191 rows × 6 columns</p>\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 24191,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1927-12-30 00:00:00-05:00\",\n        \"max\": \"2024-04-19 00:00:00-04:00\",\n        \"num_unique_values\": 24191,\n        \"samples\": [\n          \"2023-07-27 00:00:00-04:00\",\n          \"1958-02-19 00:00:00-05:00\",\n          \"2013-04-30 00:00:00-04:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1014.3741302391886,\n        \"min\": 0.0,\n        \"max\": 5257.97021484375,\n        \"num_unique_values\": 13559,\n        \"samples\": [\n          1314.449951171875,\n          1380.5799560546875,\n          330.1400146484375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1008.8432306152081,\n        \"min\": 4.400000095367432,\n        \"max\": 5264.85009765625,\n        \"num_unique_values\": 16672,\n        \"samples\": [\n          1467.93994140625,\n          1852.6500244140625,\n          4422.18017578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 997.3243294129372,\n        \"min\": 4.400000095367432,\n        \"max\": 5245.81982421875,\n        \"num_unique_values\": 16670,\n        \"samples\": [\n          2441.419921875,\n          1409.1099853515625,\n          843.2100219726562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1003.4496630965386,\n        \"min\": 4.400000095367432,\n        \"max\": 5254.35009765625,\n        \"num_unique_values\": 16670,\n        \"samples\": [\n          2444.239990234375,\n          1410.43994140625,\n          843.4299926757812\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1612241667,\n        \"min\": 0,\n        \"max\": 11456230000,\n        \"num_unique_values\": 12918,\n        \"samples\": [\n          1304400000,\n          3332510000,\n          194590000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tomorrow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1003.4628709043388,\n        \"min\": 4.400000095367432,\n        \"max\": 5254.35009765625,\n        \"num_unique_values\": 16670,\n        \"samples\": [\n          2444.239990234375,\n          1410.43994140625,\n          843.4299926757812\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500[\"Target\"] = (sp500[\"Tomorrow\"] > sp500[\"Close\"]).astype(int) #converted to integer"
      ],
      "metadata": {
        "id": "zYkd7rCfHo9U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 996
        },
        "id": "5g9_PJisIqAi",
        "outputId": "b3a81cc4-2f30-4ea2-b37f-9eefb83de040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1927-12-30 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "1928-01-03 00:00:00-05:00    17.760000    17.760000    17.760000    17.760000   \n",
              "1928-01-04 00:00:00-05:00    17.719999    17.719999    17.719999    17.719999   \n",
              "1928-01-05 00:00:00-05:00    17.549999    17.549999    17.549999    17.549999   \n",
              "1928-01-06 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "...                                ...          ...          ...          ...   \n",
              "2024-04-15 00:00:00-04:00  5149.669922  5168.430176  5052.470215  5061.819824   \n",
              "2024-04-16 00:00:00-04:00  5064.589844  5079.839844  5039.830078  5051.410156   \n",
              "2024-04-17 00:00:00-04:00  5068.970215  5077.959961  5007.250000  5022.209961   \n",
              "2024-04-18 00:00:00-04:00  5031.520020  5056.660156  5001.890137  5011.120117   \n",
              "2024-04-19 00:00:00-04:00  5005.439941  5019.020020  4953.560059  4967.229980   \n",
              "\n",
              "                               Volume     Tomorrow  Target  \n",
              "Date                                                        \n",
              "1927-12-30 00:00:00-05:00           0    17.760000       1  \n",
              "1928-01-03 00:00:00-05:00           0    17.719999       0  \n",
              "1928-01-04 00:00:00-05:00           0    17.549999       0  \n",
              "1928-01-05 00:00:00-05:00           0    17.660000       1  \n",
              "1928-01-06 00:00:00-05:00           0    17.500000       0  \n",
              "...                               ...          ...     ...  \n",
              "2024-04-15 00:00:00-04:00  3950210000  5051.410156       0  \n",
              "2024-04-16 00:00:00-04:00  4006200000  5022.209961       0  \n",
              "2024-04-17 00:00:00-04:00  3596130000  5011.120117       0  \n",
              "2024-04-18 00:00:00-04:00  3619760000  4967.229980       0  \n",
              "2024-04-19 00:00:00-04:00  2733302000          NaN       0  \n",
              "\n",
              "[24191 rows x 7 columns]"
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              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1927-12-30 00:00:00-05:00</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-03 00:00:00-05:00</th>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-04 00:00:00-05:00</th>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>0</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-05 00:00:00-05:00</th>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>0</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-06 00:00:00-05:00</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-15 00:00:00-04:00</th>\n",
              "      <td>5149.669922</td>\n",
              "      <td>5168.430176</td>\n",
              "      <td>5052.470215</td>\n",
              "      <td>5061.819824</td>\n",
              "      <td>3950210000</td>\n",
              "      <td>5051.410156</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-16 00:00:00-04:00</th>\n",
              "      <td>5064.589844</td>\n",
              "      <td>5079.839844</td>\n",
              "      <td>5039.830078</td>\n",
              "      <td>5051.410156</td>\n",
              "      <td>4006200000</td>\n",
              "      <td>5022.209961</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-17 00:00:00-04:00</th>\n",
              "      <td>5068.970215</td>\n",
              "      <td>5077.959961</td>\n",
              "      <td>5007.250000</td>\n",
              "      <td>5022.209961</td>\n",
              "      <td>3596130000</td>\n",
              "      <td>5011.120117</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-18 00:00:00-04:00</th>\n",
              "      <td>5031.520020</td>\n",
              "      <td>5056.660156</td>\n",
              "      <td>5001.890137</td>\n",
              "      <td>5011.120117</td>\n",
              "      <td>3619760000</td>\n",
              "      <td>4967.229980</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-19 00:00:00-04:00</th>\n",
              "      <td>5005.439941</td>\n",
              "      <td>5019.020020</td>\n",
              "      <td>4953.560059</td>\n",
              "      <td>4967.229980</td>\n",
              "      <td>2733302000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24191 rows × 7 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 24191,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1927-12-30 00:00:00-05:00\",\n        \"max\": \"2024-04-19 00:00:00-04:00\",\n        \"num_unique_values\": 24191,\n        \"samples\": [\n          \"2023-07-27 00:00:00-04:00\",\n          \"1958-02-19 00:00:00-05:00\",\n          \"2013-04-30 00:00:00-04:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1014.3741302391886,\n        \"min\": 0.0,\n        \"max\": 5257.97021484375,\n        \"num_unique_values\": 13559,\n        \"samples\": [\n          1314.449951171875,\n          1380.5799560546875,\n          330.1400146484375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1008.8432306152081,\n        \"min\": 4.400000095367432,\n        \"max\": 5264.85009765625,\n        \"num_unique_values\": 16672,\n        \"samples\": [\n          1467.93994140625,\n          1852.6500244140625,\n          4422.18017578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 997.3243294129372,\n        \"min\": 4.400000095367432,\n        \"max\": 5245.81982421875,\n        \"num_unique_values\": 16670,\n        \"samples\": [\n          2441.419921875,\n          1409.1099853515625,\n          843.2100219726562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1003.4496630965386,\n        \"min\": 4.400000095367432,\n        \"max\": 5254.35009765625,\n        \"num_unique_values\": 16670,\n        \"samples\": [\n          2444.239990234375,\n          1410.43994140625,\n          843.4299926757812\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1612241667,\n        \"min\": 0,\n        \"max\": 11456230000,\n        \"num_unique_values\": 12918,\n        \"samples\": [\n          1304400000,\n          3332510000,\n          194590000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tomorrow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1003.4628709043388,\n        \"min\": 4.400000095367432,\n        \"max\": 5254.35009765625,\n        \"num_unique_values\": 16670,\n        \"samples\": [\n          2444.239990234375,\n          1410.43994140625,\n          843.4299926757812\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataframe shows Target column indicating 1 if Tomorrows price is greater than todays price\n"
      ],
      "metadata": {
        "id": "w0FzaOKbI4J9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sp500 = sp500.loc[\"1990-01-01\":].copy()"
      ],
      "metadata": {
        "id": "E1BnfFT_JOay"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Figured going back to far would not be as useful for future predictions due to shifts in conditions and variables so I removed all data before 1990\n"
      ],
      "metadata": {
        "id": "uLu4SUe9JyUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 996
        },
        "id": "sfhQfMfmKahB",
        "outputId": "e6fd8be8-b2e2-4cd8-c6a6-8fcf0cc3612f"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1990-01-02 00:00:00-05:00   353.399994   359.690002   351.980011   359.690002   \n",
              "1990-01-03 00:00:00-05:00   359.690002   360.589996   357.890015   358.760010   \n",
              "1990-01-04 00:00:00-05:00   358.760010   358.760010   352.890015   355.670013   \n",
              "1990-01-05 00:00:00-05:00   355.670013   355.670013   351.350006   352.200012   \n",
              "1990-01-08 00:00:00-05:00   352.200012   354.239990   350.540009   353.790009   \n",
              "...                                ...          ...          ...          ...   \n",
              "2024-04-15 00:00:00-04:00  5149.669922  5168.430176  5052.470215  5061.819824   \n",
              "2024-04-16 00:00:00-04:00  5064.589844  5079.839844  5039.830078  5051.410156   \n",
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              "2024-04-18 00:00:00-04:00  5031.520020  5056.660156  5001.890137  5011.120117   \n",
              "2024-04-19 00:00:00-04:00  5005.439941  5019.020020  4953.560059  4967.229980   \n",
              "\n",
              "                               Volume     Tomorrow  Target  \n",
              "Date                                                        \n",
              "1990-01-02 00:00:00-05:00   162070000   358.760010       0  \n",
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              "...                               ...          ...     ...  \n",
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              "2024-04-17 00:00:00-04:00  3596130000  5011.120117       0  \n",
              "2024-04-18 00:00:00-04:00  3619760000  4967.229980       0  \n",
              "2024-04-19 00:00:00-04:00  2733302000          NaN       0  \n",
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              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 8641,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1990-01-02 00:00:00-05:00\",\n        \"max\": \"2024-04-19 00:00:00-04:00\",\n        \"num_unique_values\": 8641,\n        \"samples\": [\n          \"2022-08-05 00:00:00-04:00\",\n          \"2008-02-19 00:00:00-05:00\",\n          \"1990-11-15 00:00:00-05:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1131.1280477727364,\n        \"min\": 295.45001220703125,\n        \"max\": 5257.97021484375,\n        \"num_unique_values\": 8430,\n        \"samples\": [\n          430.7799987792969,\n          581.0399780273438,\n          1508.5999755859375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1136.9895320397425,\n        \"min\": 301.45001220703125,\n        \"max\": 5264.85009765625,\n        \"num_unique_values\": 8382,\n        \"samples\": [\n          1100.0899658203125,\n          4400.14990234375,\n          1976.6700439453125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1124.8894745356934,\n        \"min\": 294.510009765625,\n        \"max\": 5245.81982421875,\n        \"num_unique_values\": 8438,\n        \"samples\": [\n          1240.0899658203125,\n          640.8400268554688,\n          1456.6199951171875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1131.28696800196,\n        \"min\": 295.4599914550781,\n        \"max\": 5254.35009765625,\n        \"num_unique_values\": 8424,\n        \"samples\": [\n          652.5399780273438,\n          332.7200012207031,\n          1496.0699462890625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1847822799,\n        \"min\": 14990000,\n        \"max\": 11456230000,\n        \"num_unique_values\": 8406,\n        \"samples\": [\n          4088740000,\n          1172600000,\n          5926460000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tomorrow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1131.2719277462297,\n        \"min\": 295.4599914550781,\n        \"max\": 5254.35009765625,\n        \"num_unique_values\": 8423,\n        \"samples\": [\n          1136.469970703125,\n          327.989990234375,\n          1105.0899658203125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training An Initial Machine Learning Model"
      ],
      "metadata": {
        "id": "5h-9Mt8RL3Ar"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Start by using Random Forest Classifier which is best for *non-linear data relationships*.\n",
        "It works by training a bunch of individual decision trees with randomized parameters and than averaging the results from those decision trees"
      ],
      "metadata": {
        "id": "Wj_MdbtHMLqw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier(n_estimators=100, min_samples_split=100, random_state=1)\n",
        "\n",
        "train = sp500.iloc[:-100]\n",
        "test = sp500.iloc[-100]\n",
        "\n",
        "predictors = [\"Close\", \"Volume\", \"Open\" , \"High\", \"Low\"]\n",
        "model.fit(train[predictors], train[\"Target\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "FWi2ke7AL7KD",
        "outputId": "f3e2c411-cf0a-4763-e86a-8e6eda7c8661"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(min_samples_split=100, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(min_samples_split=100, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_split=100, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because this is time-series data I split it up into test set to avoid using future data to predict the past which is ridiculous in the real world, causes data leakage (Leakage means leaking info into the model)"
      ],
      "metadata": {
        "id": "k_ckZnNqNy1y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier()\n",
        "model.fit(train[predictors], train[\"Target\"])\n",
        "\n",
        "test[predictors] = test[predictors].values.reshape(-1, len(predictors))\n",
        "preds = model.predict(test[predictors])"
      ],
      "metadata": {
        "id": "jX2OP2CeQxSs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "3a8628ec-302f-43b6-c2f5-083d1d14cc69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-8f2ca6c9f1fe>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test[predictors] = test[predictors].values.reshape(-1, len(predictors))\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "cannot set using a list-like indexer with a different length than the value",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3653\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3654\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: '['Close', 'Volume', 'Open', 'High', 'Low']' is an invalid key",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mInvalidIndexError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1158\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_with_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_set_with_engine\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1221\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_with_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1222\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3659\u001b[0m             \u001b[0;31m#  the TypeError.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3660\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_indexing_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3661\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_check_indexing_error\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   5736\u001b[0m             \u001b[0;31m# would convert to numpy arrays and raise later any way) - GH29926\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5737\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5738\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidIndexError\u001b[0m: ['Close', 'Volume', 'Open', 'High', 'Low']",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-8f2ca6c9f1fe>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Target\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcacher_needs_updating\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_set_with\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1246\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1247\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1248\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1250\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_set_labels\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1254\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1255\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{key[mask]} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1256\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_set_values\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1260\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1262\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1263\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_update_cacher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36msetitem\u001b[0;34m(self, indexer, value)\u001b[0m\n\u001b[1;32m    392\u001b[0m             \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"setitem\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mputmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    350\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36msetitem\u001b[0;34m(self, indexer, value, using_cow)\u001b[0m\n\u001b[1;32m   1039\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1040\u001b[0m         \u001b[0;31m# length checking\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1041\u001b[0;31m         \u001b[0mcheck_setitem_lengths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexers/utils.py\u001b[0m in \u001b[0;36mcheck_setitem_lengths\u001b[0;34m(indexer, value, values)\u001b[0m\n\u001b[1;32m    166\u001b[0m                     \u001b[0;32mand\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m                 ):\n\u001b[0;32m--> 168\u001b[0;31m                     raise ValueError(\n\u001b[0m\u001b[1;32m    169\u001b[0m                         \u001b[0;34m\"cannot set using a list-like indexer \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m                         \u001b[0;34m\"with a different length than the value\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot set using a list-like indexer with a different length than the value"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "preds = pd.Series(preds, index=test.index)"
      ],
      "metadata": {
        "id": "MbFF5bduRfqk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "FnUpP3wZR3KC",
        "outputId": "76b06892-f772-4703-84f7-0cdc8d639136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'preds' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-c66c8509e20f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'preds' is not defined"
          ]
        }
      ]
    }
  ]
}