pH Level Forecasting_Classification.ipynb

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pd.read_csv('/content/drive/MyDrive/Colab Notebooks/pH Level Forecasting/Dataset_new.csv')\n","df.sample(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"8a-T_3MMH-Kw","executionInfo":{"status":"ok","timestamp":1717508606153,"user_tz":-60,"elapsed":978,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"231f31a4-a699-408c-9b36-ce7822abb763"},"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Unnamed: 0  Temp (oC)    pH  SEC (µS/cm)  Turbidity (<NTU)  \\\n","138         138       28.0  7.33       1053.0              1.22   \n","136         136       27.9  7.30        980.7              1.12   \n","29           29       24.4  6.22        183.2              0.24   \n","37           37       24.7  6.33        218.9              0.27   \n","82           82       26.6  6.82        467.8              0.41   \n","\n","     Total Iron (mg/l)  Titration 1  Titration 2  N_VALUE  \\\n","138               0.02        459.0        452.0     1.60   \n","136               0.02        423.0        420.0     1.60   \n","29                0.00        109.0        108.0     0.16   \n","37                0.00        124.0        118.0     0.16   \n","82                0.01        206.0        210.0     1.60   \n","\n","     Tryptophan_Probe_µgL  Final HCO3  \n","138                  0.92  512.007802  \n","136                  0.90  485.188346  \n","29                   0.20   57.418018  \n","37                   0.26   67.780080  \n","82                   0.48  158.478605  "],"text/html":["\n","  <div id=\"df-94e856fd-666c-4982-9d0a-9aa098d650f7\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table 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<td>206.0</td>\n","      <td>210.0</td>\n","      <td>1.60</td>\n","      <td>0.48</td>\n","      <td>158.478605</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94e856fd-666c-4982-9d0a-9aa098d650f7')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      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        0.1   19.261246  \n","4         50.0         49.0     0.16                   0.1   21.211752  "],"text/html":["\n","  <div id=\"df-6f85d861-8100-44a2-9969-2b34c699d7f2\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Temp (oC)</th>\n","      <th>pH</th>\n","      <th>SEC (µS/cm)</th>\n","      <th>Turbidity (&lt;NTU)</th>\n","      <th>Total Iron (mg/l)</th>\n","      <th>Titration 1</th>\n","      <th>Titration 2</th>\n","      <th>N_VALUE</th>\n","      <th>Tryptophan_Probe_µgL</th>\n","      <th>Final HCO3</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>22.4</td>\n","      <td>6.815</td>\n","      <td>31.56</td>\n","      <td>0.08</td>\n","      <td>-0.007</td>\n","      <td>28.0</td>\n","      <td>26.0</td>\n","      <td>0.16</td>\n","      <td>0.1</td>\n","      <td>10.362063</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>22.4</td>\n","      <td>5.510</td>\n","      <td>46.96</td>\n","      <td>0.08</td>\n","      <td>-0.003</td>\n","      <td>41.0</td>\n","      <td>44.0</td>\n","      <td>0.16</td>\n","      <td>0.1</td>\n","      <td>14.506888</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>23.1</td>\n","      <td>5.650</td>\n","      <td>59.19</td>\n","      <td>0.11</td>\n","      <td>-0.001</td>\n","      <td>44.0</td>\n","      <td>46.0</td>\n","      <td>0.16</td>\n","      <td>0.1</td>\n","      <td>17.310740</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>23.3</td>\n","      <td>5.680</td>\n","      <td>73.56</td>\n","      <td>0.12</td>\n","      <td>0.000</td>\n","      <td>50.0</td>\n","      <td>48.0</td>\n","      <td>0.16</td>\n","      <td>0.1</td>\n","      <td>19.261246</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>23.4</td>\n","      <td>5.810</td>\n","      <td>78.68</td>\n","      <td>0.14</td>\n","      <td>0.000</td>\n","      <td>50.0</td>\n","      <td>49.0</td>\n","      <td>0.16</td>\n","      <td>0.1</td>\n","      <td>21.211752</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f85d861-8100-44a2-9969-2b34c699d7f2')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path 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\"max\": 0.04,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          0.007,\n          0.013,\n          0.008\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Titration 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 126.35843477656769,\n        \"min\": 28.0,\n        \"max\": 610.0,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          172.0,\n          252.0,\n          80.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Titration 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131.7363120338855,\n        \"min\": 26.0,\n        \"max\": 641.0,\n        \"num_unique_values\": 128,\n        \"samples\": [\n          170.0,\n          137.0,\n          91.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N_VALUE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7080021756642713,\n        \"min\": 0.16,\n        \"max\": 1.6,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.6,\n          0.16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tryptophan_Probe_\\u00b5gL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26399905077753755,\n        \"min\": 0.1,\n        \"max\": 1.3,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          0.92,\n          1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Final HCO3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 188.66931415374404,\n        \"min\": 10.3620626600024,\n        \"max\": 749.725710106059,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          130.074363037913,\n          699.743996098988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CvKvtztEMLwh","executionInfo":{"status":"ok","timestamp":1717508606153,"user_tz":-60,"elapsed":19,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"94a28813-94bf-48a7-a3cd-8f871bbc0523"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(162, 10)"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["# pH Level Classification\n","def classify_pH(value):\n","    if 6.0 <= value <= 9.5:\n","        return 1  # Good water quality\n","    else:\n","        return 0  # Bad water quality\n","\n","# Apply the function to create the 'pH_class' column\n","df['pH_class'] = df['pH'].apply(lambda x: classify_pH(x))\n","\n","# Display the updated DataFrame\n","df.sample(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"RSEpVGL7MoJr","executionInfo":{"status":"ok","timestamp":1717508606154,"user_tz":-60,"elapsed":19,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"899dc9b9-37f1-48ac-e032-fe0ccae28e62"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Temp (oC)    pH  SEC (µS/cm)  Turbidity (<NTU)  Total Iron (mg/l)  \\\n","150       28.5  7.58      1272.00              0.40              0.040   \n","28        24.4  6.22       181.80              0.22              0.000   \n","50        24.9  6.55       284.70              0.29              0.000   \n","110       27.2  7.04       673.20              0.65              0.011   \n","2         23.1  5.65        59.19              0.11             -0.001   \n","125       27.6  7.15       798.00              0.84              0.020   \n","97        27.0  6.96       534.60              0.52              0.010   \n","23        24.2  6.11       171.20              0.21              0.000   \n","124       27.6  7.15       792.80              0.82              0.020   \n","8         23.6  5.95        89.65              0.15              0.000   \n","\n","     Titration 1  Titration 2  N_VALUE  Tryptophan_Probe_µgL  Final HCO3  \\\n","150        198.5        202.5     1.60                  1.30  685.115202   \n","28         107.0        105.0     0.16                  0.20   56.198952   \n","50         140.0        141.0     0.16                  0.30   94.477630   \n","110        293.0        290.0     1.60                  0.64  285.261490   \n","2           44.0         46.0     0.16                  0.10   17.310740   \n","125        372.0        373.0     1.60                  0.70  393.758381   \n","97         247.0        242.0     1.60                  0.52  211.507985   \n","23          83.0         91.0     0.16                  0.20   48.518835   \n","124        367.0        368.0     1.60                  0.70  391.320249   \n","8           54.0         54.0     0.16                  0.20   30.720468   \n","\n","     pH_class  \n","150         1  \n","28          1  \n","50          1  \n","110         1  \n","2           0  \n","125         1  \n","97          1  \n","23          1  \n","124         1  \n","8           0  "],"text/html":["\n","  <div id=\"df-a5c2da8f-7849-4e0a-98e5-4d850e162a2b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Temp (oC)</th>\n","      <th>pH</th>\n","      <th>SEC (µS/cm)</th>\n","      <th>Turbidity (&lt;NTU)</th>\n","      <th>Total Iron (mg/l)</th>\n","      <th>Titration 1</th>\n","      <th>Titration 2</th>\n","      <th>N_VALUE</th>\n","      <th>Tryptophan_Probe_µgL</th>\n","      <th>Final HCO3</th>\n","      <th>pH_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>150</th>\n","      <td>28.5</td>\n","      <td>7.58</td>\n","      <td>1272.00</td>\n","      <td>0.40</td>\n","      <td>0.040</td>\n","      <td>198.5</td>\n","      <td>202.5</td>\n","      <td>1.60</td>\n","      <td>1.30</td>\n","      <td>685.115202</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>24.4</td>\n","      <td>6.22</td>\n","      <td>181.80</td>\n","      <td>0.22</td>\n","      <td>0.000</td>\n","      <td>107.0</td>\n","      <td>105.0</td>\n","      <td>0.16</td>\n","      <td>0.20</td>\n","      <td>56.198952</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>24.9</td>\n","      <td>6.55</td>\n","      <td>284.70</td>\n","      <td>0.29</td>\n","      <td>0.000</td>\n","      <td>140.0</td>\n","      <td>141.0</td>\n","      <td>0.16</td>\n","      <td>0.30</td>\n","      <td>94.477630</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>110</th>\n","      <td>27.2</td>\n","      <td>7.04</td>\n","      <td>673.20</td>\n","      <td>0.65</td>\n","      <td>0.011</td>\n","      <td>293.0</td>\n","      <td>290.0</td>\n","      <td>1.60</td>\n","      <td>0.64</td>\n","      <td>285.261490</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>23.1</td>\n","      <td>5.65</td>\n","      <td>59.19</td>\n","      <td>0.11</td>\n","      <td>-0.001</td>\n","      <td>44.0</td>\n","      <td>46.0</td>\n","      <td>0.16</td>\n","      <td>0.10</td>\n","      <td>17.310740</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>125</th>\n","      <td>27.6</td>\n","      <td>7.15</td>\n","      <td>798.00</td>\n","      <td>0.84</td>\n","      <td>0.020</td>\n","      <td>372.0</td>\n","      <td>373.0</td>\n","      <td>1.60</td>\n","      <td>0.70</td>\n","      <td>393.758381</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>27.0</td>\n","      <td>6.96</td>\n","      <td>534.60</td>\n","      <td>0.52</td>\n","      <td>0.010</td>\n","      <td>247.0</td>\n","      <td>242.0</td>\n","      <td>1.60</td>\n","      <td>0.52</td>\n","      <td>211.507985</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>24.2</td>\n","      <td>6.11</td>\n","      <td>171.20</td>\n","      <td>0.21</td>\n","      <td>0.000</td>\n","      <td>83.0</td>\n","      <td>91.0</td>\n","      <td>0.16</td>\n","      <td>0.20</td>\n","      <td>48.518835</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>124</th>\n","      <td>27.6</td>\n","      <td>7.15</td>\n","      <td>792.80</td>\n","      <td>0.82</td>\n","      <td>0.020</td>\n","      <td>367.0</td>\n","      <td>368.0</td>\n","      <td>1.60</td>\n","      <td>0.70</td>\n","      <td>391.320249</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>23.6</td>\n","      <td>5.95</td>\n","      <td>89.65</td>\n","      <td>0.15</td>\n","      <td>0.000</td>\n","      <td>54.0</td>\n","      <td>54.0</td>\n","      <td>0.16</td>\n","      <td>0.20</td>\n","      <td>30.720468</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5c2da8f-7849-4e0a-98e5-4d850e162a2b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  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\"num_unique_values\": 6,\n        \"samples\": [\n          0.04,\n          0.0,\n          0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Titration 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 124.28919010830258,\n        \"min\": 44.0,\n        \"max\": 372.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          367.0,\n          107.0,\n          372.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Titration 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 123.23267288615737,\n        \"min\": 46.0,\n        \"max\": 373.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          368.0,\n          105.0,\n          373.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      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                float64\n","SEC (µS/cm)             float64\n","Turbidity (<NTU)        float64\n","Total Iron (mg/l)       float64\n","Titration 1             float64\n","Titration 2             float64\n","N_VALUE                 float64\n","Tryptophan_Probe_µgL    float64\n","Final HCO3              float64\n","pH_class                  int64\n","dtype: object"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["# input data\n","X = df.drop(['pH','pH_class'], axis=1)\n","# target variable\n","y = df.pH_class"],"metadata":{"id":"4tB3NLgQOfy3","executionInfo":{"status":"ok","timestamp":1717508606154,"user_tz":-60,"elapsed":17,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["X.sample(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"pKzO4h3JW2mK","executionInfo":{"status":"ok","timestamp":1717508606154,"user_tz":-60,"elapsed":16,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"bdbef48c-5d19-4723-ef5c-46f2ba4680ba"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Temp (oC)  SEC (µS/cm)  Turbidity (<NTU)  Total Iron (mg/l)  Titration 1  \\\n","35       24.6       207.20              0.25              0.000        119.0   \n","74       26.3       409.60              0.38              0.008        187.0   \n","1        22.4        46.96              0.08             -0.003         41.0   \n","21       24.1       159.60              0.20              0.000         80.0   \n","55       25.2       311.50              0.30              0.000        150.0   \n","\n","    Titration 2  N_VALUE  Tryptophan_Probe_µgL  Final HCO3  \n","35        116.0     0.16                  0.24   66.439108  \n","74        191.0     1.60                  0.40  136.535414  \n","1          44.0     0.16                  0.10   14.506888  \n","21         79.0     0.16                  0.20   44.495916  \n","55        154.0     0.16                  0.30  101.670121  "],"text/html":["\n","  <div id=\"df-fc84fac6-46f2-4643-bb60-f540d652e5fb\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Temp (oC)</th>\n","      <th>SEC (µS/cm)</th>\n","      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df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":454},"id":"qC8N-hTjOzC8","executionInfo":{"status":"ok","timestamp":1717508606843,"user_tz":-60,"elapsed":705,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"73b956e3-09f9-46a4-948e-89633582f7b4"},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["X_bal = X.copy()\n","y_bal = y.copy()"],"metadata":{"id":"-7uvZZPAOylb","executionInfo":{"status":"ok","timestamp":1717508606845,"user_tz":-60,"elapsed":8,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["# Balancing the data with SMOTE (Synthetic Minority Over-sampling Technique)\n","from imblearn.over_sampling import SMOTE\n","\n","# initialize and fit SMOTE\n","Smote = SMOTE(random_state=42)\n","X_smote_over, y_smote_over = Smote.fit_resample(X_bal, y_bal)\n","\n","# check new distribution\n","y_smote_over.value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i2FNVKmXR37h","executionInfo":{"status":"ok","timestamp":1717508608189,"user_tz":-60,"elapsed":1352,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"751ed2e5-8e3c-4c90-f948-27c1d70efd0e"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pH_class\n","1    153\n","0    153\n","Name: count, dtype: int64"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["# check New distribution of the pH Class\n","ax = sns.countplot(x = y_smote_over, data = df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":454},"id":"iTOKQslmR34z","executionInfo":{"status":"ok","timestamp":1717508608796,"user_tz":-60,"elapsed":609,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"e0334946-a796-471e-908d-413f853d79df"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjsAAAG1CAYAAAAfhDVuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAm50lEQVR4nO3df1DU953H8dci8qMqS1BhpYFIExOJUfDUIImTojKHmhhtaFI7nOE8TxsDpkguGuaCnp4pNU0sJyFSM/XXVZte2mqjaWk9otBGRMXYNNEYbahysQtJFQgYFpS9PzrZ6RY0Bhb2y8fnY2Zn8v18v/vlvZ3Z+pzvfhdsbrfbLQAAAEMF+HsAAACA3kTsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKP5NXYqKio0e/ZsRUdHy2azaffu3Z2OOXnypB588EHZ7XYNGjRIkyZN0rlz5zz7W1tblZWVpaFDh2rw4MFKT09XXV1dH74KAABgZX6NnZaWFiUkJKi4uLjL/X/84x81ZcoUjR49WgcOHNDbb7+t/Px8hYSEeI5ZtmyZ9uzZo1dffVXl5eU6f/68Hnroob56CQAAwOJsVvlDoDabTbt27dLcuXM9a/PmzdPAgQP13//9310+p7GxUcOHD9fOnTv19a9/XZL03nvvKT4+XpWVlZo8efJ1/eyOjg6dP39eQ4YMkc1m6/FrAQAAvc/tduuTTz5RdHS0AgKufv0msA9n+kI6Ojr0+uuva/ny5UpLS9Nbb72luLg45eXleYKourpa7e3tSk1N9Txv9OjRio2NvWbsuFwuuVwuz/aHH36oO++8s1dfDwAA6B21tbW6+eabr7rfsrFTX1+v5uZmffe739XatWu1bt06lZaW6qGHHtL+/fv11a9+VU6nU0FBQQoPD/d6blRUlJxO51XPXVBQoNWrV3dar62tVVhYmK9fCgAA6AVNTU2KiYnRkCFDrnmcZWOno6NDkjRnzhwtW7ZMkpSYmKiDBw+qpKREX/3qV7t97ry8POXm5nq2P/sfKywsjNgBAKCf+bxbUCwbO8OGDVNgYGCnj5fi4+P1u9/9TpLkcDjU1tamhoYGr6s7dXV1cjgcVz13cHCwgoODe2VuAABgLZb9PTtBQUGaNGmSTp065bX+/vvv65ZbbpEkTZgwQQMHDlRZWZln/6lTp3Tu3DklJyf36bwAAMCa/Hplp7m5WWfOnPFs19TU6Pjx44qIiFBsbKyeeuopfeMb39B9992nqVOnqrS0VHv27NGBAwckSXa7XQsXLlRubq4iIiIUFhampUuXKjk5+bq/iQUAAMzm16+eHzhwQFOnTu20npmZqa1bt0qSNm/erIKCAv3f//2f7rjjDq1evVpz5szxHNva2qonn3xSP/7xj+VyuZSWlqaXXnrpmh9j/b2mpibZ7XY1NjZyzw4AAP3E9f77bZnfs+NPxA4AAP3P9f77bdl7dgAAAHyB2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABG8+sfAr3RTHhqu79HACyn+nuP+nsEn+D9DXRmlfc3V3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABG82vsVFRUaPbs2YqOjpbNZtPu3buveuxjjz0mm82mwsJCr/ULFy4oIyNDYWFhCg8P18KFC9Xc3Ny7gwMAgH7Dr7HT0tKihIQEFRcXX/O4Xbt26dChQ4qOju60LyMjQ++++6727dunvXv3qqKiQosXL+6tkQEAQD8T6M8fPnPmTM2cOfOax3z44YdaunSpfv3rX+v+++/32nfy5EmVlpbqyJEjmjhxoiSpqKhIs2bN0vPPP99lHAEAgBuLpe/Z6ejo0Pz58/XUU09pzJgxnfZXVlYqPDzcEzqSlJqaqoCAAFVVVV31vC6XS01NTV4PAABgJkvHzrp16xQYGKgnnniiy/1Op1ORkZFea4GBgYqIiJDT6bzqeQsKCmS32z2PmJgYn84NAACsw7KxU11drf/6r//S1q1bZbPZfHruvLw8NTY2eh61tbU+PT8AALAOy8bOb3/7W9XX1ys2NlaBgYEKDAzU2bNn9eSTT2rkyJGSJIfDofr6eq/nXb58WRcuXJDD4bjquYODgxUWFub1AAAAZvLrDcrXMn/+fKWmpnqtpaWlaf78+VqwYIEkKTk5WQ0NDaqurtaECRMkSW+88YY6OjqUlJTU5zMDAADr8WvsNDc368yZM57tmpoaHT9+XBEREYqNjdXQoUO9jh84cKAcDofuuOMOSVJ8fLxmzJihRYsWqaSkRO3t7crOzta8efP4JhYAAJDk54+xjh49qvHjx2v8+PGSpNzcXI0fP14rV6687nPs2LFDo0eP1vTp0zVr1ixNmTJFmzZt6q2RAQBAP+PXKzspKSlyu93Xffyf/vSnTmsRERHauXOnD6cCAAAmsewNygAAAL5A7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBofo2diooKzZ49W9HR0bLZbNq9e7dnX3t7u1asWKGxY8dq0KBBio6O1qOPPqrz5897nePChQvKyMhQWFiYwsPDtXDhQjU3N/fxKwEAAFbl19hpaWlRQkKCiouLO+27dOmSjh07pvz8fB07dkw///nPderUKT344INex2VkZOjdd9/Vvn37tHfvXlVUVGjx4sV99RIAAIDFBfrzh8+cOVMzZ87scp/dbte+ffu81l588UXdfffdOnfunGJjY3Xy5EmVlpbqyJEjmjhxoiSpqKhIs2bN0vPPP6/o6Ohefw0AAMDa+tU9O42NjbLZbAoPD5ckVVZWKjw83BM6kpSamqqAgABVVVX5aUoAAGAlfr2y80W0trZqxYoV+uY3v6mwsDBJktPpVGRkpNdxgYGBioiIkNPpvOq5XC6XXC6XZ7upqal3hgYAAH7XL67stLe365FHHpHb7dbGjRt7fL6CggLZ7XbPIyYmxgdTAgAAK7J87HwWOmfPntW+ffs8V3UkyeFwqL6+3uv4y5cv68KFC3I4HFc9Z15enhobGz2P2traXpsfAAD4l6U/xvosdE6fPq39+/dr6NChXvuTk5PV0NCg6upqTZgwQZL0xhtvqKOjQ0lJSVc9b3BwsIKDg3t1dgAAYA1+jZ3m5madOXPGs11TU6Pjx48rIiJCI0aM0Ne//nUdO3ZMe/fu1ZUrVzz34URERCgoKEjx8fGaMWOGFi1apJKSErW3tys7O1vz5s3jm1gAAECSn2Pn6NGjmjp1qmc7NzdXkpSZman/+I//0GuvvSZJSkxM9Hre/v37lZKSIknasWOHsrOzNX36dAUEBCg9PV0bNmzok/kBAID1+TV2UlJS5Ha7r7r/Wvs+ExERoZ07d/pyLAAAYBDL36AMAADQE8QOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjObX2KmoqNDs2bMVHR0tm82m3bt3e+13u91auXKlRowYodDQUKWmpur06dNex1y4cEEZGRkKCwtTeHi4Fi5cqObm5j58FQAAwMr8GjstLS1KSEhQcXFxl/ufe+45bdiwQSUlJaqqqtKgQYOUlpam1tZWzzEZGRl69913tW/fPu3du1cVFRVavHhxX70EAABgcYH+/OEzZ87UzJkzu9zndrtVWFioZ555RnPmzJEkbd++XVFRUdq9e7fmzZunkydPqrS0VEeOHNHEiRMlSUVFRZo1a5aef/55RUdH99lrAQAA1mTZe3ZqamrkdDqVmprqWbPb7UpKSlJlZaUkqbKyUuHh4Z7QkaTU1FQFBASoqqqqz2cGAADW49crO9fidDolSVFRUV7rUVFRnn1Op1ORkZFe+wMDAxUREeE5pisul0sul8uz3dTU5KuxAQCAxVj2yk5vKigokN1u9zxiYmL8PRIAAOgllo0dh8MhSaqrq/Nar6ur8+xzOByqr6/32n/58mVduHDBc0xX8vLy1NjY6HnU1tb6eHoAAGAVlo2duLg4ORwOlZWVedaamppUVVWl5ORkSVJycrIaGhpUXV3tOeaNN95QR0eHkpKSrnru4OBghYWFeT0AAICZ/HrPTnNzs86cOePZrqmp0fHjxxUREaHY2Fjl5ORo7dq1GjVqlOLi4pSfn6/o6GjNnTtXkhQfH68ZM2Zo0aJFKikpUXt7u7KzszVv3jy+iQUAACT5OXaOHj2qqVOnerZzc3MlSZmZmdq6dauWL1+ulpYWLV68WA0NDZoyZYpKS0sVEhLiec6OHTuUnZ2t6dOnKyAgQOnp6dqwYUOfvxYAAGBNfo2dlJQUud3uq+632Wxas2aN1qxZc9VjIiIitHPnzt4YDwAAGMCy9+wAAAD4ArEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMFq3YmfatGlqaGjotN7U1KRp06b1dCYAAACf6VbsHDhwQG1tbZ3WW1tb9dvf/rbHQwEAAPhK4Bc5+O233/b894kTJ+R0Oj3bV65cUWlpqb785S/7bjoAAIAe+kKxk5iYKJvNJpvN1uXHVaGhoSoqKvLZcAAAAD31hWKnpqZGbrdbX/nKV3T48GENHz7csy8oKEiRkZEaMGCAz4cEAADori8UO7fccoskqaOjo1eGAQAA8LUvFDt/6/Tp09q/f7/q6+s7xc/KlSt7PBgAAIAvdCt2Xn75ZS1ZskTDhg2Tw+GQzWbz7LPZbMQOAACwjG7Fztq1a/Xss89qxYoVvp4HAADAp7r1e3YuXryohx9+2NezAAAA+Fy3Yufhhx/Wb37zG1/PAgAA4HPd+hjrtttuU35+vg4dOqSxY8dq4MCBXvufeOIJnwwHAADQU92KnU2bNmnw4MEqLy9XeXm51z6bzUbsAAAAy+hW7NTU1Ph6DgAAgF7RrXt2+sqVK1eUn5+vuLg4hYaG6tZbb9V//ud/yu12e45xu91auXKlRowYodDQUKWmpur06dN+nBoAAFhJt67s/Mu//Ms192/evLlbw/y9devWaePGjdq2bZvGjBmjo0ePasGCBbLb7Z6Pyp577jlt2LBB27ZtU1xcnPLz85WWlqYTJ04oJCTEJ3MAAID+q1uxc/HiRa/t9vZ2vfPOO2poaOjyD4R218GDBzVnzhzdf//9kqSRI0fqxz/+sQ4fPizpr1d1CgsL9cwzz2jOnDmSpO3btysqKkq7d+/WvHnzfDYLAADon7oVO7t27eq01tHRoSVLlujWW2/t8VCfueeee7Rp0ya9//77uv322/X73/9ev/vd77R+/XpJf713yOl0KjU11fMcu92upKQkVVZWEjsAAKD7fxvr7wUEBCg3N1cpKSlavny5T8759NNPq6mpSaNHj9aAAQN05coVPfvss8rIyJAkOZ1OSVJUVJTX86Kiojz7uuJyueRyuTzbTU1NPpkXAABYj09vUP7jH/+oy5cv++x8//M//6MdO3Zo586dOnbsmLZt26bnn39e27Zt69F5CwoKZLfbPY+YmBgfTQwAAKymW1d2cnNzvbbdbrf+/Oc/6/XXX1dmZqZPBpOkp556Sk8//bTn46ixY8fq7NmzKigoUGZmphwOhySprq5OI0aM8Dyvrq5OiYmJVz1vXl6e12toamoieAAAMFS3Yuett97y2g4ICNDw4cP1wgsvfO43tb6IS5cuKSDA++LTgAED1NHRIUmKi4uTw+FQWVmZJ26amppUVVWlJUuWXPW8wcHBCg4O9tmcAADAuroVO/v37/f1HF2aPXu2nn32WcXGxmrMmDF66623tH79ek9Q2Ww25eTkaO3atRo1apTnq+fR0dGaO3dun8wIAACsrUc3KH/00Uc6deqUJOmOO+7Q8OHDfTLUZ4qKipSfn6/HH39c9fX1io6O1re+9S2tXLnSc8zy5cvV0tKixYsXq6GhQVOmTFFpaSm/YwcAAEiSbO6//XXE16mlpUVLly7V9u3bPR8pDRgwQI8++qiKior0pS99yeeD9qampibZ7XY1NjYqLCys137OhKe299q5gf6q+nuP+nsEn+D9DXTW2+/v6/33u1vfxsrNzVV5ebn27NmjhoYGNTQ06Be/+IXKy8v15JNPdntoAAAAX+vWx1g/+9nP9NOf/lQpKSmetVmzZik0NFSPPPKINm7c6Kv5AAAAeqRbV3YuXbrU6Rf5SVJkZKQuXbrU46EAAAB8pVuxk5ycrFWrVqm1tdWz9umnn2r16tVKTk722XAAAAA91a2PsQoLCzVjxgzdfPPNSkhIkCT9/ve/V3BwsH7zm9/4dEAAAICe6FbsjB07VqdPn9aOHTv03nvvSZK++c1vKiMjQ6GhoT4dEAAAoCe6FTsFBQWKiorSokWLvNY3b96sjz76SCtWrPDJcAAAAD3VrXt2fvCDH2j06NGd1seMGaOSkpIeDwUAAOAr3Yodp9Pp9Yc3PzN8+HD9+c9/7vFQAAAAvtKt2ImJidGbb77Zaf3NN99UdHR0j4cCAADwlW7ds7No0SLl5OSovb1d06ZNkySVlZVp+fLl/AZlAABgKd2Knaeeekp/+ctf9Pjjj6utrU2SFBISohUrVigvL8+nAwIAAPREt2LHZrNp3bp1ys/P18mTJxUaGqpRo0YpODjY1/MBAAD0SLdi5zODBw/WpEmTfDULAACAz3XrBmUAAID+gtgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGM3ysfPhhx/qn/7pnzR06FCFhoZq7NixOnr0qGe/2+3WypUrNWLECIWGhio1NVWnT5/248QAAMBKLB07Fy9e1L333quBAwfqV7/6lU6cOKEXXnhBN910k+eY5557Ths2bFBJSYmqqqo0aNAgpaWlqbW11Y+TAwAAqwj09wDXsm7dOsXExGjLli2etbi4OM9/u91uFRYW6plnntGcOXMkSdu3b1dUVJR2796tefPm9fnMAADAWix9Zee1117TxIkT9fDDDysyMlLjx4/Xyy+/7NlfU1Mjp9Op1NRUz5rdbldSUpIqKyv9MTIAALAYS8fOBx98oI0bN2rUqFH69a9/rSVLluiJJ57Qtm3bJElOp1OSFBUV5fW8qKgoz76uuFwuNTU1eT0AAICZLP0xVkdHhyZOnKjvfOc7kqTx48frnXfeUUlJiTIzM7t93oKCAq1evdpXYwIAAAuz9JWdESNG6M477/Rai4+P17lz5yRJDodDklRXV+d1TF1dnWdfV/Ly8tTY2Oh51NbW+nhyAABgFZaOnXvvvVenTp3yWnv//fd1yy23SPrrzcoOh0NlZWWe/U1NTaqqqlJycvJVzxscHKywsDCvBwAAMJOlP8ZatmyZ7rnnHn3nO9/RI488osOHD2vTpk3atGmTJMlmsyknJ0dr167VqFGjFBcXp/z8fEVHR2vu3Ln+HR4AAFiCpWNn0qRJ2rVrl/Ly8rRmzRrFxcWpsLBQGRkZnmOWL1+ulpYWLV68WA0NDZoyZYpKS0sVEhLix8kBAIBVWDp2JOmBBx7QAw88cNX9NptNa9as0Zo1a/pwKgAA0F9Y+p4dAACAniJ2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYLR+FTvf/e53ZbPZlJOT41lrbW1VVlaWhg4dqsGDBys9PV11dXX+GxIAAFhKv4mdI0eO6Ac/+IHGjRvntb5s2TLt2bNHr776qsrLy3X+/Hk99NBDfpoSAABYTb+InebmZmVkZOjll1/WTTfd5FlvbGzUD3/4Q61fv17Tpk3ThAkTtGXLFh08eFCHDh3y48QAAMAq+kXsZGVl6f7771dqaqrXenV1tdrb273WR48erdjYWFVWVvb1mAAAwIIC/T3A53nllVd07NgxHTlypNM+p9OpoKAghYeHe61HRUXJ6XRe9Zwul0sul8uz3dTU5LN5AQCAtVj6yk5tba2+/e1va8eOHQoJCfHZeQsKCmS32z2PmJgYn50bAABYi6Vjp7q6WvX19fqHf/gHBQYGKjAwUOXl5dqwYYMCAwMVFRWltrY2NTQ0eD2vrq5ODofjqufNy8tTY2Oj51FbW9vLrwQAAPiLpT/Gmj59uv7whz94rS1YsECjR4/WihUrFBMTo4EDB6qsrEzp6emSpFOnTuncuXNKTk6+6nmDg4MVHBzcq7MDAABrsHTsDBkyRHfddZfX2qBBgzR06FDP+sKFC5Wbm6uIiAiFhYVp6dKlSk5O1uTJk/0xMgAAsBhLx871+P73v6+AgAClp6fL5XIpLS1NL730kr/HAgAAFtHvYufAgQNe2yEhISouLlZxcbF/BgIAAJZm6RuUAQAAeorYAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNEsHzsFBQWaNGmShgwZosjISM2dO1enTp3yOqa1tVVZWVkaOnSoBg8erPT0dNXV1flpYgAAYCWWj53y8nJlZWXp0KFD2rdvn9rb2/WP//iPamlp8RyzbNky7dmzR6+++qrKy8t1/vx5PfTQQ36cGgAAWEWgvwf4PKWlpV7bW7duVWRkpKqrq3XfffepsbFRP/zhD7Vz505NmzZNkrRlyxbFx8fr0KFDmjx5sj/GBgAAFmH5Kzt/r7GxUZIUEREhSaqurlZ7e7tSU1M9x4wePVqxsbGqrKz0y4wAAMA6LH9l5291dHQoJydH9957r+666y5JktPpVFBQkMLDw72OjYqKktPp7PI8LpdLLpfLs93U1NRrMwMAAP/qV1d2srKy9M477+iVV17p0XkKCgpkt9s9j5iYGB9NCAAArKbfxE52drb27t2r/fv36+abb/asOxwOtbW1qaGhwev4uro6ORyOLs+Vl5enxsZGz6O2trY3RwcAAH5k+dhxu93Kzs7Wrl279MYbbyguLs5r/4QJEzRw4ECVlZV51k6dOqVz584pOTm5y3MGBwcrLCzM6wEAAMxk+Xt2srKytHPnTv3iF7/QkCFDPPfh2O12hYaGym63a+HChcrNzVVERITCwsK0dOlSJScn800sAABg/djZuHGjJCklJcVrfcuWLfrnf/5nSdL3v/99BQQEKD09XS6XS2lpaXrppZf6eFIAAGBFlo8dt9v9uceEhISouLhYxcXFfTARAADoTyx/zw4AAEBPEDsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwmjGxU1xcrJEjRyokJERJSUk6fPiwv0cCAAAWYETs/OQnP1Fubq5WrVqlY8eOKSEhQWlpaaqvr/f3aAAAwM+MiJ3169dr0aJFWrBgge68806VlJToS1/6kjZv3uzv0QAAgJ/1+9hpa2tTdXW1UlNTPWsBAQFKTU1VZWWlHycDAABWEOjvAXrq448/1pUrVxQVFeW1HhUVpffee6/L57hcLrlcLs92Y2OjJKmpqan3BpV0xfVpr54f6I96+33XV3h/A5319vv7s/O73e5rHtfvY6c7CgoKtHr16k7rMTExfpgGuLHZix7z9wgAeklfvb8/+eQT2e32q+7v97EzbNgwDRgwQHV1dV7rdXV1cjgcXT4nLy9Pubm5nu2Ojg5duHBBQ4cOlc1m69V54X9NTU2KiYlRbW2twsLC/D0OAB/i/X1jcbvd+uSTTxQdHX3N4/p97AQFBWnChAkqKyvT3LlzJf01XsrKypSdnd3lc4KDgxUcHOy1Fh4e3suTwmrCwsL4P0PAULy/bxzXuqLzmX4fO5KUm5urzMxMTZw4UXfffbcKCwvV0tKiBQsW+Hs0AADgZ0bEzje+8Q199NFHWrlypZxOpxITE1VaWtrppmUAAHDjMSJ2JCk7O/uqH1sBfys4OFirVq3q9FEmgP6P9ze6YnN/3ve1AAAA+rF+/0sFAQAAroXYAQAARiN2AACA0Ygd3FCKi4s1cuRIhYSEKCkpSYcPH/b3SAB8oKKiQrNnz1Z0dLRsNpt2797t75FgIcQObhg/+clPlJubq1WrVunYsWNKSEhQWlqa6uvr/T0agB5qaWlRQkKCiouL/T0KLIhvY+GGkZSUpEmTJunFF1+U9NfftB0TE6OlS5fq6aef9vN0AHzFZrNp165dnt+qD3BlBzeEtrY2VVdXKzU11bMWEBCg1NRUVVZW+nEyAEBvI3ZwQ/j444915cqVTr9VOyoqSk6n009TAQD6ArEDAACMRuzghjBs2DANGDBAdXV1Xut1dXVyOBx+mgoA0BeIHdwQgoKCNGHCBJWVlXnWOjo6VFZWpuTkZD9OBgDobcb8IVDg8+Tm5iozM1MTJ07U3XffrcLCQrW0tGjBggX+Hg1ADzU3N+vMmTOe7ZqaGh0/flwRERGKjY3142SwAr56jhvKiy++qO9973tyOp1KTEzUhg0blJSU5O+xAPTQgQMHNHXq1E7rmZmZ2rp1a98PBEshdgAAgNG4ZwcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHwA1n5MiRKiws9PcYAPoIsQPAUlJSUpSTk9NpfevWrQoPD+/zeQD0f8QOAAAwGrEDoE+lpKQoOztb2dnZstvtGjZsmPLz8+XrP9O3Z88eTZo0SSEhIRo2bJi+9rWvXfXY9evXa+zYsRo0aJBiYmL0+OOPq7m52bP/7Nmzmj17tm666SYNGjRIY8aM0S9/+UtJ0sWLF5WRkaHhw4crNDRUo0aN0pYtW3z6WgD0TKC/BwBw49m2bZsWLlyow4cP6+jRo1q8eLFiY2O1aNEin5z/9ddf19e+9jX9+7//u7Zv3662tjZPnHQlICBAGzZsUFxcnD744AM9/vjjWr58uV566SVJUlZWltra2lRRUaFBgwbpxIkTGjx4sCQpPz9fJ06c0K9+9SsNGzZMZ86c0aeffuqT1wHAN/ir5wD6VEpKiurr6/Xuu+/KZrNJkp5++mm99tprOnHihFJSUnTw4EEFBQV5Pe/y5csKCQlRQ0PD5/6Me+65R1/5ylf0ox/9qMv9I0eOVE5OTpf3BknST3/6Uz322GP6+OOPJUnjxo1Tenq6Vq1a1enYBx98UMOGDdPmzZs/dy4A/sHHWAD63OTJkz2hI0nJyck6ffq0rly5IknKyMjQ8ePHvR5r1qy57vMfP35c06dPv+7j//d//1fTp0/Xl7/8ZQ0ZMkTz58/XX/7yF126dEmS9MQTT2jt2rW69957tWrVKr399tue5y5ZskSvvPKKEhMTtXz5ch08ePC6fy6AvkHsALAcu92u2267zesRGRl53c8PDQ297mP/9Kc/6YEHHtC4ceP0s5/9TNXV1SouLpYktbW1SZL+9V//VR988IHmz5+vP/zhD5o4caKKiookSTNnztTZs2e1bNkynT9/XtOnT9e//du/fYFXC6C3ETsA+lxVVZXX9qFDhzRq1CgNGDDAJ+cfN26cysrKruvY6upqdXR06IUXXtDkyZN1++236/z5852Oi4mJ0WOPPaaf//znevLJJ/Xyyy979g0fPlyZmZn60Y9+pMLCQm3atMknrwOAb3CDMoA+d+7cOeXm5upb3/qWjh07pqKiIr3wwgs+O/+qVas0ffp03XrrrZo3b54uX76sX/7yl1qxYkWnY2+77Ta1t7erqKhIs2fP1ptvvqmSkhKvY3JycjRz5kzdfvvtunjxovbv36/4+HhJ0sqVKzVhwgSNGTNGLpdLe/fu9ewDYA1c2QHQ5x599FF9+umnuvvuu5WVlaVvf/vbWrx4sc/On5KSoldffVWvvfaaEhMTNW3aNB0+fLjLYxMSErR+/XqtW7dOd911l3bs2KGCggKvY65cuaKsrCzFx8drxowZuv322z3f1AoKClJeXp7GjRun++67TwMGDNArr7zis9cCoOf4NhaAPpWSkqLExET+XAOAPsOVHQAAYDRiB0C/M2bMGA0ePLjLx44dO/w9HgCL4WMsAP3O2bNn1d7e3uW+qKgoDRkypI8nAmBlxA4AADAaH2MBAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjPb/jWwnrZOqTDIAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["# Train Test Split\n","from sklearn.model_selection import train_test_split\n","\n","X_train, X_test, y_train, y_test = train_test_split(X_smote_over, y_smote_over, test_size=0.20, random_state=42, stratify=y_smote_over)"],"metadata":{"id":"ERa-dl1kSqP4","executionInfo":{"status":"ok","timestamp":1717508608796,"user_tz":-60,"elapsed":7,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["# Number of classes in training Data\n","y_train.value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JMdBDrVxO4ah","executionInfo":{"status":"ok","timestamp":1717508609285,"user_tz":-60,"elapsed":495,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"13477b18-a313-488c-aafb-422b42bc4f70"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pH_class\n","1    122\n","0    122\n","Name: count, dtype: int64"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","source":["###**Logistic Regression**"],"metadata":{"id":"jmsvxomoaETB"}},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc, roc_auc_score, ConfusionMatrixDisplay\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","\n","# Initialize logistic regression model\n","log_reg = LogisticRegression(random_state=42)\n","\n","# Fit the model on the training data\n","log_reg.fit(X_train, y_train)\n","\n","# Predict probabilities on the test data\n","y_pred_proba = log_reg.predict_proba(X_test)\n","\n","# Predict binary labels on the test data\n","y_pred = log_reg.predict(X_test)\n","\n","# Evaluation metrics\n","print(\"Classification Report:\")\n","print(classification_report(y_test, y_pred))\n","\n","# Confusion matrix\n","conf_matrix = confusion_matrix(y_test, y_pred)\n","print(\"\\nConfusion Matrix:\")\n","print(conf_matrix)\n","\n","# Visualize confusion matrix\n","cm = confusion_matrix(y_test, y_pred)\n","cm_display = ConfusionMatrixDisplay(confusion_matrix = cm)\n","cm_display.plot()\n","\n","# Create a table of evaluation metrics\n","metrics_table_log = pd.DataFrame({\n","    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","    'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","              recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","})\n","print(\"\\nEvaluation Metrics:\")\n","print(metrics_table_log)\n","\n","# ROC curve\n","fpr, tpr, _ = roc_curve(y_test, log_reg.decision_function(X_test))\n","roc_auc = auc(fpr, tpr)\n","\n","plt.figure(figsize=(8, 6))\n","plt.plot(fpr, tpr, lw=2, label='ROC curve (area = {:.2f})'.format(roc_auc))\n","plt.plot([0, 1], [0, 1], color='darkorange', lw=2, linestyle='--')\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver Operating Characteristic (ROC) Curve')\n","plt.legend(loc='lower right')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"U9mmz5UNSqHh","executionInfo":{"status":"ok","timestamp":1717508611204,"user_tz":-60,"elapsed":1921,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"20441c9a-ed12-4969-be50-6174a775fe19"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        31\n","           1       1.00      1.00      1.00        31\n","\n","    accuracy                           1.00        62\n","   macro avg       1.00      1.00      1.00        62\n","weighted avg       1.00      1.00      1.00        62\n","\n","\n","Confusion Matrix:\n","[[31  0]\n"," [ 0 31]]\n","\n","Evaluation Metrics:\n","      Metric  Score\n","0   Accuracy    1.0\n","1  Precision    1.0\n","2     Recall    1.0\n","3   F1-Score    1.0\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["###**K Nearest Neighbour**"],"metadata":{"id":"jrl56uOnfRAF"}},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","\n","# Initialize KNN classifier\n","knn = KNeighborsClassifier()\n","\n","# Fit the model on the training data\n","knn.fit(X_train, y_train)\n","\n","# Predict probabilities on the test data\n","y_pred_proba = knn.predict_proba(X_test)\n","\n","# Predict binary labels on the test data\n","y_pred = knn.predict(X_test)\n","\n","# Evaluation metrics\n","print(\"Classification Report:\")\n","print(classification_report(y_test, y_pred))\n","\n","# Confusion matrix\n","conf_matrix = confusion_matrix(y_test, y_pred)\n","print(\"\\nConfusion Matrix:\")\n","print(conf_matrix)\n","\n","# Visualize confusion matrix\n","cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","cm_display.plot()\n","\n","# Create a table of evaluation metrics\n","metrics_table_knn = pd.DataFrame({\n","    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","    'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","              recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","})\n","print(\"\\nEvaluation Metrics:\")\n","print(metrics_table_knn)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":814},"id":"vGdcB8FOVrgI","executionInfo":{"status":"ok","timestamp":1717508612536,"user_tz":-60,"elapsed":1336,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"00343554-8115-44fe-a628-4ce846226243"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        31\n","           1       1.00      1.00      1.00        31\n","\n","    accuracy                           1.00        62\n","   macro avg       1.00      1.00      1.00        62\n","weighted avg       1.00      1.00      1.00        62\n","\n","\n","Confusion Matrix:\n","[[31  0]\n"," [ 0 31]]\n","\n","Evaluation Metrics:\n","      Metric  Score\n","0   Accuracy    1.0\n","1  Precision    1.0\n","2     Recall    1.0\n","3   F1-Score    1.0\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["accuracy_score_knn = metrics_table_knn[metrics_table_knn['Metric'] == 'Accuracy']['Score'].iloc[0]\n","accuracy_score_knn"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l7-_3ZYoVWVd","executionInfo":{"status":"ok","timestamp":1717508612536,"user_tz":-60,"elapsed":4,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"9bc9c9a8-eb88-4cbc-f864-1da5a97099f4"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{},"execution_count":17}]},{"cell_type":"markdown","source":["###**Decision Tree**"],"metadata":{"id":"tLEfc5_qgwsT"}},{"cell_type":"code","source":["from sklearn.tree import DecisionTreeClassifier, plot_tree\n","\n","# Initialize Decision Tree classifier\n","dt_classifier = DecisionTreeClassifier(criterion = \"entropy\", random_state=42)\n","\n","# Fit the model on the training data\n","dt_classifier.fit(X_train, y_train)\n","\n","# Predict probabilities on the test data\n","y_pred_proba = dt_classifier.predict_proba(X_test)\n","\n","# Predict binary labels on the test data\n","y_pred = dt_classifier.predict(X_test)\n","\n","# Evaluation metrics\n","print(\"Classification Report:\")\n","print(classification_report(y_test, y_pred))\n","\n","# Confusion matrix\n","conf_matrix = confusion_matrix(y_test, y_pred)\n","print(\"\\nConfusion Matrix:\")\n","print(conf_matrix)\n","# print('\\n')\n","\n","# Create a table of evaluation metrics\n","metrics_table_dt = pd.DataFrame({\n","    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","    'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","              recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","})\n","\n","# Visualize confusion matrix\n","cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","cm_display.plot()\n","print('==========================')\n","\n","print(\"\\nEvaluation Metrics:\")\n","print(metrics_table_dt)\n","print('==========================')\n","\n","# Visualize decision tree\n","plt.figure(figsize=(8, 5))\n","plot_tree(dt_classifier, filled=True, feature_names=X_train.columns)\n","plt.title('Decision Tree')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"5Wp5bv3MgxDG","executionInfo":{"status":"ok","timestamp":1717508615471,"user_tz":-60,"elapsed":2937,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"83b8d1a3-5ec7-4f35-dafd-c0cfebe68254"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        31\n","           1       1.00      1.00      1.00        31\n","\n","    accuracy                           1.00        62\n","   macro avg       1.00      1.00      1.00        62\n","weighted avg       1.00      1.00      1.00        62\n","\n","\n","Confusion Matrix:\n","[[31  0]\n"," [ 0 31]]\n","==========================\n","\n","Evaluation Metrics:\n","      Metric  Score\n","0   Accuracy    1.0\n","1  Precision    1.0\n","2     Recall    1.0\n","3   F1-Score    1.0\n","==========================\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["###**Random Forest**"],"metadata":{"id":"Fp8oMHMYn5UL"}},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","\n","# Initialize Random Forest classifier\n","rf_classifier = RandomForestClassifier(random_state=42, max_depth = 2, n_estimators = 100, criterion = \"entropy\")\n","\n","# Fit the model on the training data\n","rf_classifier.fit(X_train, y_train)\n","\n","# Predict probabilities on the test data\n","y_pred_proba = rf_classifier.predict_proba(X_test)\n","\n","# Predict binary labels on the test data\n","y_pred = rf_classifier.predict(X_test)\n","\n","# Evaluation metrics\n","print(\"Classification Report:\")\n","print(classification_report(y_test, y_pred))\n","\n","# Confusion matrix\n","conf_matrix = confusion_matrix(y_test, y_pred)\n","print(\"\\nConfusion Matrix:\")\n","print(conf_matrix)\n","\n","# Visualize confusion matrix\n","cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","cm_display.plot()\n","\n","# Create a table of evaluation metrics\n","metrics_table_rf = pd.DataFrame({\n","    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","    'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","              recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","})\n","print(\"\\nEvaluation Metrics:\")\n","print(metrics_table_rf)\n","\n","# Visualize decision trees in the Random Forest\n","plt.figure(figsize=(8, 5))\n","for estimator in rf_classifier.estimators_:\n","    plot_tree(estimator, filled=True, feature_names=X_train.columns)\n","plt.title('Random Forest Estimators')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"qEs97vAQkBa_","executionInfo":{"status":"ok","timestamp":1717508628521,"user_tz":-60,"elapsed":13052,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"80b8a91c-52d4-42e4-bd80-42fab3683ebd"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        31\n","           1       1.00      1.00      1.00        31\n","\n","    accuracy                           1.00        62\n","   macro avg       1.00      1.00      1.00        62\n","weighted avg       1.00      1.00      1.00        62\n","\n","\n","Confusion Matrix:\n","[[31  0]\n"," [ 0 31]]\n","\n","Evaluation Metrics:\n","      Metric  Score\n","0   Accuracy    1.0\n","1  Precision    1.0\n","2     Recall    1.0\n","3   F1-Score    1.0\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["###**Adaboost**"],"metadata":{"id":"1rNu1UFHrGAf"}},{"cell_type":"code","source":["# from sklearn.ensemble import AdaBoostClassifier\n","\n","# # Initialize AdaBoost classifier with 50 estimators\n","# adaboost = AdaBoostClassifier(n_estimators=50, random_state=42)\n","\n","# # Fit the model on the training data\n","# adaboost.fit(X_train, y_train)\n","\n","# # Predict probabilities on the test data\n","# y_pred_proba = adaboost.predict_proba(X_test)\n","\n","# # Predict binary labels on the test data\n","# y_pred = adaboost.predict(X_test)\n","\n","# # Evaluation metrics\n","# print(\"Classification Report:\")\n","# print(classification_report(y_test, y_pred))\n","\n","# # Confusion matrix\n","# conf_matrix = confusion_matrix(y_test, y_pred)\n","# print(\"\\nConfusion Matrix:\")\n","# print(conf_matrix)\n","\n","# # Visualize confusion matrix\n","# cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","# cm_display.plot()\n","\n","# # Create a table of evaluation metrics\n","# metrics_table_ada = pd.DataFrame({\n","#     'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","#     'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","#               recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","# })\n","# print(\"\\nEvaluation Metrics:\")\n","# print(metrics_table_ada)\n"],"metadata":{"id":"muAIUyjGrGe2","executionInfo":{"status":"ok","timestamp":1717508628522,"user_tz":-60,"elapsed":7,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}}},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":["###**Gradient Boosting**"],"metadata":{"id":"20-iFjPBsjnc"}},{"cell_type":"code","source":["from sklearn.ensemble import GradientBoostingClassifier\n","\n","# Initialize Gradient Boosting classifier with 50 estimators\n","gradient_boosting = GradientBoostingClassifier(n_estimators=50, random_state=42)\n","\n","# Fit the model on the training data\n","gradient_boosting.fit(X_train, y_train)\n","\n","# Predict probabilities on the test data\n","y_pred_proba = gradient_boosting.predict_proba(X_test)\n","\n","# Predict binary labels on the test data\n","y_pred = gradient_boosting.predict(X_test)\n","\n","# Evaluation metrics\n","print(\"Classification Report:\")\n","print(classification_report(y_test, y_pred))\n","\n","# Confusion matrix\n","conf_matrix = confusion_matrix(y_test, y_pred)\n","print(\"\\nConfusion Matrix:\")\n","print(conf_matrix)\n","\n","# Visualize confusion matrix\n","cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","cm_display.plot()\n","\n","# Create a table of evaluation metrics\n","metrics_table_gb = pd.DataFrame({\n","    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","    'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","              recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","})\n","print(\"\\nEvaluation Metrics:\")\n","print(metrics_table_gb)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":814},"id":"Ru3_sOEJsj9J","executionInfo":{"status":"ok","timestamp":1717508630139,"user_tz":-60,"elapsed":1622,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"b723ec82-c2ce-4ad3-a6ba-c85ab59945df"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        31\n","           1       1.00      1.00      1.00        31\n","\n","    accuracy                           1.00        62\n","   macro avg       1.00      1.00      1.00        62\n","weighted avg       1.00      1.00      1.00        62\n","\n","\n","Confusion Matrix:\n","[[31  0]\n"," [ 0 31]]\n","\n","Evaluation Metrics:\n","      Metric  Score\n","0   Accuracy    1.0\n","1  Precision    1.0\n","2     Recall    1.0\n","3   F1-Score    1.0\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["###**Support Vector Machine**"],"metadata":{"id":"_2J0I5UewBMj"}},{"cell_type":"code","source":["from sklearn.svm import SVC\n","\n","# Initialize SVM classifier\n","svm_classifier = SVC(kernel = 'linear', C=10, random_state=42)\n","\n","# Fit the model on the training data\n","svm_classifier.fit(X_train, y_train)\n","\n","# Predict binary labels on the test data\n","y_pred = svm_classifier.predict(X_test)\n","\n","# Evaluation metrics\n","print(\"Classification Report:\")\n","print(classification_report(y_test, y_pred))\n","\n","# Confusion matrix\n","conf_matrix = confusion_matrix(y_test, y_pred)\n","print(\"\\nConfusion Matrix:\")\n","print(conf_matrix)\n","\n","# Visualize confusion matrix\n","cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","cm_display.plot()\n","\n","# Create a table of evaluation metrics\n","metrics_table_sv = pd.DataFrame({\n","    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","    'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","              recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","})\n","print(\"\\nEvaluation Metrics:\")\n","print(metrics_table_sv)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":814},"id":"Zl9hIyrvwBp0","executionInfo":{"status":"ok","timestamp":1717508631796,"user_tz":-60,"elapsed":1661,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"65c31697-d50a-4b0c-e47a-1f00e25805a0"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.97      1.00      0.98        31\n","           1       1.00      0.97      0.98        31\n","\n","    accuracy                           0.98        62\n","   macro avg       0.98      0.98      0.98        62\n","weighted avg       0.98      0.98      0.98        62\n","\n","\n","Confusion Matrix:\n","[[31  0]\n"," [ 1 30]]\n","\n","Evaluation Metrics:\n","      Metric     Score\n","0   Accuracy  0.983871\n","1  Precision  1.000000\n","2     Recall  0.967742\n","3   F1-Score  0.983607\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["###**MultiLayer Perceptron**"],"metadata":{"id":"vKM0pvRZz4op"}},{"cell_type":"code","source":["# from sklearn.neural_network import MLPClassifier\n","\n","# # Initialize MLP classifier\n","# mlp_classifier = MLPClassifier(solver='adam', hidden_layer_sizes=(100, 200, 50, 70), random_state=42)\n","\n","# # Fit the model on the training data\n","# mlp_classifier.fit(X_train, y_train)\n","\n","# # Predict binary labels on the test data\n","# y_pred = mlp_classifier.predict(X_test)\n","\n","# # Evaluation metrics\n","# print(\"Classification Report:\")\n","# print(classification_report(y_test, y_pred))\n","\n","# # Confusion matrix\n","# conf_matrix = confusion_matrix(y_test, y_pred)\n","# print(\"\\nConfusion Matrix:\")\n","# print(conf_matrix)\n","\n","# # Visualize confusion matrix\n","# cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","# cm_display.plot()\n","\n","# # Create a table of evaluation metrics\n","# metrics_table_mlp = pd.DataFrame({\n","#     'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","#     'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","#               recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","# })\n","# print(\"\\nEvaluation Metrics:\")\n","# print(metrics_table_mlp)\n"],"metadata":{"id":"632RBLme0AjE","executionInfo":{"status":"ok","timestamp":1717508631797,"user_tz":-60,"elapsed":10,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}}},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":["###**Naive Bayes**"],"metadata":{"id":"u8VO2sxrLMEM"}},{"cell_type":"code","source":["from sklearn.naive_bayes import GaussianNB\n","\n","# Initialize Naive Bayes classifier\n","naive_bayes = GaussianNB()\n","\n","# Fit the model on the training data\n","naive_bayes.fit(X_train, y_train)\n","\n","# Predict probabilities on the test data\n","y_pred_proba = naive_bayes.predict_proba(X_test)\n","\n","# Predict binary labels on the test data\n","y_pred = naive_bayes.predict(X_test)\n","\n","# Evaluation metrics\n","print(\"Classification Report:\")\n","print(classification_report(y_test, y_pred))\n","\n","# Confusion matrix\n","conf_matrix = confusion_matrix(y_test, y_pred)\n","print(\"\\nConfusion Matrix:\")\n","print(conf_matrix)\n","\n","# Visualize confusion matrix\n","cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","cm_display.plot()\n","\n","# Create a table of evaluation metrics\n","metrics_table_nb = pd.DataFrame({\n","    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","    'Score': [accuracy_score(y_test, y_pred), precision_score(y_test, y_pred),\n","              recall_score(y_test, y_pred), f1_score(y_test, y_pred)]\n","})\n","print(\"\\nEvaluation Metrics:\")\n","print(metrics_table_nb)\n"],"metadata":{"id":"32HQzJC306lg","colab":{"base_uri":"https://localhost:8080/","height":814},"executionInfo":{"status":"ok","timestamp":1717508632947,"user_tz":-60,"elapsed":1160,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"f34520ed-5b0d-4d5c-e841-e54a0537e5dc"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.97      1.00      0.98        31\n","           1       1.00      0.97      0.98        31\n","\n","    accuracy                           0.98        62\n","   macro avg       0.98      0.98      0.98        62\n","weighted avg       0.98      0.98      0.98        62\n","\n","\n","Confusion Matrix:\n","[[31  0]\n"," [ 1 30]]\n","\n","Evaluation Metrics:\n","      Metric     Score\n","0   Accuracy  0.983871\n","1  Precision  1.000000\n","2     Recall  0.967742\n","3   F1-Score  0.983607\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["###**Conclusion**"],"metadata":{"id":"Jo9VwSReNd4I"}},{"cell_type":"code","source":["# List of metrics\n","metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score']\n","\n","# List of metrics tables\n","# metrics_tables = [metrics_table_log, metrics_table_knn, metrics_table_dt, metrics_table_rf,\n","#                   metrics_table_ada, metrics_table_gb, metrics_table_sv, metrics_table_mlp,\n","#                   metrics_table_nb]\n","metrics_tables = [metrics_table_log, metrics_table_knn, metrics_table_dt, metrics_table_rf,\n","                  metrics_table_gb, metrics_table_sv, metrics_table_nb]\n","\n","# List of algorithm names\n","# algorithm_names = ['Logistic Regression', 'KNN', 'Decision Tree', 'Random Forest',\n","#                    'Adaboost', 'Gradient Boosted', 'SVM', 'MLP', 'Naive Bayes']\n","algorithm_names = ['Logistic Regression', 'KNN', 'Decision Tree', 'Random Forest',\n","                   'Gradient Boosted', 'SVM', 'Naive Bayes']\n","\n","# Initialize lists to store scores\n","accuracy_scores = []\n","precision_scores = []\n","recall_scores = []\n","f1_scores = []\n","\n","# Extract scores for each algorithm\n","for metric_table in metrics_tables:\n","    accuracy_scores.append(metric_table[metric_table['Metric'] == 'Accuracy']['Score'].iloc[0])\n","    precision_scores.append(metric_table[metric_table['Metric'] == 'Precision']['Score'].iloc[0])\n","    recall_scores.append(metric_table[metric_table['Metric'] == 'Recall']['Score'].iloc[0])\n","    f1_scores.append(metric_table[metric_table['Metric'] == 'F1-Score']['Score'].iloc[0])\n","\n","# Create a dictionary with the extracted values\n","data = {\n","    \"Algorithm\": algorithm_names,\n","    \"Accuracy\": accuracy_scores,\n","    \"Precision\": precision_scores,\n","    \"Recall\": recall_scores,\n","    \"F1-Score\": f1_scores\n","}\n","\n","# Create a DataFrame\n","results_table = pd.DataFrame(data)\n","\n","# Display the table\n","results_table\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"98KfxrUzXNRt","executionInfo":{"status":"ok","timestamp":1717508633336,"user_tz":-60,"elapsed":391,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"ae710caf-c807-44ef-bf1c-2a8fb816fbc5"},"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["             Algorithm  Accuracy  Precision    Recall  F1-Score\n","0  Logistic Regression  1.000000        1.0  1.000000  1.000000\n","1                  KNN  1.000000        1.0  1.000000  1.000000\n","2        Decision Tree  1.000000        1.0  1.000000  1.000000\n","3        Random Forest  1.000000        1.0  1.000000  1.000000\n","4     Gradient Boosted  1.000000        1.0  1.000000  1.000000\n","5                  SVM  0.983871        1.0  0.967742  0.983607\n","6          Naive Bayes  0.983871        1.0  0.967742  0.983607"],"text/html":["\n","  <div 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 {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01574032375723439,\n        \"min\": 0.967741935483871,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.967741935483871\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007999180925807658,\n        \"min\": 0.9836065573770492,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9836065573770492\n      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right')\n","plt.legend(loc='upper left', bbox_to_anchor=(1.02, 0.5), shadow=True)\n","\n","# plt.grid(True)\n","\n","# Show plot\n","plt.tight_layout()\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"FgY1rcCEZ0eS","executionInfo":{"status":"ok","timestamp":1717508634641,"user_tz":-60,"elapsed":1310,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"5cb0fcf8-8b9c-4137-d2f2-7c005f49e574"},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1300x500 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["import joblib\n","\n","# Save the model\n","joblib.dump(gradient_boosting, '/content/drive/MyDrive/Colab Notebooks/pH Level Forecasting/model_classification.joblib')"],"metadata":{"id":"7ILKY6_Kfg9Q","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717508719438,"user_tz":-60,"elapsed":184,"user":{"displayName":"Valentine Anene","userId":"14922188338851357044"}},"outputId":"200d7cc8-9c83-41cf-da61-e90471f5f360"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/Colab Notebooks/pH Level Forecasting/model_classification.joblib']"]},"metadata":{},"execution_count":27}]}]}