diff --git a/.ipynb_checkpoints/ML_Task_1-checkpoint.ipynb b/.ipynb_checkpoints/ML_Task_1-checkpoint.ipynb
new file mode 100644
index 00000000..0866dc1f
--- /dev/null
+++ b/.ipynb_checkpoints/ML_Task_1-checkpoint.ipynb
@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "_Mo2vxkP-0JK",
+    "outputId": "85d4c99f-8b12-41bb-faf7-6a0a3f1fb237"
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'google'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-4007aab83f86>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# import google\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/content/drive'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google'"
+     ]
+    }
+   ],
+   "source": [
+    "# import google\n",
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 207
+    },
+    "colab_type": "code",
+    "id": "nmBbzycb_7O-",
+    "outputId": "7aef17ea-cf7f-46ea-9af8-0c0efddb24fb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X.shape is (12288, 307)\n",
+      "Proceed 0 of 307\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, cv2, itertools \n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "PLATE_NO_DIR = './plate_number/'\n",
+    "NEG_IMG_DIR = './negative_images/'\n",
+    "\n",
+    "ROWS = 64\n",
+    "COLS = 64\n",
+    "CHANNELS = 3\n",
+    "\n",
+    "plate_no_images = [PLATE_NO_DIR + i for i in os.listdir(PLATE_NO_DIR)]\n",
+    "neg_img = [NEG_IMG_DIR + i for i in os.listdir(NEG_IMG_DIR)]\n",
+    "\n",
+    "\n",
+    "def read_image(file_path):\n",
+    "  img = cv2.imread(file_path, cv2.IMREAD_COLOR)\n",
+    "  return cv2.resize(img, (ROWS, COLS), interpolation = cv2.INTER_CUBIC)\n",
+    "\n",
+    "def prep_data(images):\n",
+    "  m = len(images)\n",
+    "  n_x = ROWS*COLS*CHANNELS\n",
+    "  \n",
+    "  X = np.ndarray((n_x,m), dtype = np.uint8)\n",
+    "  y = np.zeros((1,m))\n",
+    "  print(\"X.shape is {}\".format(X.shape))\n",
+    "  \n",
+    "  for i,image_file in enumerate(images):\n",
+    "    image = read_image(image_file)\n",
+    "    X[:,i] = np.squeeze(image.reshape((n_x,1)))\n",
+    "    if '-' in image_file.lower() :\n",
+    "      y[0,i] = 1\n",
+    "    elif 'image' in image_file.lower() :\n",
+    "      y[0,i] = 0\n",
+    "    else : # for test data\n",
+    "      y[0,i] = image_file.split('/')[-1].split('.')[0]\n",
+    "      \n",
+    "    if i%5000 == 0 :\n",
+    "      print(\"Proceed {} of {}\".format(i, m))\n",
+    "    \n",
+    "  return X,y\n",
+    "\n",
+    "\n",
+    "X_img, y_img = prep_data(plate_no_images + neg_img)\n",
+    "\n",
+    "classes = {0: 'Normal Image',\n",
+    "           1: 'License Plate'}\n",
+    "\n",
+    "def show_images(X, y, idx) :\n",
+    "  image = X[idx]\n",
+    "  image = image.reshape((ROWS, COLS, CHANNELS))\n",
+    "  plt.figure(figsize=(4,2))\n",
+    "  plt.imshow(image)\n",
+    "  plt.title(\"This is a {}\".format(classes[y[idx,0]]))\n",
+    "  plt.show()\n",
+    "  \n",
+    "show_images(X_img.T, y_img.T, 0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "id": "cfT5L_mMAOSm",
+    "outputId": "82fde8e9-a30a-4992-9cd2-4659fb236b61"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "\n",
+    "clf = LogisticRegressionCV()\n",
+    "\n",
+    "X_img_lr, y_img_lr = X_img.T, y_img.T.ravel()\n",
+    "\n",
+    "clf.fit(X_img_lr,y_img_lr)\n",
+    "\n",
+    "print(\"Model accuracy: {:.2f}%\".format(clf.score(X_img_lr, y_img_lr)*100))\n",
+    "\n",
+    "def show_image_prediction(X, idx, model) :\n",
+    "  image = X[idx].reshape(1,-1)\n",
+    "  image_class = classes[model.predict(image).item()]\n",
+    "  image = image.reshape((ROWS, COLS, CHANNELS))\n",
+    "  plt.figure(figsize = (4,2))\n",
+    "  plt.imshow(image)\n",
+    "  plt.title(\"Test {} : I think this is a {}\".format(idx, image_class))\n",
+    "  plt.show()\n",
+    "  \n",
+    "X_img_lr, y_img_lr = X_img.T, y_img.T\n",
+    "\n",
+    "for i in np.random.randint(0, len(X_img_lr), 10) :\n",
+    "  show_image_prediction(X_img_lr, i, clf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "ML_Task_1.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/ML_Task_1.ipynb b/ML_Task_1.ipynb
index 37b63320..a476a842 100644
--- a/ML_Task_1.ipynb
+++ b/ML_Task_1.ipynb
@@ -1,339 +1,670 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
     "colab": {
-      "name": "ML_Task_1.ipynb",
-      "version": "0.3.2",
-      "provenance": []
+     "base_uri": "https://localhost:8080/",
+     "height": 122
     },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+    "colab_type": "code",
+    "id": "_Mo2vxkP-0JK",
+    "outputId": "85d4c99f-8b12-41bb-faf7-6a0a3f1fb237"
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'google'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-4007aab83f86>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# import google\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/content/drive'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google'"
+     ]
+    }
+   ],
+   "source": [
+    "# import google\n",
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 207
+    },
+    "colab_type": "code",
+    "id": "nmBbzycb_7O-",
+    "outputId": "7aef17ea-cf7f-46ea-9af8-0c0efddb24fb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X.shape is (12288, 307)\n",
+      "./plate_number/02E-225AK.jpg\n",
+      "Proceed 0 of 307\n",
+      "./plate_number/02E-48AK.jpg\n",
+      "./plate_number/05A-05AK.jpg\n",
+      "./plate_number/0K959-TTU.jpg\n",
+      "./plate_number/305-4929.jpg\n",
+      "./plate_number/50K-26FG.jpg\n",
+      "./plate_number/AA344-NSR.jpg\n",
+      "./plate_number/AA689-GDM.JPG\n",
+      "./plate_number/AA689-GGE.jpg\n",
+      "./plate_number/AAA-252EA.jpg\n",
+      "./plate_number/AAA-378AA.jpeg\n",
+      "./plate_number/AAA-474EC.jpg\n",
+      "./plate_number/AAA-878EQ.jpg\n",
+      "./plate_number/ABC-323AG.jpg\n",
+      "./plate_number/ABJ-585MB.jpg\n",
+      "./plate_number/ABJ-624PY.jpg\n",
+      "./plate_number/ABJ-669NS.jpg\n",
+      "./plate_number/ABK-308XA.jpg\n",
+      "./plate_number/ABK-505US.jpg\n",
+      "./plate_number/ABK-544AA.jpg\n",
+      "./plate_number/ABK-754AA.jpg\n",
+      "./plate_number/ABT-322AA.jpg\n",
+      "./plate_number/ACH-538AA.jpeg\n",
+      "./plate_number/AE192-KPU.jpg\n",
+      "./plate_number/AE249-NND.jpg\n",
+      "./plate_number/AED-568AA.jpg\n",
+      "./plate_number/AEE-43XA.jpg\n",
+      "./plate_number/AFG-397AA.jpg\n",
+      "./plate_number/AFG-57XA.jpg\n",
+      "./plate_number/AFH-407AA.jpg\n",
+      "./plate_number/AFH-798QB.jpg\n",
+      "./plate_number/AFR-584AA.jpg\n",
+      "./plate_number/AG887-MKR.jpg\n",
+      "./plate_number/AGL-105FX.jpg\n",
+      "./plate_number/AGL-286AG.jpg\n",
+      "./plate_number/AGL-581E.jpg\n",
+      "./plate_number/AKD-154BG.jpg\n",
+      "./plate_number/AKD-269CC.jpg\n",
+      "./plate_number/AKD-396AQ.jpg\n",
+      "./plate_number/AL454-KTN.JPG\n",
+      "./plate_number/AL629-UYY.jpg\n",
+      "./plate_number/AP214-YEN.jpg\n",
+      "./plate_number/APP-242XE.jpg\n",
+      "./plate_number/APP-456CV.jpg\n",
+      "./plate_number/APP-631EC.jpg\n",
+      "./plate_number/AW456-LND.jpeg\n",
+      "./plate_number/AY174-LSR.png\n",
+      "./plate_number/BDG-07ER.jpg\n",
+      "./plate_number/BDG-742AG.jpg\n",
+      "./plate_number/BDG-876DA.jpg\n",
+      "./plate_number/BE880-RSH.jpg\n",
+      "./plate_number/BMT-218XA.jpg\n",
+      "./plate_number/BWR-129FP.jpg\n",
+      "./plate_number/BWR-969GA.jpg\n",
+      "./plate_number/DA791-ABJ.jpg\n",
+      "./plate_number/DKA-458DM.jpg\n",
+      "./plate_number/DLA-QQQ.jpg\n",
+      "./plate_number/DUU-038QW.jpg\n",
+      "./plate_number/DUU-055QA.jpg\n",
+      "./plate_number/DUU-590AA.jpg\n",
+      "./plate_number/DW905-GGE.jpeg\n",
+      "./plate_number/EJG-604AA.jpeg\n",
+      "./plate_number/EKY-231CE.jpg\n",
+      "./plate_number/EKY-402AL.jpeg\n",
+      "./plate_number/EKY-567FD.jpg\n",
+      "./plate_number/EKY-679CG.jpg\n",
+      "./plate_number/EKY-688FC.jpg\n",
+      "./plate_number/ENU-583DL.jpg\n",
+      "./plate_number/ENW-139XA.jpg\n",
+      "./plate_number/EPE-207CB.jpg\n",
+      "./plate_number/EPE-681AY.jpg\n",
+      "./plate_number/ETN-91XA.jpg\n",
+      "./plate_number/FKJ-818BB.jpg\n",
+      "./plate_number/FKJ-829QH.jpg\n",
+      "./plate_number/FKJ-842CH.jpg\n",
+      "./plate_number/FST-130EZ.jpg\n",
+      "./plate_number/FST-485EY.jpg\n",
+      "./plate_number/FST-742DP.jpg\n",
+      "./plate_number/FV879-ABJ.jpg\n",
+      "./plate_number/FW799-ABC.jpg\n",
+      "./plate_number/FX195-EKY.jpg\n",
+      "./plate_number/GAK-872XH.jpg\n",
+      "./plate_number/GAK-874XH.jpg\n",
+      "./plate_number/GGE-40FB.jpg\n",
+      "./plate_number/GGE-418BX.jpg\n",
+      "./plate_number/GNN-898AH.jpg\n",
+      "./plate_number/JJJ-492BT.jpeg\n",
+      "./plate_number/KJA-188CZ.jpg\n",
+      "./plate_number/KJA-193AA.png\n",
+      "./plate_number/KJA-436AQ.jpg\n",
+      "./plate_number/KJA-634EW.jpg\n",
+      "./plate_number/KJA-744EZ.jpg\n",
+      "./plate_number/KPD-345AA.jpg\n",
+      "./plate_number/KRD-175AC.jpg\n",
+      "./plate_number/KRD-397DR.jpg\n",
+      "./plate_number/KRT-144XA.jpg\n",
+      "./plate_number/KSF-227AT.jpg\n",
+      "./plate_number/KSF-622AE.jpg\n",
+      "./plate_number/KSF-952DN.jpg\n",
+      "./plate_number/KTE-007VZ.jpg\n",
+      "./plate_number/KTE-678XA.jpg\n",
+      "./plate_number/KTU-509FN.jpg\n",
+      "./plate_number/KTU-639EW.jpg\n",
+      "./plate_number/KUJ-349MX.jpg\n",
+      "./plate_number/KUJ-362KZ.jpg\n",
+      "./plate_number/KUK-316.jpg\n",
+      "./plate_number/KWL-01KA.jpg\n",
+      "./plate_number/LND-330AC.jpg\n",
+      "./plate_number/LND-59AH.jpg\n",
+      "./plate_number/LND-826FA.jpg\n",
+      "./plate_number/LSD-104ES.jpg\n",
+      "./plate_number/LSD-347AS.jpg\n",
+      "./plate_number/LSD-658FB.jpg\n",
+      "./plate_number/LSD-687CW.jpg\n",
+      "./plate_number/LSD-754BC.jpg\n",
+      "./plate_number/LSR-106BC.jpg\n",
+      "./plate_number/LSR-17BZ.jpg\n",
+      "./plate_number/LSR-595EF.jpg\n",
+      "./plate_number/LSR-689CB.jpg\n",
+      "./plate_number/LSR-841AL.jpg\n",
+      "./plate_number/LSR-85EE.jpg\n",
+      "./plate_number/MDG-AMB 1.jpg\n",
+      "./plate_number/MH531-AAA.jpg\n",
+      "./plate_number/MK311-AAA.jpg\n",
+      "./plate_number/MNA-914JL.JPG\n",
+      "./plate_number/MUS-390DU.jpg\n",
+      "./plate_number/MUS-410DW.jpg\n",
+      "./plate_number/MUS-601BJ.jpg\n",
+      "./plate_number/MUS-693BH.jpg\n",
+      "./plate_number/MUS-800BG.jpg\n",
+      "./plate_number/MUS-854EY.jpg\n",
+      "./plate_number/MUS-951FW.jpg\n",
+      "./plate_number/NGD-832AA.jpg\n",
+      "./plate_number/PN803-KJA.png\n",
+      "./plate_number/PNG-795YY.jpg\n",
+      "./plate_number/RB305-KJA.jpg\n",
+      "./plate_number/RLU-916XA.jpg\n",
+      "./plate_number/RSH-890EJ.jpg\n",
+      "./plate_number/SB910-AAA.jpg\n",
+      "./plate_number/SE249-KJA.jpg\n",
+      "./plate_number/SKK-800AE.jpg\n",
+      "./plate_number/SM700-FST.jpeg\n",
+      "./plate_number/SMK-152FP.jpg\n",
+      "./plate_number/SMK-549CJ.jpg\n",
+      "./plate_number/SMK-764FC.jpg\n",
+      "./plate_number/SRA-831XA.jpg\n",
+      "./plate_number/TW336-KJA.jpg\n",
+      "./plate_number/UDU-150EM.jpg\n",
+      "./plate_number/UKP-581ZU.jpg\n",
+      "./plate_number/UWN-518XF.jpg\n",
+      "./plate_number/UYY-149YE.jpg\n",
+      "./plate_number/UYY-495XA.jpg\n",
+      "./plate_number/UYY-626YE.jpg\n",
+      "./plate_number/XB398-RSH.jpg\n",
+      "./plate_number/XC375-JJN.jpg\n",
+      "./plate_number/XF579-YAB.jpeg\n",
+      "./plate_number/YAB-998NH.jpg\n",
+      "./negative_images/1.jpg\n",
+      "./negative_images/10.png\n",
+      "./negative_images/11.jpg\n",
+      "./negative_images/12.png\n",
+      "./negative_images/13.jpg\n",
+      "./negative_images/14.jpg\n",
+      "./negative_images/15.jpg\n",
+      "./negative_images/16.jpg\n",
+      "./negative_images/17.jpg\n",
+      "./negative_images/18.jpg\n",
+      "./negative_images/19.jpg\n",
+      "./negative_images/2.jpg\n",
+      "./negative_images/20.jpg\n",
+      "./negative_images/21.jpg\n",
+      "./negative_images/22.jpg\n",
+      "./negative_images/23.jpg\n",
+      "./negative_images/24.jpg\n",
+      "./negative_images/25.jpg\n",
+      "./negative_images/26.jpg\n",
+      "./negative_images/27.jpg\n",
+      "./negative_images/28.jpg\n",
+      "./negative_images/29.jpg\n",
+      "./negative_images/3.jpg\n",
+      "./negative_images/30.jpg\n",
+      "./negative_images/31.jpg\n",
+      "./negative_images/32.jpg\n",
+      "./negative_images/33.jpg\n",
+      "./negative_images/34.jpg\n",
+      "./negative_images/35.jpg\n",
+      "./negative_images/36.jpg\n",
+      "./negative_images/37.jpg\n",
+      "./negative_images/38.jpg\n",
+      "./negative_images/39.jpg\n",
+      "./negative_images/4.jpeg\n",
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+     ]
     },
-    "accelerator": "GPU"
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "./negative_images/image_28.jpg\n",
+      "./negative_images/image_29.jpg\n",
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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os, cv2, itertools \n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "PLATE_NO_DIR = './plate_number/'\n",
+    "NEG_IMG_DIR = './negative_images/'\n",
+    "\n",
+    "ROWS = 64\n",
+    "COLS = 64\n",
+    "CHANNELS = 3\n",
+    "\n",
+    "plate_no_images = [PLATE_NO_DIR + i for i in os.listdir(PLATE_NO_DIR)]\n",
+    "neg_img = [NEG_IMG_DIR + i for i in os.listdir(NEG_IMG_DIR)]\n",
+    "\n",
+    "\n",
+    "def read_image(file_path):\n",
+    "  img = cv2.imread(file_path, cv2.IMREAD_COLOR)\n",
+    "  return cv2.resize(img, (ROWS, COLS), interpolation = cv2.INTER_CUBIC)\n",
+    "\n",
+    "def prep_data(images):\n",
+    "  m = len(images)\n",
+    "  n_x = ROWS*COLS*CHANNELS\n",
+    "  \n",
+    "  X = np.ndarray((n_x,m), dtype = np.uint8)\n",
+    "  y = np.zeros((1,m))\n",
+    "  print(\"X.shape is {}\".format(X.shape))\n",
+    "  \n",
+    "  for i,image_file in enumerate(images):\n",
+    "    print(image_file)\n",
+    "    image = read_image(image_file)\n",
+    "    X[:,i] = np.squeeze(image.reshape((n_x,1)))\n",
+    "    if '-' in image_file.lower() :\n",
+    "      y[0,i] = 1\n",
+    "    elif 'image' in image_file.lower() :\n",
+    "      y[0,i] = 0\n",
+    "    else : # for test data\n",
+    "      y[0,i] = image_file.split('/')[-1].split('.')[0]\n",
+    "      \n",
+    "    if i%5000 == 0 :\n",
+    "      print(\"Proceed {} of {}\".format(i, m))\n",
+    "    \n",
+    "  return X,y\n",
+    "\n",
+    "\n",
+    "X_img, y_img = prep_data(plate_no_images + neg_img)\n",
+    "\n",
+    "classes = {0: 'Normal Image',\n",
+    "           1: 'License Plate'}\n",
+    "\n",
+    "def show_images(X, y, idx) :\n",
+    "  image = X[idx]\n",
+    "  image = image.reshape((ROWS, COLS, CHANNELS))\n",
+    "  plt.figure(figsize=(4,2))\n",
+    "  plt.imshow(image)\n",
+    "  plt.title(\"This is a {}\".format(classes[y[idx,0]]))\n",
+    "  plt.show()\n",
+    "  \n",
+    "show_images(X_img.T, y_img.T, 0)\n",
+    "\n"
+   ]
   },
-  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "id": "cfT5L_mMAOSm",
+    "outputId": "82fde8e9-a30a-4992-9cd2-4659fb236b61"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
+      "  warnings.warn(CV_WARNING, FutureWarning)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
+      "  \"of iterations.\", ConvergenceWarning)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
+      "  \"of iterations.\", ConvergenceWarning)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
+      "  \"of iterations.\", ConvergenceWarning)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
+      "  \"of iterations.\", ConvergenceWarning)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
+      "  \"of iterations.\", ConvergenceWarning)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
+      "  \"of iterations.\", ConvergenceWarning)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
+      "  \"of iterations.\", ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model accuracy: 100.00%\n"
+     ]
+    },
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "_Mo2vxkP-0JK",
-        "colab_type": "code",
-        "outputId": "85d4c99f-8b12-41bb-faf7-6a0a3f1fb237",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 122
-        }
-      },
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
-            "\n",
-            "Enter your authorization code:\n",
-            "··········\n",
-            "Mounted at /content/drive\n"
-          ],
-          "name": "stdout"
-        }
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "nmBbzycb_7O-",
-        "colab_type": "code",
-        "outputId": "7aef17ea-cf7f-46ea-9af8-0c0efddb24fb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 207
-        }
-      },
-      "source": [
-        "import os, cv2, itertools \n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "\n",
-        "import matplotlib.pyplot as plt\n",
-        "%matplotlib inline\n",
-        "\n",
-        "PLATE_NO_DIR = './plate_number/'\n",
-        "NEG_IMG_DIR = './negative_images/'\n",
-        "\n",
-        "ROWS = 64\n",
-        "COLS = 64\n",
-        "CHANNELS = 3\n",
-        "\n",
-        "plate_no_images = [PLATE_NO_DIR + i for i in os.listdir(PLATE_NO_DIR)]\n",
-        "neg_img = [NEG_IMG_DIR + i for i in os.listdir(NEG_IMG_DIR)]\n",
-        "\n",
-        "\n",
-        "def read_image(file_path):\n",
-        "  img = cv2.imread(file_path, cv2.IMREAD_COLOR)\n",
-        "  return cv2.resize(img, (ROWS, COLS), interpolation = cv2.INTER_CUBIC)\n",
-        "\n",
-        "def prep_data(images):\n",
-        "  m = len(images)\n",
-        "  n_x = ROWS*COLS*CHANNELS\n",
-        "  \n",
-        "  X = np.ndarray((n_x,m), dtype = np.uint8)\n",
-        "  y = np.zeros((1,m))\n",
-        "  print(\"X.shape is {}\".format(X.shape))\n",
-        "  \n",
-        "  for i,image_file in enumerate(images):\n",
-        "    image = read_image(image_file)\n",
-        "    X[:,i] = np.squeeze(image.reshape((n_x,1)))\n",
-        "    if '-' in image_file.lower() :\n",
-        "      y[0,i] = 1\n",
-        "    elif 'image' in image_file.lower() :\n",
-        "      y[0,i] = 0\n",
-        "    else : # for test data\n",
-        "      y[0,i] = image_file.split('/')[-1].split('.')[0]\n",
-        "      \n",
-        "    if i%5000 == 0 :\n",
-        "      print(\"Proceed {} of {}\".format(i, m))\n",
-        "    \n",
-        "  return X,y\n",
-        "\n",
-        "\n",
-        "X_img, y_img = prep_data(plate_no_images + neg_img)\n",
-        "\n",
-        "classes = {0: 'Normal Image',\n",
-        "           1: 'License Plate'}\n",
-        "\n",
-        "def show_images(X, y, idx) :\n",
-        "  image = X[idx]\n",
-        "  image = image.reshape((ROWS, COLS, CHANNELS))\n",
-        "  plt.figure(figsize=(4,2))\n",
-        "  plt.imshow(image)\n",
-        "  plt.title(\"This is a {}\".format(classes[y[idx,0]]))\n",
-        "  plt.show()\n",
-        "  \n",
-        "show_images(X_img.T, y_img.T, 0)\n",
-        "\n"
-      ],
-      "execution_count": 167,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "X.shape is (12288, 100)\n",
-            "Proceed 0 of 100\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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GR8JLWglfVPF8/hwh/udNgyKncP3wRl/uhdLTAuJaOuBWLttAOjBEbob+2iVqmz39OU8D\n4H4nn0y7o5pqcEhtjGarxeS+PQC0MjVgi6zD9dcrv8qUQZ29/bFq1SqOWad8I6vtf5TlNE0LtM0I\nbrsSsSd30GzqTkdtI7XRLBSQdG20mqEffGagKIrgJATNY9cbOZBkfj5WKvaXbvFaLE3r5PMyCWkU\nBTRyQG+kaXAGUq+9KvbVfERKWZZdqHc4VxqON18kAsOD8pw/ft6VzrnTDrnjnZCeTrSB+pDbuPYk\nmpmjVXjPSyfV6JjBgGqjPPMPnwnA8SdoQvrAxARf+r8XAeD26f43bL+ZE+57HwDOOFOhNccccwwA\ntbTOuMGrS/Nmi7JkKFWDeOcujZlt276d1pTGzYbMWF6+QtGxi5csYelS9U5HbFmaaeesXqmfe48t\njqKAFvYTyHuMdYnJZS7MvCzLOUsldGOJRVEGeFBmsJ6I7iR1HpgoQttH9oNzYg9Al0oqSJqm4fx1\nD/NOk3AN3tmpGdgxLSEa0nE85olPXJCJ1l86+9IT6alGGxoYcidtPhkX1ZhsWlI4V41Stg8AsGzp\neoZG9Gnas08j84uXLKVwVrzS1v2Xr1nDnnF97ZfCWt1CA1nOurW6ZP7s5z/X85RlyEW++lWv0WM1\n2xy38TgdXDE3iZwXBVPTE/q6rUtM5oTU7ldtUDVgHEWkZlQ3zTmZnNBrcTgO7NfXfjnbt29f0Dhe\no/nFdmZmlkZDl8Io8rBqmJ01KJUPZWRZWDoHh4fn3IOZqSmGhhRQOTamGjlNUxYvXgzABtP6x65b\nF+oC1q1TTujCwi1tVzJh9/7Mxz2qr9H6cu+R3gIfIyFqJHSygqGGoQ5aqr3SZLHtVNIaV+O+Zsp2\n7/YdjC1TAsL9RjifTrSoF/rdpmmcfQf0w5HRYW75rRZ3DNTtqR5sgqi2+OqX/w8Ar/zvLydNVRvN\nZhodjytFMh4dMjg4aGNzTE3ouaZn1Ua6devWAOuemFANePbTzwbgwgsvDBVMi5fo9S1fs46JA7rf\nprUK+fY2W5nn1IdG7fx6nnoSUzdg5KzlgIvCEVmaYHpGtd30hGY4ZttT7Nut2mh8SjXszbf8mukJ\nvb6BYb0HZS3twqbMfmu19fiLFy+iNuh/j4WRvkbrS0+kpxqtXq+zcaMSB/rYoC+aaDX1CZ5tdui0\n9SlNzGNcXKtjMVGGDJc2PjHD2mVabDvRVE1WS9ReybIOkejrRs3j2JYwMakhktoKq1mMG3RMc0QW\nrti9azegnt6HPqgM9L44pVar8aev+jOgG1YYGxtjdko18Nq1aut87WtfAxSt4m2zyQOqZbZv38UV\n112v1zWsHuyatVoIvuXm3/CSc/9EX2/ToPVQfYibrtNGMb+64Sq9vtmSzPKYqUdrm/rvtAsimVvK\nGMUOLESzdq3aaIMDAyFsMjup2rDI1Z7cess2TtikoaWFkp5OtDRJWL18OZ08n1OzCbB/XH+IRcu6\ncaPIJ4dnC9qWOB9ebBVEtVqAAK23JahZCTPkFgMrmvYDZJ0A1dm2dS8An/7s53nkWQ8BYNUqPcaa\nY5Sw8f3vfT+LlupyXbYs2RwXzGT6EKxarEvizOQ4O/dpuMSHV7xTMFCvM2OQJF/8u27lam6wpbOY\n0s9+ccWvAagPZVz548uBbmI+y2fZu1snXeKdhrzJgC+u9tX8md6fgbQeHh5ffR/HNWamdVk8YEb+\n7va+EPLw440tljgyuIw9O9X0WCjpL5196Yn0VKNFEjFQqxPHcUBVeIjMiLnkJZD4anFDEKTiGKob\nLLmtT2mSxIxYntTnGj26WhByX7NommSi3QwBSmxJjmsl3//JT/U7Fk6oGeJ+8dKljOTqBGSl1QSQ\n8eWvf0ePYct70W5RGJfHjTdtAbocGSOjoyGf6B2K6dYsGJQ7a3vqAtVeeVGSF/r6J5f/UPefHqch\nevyBUaVXqKc1CFQIc0GRhcuo1XWZ9No0SWMk1mvet08D2SJRQJH4SvyyZUdJoDE8F959tNLXaH3p\niRxWo4nIeuAzaDcQB3zCOfcBEVkC/DPaWeNm4LnOuQOHORaNRCHEsadnqqRVQAOlHhkhHhAY5/gi\nnsJSKI16nY4HFeZzK5OKoqTma0LNVuvgGLa0ii8nKsuUzFAbIRdo2q4xNASiedii7ZmGcuLlBjC0\nc3baHTxyzdubuYE589Ix1Vb7Z9rsq2z/BIND6kiMWoJ1VayhhAzHrfvUHtt04sZwzNwKNfO8aWON\ncabFxWPhfFVWOwOzZzttG3+ZEyX6OusYOqSehKCv17rdwptK9dgCyZEsnTnwF865n4nICHCliHwT\nbTPzLefcu4xW9I3AXx3JSWtpGmgE/A8cYDGdTgD7BGRpkgTVG7D6IqSGx+9YvjJOjDlIikAZENnE\nHFm0iMIb2EaaIi1HVJsbiS/Dspp2KbWiLvVV7CujjOQlHkzAlqBJS+4P+ei+g8HEQJk+GU9CO6xz\npb8o/VeWZIbebNvyGtOFCVUrqjK/LTKokYdD1RKaZnI0RtVzdHST6q6wk0clYjncqFItZQMiHugx\nJYJzbqdz7mf2egrtKbQWZXf8J9vtn4BnLujI+vJ7JXfKGRBtEvpA4CfASusQAtpMYuVhvx8Jab1O\nJ+uEp9hDbKpFr+EJq1Tx+GXUAxrjKKI0qNGQGdoe3eCSZE6RMEDsSlod3TZpS8vq+xwX8p+7dmuI\nwuPs4yRh63Zlbpyd1uV1dHiU2JaUzCLztSimMB3csPznjgP7dVyNBrMG+4kt/jdQq3tfJDDQeGNc\nSgLGqO6vyTlqlhnIrQZVoigs085QMEmAj2cM2gkSDx+PuqZJ0TEofNyFjwcWyFqXPTK+u+o6RWQY\nuAQ4zzk3Wf3M6a96yOy8iLxCtA/nFd4L6st/PTmiaSta5nMJ8Dnn3L/a5l0isto5t1NEVqPtDA+S\nKuPjurXrXJImlK6sEJB4mHIXkOc/84OLpEs85xEP6cAAhacNnUd14Jw7iAWSEmIDIb74xS8H4KKL\nL6RV8/QLerZFS1UrTU9NM7J0iW5bqcHZ2UJoGOBisGH1l2nKjIVNhiwgPLRaA72ZRIyZxmybZh1M\nE2JfNW42VGL4w2RokJoZ5FIpNvHa3IMuB0eGcUaJMGQIDV9vmtYStl+lNHVbfqmZhNRFxFYWX3qt\nL13qMK9tfD2nOwSm7WjlSLxOQZtrXeucq3YF/hLwYrQX04uBfzvs2ZyjyPO5FxG4IIyxJ0roVJCk\nMJcqwE+uKk+tl2oFkcfNBxSpg9iWu28aJ+zjnvM03v0e9V/+8rx3AfD2d/wFAKfc/8H88Mv/ocfI\nNUXz15/6DGJsQ2MjOiE/8KY388rz3wbAoJ3zG/9yCQBnPeVsZm7T589XRo2uWBxicMsXawpql6WA\nGlEC5o3P2ERqt1skIT6o556amSWKLY52QB39+nLLYji4ZUqj/2WoHKsF1kifoUC61VKe/9azLVU5\n6hZKjkSjPRx4EXCViPzCtr0ZnWBfEJFzgVuA5y7oyPryeyWHnWjOuR/QtRfnyyH7l9/+sQjxn7A8\nek3lOWqz7CDMe7W2scoIFMIfcTznGHEchyXWa7bZ2dmu0e0dkIEB6nWD2/hEvnGhNYsmiy3zUOvY\neXJHVrNzWSjgtq3bGLGUxEff/R69UMtlPv2PzuHPX/FKAM55lf5fsmE1f/oibZG+ZEhjWwcMln3x\nRZ/nDW87H4BXvVmb9M1EcNmXlVRzyoD8T3zWs2kY5OmCt6s23fzkJwBw+lOfxv0f+EAAvvdrTcYv\nXrqya0AbuU4nawYHwjN8e2eqeu8XSvqZgb70RHrO+Oicw+GCITq/prBWoX8PfPmuCNrLG71RFAfA\nXm4QZJ8FqHKwFhU6A49+mPbUBc4xaEHfAdN8gwP6ftmiRfxyWm2dYdvWcoKz7y5eodGcZHCYN73u\nPECzBADP+oNnAfDG8/6MpK63+HOf05Djh574aAYNbOlztGvWKNxpsBZzw2U/1msxm3Ugi2hPqQ3X\nMU3ZyAr2blf6/8y04b9/5rMAPOwpTw33dsyqwkQE56mvPBlM3Aj3JoSC/KoRJ5D0GR/7ci+UHpO8\nYMgNCWGNdu75+s21bpc0S0u/mD2RpmnoktJt5SOkIZBpIQqv0eiGNfzTPdNpE9vlLrKCDrKIF75Y\nu5zcvHU7AI959NMBqNUavPkTWmtatxxpNpUFrtjvff/7ADz+eX/Ijm3aL2DVegU+nnDmgwH45YHt\nvPoVCmScOaCaZ+9kk7++4JMAjBjZzP69io8bd8JnvvlVAPZM2T1II84+5wV6jxJ/vW1WrNfimynT\nxBdeqH0Mbpycou65fSMfnCVUzGP3MUm7Qe3IN7swps1UpJLuWhjp6UTrkh5LIGSZT7IiZbeo19J4\nOLpASU8S3Gq1GDT6TWFuH6R6koQl1i/NEkW43LPAWDI5qtFMNMRwn80GnrSwQqNep5jWJHZmWPoZ\ngUG/JHuK+iTiIY94uG6zSdg2o/2Rj34Eu/dreGP5iMbivvLVr9OZUsfjWS9QR/0HP7gMgPve/wGs\nWq8J9htuuFbHPZDSsAdjbEzjesMx1I0V8y0f/hAAtxqIUkSYttyvr3JyrrtMewegVeTBxfP3z5M8\n53keSHUWSvpLZ196Ij12BkS5y4TwNPklsbA5L3FMbFy0vjEDpfLHViVJ0hDC8AiNQHFeFKFzXAiH\nZJCLcZqJR0vAoqbVLCw2AmPjCCvygtzDfowWazE5rVSXqtMf/AAApvbuRWp6/u/+/EoANg4pIY0b\nGWPSkByXfVeXxBNPewBXXalgy2mM1mDEQg7RDNu2qMG/c1rrG0465j4BxLnKltrx3ftp2RWWxv/m\nA7i1disEav29lSimnE+/EMeBSt/f5lrhmSvnhjoWQvoarS89kbvFGcjyTqimboeCEg/HLsGMUt+y\nzTnXRRiE5gtF4J0o/Y7hiUy6oQ9PhhLHwebrmAb86dcu4qEP1kDn0Kyec7RjXByr13HZRRoyaJyg\nIMSxkx7IzGXf0nGXquWKvGD1GfcH4LhT76vjNRVR7tvKzdeoZrr/Q7QIZt/4DGvH9Np/duUPANh8\n8gn+4thhxDWnnKy8I9PNTmAOv+Lyn+j+GzYGx+fWGzTMUbOc7ehQHfFQct+KKI6796/SgzSEfkyz\neURHWTiKose5zoUVbdgaRVHg9wrU6V65SkRujI+5MQdVE8sBXiRxiM6HTIItr+12+6Clc7bZrHQX\n0f1v3fIb9pyiE+AXn9dJteHMRwKwefsOLvuPLwLQ+LL+TGdfcBFb/o/ud+Okwoqmx6d54Slf0KEF\n+k5d/rZd8GnGcwW6LF+p1VW1qQNs+xclrNljy92i171VP9t9C3svVobF6ftsAmDFH7yUxCq5rvyk\ndr47/n2fYv9NOiG3fOidej+srmFHlvP4861M0ICgaSxdelSPZK5Uos2fcEWUk3p6/gWS/tLZl57I\n3RLekEiY3yPSEyML3fynp25yzoV8nJc5nYjn8ZKVZRk0WjXz0DI+2czqNBNXw3CJNH/zKwCOe5HG\nvT79ly/gD96moYMrvqFoDDe9h8v3aBjhZR/8Zz1GXATN2jLje9OohiHyRz+O4481qJFp529f+mU2\nPf3ZAKz6pToP5S6No132sc+y9rxX63g+dYF+T2DHrC7nGwZ1sPs7bZJYl+7HnaMhkltXHg/AKR1h\n/+S03SMDfVZhVj4fnHTRGxLYKE3DlTlJ2c8M9OVeKD13BpJIaOdZ0Fqh35M9QZGL6JS+X7lnuS7J\nvBPgtVcck5sj0fBM3XYeF8chHOIxXM6VOOu1M1D3TsMwW3aoMf3zWzS6/wRzFGYkJrLijYc+/UUA\nNJlmcapB3FAmkgwEQOXIgP7fvkUpD4rlS0gXbwCgZUQ0Dz33ZcgyDcquXaUYsp2xOgenvfZPcat1\n/1+aJm5JxKYVSplwlTk7q1yDvb5n1Ga1MVcv1UxBjmPfNQp8FIOsx8SBQrLwK0hWBIfJ99VwFecr\nX+CAbY+XTp1YcZJUlke/tFmRblHQNNac1BgahU64UX4JrdIqePFZhnaek6Sebt0HkLqQIR+fi+KY\nYwzA+PJna7oplPARk5ubmgTy4w5Z6Vtu6//BWo3cGtS2jNnH03x2ioLSmr+6hj4gM502I7bEfv9T\nFwOw4bW6XI6sXMqBrVpuNz5hpXVlQTGlzsWI95fKMlAmNC39JiExXoZqL6sZJkHohGaxhqqNKsAD\nH8sMHn2Vu21hpL909qUn0vPR2n85AAAa20lEQVSlM4oiIrrkJF4n+e51URQx6NlGbJuL6yQWsffc\nrUkch2XAa7KskjAOpMNl91mS1Dse+nQPD0ecPqpG9d9+TcGF5zzzFQAUM23yrdqeunbsyXqA1m4i\nH5/znV+SOFRSLTUnYHqPGvcjtQZ15+kdfD0qnDqgEf7ssQ/Tw9pnM3unuPzj6oCc8vKXAFCWbZbb\n8ScNeDA9M0k6aHler229MooqLWsrBMn+tVSM/LBtfjc8Vx7UZe9opa/R+tIT6TnwMYqiuU/YvD5I\nIhJIUtpmq1EWgYKgWrDia0LDd5MusiKgQowgBYmIPEO2v2zn+OF73w7AkBG1iCUF3/bhL/DJ8xQy\n9MJ3aOHKyOBmHrpG0R7/cu7zAHjexy/i2n2q+a78yEcAeOgTngjAdd/+dxKj2Tr9Ff+fjtXV+ce3\n/TkAj3rNXwIwbVmS6an9jO9QG23tes0MtIsOOy7TUo3TXvc3AFw7PcmqpeogcM13AIiP12N0Fo1R\nWm2qB3qW0rVVu7QH3UonX5fqtV1oIruA0tdofemJ9FyjlWUJZRlISUK+0ohUSOJuOxtLg2RleVDL\nnTiOA+ZsxngqqiGTbqP7bjC36HTbWAM0nbBH1B5csVJDGZ7nf1/e5HGvfz8A73+zap7XfuBSdu5X\nb/AVH7oQgO/deDXLj1FY95PO1XrRXVuUvmr5scexY5vi0Q5cr1qvMbicbbs0/zlhOdohq9fcuHIp\nI299i16TecjjO/Zy2xWKVzv9JX+sx9iyhdiph3vpZ9Rz3TrybR3XX/8dqe8ybIrJlWWXuyTqNtgI\nfQ78tora6Xld50KLiCBRFEhYfLrXJ4I7RX5QjE2ibmQ7qySFk3lLZ7UH+3w8fFKhSfDuvCsKJgYV\nkDhl7RS9sS9pQrpS842PeJ5G8veOb+WA5R0nDY+/9bprWGqMk7NLtaqpfrOFQO7/IM56sh5jc2KE\nxEmdZ1hF1IAVEHvU6yf/xxt5+Ou1+mnAQJ3bpqbAWCa3WQPctDXNslGtMzjnHfowjC9fo+N3xRwn\nADQkFHhlch/a6bbE9hCikKWp3L+Fkv7S2ZeeSG8DtkDHlSRRhPOPkdfQhqyI6ebgfFA0LyutnG13\ncS4A+0L/zZCz6z7BYckty1DPmRiqIY9iXna+oh/e8+6/1s8sjNJoDLDYSGG22fIadXIKCzH4/pdF\nDGlocqtL8gHTjoMnbKZtDsit08q0eMX3f8qKUxVWVE54unoNi6xcNMrIYs0WtJwa9I9/zFP57pcU\nRTK1x6qy9h0gPtauwWioJOSOIyLPnVbVUF67mclS5gWFj2X7nKgnhRGhHs/tkHe00tdofemJHLFG\nE5EYuALY4Zx7qogcB1wMLAWuBF7knPXRuT1xDleWChMOoEUVbztUjVBPzlcWebAZUu+yR12e2gDX\nnjte3a/S7Ms/1fXYw5kLvndAmbfWnak5w11b1ZD/4gX/wMvf8XcAzI4r9qwRDZPn7TnHbTYzymgu\nw7iz8EI022LJUnUyhhMNP9zwo29z7OOfCsDAiPUKtfzq/n07aZnDMrlLWbHLRZsQ0XMeM6BX+NMd\nN3G/sx4NwFRumsdZ9b04fBjcX28aRaEoJXTIi6KA3/NSJcnJy4W10e7M0vk6lIRv1N6/G3ifc+5i\nEfkYcC7w0Ts6QFGWTE5OsmrlylAk7BlyMltiXCEUHR/918/iIu9yn1UmkJ+IITNQ6WVeXTbmb/O0\n6HmtwQD6Qz38IRql32vLZZR1kGs0fnXrjxVCdOIj/oThYSv+tck1tmwZdd/X3GTD/R8EwPrFw1xz\n2Y8A2LLmGLtOx60GSVqzRsvzaqvVa40HhYkdOtEfcILCfvZ3Omy0tuBfeYfG35pDiyjP0QxG2whd\n0kFbQtMGhfPJ9K6UnW7WBHTp9PQUvqg4oJJxwelaKDmipVNE1gFnA/9o7wV4DPBF26XP+NiXO5Qj\n1WjvB94A+HYaS4Fx55xP8W9H6UbvUMrShQ4pQRt5hIEtH3lZktY86bBJJt1wSHxwZNs/iVEl1tbl\nZfU9Mbtgy9LIh0cWbyA14zizIuGNYxruOPu8v+Bvz3shAH/5To34d8YSLr9WI/ePM/qDsbExnMXD\nQnjFIDsfefN5TE8qUPJZH/k4AKODY0z825cAaI3qcnrKH/93ANYNrmTb5/8BgB/t0+XyJe+5gOsH\n1EG4z7lvBOBb7/wLSuv99MV36rbMMP7LTjmTTac9SsdrkX4X0a06q8CtonmxSY+QEaCkx+gNEXkq\nsNs5d+VdOUGV8bHZat6VQ/Tl90AO269TRP4nyo+WAw3URrsUeCKwyjmXi8iZwPnOuSfe0bFWLF/p\nnvPM57F2+QoaQ9b11lgSS2ton+cFcayvZ83mKohDc4a8EpSN5wVsqxiqqm3m3wcKgDVqEz3wrDOZ\nyefaKVHkiWOEyHybwmDTsUTE1s6mY4b/b2+9hdmmFqDs3qsR/80bjgNgMCoCimS/D9/sH2fY7NGx\nAQ3wjm7WTsr/cdGFAdXyhEc8BoB9o8tYa9tmFinqI95+PWIkfs3fKsiyaf2w2qNjTLT0Opu//I3e\ng0i6HLaVlcHTv7vU01F0bVzvYP3vT360N/06nXNvcs6tc85tAJ4P/Kdz7hzg28BzbLcjY3zsy39Z\nOZqA7V8BF4vIO4Gfo/Sjh5Uojtm/fz/rF6kd40MCiWHQarWEdke1htdP7bxLc1X9P9/GqFU6Ac9P\nS1X3D6R/caSU1XQhzoWZnVHiQs/KqUnj4CiLwJbtef03rj4GZzRU9z1ez3XZFf8JwIqNm3CRasN6\nZKWDK1eyzwymgYaavLlB1s949jlk9pM0S/W8B/KYTsOCrLM6jmL9JgacnrN1omLlanb/RuI6xQ3X\nADAb0nVR0F5VOq/grVdSVf6eLXQK6k5NNOfcd4Dv2OubgNPv1Nmco2h1mHUFkd3sQVs6Q05ShGhY\nf0zfJaWcbTJsRCcTxhUWRdFBDEO+drFeaVZfWKZYiChz/fH8cpZHQmKd4P79K8rG841va43m8mVD\njI3p2MYndRl5w2v/nsIi5k1ri5h3ZhhconlHMej3Q07XUMmByQPM2kMTovClsNTWkdSoFjrWpe+a\nG37MT378XQD2Gjft35z/QQ5YZ7rI6BXSlmM60YkzbHD3KDhGbYY8fNwe1U7ezQtXl87Q9COda4LM\nJ95ZCOlnBvrSE+ltrlOgTCLyDK6/USutTzO+1YYtoSJCaZXkyxbp01qr1TlgbIejxr44PT0dXPbQ\ng6mi4fy2uNN1djJrmDpgS+xn/uldtDsK42l2VENt2qjx6CzPYFYdkLrlL976/5/D333kH/W4Q6pR\nOkmTXbu16uiBD1BetJu3q9ZtNpsBPh6KZERCP82lw7p0tqy45ctfvIhVa3SME7PqYLQ7KbHTcTgL\nYbTKNiMGI/LhmekpuxdpwoBv/ePNklxwFsStQrRDcxDTN46uo9XnsO3LvVJ6i0dz+rSktZiOVY03\nDbQ4NtxtgOWdgNIMm9GhKLjn3v1v1Ovs3q+IiBC2qDgHbZ/5NH6LqKx1S/yMtmrPrmuJa2onRU41\nSZnoeCSGeFzDCScvPxGAq8sf8563K1/t0KB+dsWvfsmGDZpeurCu1/DIs/4QgLZkrD9G00y3WN3o\n2lXrcWbUl0vNiUl1DB/52Od42/kvBeDdf6OB2+bsOP/7Lcq8/ZLz9NyLlyf8/XtfD0Bq6alnP+1l\nAKzfdF9Kc058Ckoqec35YR+9z3MN/9+FjdZz4CNYgwoznK++SmM9S846A4Cac4gZ3D7xEMXCIivm\n9aja0YFhBi0pPWuxrdlZBQh2Oh0yf/NC1r4MMbiT76PL9fevWMu+ac0tDlpedcaqptI4oViksanf\ndNSLc0kDZ7dMjAXypOPW4qz7XbulPaA2W2/1ZmeGrTcqsvbcFyrl+4F9k5SW5927X5fHD3z4fwFw\nv/uewOysxuJuuuoGAL7w759j/Rod96c/o5Cm+qhjoO4dGj3GV76lTv/4v7R4w/P/VsdYiSF26zmt\n9rXd7oIA5vXNAvpVUH25d8phMwMLKcuXrXDPesZzEZGgmWLnO8Opun/E6Q8lwVc6qcHdLgoyW2qr\n0OyaGcS+LiCRrlMwa0QuHVum2q0205lqqN/u17rLDSecyK07FSbkn3Tf2KLK4+qr6FvNFrHRKQwO\nmmYrSnJPKWC3smZaLyuL0MmuZdmDzkyHCas8PzCu2isdUqdn//59DA7pfZg1R6SWCJnT5TwNsKmM\nWm0uGXTTQjYjg6t57sN06d76s1/qtbkSPBOAOUf1eoNWW+9HzXqWlqVvgFuEqv4PffzDvckM9KUv\nCyE9h3JXI9JVaRpY8GfXXMNp91Ooc9sM/2bWoW0to0MTMSoocK95zPxVinmjejduWpERVorWQq5Y\nrcUk+yemWGnGtMeoWQ0JRZR0W1zbOMpOh2xcd5jwfTtdSWYazePSGmbvlQA2tsG6Og9LGikbl23W\nc9j3fF4xiuOg0fzFDY+MhkbFDdM8SVRnwPYbGPQcvTbW2Rbf+bcv2/1QGRkcCNVPtVo33DIwqKEc\nb4+NjCwK4xkd9bDDhZHeUiKgS5QQdZdOnxYy0uI9u/bzg0ntHnLGmaaxs9ZBfGpZJ6dlAEk/+VyF\nDjO1pSLytQn1lAHzxpK6/h+KIoaHLbJu4zhxpUb5U5HQjttzkjrpVGg4jeA4bzMwqJH4fdYvwFBO\ntGbb5L40wjt2eRF6Uvl+ClJa8j5zRMaj5lNhrV37SK2/wKyZD7lzIYZY9+29zVcfGBjgYQ/ReN5A\noH93ATTg71UUOw1sAoUl+SMzWfI875O89OXeKXdLeCMv2wdHpY1wzOGYbKpB/s3/1KLY4447js2b\ntD7ywF6NnWWd6YPqP6sY+MLgPx43X1KG8Ee1D5U3gP14clsS6/V6WG7qkS5Zcb0Myf/SuuLVCiGN\ndPmNTIu6hu0TJaHHlIegJyKBoXIoGpkzHhxBy+SF74cVV6i9JOzmSaa9JvbdUnLnQlWTh1YVRUHu\nPBGyXl+nNRvujc8IiDXHzfKsX9fZl3un9NwZKEXmBgN91xwPx46F2NtEhry4actWbrl5GwANs7NO\nPOl41qzV6uyWBWwPGKJiZmaGpmmvUD8qXY2QtaxYS5xChYBaOTdiXpRFoEBvG2TH5QlRaVrCM0rm\nZQiDeE0SCFUi6RbVmIZIkqRb0XWQRnYhVOP/zSm0OUT/0gBt9xXotYSWrQjBSYpivE4JZmflWkM9\np0HSb89hOxrpa7S+9ER6qtEiEepxTAm0W+b9RD4oas54pdFCFTvlwXnOmLp/ftWvuOpq7bAb2s7U\n9blZsWIF69dpGmjZUoU8d8qCKcOyTY5rqmhmdpqmh2YbLeig5T6Vysm0hv3PnQueaBr5UAq0zKVM\nrB86pl0kSbo94A1l0SmLYMt1MXOePqrbbsiJD2h3wyseUxbHcZdHxGPKPB1V6UKwNdz3KELEV6Fb\ncDuKwndDDaxp8DRNGTSA50JJjyeaYzAtmchKGtadrcw8ZUG3EicS/8N5ViHByhK7VO8S07ZQgEfW\nZpn+cDfdtJWt27UtorP4WBzHpIZ2jazp6Yqly1hrtZUjIxo3Ghy0FovO0cmsq53Rr09NtGi1NcLf\nNHx93u6QWjc+387RmUEfFQ5ni0ZsBb65c93i6cyTFfssQ9cA94GaNEkOQhKLI3QHLPMudy1AWqtT\nWrjET5aoLBm0mJlvITk4OBTid5g5Mj2rD+LuXbu4Zfs2FlL6S2dfeiI91WhT09N85/s/YMnK5Ww8\nViuFUqswKnyesijmkI3AXA7Watn+fNqDbh40Djy13jDPs5xC1JCPjAb+lu3buNme3C79At3jW+jD\nB08b6TBRqucYaWiQdnhsEStX6vKcWXTehzQajUagIgjGe5oE/tvUzdVUURQF2nqvAXJcILsJdRBZ\nHsIUk7bk+/fTMzPsP6CmwbiRyMxMTTE7pUBT30JbKsdrF55DrnsevxIslPQ1Wl96Ij3VaPV6nc2b\nNnHjzVvYcp3irZ7yZOX3L8ygHkzjUMjhg5K1Wi0EVEt0m0OwmGbQbNX9fZonSIX1MCu7T2tixn/T\nG+2m0YQIZ2X0vvNe5qaplZrWGZ9UEpZ4726utwZgIVxgVFKU4KJ5BTSAmFYxMw/vB5WFJ2mhy7db\nliFf6m+MK4qQUvJteDI830ZCIXODrS5vh94KDes3KnEZGr/ldq+cOQw1qThnCyQ9nWhJkrJs+UqG\nRsfYc5vCc370IyVB2W9VP2eccQbLV2mB7xzPKkTPrWJHYojmUiE0Gt2KKufmfgbdsrJEzCsToPDL\nojFO+rVTikBNX6uQuIRufH4JqkCe5lMMl1F2UAe+oigCrb3/ho8XOsqQjfBTpZRuhVPoGiPSpS+w\n/ePwGJXknW57b4B6kgYTInjyxNQS3zDEP3jdZhfS7wXVl3uj9FSjjQyP8shHPJYf/uh7NMwZGLGG\n9LOmxq/77fVcdfXPAdizV43ZB5x8Kvc9VWmcfLEtpaMTVL31byq6EKK6QSiKlumGKApte7xWLMtA\nBE9m69igkSV3OlkXDm6SC6TOx/+6NAzzHRVv7EdRRGn082XZ7Srni3kDb69xrjXSNIQpqg5A6FVq\nwJ9SOmSu22bIThDGmaSGiLFtrRLqFuNrGfQpLuOgZmoWjmnbuYs8WnB+tL5G60tP5Ig0moiModxo\nJ6P27MuA64B/BjYANwPPdc4duMPjREIyUOehDzuTyy5TSvPVBkL0hunK5cuYNnK58QnrUtzJ+OrX\nrcW0YdDi0tGw4pQHnXIqAEuWKuVUJ8vAkAiJ1VVGcRwsfR9pr6UxUhqvbcPDx7uaIYQ8fPhEIPbV\nLpU84e01PyuKIhwjrzBa+kBpYCS39yIS8HnB+amEdvCNJkqhLL39Opf1sjoWXzkmdLMttdCmqMRb\ngpEFjBMLz7hIcHeTM/AB4GvOueeISA0YBN4MfMs59y4ReSPwRpSP43bFOUfWboMIZ531CAB+c62m\nkWqxztFmJ2HYuMoWLzPK9yJm6TG+ssgmX3MmpJSuuUEZdfJf+v7sreC5Lh5V1OjyFStYf8wGoNtL\nYLbVAo+GrbRnBOMI80lsW7LqEhGl1tvJL3/OBS/Sp49K34eqkvz2v3+apt3v4pHB3exFcDb8hIsj\n/Aoe5lsUhyau3mgP5xYXUmaeGLkoiuDapmG/krKcu8R6srpICjq9hgmJyCLgv2EkLs65jnNuHHgG\nyvQIfcbHvhxGjkSjHQfsAT4lIvdHiZFfB6x0zu20fW4DVh7+UMpRFlUS56eccgoAmVUfXXfDTYxP\naGR72HDxM+2SuKZP5JiohpJMKFZbVNyeSA//ydsdppuaBWi39P+BA/vZuVPZGn0iPY5jOpYrTANu\nXsGIK1euZNgoC5YsVYcljiKaPvluhCtZllHMiwR4GFQUdVl8PF387MwMdd/Pyse2ki6EOpkHzymK\nQltV010eO51uZsDXEbTNoXB5SmllWb6aLE2h4fnZIr2n7XabffvURLnN+k9NG219UZQhqb9QciTO\nQAI8CPioc+6BwAy6TAZxuj4ccmRVxsfJycmjHW9f7qVyJBptO7DdOfcTe/9FdKLtEpHVzrmdIrIa\n2H2oLzvnPgF8AmDTxs1OREjSNEBSvGEeGYLghBM3B9vh1j0afW/espVa6msPPSthh6FU840ecOiM\nAKbTyRgT5Tbzbn9R5LSN2rTpK6o6HZrtmTnHmJzQ93t338bWLVplXviikDwPtZveyRBJkUg1hw8Y\nY2Os1+rBGahZ9L2Moi5TpdlcsdmJRZ6HsEbD7Mip6elwDR6aneVZcBpmrG61YQU3rXazW/gTClJy\nUh/y8M3+RGhY8cqIoTyWrzTNHdeITQNeY4ySRytHwvh4G7BNRE60TY8FrgG+hDI9Qp/xsS+HkSP1\nOv8M+Jx5nDcBL0Un6RdE5FzgFuC5R3IgCfaZzXF7+nxn4VqtFkIBI4vUHjvtwQ8Ods2tOxRtsXP7\nNmLzoDw4y3cgTj0AEYI2EBHqo6oVhwO5eLeeswu27CJBQgDWvMhOpxPg1O2WlcWVElJboYjEgrpF\nniv9FV1NPJtnlJY7jcSnrrrPu/PI88luELhhWjG1NNnw8FAAQS5ZpozdHmBZr9eD5+zDLEkF05bE\nRt1lNiNAzVOGxT5kk3Yh5QskRzTRnHO/AA5VFv/YO3MyEaFWS+lk7TDBwlJUIRwZGtSbkJZWWIuE\n1oDHrFkPwOZjN4Zcp7f9br1Vjdqdu27rJrg9i2GahvrIzGcQnC59UCms9VF+yjBx40r03U+0MrPj\nJzGlJcCDGeCT2qULYRNPb9/JipAUdfNickLUbfvo557zmH8C9ClJ0wplu4UrfDjCVVC6oUeWghCq\n96NRrwcQQuzX8DAuCabEQkk/M9CXnkhPSV5EZA/qte7t2Unvuizj3jFO+N2O9Vjn3PKjPUhPJxqA\niFyxEOw0v2u5t4wT7h1j7S+dfemJ9CdaX3oid8dE+8TdcM67IveWccK9YKw9t9H68l9T+ktnX3oi\nPZtoIvIkEblORG40/No9RkRkvYh8W0SuEZGrReR1tv18EdkhIr+wv6fcA8Z6s4hcZeO5wrYtEZFv\nisgN9n/x3T3O+dKTpdP6sV8PPB5N0l8OvMA5d83v/ORHIAYKWO2c+5mIjKBQqGeiabVp59x77tYB\nVkREbgZOc87trWz7X8D+Cgh1sXPuDkGovZZeabTTgRudczc55zrAxShw8h4hzrmdzrmf2esptHf8\nYTsq34PkHg9C7dVEWwtUWUOOqDX23SEisgF4IOBhUa8RkV+JyCfvIUuSA74hIleKyCts210AofZW\n+s5ARURkGLgEOM85Nwl8FNgEPADYCbz3bhyel7Occw8Cngy8WkT+W/XDOwKh3p3Sq4m2A1hfeb/O\ntt1jRBTGcQnwOefcvwI453Y55wqnZe//wJ3tT/o7EOfcDvu/G205fjoGQoVgbx4ShHp3Sq8m2uXA\n8SJynGHano8CJ+8RIooNugC41jn395Xtqyu7PQv4da/HVhURGTJnBREZAp5gY7rHg1B7UqnunMtF\n5DXA19Fy6086567uxbmPUB4OvAi4SkR+YdveDLxARB6ALkU3A6+8e4YXZCVwqWHmEuAi59zXRORy\n7gIItZfSzwz0pSfSdwb60hPpT7S+9ET6E60vPZH+ROtLT6Q/0frSE+lPtL70RPoTrS89kf5E60tP\n5P8B+B9F1UOfv3UAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "cfT5L_mMAOSm",
-        "colab_type": "code",
-        "outputId": "82fde8e9-a30a-4992-9cd2-4659fb236b61",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LogisticRegressionCV\n",
-        "\n",
-        "clf = LogisticRegressionCV()\n",
-        "\n",
-        "X_img_lr, y_img_lr = X_img.T, y_img.T.ravel()\n",
-        "\n",
-        "clf.fit(X_img_lr,y_img_lr)\n",
-        "\n",
-        "print(\"Model accuracy: {:.2f}%\".format(clf.score(X_img_lr, y_img_lr)*100))\n",
-        "\n",
-        "def show_image_prediction(X, idx, model) :\n",
-        "  image = X[idx].reshape(1,-1)\n",
-        "  image_class = classes[model.predict(image).item()]\n",
-        "  image = image.reshape((ROWS, COLS, CHANNELS))\n",
-        "  plt.figure(figsize = (4,2))\n",
-        "  plt.imshow(image)\n",
-        "  plt.title(\"Test {} : I think this is a {}\".format(idx, image_class))\n",
-        "  plt.show()\n",
-        "  \n",
-        "X_img_lr, y_img_lr = X_img.T, y_img.T\n",
-        "\n",
-        "for i in np.random.randint(0, len(X_img_lr), 10) :\n",
-        "  show_image_prediction(X_img_lr, i, clf)"
-      ],
-      "execution_count": 169,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
-            "  warnings.warn(CV_WARNING, FutureWarning)\n",
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
-            "  \"of iterations.\", ConvergenceWarning)\n",
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
-            "  \"of iterations.\", ConvergenceWarning)\n",
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
-            "  \"of iterations.\", ConvergenceWarning)\n",
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
-            "  \"of iterations.\", ConvergenceWarning)\n",
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
-            "  \"of iterations.\", ConvergenceWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Model accuracy: 100.00%\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
-            "  \"of iterations.\", ConvergenceWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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9aCMXfuTxdZ5b7zbfJGiMc4M7BWDiOHSus0x6yGWB9SZA/GeahEIY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\ngcgDAroc/wdgxMOCe6L+fwAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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+CfMPGGD8eqtWGXPW5qmrq+Pss88G2oeRFi1axJ577gm0O+MKhQJXX301AH36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\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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x7HYCZcnLIiJ9gSEuut4BS6GlA1OBQapaWMXnd3vHhF12KyrxHHhoHwA2r1oK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\nT8DxSu7xBByv5B5PwPFK7vEEnP8HOVk0uZImqJQAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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YyL1IU9KpSe7YvpOGBSaEVX08J2bVuFIZrXgvEpJV7bKIcqQLfW7OVMk+GLSFu8PU1iFT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\ngqwnAWM/aF/zscr833lXLiQ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+nvXMZwPw9S9rjvATn/xkxkwS95vLrW5zcdF3v0snVqNY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\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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7HFMfVUk/kOm6eHpZg1Zq9QG6yvq+Hh0e8O3TJtvB+T/8Kef995cAePu71A0X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\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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urqaBhdIbpR2w9BFcEVnstfUUK6j4/c++yhHPsIVfel/ioVip3GE3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\nHqBZyA0azAM0C7lBg3mAZiE3aDAP8P8BPptEy7l1IMsAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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zP2BP3a6L8w6mrSiusnTFBsa2x2S+LEylR1VGqbkiG7ZJS9+r1+tFyqKIGo9C\nIDFSgtBY2j2fc1VS0sxIB3I1LFWrOWJyKxejzIGC7uZ1r1fPWWZGt9/89Wfz1Rs1YuyUwUcD0GkH\nVMztdPJpqgVMcDvLVmiy/k/v0fjnc5afr+0P7iIWNSCN1lTzmGxPUTMXV9W4pqvD6lKLgojc2DO9\nyynKc5JoYQXIEBifUeOfGJNo06nmMbu9A0a4ECZq7DrnvDOoVm/UNkLtx/bN9xDXTaX2xe/GdHmT\nDg4yYlFfJRR9Y+wqUeKXGUe8RP55QsQR1xOcC6gYid7AoBq0tmYqfcUJqa2XfRGydqdFYtUkBgOV\nsFEU7pXkH1j2UTuBKDJmSs9QGUDF4qpzowYS8i7lUKrrytmmGoWCgTqnnPQ4vZalJSbtNmHsifj0\nvCiICWNt9+TjlLxuvKVr4HptgMjWvL7/kBVr3clJXTc7y5Dq1HOW1DXKKyvI71pklohUcb6cTIOd\nO9W2WDHNxrMdzbZynBiDaE2l9E23b2T0BJXSS5apRM6makWNm46RL6T2fy2s4jJfrrUElBK5RIm+\nQCmReyBRm8qiO6jXa8iASuKj1q0F4NZbNObQkZOb5PE1kXABoUnbxNbKSZ4XXNcF57UJvbF//yNW\n/u4/6TUDTwQAifE9j1ro5zwVvPvbltfsfO8rAVj2+v8ktHM9FW8aOyJbZ3cyKwgXDdAxory60QWl\nNb232A0gqb7LZyY1HDMLq4wOqgto05XfBGBNx8gE4jrx414CwBKzGWz54UcZMLKB6V36WXn2X3Lv\nnRsBWLlWXV5VqyQxNzdOEKnUbZpUfczx62h1NK57xybt94uf/9tMz+o4TE6pRXv1aiUd2DXfZsSV\nASG9KCdyD0IihtwKwjAoDFNr1uuDGASXA1qULPS6Z9D1qfroJq92RyKFr7XgdDb+52S+iRPPEGLq\ndwCBGccy87MSuC7vtSXtY2pmFiZIurAETKOTMrhEI7vGr9HUw/y0s2hsuw2Auy/9JAAPO1f92sGJ\nZ0Oq7CVc8w0AZqdmqT/tpcWIAGxdrgkVq1fU2fLfamCbPfd3ARhbdR6N674KQHzOK+2eQGwc7rpZ\nXVfHnaLEAtOTU+S5TuQqmsa4adMXePzj9IU5UFXj28hwg7Sly4jUXmy5uaNqyS6yZpsSXZSqdYkS\nfYBSIvdAJCSMhxlsNIiM+9m8LYXU663a0FuLOLGaw5nnzAqCLt2OWxjlFGZ5Qbrjpbu4LilB4A1P\nPUGloZV08cfncUbUthPM2NUOY8a+fbG2EXlO7Q6bvvZvAOw6RyXt7T9QvuijjjuDe/5dVfzg0U8F\nYPut/8nSpqq3mFRdeeJRAAx1QaQBAAAgAElEQVSEaxhtXA1AdZHGdw9VH8Yt3/0UAKcerdrL/Eyz\npw6tz4wylX9uruDqzo3yJwtyTj1Vy8Ns36ZqdJoLg0O6BPDLj4qlRraCmPlmaezqRSmRS5ToA5QS\nuQc5HdqyiaASYySQDFV1PVdk8/SUeijqMqVpt+ZSq6eCxB61nzJbA+dB1i3AZpIrAnI7Pg60rWae\n9JQlteMs+CJIKgX5X1HIPMrZ+e2LAFj/7BfZNUPEiAceeoJWk7j1Kg3pXD7XJjhZg0nWn3A6AKtX\nr8HXixMLO806xtw5MMy4GbZWOl+YLiU3d5KYNhKEIQPDatxaffSxus8CWZrNFs4CXoJYP5uzu9hx\nr8ZdT0xrQM1/fvo/GLFAlOOOVbdZbGM/vHQZO+KEEl2UE7kHLs9J5uZxkhLbxHT5wqCx3nrDgSeG\nj0PSzJMMFJHYexVoq3qGyzwh8AXYnGfADIqJ7ouiEfUUd7PosMj7lRHNCaQb392RkGpF/bviC8aF\nIRh5QRBo+0NLVFXOJeWYR/26taFt3fn9ixl4+K8s6NuSmqrMuXOEQXdpAVpt0bmFxAIZjrXrlR2l\n2bRIMFsutPKU0MYhiNWIlqdfpFFXVb1pj2QyPcKY+bFTH1dd0Uk+smSAVrNUJntRjkaJEn2AUiL3\nwOVVss5DGF28hGrVk9B7CWRqb7VWSKCKlTDJcQVtjWmZxJVuSVQv2TIbbecceebLkurGDDCtuVA5\ng1y6UeRempv7KUlnSU0jMFpranlO1VxX7TmVzDXXjYv2HNkdi4meTYUR0yDmWhobvc7NEa8/GYDm\nJk3yd4mvjyx0Wj6iSxE6QcTHlGtnKxIwaFxkDh3HlsVet9IWo8YVPtn8GgBrFlWpmJ95wzqVzD+8\n4Vaq5u/2rEKRMXcGeU7qffklgFIilyjRFyglcg+a7XGuv/XT5JWNnHiavv233qO8z55Z0rlukEbi\npWOeMWCkcZl0g0T8+tavHb1Uz/Oc1BcKj+JiV8diias+kyqO9yqWnluJirvH7qQ6pH0atrIvLlpM\no2px3X4NHgT4yjY1W3rPj+vaM0tSfCG4ZVZs7Sv3jnG+aFy3VFSqbrA86e13XUm04TEL+uMCyFMp\n7kvvV4pc7aAYK22jFlUxTxpDyUkA7Nh6KRdfqvRqr32+0gvdfex6YqcupkUrNb67vUNdUxPzUwyu\nWEmJLsqJ3AuXkGbbyVoUxAIb79HQzHZHH6pABAl8tJUZsaqV4sH2iRQShl2fr0+BtOMrlUpRIbFr\nzBJCr7JXvNW6G4boUwQrZmyKFw2x+R/eBMDoa/9a+0HGWO7JCexc10JmlQX4zlm9h6Nm9Z5Gq0KS\nqBV6/PvKu/Col72Otk2guVmdOHdf/EG9ZnWIyqNetGDI8iyjasQG3ZrSjkpVXwbFS8mOD/KcdlNf\nRoMVJa1PGrM899mapnnxpf+jx9VWsnyxvkxvv01rRTV3GJvxyBCVZKHR7ZcdpWpdokQfoJTIPcjy\nkLnZQWpDo1RN/duy+24AakbTEwRSBFzFJh3zPCOqeklsKXpBj1rsDWW5uYREinMLbjCExAd0hd2o\nqCIF0txPd7VUgp4Yh8QXvkL77d1b225h7dN121SghqJ6GHPXenXzrPzS2wFIInOV5RFzxm7ZMRbQ\nZY3Ie7U44yGP12ua+2fdyDLu+PzfAnDmy9+vfQ0CxPIYu9FqkBgfd2icZF4iN9MOo4NW/K6lsd+/\ncuwJfOHTn9ZrPkzjrzsDMZvHNBUyMu1leIlWjNy+e4Jly4+iRBelRC5Rog9QSuQFmCcNfkq7M0bS\n1kybnds1Cb+VeCmWUzPDVscycCQMi2T8oEfS7hkQ4uVSu5OQ2XrYl1wNHN21d+bJ7PZmMKrWPMVN\nzvFr19o3xbc+/h6Oesk7ABidvxuARhCw/gkXADA5/kgA7vqiSsJamHDcqEq5u09QaqAwrZGHKvUD\ny346aoUam4gGeMSGNQvuSVxX4+g1zPmyq9kea+QwhlA86YESCe5qXcUbn6KF6MZGlMDh2NENfPdO\npWA7ddSk9Ij+Bld87QoCKcuq9qKUyCVK9AFKidwDEUc9TplpVqhZfHK7rVI3tqoOeQ7zVmEispIJ\nceAKd0vFCntXyAksUCI3yda2LJ5aPS4I/IzhlSzINQCkpw2XOkJRC7KPYxYLs6zmIXlRzlS3raln\n5HU9fggNsBAn1K0C6UBD4563Wn2qOIj54fu06sTwMzQzamnQohZo5tdcU6WjX7KHcU4n9/dnYxbk\nBEbn09VAoN3WfnjNI/VlaZOMeQsSWYwSBSRxlevbaplu3aDHbXjSyUxZFtaUBa3cvksDVHZMtGl3\n9luP75cO5UTuQRwNsmL5WbTvvZMdO38GKCMIQBh0i5ZJ5tVKi9hKHGHFJpWFbzUJiUTdJzXRBze3\nSZCkSZGalzh9QYR5Rh50/dLafl4kS+ShpUJGXd5s8bHZ2EsmS4nMyCWZPehhTuQVL3NhHb3ayBIQ\n6sarvWyp1l0mD/1hOPH3qRtc3mX69HDkRaRbsU2kqJnsVev5aTWmuSgkMcaRyUhrOU/fcjv1+ELd\nP6D83d/+zsVY/gk7k1sB2D6p7rCV6xYzMVtWY+xFqVqXKNEHKCVyDyToEDbugmAzd9x6D9ANchga\n0iinVp5QNYNWZuqjhFXEYqfDhuqxTloMmpSuBCo9ZuZVOg3UqoUaevOnNajj+Be8marxPWdG4dMZ\n20i4RA09RAuJDSTMCUxaN81vNT24jGGLWfYsk3kQUTEusMSpgWjnuFZuGHCOpSNWpC43n1MQIB01\n5kUW1NG0e8vaLeZNKg7YmDkXFGq/h6Nr0PJ2vlmLta4GIRVLDa3FGp21dOU8W3YqHdHUNj3hNy94\nDl/+lnKGBRZjPbtR7+ne6Xs595xnUaKLUiKXKNEH6BuJLCIhcBWwxTn3GyJyDPAZYAlwNfBC59x+\n+WGccyStlEa8ilxuBnqqJ4S+qFtUhFr6wI0oUKMSgLMQyZqrcM+Uuq52f0SDJ1a94l2AcgkE1sbo\n9msBmMog+fp7dNvj/xiApUtW0Lr8c3rub6gL6WsVNURt6Ol3bnHMa8JJtn78jwC4J9a+rXj1e9j+\nSW13+Yu0NOvUJg3RTBxgtZmGTMu4a/tGTrD1cmNANYmRphZzm9+yjWVn/4aOi91nPQjJs4VEeEEA\nbQsLveY6rSCxa7sGd6RJRidT6RzOPwqAKy+9kXPOUqk7PX0vAB/60OdwNVtfb9Wf7bofaIz40qGz\n+IsPloXOe9E3Exl4DVrkfNj+/1vg/c65z4jIPwMvBT68vwYkH6U293RGhqcZm7kE6Kbmta3GcBg7\nArMqt8yiPVQLC92m6/p1LGmoVdaJWlvnPqwVGFvHPYsVZuxa9ycfAmD33z+JuVhLqFSebKo4OTuv\n/U8A8ps/C8DxT9PKioIUBO5hW1MLb5qpMZhpP2Pz4zayKtsndSKuHtHCcccu1uPrw8JcoOr2qNVw\nHl28ytNsEVvyxPbLlZNrV7PNsaeonzq0NMKWpGyZ1kn9UFPnHXDt1crtNTVtVRaNYaU+NMi0EXdu\nn9dIs8WDz+XL/3QaAM1ElwEDlRWkjAMwOaHjNz1rvmP5NMMr5inRRV+o1iKyBngq8DH7X4BzgS/Y\nIZ8Annl4eleixIOPfpHIfw/8GTBk/y8BJp0rss83A6vvr5FWZ54b772OuH4NhCptKzWVELPT6hYJ\nolrhG43Nj1yPQkw4U6mYW0QCaiba4pcqi+WSVCVhOrIOz6OZBxozHLz8iwz7KK+8ZU1UWPRylcSb\nrT7z6nVqbLrqtq9y1CL1Cy8f0Syix/zpe5gydsmhXbdYhzLWvujPALj4r14CQPuohwFwLOu4elJV\n2h3/9ZcALMqP4WHPepne87D2beh8PX80jBFZmNBfl5Anvl61itiWFx1xBJGPM9dHLK7pvZ3/rKfx\ntc8pGf2EaSUznc8zNft5a1EHMotDYmdUSsYs0Fhs7rsso9Mpea17ccRLZBH5DWCnc+7qQzz/FSJy\nlYhc5Zi9/xNKlPgFRD9I5McCTxeR84Eaukb+ADAqIpFJ5TXAln2d7Jz7CPARgDAcdFH9U6R5yuig\nStbGiFY+2LVDF3YuS4pAkNAKpY3v3k1shq8g8UyZYUEUsGhQDVR5rnHSAy4vqIHsEIaGRrqZTp7c\nL8sJa9qP9XX9FDQ2+nFrH0puBqd7Zr4DwJKhYxgdVC1hMlfGzCUipCMaybXsBX8OwPaZOwCI0oxz\nX/kBANJcJb4kMWFVzQwRut4WHwyDKyiHfEE6JwFhYRewTKfcMVBTST9VMQKFjvb1kx/7OCOLlc6n\n0dC2piWlMqBBLfW6XisaDKlZNF3DXHqLrED7sWuOYfe0Grvu+hgl6AOJ7Jx7o3NujXPuaOBC4FLn\n3AuA7wDPtcNeDHzlMHWxRIkHHf0gke8Lrwc+IyJ/DVwLfPz+T8nJaZLHO6lVVArEJgl9IbYghMAk\nVGqBENUwpGUuoIkpdbMEdLOCfJG1IpvJSWHd9jHIUbD3OzUNg4KQL7e2gpaxjJDhYpWKaxuPB6Ad\n3utZcxkerdm1oWr5vEcvUav1I5afofuyuHClDVctpHIgo5OqpTnMVTLnrssh7UNLxfmi7A7ZQx6o\nlPbahW6Lar4qhmPHNnUjbThai7i/4vd+k1271EL9ox/8EIC7Nm1hrK2usVZi7Cw2Vt9zjtTXwioB\n9NlEds5dBlxm3+8Ezjy4FtrkcicXXnABl1hU0dhdViO48C9R+JjaVje4EobUwti2+UQJiqhkPwlD\nKxLcZp6KGX++ftU/ADBVuZ7TFquv+PRl5wOQZRHeXufJPF1u/FxBm8QnWdjOjuSFrl4x7rAISM3o\nlltUWO7TJfOUi7eqIctFep04bBDnGrd17rI32fF67UgE5wvMRZZMEtToBDrhJLWosjwjs5dXZP7p\n6VnvhkqKF9wtt6tB7m1vfyupX2rYiysMI1KLnPO0S12fftiTGloC+kC1LlGiRJ9J5AcKAarAZz/9\nX0gR02xBF77SqYRFcIOPe24mbSKTVFHFas2ECU4886YaoMbqNwDwudv+FoeR5A0ZsyaOH6UaxXX9\nz34EwPNOeRu3TGi50+s2XgfAi8/7fwB0mgm5uanCXI1H1153Nac9/FF2N1YL2QVFTHYeeQohu6fA\ncf5JStx30aY36q6O8MTVmuTv+bAt8YpOJyW08Ugq6gb74d0f5NHHKL1QNdWsKkdGZuQIPgx7w0OV\nMXPr9y4vVHW/1OikSbc4nknadtoqpEyaLiyCl6bpXhlXv+woJXKJEn2AUiL3IHOOmU6LEIhjHRpf\n5cAbhbLMkVu5UecNP2FEYu6VwHiqAxq4EV0X/tt1fwJAakEmATUQH9yg7Q8lS/jie7SY+q+/QaXN\npbd9kK0jGjzRWK60PmlT18i37f4JW7dpzvRJq84F4Kwznsx82/J+fTBFVaCt36+/WyX92euVrieX\nDCasQJutb9Nqk4qtSVPLrf78pz4KwNMueA4/2qiBG7sGrrdRi7j4Tg0IkTnVPFbFZzPeVIndbFm9\nqZVa6HzJksXs3GFx12l3zds1BHa5wHNbE/s1vqdCwuU9XOEloJzIC+EcLssJqnHhK65XNf65vkgf\nnKntEyTOW45NLU7zgiFEPBdX0OaDl78KgMaILwhnqX3SKh7c0MrDXP7lmzl9qU7ISmMzALfNX8PO\nn+kLorlDVetI1DiVph3SRRYn3dGgtW9/7MecfaHGd2+6Rdk9nnzmbzEr6iM+e+3z9PhRtUa//e1v\n4+HnW7KCsX8OtVczKRoYc9WUTtDRszRg7kfTH6Y1qhFuvtCcy5skPiJtQP3Oy1eup2r+b5urLFuq\nUWLnP+eZ/Mc/q/O3VtGXXp53yf49yb2jywcemDpfGA1Fin0lFOVrrUSJPkApkXshAkFAkmVFyZXY\nfLWNhkrVySD3tiIS8y1XelRDl3dLncYD5ooyqSRm9MpdG4ksDjvQbSdfOMJgqGmPmZUMHRwM2WQJ\n+Tta6gZrZioRb7phC6c8TtMN3bxKTGns4PJvaoL+8qFVet7crVxzrarBYu7g039Vi7StfkqTMx/2\nbAC+cuM/ApDWdnHthMbOjKWaUhirLY1obpiOrz/j6X8yR2QURrkZuBrtVYROJf1xJxwNwA//Wwu2\nnfeMZxbVMuYTo0ASCG0pU0jkHk5vr3UX7icp5c+eKEekRIk+gJSO9S6CIHBRFOECGB7V+Oj1K1Wy\njSxWqXfXpnvYPa7rTx/FEFYqBbuld89c9p0fMmVFx++YVom4uOarP0R0RCV81QjuakHXAIbtI4fI\n1tWW6lusE13qaLX1mkFqGUNpRmqundRcTkneIU8W/sahBa9AQBR1I9YARCLqJh2jyIq5STf220em\nONNYQhyRVWXLjZTwim9dRm6Gvf+5SN1nW7cqddKFL/t9Pvy+vwOgZex6Ca4IBPFSNwiC4rsveOfd\nT2ElJmlp+51W62rn3Bn8kqOcyD0QERcEAXkkrF2llt3Xvk4NVu9467sBeNKvncMl3/8uALOTamwK\nRAjNijpYU+vvQDTIZ7+lCfmRsVy2vPGGoCh37MuzBM4R2KTK7MFNaRNbOGiEGo+cj5gCAjMWeU7d\nOKsyaYwf6aRazGeaM2y7V4kFduxUa7HzwVlRpWAqmbe6x1ngqEeWMFLzYZ42kaJaoe7OGStmJ8+Y\nte8TU/r5/R98l1279Zp+kk/Nar9ChNxeej7ys5NlhbW6qHSZ9ISF+mJ4pnZnOKrmHZibmSknMqVq\nXaJEX6A0dvVAS51E5OKY3K3qc1RT9bJtRqxvfOMyHnLS8QA85eVPBmCwXi+knNdv8k7CZV+9DIBW\nUli7iuvMWmLCzq3mU+20qZjL5s47NM2w2WkxMaXcV66l71wfkxwHgrOwKWe82ZmERXK/T8JweUDH\nfNyLl+lyodFQFX++2aFmjvLaqN5nliY4y7FszqmUHl2sx09PtQh8qJYtIdIgJLWEkbVrlOigk7eL\nRBFPh/SEJz0BgG2bN3PHzRpjPe/98T3GxY6Vr+0tl+Olc+E7DrqFA0ooSolcokQfoFwj90BEXBRF\nBKFKZoCTjtYAi9u2qmsocqEPlOLotbpvYmKiSK4fGVZfTb1RY9xS82JfktUKmKdJpzDg1G0d2mo2\nSRNP8aPrv2OPP5at924FYHREk+p92mMcCaOLlGRg5zblTMjpSuJhI0QYHh0q1sZLlyyz+7S1ZpIU\nUVOetzsUmJxQF9ec0Qv51EWXZaRGJTQwrBlSO3btIjDFrmPx4612k/mWHvf2dypZ3+UXfxuAb19y\nKU3TbuaN0DAKgyLKC+9ayvOib3s9o4EUXNqddrtcI1NK5BIl+gKlRO6Bl8gSQmxW0Yee/AgAzn3i\n2QD8yz9/CGcBELWqrmmDOCrWkxseopUh5lpNBqpWfrWtkm3AKGuCKGZmRqVeauvnRUuWMDam8clJ\n4sMVO4iZt5cu1vXtuFmIB6sxO3fr+jm3nOW0kxH7Cg/mJgoIi+CNyCpZTFlfBxsVwlDXxhXTDILA\nMTSoa+INx+ma93i7pzVHraVhRAt+nZ1leUGEF1qkzPiuGS797mUAXHOlxnfnZh/o5FmhjXQSH+Ah\nhSD2Rdslp2AlSKzShU+lkjwn8JlnzWYpkSkn8gJ491MYdWsbx8ausXJQo6je+NdvojqiE3lqUifE\njp3bmZ02t49VYExmW0VC/LCpxUPDllCRR1QtUqxm5WR2ju0ujEDbTRWemp2haepnx5IPmk19KbSb\nbdJsoWEoCiA1n/HgoL5Elq5YzuROjQpbu0ZdaiecpLWQV69ey+233A7A5q0a3z0/O0vHDEkti7wq\n2o8C5q0mdMc7tkW67jL7zJwUrqIiHdTGOBAp2Fbm28YVHgku85Ul7TyCoj3/jPrfIu106wy0S9Ua\nKFXrEiX6AqVE7kEQBK5SqYLkXYniY6jN4FMJqjiLKfZ5+mEQFapjzfN5SZfJqul5sD0/VxiRhypV\nOi2TVeKKGO68J5LJ98On0XtOrkhC8sBqD9vxcRyQe9IAk4SRBEV7HetHxYxved416kW+SJwLySxS\nw7fh7y2uVmjPt+xcG49qlaztSQw853VXmnq3kken06FiLq926q8j3SgVu1bW81hmPkXU/nfOFdJ5\nbm6ulMiUErlEib5AGRCyAI7cpQhSEAkEZoXxa89U2ji/zSRchNDJLMbZwg874mAPqZ6Z0SlwMWFl\nYSUGl6WFccdrSUnak0BvFR7SoBvmGYivxKDbmu2ESsXHR1vAhkAWLSzX6nN/a2GV1PKKM18WNu4W\nqfM5xzWrddWcSwmLR0bvpd3qdOOkbSUc9NDwJBYvHfl1dhwX4Zhe4md5QtXiuhMbxzx3VKvGdW1u\nOzGtoT7QoGbbNt5yGyXKibwHhDAMSbIMMRXVJ7B7etdcHM6n8OV+sufF5Gv7B7cSFkadLPUslp5Y\nwIFxfDl8qmNeGIT8A95JU2o2WVPj5EzNou0kJTcrtL92LYx64pEVORD6ftoE877oDim5ZznJfWKE\n60ZVmbEurumkmWtO0/btWxvVOEbsReHHqF6tFvfgrcuZvRziSo3MjFW55Xe22vMF6UJN1PhXqdeK\n+5qdVUt9Yoa2VqddVL8soShV6xIl+gClRO6Bw5GmKbVag3bLynaG3UgjPUjoRlQr0jQl3EM65kla\nGKgqFq+dFm6arOs3zbzs7GZQ+eOiKKJjUVC5pwaKvFrvcIX7ySKrsrTob+BjrsOQqKqGobqpqo0B\ndU3VazXCwBuXTKOYbzM6qtFpTSOGb5mBa9GSuLj3rGNutjxjaEhTPJvzc0W7c5aF1bZPZ/eZpDmB\n3YM33DlyQp/lZX2cn58v4ql91Fzs47HTrODGLqEoJXKJEn2AvnA/icgoWhv5ZFRk/C5wK/BZ4Gjg\nbuB5zrmJ/bVTrVXdqrVrqJghBaCTqjRyiYqPTqfD0KDGJYu5ceZmZgvpktjxS1Yso2VrwVhUInZd\nPVViyz32hcAllEIq+cyhUCgMWh3LAPJBGgMDg4WEmpvTNeTunbsLiT1ifRybmmDAJPD8vGkZpl0k\nWVZ8D41ZIEmTwsBW8f01A1prbq7QDPz9ZkKxlvYlXVzeDd44+kQt+bp5o2Z0JThyczt5rSHJMiK7\nz7hq9zTfKtxURWCIXUeCADEX4Oz0dOl+on8m8ieA7zvnPiYiFaAB/Dkw7px7l4i8AVjknHv9/toJ\nwsDValUCJ3R8bWObGN6IFEVRl7GirmGNjVqdCUs08CyaFQl57oteCMBFX/iSXsD8rEnqiPIe6zaW\nyufVYZtASdopDGb+d3I9EWd+m++POEdjQI1FbZv4ad61fPsaUN5M1E7TQo2ObNJMT00WKnjdXgBp\nD8Pl7KwREPhQyswVJP69NaD8eFUa2p+WRahJ7gqffOBfWGFY1NHy1vN6vUHa1JciFi7rXwDQndRz\nk1PlRKYPVGsRGQEehxVpc851nHOTwDOAT9hhnwCeeXh6WKLEg48jXiKLyKlofeObgEcAVwOvAbY4\n50btGAEm/P/3hSAMXbVWQ7KM1CcdZJ4w3ZO2Z+TmfvKVGj1hPVBQ82RJm7iukm35UUcDsO1ujWvO\nXEDVeLEC89GGYUi76amDLFmArFCtffK+TxwICXDeCmQRXnkmRb3lwu2TpoUrqIhZ9v5q6ZZ0kR5m\nypqX+iZNfbz3mqPXs2WTMmt6U54kOZH3M1vRtaF6o0hRLGBGOEkzEnOOhSb5Y0fhmxevoaSOzLSK\naEh/tnxuuri3eUvUaM+WkV3QBxIZtbyfDnzYOXcaMAe8ofcAp0/wPt9YIvIKEblKRK460l9qJX55\n0Q8SeSVwhRU6R0TORifyQ4DHO+e2icgq4DLn3An7aysIAlep1YgC6JjEcRbM4Y035EEhAekJSvBB\nEZFJlnanVaxNo1glj3dpLV6+jNlpXWv6oIggCMgtGd8XW6tEEa2WSmlv2PIkz0uWLGZ8XG13WU/Q\nSkFaZ9duNBqF8Wx0kUo2H1QyNzeH+FRBM9INDy8q3GDLl2vG145dSqqQtHOw7C4fJVatVpmZ0Xvp\n+DVtKF0tIfXdtrEQcNa+DyAJg4iOGfFqDUv97HSKkqzZHllQIkJoxIO7t+8oJTJ9IJGdc9uBe0XE\nT9LzUDX7q8CLbduLga8chu6VKPF/gn4JCPkj4FNmsb4T+B30JfU5EXkpsAl43v22Iso0k+d5sVaT\nPYuFSVcyDFggxNzcHGkhCS3/toc8rlrxwQ6ajD85OU69qiQDWdLNtV132qkA3P7Ta7SpKGDxkFL2\nFGRzPkQyT1mzTqmGpo2kIEkS6kab67WBVqtFzUgD5manAM1Y0nsTVh2lUnfVKuXvvu66G3G2ph4f\nN6oiIx+QSsqAEQpMTs0W/QqddwsVQ4RLvQReuG/p8uVMm8vNr4GHR0aYtYoarTmzbkfBXhS5HbNo\nB0FAzWtIJYA+UK1/ngjC0FXqNSKENFc1tzKgD65n8mi35qlUdWK4wvDUTaT3vp0gkJ7JrJ+Lltik\nbDYJo4WuplanQ2ARWg2b+ImTwvUzMqJq8eSkucJd2k2M8K6eKKRlxO1Zy6uqDcQXVOuo6usZQKYm\nJwnNR+wnzdLFy5ma1hdDNbYJbAa/xpLFbN18NwCDA+qnnpyaRMzHnthypBJEpJ5o3kYgtqSIIA5p\neT+yH7LAEVoaY2Quvfn5mcLV5h/RYokwvISpSWVTmZ0u3U/QB6p1iRIlSom8ACKyC7V6jx3uvhwA\nlnJk9BMe3L6ud84te5DaPmJQTuQ9YG6oX3hV7UjpJxxZfT1SUarWJUr0AcqJXKJEH6CcyHvjI4e7\nAweII6WfcGT19YhEuUYuUaIPUErkEiX6AOVENojIU0TkVhG5w/KXf2EgImtF5DsicpOI3Cgir7Ht\nfyUiW0Tkp/Z3/i9AX+8WkeutP1fZtsUi8i0Rud0+Fx3ufvYbStUaEJEQuA14ErAZuBJ4vnPupsPa\nMYMlfaxyzl0jIkNoqnB87/YAAAGxSURBVOYz0bDTWefc3x3WDvZARO4GznDOjfVsezcHSfJQ4uBQ\nSmTFmcAdzrk7nXMd4DMoMcEvBJxz25xz19j3GeBmYPXh7dVBoSR5eJBRTmTFauDenv838ws6UUTk\naOA04Me26VUicp2I/OsviMrqgG+KyNUi8grbtsI5t82+bwdWHJ6u9S/KiXwEQUQGgS8Cf+ycmwY+\nDBwLnApsA957GLvncZZz7nTg14E/FJHH9e7cH8lDiUNHOZEVW4C1Pf+vsW2/MBCRGJ3En3LOfQnA\nObfDOZc553Lgo+gS4bDCObfFPncCX0b7tMPW+X69v/Pw9bA/UU5kxZXAcSJyjOU0X4gSE/xCwDjH\nPg7c7Jx7X8/2VT2HPQu44f+6b70QkQEzxiEiA8CTrU8lycODjH4hFnhAcM6lIvIq4H+BEPhX59yN\nh7lbvXgs8ELgehH5qW37c+D5Rj7oUO7u3zs83SuwAviy5UlHwKedc98QkSs5WJKHEgeF0v1UokQf\noFStS5ToA5QTuUSJPkA5kUuU6AOUE7lEiT5AOZFLlOgDlBO5RIk+QDmRS5ToA5QTuUSJPsD/B95N\ndIAgc+6bAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
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OlOQ6wsi2KVNTd7sut/yQBc/2G/gCn889H4CQ2OQPyQUKQwJaw7TmhXLZlLCu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\n7vEEnP8HLxdYIWCH+/QAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 288x144 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "\n",
+    "clf = LogisticRegressionCV()\n",
+    "\n",
+    "X_img_lr, y_img_lr = X_img.T, y_img.T.ravel()\n",
+    "\n",
+    "clf.fit(X_img_lr,y_img_lr)\n",
+    "\n",
+    "print(\"Model accuracy: {:.2f}%\".format(clf.score(X_img_lr, y_img_lr)*100))\n",
+    "\n",
+    "def show_image_prediction(X, idx, model) :\n",
+    "  image = X[idx].reshape(1,-1)\n",
+    "  image_class = classes[model.predict(image).item()]\n",
+    "  image = image.reshape((ROWS, COLS, CHANNELS))\n",
+    "  plt.figure(figsize = (4,2))\n",
+    "  plt.imshow(image)\n",
+    "  plt.title(\"Test {} : I think this is a {}\".format(idx, image_class))\n",
+    "  plt.show()\n",
+    "  \n",
+    "X_img_lr, y_img_lr = X_img.T, y_img.T\n",
+    "\n",
+    "for i in np.random.randint(0, len(X_img_lr), 10) :\n",
+    "  show_image_prediction(X_img_lr, i, clf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "ML_Task_1.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/negative_images/1.jpg b/negative_images/1.jpg
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index 00000000..945e0a3b
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index 00000000..72afb22e
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index 00000000..63235939
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index 00000000..ad922b3a
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diff --git a/negative_images/30.jpg b/negative_images/30.jpg
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