shipping mode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2325e9cc-641b-48e7-942f-935fb29aa09e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "data = pd.read_csv('main dataset.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec8f48b7-8596-48d2-8342-1f7d2c12bc44",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values = data.isnull().sum()\n",
    "#print(\"Missing values in each column:\\n\", missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39ee2b42-c462-4bed-bf66-b09956cd02fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "for col in ['Type', 'Delivery Status', 'Catergory_name', 'Customer City', 'Customer Country', 'Market', 'Order City', 'Order Country', 'Order Region', 'Order State', 'Order Status', 'Product Name', 'Shipping Mode']:\n",
    "    if col in data.columns:\n",
    "        data[col] = le.fit_transform(data[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f499af80-0a81-4a13-b3e3-bbaa3b60abca",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.fillna(data.median(numeric_only=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9c9af63-a179-4bb2-ac92-4c72036acd38",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data[['Days for shipping (real)', 'Days for shipment (scheduled)', 'Benefit per order', 'Sales per customer', 'Delivery Status', 'Catergory_name', 'Market', 'Order Region', 'Order State', 'Order Status', 'Product Price']]\n",
    "target = data['Shipping Mode']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0eca83d4-fe31-4f32-ab43-ba50919f03c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c9f3b402-72ed-41a9-ad7e-2a62346bb5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9c4391da-4fb5-414f-b530-c513ed906770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* 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",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --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",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, probability=True)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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(kernel=&#x27;linear&#x27;, probability=True)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear', probability=True)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model = SVC(kernel='linear',probability=True) \n",
    "svm_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9d0a099f-971f-4f90-9238-094fbb6da98f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ce2262be-1ab6-4341-91d9-bc577438ec32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      5531\n",
      "           1       1.00      1.00      1.00      1961\n",
      "           2       1.00      1.00      1.00      7026\n",
      "           3       1.00      1.00      1.00     21586\n",
      "\n",
      "    accuracy                           1.00     36104\n",
      "   macro avg       1.00      1.00      1.00     36104\n",
      "weighted avg       1.00      1.00      1.00     36104\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ba78c2c6-7b72-4bb4-b2fe-d9eae51c4216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 1.0000\n",
      "Test Accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "# Predict on the training data\n",
    "y_train_pred = svm_model.predict(X_train)\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "\n",
    "# Predict on the test data\n",
    "y_test_pred = svm_model.predict(X_test)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "\n",
    "# Print the accuracy on the training and test data\n",
    "print(f\"Training Accuracy: {train_accuracy:.4f}\")\n",
    "print(f\"Test Accuracy: {test_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b4e5f744-6739-4aab-8149-9aeb8ad866a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SVM_Vect.pkl']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(svm_model, 'SVM_Vect.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2f1a4fc1-5032-42b2-b3a0-ccb96e147efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "notebook_dir = os.getcwd()\n",
    "#print(\"Current notebook directory:\", notebook_dir)\n",
    "\n",
    "notebook_name = \"shipping mode.ipynb\"  # Replace with your actual notebook name\n",
    "\n",
    "# Combine to get the full path\n",
    "full_notebook_path = os.path.join(notebook_dir, notebook_name)\n",
    "#print(\"Full notebook path:\", full_notebook_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4aa44dfb-dd0d-46be-b317-5aae139764a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4b107907-9243-48bf-9ca1-cb2ef07217f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x=data['Shipping Mode'])\n",
    "\n",
    "plt.title('Box Plot with Outliers')\n",
    "plt.xlabel('Value')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a81f5081-56c1-445e-9f6b-eb25b29a9361",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7b816e79-791a-4f86-922e-c2f541f19e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm_normalized, annot=True, fmt=\".2f\", cmap='Blues', xticklabels=svm_model.classes_, yticklabels=svm_model.classes_)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Normalized Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9b961af7-126d-4525-96c3-c4c1beac51b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import roc_curve, auc, RocCurveDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a58280e2-8072-48ce-b7a2-04129077b015",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob = svm_model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "fe559c43-2d13-486a-be1f-e10dd6c7461c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 1.00\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob, pos_label=1)\n",
    "\n",
    "# Compute AUC\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Display AUC\n",
    "print(f'AUC: {roc_auc:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "4700b0b5-ecd0-40f2-9406-44c637f50f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ea339aa4-62d0-4aa1-ac99-502f8cb15b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.metrics import precision_recall_curve, PrecisionRecallDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "cdbd7e4f-a47e-4845-b2a8-882407109408",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision-Recall AUC: 1.00\n"
     ]
    }
   ],
   "source": [
    "precision, recall, thresholds = precision_recall_curve(y_test, y_prob,pos_label=1)\n",
    "\n",
    "# Optionally, display AUC for Precision-Recall curve\n",
    "pr_auc = auc(recall, precision)\n",
    "print(f'Precision-Recall AUC: {pr_auc:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "dbfdaf46-3aed-491f-b330-bff3072e9ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(recall, precision, color='blue', lw=2, label=f'PR curve (area = {pr_auc:.2f})')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.legend(loc='lower left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "a6c97bad-1852-46ec-a749-38c4d7a7d9b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(recall, precision, color='blue', lw=2, label=f'PR curve (area = {pr_auc:.2f})')\n",
    "\n",
    "low_precision_threshold = 0.5\n",
    "low_recall_threshold = 0.5\n",
    "\n",
    "plt.fill_between(recall, precision, where=(precision < low_precision_threshold), color='red', alpha=0.2, label='Low Precision')\n",
    "plt.fill_between(recall, precision, where=(recall < low_recall_threshold), color='orange', alpha=0.2, label='Low Recall')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve with Error Regions')\n",
    "plt.legend(loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "690be32c-cd23-492e-8186-f3b15af5490f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}