Electronics_inventory_data_defect_rates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd67e058-4cb3-463b-8cbd-a0ac7f982131",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf175537-1837-43c7-a05f-5e1bfb8ea1e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Electronics_inventory_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad40579b-0eee-482c-ba66-c17498944d58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"Availability\", \"Number of products sold\", \"Order quantities\", \"Production volumes\", \"Inspection results\", \"Defect rates\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5848f16-0267-4898-a1bb-3069eb0ea383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Pending', 'Fail', 'Pass'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Inspection results\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2888c57-5115-4d81-8280-000c59d0fcf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {'Fail': -1, 'Pass': 1, 'Pending': 0}\n",
    "df.loc[:, 'Inspection results'] = df['Inspection results'].map(mapping).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "992d076c-1b94-42d4-9d26-c10a6ac1cc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, -1, 1], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Inspection results\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03552811-3707-4c4c-b0b6-609655d559c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "corr = df.corr()\n",
    "plt.figure(figsize=(5,4))\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37c095eb-b151-4b02-8734-02c7485ec511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step - loss: 2336.8699 - val_loss: 2294.4324\n",
      "Epoch 2/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 1288.3342 - val_loss: 1205.1125\n",
      "Epoch 3/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 595.8687 - val_loss: 539.4510\n",
      "Epoch 4/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 191.6727 - val_loss: 185.2587\n",
      "Epoch 5/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 39.0176 - val_loss: 79.6713\n",
      "Epoch 6/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 74.2306 - val_loss: 125.1559\n",
      "Epoch 7/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 206.9624 - val_loss: 215.0154\n",
      "Epoch 8/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 348.1071 - val_loss: 283.3964\n",
      "Epoch 9/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 435.1407 - val_loss: 303.4430\n",
      "Epoch 10/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 448.1845 - val_loss: 278.9432\n",
      "Epoch 11/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 399.9584 - val_loss: 227.5412\n",
      "Epoch 12/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 314.4969 - val_loss: 172.5051\n",
      "Epoch 13/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 219.1416 - val_loss: 134.0767\n",
      "Epoch 14/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 134.0767 - val_loss: 123.5970\n",
      "Epoch 15/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 73.8219 - val_loss: 146.0854\n",
      "Epoch 16/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 44.1409 - val_loss: 190.2527\n",
      "Epoch 17/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 42.9627 - val_loss: 245.4552\n",
      "Epoch 18/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 63.0550 - val_loss: 303.2656\n",
      "Epoch 19/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 93.3670 - val_loss: 351.7801\n",
      "Epoch 20/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 122.9397 - val_loss: 381.9788\n",
      "Epoch 21/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 143.1192 - val_loss: 389.4272\n",
      "Epoch 22/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 149.2402 - val_loss: 374.4619\n",
      "Epoch 23/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 249ms/step - loss: 140.9878 - val_loss: 341.3202\n",
      "Epoch 24/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 121.6143 - val_loss: 296.8474\n",
      "Epoch 25/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 96.4829 - val_loss: 248.5071\n",
      "Epoch 26/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 71.4286 - val_loss: 202.8945\n",
      "Epoch 27/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 51.3613 - val_loss: 164.6643\n",
      "Epoch 28/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 39.2755 - val_loss: 136.0753\n",
      "Epoch 29/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 35.8316 - val_loss: 117.0631\n",
      "Epoch 30/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 39.5280 - val_loss: 105.8301\n",
      "Epoch 31/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 47.3713 - val_loss: 99.7492\n",
      "Epoch 32/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 55.8425 - val_loss: 96.3050\n",
      "Epoch 33/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 61.8792 - val_loss: 93.8038\n",
      "Epoch 34/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 63.6051 - val_loss: 91.6898\n",
      "Epoch 35/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 60.6366 - val_loss: 90.4356\n",
      "Epoch 36/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 53.9450 - val_loss: 91.1081\n",
      "Epoch 37/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 45.3780 - val_loss: 94.7990\n",
      "Epoch 38/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 37.0258 - val_loss: 102.1973\n",
      "Epoch 39/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 30.6199 - val_loss: 112.9305\n",
      "Epoch 40/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 27.0810 - val_loss: 125.7943\n",
      "Epoch 41/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 26.4565 - val_loss: 138.9284\n",
      "Epoch 42/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 28.0477 - val_loss: 150.2138\n",
      "Epoch 43/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 30.5810 - val_loss: 157.7502\n",
      "Epoch 44/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 32.7799 - val_loss: 160.2931\n",
      "Epoch 45/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 33.6792 - val_loss: 157.4861\n",
      "Epoch 46/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 32.8779 - val_loss: 149.8238\n",
      "Epoch 47/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 30.5682 - val_loss: 138.4941\n",
      "Epoch 48/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 27.3845 - val_loss: 125.0582\n",
      "Epoch 49/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 24.1436 - val_loss: 111.1009\n",
      "Epoch 50/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 21.5681 - val_loss: 97.9425\n",
      "Epoch 51/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 20.0772 - val_loss: 86.4813\n",
      "Epoch 52/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 19.7165 - val_loss: 77.1656\n",
      "Epoch 53/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 20.1828 - val_loss: 70.0874\n",
      "Epoch 54/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 20.9796 - val_loss: 64.5255\n",
      "Epoch 55/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 21.5871 - val_loss: 61.0957\n",
      "Epoch 56/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 21.6731 - val_loss: 59.7023\n",
      "Epoch 57/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 21.1359 - val_loss: 60.1889\n",
      "Epoch 58/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 20.0943 - val_loss: 62.3928\n",
      "Epoch 59/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 18.8247 - val_loss: 66.0647\n",
      "Epoch 60/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 17.7051 - val_loss: 70.7780\n",
      "Epoch 61/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 16.9988 - val_loss: 75.9264\n",
      "Epoch 62/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 16.7940 - val_loss: 80.7791\n",
      "Epoch 63/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 16.9642 - val_loss: 84.6098\n",
      "Epoch 64/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 17.3197 - val_loss: 86.8437\n",
      "Epoch 65/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 17.6172 - val_loss: 87.1504\n",
      "Epoch 66/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 17.6566 - val_loss: 85.5256\n",
      "Epoch 67/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 17.3941 - val_loss: 82.2725\n",
      "Epoch 68/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 16.9090 - val_loss: 77.8769\n",
      "Epoch 69/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 16.3637 - val_loss: 72.9365\n",
      "Epoch 70/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 15.8925 - val_loss: 68.0469\n",
      "Epoch 71/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 15.5907 - val_loss: 63.6772\n",
      "Epoch 72/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 15.4725 - val_loss: 60.1629\n",
      "Epoch 73/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 15.4789 - val_loss: 57.6917\n",
      "Epoch 74/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 15.5183 - val_loss: 56.3424\n",
      "Epoch 75/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 15.4955 - val_loss: 56.0871\n",
      "Epoch 76/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 15.3543 - val_loss: 56.8302\n",
      "Epoch 77/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 15.1125 - val_loss: 58.4062\n",
      "Epoch 78/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 14.8224 - val_loss: 60.5741\n",
      "Epoch 79/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 14.5433 - val_loss: 62.8082\n",
      "Epoch 80/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 14.3200 - val_loss: 65.0277\n",
      "Epoch 81/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 14.1739 - val_loss: 67.0087\n",
      "Epoch 82/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 14.1148 - val_loss: 68.4788\n",
      "Epoch 83/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 14.0850 - val_loss: 69.2536\n",
      "Epoch 84/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 14.0340 - val_loss: 69.2536\n",
      "Epoch 85/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 13.9323 - val_loss: 68.5137\n",
      "Epoch 86/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 13.7768 - val_loss: 67.1682\n",
      "Epoch 87/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 13.6079 - val_loss: 65.4317\n",
      "Epoch 88/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 13.4428 - val_loss: 63.5235\n",
      "Epoch 89/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 13.2988 - val_loss: 61.6531\n",
      "Epoch 90/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 13.1896 - val_loss: 60.0019\n",
      "Epoch 91/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 13.1054 - val_loss: 58.7042\n",
      "Epoch 92/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 13.0373 - val_loss: 57.8396\n",
      "Epoch 93/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 12.9650 - val_loss: 57.4333\n",
      "Epoch 94/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 12.8743 - val_loss: 57.4606\n",
      "Epoch 95/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 12.7638 - val_loss: 57.8571\n",
      "Epoch 96/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 12.6404 - val_loss: 58.5244\n",
      "Epoch 97/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 12.5130 - val_loss: 59.2788\n",
      "Epoch 98/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 12.3911 - val_loss: 59.9634\n",
      "Epoch 99/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 12.2922 - val_loss: 60.4911\n",
      "Epoch 100/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 12.2074 - val_loss: 60.7484\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.layers import Input\n",
    "\n",
    "X = df[[\"Availability\", \"Number of products sold\", \"Order quantities\", \"Production volumes\", \"Inspection results\"]]\n",
    "y = df[\"Defect rates\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "y_train = y_train.astype('float32')\n",
    "y_test = y_test.astype('float32')\n",
    "\n",
    "model = Sequential([\n",
    "    Input(shape=(X_train.shape[1],)),  \n",
    "    Dense(64, activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(1)\n",
    "])\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2)\n",
    "\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac17fb73-321a-4faa-a6b9-cfbdb63f9850",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "Mean Squared Error (MSE): 15.012378\n",
      "R-squared (R²): -5.397837954291194\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate MSE\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "\n",
    "# Calculate R2 score\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"R-squared (R²):\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56f3423a-eda6-45eb-80ca-16016613ed05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model successfully saved to Electronics_inventory_data_deficit_rates.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "filename = \"Electronics_inventory_data_deficit_rates.pkl\"\n",
    "\n",
    "with open(filename, \"wb\") as file:\n",
    "    pickle.dump(model, file)\n",
    "\n",
    "print(f\"Model successfully saved to {filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d8ab202-a592-436a-9328-b70362d21051",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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