InventoryDataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acc2149d-de58-4671-ad96-88fa7cb11bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Inventory_Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "01edd73c-151a-4da5-ae46-facbcd7e16f3",
   "metadata": {},
   "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>Product type</th>\n",
       "      <th>SKU</th>\n",
       "      <th>Price</th>\n",
       "      <th>Availability</th>\n",
       "      <th>Number of products sold</th>\n",
       "      <th>Revenue generated</th>\n",
       "      <th>Customer demographics</th>\n",
       "      <th>Stock levels</th>\n",
       "      <th>Lead times</th>\n",
       "      <th>Order quantities</th>\n",
       "      <th>...</th>\n",
       "      <th>Location</th>\n",
       "      <th>Lead time</th>\n",
       "      <th>Production volumes</th>\n",
       "      <th>Manufacturing lead time</th>\n",
       "      <th>Manufacturing costs</th>\n",
       "      <th>Inspection results</th>\n",
       "      <th>Defect rates</th>\n",
       "      <th>Transportation modes</th>\n",
       "      <th>Routes</th>\n",
       "      <th>Costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>SKU0</td>\n",
       "      <td>69.808006</td>\n",
       "      <td>55</td>\n",
       "      <td>802</td>\n",
       "      <td>8661.996792</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>58</td>\n",
       "      <td>7</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>NYC</td>\n",
       "      <td>29</td>\n",
       "      <td>215</td>\n",
       "      <td>29</td>\n",
       "      <td>46.279879</td>\n",
       "      <td>Pending</td>\n",
       "      <td>0.226410</td>\n",
       "      <td>Road</td>\n",
       "      <td>Route B</td>\n",
       "      <td>187.752075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Electronics</td>\n",
       "      <td>SKU1</td>\n",
       "      <td>14.843523</td>\n",
       "      <td>95</td>\n",
       "      <td>736</td>\n",
       "      <td>7460.900065</td>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>NYC</td>\n",
       "      <td>23</td>\n",
       "      <td>517</td>\n",
       "      <td>30</td>\n",
       "      <td>33.616769</td>\n",
       "      <td>Pending</td>\n",
       "      <td>4.854068</td>\n",
       "      <td>Road</td>\n",
       "      <td>Route B</td>\n",
       "      <td>503.065579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>SKU2</td>\n",
       "      <td>11.319683</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>9577.749626</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>NYC</td>\n",
       "      <td>12</td>\n",
       "      <td>971</td>\n",
       "      <td>27</td>\n",
       "      <td>30.688019</td>\n",
       "      <td>Pending</td>\n",
       "      <td>4.580593</td>\n",
       "      <td>Air</td>\n",
       "      <td>Route C</td>\n",
       "      <td>141.920282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Electronics</td>\n",
       "      <td>SKU3</td>\n",
       "      <td>61.163343</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>7766.836426</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>California</td>\n",
       "      <td>24</td>\n",
       "      <td>937</td>\n",
       "      <td>18</td>\n",
       "      <td>35.624741</td>\n",
       "      <td>Fail</td>\n",
       "      <td>4.746649</td>\n",
       "      <td>Rail</td>\n",
       "      <td>Route A</td>\n",
       "      <td>254.776159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Electronics</td>\n",
       "      <td>SKU4</td>\n",
       "      <td>4.805496</td>\n",
       "      <td>26</td>\n",
       "      <td>871</td>\n",
       "      <td>2686.505152</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>Florida</td>\n",
       "      <td>5</td>\n",
       "      <td>414</td>\n",
       "      <td>3</td>\n",
       "      <td>92.065161</td>\n",
       "      <td>Fail</td>\n",
       "      <td>3.145580</td>\n",
       "      <td>Air</td>\n",
       "      <td>Route A</td>\n",
       "      <td>923.440632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product type   SKU      Price  Availability  Number of products sold  \\\n",
       "0      Apparel  SKU0  69.808006            55                      802   \n",
       "1  Electronics  SKU1  14.843523            95                      736   \n",
       "2      Apparel  SKU2  11.319683            34                        8   \n",
       "3  Electronics  SKU3  61.163343            68                       83   \n",
       "4  Electronics  SKU4   4.805496            26                      871   \n",
       "\n",
       "   Revenue generated Customer demographics  Stock levels  Lead times  \\\n",
       "0        8661.996792            Non-binary            58           7   \n",
       "1        7460.900065                Female            53          30   \n",
       "2        9577.749626               Unknown             1          10   \n",
       "3        7766.836426            Non-binary            23          13   \n",
       "4        2686.505152            Non-binary             5           3   \n",
       "\n",
       "   Order quantities  ...    Location Lead time  Production volumes  \\\n",
       "0                96  ...         NYC        29                 215   \n",
       "1                37  ...         NYC        23                 517   \n",
       "2                88  ...         NYC        12                 971   \n",
       "3                59  ...  California        24                 937   \n",
       "4                56  ...     Florida         5                 414   \n",
       "\n",
       "  Manufacturing lead time Manufacturing costs  Inspection results  \\\n",
       "0                      29           46.279879             Pending   \n",
       "1                      30           33.616769             Pending   \n",
       "2                      27           30.688019             Pending   \n",
       "3                      18           35.624741                Fail   \n",
       "4                       3           92.065161                Fail   \n",
       "\n",
       "   Defect rates  Transportation modes   Routes       Costs  \n",
       "0      0.226410                  Road  Route B  187.752075  \n",
       "1      4.854068                  Road  Route B  503.065579  \n",
       "2      4.580593                   Air  Route C  141.920282  \n",
       "3      4.746649                  Rail  Route A  254.776159  \n",
       "4      3.145580                   Air  Route A  923.440632  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5498c741-9d82-4364-9dd3-8de9deec37ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 24 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Product type             100 non-null    object \n",
      " 1   SKU                      100 non-null    object \n",
      " 2   Price                    100 non-null    float64\n",
      " 3   Availability             100 non-null    int64  \n",
      " 4   Number of products sold  100 non-null    int64  \n",
      " 5   Revenue generated        100 non-null    float64\n",
      " 6   Customer demographics    100 non-null    object \n",
      " 7   Stock levels             100 non-null    int64  \n",
      " 8   Lead times               100 non-null    int64  \n",
      " 9   Order quantities         100 non-null    int64  \n",
      " 10  Shipping times           100 non-null    int64  \n",
      " 11  Shipping carriers        100 non-null    object \n",
      " 12  Shipping costs           100 non-null    float64\n",
      " 13  Supplier name            100 non-null    object \n",
      " 14  Location                 100 non-null    object \n",
      " 15  Lead time                100 non-null    int64  \n",
      " 16  Production volumes       100 non-null    int64  \n",
      " 17  Manufacturing lead time  100 non-null    int64  \n",
      " 18  Manufacturing costs      100 non-null    float64\n",
      " 19  Inspection results       100 non-null    object \n",
      " 20  Defect rates             100 non-null    float64\n",
      " 21  Transportation modes     100 non-null    object \n",
      " 22  Routes                   100 non-null    object \n",
      " 23  Costs                    100 non-null    float64\n",
      "dtypes: float64(6), int64(9), object(9)\n",
      "memory usage: 18.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c07fce3e-05a5-45db-bbf3-7f71e6a2c9b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = df['Product type'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2b4c04e-fdc4-412e-893c-1bcc8eccd283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Apparel', 'Electronics', 'Accessories', 'Stationery', 'Furniture'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c111b8b1-ef2c-4fcc-8977-317643665f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "separated_datasets = {category: df[df['Product type'] == category] for category in categories}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c1a1c49-8642-4fd6-9d97-7969edd9e7ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "for category, data in separated_datasets.items():\n",
    "    data.to_csv(f\"{category}_inventory_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "efe32628-168d-481b-9da2-9c4e7cf41244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 24\n",
      "19 24\n",
      "23 24\n",
      "18 24\n",
      "15 24\n"
     ]
    }
   ],
   "source": [
    "category_data = separated_datasets['Apparel']  \n",
    "print(category_data.shape[0],category_data.shape[1])\n",
    "\n",
    "category_data = separated_datasets['Electronics']  \n",
    "print(category_data.shape[0],category_data.shape[1])\n",
    "\n",
    "category_data = separated_datasets['Accessories']  \n",
    "print(category_data.shape[0],category_data.shape[1])\n",
    "\n",
    "category_data = separated_datasets['Stationery']  \n",
    "print(category_data.shape[0],category_data.shape[1])\n",
    "\n",
    "category_data = separated_datasets['Furniture']  \n",
    "print(category_data.shape[0],category_data.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52a4471f-e1f1-4597-92b6-341a8be2d739",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0c285bc-46b0-483e-9585-6dc8091138c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = ['Lead time', 'Manufacturing lead time','Lead times']  \n",
    "data_subset = data[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3faec379-8a36-4209-8c6b-03daa9759a24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Lead time  Manufacturing lead time  Lead times\n",
      "79          5                       12          25\n",
      "81         28                        1           6\n",
      "83         20                        5          24\n",
      "84         24                       22          26\n",
      "86          4                       18          14\n"
     ]
    }
   ],
   "source": [
    "print(data_subset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9462b36f-6499-4150-9254-63bf9b3a4340",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_matrix = data_subset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0fe3987d-0b38-4d81-a6c8-02d012a18720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))  \n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title(\"Heatmap of Selected Features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e7e90a46-6023-4a0c-9581-8c96aad52f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "# from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Load dataset\n",
    "data = pd.read_csv(\"Apparel_inventory_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6bd1ebe2-95f4-456b-bc93-31bdb436a998",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['Lead time', 'Manufacturing lead time']]  \n",
    "y = data['Lead times']  \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1deae507-c8be-4dce-958c-f7f17ef29e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ec0141d9-c905-4250-a218-35bb5555bbf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Mean Squared Error: 45.59466621944334\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "ridge_model = Ridge(alpha=1.0)\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "mse_ridge = mean_squared_error(y_test, y_pred)\n",
    "print(\"Ridge Mean Squared Error:\", mse_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8b6411df-e2ea-445c-8c5d-59dd921d138f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model successfully saved to Apparel_inventory_data.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "filename = \"Apparel_inventory_data.pkl\"\n",
    "\n",
    "with open(filename, \"wb\") as file:\n",
    "    pickle.dump(ridge_model, file)\n",
    "\n",
    "print(f\"Model successfully saved to {filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0bcd4367-82c5-4f53-8883-25c8c2ecef5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso Mean Squared Error: 45.66027102832497\n"
     ]
    }
   ],
   "source": [
    "lasso_model = Lasso(alpha=0.1)\n",
    "lasso_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lasso_model.predict(X_test)\n",
    "mse_lasso = mean_squared_error(y_test, y_pred)\n",
    "print(\"Lasso Mean Squared Error:\", mse_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2655a6c7-178b-4779-95b7-481f5106d30b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Mean Squared Error: 34.01343394872327\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"Electronics_inventory_data.csv\")\n",
    "X = data[['Lead time', 'Manufacturing lead time']]  \n",
    "y = data['Lead times']  \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "ridge_model = Ridge(alpha=1.0)\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "mse_ridge = mean_squared_error(y_test, y_pred)\n",
    "print(\"Ridge Mean Squared Error:\", mse_ridge)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3c64f2ed-ce7c-4806-9167-8231540c0ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model successfully saved to Electronics_inventory_data.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "filename = \"Electronics_inventory_data.pkl\"\n",
    "\n",
    "with open(filename, \"wb\") as file:\n",
    "    pickle.dump(ridge_model, file)\n",
    "\n",
    "print(f\"Model successfully saved to {filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb88fdc5-8aa5-4ff7-a0c1-d072dc72f2d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "lasso_model = Lasso(alpha=0.1)\n",
    "lasso_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lasso_model.predict(X_test)\n",
    "mse_lasso = mean_squared_error(y_test, y_pred)\n",
    "print(\"Lasso Mean Squared Error:\", mse_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "181b05bc-4ee1-417f-b556-94ad31e2b0aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Mean Squared Error: 40.90938694614016\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data = pd.read_csv(\"Furniture_inventory_data.csv\")\n",
    "X = data[['Lead time', 'Manufacturing lead time']]  \n",
    "y = data['Lead times']  \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=20)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=20)\n",
    "\n",
    "\n",
    "ridge_model = Ridge(alpha=1.0)\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "mse_ridge = mean_squared_error(y_test, y_pred)\n",
    "print(\"Ridge Mean Squared Error:\", mse_ridge)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0035dbac-cff2-493c-a9d8-bd76ce0cf03d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model successfully saved to Furniture_inventory_data.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "filename = \"Furniture_inventory_data.pkl\"\n",
    "\n",
    "with open(filename, \"wb\") as file:\n",
    "    pickle.dump(ridge_model, file)\n",
    "\n",
    "print(f\"Model successfully saved to {filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f671c3bd-272b-4990-afaa-57325df5507f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_model = Lasso(alpha=0.1)\n",
    "lasso_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lasso_model.predict(X_test)\n",
    "mse_lasso = mean_squared_error(y_test, y_pred)\n",
    "print(\"Lasso Mean Squared Error:\", mse_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fffd6737-60c6-4b7d-ae16-1a13e7d19069",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Mean Squared Error: 50.915695626773214\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"Stationery_inventory_data.csv\")\n",
    "X = data[['Lead time', 'Manufacturing lead time']]  \n",
    "y = data['Lead times']  \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "\n",
    "ridge_model = Ridge(alpha=1.0)\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "mse_ridge = mean_squared_error(y_test, y_pred)\n",
    "print(\"Ridge Mean Squared Error:\", mse_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d6f15f72-839f-4ed8-8397-001e12852b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model successfully saved to Stationery_inventory_data.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "filename = \"Stationery_inventory_data.pkl\"\n",
    "\n",
    "with open(filename, \"wb\") as file:\n",
    "    pickle.dump(ridge_model, file)\n",
    "\n",
    "print(f\"Model successfully saved to {filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b471736-937a-4611-9769-5c80bf620380",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_model = Lasso(alpha=0.1)\n",
    "lasso_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lasso_model.predict(X_test)\n",
    "mse_lasso = mean_squared_error(y_test, y_pred)\n",
    "print(\"Lasso Mean Squared Error:\", mse_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a4dcc101-ecde-4b80-9903-1cb86d8db2c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Mean Squared Error: 51.83085197156589\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"Accessories_inventory_data.csv\")\n",
    "X = data[['Lead time', 'Manufacturing lead time']]  \n",
    "y = data['Lead times']  \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=21)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=21)\n",
    "\n",
    "\n",
    "ridge_model = Ridge(alpha=1.0)\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "mse_ridge = mean_squared_error(y_test, y_pred)\n",
    "print(\"Ridge Mean Squared Error:\", mse_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a814292b-708e-4537-b468-2b0d32a181d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model successfully saved to Accessories_inventory_data.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "filename = \"Accessories_inventory_data.pkl\"\n",
    "\n",
    "with open(filename, \"wb\") as file:\n",
    "    pickle.dump(ridge_model, file)\n",
    "\n",
    "print(f\"Model successfully saved to {filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6287980f-891a-4688-a7d4-e9e51fd9e017",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18 entries, 0 to 17\n",
      "Data columns (total 2 columns):\n",
      " #   Column                   Non-Null Count  Dtype\n",
      "---  ------                   --------------  -----\n",
      " 0   Lead time                18 non-null     int64\n",
      " 1   Manufacturing lead time  18 non-null     int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 420.0 bytes\n"
     ]
    }
   ],
   "source": [
    "X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "507d7cb6-a4db-412e-ac2b-4291824a6650",
   "metadata": {},
   "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>Lead time</th>\n",
       "      <th>Manufacturing lead time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.944444</td>\n",
       "      <td>14.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.825090</td>\n",
       "      <td>9.191677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>23.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Lead time  Manufacturing lead time\n",
       "count  18.000000                18.000000\n",
       "mean   15.944444                14.611111\n",
       "std     7.825090                 9.191677\n",
       "min     1.000000                 1.000000\n",
       "25%    10.000000                 7.000000\n",
       "50%    17.500000                11.000000\n",
       "75%    22.500000                23.250000\n",
       "max    29.000000                28.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "76a30ba3-7c75-43ca-9721-61b504863a21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18.000000\n",
       "mean     17.888889\n",
       "std       7.583953\n",
       "min       1.000000\n",
       "25%      14.250000\n",
       "50%      19.000000\n",
       "75%      23.750000\n",
       "max      27.000000\n",
       "Name: Lead times, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d6744f8-f246-488f-b8e2-acb263b03c38",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}