Bank Note Classification Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0442594d-bb48-4e87-917e-42600958a2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import warnings\n",
    "\n",
    "# Suppress all warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5b28e5a3-17ee-43a5-9fa6-e3397464801c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data were extracted from images that were taken from genuine and forged banknote-like specimens. \n",
    "#For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. \n",
    "#Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. \n",
    "#Wavelet Transform tool were used to extract features from images.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d3e929ef-43e0-4322-a899-5238a5cbabfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this dataset 5 factors were observed: \n",
    "# 1) Variance: in pixel intensity compared to standard bank note\n",
    "# 2) Skew: Assymetry in pixel distribution\n",
    "# 3) Curtosis: Tailedness in distribution\n",
    "# 4) Entropy: Randomness/complexity of pixel intensity\n",
    "# 5) Class: Whether it was fake(0) or authentic(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b3a2ffa3-2470-433b-b9fa-ea156e499a21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image.Var</th>\n",
       "      <th>Image.Skew</th>\n",
       "      <th>Image.Curt</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.62160</td>\n",
       "      <td>8.6661</td>\n",
       "      <td>-2.8073</td>\n",
       "      <td>-0.44699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.54590</td>\n",
       "      <td>8.1674</td>\n",
       "      <td>-2.4586</td>\n",
       "      <td>-1.46210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.86600</td>\n",
       "      <td>-2.6383</td>\n",
       "      <td>1.9242</td>\n",
       "      <td>0.10645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45660</td>\n",
       "      <td>9.5228</td>\n",
       "      <td>-4.0112</td>\n",
       "      <td>-3.59440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32924</td>\n",
       "      <td>-4.4552</td>\n",
       "      <td>4.5718</td>\n",
       "      <td>-0.98880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Image.Var  Image.Skew  Image.Curt  Entropy  Class\n",
       "0    3.62160      8.6661     -2.8073 -0.44699      0\n",
       "1    4.54590      8.1674     -2.4586 -1.46210      0\n",
       "2    3.86600     -2.6383      1.9242  0.10645      0\n",
       "3    3.45660      9.5228     -4.0112 -3.59440      0\n",
       "4    0.32924     -4.4552      4.5718 -0.98880      0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('bank_note_data.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4279cdd9-1bd5-4222-82cb-e964d17d6d9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Class', data=data)\n",
    "plt.xticks(ticks=[0, 1], labels=['Fake', 'Authentic'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ad02dfad-b14a-4686-badc-156030a185df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scales values for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2d5c4aad-1a32-48c8-a22f-fdb56d8adfbb",
   "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>Image.Var</th>\n",
       "      <th>Image.Skew</th>\n",
       "      <th>Image.Curt</th>\n",
       "      <th>Entropy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.121806</td>\n",
       "      <td>1.149455</td>\n",
       "      <td>-0.975970</td>\n",
       "      <td>0.354561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.447066</td>\n",
       "      <td>1.064453</td>\n",
       "      <td>-0.895036</td>\n",
       "      <td>-0.128767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.207810</td>\n",
       "      <td>-0.777352</td>\n",
       "      <td>0.122218</td>\n",
       "      <td>0.618073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.063742</td>\n",
       "      <td>1.295478</td>\n",
       "      <td>-1.255397</td>\n",
       "      <td>-1.144029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.036772</td>\n",
       "      <td>-1.087038</td>\n",
       "      <td>0.736730</td>\n",
       "      <td>0.096587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Image.Var  Image.Skew  Image.Curt   Entropy\n",
       "0   1.121806    1.149455   -0.975970  0.354561\n",
       "1   1.447066    1.064453   -0.895036 -0.128767\n",
       "2   1.207810   -0.777352    0.122218  0.618073\n",
       "3   1.063742    1.295478   -1.255397 -1.144029\n",
       "4  -0.036772   -1.087038    0.736730  0.096587"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(data.drop('Class',axis=1))\n",
    "scaled_features = scaler.fit_transform(data.drop('Class',axis=1))\n",
    "df_feat = pd.DataFrame(scaled_features,columns=data.columns[:-1])\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0c584b3e-0fc7-48da-8ef3-bfc82e72e04d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separate data into training and testing samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5a5b92c1-5225-42ff-a85c-a0a57abf989c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_feat\n",
    "y = data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c5b10cdc-efdd-471d-86ed-9e9b4b89e809",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a neural network model with the Keras API using a ReLU activation function for the hidden layers and a softmax for the output layer\n",
    "\n",
    "#Use a neural network because these visual patterns are non-linear and a neural network can help uncover these relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ddb139e5-9f35-4023-b391-8b4c7ca30b9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6692 - loss: 0.6548 - val_accuracy: 0.8495 - val_loss: 0.5765\n",
      "Epoch 2/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8804 - loss: 0.5409 - val_accuracy: 0.8738 - val_loss: 0.4401\n",
      "Epoch 3/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9215 - loss: 0.3928 - val_accuracy: 0.9005 - val_loss: 0.3134\n",
      "Epoch 4/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9607 - loss: 0.2539 - val_accuracy: 0.9442 - val_loss: 0.2170\n",
      "Epoch 5/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9611 - loss: 0.1881 - val_accuracy: 0.9830 - val_loss: 0.1468\n",
      "Epoch 6/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9849 - loss: 0.1158 - val_accuracy: 0.9903 - val_loss: 0.0991\n",
      "Epoch 7/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9942 - loss: 0.0754 - val_accuracy: 0.9951 - val_loss: 0.0690\n",
      "Epoch 8/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9953 - loss: 0.0562 - val_accuracy: 0.9951 - val_loss: 0.0512\n",
      "Epoch 9/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9951 - loss: 0.0441 - val_accuracy: 0.9951 - val_loss: 0.0406\n",
      "Epoch 10/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9935 - loss: 0.0305 - val_accuracy: 0.9951 - val_loss: 0.0339\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x228c6743bd0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# Define your model\n",
    "model = Sequential([\n",
    "    Dense(10, activation='relu', input_shape=(len(feat_cols),)),  \n",
    "    Dense(20, activation='relu'),\n",
    "    Dense(10, activation='relu'),\n",
    "    Dense(2, activation='softmax')  # For binary classification, change to 2 units for 2 classes\n",
    "])\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',  # Use categorical_crossentropy if y is one-hot encoded\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d97c8f1e-f8cd-4306-9051-752f7ede7d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model clearly improves in accuracy as it passes through the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0993af2a-ecfb-48f0-b8c9-f2022b2a5b06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1000us/step - accuracy: 0.9989 - loss: 0.0319\n",
      "Test Loss: 0.03386180102825165\n",
      "Test Accuracy: 0.9951456189155579\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test data\n",
    "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
    "\n",
    "print(f'Test Loss: {test_loss}')\n",
    "print(f'Test Accuracy: {test_accuracy}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "61fdab2f-aae7-4a42-9f0f-f46b66934f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Confusion Matrix:\n",
      "[[226   2]\n",
      " [  0 184]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00       228\n",
      "           1       0.99      1.00      0.99       184\n",
      "\n",
      "    accuracy                           1.00       412\n",
      "   macro avg       0.99      1.00      1.00       412\n",
      "weighted avg       1.00      1.00      1.00       412\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "# Generate the predicted class labels\n",
    "predicted_classes = model.predict(X_test).argmax(axis=1)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, predicted_classes)\n",
    "print(f'Confusion Matrix:\\n{conf_matrix}')\n",
    "\n",
    "# Classification Report\n",
    "class_report = classification_report(y_test, predicted_classes)\n",
    "print(f'Classification Report:\\n{class_report}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "79380cb1-fa34-4ec8-a16b-c59d029ac375",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9960 - loss: 0.0288 - val_accuracy: 0.9951 - val_loss: 0.0279\n",
      "Epoch 2/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9936 - loss: 0.0233 - val_accuracy: 0.9951 - val_loss: 0.0238\n",
      "Epoch 3/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9937 - loss: 0.0214 - val_accuracy: 0.9951 - val_loss: 0.0206\n",
      "Epoch 4/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9965 - loss: 0.0175 - val_accuracy: 0.9951 - val_loss: 0.0188\n",
      "Epoch 5/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9943 - loss: 0.0171 - val_accuracy: 0.9976 - val_loss: 0.0170\n",
      "Epoch 6/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0124 - val_accuracy: 0.9976 - val_loss: 0.0145\n",
      "Epoch 7/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0111 - val_accuracy: 0.9976 - val_loss: 0.0138\n",
      "Epoch 8/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0100 - val_accuracy: 0.9976 - val_loss: 0.0121\n",
      "Epoch 9/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0089 - val_accuracy: 0.9976 - val_loss: 0.0116\n",
      "Epoch 10/10\n",
      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0089 - val_accuracy: 0.9976 - val_loss: 0.0103\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))\n",
    "\n",
    "# Plotting accuracy over epochs\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plotting loss over epochs\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a953bc84-1074-439d-b32b-4ea9e376457d",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Graph suggests the model is learning from the training data as accuracy in trianing and validation data is increasing while the loss is decreasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d95cdac9-d5a8-42b2-864d-052684568ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "132dee76-8d6a-42ae-824f-03ab85a7d03d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Use of numerous decision trees will be valuable for classification task in which accuracy is crucial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "07ee71c0-81ca-4d10-9a06-3096865785fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=200)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=200)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200)\n",
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "928f00d6-7f05-4ac8-bf0c-d0d21fb6fe36",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_preds = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "be21130f-b9d4-49cf-a0ce-95e85aea6d7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99       228\n",
      "           1       0.97      0.99      0.98       184\n",
      "\n",
      "    accuracy                           0.99       412\n",
      "   macro avg       0.98      0.99      0.99       412\n",
      "weighted avg       0.99      0.99      0.99       412\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,rfc_preds))"
   ]
  }
 ],
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