decision tree shipment-Copy2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a927631",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "data = pd.read_csv('main dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b928302d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#print(data.info())\n",
    "columns_to_drop = ['Customer Email', 'Customer Password', 'Customer Street', 'Customer Zipcode', \n",
    "                   'Order City', 'Order Country', 'Order Customer Id', 'Order Region', 'Order Zipcode']\n",
    "data = data.drop(columns=columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29ad6b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import hstack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9704d96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()\n",
    "\n",
    "def limit_categories(series, threshold=0.01):\n",
    "    value_counts = series.value_counts(normalize=True)\n",
    "    mask = series.isin(value_counts[value_counts >= threshold].index)\n",
    "    return series.where(mask, other='Other')\n",
    "\n",
    "if 'Customer City' in data.columns:\n",
    "    data['Customer City'] = limit_categories(data['Customer City'])\n",
    "if 'Product Name' in data.columns:\n",
    "    data['Product Name'] = limit_categories(data['Product Name'])\n",
    "\n",
    "if 'Customer Country' in data.columns:\n",
    "    data['Customer Country'] = data['Customer Country'].astype(str)\n",
    "if 'Customer State' in data.columns:\n",
    "    data['Customer State'] = data['Customer State'].astype(str)\n",
    "\n",
    "high_cardinality_columns = ['Customer Country', 'Customer State']\n",
    "label_encoders = {}\n",
    "for column in high_cardinality_columns:\n",
    "    le = LabelEncoder()\n",
    "    data[column] = le.fit_transform(data[column].astype(str))\n",
    "    label_encoders[column] = le\n",
    "\n",
    "for column in data.select_dtypes(include=['object']).columns:\n",
    "    data[column] = data[column].astype(str)\n",
    "\n",
    "categorical_columns = data.select_dtypes(include=['object']).columns\n",
    "one_hot_encoder = OneHotEncoder(sparse_output=True)\n",
    "one_hot_encoded = one_hot_encoder.fit_transform(data[categorical_columns])\n",
    "\n",
    "data = data.drop(columns=categorical_columns)\n",
    "data_sparse = hstack([data.values, one_hot_encoded])\n",
    "\n",
    "target = 'Days for shipment (scheduled)'\n",
    "features = data.drop(columns=[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f784e094",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "680c6d23",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features, data[target], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a26006b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.4329954019167913\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the Model\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf1b1cc2",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "got an unexpected keyword argument 'orientation'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m20\u001b[39m,\u001b[38;5;241m10\u001b[39m))\n\u001b[1;32m----> 5\u001b[0m plot_tree(model, feature_names\u001b[38;5;241m=\u001b[39mfeatures\u001b[38;5;241m.\u001b[39mcolumns, filled\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, rounded\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,orientation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorizontal\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mgca()\u001b[38;5;241m.\u001b[39minvert_yaxis()\n\u001b[0;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:191\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    188\u001b[0m func_sig \u001b[38;5;241m=\u001b[39m signature(func)\n\u001b[0;32m    190\u001b[0m \u001b[38;5;66;03m# Map *args/**kwargs to the function signature\u001b[39;00m\n\u001b[1;32m--> 191\u001b[0m params \u001b[38;5;241m=\u001b[39m func_sig\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    192\u001b[0m params\u001b[38;5;241m.\u001b[39mapply_defaults()\n\u001b[0;32m    194\u001b[0m \u001b[38;5;66;03m# ignore self/cls and positional/keyword markers\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\inspect.py:3267\u001b[0m, in \u001b[0;36mSignature.bind\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3262\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbind\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m/\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   3263\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Get a BoundArguments object, that maps the passed `args`\u001b[39;00m\n\u001b[0;32m   3264\u001b[0m \u001b[38;5;124;03m    and `kwargs` to the function's signature.  Raises `TypeError`\u001b[39;00m\n\u001b[0;32m   3265\u001b[0m \u001b[38;5;124;03m    if the passed arguments can not be bound.\u001b[39;00m\n\u001b[0;32m   3266\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 3267\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bind(args, kwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\inspect.py:3256\u001b[0m, in \u001b[0;36mSignature._bind\u001b[1;34m(self, args, kwargs, partial)\u001b[0m\n\u001b[0;32m   3246\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   3247\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgot some positional-only arguments passed as \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   3248\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkeyword arguments: \u001b[39m\u001b[38;5;132;01m{arg!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3253\u001b[0m             ),\n\u001b[0;32m   3254\u001b[0m         )\n\u001b[0;32m   3255\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 3256\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   3257\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgot an unexpected keyword argument \u001b[39m\u001b[38;5;132;01m{arg!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m   3258\u001b[0m                 arg\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(kwargs))))\n\u001b[0;32m   3260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_arguments_cls(\u001b[38;5;28mself\u001b[39m, arguments)\n",
      "\u001b[1;31mTypeError\u001b[0m: got an unexpected keyword argument 'orientation'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plot_tree(model, feature_names=features.columns, filled=True, rounded=True,orientation='horizontal')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "626c22cb-a940-4c8b-9048-77372a0f31be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 10))  \n",
    "plot_tree(model, feature_names=features.columns, filled=True, rounded=True, ax=ax)\n",
    "\n",
    "plt.gca().invert_yaxis()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "142876c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1988     0     0     0]\n",
      " [    0  5492    36    80]\n",
      " [    0    41  5281  1656]\n",
      " [    0   103  1787 19638]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1988\n",
      "           1       0.97      0.98      0.98      5608\n",
      "           2       0.74      0.76      0.75      6978\n",
      "           4       0.92      0.91      0.92     21528\n",
      "\n",
      "    accuracy                           0.90     36102\n",
      "   macro avg       0.91      0.91      0.91     36102\n",
      "weighted avg       0.90      0.90      0.90     36102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix , classification_report\n",
    "cm= confusion_matrix(y_test, y_pred) \n",
    "print(cm)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c1700950",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 1.0000\n",
      "Test Accuracy: 0.8974\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_train_pred = 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 = 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": 24,
   "id": "b15d48f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['deccisiontree.pkl']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(model, 'deccisiontree.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e138d0aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b96282f6-d240-4c4c-964b-47bc99904639",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1988     0     0     0]\n",
      " [    0  5492    36    80]\n",
      " [    0    41  5281  1656]\n",
      " [    0   103  1787 19638]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1988\n",
      "           1       0.97      0.98      0.98      5608\n",
      "           2       0.74      0.76      0.75      6978\n",
      "           4       0.92      0.91      0.92     21528\n",
      "\n",
      "    accuracy                           0.90     36102\n",
      "   macro avg       0.91      0.91      0.91     36102\n",
      "weighted avg       0.90      0.90      0.90     36102\n",
      "\n",
      "Training Accuracy: 1.0000\n",
      "Test Accuracy: 0.8974\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix , classification_report, accuracy_score\n",
    "cm= confusion_matrix(y_test, y_pred) \n",
    "print(cm)\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "y_train_pred = model.predict(X_train)\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "\n",
    "y_test_pred = model.predict(X_test)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "\n",
    "print(f\"Training Accuracy: {train_accuracy:.4f}\")\n",
    "print(f\"Test Accuracy: {test_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "821629c7-fd6c-49ef-9127-ea755f58dc17",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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