Final Project_Water Quality.ipynb

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        "id": "w3G_SSS_N2xz"
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      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## DRINKING WATER QUALITY\n",
        "\n",
        "Dataset: The city of New York\n",
        "\n",
        "### OBJECTIVES\n",
        "\n",
        "### GUIDING QUESTIONS\n",
        "1. Which sample class consistently demonstrates higher levels of key water quality indicators? (Tableau)\n",
        "2. Trend over years of water quality indicators? (Tableau)\n",
        "3. Which sample site has highest average chemical residues and microbial measurements like Coliform over years? (Tableau)\n",
        "4. K means clustering water samples based on turbidity & residual free chlorine levels – Compliance Samples\n",
        "5. Can we predict the classification of water samples as 'Compliance' or 'Operational' based on their residual free chlorine levels and coliform?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# !pip install scikit-learn\n",
        "# !pip install matplotlib\n",
        "# !pip install altair\n",
        "# !pip install seaborn\n",
        "# !pip install plotly\n",
        "# !pip install --upgrade nbformat\n",
        "# !pip install statsmodels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "nY6pMLW_N2x1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn as sk\n",
        "import sklearn.metrics as skm\n",
        "import sklearn.cluster as skc\n",
        "import sklearn.preprocessing as skp\n",
        "import matplotlib.pyplot as plt\n",
        "import altair as alt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zoIttb6N2x2"
      },
      "source": [
        "### A. Data Wrangling \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "VvL4sDaQN2x2",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "0f944b4c-7ae5-4ce6-9ba9-d37f28435c60"
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      "outputs": [
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          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample Number</th>\n",
              "      <th>Sample Date</th>\n",
              "      <th>Sample Time</th>\n",
              "      <th>Sample Site</th>\n",
              "      <th>Sample class</th>\n",
              "      <th>Residual Free Chlorine (mg/L)</th>\n",
              "      <th>Turbidity (NTU)</th>\n",
              "      <th>Fluoride (mg/L)</th>\n",
              "      <th>Coliform (Quanti-Tray) (MPN /100mL)</th>\n",
              "      <th>E.coli(Quanti-Tray) (MPN/100mL)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>201655703</td>\n",
              "      <td>08/09/2016</td>\n",
              "      <td>10:31</td>\n",
              "      <td>43250</td>\n",
              "      <td>Compliance</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.74</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>&lt;1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>201655704</td>\n",
              "      <td>08/09/2016</td>\n",
              "      <td>11:27</td>\n",
              "      <td>46850</td>\n",
              "      <td>Compliance</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>&lt;1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>201655705</td>\n",
              "      <td>08/09/2016</td>\n",
              "      <td>10:14</td>\n",
              "      <td>50200</td>\n",
              "      <td>Operational</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.74</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>&lt;1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>201655706</td>\n",
              "      <td>08/09/2016</td>\n",
              "      <td>12:12</td>\n",
              "      <td>50300</td>\n",
              "      <td>Operational</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.63</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>&lt;1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>201655707</td>\n",
              "      <td>08/09/2016</td>\n",
              "      <td>10:17</td>\n",
              "      <td>76550</td>\n",
              "      <td>Compliance</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>&lt;1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Sample Number Sample Date Sample Time Sample Site Sample class  \\\n",
              "0      201655703  08/09/2016       10:31       43250   Compliance   \n",
              "1      201655704  08/09/2016       11:27       46850   Compliance   \n",
              "2      201655705  08/09/2016       10:14       50200  Operational   \n",
              "3      201655706  08/09/2016       12:12       50300  Operational   \n",
              "4      201655707  08/09/2016       10:17       76550   Compliance   \n",
              "\n",
              "   Residual Free Chlorine (mg/L) Turbidity (NTU) Fluoride (mg/L)  \\\n",
              "0                           0.68            0.74             NaN   \n",
              "1                           0.81            1.45             NaN   \n",
              "2                           0.79            0.74             NaN   \n",
              "3                           0.92            0.63             NaN   \n",
              "4                           0.06             0.6             NaN   \n",
              "\n",
              "  Coliform (Quanti-Tray) (MPN /100mL) E.coli(Quanti-Tray) (MPN/100mL)  \n",
              "0                                  <1                              <1  \n",
              "1                                  <1                              <1  \n",
              "2                                  <1                              <1  \n",
              "3                                  <1                              <1  \n",
              "4                                  <1                              <1  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load dataset\n",
        "df_url='C:/Users/Admin/My Drive/MDSA/605/Water Quality.csv'\n",
        "df = pd.read_csv(df_url)\n",
        "#df.index += 1\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sample Number                            int64\n",
            "Sample Date                             object\n",
            "Sample Time                             object\n",
            "Sample Site                             object\n",
            "Sample class                            object\n",
            "Residual Free Chlorine (mg/L)          float64\n",
            "Turbidity (NTU)                         object\n",
            "Fluoride (mg/L)                         object\n",
            "Coliform (Quanti-Tray) (MPN /100mL)     object\n",
            "E.coli(Quanti-Tray) (MPN/100mL)         object\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# inspect data types\n",
        "data_types = df.dtypes\n",
        "print(data_types)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Sample Number                               0\n",
              "Sample Date                                 0\n",
              "Sample Time                                 0\n",
              "Sample Site                                 3\n",
              "Sample class                                0\n",
              "Residual Free Chlorine (mg/L)              49\n",
              "Turbidity (NTU)                            52\n",
              "Fluoride (mg/L)                        123714\n",
              "Coliform (Quanti-Tray) (MPN /100mL)       116\n",
              "E.coli(Quanti-Tray) (MPN/100mL)           116\n",
              "dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# null values\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0         0.9\n",
            "1         0.9\n",
            "2         0.9\n",
            "3         0.9\n",
            "4         0.9\n",
            "         ... \n",
            "142527    0.9\n",
            "142528    0.9\n",
            "142529    0.9\n",
            "142530    0.9\n",
            "142531    0.9\n",
            "Name: Coliform (Quanti-Tray) (MPN /100mL), Length: 142532, dtype: object\n",
            "0         0.9\n",
            "1         0.9\n",
            "2         0.9\n",
            "3         0.9\n",
            "4         0.9\n",
            "         ... \n",
            "142527    0.9\n",
            "142528    0.9\n",
            "142529    0.9\n",
            "142530    0.9\n",
            "142531    0.9\n",
            "Name: E.coli(Quanti-Tray) (MPN/100mL), Length: 142532, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# replace some special charaters in column coliform\n",
        "\n",
        "df['Coliform (Quanti-Tray) (MPN /100mL)'] = df['Coliform (Quanti-Tray) (MPN /100mL)'].replace('<1', 0.9)\n",
        "df['Coliform (Quanti-Tray) (MPN /100mL)'] = df['Coliform (Quanti-Tray) (MPN /100mL)'].replace('>200.5', 201)\n",
        "df['E.coli(Quanti-Tray) (MPN/100mL)'] = df['E.coli(Quanti-Tray) (MPN/100mL)'].replace('<1', 0.9)\n",
        "df['Turbidity (NTU)'] = df['Turbidity (NTU)'].replace('<0.10', 0.09)\n",
        "\n",
        "\n",
        "print(df['Coliform (Quanti-Tray) (MPN /100mL)'])\n",
        "print(df['E.coli(Quanti-Tray) (MPN/100mL)'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "## Replace special values to numerical format\n",
        "# Define a mapping of values to convert\n",
        "values_to_convert = {'1S03':1001,'1S03A':1002, '1S03B':1003, '1S04':1004, '1S07':1005,'1SCH3':1006,'1SCH4':1007, '1SCHA':1008, '1SCL1':1009, '3ISL3':1010, '3ISL4':1011, '3SC26':1012, '4ISL5':1013}\n",
        "\n",
        "# Replace the values in the DataFrame\n",
        "df['Sample Site'] = df['Sample Site'].replace(values_to_convert)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sample Number                            int64\n",
            "Sample Date                             object\n",
            "Sample Time                             object\n",
            "Sample Site                            float64\n",
            "Sample class                            object\n",
            "Residual Free Chlorine (mg/L)          float64\n",
            "Turbidity (NTU)                        float64\n",
            "Fluoride (mg/L)                         object\n",
            "Coliform (Quanti-Tray) (MPN /100mL)    float64\n",
            "E.coli(Quanti-Tray) (MPN/100mL)        float64\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# convert object to float\n",
        "df['Coliform (Quanti-Tray) (MPN /100mL)'] = df['Coliform (Quanti-Tray) (MPN /100mL)'].astype(float)\n",
        "df['E.coli(Quanti-Tray) (MPN/100mL)'] = df['E.coli(Quanti-Tray) (MPN/100mL)'].astype(float)\n",
        "df['Turbidity (NTU)'] = df['Turbidity (NTU)'].astype(float)\n",
        "df['Sample Site'] = df['Sample Site'].astype(float)\n",
        "data_types = df.dtypes\n",
        "print(data_types)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sample Number                            int64\n",
            "Sample Date                             object\n",
            "Sample Time                             object\n",
            "Sample Site                            float64\n",
            "Sample class                            object\n",
            "Residual Free Chlorine (mg/L)          float64\n",
            "Turbidity (NTU)                        float64\n",
            "Fluoride (mg/L)                         object\n",
            "Coliform (Quanti-Tray) (MPN /100mL)    float64\n",
            "E.coli(Quanti-Tray) (MPN/100mL)        float64\n",
            "dtype: object\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Sample Number                               0\n",
              "Sample Date                                 0\n",
              "Sample Time                                 0\n",
              "Sample Site                                 3\n",
              "Sample class                                0\n",
              "Residual Free Chlorine (mg/L)              49\n",
              "Turbidity (NTU)                            52\n",
              "Fluoride (mg/L)                        123714\n",
              "Coliform (Quanti-Tray) (MPN /100mL)       116\n",
              "E.coli(Quanti-Tray) (MPN/100mL)           116\n",
              "dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_types = df.dtypes\n",
        "print(data_types)\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# # export to csv file\n",
        "# df.to_csv('Water Quality01.csv', index=False) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "# drop NA so we can perform K clustering\n",
        "df = df.dropna(subset=['Turbidity (NTU)'])\n",
        "df = df.dropna(subset=['Residual Free Chlorine (mg/L)'])\n",
        "df = df.dropna(subset=['Coliform (Quanti-Tray) (MPN /100mL)'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 142412 entries, 0 to 142531\n",
            "Data columns (total 10 columns):\n",
            " #   Column                               Non-Null Count   Dtype  \n",
            "---  ------                               --------------   -----  \n",
            " 0   Sample Number                        142412 non-null  int64  \n",
            " 1   Sample Date                          142412 non-null  object \n",
            " 2   Sample Time                          142412 non-null  object \n",
            " 3   Sample Site                          142409 non-null  float64\n",
            " 4   Sample class                         142412 non-null  object \n",
            " 5   Residual Free Chlorine (mg/L)        142412 non-null  float64\n",
            " 6   Turbidity (NTU)                      142412 non-null  float64\n",
            " 7   Fluoride (mg/L)                      18811 non-null   object \n",
            " 8   Coliform (Quanti-Tray) (MPN /100mL)  142412 non-null  float64\n",
            " 9   E.coli(Quanti-Tray) (MPN/100mL)      142412 non-null  float64\n",
            "dtypes: float64(5), int64(1), object(4)\n",
            "memory usage: 12.0+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### B. EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "g5B5MkeFWzTK",
        "outputId": "5282634f-9d20-4b3c-a5bf-b7a8201a2921"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title Coliform by Sample class\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(15, 6))\n",
        "ax=df.boxplot(by='Sample class', column='Turbidity (NTU)', grid=False)\n",
        "plt.xticks(rotation=45, fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title Residual by Sample class\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(15, 6))\n",
        "ax=df.boxplot(by='Sample class', column='Residual Free Chlorine (mg/L)', grid=False)\n",
        "plt.xticks(rotation=45, fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title Coliform by Sample class\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(15, 6))\n",
        "ax=df.boxplot(by='Sample class', column='Coliform (Quanti-Tray) (MPN /100mL)', grid=False)\n",
        "plt.xticks(rotation=45, fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "P37kOHlrtbpn",
        "outputId": "7361635b-03c1-4fa3-d283-e7a67087b6f2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# prompt: generate scatter plot\n",
        "\n",
        "df.plot.scatter(x='Sample class', y='Residual Free Chlorine (mg/L)')\n",
        "plt.xticks(rotation=45, fontsize=8)\n",
        "plt.show()\n",
        "\n",
        "#df_frame.boxplot(by='Education', column='LoanAmount', grid=False)\n",
        "#plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### C. K-means Analysis \n",
        "K means clustering water samples based on turbidity & residual free chlorine levels – Compliance Samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "p-A6uUltuL3_"
      },
      "outputs": [],
      "source": [
        "##Load Libraries for PCA\n",
        "from sklearn.preprocessing import scale # Data scaling\n",
        "from sklearn import decomposition #PCA\n",
        "import pandas as pd # pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Residual Free Chlorine (mg/L)</th>\n",
              "      <th>Turbidity (NTU)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.81</td>\n",
              "      <td>1.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06</td>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.13</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.61</td>\n",
              "      <td>2.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Residual Free Chlorine (mg/L)  Turbidity (NTU)\n",
              "0                            0.68             0.74\n",
              "1                            0.81             1.45\n",
              "4                            0.06             0.60\n",
              "5                            0.13             0.75\n",
              "11                           0.61             2.53"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Filter the DataFrame to include only specific rows where 'Sample Class' is either 'Compliance' or 'Resampling Compliance'\n",
        "filtered_df = df[df['Sample class'].isin(['Compliance', 'Resample_Compliance'])]\n",
        "df01 = filtered_df[['Residual Free Chlorine (mg/L)', 'Turbidity (NTU)']]\n",
        "df01.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sRdgCwUeN2x4",
        "outputId": "d1da33b4-153a-490f-f2b0-e684d06aed5b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Residual Free Chlorine (mg/L)</th>\n",
              "      <th>Turbidity (NTU)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.81</td>\n",
              "      <td>1.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06</td>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.13</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.61</td>\n",
              "      <td>2.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Residual Free Chlorine (mg/L)  Turbidity (NTU)\n",
              "0                            0.68             0.74\n",
              "1                            0.81             1.45\n",
              "4                            0.06             0.60\n",
              "5                            0.13             0.75\n",
              "11                           0.61             2.53"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Standardize (scale, recenter) the height and weight columns - otherwise distances will mostly be based on weight\n",
        "scaler = skp.StandardScaler()\n",
        "df01_standardized = scaler.fit_transform(df01)\n",
        "df01_standardized = pd.DataFrame(df01)\n",
        "df01_standardized.columns = df01.columns\n",
        "df01_standardized.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lR-dq1cs050a"
      },
      "source": [
        "**Run PCA on the feature set dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NP2---4rxdbF",
        "outputId": "22e2321a-7e1a-477c-e1a1-0c81c1f85afe"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.036452</td>\n",
              "      <td>-0.183968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.757986</td>\n",
              "      <td>-0.203688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.196858</td>\n",
              "      <td>0.407273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.037906</td>\n",
              "      <td>0.361074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.794610</td>\n",
              "      <td>0.159370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        PC1       PC2\n",
              "0  0.036452 -0.183968\n",
              "1  0.757986 -0.203688\n",
              "2 -0.196858  0.407273\n",
              "3 -0.037906  0.361074\n",
              "4  1.794610  0.159370"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# prompt: Generate principal components for table above\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA \n",
        "pca = PCA(n_components=2)\n",
        "df01_pca = pca.fit_transform(df01_standardized)\n",
        "df01_pca = pd.DataFrame(df01_pca)\n",
        "df01_pca.columns = ['PC1', 'PC2']\n",
        "df01_pca.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zTN8ChHKzc8j",
        "outputId": "d5da8574-44e6-46a3-a1cb-42db587efd20"
      },
      "outputs": [
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              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>-0.203688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.196858</td>\n",
              "      <td>0.407273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.037906</td>\n",
              "      <td>0.361074</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.159370</td>\n",
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            "text/plain": [
              "        PC1       PC2\n",
              "0  0.036452 -0.183968\n",
              "1  0.757986 -0.203688\n",
              "2 -0.196858  0.407273\n",
              "3 -0.037906  0.361074\n",
              "4  1.794610  0.159370"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# prompt: load all pca score with column names\n",
        "\n",
        "pca_columns = ['PC' + str(i) for i in range(1, len(df01_pca.columns) + 1)]\n",
        "df01_pca.columns = pca_columns\n",
        "df01_pca.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1STXWRnv0w45"
      },
      "source": [
        "**Explained variance for each PC**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHyriW7R0Zqj",
        "outputId": "96489ae3-b628-4c53-a40b-7740aa0747d4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Explained Variance\n",
            "PC1            0.647324\n",
            "PC2            0.352676\n"
          ]
        }
      ],
      "source": [
        "# prompt: generate the explain variance for the table above\n",
        "\n",
        "pca_variances = pca.explained_variance_ratio_\n",
        "pca_variances = pd.DataFrame(pca_variances)\n",
        "pca_variances.columns = ['Explained Variance']\n",
        "pca_variances.index = pca_columns\n",
        "print(pca_variances)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
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        "id": "2Hu0-g8G1-zb",
        "outputId": "f8c6a73e-faad-46eb-c28b-7cb8f4c568a7"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Explained Variance</th>\n",
              "      <th>Cumulative Variance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>PC1</th>\n",
              "      <td>0.647324</td>\n",
              "      <td>0.647324</td>\n",
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              "    <tr>\n",
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              "     Explained Variance  Cumulative Variance\n",
              "PC1            0.647324             0.647324\n",
              "PC2            0.352676             1.000000"
            ]
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          "execution_count": 26,
          "metadata": {},
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      ],
      "source": [
        "# prompt: generate cumulative variance and explained variance\n",
        "\n",
        "df01_pca['Cumulative Variance'] = df01_pca['PC1'] + df01_pca['PC2']\n",
        "df01_pca.head()\n",
        "\n",
        "pca_variances['Cumulative Variance'] = pca_variances['Explained Variance'].cumsum()\n",
        "pca_variances.head()\n"
      ]
    },
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      "cell_type": "code",
      "execution_count": 27,
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                          0.5555555555555556,
                          "#d8576b"
                        ],
                        [
                          0.6666666666666666,
                          "#ed7953"
                        ],
                        [
                          0.7777777777777778,
                          "#fb9f3a"
                        ],
                        [
                          0.8888888888888888,
                          "#fdca26"
                        ],
                        [
                          1,
                          "#f0f921"
                        ]
                      ]
                    },
                    "colorway": [
                      "#636efa",
                      "#EF553B",
                      "#00cc96",
                      "#ab63fa",
                      "#FFA15A",
                      "#19d3f3",
                      "#FF6692",
                      "#B6E880",
                      "#FF97FF",
                      "#FECB52"
                    ],
                    "font": {
                      "color": "#2a3f5f"
                    },
                    "geo": {
                      "bgcolor": "white",
                      "lakecolor": "white",
                      "landcolor": "#E5ECF6",
                      "showlakes": true,
                      "showland": true,
                      "subunitcolor": "white"
                    },
                    "hoverlabel": {
                      "align": "left"
                    },
                    "hovermode": "closest",
                    "mapbox": {
                      "style": "light"
                    },
                    "paper_bgcolor": "white",
                    "plot_bgcolor": "#E5ECF6",
                    "polar": {
                      "angularaxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      },
                      "bgcolor": "#E5ECF6",
                      "radialaxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      }
                    },
                    "scene": {
                      "xaxis": {
                        "backgroundcolor": "#E5ECF6",
                        "gridcolor": "white",
                        "gridwidth": 2,
                        "linecolor": "white",
                        "showbackground": true,
                        "ticks": "",
                        "zerolinecolor": "white"
                      },
                      "yaxis": {
                        "backgroundcolor": "#E5ECF6",
                        "gridcolor": "white",
                        "gridwidth": 2,
                        "linecolor": "white",
                        "showbackground": true,
                        "ticks": "",
                        "zerolinecolor": "white"
                      },
                      "zaxis": {
                        "backgroundcolor": "#E5ECF6",
                        "gridcolor": "white",
                        "gridwidth": 2,
                        "linecolor": "white",
                        "showbackground": true,
                        "ticks": "",
                        "zerolinecolor": "white"
                      }
                    },
                    "shapedefaults": {
                      "line": {
                        "color": "#2a3f5f"
                      }
                    },
                    "ternary": {
                      "aaxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      },
                      "baxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      },
                      "bgcolor": "#E5ECF6",
                      "caxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      }
                    },
                    "title": {
                      "x": 0.05
                    },
                    "xaxis": {
                      "automargin": true,
                      "gridcolor": "white",
                      "linecolor": "white",
                      "ticks": "",
                      "title": {
                        "standoff": 15
                      },
                      "zerolinecolor": "white",
                      "zerolinewidth": 2
                    },
                    "yaxis": {
                      "automargin": true,
                      "gridcolor": "white",
                      "linecolor": "white",
                      "ticks": "",
                      "title": {
                        "standoff": 15
                      },
                      "zerolinecolor": "white",
                      "zerolinewidth": 2
                    }
                  }
                },
                "title": {
                  "text": "Cumulative Variance by PC"
                },
                "xaxis": {
                  "anchor": "y",
                  "domain": [
                    0,
                    1
                  ],
                  "title": {
                    "text": "x"
                  }
                },
                "yaxis": {
                  "anchor": "x",
                  "domain": [
                    0,
                    1
                  ],
                  "title": {
                    "text": "Cumulative Variance"
                  }
                }
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# prompt: plot the table above\n",
        "import plotly.express as px\n",
        "fig = px.bar(pca_variances, x=pca_columns, y='Explained Variance', title='Explained Variance by PC',\n",
        "             labels={'Explained Variance': 'Explained Variance', 'PC': 'PC'},\n",
        "             color_discrete_sequence=px.colors.qualitative.Pastel1)\n",
        "fig.show()\n",
        "fig = px.bar(pca_variances, x=pca_columns, y='Cumulative Variance', title='Cumulative Variance by PC',\n",
        "             labels={'Cumulative Variance': 'Cumulative Variance', 'PC': 'PC'},\n",
        "             color_discrete_sequence=px.colors.qualitative.Pastel1)\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Elbow Method is a common technique used to determine the optimal number of clusters\n",
        "# Selecting relevant features\n",
        "from sklearn.cluster import KMeans\n",
        "data = df01_standardized[['Turbidity (NTU)', 'Residual Free Chlorine (mg/L)']]\n",
        "\n",
        "# It's a good practice to scale your data for k-means clustering\n",
        "scaler = StandardScaler()\n",
        "data_scaled = scaler.fit_transform(data)\n",
        "\n",
        "# Range of k we want to try\n",
        "k_values = range(1, 10)\n",
        "inertias = []\n",
        "\n",
        "for k in k_values:\n",
        "    model = KMeans(n_clusters=k, random_state=1)\n",
        "    model.fit(data_scaled)\n",
        "    inertias.append(model.inertia_)\n",
        "\n",
        "# Plotting the elbow curve\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.plot(k_values, inertias, 'bo-')\n",
        "plt.xlabel('Number of Clusters (k)')\n",
        "plt.ylabel('Inertia')\n",
        "plt.title('Elbow Method For Optimal k')\n",
        "plt.xticks(k_values)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Applying K-means clustering\n",
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters=3, random_state=42)  # Adjust the number of clusters as needed\n",
        "df01_standardized['Cluster'] = kmeans.fit_predict(df01_standardized[['Turbidity (NTU)', 'Residual Free Chlorine (mg/L)']])\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "colors = ['blue', 'orange', 'green']  # Color for each cluster\n",
        "for cluster in df01_standardized['Cluster'].unique():\n",
        "    clustered_data = df01_standardized[df01_standardized['Cluster'] == cluster]\n",
        "    plt.scatter(clustered_data['Turbidity (NTU)'], clustered_data['Residual Free Chlorine (mg/L)'], label=f'Cluster {cluster}', color=colors[cluster])\n",
        "    \n",
        "# Plot centroids\n",
        "centroids = kmeans.cluster_centers_\n",
        "plt.scatter(centroids[:, 0], centroids[:, 1], s=50, c='red', marker='X', label='Centroids')\n",
        "\n",
        "plt.title('K-means Clustering of Turbidity and Residual Free Chlorine')\n",
        "plt.xlabel('Turbidity (NTU)')\n",
        "plt.ylabel('Residual Free Chlorine (mg/L)')\n",
        "#plt.xticks(range(min(df['Turbidity (NTU)']), max(df['Turbidity (NTU)'])+1))  # Setting x-ticks to show all encoded values\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "# remove outliers\n",
        "import pandas as pd\n",
        "\n",
        "# Calculate Q1 and Q3\n",
        "Q1A = df01_standardized[['Turbidity (NTU)', 'Residual Free Chlorine (mg/L)']].quantile(0.25)\n",
        "Q3A = df01_standardized[['Turbidity (NTU)', 'Residual Free Chlorine (mg/L)']].quantile(0.75)\n",
        "\n",
        "# Compute the IQR by subtracting Q1 from Q3\n",
        "IQRA = Q3A - Q1A\n",
        "\n",
        "# Filter out outliers\n",
        "outliers = (\n",
        "    (df01_standardized['Turbidity (NTU)'] >= (Q1A['Turbidity (NTU)'] - 1.5 * IQRA['Turbidity (NTU)'])) &\n",
        "    (df01_standardized['Turbidity (NTU)'] <= (Q3A['Turbidity (NTU)'] + 1.5 * IQRA['Turbidity (NTU)'])) &\n",
        "    (df01_standardized['Residual Free Chlorine (mg/L)'] >= (Q1A['Residual Free Chlorine (mg/L)'] - 1.5 * IQRA['Residual Free Chlorine (mg/L)'])) &\n",
        "    (df01_standardized['Residual Free Chlorine (mg/L)'] <= (Q3A['Residual Free Chlorine (mg/L)'] + 1.5 * IQRA['Residual Free Chlorine (mg/L)']))\n",
        ")\n",
        "\n",
        "# Apply the filter to the DataFrame to exclude outliers\n",
        "df01_filtered= df01_standardized[outliers]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Applying K-means clustering\n",
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters=3, random_state=42)  # Adjust the number of clusters as needed\n",
        "df01_filtered['Cluster'] = kmeans.fit_predict(df01_filtered[['Turbidity (NTU)', 'Residual Free Chlorine (mg/L)']])\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "colors = ['blue', 'orange', 'green']  # Color for each cluster\n",
        "for clust in df01_filtered['Cluster'].unique():\n",
        "    clust_data = df01_filtered[df01_filtered['Cluster'] == clust]\n",
        "    plt.scatter(clust_data['Turbidity (NTU)'], clust_data['Residual Free Chlorine (mg/L)'], label=f'Cluster {clust}', color=colors[clust])\n",
        "    \n",
        "# Plot centroids\n",
        "centroids01 = kmeans.cluster_centers_\n",
        "plt.scatter(centroids01[:, 0], centroids01[:, 1], s=50, c='red', marker='X', label='Centroids')\n",
        "\n",
        "plt.title('K-means Clustering of Turbidity and Residual Free Chlorine')\n",
        "plt.xlabel('Turbidity (NTU)')\n",
        "plt.ylabel('Residual Free Chlorine (mg/L)')\n",
        "#plt.xticks(range(min(df['Turbidity (NTU)']), max(df['Turbidity (NTU)'])+1))  # Setting x-ticks to show all encoded values\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Residual Free Chlorine (mg/L)</th>\n",
              "      <th>Turbidity (NTU)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.81</td>\n",
              "      <td>1.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06</td>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.13</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.61</td>\n",
              "      <td>2.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142525</th>\n",
              "      <td>0.04</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142528</th>\n",
              "      <td>0.26</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142529</th>\n",
              "      <td>0.33</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142530</th>\n",
              "      <td>0.33</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142531</th>\n",
              "      <td>0.04</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>89336 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Residual Free Chlorine (mg/L)  Turbidity (NTU)\n",
              "0                                0.68             0.74\n",
              "1                                0.81             1.45\n",
              "4                                0.06             0.60\n",
              "5                                0.13             0.75\n",
              "11                               0.61             2.53\n",
              "...                               ...              ...\n",
              "142525                           0.04             0.79\n",
              "142528                           0.26             0.78\n",
              "142529                           0.33             0.79\n",
              "142530                           0.33             0.83\n",
              "142531                           0.04             0.70\n",
              "\n",
              "[89336 rows x 2 columns]"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df01_standardized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming 'df' is your initial DataFrame already loaded with data\n",
        "# Filter the DataFrame to include only specific rows where 'Sample Class' is either 'Compliance' or 'Resampling Compliance'\n",
        "filtered_df = df[df['Sample class'].isin(['Compliance', 'Resample_Compliance'])]\n",
        "df01 = filtered_df[['Residual Free Chlorine (mg/L)', 'Turbidity (NTU)']]\n",
        "\n",
        "# Standardize the selected columns\n",
        "scaler = StandardScaler()\n",
        "df01_standardized = scaler.fit_transform(df01)\n",
        "df01_standardized = pd.DataFrame(df01_standardized, columns=df01.columns)\n",
        "\n",
        "# Splitting the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df01_standardized,\n",
        "    filtered_df['Sample class'],  # Ensure to use the filtered_df for corresponding target values\n",
        "    test_size=0.3, random_state=42)\n",
        "\n",
        "# Creating and training the KNeighborsClassifier model\n",
        "df_neigh = KNeighborsClassifier(n_neighbors=3)\n",
        "df_neigh.fit(X_train, y_train)\n",
        "\n",
        "# Making predictions\n",
        "y_pred = df_neigh.predict(X_test)\n",
        "\n",
        "# Generating the confusion matrix\n",
        "labels = np.unique(filtered_df['Sample class'])\n",
        "cm = confusion_matrix(y_test, y_pred, labels=labels)\n",
        "\n",
        "# Displaying the confusion matrix with labels and scores\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "cax = ax.matshow(cm, cmap=plt.cm.Blues)\n",
        "plt.title('Confusion Matrix', pad=20)\n",
        "fig.colorbar(cax)\n",
        "\n",
        "# Set labels for axes\n",
        "ax.set_xticklabels([''] + list(labels), rotation=90)\n",
        "ax.set_yticklabels([''] + list(labels))\n",
        "\n",
        "# Display the scores on the confusion matrix\n",
        "for (i, j), val in np.ndenumerate(cm):\n",
        "    ax.text(j, i, f'{val}', ha='center', va='center', color='black')\n",
        "\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### D. Classification_ Logistic Regression\n",
        "Can we predict the classification of water samples as 'Compliance' or 'Operational' based on their residual free chlorine levels and coliform?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.707307368223949\n",
            "Confusion Matrix:\n",
            " [[ 8100  8179]\n",
            " [ 4326 22119]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      0.50      0.56     16279\n",
            "           1       0.73      0.84      0.78     26445\n",
            "\n",
            "    accuracy                           0.71     42724\n",
            "   macro avg       0.69      0.67      0.67     42724\n",
            "weighted avg       0.70      0.71      0.70     42724\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "\n",
        "# Filter necessary columns\n",
        "df_filtered = df[['Sample class', 'Residual Free Chlorine (mg/L)', 'Coliform (Quanti-Tray) (MPN /100mL)']]\n",
        "\n",
        "# Convert Sample Class into a binary variable\n",
        "df_filtered['Sample class'] = df_filtered['Sample class'].apply(lambda x: 1 if x == 'Compliance' else 0)\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X = df_filtered[['Residual Free Chlorine (mg/L)', 'Coliform (Quanti-Tray) (MPN /100mL)']]\n",
        "y = df_filtered['Sample class']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Create and train the logistic regression model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Log Regression Formula "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic Regression Formula:\n",
            "p = 1 / (1 + exp(-(3.4886 + -5.0594*Residual Free Chlorine (mg/L) + -0.0068*Coliform (Quanti-Tray) (MPN /100mL))))\n"
          ]
        }
      ],
      "source": [
        "# # Assuming 'model' is your trained Logistic Regression model\n",
        "# intercept = model.intercept_\n",
        "# coefficients = model.coef_\n",
        "\n",
        "# print(\"Intercept (β0):\", intercept)\n",
        "# print(\"Coefficients (β1, β2, ...):\", coefficients)\n",
        "\n",
        "# Assuming 'model' is your trained Logistic Regression model\n",
        "intercept = model.intercept_[0]  # Intercept β0\n",
        "coefficients = model.coef_[0]    # Coefficients β1, β2 for each feature\n",
        "\n",
        "# Feature names, corresponding to x1, x2\n",
        "features = ['Residual Free Chlorine (mg/L)', 'Coliform (Quanti-Tray) (MPN /100mL)']\n",
        "\n",
        "# Constructing the formula string\n",
        "formula = f\"p = 1 / (1 + exp(-({intercept:.4f}\"\n",
        "for coef, feature in zip(coefficients, features):\n",
        "    formula += f\" + {coef:.4f}*{feature}\"\n",
        "formula += \")))\"\n",
        "\n",
        "print(\"Logistic Regression Formula:\")\n",
        "print(formula)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Boundaries Plot "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Ensure X_train and X_test are numpy arrays if not already\n",
        "X_train = X_train.values\n",
        "X_test = X_test.values\n",
        "\n",
        "# Plotting decision boundaries\n",
        "x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1\n",
        "y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
        "                     np.arange(y_min, y_max, 0.1))\n",
        "\n",
        "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "\n",
        "plt.contourf(xx, yy, Z, alpha=0.4)\n",
        "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=20, edgecolor='k')\n",
        "plt.title('Logistic Regression Decision Boundary')\n",
        "plt.xlabel('Residual Free Chlorine (mg/L)')\n",
        "plt.ylabel('Coliform (Quanti-Tray) (MPN /100mL)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Compute the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "labels = ['Operational', 'Compliance']\n",
        "\n",
        "# Plot the confusion matrix using seaborn\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.show()\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}