Pipe_Line_One_Layer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bd20b130",
   "metadata": {},
   "source": [
    "# بسم الله الرحمن الرحيم"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d9ed4de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd \n",
    "# import numpy as np \n",
    "# from matplotlib import pyplot as plt \n",
    "# import seaborn as sns \n",
    "# plt.style.use('default')\n",
    "# #-------------------------------------------------------------------------------------\n",
    "# from sklearn.utils import resample \n",
    "# from sklearn import preprocessing\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.model_selection import train_test_split\n",
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# from sklearn.tree import DecisionTreeClassifier\n",
    "# from sklearn.svm import SVC\n",
    "# from sklearn.pipeline import make_pipeline\n",
    "# from sklearn.metrics import confusion_matrix\n",
    "# from sklearn.model_selection import cross_validate\n",
    "\n",
    "# from sklearn.naive_bayes import GaussianNB\n",
    "# from sklearn.metrics import confusion_matrix\n",
    "# from sklearn.metrics import classification_report\n",
    "# from sklearn.metrics import precision_score\n",
    "# from sklearn.metrics import recall_score\n",
    "# from sklearn.pipeline import Pipeline \n",
    "\n",
    "# import tensorflow_addons as tfa\n",
    "# import tensorflow as tf \n",
    "# from tensorflow import keras \n",
    "# from tensorflow.keras import layers \n",
    "# from focal_loss import SparseCategoricalFocalLoss\n",
    "\n",
    "# #-------------------------------------------------------------------------------------\n",
    "\n",
    "# from keras.callbacks import EarlyStopping\n",
    "# from keras.callbacks import ModelCheckpoint\n",
    "# #-------------------------------------------------------------------------------------\n",
    "# from imblearn.over_sampling import SMOTE\n",
    "# from utils import *\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e76e207c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "import pandas as pd \n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf \n",
    "import tensorflow.keras \n",
    "import pickle \n",
    "import seaborn as sns \n",
    "from matplotlib import pyplot as plt \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02bdbad",
   "metadata": {},
   "source": [
    "## Reading the Dataset, cleaning , preparing it for the training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ae5c39bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"dataset/body_level_classification_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af2862f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_original= cleaning_data(df).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c3b68f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_class_train,all_class_test=data_spliting(df_original,1/4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ce7e5589",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=all_class_train.drop([\"Body_Level\"],axis=1).to_numpy()\n",
    "Y_train=all_class_train[\"Body_Level\"].to_numpy()\n",
    "X_test =all_class_test.drop([\"Body_Level\"],axis=1).to_numpy()\n",
    "Y_test =all_class_test[\"Body_Level\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ee1fc46e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, scaler= data_scaling(X_train)\n",
    "with open(f\"scaler\",\"wb\") as fp : \n",
    "    pickle.dump(scaler,fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4858c01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val, X_test, Y_val, Y_test = train_test_split(X_test\n",
    "                                                     , Y_test, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f8a9ce06",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = scaler.transform(X_val)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "212e3b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_one_layer  = keras.Sequential([keras.Input(shape=(23)),\n",
    "                           layers.Dense(4,activation='relu'),\n",
    "                           \n",
    "                        layers.Dense(4,activation='softmax')])\n",
    "model_one_layer.compile(loss=SparseCategoricalFocalLoss(from_logits=False,gamma=2),\n",
    "          optimizer=keras.optimizers.Adam(learning_rate=0.01),\n",
    "          metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "83113f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model=build_one_layer_model(2,0.01)\n",
    "model=model_one_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0b904b40",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "35/35 [==============================] - 1s 8ms/step - loss: 0.7133 - accuracy: 0.4914 - val_loss: 0.5242 - val_accuracy: 0.5217\n",
      "Epoch 2/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.4086 - accuracy: 0.5726 - val_loss: 0.4040 - val_accuracy: 0.5707\n",
      "Epoch 3/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.3270 - accuracy: 0.6384 - val_loss: 0.3517 - val_accuracy: 0.6467\n",
      "Epoch 4/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.2795 - accuracy: 0.6952 - val_loss: 0.3064 - val_accuracy: 0.7174\n",
      "Epoch 5/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.2320 - accuracy: 0.7628 - val_loss: 0.2454 - val_accuracy: 0.7826\n",
      "Epoch 6/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.1959 - accuracy: 0.7989 - val_loss: 0.2077 - val_accuracy: 0.8424\n",
      "Epoch 7/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.1631 - accuracy: 0.8467 - val_loss: 0.1761 - val_accuracy: 0.8370\n",
      "Epoch 8/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.1337 - accuracy: 0.8647 - val_loss: 0.1378 - val_accuracy: 0.8696\n",
      "Epoch 9/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.1104 - accuracy: 0.8882 - val_loss: 0.1153 - val_accuracy: 0.8750\n",
      "Epoch 10/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0954 - accuracy: 0.9080 - val_loss: 0.0957 - val_accuracy: 0.8967\n",
      "Epoch 11/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0822 - accuracy: 0.9270 - val_loss: 0.0790 - val_accuracy: 0.9130\n",
      "Epoch 12/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0708 - accuracy: 0.9333 - val_loss: 0.0729 - val_accuracy: 0.9130\n",
      "Epoch 13/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0634 - accuracy: 0.9495 - val_loss: 0.0644 - val_accuracy: 0.9130\n",
      "Epoch 14/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0572 - accuracy: 0.9441 - val_loss: 0.0601 - val_accuracy: 0.9130\n",
      "Epoch 15/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0510 - accuracy: 0.9549 - val_loss: 0.0540 - val_accuracy: 0.9293\n",
      "Epoch 16/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0462 - accuracy: 0.9576 - val_loss: 0.0471 - val_accuracy: 0.9293\n",
      "Epoch 17/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0431 - accuracy: 0.9603 - val_loss: 0.0453 - val_accuracy: 0.9293\n",
      "Epoch 18/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0403 - accuracy: 0.9639 - val_loss: 0.0422 - val_accuracy: 0.9402\n",
      "Epoch 19/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0366 - accuracy: 0.9648 - val_loss: 0.0370 - val_accuracy: 0.9511\n",
      "Epoch 20/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0338 - accuracy: 0.9748 - val_loss: 0.0337 - val_accuracy: 0.9565\n",
      "Epoch 21/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0338 - accuracy: 0.9657 - val_loss: 0.0320 - val_accuracy: 0.9565\n",
      "Epoch 22/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0315 - accuracy: 0.9702 - val_loss: 0.0276 - val_accuracy: 0.9728\n",
      "Epoch 23/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0289 - accuracy: 0.9729 - val_loss: 0.0284 - val_accuracy: 0.9674\n",
      "Epoch 24/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0268 - accuracy: 0.9748 - val_loss: 0.0285 - val_accuracy: 0.9674\n",
      "Epoch 25/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0241 - accuracy: 0.9811 - val_loss: 0.0243 - val_accuracy: 0.9783\n",
      "Epoch 26/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0240 - accuracy: 0.9766 - val_loss: 0.0217 - val_accuracy: 0.9728\n",
      "Epoch 27/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0221 - accuracy: 0.9757 - val_loss: 0.0221 - val_accuracy: 0.9728\n",
      "Epoch 28/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0229 - accuracy: 0.9802 - val_loss: 0.0219 - val_accuracy: 0.9728\n",
      "Epoch 29/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0233 - accuracy: 0.9739 - val_loss: 0.0264 - val_accuracy: 0.9674\n",
      "Epoch 30/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0207 - accuracy: 0.9766 - val_loss: 0.0166 - val_accuracy: 0.9837\n",
      "Epoch 31/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0208 - accuracy: 0.9766 - val_loss: 0.0161 - val_accuracy: 0.9728\n",
      "Epoch 32/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0200 - accuracy: 0.9766 - val_loss: 0.0176 - val_accuracy: 0.9783\n",
      "Epoch 33/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0181 - accuracy: 0.9775 - val_loss: 0.0176 - val_accuracy: 0.9837\n",
      "Epoch 34/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0186 - accuracy: 0.9784 - val_loss: 0.0140 - val_accuracy: 0.9837\n",
      "Epoch 35/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0177 - accuracy: 0.9802 - val_loss: 0.0148 - val_accuracy: 0.9837\n",
      "Epoch 36/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0161 - accuracy: 0.9847 - val_loss: 0.0143 - val_accuracy: 0.9837\n",
      "Epoch 37/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0153 - accuracy: 0.9838 - val_loss: 0.0156 - val_accuracy: 0.9783\n",
      "Epoch 38/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0157 - accuracy: 0.9829 - val_loss: 0.0150 - val_accuracy: 0.9783\n",
      "Epoch 39/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0151 - accuracy: 0.9874 - val_loss: 0.0155 - val_accuracy: 0.9783\n",
      "Epoch 40/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0144 - accuracy: 0.9856 - val_loss: 0.0145 - val_accuracy: 0.9783\n",
      "Epoch 41/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0149 - accuracy: 0.9856 - val_loss: 0.0153 - val_accuracy: 0.9783\n",
      "Epoch 42/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0141 - accuracy: 0.9865 - val_loss: 0.0141 - val_accuracy: 0.9837\n",
      "Epoch 43/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0132 - accuracy: 0.9847 - val_loss: 0.0126 - val_accuracy: 0.9837\n",
      "Epoch 44/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0155 - accuracy: 0.9820 - val_loss: 0.0191 - val_accuracy: 0.9728\n",
      "Epoch 45/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0183 - accuracy: 0.9729 - val_loss: 0.0162 - val_accuracy: 0.9837\n",
      "Epoch 46/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0156 - accuracy: 0.9829 - val_loss: 0.0129 - val_accuracy: 0.9837\n",
      "Epoch 47/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0132 - accuracy: 0.9847 - val_loss: 0.0144 - val_accuracy: 0.9837\n",
      "Epoch 48/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0132 - accuracy: 0.9838 - val_loss: 0.0106 - val_accuracy: 0.9837\n",
      "Epoch 49/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0137 - accuracy: 0.9856 - val_loss: 0.0110 - val_accuracy: 0.9837\n",
      "Epoch 50/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0126 - accuracy: 0.9847 - val_loss: 0.0109 - val_accuracy: 0.9837\n",
      "Epoch 51/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0116 - accuracy: 0.9892 - val_loss: 0.0121 - val_accuracy: 0.9783\n",
      "Epoch 52/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0116 - accuracy: 0.9838 - val_loss: 0.0108 - val_accuracy: 0.9837\n",
      "Epoch 53/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0106 - accuracy: 0.9874 - val_loss: 0.0109 - val_accuracy: 0.9783\n",
      "Epoch 54/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0109 - accuracy: 0.9865 - val_loss: 0.0123 - val_accuracy: 0.9783\n",
      "Epoch 55/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0106 - accuracy: 0.9892 - val_loss: 0.0105 - val_accuracy: 0.9837\n",
      "Epoch 56/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0109 - accuracy: 0.9865 - val_loss: 0.0123 - val_accuracy: 0.9837\n",
      "Epoch 57/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0097 - accuracy: 0.9910 - val_loss: 0.0141 - val_accuracy: 0.9783\n",
      "Epoch 58/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0100 - accuracy: 0.9901 - val_loss: 0.0115 - val_accuracy: 0.9783\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0109 - accuracy: 0.9865 - val_loss: 0.0146 - val_accuracy: 0.9783\n",
      "Epoch 60/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0160 - accuracy: 0.9829 - val_loss: 0.0225 - val_accuracy: 0.9728\n",
      "Epoch 61/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0143 - accuracy: 0.9820 - val_loss: 0.0145 - val_accuracy: 0.9728\n",
      "Epoch 62/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0112 - accuracy: 0.9865 - val_loss: 0.0122 - val_accuracy: 0.9783\n",
      "Epoch 63/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0098 - accuracy: 0.9910 - val_loss: 0.0105 - val_accuracy: 0.9837\n",
      "Epoch 64/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0094 - accuracy: 0.9910 - val_loss: 0.0106 - val_accuracy: 0.9891\n",
      "Epoch 65/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0104 - accuracy: 0.9874 - val_loss: 0.0126 - val_accuracy: 0.9837\n",
      "Epoch 66/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0111 - accuracy: 0.9865 - val_loss: 0.0116 - val_accuracy: 0.9783\n",
      "Epoch 67/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0099 - accuracy: 0.9910 - val_loss: 0.0089 - val_accuracy: 0.9891\n",
      "Epoch 68/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0088 - accuracy: 0.9892 - val_loss: 0.0093 - val_accuracy: 0.9783\n",
      "Epoch 69/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0082 - accuracy: 0.9928 - val_loss: 0.0106 - val_accuracy: 0.9837\n",
      "Epoch 70/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0077 - accuracy: 0.9919 - val_loss: 0.0092 - val_accuracy: 0.9783\n",
      "Epoch 71/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0079 - accuracy: 0.9937 - val_loss: 0.0167 - val_accuracy: 0.9783\n",
      "Epoch 72/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0087 - accuracy: 0.9901 - val_loss: 0.0093 - val_accuracy: 0.9728\n",
      "Epoch 73/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0087 - accuracy: 0.9901 - val_loss: 0.0120 - val_accuracy: 0.9783\n",
      "Epoch 74/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0087 - accuracy: 0.9874 - val_loss: 0.0129 - val_accuracy: 0.9837\n",
      "Epoch 75/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0084 - accuracy: 0.9901 - val_loss: 0.0216 - val_accuracy: 0.9783\n",
      "Epoch 76/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0090 - accuracy: 0.9928 - val_loss: 0.0151 - val_accuracy: 0.9728\n",
      "Epoch 77/300\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0074 - accuracy: 0.9937 - val_loss: 0.0074 - val_accuracy: 0.9891\n",
      "Epoch 78/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0080 - accuracy: 0.9892 - val_loss: 0.0146 - val_accuracy: 0.9837\n",
      "Epoch 79/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0080 - accuracy: 0.9883 - val_loss: 0.0185 - val_accuracy: 0.9837\n",
      "Epoch 80/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0092 - accuracy: 0.9883 - val_loss: 0.0127 - val_accuracy: 0.9837\n",
      "Epoch 81/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0074 - accuracy: 0.9910 - val_loss: 0.0133 - val_accuracy: 0.9728\n",
      "Epoch 82/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0081 - accuracy: 0.9892 - val_loss: 0.0097 - val_accuracy: 0.9946\n",
      "Epoch 83/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0081 - accuracy: 0.9928 - val_loss: 0.0113 - val_accuracy: 0.9837\n",
      "Epoch 84/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0075 - accuracy: 0.9910 - val_loss: 0.0115 - val_accuracy: 0.9837\n",
      "Epoch 85/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0067 - accuracy: 0.9937 - val_loss: 0.0113 - val_accuracy: 0.9783\n",
      "Epoch 86/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0078 - accuracy: 0.9910 - val_loss: 0.0165 - val_accuracy: 0.9837\n",
      "Epoch 87/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0065 - accuracy: 0.9973 - val_loss: 0.0150 - val_accuracy: 0.9728\n",
      "Epoch 88/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0065 - accuracy: 0.9955 - val_loss: 0.0130 - val_accuracy: 0.9783\n",
      "Epoch 89/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0057 - accuracy: 0.9955 - val_loss: 0.0170 - val_accuracy: 0.9783\n",
      "Epoch 90/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0066 - accuracy: 0.9919 - val_loss: 0.0119 - val_accuracy: 0.9783\n",
      "Epoch 91/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0064 - accuracy: 0.9919 - val_loss: 0.0159 - val_accuracy: 0.9837\n",
      "Epoch 92/300\n",
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      "Epoch 106/300\n",
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      "Epoch 107/300\n",
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      "Epoch 111/300\n",
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      "Epoch 115/300\n",
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      "Epoch 116/300\n",
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      "35/35 [==============================] - 0s 3ms/step - loss: 0.0128 - accuracy: 0.9829 - val_loss: 0.0232 - val_accuracy: 0.9674\n",
      "Epoch 118/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0114 - accuracy: 0.9856 - val_loss: 0.0110 - val_accuracy: 0.9783\n",
      "Epoch 119/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0074 - accuracy: 0.9901 - val_loss: 0.0142 - val_accuracy: 0.9837\n",
      "Epoch 120/300\n",
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      "Epoch 122/300\n",
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      "Epoch 148/300\n",
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      "Epoch 149/300\n",
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      "Epoch 150/300\n",
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      "Epoch 154/300\n",
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      "Epoch 157/300\n",
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      "Epoch 158/300\n",
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      "Epoch 159/300\n",
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      "Epoch 162/300\n",
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      "Epoch 164/300\n",
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      "Epoch 165/300\n",
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      "Epoch 172/300\n",
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      "Epoch 173/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9991 - val_loss: 0.0165 - val_accuracy: 0.9783\n",
      "Epoch 174/300\n",
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      "Epoch 186/300\n",
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      "35/35 [==============================] - 0s 3ms/step - loss: 0.0064 - accuracy: 0.9910 - val_loss: 0.0408 - val_accuracy: 0.9783\n",
      "Epoch 188/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0084 - accuracy: 0.9856 - val_loss: 0.0304 - val_accuracy: 0.9837\n",
      "Epoch 189/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0128 - accuracy: 0.9829 - val_loss: 0.0463 - val_accuracy: 0.9674\n",
      "Epoch 190/300\n",
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      "Epoch 191/300\n",
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      "Epoch 193/300\n",
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      "Epoch 195/300\n",
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      "Epoch 196/300\n",
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      "Epoch 198/300\n",
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      "Epoch 202/300\n",
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      "Epoch 203/300\n",
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      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0225 - val_accuracy: 0.9783\n",
      "Epoch 217/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0171 - val_accuracy: 0.9837\n",
      "Epoch 218/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 0.9982 - val_loss: 0.0195 - val_accuracy: 0.9674\n",
      "Epoch 219/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0197 - val_accuracy: 0.9728\n",
      "Epoch 220/300\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0184 - val_accuracy: 0.9783\n",
      "Epoch 221/300\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0197 - val_accuracy: 0.9728\n",
      "Epoch 222/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0192 - val_accuracy: 0.9837\n",
      "Epoch 223/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0208 - val_accuracy: 0.9837\n",
      "Epoch 224/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0204 - val_accuracy: 0.9783\n",
      "Epoch 225/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0176 - val_accuracy: 0.9837\n",
      "Epoch 226/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 0.9973 - val_loss: 0.0198 - val_accuracy: 0.9783\n",
      "Epoch 227/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0304 - val_accuracy: 0.9728\n",
      "Epoch 228/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0017 - accuracy: 0.9982 - val_loss: 0.0152 - val_accuracy: 0.9837\n",
      "Epoch 229/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0226 - val_accuracy: 0.9728\n",
      "Epoch 230/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 0.9964 - val_loss: 0.0268 - val_accuracy: 0.9728\n",
      "Epoch 231/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9973 - val_loss: 0.0168 - val_accuracy: 0.9837\n",
      "Epoch 232/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9973 - val_loss: 0.0189 - val_accuracy: 0.9783\n",
      "Epoch 233/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9991 - val_loss: 0.0318 - val_accuracy: 0.9783\n",
      "Epoch 234/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0225 - val_accuracy: 0.9728\n",
      "Epoch 235/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0341 - val_accuracy: 0.9728\n",
      "Epoch 236/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0192 - val_accuracy: 0.9783\n",
      "Epoch 237/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0286 - val_accuracy: 0.9728\n",
      "Epoch 238/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0329 - val_accuracy: 0.9674\n",
      "Epoch 239/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0022 - accuracy: 0.9964 - val_loss: 0.0218 - val_accuracy: 0.9783\n",
      "Epoch 240/300\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0187 - val_accuracy: 0.9783\n",
      "Epoch 241/300\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0019 - accuracy: 0.9982 - val_loss: 0.0230 - val_accuracy: 0.9891\n",
      "Epoch 242/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0021 - accuracy: 0.9973 - val_loss: 0.0174 - val_accuracy: 0.9837\n",
      "Epoch 243/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0017 - accuracy: 0.9991 - val_loss: 0.0325 - val_accuracy: 0.9728\n",
      "Epoch 244/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0016 - accuracy: 0.9991 - val_loss: 0.0358 - val_accuracy: 0.9728\n",
      "Epoch 245/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 0.9991 - val_loss: 0.0294 - val_accuracy: 0.9728\n",
      "Epoch 246/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0032 - accuracy: 0.9955 - val_loss: 0.0217 - val_accuracy: 0.9837\n",
      "Epoch 247/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0034 - accuracy: 0.9946 - val_loss: 0.0137 - val_accuracy: 0.9891\n",
      "Epoch 248/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0067 - accuracy: 0.9937 - val_loss: 0.0133 - val_accuracy: 0.9946\n",
      "Epoch 249/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0152 - accuracy: 0.9847 - val_loss: 0.0275 - val_accuracy: 0.9783\n",
      "Epoch 250/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0181 - accuracy: 0.9811 - val_loss: 0.0495 - val_accuracy: 0.9620\n",
      "Epoch 251/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0172 - accuracy: 0.9793 - val_loss: 0.0564 - val_accuracy: 0.9783\n",
      "Epoch 252/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0103 - accuracy: 0.9829 - val_loss: 0.0223 - val_accuracy: 0.9620\n",
      "Epoch 253/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0028 - accuracy: 0.9964 - val_loss: 0.0243 - val_accuracy: 0.9728\n",
      "Epoch 254/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 0.9982 - val_loss: 0.0201 - val_accuracy: 0.9783\n",
      "Epoch 255/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0136 - val_accuracy: 0.9837\n",
      "Epoch 256/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0153 - val_accuracy: 0.9837\n",
      "Epoch 257/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 0.9991 - val_loss: 0.0150 - val_accuracy: 0.9837\n",
      "Epoch 258/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0012 - accuracy: 0.9991 - val_loss: 0.0194 - val_accuracy: 0.9728\n",
      "Epoch 259/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 0.9982 - val_loss: 0.0196 - val_accuracy: 0.9783\n",
      "Epoch 260/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0165 - val_accuracy: 0.9783\n",
      "Epoch 261/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0134 - val_accuracy: 0.9837\n",
      "Epoch 262/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0145 - val_accuracy: 0.9837\n",
      "Epoch 263/300\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 9.9296e-04 - accuracy: 1.0000 - val_loss: 0.0160 - val_accuracy: 0.9837\n",
      "Epoch 264/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 9.1389e-04 - accuracy: 1.0000 - val_loss: 0.0166 - val_accuracy: 0.9783\n",
      "Epoch 265/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 9.0551e-04 - accuracy: 1.0000 - val_loss: 0.0181 - val_accuracy: 0.9783\n",
      "Epoch 266/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0175 - val_accuracy: 0.9783\n",
      "Epoch 267/300\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0175 - val_accuracy: 0.9783\n",
      "Epoch 268/300\n",
      "35/35 [==============================] - 0s 4ms/step - loss: 9.3893e-04 - accuracy: 1.0000 - val_loss: 0.0138 - val_accuracy: 0.9837\n",
      "Epoch 269/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 9.6000e-04 - accuracy: 1.0000 - val_loss: 0.0222 - val_accuracy: 0.9783\n",
      "Epoch 270/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0138 - val_accuracy: 0.9837\n",
      "Epoch 271/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 0.9982 - val_loss: 0.0193 - val_accuracy: 0.9783\n",
      "Epoch 272/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0204 - val_accuracy: 0.9728\n",
      "Epoch 273/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 8.8852e-04 - accuracy: 1.0000 - val_loss: 0.0151 - val_accuracy: 0.9837\n",
      "Epoch 274/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 8.5129e-04 - accuracy: 0.9991 - val_loss: 0.0197 - val_accuracy: 0.9783\n",
      "Epoch 275/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 9.4610e-04 - accuracy: 1.0000 - val_loss: 0.0171 - val_accuracy: 0.9783\n",
      "Epoch 276/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0194 - val_accuracy: 0.9783\n",
      "Epoch 277/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 9.7002e-04 - accuracy: 1.0000 - val_loss: 0.0181 - val_accuracy: 0.9783\n",
      "Epoch 278/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 8.4374e-04 - accuracy: 1.0000 - val_loss: 0.0170 - val_accuracy: 0.9837\n",
      "Epoch 279/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 8.0308e-04 - accuracy: 1.0000 - val_loss: 0.0212 - val_accuracy: 0.9783\n",
      "Epoch 280/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 8.6285e-04 - accuracy: 1.0000 - val_loss: 0.0209 - val_accuracy: 0.9783\n",
      "Epoch 281/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0188 - val_accuracy: 0.9783\n",
      "Epoch 282/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 9.4780e-04 - accuracy: 0.9991 - val_loss: 0.0147 - val_accuracy: 0.9783\n",
      "Epoch 283/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0014 - accuracy: 0.9982 - val_loss: 0.0175 - val_accuracy: 0.9837\n",
      "Epoch 284/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0237 - val_accuracy: 0.9728\n",
      "Epoch 285/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 9.4530e-04 - accuracy: 1.0000 - val_loss: 0.0230 - val_accuracy: 0.9783\n",
      "Epoch 286/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 0.9973 - val_loss: 0.0155 - val_accuracy: 0.9837\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 287/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 0.9982 - val_loss: 0.0148 - val_accuracy: 0.9783\n",
      "Epoch 288/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0052 - accuracy: 0.9919 - val_loss: 0.0235 - val_accuracy: 0.9783\n",
      "Epoch 289/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0156 - accuracy: 0.9802 - val_loss: 0.0921 - val_accuracy: 0.9674\n",
      "Epoch 290/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9829 - val_loss: 0.0250 - val_accuracy: 0.9946\n",
      "Epoch 291/300\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 0.0193 - accuracy: 0.9829 - val_loss: 0.0347 - val_accuracy: 0.9783\n",
      "Epoch 292/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0167 - accuracy: 0.9829 - val_loss: 0.0278 - val_accuracy: 0.9783\n",
      "Epoch 293/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0186 - accuracy: 0.9892 - val_loss: 0.0570 - val_accuracy: 0.9674\n",
      "Epoch 294/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0195 - accuracy: 0.9811 - val_loss: 0.0440 - val_accuracy: 0.9728\n",
      "Epoch 295/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0076 - accuracy: 0.9892 - val_loss: 0.0245 - val_accuracy: 0.9837\n",
      "Epoch 296/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0059 - accuracy: 0.9928 - val_loss: 0.0368 - val_accuracy: 0.9674\n",
      "Epoch 297/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 0.9964 - val_loss: 0.0303 - val_accuracy: 0.9783\n",
      "Epoch 298/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0018 - accuracy: 0.9982 - val_loss: 0.0297 - val_accuracy: 0.9837\n",
      "Epoch 299/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0016 - accuracy: 0.9991 - val_loss: 0.0311 - val_accuracy: 0.9837\n",
      "Epoch 300/300\n",
      "35/35 [==============================] - 0s 3ms/step - loss: 0.0018 - accuracy: 0.9982 - val_loss: 0.0314 - val_accuracy: 0.9783\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,Y_train,validation_data=[X_val,Y_val] \n",
    "             ,batch_size=32, epochs=300 , verbose =1, \n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a6b650d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "accurate_train_accuracy,classify_report= test_model(model,X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "df8360d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accurate_train_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c8fb6bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       143\n",
      "           1       1.00      1.00      1.00       151\n",
      "           2       1.00      1.00      1.00       305\n",
      "           3       1.00      1.00      1.00       510\n",
      "\n",
      "    accuracy                           1.00      1109\n",
      "   macro avg       1.00      1.00      1.00      1109\n",
      "weighted avg       1.00      1.00      1.00      1109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classify_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1957871f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "accurate_val_accuracy,classify_report= test_model(model,X_val,Y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5998fbb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9782608695652174"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accurate_val_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5d4b1ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.90      0.92        20\n",
      "           1       0.92      0.92      0.92        24\n",
      "           2       0.98      1.00      0.99        50\n",
      "           3       1.00      1.00      1.00        90\n",
      "\n",
      "    accuracy                           0.98       184\n",
      "   macro avg       0.96      0.95      0.96       184\n",
      "weighted avg       0.98      0.98      0.98       184\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classify_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f782e880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "accurate_test_accuracy,classify_report= test_model(model,X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e7857271",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9619565217391305"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accurate_test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0d5fda85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      1.00      0.93        27\n",
      "           1       1.00      0.77      0.87        26\n",
      "           2       0.96      0.98      0.97        51\n",
      "           3       0.99      1.00      0.99        80\n",
      "\n",
      "    accuracy                           0.96       184\n",
      "   macro avg       0.96      0.94      0.94       184\n",
      "weighted avg       0.97      0.96      0.96       184\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classify_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcfdab7f",
   "metadata": {},
   "source": [
    "## important-Note\n",
    "gamma=1 on-test(0.967479674796748) on-val(0.9836734693877551)\n",
    "gamma=2 on-test(0.967479674796748) on-val(0.9836734693877551) \n",
    "gamma=3 on-test(0.975609756097561) on-val(0.9836734693877551)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "256d9447",
   "metadata": {},
   "outputs": [],
   "source": [
    "## I can't consider observations in the  age to be false outlier \n",
    "## I will consider it a true outlier, I will just make a log transformation for it \n",
    "## bc it's so skewed , it have skewness value 1.481576 \n",
    "\n",
    "# you can't do log transformation in the Time_E_DEV column \n",
    "\n",
    "\n",
    "# meal_Count log transforamtion has increase the skewness \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1b49dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.skew().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fda283e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp= df.copy()\n",
    "\n",
    "sns.boxplot(x=np.log(df_temp[\"Age\"]))\n",
    "plt.show()\n",
    "\n",
    "df_temp[df_temp[\"Age\"] < df_temp[\"Age\"].quantile(0.25)]\n",
    "\n",
    "\n",
    "plt.hist(df_temp[\"Age\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43070041",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.Series(np.log(df_temp[\"Time_E_Dev\"])).skew()\n",
    "\n",
    "df_temp=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df3e6b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(np.log10(df_temp[\"Meal_Count\"]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf6de29",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series(np.log10(df_temp[\"Meal_Count\"])).skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beb96fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series(X_train[:,14]).skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdb1b3d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[:,14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f10fd7cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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