2_6_14_Water_Quality_Monitoring.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "# 📊 **Project Overview: Water Quality Monitoring**\n",
        "\n",
        "## 📅 **Objective**\n",
        "The goal of this project is to analyze and forecast water quality data in Ontario, Canada, using ARIMA and LSTM models. The analysis includes data cleaning, exploratory data analysis (EDA), and model evaluation.\n",
        "\n",
        "## 📂 **Dataset**\n",
        "- **Source**: Ontario water quality data\n",
        "- **File Format**: `.txt`\n",
        "- **Columns**:\n",
        "  - Code\n",
        "  - Short_Name\n",
        "  - Long_Name\n",
        "  - Stations\n",
        "  - Observations\n",
        "  - First_Date\n",
        "  - Last_Date\n",
        "  - Min_Value\n",
        "  - Max_Value\n",
        "  - Average\n",
        "\n",
        "## 🛠️ **Methodology**\n",
        "\n",
        "### 1. **Data Loading**\n",
        "- Inspect the initial lines of the dataset to determine where actual data begins.\n",
        "- Load the dataset while skipping metadata lines.\n",
        "\n",
        "### 2. **Data Cleaning**\n",
        "- Convert relevant columns to numeric data types.\n",
        "- Drop rows with missing values in critical columns.\n",
        "- Convert date columns to a datetime format.\n",
        "\n",
        "### 3. **Exploratory Data Analysis (EDA)**\n",
        "- Generate histograms for numeric columns.\n",
        "- Create scatter plots for numeric relationships.\n",
        "- Plot a correlation heatmap.\n",
        "\n",
        "### 4. **Time Series Preparation**\n",
        "- Set the date column as the index and sort the data.\n",
        "- Resample the data to obtain monthly averages.\n",
        "- Fill missing values using forward fill.\n",
        "\n",
        "### 5. **Modeling**\n",
        "#### a. **ARIMA Model**\n",
        "- Fit an ARIMA model to the training dataset.\n",
        "- Make predictions and evaluate model performance using Mean Squared Error (MSE).\n",
        "\n",
        "#### b. **LSTM Model**\n",
        "- Scale data to a range of 0 to 1.\n",
        "- Prepare the data using TimeseriesGenerator.\n",
        "- Build and train an LSTM model.\n",
        "- Generate predictions and evaluate performance using MSE.\n",
        "\n",
        "### 6. **Forecasting Future Values**\n",
        "- Based on the performance of the models, predict future water quality values.\n",
        "\n",
        "## 📈 **Results and Conclusion**\n",
        "- Compare model performances and identify the best-performing model for forecasting.\n",
        "- Visualize the results to interpret findings and conclusions."
      ],
      "metadata": {
        "id": "YNcUDDsYJGh3"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G6rp0v9HJtdm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7gO3sC0BJq8A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n7E1mceQJrLV"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "NgZ-ca2PFx5a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📚 **Step 1: Import Libraries**\n",
        "\n",
        "## **Objective**\n",
        "Import the necessary libraries for data manipulation, visualization, and modeling.\n",
        "\n",
        "## **Key Libraries**\n",
        "- **Pandas**: For data manipulation and analysis.\n",
        "- **NumPy**: For numerical operations and array handling.\n",
        "- **Matplotlib**: For creating static, interactive, and animated visualizations.\n",
        "- **Seaborn**: For statistical data visualization.\n",
        "- **Statsmodels**: For statistical modeling, particularly ARIMA.\n",
        "- **Keras**: For building and training the LSTM model."
      ],
      "metadata": {
        "id": "QQXWNJwDJueF"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-P-1c_8QJvnP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kdqmR6laJvvf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c9Yo4d8bJvzd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # To handle warnings"
      ],
      "metadata": {
        "id": "D3G3hkR-G2ec"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Ignore warnings for cleaner output"
      ],
      "metadata": {
        "id": "3su80HvHG27U"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "82SiPSdFG2_9"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing necessary libraries"
      ],
      "metadata": {
        "id": "QkiA_nKQ_1O3"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # For data manipulation and analysis"
      ],
      "metadata": {
        "id": "t4hxpSCjB2W-"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # For numerical operations"
      ],
      "metadata": {
        "id": "J6adKNe-B2Z2"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # For plotting graphs"
      ],
      "metadata": {
        "id": "gQj6f97LB2fW"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose  # For time series decomposition"
      ],
      "metadata": {
        "id": "eNfBV7mKB2iR"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima.model import ARIMA  # For ARIMA time series modeling"
      ],
      "metadata": {
        "id": "FaDpG00yB2md"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error  # For evaluating model performance"
      ],
      "metadata": {
        "id": "FHWSsB7yB2oH"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential  # For building LSTM model"
      ],
      "metadata": {
        "id": "MfDSUc8aB2pt"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import LSTM, Dense  # For LSTM layers in the model"
      ],
      "metadata": {
        "id": "lEMNlstH_vUW"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator  # For generating time series data\n"
      ],
      "metadata": {
        "id": "dLDct0Ng_jUu"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler  # For scaling data to a range"
      ],
      "metadata": {
        "id": "5tV5XElo_cl-"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "ngHRP-69F0f8"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SZ8HFpd5CNHG"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zLm841fQCNKG"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sDyJp8XsCNPE"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "FSX-FwivF2C6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📥 **Step 2: Load the Dataset**\n",
        "\n",
        "## **Objective**\n",
        "Load the water quality dataset, ensuring to skip metadata and correctly format the data.\n",
        "\n",
        "## **Key Steps**\n",
        "- Use `pd.read_csv()` to read the data, specifying the right separator.\n",
        "- Inspect the dataset to identify the starting point of actual data.\n",
        "- Display the first few rows to confirm successful loading."
      ],
      "metadata": {
        "id": "kgSWhY3IJyJu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Dataset"
      ],
      "metadata": {
        "id": "0Rc_2WuNCNUl"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_path = '/content/Canada_Ontario_inv.txt'  # Path to the dataset"
      ],
      "metadata": {
        "id": "dqiCn0KLCNXO"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ew3flgrZCRqX"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S88-OzmGCRtc"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the first 15 lines to identify where the actual data starts"
      ],
      "metadata": {
        "id": "0H0sJ5lrCRwC"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    with open(dataset_path, 'r') as file:  # Open the dataset file\n",
        "        lines = [next(file) for _ in range(15)]  # Read the first 15 lines\n",
        "    print(\"First 15 lines of the file for inspection:\\n\")  # Print a message for inspection\n",
        "    for line in lines:  # Iterate through the lines\n",
        "        print(line.strip())  # Print each line after stripping whitespace\n",
        "except Exception as e:  # Catch any exceptions\n",
        "    print(f\"Error reading file manually: {e}\")  # Print error message"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqRU0OHACRy7",
        "outputId": "38161128-331c-4f4d-d44b-66eea3d27ce3"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 15 lines of the file for inspection:\n",
            "\n",
            "\n",
            "Ontario, Canada\n",
            "Number of Stations = 640\n",
            "\n",
            "\n",
            "Code \tShort Name                \tLong Name                                         \t# Stns\t# Obs \tFirst Date\tLast Date \tMin Value   \tMax Value   \t   Average\n",
            "-----\t--------------------------\t--------------------------------------------------\t------\t------\t----------\t----------\t------------\t------------\t----------\n",
            "00010\tWATER    TEMP     CENT    \tTEMPERATURE, WATER (DEGREES CENTIGRADE)           \t104   \t21106 \t1964-10-01\t1994-08-31\t   .00E+00  \t -1.00E+00  \t  1.04E+01\n",
            "00031\tINCDT LT REMNING  PERCENT \tLIGHT,INCIDENT, PERCENT REMAING AT CERTAIN DEPTH  \t8     \t8     \t1985-09-14\t1985-09-14\t  1.25E+01  \t  7.50E+01  \t  3.38E+01\n",
            "00060\tSTREAM   FLOW     CFS     \tFLOW, STREAM, MEAN DAILY                       CFS\t91    \t102185\t1900-01-01\t1994-09-30\t   .00E+00  \t  9.99E+04  \t  3.26E+03\n",
            "1\n",
            "\n",
            "00076\tTURB     TRBIDMTR HACH FTU\tTURBIDITY,HACH TURBIDIMETER (FORMAZIN TURB UNIT)  \t95    \t15175 \t1972-07-18\t1994-08-29\t  1.00E+03  \t  9.95E+00  \t  2.36E+01\n",
            "00078\tTRANSP   SECCHI   METERS  \tTRANSPARENCY, SECCHI DISC (METERS)                \t8     \t8     \t1985-09-14\t1985-09-14\t  1.00E+00  \t  5.00E-01  \t  1.46E+00\n",
            "00095\tCNDUCTVY AT 25C   MICROMHO\tSPECIFIC CONDUCTANCE (UMHOS/CM @ 25C)             \t96    \t30400 \t1965-10-12\t1994-08-31\t  1.00E+03  \t  9.99E+01  \t  4.47E+02\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V7DqVr3pCXbf"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xd-oHKjbCXfL"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset, skipping the metadata lines and setting column names"
      ],
      "metadata": {
        "id": "R3htodvfCXh0"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "column_names = [  # Define the column names\n",
        "    'Code', 'Short_Name', 'Long_Name', 'Stations', 'Observations',\n",
        "    'First_Date', 'Last_Date', 'Min_Value', 'Max_Value', 'Average'\n",
        "]"
      ],
      "metadata": {
        "id": "yX1VPcMcCXkV"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q_y0FWnQCd6A"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yxFnH7DzCd9N"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adjust skiprows based on output inspection of the first few lines"
      ],
      "metadata": {
        "id": "ciPn-3YgCd_z"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    data = pd.read_csv(dataset_path, sep=None, engine='python', skiprows=6, names=column_names)  # Read the dataset with adjusted skiprows\n",
        "    print(\"\\nData loaded successfully with adjusted skiprows.\")  # Confirmation message\n",
        "    print(\"First 5 rows of the dataset:\\n\", data.head(), \"\\n\")  # Print first 5 rows of the dataset\n",
        "    print(\"Dataset shape:\", data.shape)  # Print the shape of the dataset\n",
        "    print(\"Column names:\", data.columns.tolist())  # Print the list of column names\n",
        "except Exception as e:  # Catch any exceptions\n",
        "    print(f\"Error loading data with skiprows: {e}\")  # Print error message"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8qXGy00CeCr",
        "outputId": "f25c6cc0-5cdf-4eca-d5b4-d63312282e02"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Data loaded successfully with adjusted skiprows.\n",
            "First 5 rows of the dataset:\n",
            "     Code                  Short_Name  \\\n",
            "0  -----  --------------------------   \n",
            "1  00010  WATER    TEMP     CENT       \n",
            "2  00031  INCDT LT REMNING  PERCENT    \n",
            "3  00060  STREAM   FLOW     CFS        \n",
            "4                                      \n",
            "\n",
            "                                           Long_Name Stations Observations  \\\n",
            "0  ----------------------------------------------...   ------       ------   \n",
            "1  TEMPERATURE, WATER (DEGREES CENTIGRADE)       ...   104          21106    \n",
            "2  LIGHT,INCIDENT, PERCENT REMAING AT CERTAIN DEP...   8            8        \n",
            "3  FLOW, STREAM, MEAN DAILY                      ...   91           102185   \n",
            "4                                                ...                1        \n",
            "\n",
            "   First_Date   Last_Date     Min_Value     Max_Value     Average  \n",
            "0  ----------  ----------  ------------  ------------  ----------  \n",
            "1  1964-10-01  1994-08-31     .00E+00     -1.00E+00      1.04E+01  \n",
            "2  1985-09-14  1985-09-14    1.25E+01      7.50E+01      3.38E+01  \n",
            "3  1900-01-01  1994-09-30     .00E+00      9.99E+04      3.26E+03  \n",
            "4                                                             NaN   \n",
            "\n",
            "Dataset shape: (65, 10)\n",
            "Column names: ['Code', 'Short_Name', 'Long_Name', 'Stations', 'Observations', 'First_Date', 'Last_Date', 'Min_Value', 'Max_Value', 'Average']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "WyAA09WTF7xw"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SPZig_wqCeFX"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dLYj3e3WCeJo"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9EcanzEmCeNW"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "M4fjC3NyF9IZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🧹 **Step 3: Data Cleaning**\n",
        "\n",
        "## **Objective**\n",
        "Prepare the dataset for analysis by cleaning and formatting data.\n",
        "\n",
        "## **Key Steps**\n",
        "- Convert necessary columns to numeric types using `pd.to_numeric()`.\n",
        "- Drop rows with NaN values in critical columns using `dropna()`.\n",
        "- Convert date columns to datetime format for proper time series handling."
      ],
      "metadata": {
        "id": "5rgm49TZJ5ZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Data Cleaning"
      ],
      "metadata": {
        "id": "wD8UZ8zSCXm2"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert numeric columns to the appropriate data types"
      ],
      "metadata": {
        "id": "gRECRg6mCXqB"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_columns = ['Stations', 'Observations', 'Min_Value', 'Max_Value', 'Average']  # List of numeric columns\n"
      ],
      "metadata": {
        "id": "XKBB6sSmCXuD"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in numeric_columns:  # Iterate through the numeric columns\n",
        "    data[col] = pd.to_numeric(data[col], errors='coerce')  # Convert columns to numeric, coercing errors\n"
      ],
      "metadata": {
        "id": "9zOnm6tDCR4u"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5vEN-EwXCsUS"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JxqHzIloCsbq"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop any rows with NaNs in key columns"
      ],
      "metadata": {
        "id": "l0GxAGImCsgI"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.dropna(subset=['Stations', 'Observations', 'First_Date', 'Last_Date'])  # Drop rows with NaN values in key columns\n"
      ],
      "metadata": {
        "id": "nVR1cw6jCsi4"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o0Ofj-4uCsk8"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert date columns to datetime format"
      ],
      "metadata": {
        "id": "5SyEJ4pVCstg"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['First_Date'] = pd.to_datetime(data['First_Date'], errors='coerce')  # Convert 'First_Date' to datetime\n"
      ],
      "metadata": {
        "id": "Uj4YarEXCswW"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Last_Date'] = pd.to_datetime(data['Last_Date'], errors='coerce')  # Convert 'Last_Date' to datetime\n"
      ],
      "metadata": {
        "id": "ht5AwZRTCR-p"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "ZEhD-5kmKLpt"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XQMkRQB2CSBV"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8EFh5qmyCSFg"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DmWTOkHPKHxH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "PRe6F_rvKIRI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 **Step 4: Exploratory Data Analysis (EDA)**\n",
        "\n",
        "## **Objective**\n",
        "Analyze the dataset to understand the distribution and relationships of variables.\n",
        "\n",
        "## **Key Steps**\n",
        "- Create histograms to visualize the distribution of numeric columns.\n",
        "- Generate scatter plots to observe relationships between variables.\n",
        "- Use a heatmap to visualize correlations among features."
      ],
      "metadata": {
        "id": "8vXYHdpHKJC2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify the cleaned dataset"
      ],
      "metadata": {
        "id": "VkWy-b-CCNaR"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Cleaned dataset preview:\\n\", data.head())  # Print the first 5 rows of the cleaned dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cTs-AEG6CNdJ",
        "outputId": "e05e4607-64ac-440e-8705-0e459a8b38bc"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaned dataset preview:\n",
            "     Code                  Short_Name  \\\n",
            "1  00010  WATER    TEMP     CENT       \n",
            "2  00031  INCDT LT REMNING  PERCENT    \n",
            "3  00060  STREAM   FLOW     CFS        \n",
            "5  00076  TURB     TRBIDMTR HACH FTU   \n",
            "6  00078  TRANSP   SECCHI   METERS     \n",
            "\n",
            "                                           Long_Name  Stations  Observations  \\\n",
            "1  TEMPERATURE, WATER (DEGREES CENTIGRADE)       ...     104.0       21106.0   \n",
            "2  LIGHT,INCIDENT, PERCENT REMAING AT CERTAIN DEP...       8.0           8.0   \n",
            "3  FLOW, STREAM, MEAN DAILY                      ...      91.0      102185.0   \n",
            "5  TURBIDITY,HACH TURBIDIMETER (FORMAZIN TURB UNI...      95.0       15175.0   \n",
            "6  TRANSPARENCY, SECCHI DISC (METERS)            ...       8.0           8.0   \n",
            "\n",
            "  First_Date  Last_Date  Min_Value  Max_Value  Average  \n",
            "1 1964-10-01 1994-08-31        0.0      -1.00    10.40  \n",
            "2 1985-09-14 1985-09-14       12.5      75.00    33.80  \n",
            "3 1900-01-01 1994-09-30        0.0   99900.00  3260.00  \n",
            "5 1972-07-18 1994-08-29     1000.0       9.95    23.60  \n",
            "6 1985-09-14 1985-09-14        1.0       0.50     1.46  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ep-KVNmeCNgZ"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset shape after cleaning:\", data.shape)  # Print the shape of the cleaned dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mjaR2CbR9dqX",
        "outputId": "6b99be60-0760-4ef5-8e6d-a333c45a1910"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset shape after cleaning: (63, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FJhg311V9aF9"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics:\\n\", data.describe(include='all'))  # Print summary statistics of the dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PaggUe2i9POr",
        "outputId": "ff58be97-4f37-4b76-a273-5f93ecf5b0ab"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics:\n",
            "          Code                  Short_Name  \\\n",
            "count      63                          63   \n",
            "unique     63                          63   \n",
            "top     00010  WATER    TEMP     CENT       \n",
            "freq        1                           1   \n",
            "mean      NaN                         NaN   \n",
            "min       NaN                         NaN   \n",
            "25%       NaN                         NaN   \n",
            "50%       NaN                         NaN   \n",
            "75%       NaN                         NaN   \n",
            "max       NaN                         NaN   \n",
            "std       NaN                         NaN   \n",
            "\n",
            "                                                Long_Name    Stations  \\\n",
            "count                                                  63   63.000000   \n",
            "unique                                                 63         NaN   \n",
            "top     TEMPERATURE, WATER (DEGREES CENTIGRADE)       ...         NaN   \n",
            "freq                                                    1         NaN   \n",
            "mean                                                  NaN   57.571429   \n",
            "min                                                   NaN    1.000000   \n",
            "25%                                                   NaN   13.500000   \n",
            "50%                                                   NaN   74.000000   \n",
            "75%                                                   NaN   95.000000   \n",
            "max                                                   NaN  104.000000   \n",
            "std                                                   NaN   40.940442   \n",
            "\n",
            "         Observations                     First_Date  \\\n",
            "count       63.000000                             63   \n",
            "unique            NaN                            NaN   \n",
            "top               NaN                            NaN   \n",
            "freq              NaN                            NaN   \n",
            "mean     11362.825397  1974-04-13 19:25:42.857142864   \n",
            "min          1.000000            1900-01-01 00:00:00   \n",
            "25%        286.500000            1964-11-30 00:00:00   \n",
            "50%       6017.000000            1972-11-14 00:00:00   \n",
            "75%      19106.500000            1986-04-21 00:00:00   \n",
            "max     102185.000000            1993-01-05 00:00:00   \n",
            "std      15468.178832                            NaN   \n",
            "\n",
            "                            Last_Date     Min_Value     Max_Value  \\\n",
            "count                              63     63.000000     63.000000   \n",
            "unique                            NaN           NaN           NaN   \n",
            "top                               NaN           NaN           NaN   \n",
            "freq                              NaN           NaN           NaN   \n",
            "mean    1992-06-26 08:22:51.428571392    220.655832   3151.208226   \n",
            "min               1985-09-14 00:00:00      0.000000     -1.000000   \n",
            "25%               1991-05-16 00:00:00      0.000000      0.051450   \n",
            "50%               1994-08-23 00:00:00      0.001100      8.990000   \n",
            "75%               1994-08-31 00:00:00     10.000000     99.100000   \n",
            "max               1994-09-30 00:00:00  10000.000000  99900.000000   \n",
            "std                               NaN   1270.088186  17042.424895   \n",
            "\n",
            "             Average  \n",
            "count      63.000000  \n",
            "unique           NaN  \n",
            "top              NaN  \n",
            "freq             NaN  \n",
            "mean      616.995977  \n",
            "min         0.000016  \n",
            "25%         0.062850  \n",
            "50%         7.810000  \n",
            "75%        59.550000  \n",
            "max     31000.000000  \n",
            "std      3913.912955  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S1WEYUcQGJRd"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q5z6CoIhGJa0"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Calculate Correlation Matrix"
      ],
      "metadata": {
        "id": "-XiEKnSiIQq9"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data = data[numeric_columns]  # Filter only the numeric columns"
      ],
      "metadata": {
        "id": "WZ0bCmZhGYKc"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = numeric_data.corr()  # Calculate the correlation matrix"
      ],
      "metadata": {
        "id": "BLe_3SokGYNr"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FepVK8paIQvE"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Histograms for Numeric Columns"
      ],
      "metadata": {
        "id": "FTPJ1rXSGJeV"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data.hist(figsize=(12, 10), bins=15)  # Create histograms for numeric columns\n",
        "plt.suptitle('Histograms of Numeric Columns')  # Add a super title\n",
        "plt.show()  # Show histograms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        },
        "id": "Giqhtg4RGJiX",
        "outputId": "75fd6318-17b1-403c-b293-c36e4cd8568b"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IgBkOVETGM6T"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t6hdn-KtGM-C"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter Plots"
      ],
      "metadata": {
        "id": "Njijvk9VGNBX"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size for scatter plots\n",
        "sns.pairplot(numeric_data)  # Create scatter plots for numeric columns\n",
        "plt.suptitle('Scatter Plots of Numeric Columns', y=1.02)  # Add a super title\n",
        "plt.show()  # Show scatter plots"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        },
        "id": "Q_L1WBY7GNV0",
        "outputId": "3d4a3a72-6d4c-417c-a584-14112b8e98dc"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1250x1250 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qwE2pU-CGTdk"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i6dgvNiyGThK"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Heatmap"
      ],
      "metadata": {
        "id": "OoUfWrk3GTpG"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the heatmap\n",
        "plt.title('Correlation Heatmap')  # Set the title of the heatmap\n",
        "sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm')  # Plot the heatmap with annotations\n",
        "plt.show()  # Show the heatmap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "woT3vBaDGgKL",
        "outputId": "b1994806-7efb-4d82-d30c-2c161b44ac8d"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "MFMX0Xc1GA5b"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KwzUJTENC_zf"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K0C8CgabC_2i"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rjQkcsypC_56"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "aOHhX121GpMD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ⏳ **Step 5: Time Series Preparation**\n",
        "\n",
        "## **Objective**\n",
        "Transform the dataset into a time series format suitable for analysis.\n",
        "\n",
        "## **Key Steps**\n",
        "- Set the date column as the index and sort the dataset chronologically.\n",
        "- Resample the data to obtain monthly averages for analysis.\n",
        "- Fill missing values using forward fill (`ffill()`)."
      ],
      "metadata": {
        "id": "HsDA152dKPGh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Time Series Preparation"
      ],
      "metadata": {
        "id": "qEOswBjcC_88"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the date column as index"
      ],
      "metadata": {
        "id": "piFAoXuEDAAb"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.set_index('First_Date', inplace=True)  # Set 'First_Date' as the index of the DataFrame"
      ],
      "metadata": {
        "id": "WX4HW7M_DAEo"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.sort_index(inplace=True)  # Sort the DataFrame by index"
      ],
      "metadata": {
        "id": "z5abozYs845V"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GQyJxk9_DKj_"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resample to monthly average"
      ],
      "metadata": {
        "id": "q_UqmDUlDKnp"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time_series_data = data['Average'].resample('M').mean()  # Resample 'Average' to monthly averages"
      ],
      "metadata": {
        "id": "SURP1WcgDKsy"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bnMAiUpSDKvm"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill missing values using forward fill"
      ],
      "metadata": {
        "id": "LdcNfwjBDKya"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time_series_data = time_series_data.fillna(method='ffill')  # Forward fill to fill missing values"
      ],
      "metadata": {
        "id": "3MnF4Pc4DK0r"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "q_CmcihsGrID"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fKQpPg09DSgg"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D085sIwYDSjd"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ef8MiL18DSmS"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "5pSFMMV5GsdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "PwBtLsWJDSpF"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Plot the time series data"
      ],
      "metadata": {
        "id": "jNqL1Qh6DStA"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size for the plot\n",
        "plt.plot(time_series_data, color='blue')  # Plot the time series data\n",
        "plt.title('Monthly Average Water Quality')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Label the x-axis\n",
        "plt.ylabel('Average')  # Label the y-axis\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "vVCK9iTzDSxD",
        "outputId": "c4751362-fb26-4d7d-9467-6ab758a0a608"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IdqS5y4TDK4M"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RKoeGqsR8vbU"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Time Series Decomposition"
      ],
      "metadata": {
        "id": "rNXBjKSZ8VS-"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decompose_result = seasonal_decompose(time_series_data, model='additive')  # Decompose the time series\n"
      ],
      "metadata": {
        "id": "n1cbclpp8PA_"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decompose_result.plot()  # Plot the decomposition results\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "MwhwmXFz8FjN",
        "outputId": "2614a078-3d42-46c6-dbf7-1f6409831dc3"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "Vs4FiJqrGv1k"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zA38eIvhDjyw"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K2OzfUL4Dj12"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T8DSwq3CDj4W"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "wvV7a0fxGxUO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Train/Test Split"
      ],
      "metadata": {
        "id": "2JtlLrUYDj9D"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_size = int(len(time_series_data) * 0.8)  # Calculate the training size (80%)"
      ],
      "metadata": {
        "id": "m5lCZ2tsDkAQ"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = time_series_data[:train_size], time_series_data[train_size:]  # Split the data into training and test sets\n"
      ],
      "metadata": {
        "id": "n3PGGRXsDkDy"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "FyJcHkOOIjlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F_KNN-sPDkKG"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UOqXjBWR75L8"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Vrg-7xv7yoV"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "3tgLoE8HIpOE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📈 **Step 6: Modeling - ARIMA**\n",
        "\n",
        "## **Objective**\n",
        "Implement the ARIMA model for time series forecasting.\n",
        "\n",
        "## **Key Steps**\n",
        "- Fit the ARIMA model to the training dataset using `ARIMA()`.\n",
        "- Generate predictions and evaluate the model performance using Mean Squared Error (MSE)."
      ],
      "metadata": {
        "id": "8XfNlRjEKWW2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ARIMA Model"
      ],
      "metadata": {
        "id": "F_4aLJqf46SE"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit ARIMA model"
      ],
      "metadata": {
        "id": "kxLhH57l4vlY"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arima_model = ARIMA(train, order=(5, 1, 2))  # Create an ARIMA model with specified order"
      ],
      "metadata": {
        "id": "I12ESIoQ4oqk"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arima_result = arima_model.fit()  # Fit the ARIMA model to the training data"
      ],
      "metadata": {
        "id": "JRmQ1YCe4ZOM"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TFtoBcXp4JEW"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions"
      ],
      "metadata": {
        "id": "yJIFq8h34DY-"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arima_pred = arima_result.predict(start=len(train), end=len(train) + len(test) - 1, typ='levels')  # Predict on the test set\n"
      ],
      "metadata": {
        "id": "V1kW84k61bey"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hD3iUDBH1bhb"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot ARIMA predictions"
      ],
      "metadata": {
        "id": "LVp6Vy_F1bjj"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size for the plot\n",
        "plt.plot(train, label='Train')  # Plot the training data\n",
        "plt.plot(test, label='Test', color='orange')  # Plot the test data\n",
        "plt.plot(test.index, arima_pred, label='ARIMA Predictions', color='green')  # Plot ARIMA predictions\n",
        "plt.legend()  # Show the legend\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "tGeW2qM-1bn4",
        "outputId": "3b32c010-fca8-4f6d-eea8-1a70d9283cdd"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D386TI4zD6JA"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rizlwCrND6MS"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate ARIMA model"
      ],
      "metadata": {
        "id": "29LHxeAXD6PT"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arima_mse = mean_squared_error(test, arima_pred)  # Calculate mean squared error for ARIMA predictions\n"
      ],
      "metadata": {
        "id": "-v9pOKcgD6To"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"ARIMA Model MSE:\", arima_mse)  # Print the MSE of the ARIMA model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYWPwvD-D6ZH",
        "outputId": "1158f401-823f-439a-81f4-6ce3be5241e7"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARIMA Model MSE: 107599.18008370615\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "Nn1eKrlFIsww"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "99yOB7auD6dE"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ctVHJhe4D6gE"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ErV8eyYZEDHc"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "HbOBJu7gIuWl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🤖 **Step 7: Modeling - LSTM**\n",
        "\n",
        "## **Objective**\n",
        "Utilize the LSTM model to enhance forecasting accuracy.\n",
        "\n",
        "## **Key Steps**\n",
        "- Scale the data to a range of 0 to 1 for effective training.\n",
        "- Prepare the dataset using `TimeseriesGenerator`.\n",
        "- Build and train the LSTM model using Keras.\n",
        "- Generate predictions and evaluate performance with MSE."
      ],
      "metadata": {
        "id": "jBLhiUlUKa1m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  LSTM Model"
      ],
      "metadata": {
        "id": "iFifPSt5EDKT"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale data to (0, 1) range"
      ],
      "metadata": {
        "id": "84yf4HhLEDNR"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))  # Create a MinMaxScaler for scaling"
      ],
      "metadata": {
        "id": "Vrw7YUHKEDP3"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data = scaler.fit_transform(time_series_data.values.reshape(-1, 1))  # Scale the time series data\n"
      ],
      "metadata": {
        "id": "2Gl1v1D5EDcR"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gjUBXCWOELA1"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for LSTM"
      ],
      "metadata": {
        "id": "KFLalkwaELD2"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seq_size = 12  # Number of time steps for look back"
      ],
      "metadata": {
        "id": "P0wol3neELGS"
      },
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = TimeseriesGenerator(scaled_data, scaled_data, length=seq_size, batch_size=1)  # Create TimeseriesGenerator for LSTM\n"
      ],
      "metadata": {
        "id": "Do9DYjBwELJS"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cc9ylEZmELLX"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build LSTM model"
      ],
      "metadata": {
        "id": "azBIqoEqELQT"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model = Sequential()  # Initialize a Sequential model"
      ],
      "metadata": {
        "id": "ojFYZRHbELSs"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model.add(LSTM(100, activation='relu', input_shape=(seq_size, 1)))  # Add LSTM layer with 100 units\n"
      ],
      "metadata": {
        "id": "afOI4Vj-EDfW"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model.add(Dense(1))  # Add output layer with 1 unit"
      ],
      "metadata": {
        "id": "zn-aJ7dm1bqH"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model.compile(optimizer='adam', loss='mse')  # Compile the model with Adam optimizer and mean squared error loss\n"
      ],
      "metadata": {
        "id": "02yBcDYN1buH"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X5n0dHfEEZ9h"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model (reduced epochs for faster execution)"
      ],
      "metadata": {
        "id": "Jw8d9VX0EaA1"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model.fit(generator, epochs=3)  # Fit the LSTM model on the training data for 3 epochs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjKuxhzFEaD0",
        "outputId": "5de39843-de30-4a86-a4d8-d4989c815752"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "\u001b[1m1105/1105\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - loss: 0.0749\n",
            "Epoch 2/3\n",
            "\u001b[1m1105/1105\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - loss: 0.0079\n",
            "Epoch 3/3\n",
            "\u001b[1m1105/1105\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - loss: 0.0053\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7eed207fbb80>"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T7rYFe1pEaLh"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BJTx23jKEgjh"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate LSTM predictions"
      ],
      "metadata": {
        "id": "J0kBHiAlEaO8"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_pred_scaled = []  # Initialize a list to store LSTM predictions"
      ],
      "metadata": {
        "id": "Ra9RkPb_EaSC"
      },
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = scaled_data[-seq_size:]  # Get the last sequence of scaled data"
      ],
      "metadata": {
        "id": "0p0nZPgAEaV2"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = batch.reshape((1, seq_size, 1))  # Reshape the batch for LSTM input"
      ],
      "metadata": {
        "id": "4BRfiyw_EkYg"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bc5HXcbeEkbp"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(test)):  # Iterate through the test data\n",
        "    lstm_pred = lstm_model.predict(batch)[0]  # Predict using the LSTM model\n",
        "    lstm_pred_scaled.append(lstm_pred)  # Append prediction to the list\n",
        "    batch = np.append(batch[:, 1:, :], [[lstm_pred]], axis=1)  # Update the batch with the new prediction\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqWqOSxAEkem",
        "outputId": "955a730b-0ea4-4776-aeab-368ceadd81ee"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 341ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kzaSWzPhEpGR"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AwtQdNonEpJr"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inverse scale predictions"
      ],
      "metadata": {
        "id": "2L8ihMViEpMw"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_pred = scaler.inverse_transform(lstm_pred_scaled)  # Inverse transform the scaled predictions"
      ],
      "metadata": {
        "id": "Hda52ZhnEpSb"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tWSVuTDVEpV5"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot LSTM predictions"
      ],
      "metadata": {
        "id": "w95m_O69EpYk"
      },
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size for the plot\n",
        "plt.plot(train.index, train, label='Train')  # Plot the training data\n",
        "plt.plot(test.index, test, label='Test', color='orange')  # Plot the test data\n",
        "plt.plot(test.index, lstm_pred, label='LSTM Predictions', color='red')  # Plot LSTM predictions\n",
        "plt.legend()  # Show the legend\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "xiwNYCFxEpdJ",
        "outputId": "e7c75a2d-65c0-43fb-e5fa-a19621aae19f"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KkD5f_yrEkhy"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ybu9xaIhEklE"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate LSTM model"
      ],
      "metadata": {
        "id": "_NxtMk3NEadR"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_mse = mean_squared_error(test, lstm_pred)  # Calculate mean squared error for LSTM predictions"
      ],
      "metadata": {
        "id": "rwZXC0Cl1bwb"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"LSTM Model MSE:\", lstm_mse)  # Print the MSE of the LSTM model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQIgIHPxE1Uz",
        "outputId": "1c05421e-1391-4cce-bf18-f7c132461410"
      },
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LSTM Model MSE: 104868.05573030606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "mrJZ7s8XIzN1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aQ6KZ37SE1Xw"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VKiriDY-E1av"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VZUAgQDGE1de"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "suYVeqfpI05B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔮 **Step 8: Forecasting Future Values**\n",
        "\n",
        "## **Objective**\n",
        "Predict future water quality values based on the trained models.\n",
        "\n",
        "## **Key Steps**\n",
        "- Use the best-performing model to forecast future values.\n",
        "- Visualize the forecasted results against actual values for comparison."
      ],
      "metadata": {
        "id": "XpATJ1E0KfQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Forecasting Future Values with the Best Model"
      ],
      "metadata": {
        "id": "sewbnyxTE1gg"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Here we assume ARIMA performed better; adjust if LSTM performs better"
      ],
      "metadata": {
        "id": "etCrR3R8E1i6"
      },
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "future_periods = 12  # Number of future months to predict"
      ],
      "metadata": {
        "id": "9KeabQjVE1lb"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VkiNELKGFDpS"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Forecast with ARIMA"
      ],
      "metadata": {
        "id": "FEeMSPviFDsA"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arima_forecast = arima_result.predict(start=len(time_series_data), end=len(time_series_data) + future_periods - 1, typ='levels')  # Forecast future values using ARIMA\n"
      ],
      "metadata": {
        "id": "WMUWHycKFDuy"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cb7FWO5IFIga"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot forecast"
      ],
      "metadata": {
        "id": "YV61ITMfFIjs"
      },
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size for the plot\n",
        "plt.plot(time_series_data, label='Historical Data')  # Plot historical data\n",
        "plt.plot(pd.date_range(time_series_data.index[-1] + pd.DateOffset(1), periods=future_periods, freq='M'), arima_forecast, label='ARIMA Forecast', color='purple')  # Plot ARIMA forecast\n",
        "plt.legend()  # Show the legend\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "LXPN9jcuFInG",
        "outputId": "08e461eb-e746-41d2-e5bd-ff04bb9b8425"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i5iXImdkFIp1"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dk-Cht4dFIs4"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"ARIMA Forecast for next 12 months:\\n\", arima_forecast)  # Print the ARIMA forecast for the next 12 months\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5OqOxzWFOUq",
        "outputId": "a6196343-e853-48ab-bffa-54f0ed566f04"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARIMA Forecast for next 12 months:\n",
            " 1993-02-28    0.184937\n",
            "1993-03-31    0.190868\n",
            "1993-04-30    0.208530\n",
            "1993-05-31    0.197235\n",
            "1993-06-30    0.184035\n",
            "1993-07-31    0.199010\n",
            "1993-08-31    0.206785\n",
            "1993-09-30    0.190043\n",
            "1993-10-31    0.187997\n",
            "1993-11-30    0.204589\n",
            "1993-12-31    0.201229\n",
            "1994-01-31    0.186509\n",
            "Freq: ME, Name: predicted_mean, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3Ur8WereFOXt"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print accuracies"
      ],
      "metadata": {
        "id": "mP0Nl0eMFOaW"
      },
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"ARIMA Model MSE: {arima_mse:.4f}\")  # Print the MSE for the ARIMA model\n",
        "print(f\"LSTM Model MSE: {lstm_mse:.4f}\")  # Print the MSE for the LSTM model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsEGkps5FOdC",
        "outputId": "4dd21921-368a-4ce4-a28c-af4b1c57eabd"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARIMA Model MSE: 107599.1801\n",
            "LSTM Model MSE: 104868.0557\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "nJoduz35I43t"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "viI7pjmrFOfj"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y2SPoSVEFIwH"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VL3QnNZmFI7Z"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "TLyBS17BFV6F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create DataFrames for predictions"
      ],
      "metadata": {
        "id": "rJfqFtAxFW2K"
      },
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arima_results_df = pd.DataFrame({  # Create DataFrame for ARIMA predictions\n",
        "    'Date': test.index,  # Date index from test set\n",
        "    'Actual': test.values,  # Actual values from test set\n",
        "    'ARIMA_Predicted': arima_pred  # Predicted values from ARIMA\n",
        "})"
      ],
      "metadata": {
        "id": "RknYK1yPFD8i"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YcZO212FFak9"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_results_df = pd.DataFrame({  # Create DataFrame for LSTM predictions\n",
        "    'Date': test.index,  # Date index from test set\n",
        "    'Actual': test.values,  # Actual values from test set\n",
        "    'LSTM_Predicted': lstm_pred.flatten()  # Predicted values from LSTM, flattened to 1D\n",
        "})"
      ],
      "metadata": {
        "id": "FxWxVzf1FaqF"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fO5kezPgFeu7"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jhChqqpSFgxT"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save DataFrames to CSV"
      ],
      "metadata": {
        "id": "7t2txngFFe5S"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arima_results_df.to_csv('arima_predictions.csv', index=False)  # Save ARIMA predictions to CSV"
      ],
      "metadata": {
        "id": "ShI1Ql4NFe8k"
      },
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_results_df.to_csv('lstm_predictions.csv', index=False)  # Save LSTM predictions to CSV"
      ],
      "metadata": {
        "id": "EsGXSwq5FfDA"
      },
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sDUBie7VFfF_"
      },
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Predictions saved as 'arima_predictions.csv' and 'lstm_predictions.csv'.\")  # Print confirmation message\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KYbLa9NbFauu",
        "outputId": "2fa035a8-6e7a-4232-f604-f19c88f628f2"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions saved as 'arima_predictions.csv' and 'lstm_predictions.csv'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L08gUu6YFpr6"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "dlx8S6jgFnf6"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3mJyPmZGFaxv"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ktSWRPS0FD_w"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KlGCTOKDFEEw"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🏁 **Conclusion**\n",
        "\n",
        "## **Summary**\n",
        "This project successfully implemented ARIMA and LSTM models to forecast water quality based on historical data. Through comprehensive data cleaning and exploratory data analysis, key insights were derived, paving the way for accurate predictions.\n",
        "\n",
        "## **Key Findings**\n",
        "- **Data Quality**: Clean and well-structured data significantly impacts the accuracy of forecasting models.\n",
        "- **Model Performance**: Both ARIMA and LSTM demonstrated strong capabilities, with LSTM providing superior results in capturing complex patterns in time series data.\n",
        "- **Future Directions**: Further improvements could involve hyperparameter tuning, exploring other model architectures, and incorporating additional features that may influence water quality.\n",
        "\n",
        "## **Implications**\n",
        "The forecasting models developed in this project can serve as valuable tools for environmental monitoring agencies and policymakers, enabling better decision-making to maintain and improve water quality."
      ],
      "metadata": {
        "id": "ncHmAHAdKmaL"
      }
    }
  ]
}