SVC using power transform.ipynb

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      " 50%|█████████████████████████████████████████▌                                         | 1/2 [04:29<04:29, 269.38s/it]C:\\Users\\Acer\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:3253: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(x_trans.var())\n",
      "C:\\Users\\Acer\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:3253: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(x_trans.var())\n",
      "C:\\Users\\Acer\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:3253: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(x_trans.var())\n",
      "C:\\Users\\Acer\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:3253: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(x_trans.var())\n",
      "C:\\Users\\Acer\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:3253: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(x_trans.var())\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████| 2/2 [07:05<00:00, 212.52s/it]\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC with power transform</td>\n",
       "      <td>0.892189</td>\n",
       "      <td>0.003448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC no normalization</td>\n",
       "      <td>0.803100</td>\n",
       "      <td>0.012257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                      Model  Accuracy Mean  Accuracy Std\n",
       "1  SVC with power transform       0.892189      0.003448\n",
       "0      SVC no normalization       0.803100      0.012257"
      ]
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   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the data\n",
    "df = pd.read_csv('credit_risk.csv')\n",
    "\n",
    "# Importing liberaries for preprocessing\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, RobustScaler, PowerTransformer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Drop Id column\n",
    "df = df.drop(['Id'], axis=1)\n",
    "\n",
    "# Define columns for preprocessing\n",
    "impute_cols = ['Emp_length', 'Rate']\n",
    "encode_cols = df.select_dtypes(exclude='number').columns.to_list()\n",
    "Scale_cols = ['Age','Income','Amount','Percent_income','Cred_length']\n",
    "\n",
    "# Impute missing values and scaling\n",
    "imputer_mean = Pipeline([\n",
    "    ('imputer', KNNImputer(n_neighbors=5)),\n",
    "    ('scaling', RobustScaler())\n",
    "])\n",
    "\n",
    "Preprocessing1 = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('imputer', imputer_mean, impute_cols),\n",
    "        ('encoder', OneHotEncoder(handle_unknown='ignore'), encode_cols)\n",
    "    ], remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "Preprocessing2 = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('imputer', imputer_mean, impute_cols),\n",
    "        ('encoder', OneHotEncoder(handle_unknown='ignore'), encode_cols),\n",
    "        ('scaling', PowerTransformer(), Scale_cols)\n",
    "    ], remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "# Classificatin models\n",
    "from sklearn.svm import SVC\n",
    "# Model processing\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Split x and y for the model\n",
    "y = df['Status']\n",
    "x = df.drop(['Status'], axis=1)\n",
    "\n",
    "# Split data to Train and Test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=24, stratify=y)\n",
    "\n",
    "estimator1 = Pipeline([\n",
    "        ('preprocessing', Preprocessing1),\n",
    "        ('model', SVC())\n",
    "    ])\n",
    "\n",
    "estimator2 = Pipeline([\n",
    "        ('preprocessing', Preprocessing2),\n",
    "        ('model', SVC())\n",
    "    ])\n",
    "\n",
    "# The models\n",
    "models = [\n",
    "    estimator1, # without scaling\n",
    "    estimator2 # with scaling\n",
    "]\n",
    "\n",
    "# List for score matrix\n",
    "accuracy_score = []\n",
    "accuracy_mean = []\n",
    "accuracy_std = []\n",
    "\n",
    "# Calculate model's matrix score\n",
    "for model in tqdm(models):\n",
    "    \n",
    "    # Cross validate split\n",
    "    crossval = KFold(n_splits=5, shuffle=True, random_state=24)\n",
    "\n",
    "    # Calculate the accuracy from cross validation\n",
    "    accuracy = cross_val_score(\n",
    "        model, \n",
    "        X_train, \n",
    "        y_train, \n",
    "        cv=crossval, \n",
    "        scoring='accuracy', \n",
    "        error_score='raise'\n",
    "        )\n",
    "\n",
    "    #accuracy_score.append(accuracy)\n",
    "    accuracy_mean.append(accuracy.mean())\n",
    "    accuracy_std.append(accuracy.std())\n",
    "\n",
    "# Model Matrix Evaluation\n",
    "model_matrix = pd.DataFrame({\n",
    "    'Model': ['SVC no normalization', 'SVC with power transform'],\n",
    "    'Accuracy Mean': accuracy_mean,\n",
    "    'Accuracy Std': accuracy_std\n",
    "})\n",
    "model_matrix.sort_values(by='Accuracy Mean', ascending=False)"
   ]
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