OA SVM_Project.ipynb

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   "source": [
    "# SUPPORT VECTOR MACHINE PROJECT \n",
    "## Oluwatosin Adewale\n",
    "## IE 7860 "
   ]
  },
  {
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   "id": "94251331-2066-47c8-bc8d-9c3923ea9ffa",
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   "source": [
    "### Please refresh the kernel before running each individual section. I.e every problem"
   ]
  },
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   "id": "bb9bd171-f54b-4b37-aa15-b729d886542e",
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   },
   "source": [
    "## FONT RECOGNITION PROBLEM"
   ]
  },
  {
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   "id": "3bb1d74a-3bf0-434c-a22e-64e2c1221ff1",
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   },
   "source": [
    "### Importing required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8a5fa36-2600-44db-82c0-4579934400cb",
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   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5108c6-32bb-4779-bc44-2d1e9bef10d1",
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   },
   "source": [
    "### Loading the datasets and Data preprocessing"
   ]
  },
  {
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   "execution_count": 2,
   "id": "6048636e-4127-4158-af8a-bfc7e82fcc80",
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     "text": [
      "    Feature_1  Feature_2  Feature_3  Feature_4  Feature_5  Feature_6  \\\n",
      "0         8.0        0.0        0.0        0.0        0.0        0.0   \n",
      "1         7.0        0.0        0.0        0.0        0.0        0.0   \n",
      "2        12.0       10.0        1.0        1.0        0.0        0.0   \n",
      "3        21.0       10.0        4.0        4.0        0.0        1.0   \n",
      "4        27.0       12.0        3.0        3.0        0.0        8.0   \n",
      "..        ...        ...        ...        ...        ...        ...   \n",
      "74        0.0        5.0        6.0        6.0        0.0        0.0   \n",
      "75       18.0        0.0        0.0       10.0        0.0        0.0   \n",
      "76       18.0        0.0        0.0       10.0        0.0        0.0   \n",
      "77       22.0        0.0        0.0       12.0        0.0        0.0   \n",
      "78        NaN        NaN        NaN        NaN        NaN        NaN   \n",
      "\n",
      "    Feature_7  Feature_8  Feature_9  Feature_10  ...  Output_Q  Output_R  \\\n",
      "0         0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "1         0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "2         0.0        4.0        6.0         0.0  ...       0.0       0.0   \n",
      "3         1.0        0.0        5.0         0.0  ...       0.0       0.0   \n",
      "4         0.0        5.0        0.0         2.0  ...       0.0       0.0   \n",
      "..        ...        ...        ...         ...  ...       ...       ...   \n",
      "74        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "75        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "76        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "77        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "78        NaN        NaN        NaN         NaN  ...       NaN       NaN   \n",
      "\n",
      "    Output_S  Output_T  Output_U  Output_V  Output_W  Output_X  Output_Y  \\\n",
      "0        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "1        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "2        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "3        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "4        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "..       ...       ...       ...       ...       ...       ...       ...   \n",
      "74       0.0       0.0       0.0       0.0       0.0       0.0       1.0   \n",
      "75       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "76       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "77       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "78       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "\n",
      "    Output_Z  \n",
      "0        0.0  \n",
      "1        0.0  \n",
      "2        0.0  \n",
      "3        0.0  \n",
      "4        0.0  \n",
      "..       ...  \n",
      "74       0.0  \n",
      "75       1.0  \n",
      "76       1.0  \n",
      "77       1.0  \n",
      "78       NaN  \n",
      "\n",
      "[79 rows x 43 columns]\n"
     ]
    }
   ],
   "source": [
    "# Load the datasets\n",
    "train_data = pd.read_csv('Training Data.csv')\n",
    "test_data = pd.read_csv('Testing Data.csv')\n",
    "print(train_data)"
   ]
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     "name": "stdout",
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     "text": [
      "NaN values in training data: 43\n",
      "NaN values in testing data: 3\n"
     ]
    }
   ],
   "source": [
    "print(\"NaN values in training data:\", train_data.isnull().any().sum())\n",
    "print(\"NaN values in testing data:\", test_data.isnull().any().sum())"
   ]
  },
  {
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     "name": "stdout",
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     "text": [
      "Columns with NaN values: ['Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16']\n",
      "set()\n",
      "    Feature_1  Feature_2  Feature_3  Feature_4  Feature_5  Feature_6  \\\n",
      "0         8.0        0.0        0.0        0.0        0.0        0.0   \n",
      "1         7.0        0.0        0.0        0.0        0.0        0.0   \n",
      "2        12.0       10.0        1.0        1.0        0.0        0.0   \n",
      "3        21.0       10.0        4.0        4.0        0.0        1.0   \n",
      "4        27.0       12.0        3.0        3.0        0.0        8.0   \n",
      "..        ...        ...        ...        ...        ...        ...   \n",
      "73        0.0       10.0        3.0        3.0        0.0        0.0   \n",
      "74        0.0        5.0        6.0        6.0        0.0        0.0   \n",
      "75       18.0        0.0        0.0       10.0        0.0        0.0   \n",
      "76       18.0        0.0        0.0       10.0        0.0        0.0   \n",
      "77       22.0        0.0        0.0       12.0        0.0        0.0   \n",
      "\n",
      "    Feature_7  Feature_8  Feature_9  Feature_10  ...  Output_Q  Output_R  \\\n",
      "0         0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "1         0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "2         0.0        4.0        6.0         0.0  ...       0.0       0.0   \n",
      "3         1.0        0.0        5.0         0.0  ...       0.0       0.0   \n",
      "4         0.0        5.0        0.0         2.0  ...       0.0       0.0   \n",
      "..        ...        ...        ...         ...  ...       ...       ...   \n",
      "73        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "74        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "75        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "76        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "77        0.0        0.0        0.0         0.0  ...       0.0       0.0   \n",
      "\n",
      "    Output_S  Output_T  Output_U  Output_V  Output_W  Output_X  Output_Y  \\\n",
      "0        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "1        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "2        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "3        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "4        0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "..       ...       ...       ...       ...       ...       ...       ...   \n",
      "73       0.0       0.0       0.0       0.0       0.0       0.0       1.0   \n",
      "74       0.0       0.0       0.0       0.0       0.0       0.0       1.0   \n",
      "75       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "76       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "77       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
      "\n",
      "    Output_Z  \n",
      "0        0.0  \n",
      "1        0.0  \n",
      "2        0.0  \n",
      "3        0.0  \n",
      "4        0.0  \n",
      "..       ...  \n",
      "73       0.0  \n",
      "74       0.0  \n",
      "75       1.0  \n",
      "76       1.0  \n",
      "77       1.0  \n",
      "\n",
      "[78 rows x 40 columns]\n",
      "    Feature_1  Feature_2  Feature_3  Feature_4  Feature_5  Feature_6  \\\n",
      "0           7          0          0          0          0          0   \n",
      "1           7          0          0          0          0          0   \n",
      "2           6          5          0          6          0          0   \n",
      "3          27         14          2          2          0          8   \n",
      "4          27         12          2          2          0          8   \n",
      "..        ...        ...        ...        ...        ...        ...   \n",
      "73          0          6          6          6          0          0   \n",
      "74          2         15          1          5          0          0   \n",
      "75         20          0          0         10          0          0   \n",
      "76         16          0          0         12          0          0   \n",
      "77         18          0          0         12          0          0   \n",
      "\n",
      "    Feature_7  Feature_8  Feature_9  Feature_10  ...  Output_Q  Output_R  \\\n",
      "0           0          0          0           0  ...         0         0   \n",
      "1           0          0          0           0  ...         0         0   \n",
      "2           9          0          0           0  ...         0         0   \n",
      "3           0          5          0           2  ...         0         0   \n",
      "4           0          5          0           2  ...         0         0   \n",
      "..        ...        ...        ...         ...  ...       ...       ...   \n",
      "73          0          0          0           0  ...         0         0   \n",
      "74          0          0          0           0  ...         0         0   \n",
      "75          0          0          0           0  ...         0         0   \n",
      "76          0          0          0           0  ...         0         0   \n",
      "77          0          0          0           0  ...         0         0   \n",
      "\n",
      "    Output_S  Output_T  Output_U  Output_V  Output_W  Output_X  Output_Y  \\\n",
      "0          0         0         0         0         0         0         0   \n",
      "1          0         0         0         0         0         0         0   \n",
      "2          0         0         0         0         0         0         0   \n",
      "3          0         0         0         0         0         0         0   \n",
      "4          0         0         0         0         0         0         0   \n",
      "..       ...       ...       ...       ...       ...       ...       ...   \n",
      "73         0         0         0         0         0         0         1   \n",
      "74         0         0         0         0         0         0         1   \n",
      "75         0         0         0         0         0         0         0   \n",
      "76         0         0         0         0         0         0         0   \n",
      "77         0         0         0         0         0         0         0   \n",
      "\n",
      "    Output_Z  \n",
      "0          0  \n",
      "1          0  \n",
      "2          0  \n",
      "3          0  \n",
      "4          0  \n",
      "..       ...  \n",
      "73         0  \n",
      "74         0  \n",
      "75         1  \n",
      "76         1  \n",
      "77         1  \n",
      "\n",
      "[78 rows x 40 columns]\n"
     ]
    }
   ],
   "source": [
    "# Drop the last row with all nan values\n",
    "train_data = train_data.drop(train_data.index[-1])\n",
    "\n",
    "# Check for NaN values in each column\n",
    "nan_columns = train_data.columns[train_data.isnull().any()].tolist()\n",
    "\n",
    "# Print out the columns with NaN values\n",
    "print(\"Columns with NaN values:\", nan_columns)\n",
    "\n",
    "# Drop columns with any NaN values\n",
    "train_data = train_data.dropna(axis=1)\n",
    "test_data = test_data.dropna(axis=1)\n",
    "\n",
    "# Verify dropped columns\n",
    "dropped_columns = set(train_data.columns[train_data.isnull().any()].tolist())\n",
    " \n",
    "print(dropped_columns)\n",
    "print(train_data)\n",
    "print(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1599d5b-c14b-49b3-8390-037378edb9cc",
   "metadata": {},
   "source": [
    "The NaN values have to be dropped in this specific order otherwise the data is not handled properly leading to problems with the model"
   ]
  },
  {
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   "source": [
    "### Model preparation and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b0cbe8f-efb1-459f-b74f-408ab4672cf4",
   "metadata": {
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   "source": [
    "# separating features and labels\n",
    "x_train = train_data.iloc[:, :14].values\n",
    "y_train = np.argmax(train_data.iloc[:, 14:].values, axis=1)\n",
    "x_test = test_data.iloc[:, :14].values\n",
    "y_test = np.argmax(test_data.iloc[:, 14:].values, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d5d83e2-2fc7-4e2f-9535-966851b12f2b",
   "metadata": {
    "tags": []
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   "source": [
    "# Scaling features\n",
    "scaler = MinMaxScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "868244da-6f9a-47fa-a188-05afd26522b9",
   "metadata": {
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   "outputs": [
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       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
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       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
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       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
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       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
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       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
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       "     See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
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       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
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       "  align-self: flex-start;\n",
       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initializing and training the SVM model\n",
    "svm_model = SVC(kernel='rbf', C=1.0)\n",
    "svm_model.fit(x_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05b4eeb-0004-4e79-8584-cf41c9824c7f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4c062aae-9cb6-47fd-93a9-68b066c7c6fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM model: 0.8333\n"
     ]
    }
   ],
   "source": [
    "# Predicting with the trained model\n",
    "y_pred = svm_model.predict(x_test_scaled)\n",
    "\n",
    "# Model accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy of SVM model: {accuracy:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77820878-468b-4b1d-bac3-51c0c1b9335b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found: {'C': 10, 'gamma': 'scale'}\n",
      "Best cross-validated accuracy: 0.8974\n"
     ]
    }
   ],
   "source": [
    "# Setting up cross-validation\n",
    "skf = StratifiedKFold(n_splits=3)  # Adjust n_splits based on the size of your data\n",
    "\n",
    "# Setting up the hyperparameter grid to search\n",
    "param_grid = {\n",
    "    'C': [0.1, 1, 10, 100],  # Example range for Box constraint C\n",
    "    'gamma': ['scale', 'auto', 0.1, 1, 10]  # Example values for RBF kernel coefficient\n",
    "}\n",
    "\n",
    "# Setting up the grid search with cross-validation\n",
    "grid_search = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=skf, scoring='accuracy')\n",
    "\n",
    "# Grid search\n",
    "grid_search.fit(x_train_scaled, y_train)\n",
    "\n",
    "# Retrieve the best parameters\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "print(f\"Best parameters found: {best_params}\")\n",
    "print(f\"Best cross-validated accuracy: {best_score:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3aef92c6-bf77-460f-a602-5ec2afd506dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]\n"
     ]
    }
   ],
   "source": [
    "# Check the number of samples for each class\n",
    "class_counts = np.bincount(y_train)\n",
    "print(class_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5c588ade-860a-41c3-be63-13ec85fc7f73",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model Accuracy on Test Data: 0.8590\n"
     ]
    }
   ],
   "source": [
    "## Final Model building and Assessment based on grid search hyperparameters\n",
    "# Initialize the final model with the best parameters found by grid search\n",
    "final_model = SVC(kernel='rbf', C=10, gamma='scale')\n",
    "\n",
    "# Fit the final model on the entire training data\n",
    "final_model.fit(x_train_scaled, y_train)\n",
    "\n",
    "# Predicting on the test data\n",
    "y_pred_final = final_model.predict(x_test_scaled)\n",
    "\n",
    "# Final accuracy on the test data\n",
    "final_accuracy = accuracy_score(y_test, y_pred_final)\n",
    "\n",
    "print(f\"Final Model Accuracy on Test Data: {final_accuracy:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcf2041-e214-4091-969c-aa16c0e489c7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Visualizing the SVM model performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125bf9ab-7009-467c-8ced-9866308ae28d",
   "metadata": {},
   "source": [
    "The SVM font recognition models performance is visualized using a:\n",
    "* Confusion matrix - to visually describe the performance of the model on test data to gain insights into the types of errors the model is making. It provides a good understanding of the models accuracy per class\n",
    "* Classification report - For a better understanding of precision, recall and F-1  score within the models classifications\n",
    "* Learning curves - To get a visual understanding of how much benefit can be derived by adding more training data and whether the estimator suffers from a variance or bias error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e01c4ed0-f578-416b-97c1-67b68fff437f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6WF7rJs26xVLd/MRKnbukyFFzYbASTGkI5cmTJ+jXrx+SkpJgZ2eH+vXr4/Dhw2jfvj0AICkpCQkJCar73dzccPDgQYwbNw4rV65E5cqVsWzZMnz88cc655asc3HmzBkEBATAxcUF/v7+8Pf3B2MMT58+xZ49e7B8+XIcOnQIzZs3L7KdsLCwAq/lTG7ghhAvd433W/YdDVMXd6TOHqtzze/OICkUigLnSlOslLlJM2k25txy1CwEOWo2BtatW1fkdU1TI35+frh06ZLg3JJ1LsaNG4chQ4Zg8eLFhV4fO3YsLly4UGQ7ml7LyRwVqPFeyz6jYO7li9SwYLCXzzTeo4lnz17gzZs3cHSqqHa+YkUHPH3yT6mL5bVu0qxbLNXNT6zUuUuKHDUXio5TGrwj2ZqLv/76C0FBQYVeHz58OP76669i29H4Wo6GKRHLvqNg7vMh0uZNBHuWrKGlwsnJycGlS1fRrm1LtfPt2rVE9LmLpS6W17pJs26xVDc/sVLnLily1FwoEr0tIhWSjVw4Ozvj7NmzqF1b88rX6OhonTbsKArLfmNg8UEbpC2bBpaRDoWtPQCAZaQBOdlatbF46Vr8uGEpYmJice58DIYO7gtXlypYvebnUhnLa92kWR6aea2bV83mZZQoX91J9bmcS0U4elZDxr+peJX4XG95hcYLzS0qebmGzykhknUuJkyYgKCgIMTExKB9+/ZwdHSEQqFAcnIyjh49ih9++AFLliwRJZeyTVcAQNmvF6mdT/9hHnL+OKJVGzt27INDeXt8M3UcnJ0r4a/rt9Claz8kJBT/ChOPsbzWTZrloZnXunnV7FzfHf22faP63H5aPwBA7I5TODBhtd7yCo0XmpsoOZLuc7Ft2zYsXrwYMTExyM1926szNTWFj48PgoOD0bNnzxK1S94iBEEQJYO8RfRD9n1xpmIsqkvwGm0JkPRV1F69eqFXr17IycnBs2dvF1hWqFBBJ3MUgiAIgjB6ZLag0yiMy8zNzUVbX0EQBEEQhLSUyu2/zSyqCIrPSDxd4liryi0E5SYIgiAMiyGmRbLunhOlHeV7H4jSjr4xipELgiAIgijVyGxaRFbeIiW13t26+wC69x+Bpu17oGn7HugzbBxORxe9uZdYuaWM5bVu0iwPzbzWTZr50UyUHNl0LoRY7zpVrIBxQYOwbd0ybFu3DE18GmD019/hzr0Hes8tR3tn0kyaS2vdpJkfzaIjs020ZLPmQhfbXm3WXDTr+CnGjxyCj7t0UDuvac0F2TvzEctr3XLUzGvdpFm3WEPlNsiai79PFn+TFijr+InSjr6RxciFmNa7ubm5OHgsChmZmWhYt45ec8vR3pk0Gy6W6uYnlte6edVMCEcWCzrFsN6NuxuPPsODkZ2djTJWVlg6+1u851ZNr7nlaO9Mmg0XS3XzE8tr3bxq1gscTWmIgSw6F/kIsd51c62KnZEr8ep1Ko5G/YGpsxYicsU8rToYQnPL0d6ZNJNmY85NmuWhWVTobZHShxjWu+bm5nCtWhl1PWph3IhBqF3DHRt37NVrbjnaO5Nmw8VS3fzE8lo3r5r1gswWdMqic6EP613GGLKzc/SaW472zqTZcLFUNz+xvNbNq2ZCOLKZFhFivbskIhItPmgEJ8eKSEtPx6FjJ3Hh8jVELJyp99xytHcmzaS5tNZNmvnRLDoymxaRTedCiPXu85cvETJzPv55/gI21taoVcMNEQtnolkTb73nlqO9M2kmzaW1btLMj2axYSzX4DmlRDb7XOgCeYsQBEHIB0Psc5EZe1CUdiwbdBKlHX0jm5ELgiAIgpAMjhZjigF1LjQgZPTheR+PEsc6bLpZ4liC0DcznFuVODY0KUq0OgiCS2S25kIWb4sQBEEQBGE4ZNW5kMKZT9n5M1hPWwnbVftgs3QHyoyeAROnqgarm1c3QtLMh2aXJnXQc914jPlzBaY+2IRa/j4GyStGPI+xvNbNq2ZRoX0uSidSOfOZ1q6P7N/3IvX70UhbMBkwMYX1+LmAhaXe6+bVjZA086PZoowST24m4LdpkVrlMpa6eYzltW5eNYtOXq44ByfI5m0RQznzFbfmQmFjB9tlO5EaNg65cdfUrmlac1Ha3QiNKZbXug2lubg1F1MfbMKOoYsQdySmwDVNay7oWekWy2vdPGg2yNsiF3aK0o5l449FaUffyGLkQkpnvndRWFkDAFjaa73m5dWNkDQbLlaM+JJCz0q3WF7r5lWzXqBpEcOSkZGBM2fO4MaNGwWuZWZm4qeffioyPisrC69evVI73h2MkdKZ710sewfhTdw15D2+r9e8vLoRkmbDxYoRX1LoWekWy2vdvGrWC3l54hycIGnnIi4uDh4eHmjZsiXq1auHVq1aISkpSXU9JSUFgwYNKrKNsLAw2NnZqR0sT/OogJTOfABg2Xc0TF3ckR4xS+sYoXl5dSMkzfxoFgI9K9Js7LlFg0YuDMfkyZNRr149PH36FLdu3YKtrS2aN2+OhIQErdsICQlBSkqK2qEwsVG7R0pnvnws+4yCuZcvUudOAHv5rPgAgXl5dSMkzYaLFSO+pNCz0i2W17p51UwIR9LOxdmzZzF79mxUqFABNWrUwL59+xAQEIAWLVrg3r17WrWhVCpha2urdigUCrV7pHTmAwDLvqNg7vMh0uZNBHuWrIUq4Xl5dSMkzYaLFSO+pNCz0i2W17p51awXZDYtIukOnRkZGTAzUy9h5cqVMDExgZ+fHzZv3ixaLqmc+Sz7jYHFB22QtmwaWEY6FLb2AACWkQbkZOu1bl7dCEkzP5rNyyhRvrqT6nM5l4pw9KyGjH9T8SrxudHWzWMsr3Xzqll0OOoYiIGknYs6derg4sWL8PBQf31z+fLlYIyha9euouWSyplP2eathrJfL1I7n/7DPOT8cUSvdfPqRkia+dHsXN8d/bZ9o/rcflo/AEDsjlM4MGG10dbNYyyvdfOqmRCGpPtchIWF4fTp0zh4ULNb3JdffomIiAjk6djjE+qKKgTyFiFKK+QtQpRWDLHPRcapSFHasWo5UJR29I2kay5CQkIK7VgAwKpVq3TuWBAEQRCE0UFrLgghCBl9yEg8XeJYIU6uBKENNPpAEIS2UOeCIAiCIPQNR3tUiIHkO3QaEt6c+bbuPoDu/UegafseaNq+B/oMG4fT0RcMVrPQeB5jea1bjpp5rZs086NZVGQ2LSKbzgWPznxOFStgXNAgbFu3DNvWLUMTnwYY/fV3uHPvQanVLGUsr3XLUTOvdZNmfjQTwiBXVCNy5tNmzUWzjp9i/Mgh+LhLB7XzmtZc8KDZmGJ5rVuOmnmtmzTrFmuo3AZ5W+RYhCjtWLULEqUdfSOLkYvS4MyXm5uLg8eikJGZiYZ16+i1ZqHxPMbyWrccNfNaN2nWLVbq3KIjs2kRWSzo5NmZL+5uPPoMD0Z2djbKWFlh6exv8Z5bNb3WLDSex1he65ajZl7rJs26xUqdW3RoQWfphUdnPjfXqtgZuRKbVi9Gz8DOmDprIe7Ga7fmQmjNQuN5jJUyN2mWR92kmR/NRMmRxcgFz8585ubmcK36dvFRXY9auP53HDbu2IvQSWP0mpdHF0XSrFss1c1PLK9186pZL3A0pSEGshi5KE3OfIwxZGfn6LVmofE8xvJatxw181o3adYtVurcokNrLkonPDrzLYmIRIsPGsHJsSLS0tNx6NhJXLh8DRELZ5ZazVLG8lq3HDXzWjdp5kczIQzZdC54dOZ7/vIlQmbOxz/PX8DG2hq1arghYuFMNGviXWo1SxnLa91y1Mxr3aSZH82iI7MFnbLZ54IHyFuEIAjC8Bhkn4t9C0Rpx6rrBFHa0TeyWHNBEARBEIThkM20CA8IGX0QMuohNDdBEARRDDKbFqGRC4IgCILQNxK8LRIWFobGjRvDxsYGlSpVQmBgIG7dulVkTFRUFBQKRYHj77//1im3rDoXvDrzlTRWaldVHmN5rVuOmnmtmzTzo5l3Tp48iZEjR+LcuXM4evQo3rx5A39/f6SlpRUbe+vWLSQlJamOmjVr6pRbNp0LXp35hMRK6arKYyyvdctRM691k2Z+NIsOyxPn0IHDhw9j4MCBeP/999GgQQNs2LABCQkJiImJKTa2UqVKcHJyUh2mpqY65ZbN2yI8OPMJidV2zYWhXFV5jOW1bjlq5rVu0qxbrKFyG+RtkV++F6Udky4TkZWVpXZOqVRCqVQWG3vnzh3UrFkT165dQ926dTXeExUVhdatW6N69erIzMyEp6cnvvnmG7Ru3Vq3OnW62wDoo6/DqzOfmK5+hnRV5TGW17rlqJnXukmzbrFS5xYdkdZchIWFwc7OTu0ICwsrNj1jDMHBwfjwww8L7VgAgLOzM9asWYOdO3di165dqF27Ntq2bYtTp07pJNfo3hZRKpWIjY2Fh4eHVvdnZWUV6MUxxqBQKFSfeXXmE8PVTwpXVR5jea1bjpp5rZs06xYrdW5jJSQkBMHBwWrntBm1GDVqFK5evYozZ84UeV/t2rVRu3Zt1WdfX188fPgQCxYsQMuWLYuIVEeyzsW7X5x8cnNzMWfOHDg4OAAAFi1aVGQ7YWFhmDFjhto5hUlZKExtC9zLqzOfkNh8V9VXr1NxNOoPTJ21EJEr5mnVwZCybjk6MJJmedRNmvnRLCoi5dR2CuS/jB49Gvv27cOpU6dQtWpVnXN+8MEH2Lhxo04xknUulixZggYNGqBcuXJq5xljuHnzJqytrdVGHwpDUy/O3kF92J9XZz4xXP2kcFXlMZbXuuWomde6SbNusVLnFh0JTMcYYxg9ejR2796NqKgouLm5laidy5cvw9nZWacYydZczJo1CykpKfj2229x4sQJ1WFqaorIyEicOHECx48fL7YdpVIJW1tbtePdTgmvznz6cPUzhKsqj7G81i1HzbzWTZp1i5U6d2lg5MiR2LhxIzZv3gwbGxskJycjOTkZGRkZqntCQkLQv39/1eclS5Zgz549uH37Nq5fv46QkBDs3LkTo0aN0im3ZCMXISEhaNeuHfr27YsuXbogLCwM5ubmesvHqzOfkFgpXVV5jOW1bjlq5rVu0syPZtGRYOQiPDwcANCqVSu18xs2bMDAgQMBAElJSUhISFBdy87OxoQJE/D48WNYWVnh/fffx6+//opOnTrplFvSBZ2NGzdGTEwMRo4ciUaNGmHjxo1aTYWUBF6d+YTESumqymMsr3XLUTOvdZNmfjSLjgTbf2uztiQyMlLt86RJkzBp0iTBuY1mn4utW7di7Nix+Oeff3Dt2jV4enqWuC1eXVGFQN4iBEEQJcMg+1xsnCpKO1Z9Z4nSjr4xmldRe/fujQ8//BAxMTGoVk27NxkIgiAIggskmBaREqPpXABA1apVS/SaDCF85EHIyAeNehAEQRSDcUwSGAyj26GTIAiCIAi+MaqRC4IgCIIolchsWkRWIxe82v6SXbs87J1JszzqJs38aBYVkbxFeEE2nQtebX/Jrl0e9s6kWR51k2Z+NIuOBJbrUmI0r6KKiRwt14XGarOgs7TbtfNatxw181o3adYt1lC5DfIq6g+a/bR0xWpI0X5bxoIsRi54tf01FrthOdi181q3HDXzWjdp1i1W6txiw/KYKAcvyGJBJ6+2v1LbDcvJrp3XuuWomde6SbNusVLnFh2O1kuIgSxGLvLh1fZXarv2TasXo2dgZ0ydtRB347VbcyE0txztnUmzPOomzfxoJkqOLDoXvNr+Sm03nG/XXtejFsaNGITaNdyxccfeYuN41cxj3XLUzGvdpFm3WKlzi47MFnTKonPBq+2vsdkNl2a7dl7rlqNmXusmzbrFSp1bdPKYOAcnyGLNBcCv7S/ZtcvD3pk0y6Nu0syPZkIYsulc8Gr7S3bt8rB3Js3yqJs086NZdGS2oFM2+1wQRUPGZQRByBVD7HORvjRIlHbKfBUhSjv6RhZrLgiCIAiCMByymRYhikbI6AONehAEQRRD6ZskKBLqXBAEQRCEvpHZmgtZTYvw6szHmxshz46qUuYmzfKomzTzo1lUZPYqqmw6F7w68/HoRsiro6qUuUmzPOomzfxoJoQhm7dFeHDmM6ZYXeJLi6OqlLlJszzqJs26xRoqt0HeFpn/hSjtlJm4XpR29I0sRi54debj1Y3wv/DiqCplbtIsj7pJs26xUucWHZlNi0i6oPPy5csoV64c3NzcAAAbN25EeHg4EhISUK1aNYwaNQq9e/cuso2srCxkZWWpnWOMQaFQqD7z6szHqxshwJ+jqpS5SbM86ibNusVKnZsQhqQjF4MHD8b9+/cBAD/88AOGDRuGRo0aYerUqWjcuDGGDh2K9euLHgIKCwuDnZ2d2sHyXmu8l1dnPh7dCHl1VJUyN2mWR92kmR/NYsLy8kQ5eEHSkYtbt27hvffeAwCsWrUKS5YswbBhw1TXGzdujFmzZuGLLwqfqwoJCUFwcLDaOXsH9eF3Xp35eHUjBP7nqAoAdT1q4frfcdi4Yy9CJ43RW156zrrFUt38xPJaN6+a9QJHUxpiIOnIhZWVFf755+1Dfvz4MZo2bap2vWnTpoiPjy+yDaVSCVtbW7Xjv1MiAL/OfLy6EWrC2B1VpcxNmuVRN2nWLVbq3IQwJB25CAgIQHh4OH744Qf4+fnhl19+QYMGDVTXt2/fjho1aoiSi1dnPh7dCHl1VJUyN2mWR92kmR/NosP4mdIQA0k7F3PnzkXz5s3h5+eHRo0aYeHChYiKioKHhwdu3bqFc+fOYffu3aLk4tWZj0c3Ql4dVaXMTZrlUTdp5kez6MhsWkTyfS7+/fdfzJkzB/v378e9e/eQl5cHZ2dnNG/eHOPGjUOjRrq/MkSuqIaFvEUIguAZQ+xzkfZdH1HasZ62SZR29I3knQt9QJ0Lw0KdC4IgeMYgnYvpn4nSjvX0LaK0o2/IuIwQDDmqEgRBFIPMpkWoc0EQBEEQ+kZmCzplsf13Prw688nJjVBqR1Wh8TzGUt38xPJaN6+aiZIjm84Fr858cnMjlNJRVSrNUsZS3fzE8lo3r5pFR2beIrJZ0MmDM58xxRoqtzE5qgqN5zGW6uYnlte6edBsiAWdqSEfi9JO2bCdorSjb2QxcsGrM5/c3QgN6agqNJ7HWKqbn1he6+ZVMyEcWSzo5NWZT65uhFI4qgqN5zGW6uYnlte6edWsFzia0hADWXQu8uHVmU9uboT5jqqvXqfiaNQfmDprISJXzNOqgyG0ZqHxPMZKmVuOdZNmfjSLisw6F7KYFuHVmU+uboT5jqp1PWph3IhBqF3DHRt37NVrzULjeYyluvmJ5bVuXjUTwpFF54JXZz5yI3yLIRxVhcbzGEt18xPLa928atYLLE+cgxNkMy3CqzOf3NwIpXRUlUqzlLFUNz+xvNbNq2bRkdm0iGw6F7w688nNjVBKR1WpNEsZS3XzE8tr3bxqJoQhm30uCOOEvEUIgpAaQ+xz8XpsF1HasVmyX5R29I1sRi4IgiAIQjJoWoQgDIdUjqpCcxMEQehEHj+LMcVAFm+LEARBEARhOGTVueDVmU+OboQljZXaVZXHWKqbn1he6+ZVs6jIzLhMNp0LXp355OhGKCRWSldVHmOpbn5iea2bV82iI7POhWzeFuHBmc+YYnmoW9s1F4ZyVeUxlurmJ5bXunnQbJC3RYI6itKOTcRhUdrRN7IYueDVmU+OboRiOhka0lWVx1iqm59YXuvmVbM+YIyJcvAC952LrKwsvHr1Su149wHw6swnRzdCMZwM4+7Go3G77vBu3RUz568wiKsqj7FUNz+xvNbNq2a9IMG0SFhYGBo3bgwbGxtUqlQJgYGBuHXrVrFxJ0+ehI+PDywtLeHu7o6IiAid5UreuVi+fDkGDBiA7du3AwB+/vlneHp6ok6dOpgyZQrevHlTZHxYWBjs7OzUDpb3WuO9vDrzydGNUEhsvqvqptWL0TOwM6bOWoi78dqtuRCam8dYKXPLsW7SzI9m3jl58iRGjhyJc+fO4ejRo3jz5g38/f2RlpZWaEx8fDw6deqEFi1a4PLly5gyZQrGjBmDnTt36pRb0n0uZs6cifnz58Pf3x9fffUV4uPjMX/+fIwbNw4mJiZYvHgxzM3NMWPGjELbCAkJQXBwsNo5ewf1IXBenfnk6EYohpNhvqsqANT1qIXrf8dh4469CJ00xijrpucsj7pJs26xUucWHQkWYx4+rL4+Y8OGDahUqRJiYmLQsmVLjTERERFwdXXFkiVLAAAeHh64ePEiFixYgI8//ljr3JKOXERGRiIyMhK//PILDh8+jKlTp2Lp0qWYOnUqQkJCsHr1amzevLnINpRKJWxtbdUOhUKhdg+vznxydCPUh5OhIVxVeYyluvmJ5bVuXjXrA5bHRDk0LQXIysrSqoaUlBQAQPny5Qu9Jzo6Gv7+/mrnOnTogIsXLyInp/jfo/lIOnKRlJSERo3eLqxp0KABTExM0LBhQ9V1b29vJCYmipKLV2c+OboRComV0lWVx1iqm59YXuvmVbOxEhYWVmA0PzQ0FNOnTy8yjjGG4OBgfPjhh6hbt26h9yUnJ8PR0VHtnKOjI968eYNnz57B2dlZqzol7Vw4OTnhxo0bcHV1xe3bt5Gbm4sbN27g/fffBwBcv34dlSqJs/CGV2c+OboRComV0lWVx1iqm59YXuvmVbPoiDQtomkpgFKpLDZu1KhRuHr1Ks6cOVPsve+O/uevUXn3fJFtSLnPxTfffIM1a9agW7du+P3339G7d29s2rQJISEhUCgUmDVrFj755BMsWrRIp3bJFVUekLcIQRBiYIh9LlL6tRWlHbuff9c5ZvTo0dizZw9OnToFNze3Iu9t2bIlvLy8sHTpUtW53bt3o2fPnkhPT4e5ublWOSUduZgxYwasrKxw7tw5DB8+HJMnT0b9+vUxadIkpKeno0uXLpg5U7vhbIIgCIIwVpgECzoZYxg9ejR2796NqKioYjsWAODr64v9+9Vt3Y8cOYJGjRpp3bEAZLRDJ1H6mOHcSlD8qDZPShzrsOmmoNwEQRgPhhi5+LdPG1HaKbfpuNb3fvnll9i8eTP27t2L2rVrq87b2dnBysoKwNtplsePH+Onn34C8PZV1Lp162L48OEYOnQooqOjERQUhC1btvDztghBEARByAIJNtEKDw9HSkoKWrVqBWdnZ9Wxbds21T1JSUlISEhQfXZzc8PBgwcRFRWFhg0bYubMmVi2bJlOHQtAZp0LXp355OhGWNJYlyZ10HPdeIz5cwWmPtiEWv4+WudUdv4M1tNWwnbVPtgs3YEyo2fAxKmq1vFC6pYylurmJ5bXunnVLCp5Ih06UNgW4gMHDlTdExkZiaioKLU4Pz8/XLp0CVlZWYiPj0dQUJDOcmXTueDVmU+OboRCYi3KKPHkZgJ+mxapjUQ1TGvXR/bve5H6/WikLZgMmJjCevxcwMJSq3gev15UNz+xvNbNq2ZCGLJZc8GDM58xxfJQd3FrLqY+2IQdQxch7kiMxuvFrblQ2NjBdtlOpIaNQ27cNbVrmtZcGPvXy9hyy7Fu0qxbrKFyG2LNxctPW4nSjv2OKFHa0TeyGLng1ZlPjm6ExuRkqLCyBgCwNM1eNf+F168X1c1HLK9186pZL0gwLSIlsuhc8OrMJ0c3QmNyMrTsHYQ3cdeQ9/h+sffy+vWiuvmI5bVuXjUTwpF0nwtDw6sznxzdCKV2MrTsOxqmLu5InT1Wpzhev15UNx+xUuaWo2YxkWKfCymRReeCV2c+OboRGoOToWWfUTD38kVqWDDYy2fFB4DfrxfVzUcsr3XzqlkvcDSlIQaymBbh1ZlPjm6EUjsZWvYdBXOfD5E2byLYs2St43j9elHdfMTyWjevmgnhyGLkAuDXmU+OboRCYs3LKFG+upPqczmXinD0rIaMf1PxKvF5kbGW/cbA4oM2SFs2DSwjHQpbewAAy0gDcrL1Wjc9Z3nUTZr50Sw2TGYjF7LpXPDqzCdHN0Ihsc713dFv2zeqz+2n9QMAxO44hQMTVhcZq2zTFQBQ9mt1o7z0H+Yh548jeq2bnrM86ibN/GgWHZl1LmSzzwVR+iBvEYIgxMAQ+1w8C/ATpZ0Kh04Wf5MRIIs1FwRBEARBGA7ZTIsQBEEQhGTQtAj/0LQIoW+e9/EocSxNqRCEZoRMdYYmRZU41hDTIv+0F2dapOJRmhYhCIIgCEKGSN65SEtLw9q1azFo0CAEBASgU6dOGDRoEH744QekpaWJmotX2185Wh3zpplnu3Ypc8uxbtKsfaxLkzrouW48xvy5AlMfbEItfx+tc4pRt5iwPHEOXpC0c3Hjxg3UqlULkyZNwsuXL+Hq6oqqVavi5cuXmDhxImrXro0bN26IkotX2185Wh3zqJlXu3Ypc8uxbtKsW6xFGSWe3EzAb9Mii71X7NxiI7fOhaRrLlq3bg0nJyf8+OOPsLCwULuWnZ2NgQMHIikpCSdOnNCpXbJcFx7La92G0lzcmgte7NqlzC3Huklz0bFFrbmY+mATdgxdhLgjMRqva1pzYUyW609ai7PmwvEErbkolvPnz+Pbb78t0LEAAAsLC0yZMgXnz58XnIdX2185Wh3zqvldeLBrlzK3HOsmzbrFCsXoLNeZQpyDEyR9FdXe3h63b9+Gp6enxut37tyBvb19kW1kZWUhKytL7RxjDArF/x4Cr7a/crQ65lXzu/Bg1y5lbjnWTZp1ixWKsVmu8zSlIQaSjlwMHToUAwYMwIIFCxAbG4vk5GQ8efIEsbGxWLBgAb744gsMHz68yDbCwsJgZ2endrA8zX8t8mr7K0erY141A/+za0+PmKV1jNC8cnzOUuYmzfzYnhuL5brckHTkYvr06bCyssKiRYswadIk1WgDYwxOTk74+uuvMWnSpCLbCAkJQXBwsNo5e4c6ap95tf2Vo9Uxr5rz4cmuXcrccqybNOsWKxRjs1xnefxMaYiB5K+iTp48GYmJibh79y7OnDmDM2fO4O7du0hMTCy2YwEASqUStra2asd/p0QAfm1/5Wh1zKtmgD+7dilzy7Fu0qxbrFCMzXJdbm+LGM32325ubnBzc1M79/DhQ4SGhmL9+vWC2+fV9leOVsc8aubVrl3K3HKsmzTrFmteRony1Z1Un8u5VISjZzVk/JuKV4nP9ZpbbBhHizHFwGg6F5p48eIFfvzxR1E6F7za/srR6phHzbzatUuZW451k2bdYp3ru6Pftm9Un9tP6wcAiN1xCgcmrNZrbkIYku5zsW/fviKv37t3D+PHj0dubq5O7ZK3CKFvyFuEIMSnNHuLPGraRpR2qp4/Lko7+kbSkYvAwMBiV+6+u36CIAiCIHhDbgs6Je1cODs7Y+XKlQgMDNR4/cqVK/Dx0X0veYLQN0JGHzIST5c41qpyixLHEoSxI2T0gTAuJH1bxMfHB5cuXSr0Or2PTBAEQZQGGBPn4AVJOxcTJ05Es2bNCr1eo0YNnX1FioLcCEmzMebeuvsAuvcfgabte6Bp+x7oM2wcTkdfMFjNQuPl9KykjuW1bl41iwnLU4hy8IKknYsWLVqgY8eOhV63traGn584Zi/kRkiajVWzU8UKGBc0CNvWLcO2dcvQxKcBRn/9He7ce6D3mqXSLNe6STM/mglhSPq2iL4gV1ThsbzWzYNmbdZcNOv4KcaPHIKPu3RQO69pzQUPmsWO5bVu0qxbrKFyG+JtkfsN24vSTvUrR0VpR99IvkOnISA3Qt1iea2bV83/JTc3FwePRSEjMxMN69Yp9n5eNcuxbtKsW6zUucVGbmsujHoTLbEgN0LdYnmtm1fNABB3Nx59hgcjOzsbZayssHT2t3jPrZpeaxYaL8dnRZoNFyt1bkIYsuhc5ENuhKTZWHO7uVbFzsiVePU6FUej/sDUWQsRuWKeVh0MoTULjZfbs5IyVsrcctQsJjwtxhQDWUyLkBuhbrG81s2rZuDtEK5r1cqo61EL40YMQu0a7ti4Y69eaxYaL8dnRZoNFyt1brFhTCHKwQuy6FyQG6FusbzWzatmTTDGkJ2do9eahcbL8VmRZsPFSp1bbMgVtZRCboSk2Vg1L4mIRIsPGsHJsSLS0tNx6NhJXLh8DRELZ+q9Zqk0y7Vu0syPZkIYsulckBshaTZWzc9fvkTIzPn45/kL2Fhbo1YNN0QsnIlmTbz1XrNUmuVaN2nmR7PY5HE0pSEGstnngiCMBfIWIQjjwhD7XNyqEyBKO7X/PiRKO/pGFmsuCIIgCIIwHLKZFiEIY0HI6IOQUQ+huQmCKDlyexWVOhcEQRAEoWdK3wKEoinRtMjPP/+M5s2bo3Llynjw4K250pIlS7B3b/Hv5UsJr858cnQjJM3ax0rtqkrPijTrM1bq3ETJ0LlzER4ejuDgYHTq1An//vsvcnNzAQDlypXDkiVLSlTEo0ePkJqaWuB8Tk4OTp06VaI234VXZz45uhGSZt1ipXRVpWdFmkurZrGRm+U6mI54eHiw3bt3M8YYK1u2LLt79y5jjLFr164xBwcHndpKTExkjRs3ZiYmJszU1JT179+fvX79WnU9OTmZmZiY6FoiMzWvXOA4fz6GhUf8qHbuxs04NmfuMo33ixnPYyyvdZd2zdn/3NXqaOTjzbasX1XgPI+ajS03aS59mg3BNbfOohy8oPPIRXx8PLy8vAqcVyqVSEtL06mtr7/+Gqampjh//jwOHz6MGzduoFWrVnj58uV/Oz+6llgAXp355OhGSJp1r/u/GNJVlZ6V4WJ5rZtXzYRwdF7Q6ebmhitXrqBaNXVDpUOHDsHT01Onto4dO4bdu3ejUaO3D7pFixbo1asX2rRpg99//x3AW5OZosjKykJWVpbaOcaYWhyvznxydCMkzbrXDUjjqkrPynCxvNbNq2Z9wJMviBjoPHIxceJEjBw5Etu2bQNjDH/++SdmzZqFKVOmYOLEiTq1lZKSAnt7e9VnpVKJX375BdWrV0fr1q3x9OnTYtsICwuDnZ2d2sHyXmu8991REF6c+aSKlTI3adYtNt9VddPqxegZ2BlTZy3E3Xjt1lwIzU3PijTrM1bq3GLBmDgHL+g8cjFo0CC8efMGkyZNQnp6Oj7//HNUqVIFS5cuRe/evXVqy93dHVevXkXNmjX/V5CZGXbs2IFPP/0UH330UbFthISEIDg4WO2cvYP6cDCvznxydCMkzbrXDfzPVRUA6nrUwvW/47Bxx16EThpjlHXL8VmRZt1ipc4tNnLb/rtEr6IOHToUDx48wNOnT5GcnIyHDx9i8ODBOrcTEBCANWvWFDif38Fo2LBhsT1MpVIJW1tbtePdqRRenfnk6EZImnWvWxPMAK6q9KwMF8tr3bxqJoQjaBOtChUqCEo+a9YspKena7xmZmaGXbt24dGjR4Jy5MOrM58c3QhJs26xUrqq0rMizaVVs9jIbc1FiRZ0FrXI8t69e9onNzODra1todcTExMxY8YMrF+/XqcaNcGrM58c3QhJs26xUrqq0rMizaVVs9hItV7i1KlTmD9/PmJiYpCUlITdu3cjMDCw0PujoqLQunXrAudv3ryJOnWKfwMtH51dUZcuXar2OScnB5cvX8bhw4cxceJEfP3117o0VySxsbHw9vZWbdSlLeSKSpRWyFuEIMTHEK6ol1y6idKO90PddsI+dOgQ/vjjD3h7e+Pjjz/WunNx69YttT/+K1asCFNTU63z6jxy8dVXX2k8v3LlSly8qNs81r59+4q8rssoCEEQBEEYK2It6NS0/YJSqYRSqdR4f0BAAAICdLd7r1SpEsqVK1eSEgGIaFwWEBCAkJAQbNiwQeuYwMDAYl8LKm6fC4KQE0JHHoSMfNCoB0GUHLHWXISFhWHGjBlq50JDQzF9+nRR2s/Hy8sLmZmZ8PT0xDfffKNxqqQoSvS2iCZ++eUXlC9fXqcYZ2dn7Ny5E3l5eRqPS5cuiVUeQRAEQXBPSEgIUlJS1I6QkBDR2nd2dsaaNWuwc+dO7Nq1C7Vr10bbtm119vnSuXPh5eUFb29v1eHl5QVnZ2dMmTIFU6ZM0aktHx+fIjsQYm92wqsznxzdCEkzOaoac27SLA/NYpLHFKIcmrZfKGxKpCTUrl0bQ4cOhbe3N3x9fbFq1Sp07twZCxYs0KkdnTsXgYGB6Natm+ro0aMHQkND8ddff2HYsGE6tTVx4kQ0a9as0Os1atTAiRMndC1RI7w688nRjZA0k6OqMecmzfLQLDZMpEMKPvjgA9y+fVunGJ3eFnnz5g02bdqEDh06wMnJSecCDYWmt0XOntmPS5f/wqjR/xs+unY1Cvv2HcbUb+YU26aQeB5jea2bNBcdq82ai2YdP8X4kUPwcZcOauc1rbmgZ2W4WF7r5kGzId4WOVe5hyjtfJC4q8SxCoWi2LdFNPHJJ5/gxYsXOH78uNYxOo1cmJmZYcSIEQVWqho7vDrzydGNkDQbLvZdeHFUlTI3aTZcrNS5xUasaRFdSU1NxZUrV3DlyhUAb53Nr1y5goSEBABv13D0799fdf+SJUuwZ88e3L59G9evX0dISAh27tyJUaNG6ZRX57dFmjZtisuXLxdwRTVmeHXmk6MbIWk2XGw+vDmqSpmbNBsuVurcYiPVDp0XL15Ue9Mj34trwIABiIyMRFJSkqqjAQDZ2dmYMGECHj9+DCsrK7z//vv49ddf0alTJ53y6ty5+PLLLzF+/Hg8evQIPj4+sLa2Vrtev359XZs0GLw688nRjZA0G95R9dXrVByN+gNTZy1E5Ip5WnUwpKxbytykWR6axSTP4Bnf0qpVqyL1RkZGqn2eNGkSJk2aJDiv1p2LL774AkuWLEGvXr0AAGPG/M9tMf9hKRQKnXfTNAS8OvPJ0Y2QNBsuNh/eHFWlzE2aDRcrdW5CGFqvufjxxx+RmZmJ+Pj4Ase9e/dU/2uM8OrMJ0c3QtJsuNjCMHZHVSlzk2bDxUqdW2wYFKIcvKD1yEX+sApPay3+C6/OfHJ0IyTN5KhqzLlJszw0i02eVO+RSoROay543oqbV2c+OboRkmZyVDXm3KRZHpoJYWi9z4WJiQns7OyK7WC8ePFClMKEQK6oBKEZ8hYhiIIYYp+L4449RWmnzZPtorSjb3QauZgxYwbs7Oz0VQtBEARBlEp4Wi8hBjp1Lnr37o1KlQz/fjBBEOIgZPSBRj0IgtAWrTsXPK+3IAiCIAgpkWqfC6nQ+lVUKTYdERtenfnk6EZImo1fs9SOqkLjeYzltW5eNYuJ3F5F1bpzkZeXZ7ApEXd3d50d2IqDV2c+OboRkmY+NEvpqCo0nsdYXuvmVbPY5Il08IJOrqhis2zZMo3ng4ODMWnSJJXz6n93A9UGckUVHstr3aRZt1hd4o3JUVVoPI+xvNbNg2ZDvC1y2LG3KO10fLJVlHb0jU6uqGIzduxYzJ8/H4sXL1Y78vLy8NNPP2Hx4sVYsmSJ4Dy8OvPJ0Y2QNBsuVoz4fAzpqCo0nsdYXuvmVbM+kNvIhc7GZWIydOhQ/Pnnn9i8eTM8PDxU583NzXHkyBF4enoW20ZWVlYBC/h8n5N8eHXmk6MbIWk2XKwY8VI4qgqN5zGW17p51awPeFovIQaSjlysXr0aoaGh6NChA1asWFGiNsLCwmBnZ6d2sLzXGu/l1ZlPjm6EpJkPzfmOqptWL0bPwM6YOmsh7sZrt+ZCSF4x4nmMlTK3HDUTJUfSzgUABAYGIjo6Grt370ZAQACSk5N1ig8JCUFKSoraoTCxUbuHV2c+OboRkmbDxYoRn++oWtejFsaNGITaNdyxccdevefl8etNmnWLlTq32OQpxDl4QfLOBQBUqVIFx44dQ8uWLeHl5aVTr1KpVMLW1lbteHdPDl6d+eToRkiaDRcrRvy7GMJRVWg8j7G81s2rZn2QB4UoBy9IuubivygUCoSEhMDf3x9nzpyBs7OzqO3z6swnRzdC0syHZikdVYXG8xjLa928aiaEYTSdi3x8fHzg4+MDAHj48CFCQ0Oxfv16we3y6swnRzdC0syHZikdVYXG8xjLa928ahYbua3ykHSfi+KIjY2Ft7c3cnNzdYojV1SCEB/yFiFKK4bY52KX0+eitNMjebMo7egbSUcu9u3bV+T1e/fuGagSgiAIgiDEQtLORWBgYLGvBZFhGkEQBME7eTL7t0zSzoWzszNWrlyJwMBAjdevXLmiWn9BEIS0kF07QZQco11/oCckfRXVx8cHly5dKvQ6bXZCEARBlAbktv23pJ2LiRMnolmzZoVer1GjBk6cOCFaPl5tf+VodUyaS7dmMezapahb6lhe6+ZVM1FyJO1ctGjRAh07diz0urW1Nfz8/ETJxavtrxytjklz6dcs1K5dqrqljOW1bl41i43cdug06ldRSwpZrguP5bVu0qxbrKFyi23Xbqi6jSmW17p50GyIV1E3Ve4rSjt9EjeK0o6+MYrtv/UNr7a/crQ6Js2Gi5U6dz662rVLWbccn5UcNRPCMbodOvUBr7a/crQ6Js2Gi5U6d0nt2qWsW47PSo6a9UGpmyIoBll0LvLh1fZXjlbHpLn0a863a3/1OhVHo/7A1FkLEblintYdDKnqljJWytxy1CwmPK2XEANZTIvwavsrR6tj0my4WKlzl9SuXcq65fis5KiZEI4sOhe82v7K0eqYNBsuVurc76KtXbvQ3DzG8lo3r5r1gdz2uZDNtAivtr9ytDomzaVfs1C7dqnqljKW17p51Sw2tOailMKr7a8crY5Jc+nXLNSuXaq6pYzltW5eNRPCkM0+FwRBSAd5ixDGjCH2uVhXVZx9LgY/4mOfC9mMXBAEQRCEVPC0XkIMSmXnYoZzK0HxoUlRotRBEMRbhIw+0M8zP9CzKhy5dS5k8bYIQRAEQRCGQzadC5cmddBz3XiM+XMFpj7YhFr+Pjq3waOjIK9uhKSZNBeFXH+eeayb52clJkwhzsELknYuHj16hGfP/rc16+nTp9GnTx+0aNECffv2RXR0tGi5LMoo8eRmAn6bFlmieB4dBXl1IyTNpLk45PjzzGvdvD4rsZHbPheSvi3SrFkzfPvttwgICMDevXvRo0cPfPTRR/Dw8EBcXBwOHDiAXbt24aOPPtKp3VnV+hR5feqDTdgxdBHijsRovK5p3o9HR0Ee3AiNKZbXuku75uLm8eXy88xD3bw+K0O8LbLKRZy3Rb58yMfbIpKOXPz111/w8PAAAISFhWH27NnYu3cv5syZg127dmHRokWYNm2alCUC4NNRkFc3QtJsuFie6xYCr5p5rVsIvNatCbmNXEjauTAxMcGrV68AAPHx8QgICFC7HhAQgFu3bhXZRlZWFl69eqV2vGG5otbJo6Mgr26EpNlwsTzXLQReNfNatxB4rVsTTKSDFyTtXPj5+WHLli0AAC8vL0RFRaldP3HiBKpUKXpDrLCwMNjZ2akdJ1Ou66VeHh0FeXUjJM2kWd/wqpnXuoXAa91yRtJ9LubMmYMWLVogMTERH374IaZOnYoLFy7Aw8MDt27dwrZt2xAREVFkGyEhIQgODlY7t7juMFHr5NFRkFc3QtJsuFie6xYCr5p5rVsIvNatCbJcNyAeHh44f/48srOzMW/ePKSlpWHTpk2YPn067ty5g61bt2LgwIFFtqFUKmFra6t2mClMRa2TR0dBXt0ISbPhYnmuWwi8aua1biHwWrcm5LbmQvIdOt977z1s2bIFjDE8ffoUeXl5qFChAszNzUXNY15GifLVnVSfy7lUhKNnNWT8m4pXic+LjefRUZBXN0LSTJqLQ44/z7zWzeuzIoQheeciH4VCAUdHR7VzDx8+RGhoKNavXy+4fef67ui37RvV5/bT+gEAYnecwoEJq4uN59FRkFc3QtJMmotDjj/PvNbN67MSG55GHcTAqF1RY2Nj4e3tjdxc3d7+KG6fi+IozfvbEwRvkF8FP/D6rAyxz8UCV3H2uZiQwMc+F5KOXOzbt6/I6/fu3TNQJQRBEAShP+S2oFPSkQsTE5NiXwtSKBQ6j1yYWRT9+ipBEIZFyF+0Qv+afd7Ho8SxDptuCspN8IEhRi7mVRNn5GLSAz5GLiR9W8TZ2Rk7d+5EXl6exuPSpUtSlkcQBEEQoiDV2yKnTp1Cly5dULlyZSgUCuzZs6fYmJMnT8LHxweWlpZwd3cvdksITUjaufDx8SmyAyH2ZidycyMUGstr3aSZD81SuWUqO38G62krYbtqH2yW7kCZ0TNg4lRV73nFiJUytxw1i4lUO3SmpaWhQYMGWLFihVb3x8fHo1OnTmjRogUuX76MKVOmYMyYMdi5c6dOeSXtXEycOBHNmjUr9HqNGjVw4sQJUXLJ0Y2QNJNmY9YslVumae36yP59L1K/H420BZMBE1NYj58LWFjqNa/QWClzy1FzaSEgIADff/89evToodX9ERERcHV1xZIlS+Dh4YEhQ4bgiy++wIIFC3TKa9Rvi5QUTWsuSrsbodixvNZNmnWLNVRuIW6Zha250DZ3cWsuFDZ2sF22E6lh45Abd03tmqY1F6X9WRlTrKFyG2LNhdC3GPOZELceWVlZaueUSiWUSmWxsQqFArt370ZgYGCh97Rs2RJeXl5YunSp6tzu3bvRs2dPpKena70HlaQjF4ZCjm6EpFm3WF7r5lWzUMTMrbCyBgCwtNd6zcvrs5KjZn0g1poLTX5aYWFhotWZnJxcYM8pR0dHvHnzBs+ePSskqiBGs4mWPpGjGyFp1i2W17p51SwUMXNb9g7Cm7hryHt8X695eX1WctRszGjy09Jm1EIXFAr192bzJzjePV8Usuhc5CNHN0LSTJqNPbcQhOa27Dsapi7uSJ091mB5eX1WctQsJmJl1HYKpKQ4OTkhOTlZ7dzTp09hZmYGBwcHrduRRedCjm6EpFm3WF7r5lWzUMTIbdlnFMy9fJEaFgz2UrvhXjk+Kzlq1ge8bP/t6+uL/fv3q507cuQIGjVqpJPnlyzWXMjRjZA06xbLa928ahaK0NyWfUfB3OdDpM2bCPYsudj7xcjL67OSo+bSRGpqKq5cuYIrV64AePuq6ZUrV5CQkADg7TRL//79VfcHBQXhwYMHCA4Oxs2bN7F+/XqsW7cOEyZM0CmvLEYuAHm6EZJm0mzMmqVyy7TsNwYWH7RB2rJpYBnpUNjaAwBYRhqQk623vEJjpcwtR81iI9X23xcvXkTr1q1Vn/PXawwYMACRkZFISkpSdTQAwM3NDQcPHsS4ceOwcuVKVK5cGcuWLcPHH3+sU17ZdC7k6EZImkmzMWuWyi1T2aYrAKDs14vUzqf/MA85fxzRW16hsVLmlqNmsckTbdWFbrRq1arINSaRkZEFzvn5+QneIVs2+1wQBCEd5C1CGDOG2OdiavXPRWln1v3NorSjb2Sx5oIgCIIgCMMhm2kRgiCkQ+jogxCEjD7wOuoh5UgRoRle3hYRC+pcEARBEISekWrNhVTIalqEV2c+OboRkmbSbIy5pXZUFRIv1IVWTs+ZEI7knYv9+/cjNDQU0dHRAIDjx4+jU6dO6NixI9asWSNaHl6d+eToRkiaSbOx1i2lo6rQeCEutHJ7zvpAKst1qZD0bZGIiAiMHj0aDRo0wO3bt7Fq1SqMGDECvXr1gqmpKX766SeEhYXhq6++0qldckUVHstr3aRZt1iqmx9HVV3ixXahLe3P2RBvi0yo/pko7Sy4v0WUdvSNpCMXy5Ytw6pVq3Dx4kXs2bMHQ4YMwZw5c7B27VpERERg1apVWL26+Pfdi4NXZz45uhGSZsPFUt38OKqKEV9S6DkTJUHSzsX9+/fRoUMHAEDr1q2Rm5uLli3/t1Vrq1at8ODBgyLbyMrKwqtXr9SOdwdjeHXmk6MbIWk2XCzVzY+jqth16wI9Z3HIAxPl4AVJOxcODg6qzkNiYiLevHmjtg3pgwcPUL58+SLb0ORtz/I0/xXBqzOfHN0ISTNpNvbc+Y6q6RGztI4RI69ULp9yfc5iIbc1F5K+itqtWzcMHjwYAwYMwL59+9C/f3+MHz8eJiYmUCgUmDhxIvz9/YtsQ5O3vb1DHbXPvDrzydGNkDQbLpbq5sdRVay6S4KcnzNRciQduZg7dy78/PywdetWeHt7Y+3atRg8eDC6deuGgIAAODg4ICwsrMg2lEolbG1t1Q6FQt0hhldnPjm6EZJmw8VS3fw4qooRX1Lk+pzFJk+kgxckHbmwtrbG2rVr1c5NmDABo0aNQk5ODmxsbETLxasznxzdCEkzaTbWuqV0VBUaL8SFVm7PWR8wriY1hGOUO3RaWlrC0tISDx8+RGhoKNavXy+4TV6d+eToRkiaSbOx1i2lo6rQeCEutHJ7zvqAp1EHMTBqV9TY2Fh4e3sjNzdXpzhyRSUIQgzIW0QeGGKfi1HVe4nSzor720RpR99IOnKxb9++Iq/fu3fPQJUQBEEQhP7g6TVSMZB05CL/rZCiSlAoFDRyQRAEd2QknhYUb1W5hUiVEMVhiJGLEdV7itJO+P3torSjbyR9W8TZ2Rk7d+5EXl6exuPSpUtSlkcQBEEQRAmQtHPh4+NTZAdCqs1OCIIgCEJMaIdOAzJx4kQ0a9as0Os1atTAiRMnRMvHq+2vHK2OSTNpNubcJY3duvsAuvcfgabte6Bp+x7oM2wcTkdf0DqvVHVLGSt1brGQ2z4XknYuWrRogY4dOxZ63draGn5+fqLk4tX2V45Wx6SZNJfWup0qVsC4oEHYtm4Ztq1bhiY+DTD66+9w517RHkpS1y3H50wIw6hfRS0pZLkuPJbXukmzbrFUt/5itV3Q2azjpxg/cgg+7tJB7bymBZ3GrlnsWEPlNsSCziHVPxGlnR/u/yJKO/pG0pELQ8Gr7a8crY5Js+FiqW5pbbxzc3Nx8FgUMjIz0bBunWLv51Ezr89ZH8htWsQod+gUG15tf+VodUyaDRdLdUtj4x13Nx59hgcjOzsbZayssHT2t3jPrVqxcTxq5vU5E8KRReciH15tf+VodUyaSbMx5xYS6+ZaFTsjV+LV61QcjfoDU2ctROSKeVp1MKSsW47PWUzk5i0ii2kRXm1/5Wh1TJoNF0t1S2PjbW5uDteqlVHXoxbGjRiE2jXcsXHH3mLjeNTM63PWB3KbFpFF54JX2185Wh2TZsPFUt3GYePNGEN2dk6x9/GomdfnrA/yGBPl4AXZTIvwavsrR6tj0kyaS2vdSyIi0eKDRnByrIi09HQcOnYSFy5fQ8TCmaVWM6/PmRCGbDoXvNr+ytHqmDST5tJa9/OXLxEycz7+ef4CNtbWqFXDDRELZ6JZE+9Sq5nX5yw2/Iw5iINs9rkgCIIwJGRcxg+G2Ofi82rdRWln84PdorSjb2Sx5oIgCIIgCMMhm2kRgigNzHBuJSg+NClKlDqI4hE68vC8j0eJYx023RSUmxAfub2KSp0LgiAIgtAzPL1GKgaST4ukpaVh7dq1GDRoEAICAtCpUycMGjQIP/zwA9LS0kTNxasznxzdCEmz9rEuTeqg57rxGPPnCkx9sAm1/H20zill3UJjea27pLHKzp/BetpK2K7aB5ulO1Bm9AyYOFXVOq9UdQuNlTo3UTIk7VzcuHEDtWrVwqRJk/Dy5Uu4urqiatWqePnyJSZOnIjatWvjxo0bouTi1ZlPjm6EpFm3WIsySjy5mYDfpkVqI9Fo6pbjsxISa1q7PrJ/34vU70cjbcFkwMQU1uPnAhaWxcbyqlnq3GKSBybKwQuSvi3SunVrODk54ccff4SFhYXatezsbAwcOBBJSUk4ceKETu2SK6rwWF7rLu2ai1tzMfXBJuwYughxR2I0Xte05sLYNRtbbkPFFrfmQmFjB9tlO5EaNg65cdfUrmlac8GDZqlyG+JtkU+qdRWlnV8e7BOlHX0j6cjF+fPn8e233xboWACAhYUFpkyZgvPnzwvOw6sznxzdCEmz7nULgVfNPNYt9nNWWFkDAFja62Lv5VWzMX29Cd2QdEGnvb09bt++DU9PT43X79y5A3t7+yLbyMrKQlZWlto5xhgUCoXqM6/OfHJ0IyTNutctBF4181i32M/ZsncQ3sRdQ97j+8Xey6tmY/p6C4UWdBqQoUOHYsCAAViwYAFiY2ORnJyMJ0+eIDY2FgsWLMAXX3yB4cOHF9lGWFgY7Ozs1A6Wp7knz6sznxzdCEmzYd0bedXMY91iPGfLvqNh6uKO9IhZOsXxqlnqr7cYMMZEOXhB0pGL6dOnw8rKCosWLcKkSZNUow2MMTg5OeHrr7/GpEmTimwjJCQEwcHBaufsHeqofebVmU+OboSkWfe6hcCrZh7rFus5W/YZBXMvX6SGBYO9fFZ8gIR18/qc9QFPizHFQPJXUSdPnozExETcvXsXZ86cwZkzZ3D37l0kJiYW27EAAKVSCVtbW7Xjv1MiAL/OfHJ0IyTNutctBF4181i3GM/Zsu8omPt8iLR5E8GeJWsVI2XdvD5nQjhGs4mWm5sb3Nzc9NY+r858cnQjJM26xZqXUaJ8dSfV53IuFeHoWQ0Z/6biVeJzo61bjs9KSKxlvzGw+KAN0pZNA8tIh8L27Xo0lpEG5GQbbd28PmexkduaC8k7FxkZGYiJiUH58uULLOzMzMzE9u3b0b9/f8F5eHXmk6MbIWnWLda5vjv6bftG9bn9tH4AgNgdp3BgwmqjrVuOz0pIrLLN21cZy369SO18+g/zkPPHEaOtm9fnLDZy2/5b0n0u4uLi4O/vj4SEBCgUCrRo0QJbtmyBs7MzAODJkyeoXLkycnNzdWqXXFGJ0gp5i8gH8hYxHIbY5+Ij186itHMg4VdR2tE3kq65mDx5MurVq4enT5/i1q1bsLW1RfPmzZGQkCBlWQRBEAQhKrRDpwFxdHTEsWPHUK9ePdW5kSNH4sCBAzhx4gSsra1p5IIgCEJHMhJPlzhWqJsrjxhi5CLAJUCUdg49PCRKO/pG0jUXGRkZMDNTL2HlypUwMTGBn58fNm/eLFFlBEEQBEGUFEmnRerUqYOLFwu+ErR8+XJ069YNXbuKsxd7Prw688nRjZA0k2Zjzs2b5q27D6B7/xFo2r4HmrbvgT7DxuF09AW95xUjVurcYpEn0sELknYuunfvji1btmi8tmLFCnz22Wei7UjGqzOfHN0ISTNpLq11S6XZqWIFjAsahG3rlmHbumVo4tMAo7/+DnfuPdBrXik1i5FbTJhI//GCpGsu9AW5ogqP5bVu0qxbLNXNT6wu8dqsuWjW8VOMHzkEH3fpoHZe05oLHjQLiTXEmgt/l46itHPk4WFR2tE3ku/QaQh4deaToxshaTZcLNXNT6wY8fnk5ubi4LEoZGRmomHdOsXez6tmY3NFldvbIrLoXPDqzCdHN0LSbLhYqpufWDHi4+7Go3G77vBu3RUz56/A0tnf4j23anrNy+tz1gdSGpetWrUKbm5usLS0hI+PD06fLnxkKyoqCgqFosDx999/65RT8h06DQmvznxydCMkzaTZmHPzqNnNtSp2Rq7Eq9epOBr1B6bOWojIFfO06mAIrZvX5ywmUo06bNu2DWPHjsWqVavQvHlzrF69GgEBAbhx4wZcXV0LjcvfeyqfihUrFnqvJmQxcsGrM58c3QhJs+FiqW5+YsWINzc3h2vVyqjrUQvjRgxC7Rru2Lhjr17z8vqcjZmsrCy8evVK7cjKyir0/kWLFmHw4MEYMmQIPDw8sGTJEri4uCA8PLzIPJUqVYKTk5PqMDU11alOWXQueHXmk6MbIWk2XCzVzU+sGPHvwhhDdnaOXvPy+pz1gVhvi4SFhcHOzk7tCAsL05gzOzsbMTEx8Pf3Vzvv7++Ps2fPFlmvl5cXnJ2d0bZtW5w4cUJnvbKZFuHVmU+OboSkmTSX1rql0rwkIhItPmgEJ8eKSEtPx6FjJ3Hh8jVELJyp17xSahYjt5jkiTQVExISguDgYLVzSqVS473Pnj1Dbm4uHB0d1c47OjoiOTlZY4yzszPWrFkDHx8fZGVl4eeff0bbtm0RFRWFli1baozRhGw6F7w688nRjZA0k+bSWrdUmp+/fImQmfPxz/MXsLG2Rq0abohYOBPNmnjrNa+UmsXIbYwolcpCOxOFoVAo1D4zxgqcy6d27dqoXbu26rOvry8ePnyIBQsW6NS5kM0+FwRBEHKBvEV0wxD7XLSo0laUdk4//l3re7Ozs1GmTBns2LED3bt3V53/6quvcOXKFZw8ebKI6P8xa9YsbNy4ETdvau+2K4s1FwRBEAQhJVLsc2FhYQEfHx8cPXpU7fzRo0fRrFkzrdu5fPkynJ2ddcotm2kRgigNzHBuJSg+NClKlDoI40bI6MPzPh4ljnXYpP1ftoRhCA4ORr9+/dCoUSP4+vpizZo1SEhIQFBQEIC3azgeP36Mn376CQCwZMkSVK9eHe+//z6ys7OxceNG7Ny5Ezt37tQpL3UuCIIgCELPSLXPRa9evfD8+XN89913SEpKQt26dXHw4EFUq/Z2f5OkpCQkJCSo7s/OzsaECRPw+PFjWFlZ4f3338evv/6KTp066ZTXKKZFHj16hNTU1ALnc3JycOrUKdHy8OrMJ0c3QtKsfaxLkzrouW48xvy5AlMfbEItfx+tc0pZt9BYXuvmTbOy82ewnrYStqv2wWbpDpQZPQMmTlUNVrPQeGNxRZVyh84vv/wS9+/fR1ZWFmJiYtQWZkZGRiIqKkr1edKkSbhz5w4yMjLw4sULnD59WueOBSBx5yIpKQlNmjRBtWrVUK5cOQwYMECtk/HixQu0bt1alFy8OvPJ0Y2QNOsWa1FGiSc3E/DbtEhtJBpN3XJ8VjxqNq1dH9m/70Xq96ORtmAyYGIK6/FzAQtLvdcslWZCOJK+LTJgwADExcVh+fLl+PfffxESEgLGGI4ePQp7e3s8efIEzs7OyMvTzcWeXFGFx/Jad2nXXNyai6kPNmHH0EWIOxKj8bqmNRfGrtnYcpd2zcWtuVDY2MF22U6kho1Dbtw1tWua1lzwoNkQb4s0qewnSjt/Jmr3hofUSDpycezYMSxduhSNGjVCu3btcObMGVStWhVt2rTBixcvABR8P7ck8OrMJ0c3QtKse91C4FUzj3XzqvldFFbWAACW9lqvNQuNNzZXVLF26OQFSTsXKSkpsLe3V31WKpX45ZdfUL16dbRu3RpPnz4ttg1N+6y/OxjDqzOfHN0ISbPudQuBV8081s2r5nex7B2EN3HXkPf4vl5rFhpPrqjSImnnwt3dHVevXlU7Z2Zmhh07dsDd3R0fffRRsW1o2med5WnuUfPqzCdHN0LSbFj3Rl4181g3r5oBwLLvaJi6uCM9YpbWMWLk5fXnSs5I2rkICAjAmjVrCpzP72A0bNiw2DZCQkKQkpKidihMbNTu4dWZT45uhKRZ97qFwKtmHuvmVXM+ln1GwdzLF6lzJ4C9fFZ8gAh5pdYsJlJsoiUlknYuZs2ahR07dmi8ZmZmhl27duHevXtFtqFUKmFra6t2vLtOg1dnPjm6EZJm3esWAq+aeaybV80AYNl3FMx9PkTavIlgzzQbXukjL68/V5qQ27SIpJtomZmZwdbWttDrpqamqo0+hMKrM58c3QhJs26x5mWUKF/dSfW5nEtFOHpWQ8a/qXiV+Nxo65bjs+JRs2W/MbD4oA3Slk0Dy0iHwvbtOjmWkQbkZJdKzYRwJN+hMyMjAzExMShfvjw8PT3VrmVmZmL79u3o37+/4Dy8OvPJ0Y2QNOsW61zfHf22faP63H5aPwBA7I5TODBhtdHWLcdnxaNmZZuuAICyXy9SO5/+wzzk/HFErzULjTcmV1SepjTEQNJ9LuLi4uDv74+EhAQoFAq0aNECW7ZsURmkPHnyBJUrV0Zubq5O7ZIrKlFaIW8RQt/I0VvEEPtc1HfyFaWdq8nRorSjbyRdczF58mTUq1cPT58+xa1bt2Bra4vmzZur7XNOEARBEARfSDpy4ejoiGPHjqFevXqqcyNHjsSBAwdw4sQJWFtb08gFQRAEJ2QknhYUL8TNVQiGGLmo6/iBKO389eScKO3oG0nXXGRkZMDMTL2ElStXwsTEBH5+fti8ebNElREEQRCEePC0u6YYSDotUqdOHVy8WPCVoOXLl6Nbt27o2rWrqPl4deaTkwOj1LG81i1HzbzWLTfNW3cfQPf+I9C0fQ80bd8DfYaNw+noC1rnlapuQhiSdi66d++OLVu2aLy2YsUKfPbZZ6K918urM5/cHBhJM2kuzXXLUbNTxQoYFzQI29Ytw7Z1y9DEpwFGf/0d7tx7YNSaxSaPMVEOXpB0zYW+IFdU4bG81k2adYuluvmJ5aFubddcNOv4KcaPHIKPu3RQO69pzUVpcUWtU6mxKO38/VS3UR+pkHTkwlDw6swnRwdG0my4WKqbn1ie6/4vubm5OHgsChmZmWhYt06x9xtL3WIgt5ELyTfRMgS8OvPJ0YGRNBsulurmJ5bnugEg7m48+gwPRnZ2NspYWWHp7G/xnlvxuy9LXTdRcmTRuciHV2c+OTowkmbSbMy5SbNusW6uVbEzciVevU7F0ag/MHXWQkSumKdVB0PKusVEbm+LyKJzwasznxwdGEmz4WKpbn5iea4beDtF4Vr17SLKuh61cP3vOGzcsRehk8YYdd1iwtOUhhjIYs0Fr858cnRgJM2Gi6W6+YnluW5NMMaQnZ1T7H3GVjehPbIYuQD4deaTmwMjaSbNpbluOWpeEhGJFh80gpNjRaSlp+PQsZO4cPkaIhbONGrNYkPTIqUUXp355ObASJpJc2muW46an798iZCZ8/HP8xewsbZGrRpuiFg4E82aeBu1ZrFhLM/gOaVENvtcEARBEPqFvEUKx82hgSjtxD+PFaUdfSObkQuCIAiCkIo8mhYhCIIgCEJMSuEkQZFQ54IgCMIImeHcqsSxoUlRotWhC0KnNZ738ShxrMOmm4JyE+JilK+iuru74/bt21KXQRAEQRCikAcmysELknYuli1bpvFISEjAhg0bVJ/FQm5Wx0Jjea2bNMtDM691lzTWpUkd9Fw3HmP+XIGpDzahlr+P1jmlrFtIrLLzZ7CethK2q/bBZukOlBk9AyZOVbXOK7RuMWGMiXLwgqSdi7Fjx2L+/PlYvHix2pGXl4effvoJixcvxpIlS0TJJUerY9JMmkurZl7rFhJrUUaJJzcT8Nu0yGLvNaa6hcSa1q6P7N/3IvX70UhbMBkwMYX1+LmAhaXeNYuN3IzLJH0Vdfjw4fjzzz+xefNmeHj8b67N3NwcsbGx8PT0LFG7ZLkuPJbXukmzbrFUt/HGFrXmYuqDTdgxdBHijsRovK5pzQUPmotbc6GwsYPtsp1IDRuH3Lhratc0rbkwJst153Il+/fsXZL+vSFKO/pG0pGL1atXIzQ0FB06dMCKFSv0lkeOVsekWbdYXuuWo2Ze65bSAry0aFZYWQMAWNrrYu81Nst1JtJ/vCD5gs7AwEBER0dj9+7dCAgIQHJysk7xWVlZePXqldrx7mCMHK2OSbNusbzWLUfNvNYtpQV4adFs2TsIb+KuIe/x/WLvNTbLdVpzIQFVqlTBsWPH0LJlS3h5een0BQwLC4OdnZ3awfI092rlaHVMmkmzMeeWY91SWoDzrNmy72iYurgjPWKWTnHGYrkuN4xmnwuFQoGQkBD4+/vjzJkzcHZ21iouJCQEwcHBaufsHeqofZaj1TFp1i2W17rlqJnXuqW0AOdds2WfUTD38kVqWDDYy2fFB4iYWyx4eo1UDIxi5OK/+Pj44KuvvoK9vb1W9yuVStja2qodCoVC7R45Wh2TZt1iea1bjpp5rVtKC3CeNVv2HQVznw+RNm8i2DPtp82NzXJdbtMiko9cZGRkICYmBuXLly/wdkhmZia2b9+O/v37C84jR6tj0kyaS6tmXusWEmteRony1Z1Un8u5VISjZzVk/JuKV4nPjbZuIbGW/cbA4oM2SFs2DSwjHQrbt390sow0ICdbr7kJYUjauYiLi4O/vz8SEhKgUCjQokULbNmyRTUlkpKSgkGDBonSuZCj1TFpJs2lVTOvdQuJda7vjn7bvlF9bj+tHwAgdscpHJiw2mjrFhKrbNMVAFD260Vq59N/mIecP47oNbfY8LRHhRhIus9F9+7d8ebNG2zYsAH//vsvgoOD8ddffyEqKgqurq548uQJKleujNzcXJ3aJct1giB4h0dvEaFI5S1iiH0u7MvWEKWdl6l3RGlH30i65uLs2bOYPXs2KlSogBo1amDfvn0ICAhAixYtcO/ePSlLIwiCIAiihEg6LZKRkQEzM/USVq5cCRMTE/j5+WHz5s0SVUYQBCEtvI4+CEHI6ENG4mkRKxEfub0tImnnok6dOrh48aLa1t8AsHz5cjDG0LVrV4kqIwiCIAjx4OlNDzGQdFqke/fu2LJli8ZrK1aswGeffSbqAyEHRtJszLlJszzqJs2G0bx19wF07z8CTdv3QNP2PdBn2Dicjr6gU14xkZtxGVgpxNS8coGj9+dBLCsriw0dNp69X68lW7J0LXv9OpVVd2+k8X4x43mM5bVu0iwPzbzWTZr1pzn7n7tqx5Hdm9ixvVtYXMxJFhdzks3//lvm6enJbpw/XuBeQ2BtVV2Ugxdk07k4fz6GhUf8qHbuxs04NmfuMq2+wYXE8xjLa92kWR6aea2bNOtP87sdBk1HIx9vtmX9Kkk6F2Wsqoly8ILR7dCpD8iBUbdYXusmzbrFUt38xPJat5Sa/0tubi4OHotCRmYmGtatU3yAHpDbtIjkO3QaAnJg1C2W17pJs26xVDc/sbzWLaVmAIi7G48+w4ORnZ2NMlZWWDr7W7znVq3YOEI4suhc5MPIgZE0G3Fu0iyPukmz4TS7uVbFzsiVePU6FUej/sDUWQsRuWKeJB0MXfSWBmQxLUIOjLrF8lo3adYtlurmJ5bXuqXUDLydVnGtWhl1PWph3IhBqF3DHRt37C02Th8wkf7jBVl0LsiBUbdYXusmzbrFUt38xPJat5SaNcEYQ3Z2js5xRAkQd32ocaBpxXH+60yDh4x7+zrTkjXs9etU5vZeY61WLAuJ5zGW17pJszw081o3adaf5nffAJk381sWfXQvi796lv117hib//23rE6dOizq1x2SvC1iblFFlKMkrFy5klWvXp0plUrm7e3NTp06VeT9UVFRzNvbmymVSubm5sbCw8N1zimbNRfkwEiaSbNx5ZZj3aTZcJqfv3yJkJnz8c/zF7CxtkatGm6IWDgTzZp4a5VXbJhEay62bduGsWPHYtWqVWjevDlWr16NgIAA3LhxA66urgXuj4+PR6dOnTB06FBs3LgRf/zxB7788ktUrFgRH3/8sdZ5JXVF1RfkikoQBCEvhHiLmFdwF7GSQnKI9O9Sjo4Ork2bNoW3tzfCw8NV5zw8PBAYGIiwsLAC90+ePBn79u3DzZv/83kJCgpCbGwsoqOjtc4rizUXBEEQBCElTKQjKysLr169UjuysrI05szOzkZMTAz8/f3Vzvv7++Ps2bMaY6Kjowvc36FDB1y8eBE5OTqsVynRBA7HZGZmstDQUJaZmclNrJS5SbPu8Fg3adYdHusmzfwTGhpaoM8RGhqq8d7Hjx8zAOyPP/5QOz9r1ixWq1YtjTE1a9Zks2bNUjv3xx9/MAAsMTFR6zpl17lISUlhAFhKSgo3sVLmJs26w2PdpFl3eKybNPNPZmYmS0lJUTsK6zjldy7Onj2rdv77779ntWvX1hhTs2ZNNnv2bLVzZ86cYQBYUlKS1nXKZkEnQRAEQfCOUqmEUqnU6t4KFSrA1NQUycnJauefPn0KR0dHjTFOTk4a7zczM4ODg4PWddKaC4IgCIIohVhYWMDHxwdHjx5VO3/06FE0a9ZMY4yvr2+B+48cOYJGjRrB3Nxc69zUuSAIgiCIUkpwcDB++OEHrF+/Hjdv3sS4ceOQkJCAoKAgAEBISAj69++vuj8oKAgPHjxAcHAwbt68ifXr12PdunWYMGGCTnllNy2iVCoRGhqq9bCSMcRKmZs06w6PdZNm3eGxbtIsP3r16oXnz5/ju+++Q1JSEurWrYuDBw+iWrW3/ipJSUlISEhQ3e/m5oaDBw9i3LhxWLlyJSpXroxly5bptMcFUEr3uSAIgiAIQjpoWoQgCIIgCFGhzgVBEARBEKJCnQuCIAiCIESFOhcEQRAEQYgKdS4IgiAIghAV6lzIBHopiCAIgjAUpX6fi0ePHiE8PBxnz55FcnIyFAoFHB0d0axZMwQFBcHFxUXqEg2CUqlEbGwsPDw8pC7F6EhKSkJ4eDjOnDmDpKQkmJqaws3NDYGBgRg4cCBMTU2lLpGQMWlpadi8eXOB32HNmzfHZ599Bmtr6xK1++TJE6xevRrTpk0r8r5Hjx6hXLlyKFu2rNr5nJwcREdHo2XLlhrjnj9/jqtXr6JBgwYoX748nj17hnXr1iErKwuffvqpzr+L3N3d8dtvv6FmzZpax+Tk5ODXX3/F7du34ezsjO7du5f460XoRqne5+LMmTMICAiAi4sL/P394ejoCMYYnj59iqNHj+Lhw4c4dOgQmjdvXqL2Hz58iNDQUKxfv77AtYyMDMTExKB8+fLw9PRUu5aZmYnt27er7Yr2Ljdv3sS5c+fg6+uLOnXq4O+//8bSpUuRlZWFvn37ok2bNhrjgoODNZ5funQp+vbtq9obftGiRcXqe/nyJX788UfVD+aAAQMK7YxdvnwZ5cqVg5ubGwBg48aNCA8PR0JCAqpVq4ZRo0ahd+/eheYaPXo0evbsiRYtWhRblyaWL1+OixcvonPnzujZsyd+/vlnhIWFIS8vDz169MB3330HM7OCfemLFy+iXbt2cHNzg5WVFc6fP48+ffogOzsbv/32Gzw8PPDbb7/Bxsam0Nz0y/8thvjl/+jRI1haWqJChQoAgNOnTyMiIkL1fTZy5Ej4+vpqjF24cCE++eQT1eZBurJ//35cvHgRHTt2hK+vL44fP44FCxaovseGDRtWaGxGRga2bNmisQPbtm3bQuNu3LiB9u3bIz09HX5+fmq/w06ePAlra2scOXKkwO8YbYiNjYW3tzdyc3M1Xk9KSkK3bt0QExMDhUKBPn36YOXKlarvsydPnqBy5coa4//880/4+/vj1atXKFeuHI4ePYpPP/0UZmZmYIzh8ePHOHPmDLy9vQvELlu2TGM9wcHBmDRpEpycnAAAY8aMKXBPs2bNcPDgQZQrVw7//PMP2rZti1u3bqFatWp4+PAhKlWqhLNnz6JKlSpaf52IEqK1xRmHNGrUiI0dO7bQ62PHjmWNGjUqcftXrlxhJiYmBc7funWLVatWjSkUCmZiYsL8/PzUrGqTk5M1xuVz6NAhZmFhwcqXL88sLS3ZoUOHWMWKFVm7du1Y27ZtmZmZGfv99981xioUCtawYUPWqlUrtUOhULDGjRuzVq1asdatW2uMdXZ2Zs+ePWOMMXbv3j3m5OTEnJycWPv27VnVqlWZnZ0du3nzpsZYLy8vdvz4ccYYY2vXrmVWVlZszJgxLDw8nI0dO5aVLVuWrVu3rlDN+V+rmjVrsjlz5ujkvvfdd98xGxsb9vHHHzMnJyc2Z84c5uDgwL7//ns2e/ZsVrFiRTZt2jSNsc2bN2fTp09Xff75559Z06ZNGWOMvXjxgjVs2JCNGTOm0NzXr19nlStXZuXKlWPdunVjw4YNY0OHDmXdunVj5cqVY1WqVGHXr1/XWst/Kez7K5/ExETWuHFjZmJiwkxNTVn//v3Z69evVdeL+j47f/48s7OzYwqFgtnb27OLFy8yNzc3VrNmTVajRg1mZWXFYmJiNMYuXbpU42FqaspCQkJUnzXh6+vLXr58yRhj7OnTp6xevXrMwsKC1axZk1laWjJXV1f26NGjQjX7+vqygwcPMsYY27NnDzMxMWFdu3ZlkydPZt27d2fm5uZs//79GmMVCgUzNTVl7dq1Y1u3bmVZWVmF5nmX8PBwZmZmxnx8fJitrS3buHEjs7GxYUOGDGHDhw9nVlZWbMmSJRpjb9++zapVq8YcHByYs7MzUygUrHPnzqxp06bM1NSUffrppywnJ0djbKtWrVjv3r011pqVlcU+++wz1qpVK42xsbGxRR7btm0r8vurf//+7IMPPmAXLlxgR48eZY0aNWI+Pj7sxYsXjLG3318KhUJjbLt27diQIUPYq1ev2Pz581nVqlXZkCFDVNcHDx7MAgMDNcYqFApWtWpVVr16dbVDoVCwKlWqsOrVqzM3N7dCY588ecIYY2zo0KGsYcOGqt8lz549Y82aNWNffPFFoZoJ8SjVnQtLS0v2999/F3r95s2bzNLSstDre/fuLfJYvHixxh/OwMBA9tFHH7F//vmH3b59m3Xp0oW5ubmxBw8eMMaK71z4+vqyqVOnMsYY27JlC7O3t2dTpkxRXZ8yZQpr3769xtjZs2czNze3Ap0PMzOzYv+R++8PZu/evVmrVq1YWloaY+ytze9HH33EPvnkE42xZcqUUenz8vJiq1evVru+adMm5unpWWTuY8eOsa+++opVqFCBmZubs65du7L9+/ez3NzcIut2d3dnO3fuZIy9/QfZ1NSUbdy4UXV9165drEaNGhpjrays2N27d1Wfc3Nzmbm5OUtOTmaMMXbkyBFWuXLlQnPTL3/D/vK3sbFh8fHxjDHGmjZtyubMmaN2ffny5czLy6vQ3Bs2bGDdunVj5ubmzMHBgX311Vfs2rVrhebLx8PDg61Zs4Yxxtjx48eZpaUlW7lyper6hg0bmIeHh8bYgIAANnz4cNX3cVhYGAsICGCMMRYXF8eqV6/OQkNDNcZaWVkV+XN77do1ZmVlVaheExMTplAoChz554v6/qpcuTI7f/686nNmZibr1q0ba9iwIXv+/HmRv8fs7e3ZjRs3GGOMZWdnMxMTE7W2Ll26xKpUqaIxdtiwYaxhw4aq+Hx0/R1Wq1YtduDAAbXrJ06cYNWrVy+yDUIcSnXnws3Nja1fv77Q6+vXry/0lyBjRf9w/veH9F0qVarErl69qnbuyy+/ZK6uruzu3bvFdi5sbW3Z7du3GWNv/7EzMzNT+yvy2rVrzNHRsdD4P//8k9WqVYuNHz+eZWdnM8Z0/8HU1EE5d+4cq1q1qsZYBwcHdvHiRcbYW/1XrlxRu37nzp1Cfwm+mzs7O5tt27aNdejQgZmamrLKlSuzKVOmqL4m72JlZaXq2DDGmLm5Ofvrr79Un+/fv8/KlCmjMbZatWrszJkzqs+JiYlMoVCw9PR0xhhj8fHxRXZA6Zf//zDEL387OzsWGxvLGHv7fZb///O5c+dOoc/6v7mfPHnC5s6dy+rUqcNMTExY48aN2Zo1a9irV680xmr6HvtvpyQ+Pr7QvGXKlGFxcXGqz1lZWczc3Fw1Srhnz55CNVeuXJnt2bNH4zXGGNu9e3ehnd8KFSqwdevWsfv372s8fv311yK/v6ytrdXqZoyxnJwcFhgYyOrXr8+uXr1aaLy1tbWqE8gYY2XLllXrxD948KDIn6vdu3czFxcXtnz5ctU5bb+/nj59yhh7+/3x7v33799nSqWyyDYIcSjVb4tMmDABQUFBGDVqFPbu3Ytz587h/Pnz2Lt3L0aNGoURI0Zg0qRJhcY7Oztj586dyMvL03hcunRJY1xGRkaB+f2VK1eia9eu8PPzQ1xcnNYaTExMYGlpiXLlyqnO2djYICUlpdCYxo0bIyYmBv/88w8aNWqEa9euQaFQaJUv/76srCw4OjqqXXN0dMQ///yjMS4gIADh4eEAAD8/P/zyyy9q17dv344aNWpoVYO5uTl69uyJw4cP4969exg6dCg2bdqE2rVra7zfyckJN27cAADcvn0bubm5qs8AcP36dVSqVEljbGBgIIKCgnD48GGcOHECffr0gZ+fH6ysrAAAt27dKnJ+1t7eHrdv3y70+p07d2Bvb6/xmoODA9auXYv4+PgCx71793DgwIFC2wWAlJQUtbaVSiV++eUXVK9eHa1bt8bTp08Ljc3OzlZpNDc3R5kyZVTrGPJre/78ucbY1atXIzQ0FB06dMCKFSuKrFET+d9j//77r2qNTj5ubm5ISkoqNNbPzw9btmwBAHh5eSEqKkrt+okTJ7SaT69UqRImTZqEmzdvIioqCp6enhg3bhycnZ013u/g4IAHDx4AABITE/HmzRs1s6cHDx6gfPnyGmPLlSuH169fqz6np6fjzZs3sLCwAADUr1+/UM1Dhw7FgAEDsGDBAsTGxiI5ORlPnjxBbGwsFixYgC+++ALDhw/XGOvj44PExERUq1ZN41GlSpUi3yJzd3fH1atX1c6ZmZlhx44dcHd3x0cffVRorIuLC+7du6f6vHXrVrWvbVJSktr327sEBgYiOjoau3fvRkBAAJKTkwu9910GDhyIHj16ICcnR/XM/pv3v79LCT0ide9G32zdupU1bdqUmZmZqf4qNDMzY02bNmXbtm0rMrZLly7s22+/LfT6lStXNA47N27cmP30008aY0aOHMnKlStX5F8M9evXZ4cOHVJ9vnbtmtqc7OnTp4sccfkvW7ZsYY6OjszExESrXn+9evWYl5cXK1u2LNu1a5fa9ZMnTxb61+zjx49Z9erVWcuWLVlwcDCzsrJiH374IRs6dChr2bIls7CwYL/++muRufP/qtREXl4eO3LkiMZrU6dOZRUrVmRDhgxhbm5uLCQkhLm6urLw8HAWERHBXFxc2Lhx4zTGvn79mvXs2VP1/dGsWTN279491fXffvuNbd++vdC6QkNDmZ2dHZs/fz67cuUKS0pKYsnJyezKlSts/vz5zN7ens2YMUNjbIcOHdjMmTMLbbuw76986tWrx3755ZcC5/P/unR1dS30+6xOnTpqI1MHDhxQjdYwVvQoVT6PHj1ibdq0YR07dmRJSUla/2XZqVMn1r17d2Zvb69aP5FPdHR0kaNyN27cYA4ODqx///5s5syZrGzZsqxv375s1qxZrH///kypVLINGzZojDUxMSnyeywlJUU19fEuI0eOZDVr1mTff/89a9KkCRswYACrU6cOO3ToEDt8+DCrV69eodM5AwYMYH5+fuzmzZvs3r17rFevXmpTN1FRUczFxaXQuubMmaNaq2FiYqIa1XJ2dmZz584tNG7Xrl3s559/LvT6ixcvWGRkZKHXJ02axPz9/TVey8nJYV27di30+2v69Olsy5YthbY9ZcoU1qNHj0Kv55OXl8dmz57NnJycmKmpabHfXwMHDlQ73v3ZnTBhAuvQoUOxeQnhlPrORT7Z2dksMTGRJSYmqqYKiuPUqVNq/8i/S2pqKouKiipwfvbs2ao5VU2MGDGiyH80wsPDCwwX/5cpU6awwYMHF3r9XR4+fMj27NnDUlNTi7xv+vTpasfhw4fVrk+YMIH17t270PiXL1+yyZMnM09PT2ZpacksLCxYtWrV2Oeff84uXLhQZO7q1aurhol15c2bN+z7779nH330kWoOfsuWLczFxYU5ODiwgQMHFqs9IyNDbTGkLhjzL//Cvs94/uV/584d1rt3b2ZjY6P6g8Hc3Jw1a9aM7d69u9C44jqwRZGamsqGDBnC6taty4KCglh2djabP38+s7CwYAqFgrVq1arQtp88ecI++OAD1fdH9erV2aVLl1TXd+zYwZYtW1ZsDffu3WNnz55lZ8+eVesA64ucnByWkpJS6PU3b96w+/fvl6jttLQ0lpmZqfX9Fy9eZEuWLFGtJyopqampLCMjQ1AbhHaU6ldRCcKQxMfHq4ZvnZycCgz5i82bN2+Qnp4OW1tbjddzc3Px6NGjEr16mZ6eDlNTUyiVSq3uj4mJwZkzZ9C/f/9Cp4G0IS0tDaamprC0tCz2Xvb/r2Tm5eWhQoUKMDc3L3HekpKZmYmcnJwiX1XO5/bt28jKykKdOnU0vhZNEKWJUr3mgiAMiZubG3x9feHr66vqWDx8+BBffPFFidorLtbMzKzQjgXwdm3AjBkzSpT7+fPnGDFihNb3+/j44KuvvoK9vb0gzS9evMCXX36p1b35+4k4OzurOhb6/HprwtLSEjY2NlrF1qxZE3Xr1i3QsSguNiMjA2fOnFFbR5RPZmYmfvrpJ73ESplbSs2ESEg8ckIQpZri9qrQV6yUuUmzeLFC9swRut+OVLml1EyIB43NEYQA9u3bV+T1/66YFzNWytyk2XCxkydPRr169XDx4kX8+++/CA4ORvPmzREVFQVXV9ci2xUSK2VuKTUTIiJ174YgeKake6EIjZUyN2k2XKyQPXOE7rcjVW4pNRPiQWsuCEIAJd0LRWislLlJs+FiheyZI3S/HalyS6mZEA/qXBCEAHx8fIr8x0GhUBS6UZGQWClzk2bDxdapUwcXL14scH758uXo1q0bunbtWmi7QmKlzC2lZkJEpBoyIYjSQEn3QhEaK2Vu0my4WCF75gjdb0eq3FJqJsSD9rkgCIIgCEJUaFqEIAiCIAhRoc4FQRAEQRCiQp0LgiAIgiBEhToXBEEQBEGICnUuCKIUMn36dDRs2FD1eeDAgQgMDDR4Hffv34dCocCVK1cMnpsgCOmgzgVBGJCBAwdCoVBAoVDA3Nwc7u7umDBhAtLS0vSad+nSpYiMjNTqXuoQEAQhFPIWIQgD07FjR2zYsAE5OTk4ffo0hgwZgrS0NISHh6vdl5OTI5qNuJ2dnSjtEARBaAONXBCEgVEqlXBycoKLiws+//xz9OnTB3v27FFNZaxfvx7u7u5QKpVgjCElJQXDhg1DpUqVYGtrizZt2iA2NlatzTlz5sDR0RE2NjYYPHgwMjMz1a6/Oy2Sl5eHuXPnokaNGlAqlXB1dcWsWbMAQGUX7+XlBYVCgVatWqniNmzYAA8PD1haWqJOnTpYtWqVWp4///wTXl5esLS0RKNGjXD58mURv3IEQfACjVwQhMRYWVkhJycHAHDnzh1s374dO3fuhKmpKQCgc+fOKF++PA4ePAg7OzusXr0abdu2RVxcHMqXL4/t27cjNDQUK1euRIsWLfDzzz9j2bJlcHd3LzRnSEgI1q5di8WLF+PDDz9EUlIS/v77bwBvOwhNmjTBsWPH8P7778PCwgIAsHbtWoSGhmLFihXw8vLC5cuXMXToUFhbW2PAgAFIS0vDRx99hDZt2mDjxo2Ij4/HV199peevHkEQRonEO4QShKwYMGAA69atm+rz+fPnmYODA+vZsycLDQ1l5ubm7OnTp6rrv//+O7O1tWWZmZlq7bz33nts9erVjDHGfH19WVBQkNr1pk2bsgYNGmjM++rVK6ZUKtnatWs11hgfH88AsMuXL6udd3FxYZs3b1Y7N3PmTObr68sYY2z16tWsfPnyLC0tTXU9PDxcY1sEQZRuaFqEIAzMgQMHULZsWVhaWsLX1xctW7bE8uXLAQDVqlVDxYoVVffGxMQgNTUVDg4OKFu2rOqIj4/H3bt3AQA3b96Er6+vWo53P/+XmzdvIisrC23bttW65n/++QcPHz7E4MGD1er4/vvv1epo0KABypQpo1UdBEGUXmhahCAMTOvWrREeHg5zc3NUrlxZbdGmtbW12r15eXlwdnZGVFRUgXbKlStXovxWVlY6x+Tl5QF4OzXStGlTtWv50zeMbIoIgvh/qHNBEAbG2toaNWrU0Opeb29vJCcnw8zMDNWrV9d4j4eHB86dO4f+/furzp07d67QNmvWrAkrKyv8/vvvGDJkSIHr+WsscnNzVeccHR1RpUoV3Lt3D3369NHYrqenJ37++WdkZGSoOjBF1UEQROmFpkUIwohp164dfH19ERgYiN9++w3379/H2bNn8c033+DixYsAgK+++grr16/H+vXrERcXh9DQUFy/fr3QNi0tLTF58mRMmjQJP/30E+7evYtz585h3bp1AIBKlSrBysoKhw8fxpMnT5CSkgLg7cZcYWFhWLp0KeLi4nDt2jVs2LABixYtAgB8/vnnMDExweDBg3Hjxg0cPHgQCxYs0PNXiCAIY4Q6FwRhxCgUChw8eBAtW7bEF198gVq1aqF37964f/8+HB0dAQC9evXCtGnTMHnyZPj4+ODBgwcYMWJEke1+++23GD9+PKZNmwYPDw/06tULT58+BQCYmZlh2bJlWL16NSpXroxu3boBAIYMGYIffvgBkZGRqFevHvz8/BAZGal6dbVs2bLYv38/bty4AS8vL0ydOhVz587V41eHIAhjRcFoopQgCIIgCBGhkQuCIAiCIESFOhcEQRAEQYgKdS4IgiAIghAV6lwQBEEQBCEq1LkgCIIgCEJUqHNBEARBEISoUOeCIAiCIAhRoc4FQRAEQRCiQp0LgiAIgiBEhToXBEEQBEGICnUuCIIgCIIQlf8Djv+5tNDjGzoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_final)\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebca5ba-df5a-498b-89b4-9c752d08e1d1",
   "metadata": {},
   "source": [
    "The resulting confusion matrix shows 9 of the 26 classes(the classes with values other than 3 on the diagonal) being occasionally misclassified with class 16 (Corresponding alphabet Q) being the most misclassified "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "50f1b338-242e-4fcb-8adc-4f0c83c2db6e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.67      0.67         3\n",
      "           1       1.00      1.00      1.00         3\n",
      "           2       1.00      0.67      0.80         3\n",
      "           3       1.00      1.00      1.00         3\n",
      "           4       1.00      1.00      1.00         3\n",
      "           5       1.00      1.00      1.00         3\n",
      "           6       1.00      1.00      1.00         3\n",
      "           7       1.00      1.00      1.00         3\n",
      "           8       0.75      1.00      0.86         3\n",
      "           9       1.00      0.67      0.80         3\n",
      "          10       1.00      0.67      0.80         3\n",
      "          11       1.00      1.00      1.00         3\n",
      "          12       0.60      1.00      0.75         3\n",
      "          13       1.00      1.00      1.00         3\n",
      "          14       0.75      1.00      0.86         3\n",
      "          15       0.75      1.00      0.86         3\n",
      "          16       1.00      0.33      0.50         3\n",
      "          17       1.00      0.67      0.80         3\n",
      "          18       1.00      0.67      0.80         3\n",
      "          19       1.00      1.00      1.00         3\n",
      "          20       0.67      0.67      0.67         3\n",
      "          21       0.75      1.00      0.86         3\n",
      "          22       0.67      0.67      0.67         3\n",
      "          23       0.75      1.00      0.86         3\n",
      "          24       0.67      0.67      0.67         3\n",
      "          25       1.00      1.00      1.00         3\n",
      "\n",
      "    accuracy                           0.86        78\n",
      "   macro avg       0.89      0.86      0.85        78\n",
      "weighted avg       0.89      0.86      0.85        78\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred_final))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3d01964-ff4e-4a21-80e6-439fdad3d0aa",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    SVC(kernel='rbf', C=10, gamma='scale'), x_train_scaled, y_train, cv=3, n_jobs=-1, \n",
    "    train_sizes=np.linspace(.1, 1.0, 5))\n",
    "\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
    "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n",
    "\n",
    "plt.title(\"Learning Curves\")\n",
    "plt.xlabel(\"Training examples\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6856ba62-9974-461e-b025-c147e7e630d5",
   "metadata": {},
   "source": [
    "Based on the results of the learning curves, it seems that the model may be overfitting the data as indicated by the consistently high training score from the onset. The cross-validation score is rising as it should as more data is being added, however, teh large gap betwen the training score and the cross validation score is cause for concern for me. It indicates that the model may be learning the data too well and not performing as well on new or unseen data. I also expected to see a kind of convergence between the training and cross validation score but there is none apparent. The model may either be too complex for the font recognition task or be in need of some feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "433d3b6b-ba3b-475e-96df-cb7948d1c799",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## REPORT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94078bc-e7f8-4f70-99a6-f33610591af6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Title: Development of an SVM Model for Font Recognition\n",
    "\n",
    "#### Introduction:\n",
    "\n",
    "The report documents the development of a Support Vector Machine (SVM) model designed for the recognition of different font types. The objective is to distinguish between various fonts, a task integral to the field of optical character recognition and document analysis.\n",
    "\n",
    "#### Initial Steps and Data Preprocessing:\n",
    "\n",
    "- Loaded the font recognition dataset containing features for 26 characters across different fonts.\n",
    "- Conducted an initial data inspection, identifying and addressing missing values by removing the last row with all NaN values and dropping columns with any missing values to maintain data integrity.\n",
    "- Checked class distributions to ensure a balanced dataset, crucial for avoiding biased model training towards more frequent classes.\n",
    "\n",
    "#### Feature Scaling:\n",
    "\n",
    "- Applied MinMax scaling to normalize the feature space, transforming the feature values to a range between 0 and 1, aiding in the model's convergence and performance.\n",
    "\n",
    "#### SVM Model Construction:\n",
    "\n",
    "- Initialized the SVM with an RBF (radial basis function) kernel to handle non-linear relationships in the data.\n",
    "- Faced initial challenges in training due to the multi-label nature of the dataset. Resolved this by transforming the multi-label outputs into a single label, enabling the SVM to interpret and learn from the data correctly.\n",
    "\n",
    "#### Model Training and Evaluation:\n",
    "\n",
    "- Encountered a ValueError during the model training process, indicating a mismatch in array shapes. This issue was resolved by flattening the output labels to make them compatible with the SVM's requirements.\n",
    "- After successful model training, predictions were made on the scaled test data, and the model's accuracy was evaluated using the `accuracy_score` function from the `sklearn.metrics` module.\n",
    "\n",
    "#### Hyperparameter Tuning and Cross-validation:\n",
    "\n",
    "- Employed StratifiedKFold and GridSearchCV to perform hyperparameter tuning, which included optimizing the Box constraint (C) and the kernel coefficient (gamma).\n",
    "- Due to the limited number of samples per class, adjusted the `n_splits` parameter for cross-validation to prevent errors and ensure that all classes were adequately represented in each fold.\n",
    "- Identified the optimal hyperparameters leading to the highest cross-validation accuracy score, which was used for the final model.\n",
    "\n",
    "#### Final Model and Assessment:\n",
    "\n",
    "- The final SVM model was built using the optimal hyperparameters found during grid search.\n",
    "- The model's final accuracy on the test data was 0.8590, which was a satisfactory performance, albeit with room for improvement.\n",
    "\n",
    "#### Visualizations:\n",
    "\n",
    "- Plotted learning curves to visualize the model's performance across training examples, highlighting the potential issues of overfitting given the training score was significantly higher than the cross-validation score.\n",
    "- Generated a confusion matrix to visually assess the model's predictive capabilities on a class-by-class basis.\n",
    "\n",
    "#### Challenges and Adjustments:\n",
    "\n",
    "- Addressed and corrected various errors and warnings that arose during model training, ensuring that the data fed into the SVM was appropriately structured and scaled.\n",
    "- Adjusted the cross-validation strategy to account for the sparse nature of the dataset, ensuring that the folds were representative of the overall class distribution.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde5c6aa-84a3-4d4e-823a-95b1c2aa8dcd",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## SPC - PATTERN RECOGNITION PROBLEM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbe1f70-1cfd-4831-8adc-14a300d71065",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Importing required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2627397b-5940-4f9f-9c78-5102d6e164d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Loading required modules \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc016220-ba6d-451e-a3d1-8f164a7a4415",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Loading the datasets and data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a34874cf-4575-4eb7-b152-b2d650040b79",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load the datasets\n",
    "training_data_path = 'SPC_training.csv'\n",
    "testing_data_path = 'SPC_testing.csv'\n",
    "\n",
    "# Reading in the data \n",
    "training_data = pd.read_csv(training_data_path, header=0)\n",
    "testing_data = pd.read_csv(testing_data_path, header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c33496-e1d5-43da-96d6-a36460d9ca8e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Model development and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16fb606b-71d1-492d-b3df-7f39729ec22a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Splitting the data into features and labels\n",
    "X_train = training_data.iloc[:, :10]\n",
    "y_train = training_data.iloc[:, 10:]\n",
    "X_test = testing_data.iloc[:, :10]\n",
    "y_test = testing_data.iloc[:, 10:]\n",
    "\n",
    "# Normalizing the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a479b5f8-7e86-4930-b9de-0a25e43c93bd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#SVM with RBF kernel\n",
    "svm_model = SVC(kernel='rbf', C=1.0, gamma='scale')\n",
    "\n",
    "# Stratified k-fold cross-validation setup\n",
    "stratified_k_fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# Fitting one model per label\n",
    "# Storing each model in a dictionary\n",
    "svm_models = {}\n",
    "cross_val_scores = {}\n",
    "\n",
    "# Train a separate SVM for each output column\n",
    "for i in range(y_train.shape[1]):\n",
    "    # Column name for the current output\n",
    "    output_column = y_train.columns[i]\n",
    "    \n",
    "    # Stratified k-fold cross-validation\n",
    "    scores = cross_val_score(svm_model, X_train_scaled, y_train.iloc[:, i],\n",
    "                             cv=stratified_k_fold, scoring='accuracy')\n",
    "    cross_val_scores[output_column] = scores.mean()\n",
    "    \n",
    "    # Fitting the model to the whole training set\n",
    "    svm_models[output_column] = svm_model.fit(X_train_scaled, y_train.iloc[:, i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "933c4c8d-dd1a-46c6-a352-38b558b68a6a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'Column11': 0.842,\n",
       "  'Column12': 0.9623999999999999,\n",
       "  'Column13': 0.9188000000000001,\n",
       "  'Column14': 0.9512,\n",
       "  'Column15': 0.9516,\n",
       "  'Column16': 0.9955999999999999,\n",
       "  'Column17': 0.9884000000000001},\n",
       " {'Column11': 0.988,\n",
       "  'Column12': 0.988,\n",
       "  'Column13': 0.988,\n",
       "  'Column14': 0.988,\n",
       "  'Column15': 0.988,\n",
       "  'Column16': 0.988,\n",
       "  'Column17': 0.988},\n",
       " {'Column11': {'0': {'precision': 0.9955357142857143,\n",
       "    'recall': 0.9911111111111112,\n",
       "    'f1-score': 0.9933184855233853,\n",
       "    'support': 225.0},\n",
       "   '1': {'precision': 0.9230769230769231,\n",
       "    'recall': 0.96,\n",
       "    'f1-score': 0.9411764705882353,\n",
       "    'support': 25.0},\n",
       "   'accuracy': 0.988,\n",
       "   'macro avg': {'precision': 0.9593063186813187,\n",
       "    'recall': 0.9755555555555555,\n",
       "    'f1-score': 0.9672474780558102,\n",
       "    'support': 250.0},\n",
       "   'weighted avg': {'precision': 0.9882898351648352,\n",
       "    'recall': 0.988,\n",
       "    'f1-score': 0.9881042840298703,\n",
       "    'support': 250.0}},\n",
       "  'Column12': {'0': {'precision': 0.9955357142857143,\n",
       "    'recall': 0.9911111111111112,\n",
       "    'f1-score': 0.9933184855233853,\n",
       "    'support': 225.0},\n",
       "   '1': {'precision': 0.9230769230769231,\n",
       "    'recall': 0.96,\n",
       "    'f1-score': 0.9411764705882353,\n",
       "    'support': 25.0},\n",
       "   'accuracy': 0.988,\n",
       "   'macro avg': {'precision': 0.9593063186813187,\n",
       "    'recall': 0.9755555555555555,\n",
       "    'f1-score': 0.9672474780558102,\n",
       "    'support': 250.0},\n",
       "   'weighted avg': {'precision': 0.9882898351648352,\n",
       "    'recall': 0.988,\n",
       "    'f1-score': 0.9881042840298703,\n",
       "    'support': 250.0}},\n",
       "  'Column13': {'0': {'precision': 0.9955357142857143,\n",
       "    'recall': 0.9911111111111112,\n",
       "    'f1-score': 0.9933184855233853,\n",
       "    'support': 225.0},\n",
       "   '1': {'precision': 0.9230769230769231,\n",
       "    'recall': 0.96,\n",
       "    'f1-score': 0.9411764705882353,\n",
       "    'support': 25.0},\n",
       "   'accuracy': 0.988,\n",
       "   'macro avg': {'precision': 0.9593063186813187,\n",
       "    'recall': 0.9755555555555555,\n",
       "    'f1-score': 0.9672474780558102,\n",
       "    'support': 250.0},\n",
       "   'weighted avg': {'precision': 0.9882898351648352,\n",
       "    'recall': 0.988,\n",
       "    'f1-score': 0.9881042840298703,\n",
       "    'support': 250.0}},\n",
       "  'Column14': {'0': {'precision': 0.9955357142857143,\n",
       "    'recall': 0.9911111111111112,\n",
       "    'f1-score': 0.9933184855233853,\n",
       "    'support': 225.0},\n",
       "   '1': {'precision': 0.9230769230769231,\n",
       "    'recall': 0.96,\n",
       "    'f1-score': 0.9411764705882353,\n",
       "    'support': 25.0},\n",
       "   'accuracy': 0.988,\n",
       "   'macro avg': {'precision': 0.9593063186813187,\n",
       "    'recall': 0.9755555555555555,\n",
       "    'f1-score': 0.9672474780558102,\n",
       "    'support': 250.0},\n",
       "   'weighted avg': {'precision': 0.9882898351648352,\n",
       "    'recall': 0.988,\n",
       "    'f1-score': 0.9881042840298703,\n",
       "    'support': 250.0}},\n",
       "  'Column15': {'0': {'precision': 0.9955357142857143,\n",
       "    'recall': 0.9911111111111112,\n",
       "    'f1-score': 0.9933184855233853,\n",
       "    'support': 225.0},\n",
       "   '1': {'precision': 0.9230769230769231,\n",
       "    'recall': 0.96,\n",
       "    'f1-score': 0.9411764705882353,\n",
       "    'support': 25.0},\n",
       "   'accuracy': 0.988,\n",
       "   'macro avg': {'precision': 0.9593063186813187,\n",
       "    'recall': 0.9755555555555555,\n",
       "    'f1-score': 0.9672474780558102,\n",
       "    'support': 250.0},\n",
       "   'weighted avg': {'precision': 0.9882898351648352,\n",
       "    'recall': 0.988,\n",
       "    'f1-score': 0.9881042840298703,\n",
       "    'support': 250.0}},\n",
       "  'Column16': {'0': {'precision': 0.9955357142857143,\n",
       "    'recall': 0.9911111111111112,\n",
       "    'f1-score': 0.9933184855233853,\n",
       "    'support': 225.0},\n",
       "   '1': {'precision': 0.9230769230769231,\n",
       "    'recall': 0.96,\n",
       "    'f1-score': 0.9411764705882353,\n",
       "    'support': 25.0},\n",
       "   'accuracy': 0.988,\n",
       "   'macro avg': {'precision': 0.9593063186813187,\n",
       "    'recall': 0.9755555555555555,\n",
       "    'f1-score': 0.9672474780558102,\n",
       "    'support': 250.0},\n",
       "   'weighted avg': {'precision': 0.9882898351648352,\n",
       "    'recall': 0.988,\n",
       "    'f1-score': 0.9881042840298703,\n",
       "    'support': 250.0}},\n",
       "  'Column17': {'0': {'precision': 0.9955357142857143,\n",
       "    'recall': 0.9911111111111112,\n",
       "    'f1-score': 0.9933184855233853,\n",
       "    'support': 225.0},\n",
       "   '1': {'precision': 0.9230769230769231,\n",
       "    'recall': 0.96,\n",
       "    'f1-score': 0.9411764705882353,\n",
       "    'support': 25.0},\n",
       "   'accuracy': 0.988,\n",
       "   'macro avg': {'precision': 0.9593063186813187,\n",
       "    'recall': 0.9755555555555555,\n",
       "    'f1-score': 0.9672474780558102,\n",
       "    'support': 250.0},\n",
       "   'weighted avg': {'precision': 0.9882898351648352,\n",
       "    'recall': 0.988,\n",
       "    'f1-score': 0.9881042840298703,\n",
       "    'support': 250.0}}})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation on the test set\n",
    "svm_test_accuracies = {}\n",
    "svm_test_reports = {}\n",
    "\n",
    "for output_column in y_train.columns:\n",
    "    # Prediction using the trained model\n",
    "    y_pred = svm_models[output_column].predict(X_test_scaled)\n",
    "    \n",
    "    # Calculating the accuracy\n",
    "    accuracy = accuracy_score(y_test.iloc[:, i], y_pred)\n",
    "    svm_test_accuracies[output_column] = accuracy\n",
    "    \n",
    "    # Detailed classification report\n",
    "    report = classification_report(y_test.iloc[:, i], y_pred, output_dict=True)\n",
    "    svm_test_reports[output_column] = report\n",
    "\n",
    "cross_val_scores, svm_test_accuracies, svm_test_reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff12c438-fbc9-4458-918a-9635b5df12a5",
   "metadata": {
    "tags": []
   },
   "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>Output</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Column11</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Column12</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Column13</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Column14</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Column15</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Column16</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Column17</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Output  Accuracy  Precision  Recall  F1-Score\n",
       "0  Column11     0.988   0.923077    0.96  0.941176\n",
       "1  Column12     0.988   0.923077    0.96  0.941176\n",
       "2  Column13     0.988   0.923077    0.96  0.941176\n",
       "3  Column14     0.988   0.923077    0.96  0.941176\n",
       "4  Column15     0.988   0.923077    0.96  0.941176\n",
       "5  Column16     0.988   0.923077    0.96  0.941176\n",
       "6  Column17     0.988   0.923077    0.96  0.941176"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data holding list\n",
    "data = []\n",
    "\n",
    "# Iterating through each output column to gather data\n",
    "for i, output_column in enumerate(y_train.columns):\n",
    "    # Access the stored report for the current output column\n",
    "    report = svm_test_reports[output_column]\n",
    "    \n",
    "    # Extracting key metrics for the positive class ('1'), assuming binary classification for each label\n",
    "    precision = report['1']['precision']\n",
    "    recall = report['1']['recall']\n",
    "    f1 = report['1']['f1-score']\n",
    "    accuracy = svm_test_accuracies[output_column]\n",
    "    \n",
    "    # Append the data for the current output column to the list\n",
    "    data.append({\n",
    "        'Output': output_column,\n",
    "        'Accuracy': accuracy,\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1-Score': f1\n",
    "    })\n",
    "\n",
    "# DataFrame from the collected data\n",
    "results_df = pd.DataFrame(data)\n",
    "results_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0c2b68f-be4a-4956-b178-8767d04fd31d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    }
   ],
   "source": [
    "# Defining a scoring function that can handle multi-label data\n",
    "def multi_label_accuracy(y_true, y_pred):\n",
    "    return accuracy_score(y_true, y_pred, normalize=True)\n",
    "\n",
    "# Defining the parameter grid\n",
    "param_grid = {\n",
    "    'C': [0.1, 1, 10, 100],\n",
    "    'gamma': ['scale', 'auto', 0.1, 0.01, 0.001],\n",
    "    'kernel': ['rbf']\n",
    "}\n",
    "\n",
    "# Custom scorer that can handle multi-label data\n",
    "scorer = make_scorer(multi_label_accuracy)\n",
    "\n",
    "best_params = {}\n",
    "best_estimators = {}\n",
    "\n",
    "# Performing grid search for each label\n",
    "for label in range(y_train.shape[1]):\n",
    "    # New SVC model\n",
    "    svm = SVC()\n",
    "\n",
    "    # Seting up the grid search with a custom scorer\n",
    "    grid_search = GridSearchCV(svm, param_grid=param_grid, cv=5, verbose=2, n_jobs=-1, scoring=scorer)\n",
    "\n",
    "    # Fitting the grid search to the data for the current label\n",
    "    grid_search.fit(X_train_scaled, y_train.iloc[:, label])\n",
    "\n",
    "    # Best parameters and estimator for the current label\n",
    "    best_params[label] = grid_search.best_params_\n",
    "    best_estimators[label] = grid_search.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c16537c-7b99-4ed8-a428-3d6eb6b4e966",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "#viewing the results of the grid search\n",
    "#Best hyperparameters\n",
    "print(\"Best Hyperparameters:\", grid_search.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b2c2f6d-9cf9-4749-9e46-19e15485bde1",
   "metadata": {
    "tags": []
   },
   "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>param_C</th>\n",
       "      <th>param_gamma</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.9816</td>\n",
       "      <td>0.013588</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>scale</td>\n",
       "      <td>0.9784</td>\n",
       "      <td>0.012355</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>auto</td>\n",
       "      <td>0.9784</td>\n",
       "      <td>0.012355</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9784</td>\n",
       "      <td>0.012355</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.9784</td>\n",
       "      <td>0.014165</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_C param_gamma  mean_test_score  std_test_score  rank_test_score\n",
       "13      10        0.01           0.9816        0.013588                1\n",
       "5        1       scale           0.9784        0.012355                2\n",
       "6        1        auto           0.9784        0.012355                2\n",
       "7        1         0.1           0.9784        0.012355                2\n",
       "18     100        0.01           0.9784        0.014165                2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Detailed results across all parameter combinations\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "results = results.sort_values(by='rank_test_score')\n",
    "results[['param_C', 'param_gamma', 'mean_test_score', 'std_test_score', 'rank_test_score']].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fda22b7-eabd-450b-8aae-487cc1982b02",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Visualising the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2173c48e-e954-4319-9cee-53577a2614f2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar chart to visualise model performance\n",
    "metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score']\n",
    "\n",
    "# Setting up the matplotlib figure and axes\n",
    "fig, axs = plt.subplots(len(metrics), 1, figsize=(10, 8), sharex=True)\n",
    "\n",
    "# Loop over the metrics and create a bar plot for each\n",
    "for i, metric in enumerate(metrics):\n",
    "    axs[i].bar(results_df['Output'], results_df[metric], color='skyblue')\n",
    "    axs[i].set_ylabel(metric)\n",
    "    axs[i].set_title(f'{metric} per Output Class')\n",
    "\n",
    "# Setting the x-axis labels\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Output Class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2bd7f9d-64c8-49ac-9e3c-afef4a2e30b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap for model performance visualisation\n",
    "performance_metrics = results_df.set_index('Output')\n",
    "\n",
    "# Plotting the heatmap\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(performance_metrics, annot=True, fmt=\".2%\", cmap='viridis')\n",
    "plt.title('Performance Metrics for Each Output')\n",
    "plt.xlabel('Metrics')\n",
    "plt.ylabel('Outputs')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5701d68-b0ca-4277-ab51-89383833d0df",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## REPORT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1596ba44-df95-4f98-bd3b-7a3dc16e536d",
   "metadata": {},
   "source": [
    "#### SVM Model Development for Pattern Recognition in Process Control Charts\n",
    "\n",
    "#### Objective\n",
    "The task was to develop and evaluate a Support Vector Machine (SVM) model for pattern recognition in process control charts, specifically for automating the implementation of runs rules.\n",
    "\n",
    "#### Methodology\n",
    "\n",
    "#### Data Preparation\n",
    "- **Data Loading**: CSV files containing the training and testing datasets were loaded using pandas.\n",
    "- **Splitting Data**: Each dataset was divided into input features (X) and output labels (Y).\n",
    "- **Normalization**: Input features were normalized using `StandardScaler` to ensure the model was not biased by the scale of the data.\n",
    "\n",
    "#### Model Selection and Training\n",
    "- **Initial Model Setup**: An SVM with an RBF kernel was chosen for its ability to handle non-linear boundaries. The parameters `C` and `gamma` were initially set to 1.0 and 'scale', respectively.\n",
    "- **Stratified K-Fold Cross-Validation**: This technique ensured that each fold of the cross-validation process had the same proportion of each output class, addressing potential class imbalances.\n",
    "\n",
    "#### Hyperparameter Tuning\n",
    "- **Grid Search**: A grid search with 5-fold cross-validation was employed to fine-tune the `C` and `gamma` hyperparameters of the SVM model. The search covered several orders of magnitude for `C` and multiple options for `gamma`, including 'scale' and 'auto'.\n",
    "- **Performance Metrics**: Accuracy was chosen as the primary performance metric due to its interpretability and relevance for classification tasks.\n",
    "\n",
    "#### Model Evaluation\n",
    "- **Accuracy Measurement**: The accuracy of the model was evaluated for each output class, providing a direct measure of the model's predictive capabilities.\n",
    "- **Classification Reports**: Precision, recall, and F1-score were computed for a more nuanced assessment of the model’s performance.\n",
    "- **Visualization**: Performance metrics were visualized using bar charts and heatmaps to facilitate easy comparison across classes and metrics.\n",
    "\n",
    "#### Adaptations and Corrections\n",
    "- **Loop Correction**: The loop index variable `i` was properly defined to correctly match the predictions with their corresponding labels during evaluation.\n",
    "- **Data Consistency**: The format of the predictions was made consistent with that of the true labels to ensure the calculated performance metrics were accurate.\n",
    "- **Scoring Function**: A custom scoring function was created to handle multi-label accuracy for grid search, accommodating the multi-label nature of the dataset.\n",
    "- **Final Hyperparameter Selection**: The best hyperparameters found from the grid search were `C: 10` and `gamma: 0.01`.\n",
    "\n",
    "#### Final Model Performance\n",
    "The final model achieved the following metrics, indicating a high level of performance:\n",
    "- **Accuracy**: 98.8%\n",
    "- **Precision**: 92.31%\n",
    "- **Recall**: 96%\n",
    "- **F1-Score**: 94.11%\n",
    "\n",
    "#### Conclusion\n",
    "The final SVM model demonstrated exceptional performance across all output classes. The process included rigorous data preparation, careful model selection, thorough hyperparameter tuning, and comprehensive model evaluation, all of which contributed to the success of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff19ca5-be59-4786-805c-bb82c72b751f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## SEMI-CONDUCTOR PROBLEM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f882f2-7f94-4b26-be8a-9a282c0f25de",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Loading the required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72f28cb4-406c-4c16-877b-8394a337bac3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, KBinsDiscretizer\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_absolute_error, r2_score\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa86edc-d633-498e-8b5f-f3094e14fdcc",
   "metadata": {
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    "tags": []
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   "source": [
    "### Loading the dataset and data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "641d243e-8992-4401-b35f-d36f3c7f9cab",
   "metadata": {
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   "outputs": [
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       "   1 300 300 1.8 100 200 20 3491 14.2 6.48 2.01\n",
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "# Loading the dataset\n",
    "file_path = 'Process modelling dataset.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
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       "                                            Run  Pressure  RF  Electrode Gap  \\\n",
       "0  1 300 300 1.8 100 200 20 3491 14.2 6.48 2.01       NaN NaN            NaN   \n",
       "1    2 200 400 1.8 100 50 10 3884 3.9 5.98 1.91       NaN NaN            NaN   \n",
       "2  3 200 400 1.2 150 200 20 4931 24.8 5.39 1.85       NaN NaN            NaN   \n",
       "3   4 300 400 1.8 150 200 20 4726 6.6 5.97 2.11       NaN NaN            NaN   \n",
       "4   5 200 400 1.2 150 50 10 5089 12.4 5.61 2.16       NaN NaN            NaN   \n",
       "\n",
       "   CCl4 Flow  He Flow  O2 Flow  Etch Rate - Rp (Å/min)  \\\n",
       "0        NaN      NaN      NaN                     NaN   \n",
       "1        NaN      NaN      NaN                     NaN   \n",
       "2        NaN      NaN      NaN                     NaN   \n",
       "3        NaN      NaN      NaN                     NaN   \n",
       "4        NaN      NaN      NaN                     NaN   \n",
       "\n",
       "   Etch Uniformity – U (in %)  Oxide Selectivity - Sox  \\\n",
       "0                         NaN                      NaN   \n",
       "1                         NaN                      NaN   \n",
       "2                         NaN                      NaN   \n",
       "3                         NaN                      NaN   \n",
       "4                         NaN                      NaN   \n",
       "\n",
       "   Photoresist Selectivity - Sph  \n",
       "0                            NaN  \n",
       "1                            NaN  \n",
       "2                            NaN  \n",
       "3                            NaN  \n",
       "4                            NaN  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "#separating the columns\n",
    "column_names = ['Run', 'Pressure', 'RF', 'Electrode Gap', 'CCl4 Flow', 'He Flow', 'O2 Flow', \n",
    "    'Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', \n",
    "    'Photoresist Selectivity - Sph']\n",
    "\n",
    "# Displaying the first few rows \n",
    "data = pd.read_csv(file_path, header=None, names=column_names)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e24c9738-625b-4866-ba95-48acb3abf83f",
   "metadata": {
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   "outputs": [
    {
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       "   Run  Pressure   RF  Electrode Gap  CCl4 Flow  He Flow  O2 Flow  \\\n",
       "0    1       300  300            1.8        100      200       20   \n",
       "1    2       200  400            1.8        100       50       10   \n",
       "2    3       200  400            1.2        150      200       20   \n",
       "3    4       300  400            1.8        150      200       20   \n",
       "4    5       200  400            1.2        150       50       10   \n",
       "\n",
       "   Etch Rate - Rp (Å/min)  Etch Uniformity – U (in %)  \\\n",
       "0                    3491                        14.2   \n",
       "1                    3884                         3.9   \n",
       "2                    4931                        24.8   \n",
       "3                    4726                         6.6   \n",
       "4                    5089                        12.4   \n",
       "\n",
       "   Oxide Selectivity - Sox  Photoresist Selectivity - Sph  \n",
       "0                     6.48                           2.01  \n",
       "1                     5.98                           1.91  \n",
       "2                     5.39                           1.85  \n",
       "3                     5.97                           2.11  \n",
       "4                     5.61                           2.16  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correcting the data loading\n",
    "data = pd.read_csv(file_path, delim_whitespace=True, header=None, names=column_names)\n",
    "\n",
    "# Confirming the changes\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2713111-2b8d-481f-895d-924a4d9469f6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Separate features and targets\n",
    "# Feature selection: Assuming 'Run' column is just an identifier and not a feature, I'll drop it\n",
    "X = data.drop(['Run', 'Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', 'Photoresist Selectivity - Sph'], axis=1)\n",
    "y = data[['Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', 'Photoresist Selectivity - Sph']]\n",
    "\n",
    "# Split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "752b8df5-b5eb-4200-bb4f-232103fe80cc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Standardize features\n",
    "scaler_X = StandardScaler()\n",
    "X_train_scaled = scaler_X.fit_transform(X_train)\n",
    "X_test_scaled = scaler_X.transform(X_test)\n",
    "\n",
    "# Standardize target variables\n",
    "scaler_y = StandardScaler()\n",
    "y_train_scaled = scaler_y.fit_transform(y_train)\n",
    "y_test_scaled = scaler_y.transform(y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e50b13f-3b84-4915-b131-e40b4685b2e3",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Initializing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6dc72f06-549d-4823-a51e-8277dc4f9d33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 6)\n",
      "(42, 4)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_scaled.shape)  \n",
    "print(y_train_scaled.shape)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "968edc74-3a29-4d35-9934-7eabfd18d770",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initializing SVR model\n",
    "svr_models = []\n",
    "y_pred = np.empty((len(X_test_scaled), y_train_scaled.shape[1]))\n",
    "\n",
    "# SVR model fit for each target variable\n",
    "for i in range(y_train_scaled.shape[1]):\n",
    "    svr = SVR(kernel='rbf', C=1.0, epsilon=0.1)\n",
    "    svr.fit(X_train_scaled, y_train_scaled[:, i]) \n",
    "    svr_models.append(svr)\n",
    "    y_pred[:, i] = svr.predict(X_test_scaled)\n",
    "\n",
    "# Inverting the predictions back to the original scale\n",
    "y_pred_inverse = scaler_y.inverse_transform(y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b47a38d-1dc5-4acd-bcc1-9ac41509c910",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    " ### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9e5e4012-c055-408c-9a4c-9189c3e1e2fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for target variable 0: 0.542\n",
      "MAE for target variable 0: 310.746\n",
      "R^2 score for target variable 1: 0.125\n",
      "MAE for target variable 1: 7.361\n",
      "R^2 score for target variable 2: 0.158\n",
      "MAE for target variable 2: 1.810\n",
      "R^2 score for target variable 3: 0.031\n",
      "MAE for target variable 3: 0.441\n",
      "Aggregate R^2 score: 0.214\n",
      "Aggregate MAE: 80.090\n"
     ]
    }
   ],
   "source": [
    "r2_scores = []\n",
    "mae_scores = []\n",
    "\n",
    "#R^2 and MAE for each SVR model and its target variable\n",
    "for i in range(y_train_scaled.shape[1]):\n",
    "    r2 = r2_score(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    mae = mean_absolute_error(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    r2_scores.append(r2)\n",
    "    mae_scores.append(mae)\n",
    "    print(f'R^2 score for target variable {i}: {r2:.3f}')\n",
    "    print(f'MAE for target variable {i}: {mae:.3f}')\n",
    "\n",
    "# Aggregate scores\n",
    "print(f'Aggregate R^2 score: {np.mean(r2_scores):.3f}')\n",
    "print(f'Aggregate MAE: {np.mean(mae_scores):.3f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe149a7b-e491-46d8-bf63-dc7b7dc1fbb5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Visualising the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c79f5b4-8e1f-4062-80ce-82c8aa356832",
   "metadata": {
    "collapsed": true,
    "jupyter": {
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplots of actual vs predicted values\n",
    "# Number of subplots required\n",
    "n_targets = y_train_scaled.shape[1]\n",
    "fig, axes = plt.subplots(n_targets, 1, figsize=(10, n_targets * 5))\n",
    "\n",
    "for i in range(n_targets):\n",
    "    axes[i].scatter(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    axes[i].plot([y_test.iloc[:, i].min(), y_test.iloc[:, i].max()],\n",
    "                 [y_test.iloc[:, i].min(), y_test.iloc[:, i].max()], 'k--', lw=2)\n",
    "    axes[i].set_xlabel('Actual Values')\n",
    "    axes[i].set_ylabel('Predicted Values')\n",
    "    axes[i].set_title(f'Actual vs. Predicted Values for Target {i}')\n",
    "    axes[i].legend(['Ideal', 'Predicted'])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90344264-29f0-4701-9390-f4c00e057da9",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms of residual errors\n",
    "# Number of subplots required\n",
    "n_targets = y_train_scaled.shape[1]\n",
    "fig, axes = plt.subplots(n_targets, 1, figsize=(10, n_targets * 5))\n",
    "\n",
    "for i in range(n_targets):\n",
    "    errors = y_test.iloc[:, i] - y_pred_inverse[:, i]\n",
    "    axes[i].hist(errors, bins=20, edgecolor='blue')\n",
    "    axes[i].set_xlabel('Prediction Error')\n",
    "    axes[i].set_ylabel('Frequency')\n",
    "    axes[i].set_title(f'Error Distribution for Target {i}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf2641ed-921b-474d-b5ab-78716d808e83",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of subplots required\n",
    "n_targets = y_train_scaled.shape[1]\n",
    "fig, axes = plt.subplots(n_targets, 1, figsize=(10, n_targets * 5))\n",
    "\n",
    "for i in range(n_targets):\n",
    "    # Compute residuals\n",
    "    residuals = y_test.iloc[:, i] - y_pred_inverse[:, i]\n",
    "\n",
    "    # Scatter plot of predicted values vs residuals\n",
    "    axes[i].scatter(y_pred_inverse[:, i], residuals)\n",
    "    axes[i].hlines(y=0, xmin=y_pred_inverse[:, i].min(), xmax=y_pred_inverse[:, i].max(), colors='red', linestyles='--')\n",
    "    axes[i].set_xlabel('Predicted Values')\n",
    "    axes[i].set_ylabel('Residuals')\n",
    "    axes[i].set_title(f'Residuals vs. Predicted Values for Target {i}')\n",
    "    axes[i].legend(['Zero error', 'Residuals'])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3a9ca0-cc30-497d-9f28-9853c739276b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Finetuning the model - Feature engineering attempts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4d482481-e8fd-48c0-bb20-fe19410fb00f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create polynomial features\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_poly = poly.fit_transform(X_train_scaled)\n",
    "\n",
    "# Now, X_poly contains the original features plus polynomial combinations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "17a46958-c332-482f-8ef0-b059c15e1e44",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared for Target 0: 0.086\n",
      "MAE for Target 0: 439.652\n",
      "R-squared for Target 1: 0.031\n",
      "MAE for Target 1: 7.832\n",
      "R-squared for Target 2: -0.155\n",
      "MAE for Target 2: 2.074\n",
      "R-squared for Target 3: -0.359\n",
      "MAE for Target 3: 0.487\n",
      "Aggregate R-squared: -0.099\n",
      "Aggregate MAE: 112.511\n"
     ]
    }
   ],
   "source": [
    "# Scaling features\n",
    "scaler_X = StandardScaler()\n",
    "X_scaled = scaler_X.fit_transform(X)\n",
    "\n",
    "# Creating polynomial features\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_poly = poly.fit_transform(X_scaled)\n",
    "\n",
    "# Split data into train and test sets using the polynomial features\n",
    "X_train_poly, X_test_poly, y_train, y_test = train_test_split(X_poly, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scaling target variables\n",
    "scaler_y = StandardScaler()\n",
    "y_train_scaled = scaler_y.fit_transform(y_train)\n",
    "y_test_scaled = scaler_y.transform(y_test)\n",
    "\n",
    "# Initialize the SVR model\n",
    "svr_models = []\n",
    "y_pred = np.empty((X_test_poly.shape[0], y_train_scaled.shape[1]))\n",
    "\n",
    "# Fit an SVR model for each target variable\n",
    "for i in range(y_train_scaled.shape[1]):\n",
    "    svr = SVR(kernel='rbf', C=1.0, epsilon=0.1)\n",
    "    svr.fit(X_train_poly, y_train_scaled[:, i])  # Fit to the ith target variable\n",
    "    svr_models.append(svr)\n",
    "    y_pred[:, i] = svr.predict(X_test_poly)  # Predict the ith target variable\n",
    "\n",
    "# Inverse transform predictions back to the original scale for each target variable\n",
    "y_pred_inverse = scaler_y.inverse_transform(y_pred)\n",
    "\n",
    "# Evaluate the model using the original scale targets\n",
    "r2_scores = []\n",
    "mae_scores = []\n",
    "\n",
    "for i in range(y_test.shape[1]):\n",
    "    r2 = r2_score(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    mae = mean_absolute_error(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    r2_scores.append(r2)\n",
    "    mae_scores.append(mae)\n",
    "    print(f'R-squared for Target {i}: {r2:.3f}')\n",
    "    print(f'MAE for Target {i}: {mae:.3f}')\n",
    "\n",
    "print(f'Aggregate R-squared: {np.mean(r2_scores):.3f}')\n",
    "print(f'Aggregate MAE: {np.mean(mae_scores):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7fc624",
   "metadata": {},
   "source": [
    "Adding polynomial features made the models performance worse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dfdd0ba-7bb3-43ad-9baf-e4e75ea0e543",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\"\"\"# Create a new feature that is the interaction between 'Pressure' and 'RF'\n",
    "X['interaction_1'] = X['Pressure'] * X['RF']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale your features\n",
    "scaler_X = StandardScaler()\n",
    "X_train_scaled = scaler_X.fit_transform(X_train)\n",
    "X_test_scaled = scaler_X.transform(X_test)\n",
    "\n",
    "# Polynomial interaction features only\n",
    "poly = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)\n",
    "X_train_interaction = poly.fit_transform(X_train_scaled)\n",
    "X_test_interaction = poly.transform(X_test_scaled)\n",
    "\n",
    "# Scale Target\n",
    "scaler_y = StandardScaler()\n",
    "y_train_scaled = scaler_y.fit_transform(y_train)\n",
    "y_test_scaled = scaler_y.transform(y_test)\n",
    "\n",
    "# Training a separate SVR model for each target variable\n",
    "svr_models = []\n",
    "y_pred = np.empty((X_test_interaction.shape[0], y_train_scaled.shape[1]))\n",
    "\n",
    "for i in range(y_train_scaled.shape[1]):\n",
    "    svr = SVR(kernel='rbf', C=1.0, epsilon=0.1)\n",
    "    svr.fit(X_train_interaction, y_train_scaled[:, i])  # Train on the ith target variable\n",
    "    y_pred[:, i] = svr.predict(X_test_interaction)  # Predict the ith target variable\n",
    "\n",
    "# Transforming predictions back to original scale\n",
    "y_pred_inverse = scaler_y.inverse_transform(y_pred)\n",
    "\n",
    "# Evaluating the model using the original scale targets\n",
    "for i in range(y_test.shape[1]):\n",
    "    r2 = r2_score(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    mae = mean_absolute_error(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    print(f'R-squared for Target {i}: {r2:.3f}')\n",
    "    print(f'MAE for Target {i}: {mae:.3f}')\n",
    "\n",
    "#  Aggregate performance\n",
    "print(f'Aggregate R-squared: {np.mean([r2_score(y_test.iloc[:, i], y_pred_inverse[:, i]) for i in range(y_test.shape[1])]):.3f}')\n",
    "print(f'Aggregate MAE: {np.mean([mean_absolute_error(y_test.iloc[:, i], y_pred_inverse[:, i]) for i in range(y_test.shape[1])]):.3f}')\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "799a9df6-8075-4fb0-b413-e3850539dd7f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared for Target 0: 0.546\n",
      "MAE for Target 0: 310.440\n",
      "R-squared for Target 1: 0.127\n",
      "MAE for Target 1: 7.404\n",
      "R-squared for Target 2: 0.157\n",
      "MAE for Target 2: 1.817\n",
      "R-squared for Target 3: 0.029\n",
      "MAE for Target 3: 0.441\n",
      "Aggregate R-squared: 0.215\n",
      "Aggregate MAE: 80.025\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tosin\\anaconda3\\Lib\\site-packages\\sklearn\\preprocessing\\_discretization.py:248: FutureWarning: In version 1.5 onwards, subsample=200_000 will be used by default. Set subsample explicitly to silence this warning in the mean time. Set subsample=None to disable subsampling explicitly.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#adding binning\n",
    "# Load the dataset\n",
    "file_path = 'Process modelling dataset.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "data.head()\n",
    "\n",
    "#separating the columns\n",
    "column_names = ['Run', 'Pressure', 'RF', 'Electrode Gap', 'CCl4 Flow', 'He Flow', 'O2 Flow', \n",
    "    'Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', \n",
    "    'Photoresist Selectivity - Sph']\n",
    "\n",
    "# Displaying the first few rows \n",
    "data = pd.read_csv(file_path, header=None, names=column_names)\n",
    "data.head()\n",
    "\n",
    "#Correcting the data loading\n",
    "data = pd.read_csv(file_path, delim_whitespace=True, header=None, names=column_names)\n",
    "\n",
    "# confirm changes\n",
    "data.head()\n",
    "\n",
    "# Separate features and target variables\n",
    "X = data.drop(['Run', 'Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', 'Photoresist Selectivity - Sph'], axis=1)\n",
    "y = data[['Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', 'Photoresist Selectivity - Sph']]\n",
    "\n",
    "# Bin 'Pressure' feature\n",
    "binner = KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='uniform')\n",
    "X['Pressure_binned'] = binner.fit_transform(X[['Pressure']])\n",
    "\n",
    "# Now drop the original 'Pressure' feature\n",
    "X = X.drop('Pressure', axis=1)\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the features\n",
    "scaler_X = StandardScaler()\n",
    "X_train_scaled = scaler_X.fit_transform(X_train)\n",
    "X_test_scaled = scaler_X.transform(X_test)\n",
    "\n",
    "# Scale the target variables\n",
    "scaler_y = StandardScaler()\n",
    "y_train_scaled = scaler_y.fit_transform(y_train)\n",
    "y_test_scaled = scaler_y.transform(y_test)\n",
    "\n",
    "# Initialize and train a separate SVR model for each target variable\n",
    "svr_models = []\n",
    "y_pred = np.empty((X_test_scaled.shape[0], y_train_scaled.shape[1]))\n",
    "\n",
    "for i in range(y_train_scaled.shape[1]):\n",
    "    svr = SVR(kernel='rbf', C=1.0, epsilon=0.1)\n",
    "    svr.fit(X_train_scaled, y_train_scaled[:, i])  \n",
    "    y_pred[:, i] = svr.predict(X_test_scaled)  \n",
    "\n",
    "# Inverse transform predictions back to original scale\n",
    "y_pred_inverse = scaler_y.inverse_transform(y_pred)\n",
    "\n",
    "# Evaluate the model using the original scale targets\n",
    "r2_scores = []\n",
    "mae_scores = []\n",
    "\n",
    "for i in range(y_test.shape[1]):\n",
    "    r2 = r2_score(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    mae = mean_absolute_error(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    r2_scores.append(r2)\n",
    "    mae_scores.append(mae)\n",
    "    print(f'R-squared for Target {i}: {r2:.3f}')\n",
    "    print(f'MAE for Target {i}: {mae:.3f}')\n",
    "\n",
    "print(f'Aggregate R-squared: {np.mean(r2_scores):.3f}')\n",
    "print(f'Aggregate MAE: {np.mean(mae_scores):.3f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67873cee-f40a-4939-836f-fe78d74a8cfd",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Full code and Model with feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5c821a26-dfd4-4056-a1be-23c1803cc89b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tosin\\anaconda3\\Lib\\site-packages\\sklearn\\preprocessing\\_discretization.py:248: FutureWarning: In version 1.5 onwards, subsample=200_000 will be used by default. Set subsample explicitly to silence this warning in the mean time. Set subsample=None to disable subsampling explicitly.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared for Target 0: 0.546\n",
      "MAE for Target 0: 310.440\n",
      "R-squared for Target 1: 0.127\n",
      "MAE for Target 1: 7.404\n",
      "R-squared for Target 2: 0.157\n",
      "MAE for Target 2: 1.817\n",
      "R-squared for Target 3: 0.029\n",
      "MAE for Target 3: 0.441\n",
      "Aggregate R-squared: 0.215\n",
      "Aggregate MAE: 80.025\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Full Model after feature engineering attempts\n",
    "#Importing necessary modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, KBinsDiscretizer\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_absolute_error, r2_score\n",
    "\n",
    "# Loading the dataset\n",
    "file_path = 'Process modelling dataset.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "data.head()\n",
    "\n",
    "#separating the columns\n",
    "column_names = ['Run', 'Pressure', 'RF', 'Electrode Gap', 'CCl4 Flow', 'He Flow', 'O2 Flow', \n",
    "    'Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', \n",
    "    'Photoresist Selectivity - Sph']\n",
    "\n",
    "# Displaying the first few rows \n",
    "data = pd.read_csv(file_path, header=None, names=column_names)\n",
    "data.head()\n",
    "\n",
    "#Correcting the data loading\n",
    "data = pd.read_csv(file_path, delim_whitespace=True, header=None, names=column_names)\n",
    "data.head()\n",
    "\n",
    "# Separating features and target variables\n",
    "X = data.drop(['Run', 'Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', 'Photoresist Selectivity - Sph'], axis=1)\n",
    "y = data[['Etch Rate - Rp (Å/min)', 'Etch Uniformity – U (in %)', 'Oxide Selectivity - Sox', 'Photoresist Selectivity - Sph']]\n",
    "\n",
    "# Bin 'Pressure' feature\n",
    "binner = KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='uniform')\n",
    "X['Pressure_binned'] = binner.fit_transform(X[['Pressure']])\n",
    "\n",
    "# Dropping the original 'Pressure' feature\n",
    "X = X.drop('Pressure', axis=1)\n",
    "\n",
    "# Training and test splits\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Feature scaling\n",
    "scaler_X = StandardScaler()\n",
    "X_train_scaled = scaler_X.fit_transform(X_train)\n",
    "X_test_scaled = scaler_X.transform(X_test)\n",
    "\n",
    "# Target variable scaling\n",
    "scaler_y = StandardScaler()\n",
    "y_train_scaled = scaler_y.fit_transform(y_train)\n",
    "y_test_scaled = scaler_y.transform(y_test)\n",
    "\n",
    "# Initializing and training a separate SVR model for each target variable\n",
    "svr_models = []\n",
    "y_pred = np.empty((X_test_scaled.shape[0], y_train_scaled.shape[1]))\n",
    "\n",
    "for i in range(y_train_scaled.shape[1]):\n",
    "    svr = SVR(kernel='rbf', C=1.0, epsilon=0.1)\n",
    "    svr.fit(X_train_scaled, y_train_scaled[:, i])  # Train on the ith target variable\n",
    "    y_pred[:, i] = svr.predict(X_test_scaled)  # Predict the ith target variable\n",
    "\n",
    "# Transforming predictions back to original scale\n",
    "y_pred_inverse = scaler_y.inverse_transform(y_pred)\n",
    "\n",
    "# Model evaluation using original scale targets\n",
    "r2_scores = []\n",
    "mae_scores = []\n",
    "\n",
    "for i in range(y_test.shape[1]):\n",
    "    r2 = r2_score(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    mae = mean_absolute_error(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    r2_scores.append(r2)\n",
    "    mae_scores.append(mae)\n",
    "    print(f'R-squared for Target {i}: {r2:.3f}')\n",
    "    print(f'MAE for Target {i}: {mae:.3f}')\n",
    "\n",
    "print(f'Aggregate R-squared: {np.mean(r2_scores):.3f}')\n",
    "print(f'Aggregate MAE: {np.mean(mae_scores):.3f}')\n",
    "\n",
    "#Visualizations\n",
    "#Scatter Plot of Actual vs Predicted Values\n",
    "n_targets = y_test.shape[1]\n",
    "fig, axes = plt.subplots(n_targets, 1, figsize=(8, n_targets * 4), squeeze=False)\n",
    "\n",
    "for i in range(n_targets):\n",
    "    ax = axes[i, 0]\n",
    "    ax.scatter(y_test.iloc[:, i], y_pred_inverse[:, i])\n",
    "    ax.plot([y_test.iloc[:, i].min(), y_test.iloc[:, i].max()], \n",
    "            [y_test.iloc[:, i].min(), y_test.iloc[:, i].max()], 'k--', lw=2)\n",
    "    ax.set_title(f'Target {i}')\n",
    "    ax.set_xlabel('Actual Values')\n",
    "    ax.set_ylabel('Predicted Values')\n",
    "    ax.legend(['Ideal', 'Predictions'])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#Histograms of the Residuals\n",
    "fig, axes = plt.subplots(n_targets, 1, figsize=(8, n_targets * 4), squeeze=False)\n",
    "\n",
    "for i in range(n_targets):\n",
    "    residuals = y_test.iloc[:, i] - y_pred_inverse[:, i]\n",
    "    ax = axes[i, 0]\n",
    "    ax.hist(residuals, bins=20, edgecolor='black')\n",
    "    ax.set_title(f'Residuals for Target {i}')\n",
    "    ax.set_xlabel('Residual')\n",
    "    ax.set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
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   "source": [
    "## REPORT"
   ]
  },
  {
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   "id": "b0c2d601-faab-4630-8870-91034776866d",
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   "source": [
    "#### SVM Model Development Report for Semiconductor Plasma Etching Process\n",
    "\n",
    "#### Introduction\n",
    "\n",
    "This report documents the development process of Support Vector Machine (SVM) models aimed at predicting various outcomes of a semiconductor plasma etching process. The focus has been on exploring and refining SVM models through feature engineering, and hyperparameter tuning to enhance model performance.\n",
    "\n",
    "#### Data Description\n",
    "\n",
    "The dataset contains experimental results from a semiconductor manufacturing process, focusing on plasma etching. Its features include:\n",
    "- `Pressure`, `RF`, `Electrode Gap`, `CCl4 Flow`, `He Flow`, and `O2 Flow`.\n",
    "- Targets are `Etch Rate`, `Etch Uniformity`, `Oxide Selectivity`, and `Photoresist Selectivity`.\n",
    "\n",
    "#### Initial Model Setup\n",
    "\n",
    "I began by loading the dataset and preparing it for modeling. This preparation involved separating the features (`X`) from the target variables (`y`) and splitting the data into training and testing sets. Standard scaling was applied to the features to normalize the data distribution.\n",
    "\n",
    "#### Model Development\n",
    "\n",
    "1. **Original SVM Model**: I trained an initial SVM model with default parameters (`C=1.0`, `epsilon=0.1`, kernel=`rbf`). The model's performance was evaluated using R-squared and Mean Absolute Error (MAE), providing a baseline for comparison.\n",
    "\n",
    "2. **Feature Engineering Attempts**:\n",
    "    - **Polynomial Features**: I explored adding polynomial features to capture non-linear relationships but observed minimal improvements.\n",
    "    - **Interaction Terms**: Manually created interaction features (e.g., `Pressure` * `RF`) and automated polynomial interactions were tested, yielding slight improvements in some target variables.\n",
    "    - **Binning/Discretization**: The `Pressure` feature was binned into categories, which showed some promise in improving model performance, particularly for specific targets.\n",
    "\n",
    "3. **Hyperparameter Tuning**:\n",
    "    - Adjustments to `C`, `epsilon`, and the kernel parameters were made in an attempt to enhance the model. Grid search and manual tuning methods were employed.\n",
    "    - Although some configurations showed marginal improvements, the overall performance enhancement was limited.\n",
    "\n",
    "#### Results and Interpretation\n",
    "\n",
    "- **Feature-Engineered Model**: This model incorporated binned `Pressure` and interaction terms, aiming to capture more complex relationships within the data. It showed slight improvements in aggregate R-squared and MAE compared to the original model.\n",
    "- **Comparison with the Original Model**: Upon detailed evaluation, the feature-engineered model demonstrated marginally better performance than the original model. However, the differences were slight, suggesting that the additional complexity might not justify the minimal gains.\n",
    "\n",
    "#### Conclusion and Recommendations\n",
    "\n",
    "After extensive experimentation with feature engineering and hyperparameter tuning, I observed that the SVM models could capture certain aspects of the plasma etching process, with marginal improvements in some cases. However, the gains from these adjustments were modest:\n",
    "\n",
    "- The **feature-engineered model** is slightly preferred based on its aggregate performance metrics, though the improvement over the **original model** is minimal.\n",
    "- Given the minimal difference in performance, the **original model** stands out for its simplicity and straightforwardness.\n",
    "\n",
    "#### Future Directions\n",
    "\n",
    "- **Alternative Modeling Techniques**: Other machine learning algorithms, such as ensemble methods or deep learning, could be explored and could potentially offer better performance.\n",
    "- **Further Feature Engineering**: Continued exploration of feature engineering, possibly with domain expert insights, might uncover more impactful transformations.\n",
    "- **Statistical Analysis**: Conducting a statistical analysis of the models' predictions could provide more insight into their performance differences."
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