diff --git a/EDA.py b/EDA.py
new file mode 100644
index 0000000000000000000000000000000000000000..7852c8eaa04445025328d9da394891cd905b97c6
--- /dev/null
+++ b/EDA.py
@@ -0,0 +1,61 @@
+from sklearn.decomposition import PCA
+from math import ceil
+import numpy as np
+import matplotlib.pyplot as plt
+
+def find_mean_img(full_mat, title):
+    """[summary]
+
+    Args:
+        full_mat ([type]): [description]
+        title ([type]): [description]
+
+    Returns:
+        [type]: [description]
+    """
+    # calculate the average
+    mean_img = np.mean(full_mat, axis = 0)
+    # reshape it back to a matrix
+    mean_img = mean_img.reshape((300,225))
+    plt.imshow(mean_img, vmin=0, vmax=255, cmap='Greys_r')
+    plt.title(f'Average {title}')
+    plt.axis('off')
+    plt.show()
+    return mean_img
+
+def eigenimages(full_mat, title, n_comp = 0.7, size = (300,225)):
+    """[summary]
+
+    Args:
+        full_mat ([type]): [description]
+        title ([type]): [description]
+        n_comp (float, optional): [description]. Defaults to 0.7.
+        size (tuple, optional): [description]. Defaults to (300,225).
+
+    Returns:
+        [type]: [description]
+    """
+    # fit PCA to describe n_comp * variability in the class
+    pca = PCA(n_components = n_comp, whiten = True)
+    pca.fit(full_mat)
+    print('Number of PC: ', pca.n_components_)
+    return pca
+  
+def plot_pca(pca, size = (300,225)):
+    """[summary]
+
+    Args:
+        pca ([type]): [description]
+        size (tuple, optional): [description]. Defaults to (300,225).
+    """
+    # plot eigenimages in a grid
+    n = pca.n_components_
+    fig = plt.figure(figsize=(8, 8))
+    r = int(n**.5)
+    c = ceil(n/ r)
+    for i in range(n):
+        ax = fig.add_subplot(r, c, i + 1, xticks = [], yticks = [])
+        ax.imshow(pca.components_[i].reshape(size), 
+                  cmap='Greys_r')
+    plt.axis('off')
+    plt.show()
\ No newline at end of file
diff --git a/data_processing.py b/data_processing.py
index f2469c7629cfb9ffe2e0663e14cefa02644d61f4..66fc35020faaa894a798691df81a080f43a82b0e 100644
--- a/data_processing.py
+++ b/data_processing.py
@@ -1,28 +1,58 @@
 import os
-import cv2 #vision task package opencv-python
+import shutil
 import pandas as pd
-import glob
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
 import numpy as np
 
-def load_transform_images(folder):
+
+def load_sort_data(meta_filename = str, image_folder = str, output_folder = str):
     """[summary]
 
     Args:
-        filename ([type]): [description]
-    """
-    images = [cv2.imread(file, flags=cv2.IMREAD_GRAYSCALE) for file in glob.glob("./data/"+ folder+"/*.jpg")]
-    return images
+        meta_filename ([type], optional): [description]. Defaults to str.
+        image_folder ([type], optional): [description]. Defaults to str.
+        output_folder ([type], optional): [description]. Defaults to str.
 
-def transform(data):
-    flat = []
-    df = pd.DataFrame()
+    Returns:
+        [type]: [description]
+    """
+    data_dir = os.getcwd() + "/data/"
+    dest_dir = data_dir + output_folder
+    metadata = pd.read_csv(data_dir + '/' + meta_filename)
+    labels = metadata['dx'].unique()
+    label_images = []
     
-    for i,img in enumerate(data):
-        scale = (img.astype(np.float32) - 127.5)/127.5
-        scale = scale.reshape(1,-1)
-        df = df.append(pd.Series(scale[0]), ignore_index=True)
-        
-    return df
+    for i in labels:
+        if os.path.exists(dest_dir + str(i) + '/'):
+            shutil.rmtree(dest_dir + str(i) + '/')
+        os.mkdir(dest_dir + str(i) + '/')
+        sample = metadata[metadata['dx'] == i]['image_id']
+        label_images.extend(sample)
+        for id in label_images:
+            shutil.copyfile((data_dir + image_folder + '/' + id + '.jpg'), (dest_dir + i + '/' + id + '.jpg'))
+        label_images = []
+    
+    return metadata, dest_dir
+
+def transform(path, size = (300, 225)):
+    # create a list of images
+    img_list = [fn for fn in os.listdir(path) if fn.endswith('.jpg')]
+    #iterating over each .jpg
+    for fn in img_list:
+        fp = path + '/' + fn
+        current_image = image.load_img(fp, target_size = size, 
+                                    color_mode = 'grayscale')
+        # covert image to a matrix
+        img_ts = image.img_to_array(current_image)
+        # turn that into a vector / 1D array
+        img_ts = [img_ts.ravel()]
+        try:
+            # concatenate different images
+            full_mat = np.concatenate((full_mat, img_ts))
+        except UnboundLocalError: 
+            # if not assigned yet, assign one
+            full_mat = img_ts
+    return full_mat
 
-# def batch_data(data):
     
diff --git a/image_preprocessing.ipynb b/image_preprocessing.ipynb
index f140112008e9326dbb5c3ad4ac9579f77c32e6d8..c2737bfbc0a742b0ef6fc282b7c15f0eda81b6f6 100644
--- a/image_preprocessing.ipynb
+++ b/image_preprocessing.ipynb
@@ -13,235 +13,613 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 39,
    "id": "d7e56e0e-7eec-429d-940b-c3337db4b4dc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from data_processing import load_transform_images, transform\n",
-    "import matplotlib.pyplot as plt"
+    "import tensorflow as tf\n",
+    "import importlib as lib\n",
+    "from data_processing import load_sort_data, transform\n",
+    "import EDA\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caa99aac",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "The project we are presenting is a multi-label image classification task based on the 2018 Human vs Machine skin lesion analysis toward melanoma detection hosted by the International Skin Imaging Collaboration (ISIC).\n",
+    "\n",
+    "This notebook will contain the following sections:\n",
+    " 1. Problem Definition & Data Description\n",
+    " 2. Data Preparation\n",
+    " 3. Exploratory Analysis\n",
+    " 4. Data Processing for Model Ingestion\n",
+    " 5. Model Creation\n",
+    " 6. Model Scoring & Evaluation\n",
+    " 7. Interpretation of Results"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "67180bd5-c307-4d97-ac64-a6f7980ce32d",
+   "id": "830dee53",
    "metadata": {},
    "source": [
-    "## Load Data"
+    "# 1. Problem Definition & Data description\n",
+    "\n",
+    "#### Problem Definition:\n",
+    "Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. With a sufficiently large and diverse collection of skin lesions we will develop a method to automate the prediction of disease classification within dermoscopic images. The project is meant to human computer computer collaboration and not intended to replace traditional forms of diagnosis.  \n",
+    "\n",
+    "Possible disease categories (and abbreviation) for classification are:\n",
+    " 1. Melanoma (mel)\n",
+    " 2. Melanocytic Nevus (nv)\n",
+    " 3. Basal Cell Carcinoma (bcc)\n",
+    " 4. Actinic Keratosis / Bowen's Disease (akiec)\n",
+    " 5. Benign Keratosis (bkl)\n",
+    " 6. Dermatofibroma (df)\n",
+    " 7. Vascular Lesion (vasc)\n",
+    "\n",
+    "#### Data Description\n",
+    "- Data images are in JPEG format using the naming scheme `ISIC_.jpg` where _ is a 7 digit unique identifier of the image.\n",
+    "- There are a total of 10,015 600x450 pixel color images contained in the training data folder.\n",
+    "- There are a total of 193 600x450 pixel color images contained in the validation data folder.\n",
+    "- The training metadata is a 10,015x8 .csv file containing the following variables*:\n",
+    "  - lesion_id: Unique identifier of a legion with multiple images.\n",
+    "  - image_id: Unique identifier of the associated image file.\n",
+    "  - dx: Prediction label containing the 7 abbreviated disease categories.\n",
+    "  - dx_type: Method of how the diagnosis was confirmed. \n",
+    "  - age: Numeric year age of the patient.\n",
+    "  - sex: String binary value 'male' or 'female'.\n",
+    "  - localization: Categorical location on the body the image was taken. \n",
+    "  - dataset: Image source.\n",
+    "\n",
+    "*Further details of the data will be provided in the data preparation section."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "abf26e08-49e0-4f39-8b1e-66504dd70fbf",
+   "id": "96ff082e",
    "metadata": {},
    "source": [
-    "# Image Processing and Transformation"
+    "# 2. Data Preparation\n",
+    "\n",
+    "#### Step 1. Load and Sort\n",
+    "First we will load the data using the function `load_sort_data()`.\n",
+    "\n",
+    "The `load_sort_data()` function sorts the images into folders based on the diagnosis label.  This will help reduce the overall size of the dataset and make preprocessing the images much faster.  The function will return the metadata as a pandas DataFrame and the path of the sorted image folders. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9997df6a-7b70-4060-9e0c-a8c0ed6c3737",
+   "id": "b8c4f292",
    "metadata": {},
    "outputs": [],
    "source": [
-    "training_data = load_transform_images(\"ISIC2018_Task3_Training_Input\")"
+    "# function takes 3 parameters: metadata filename, the folder of the raw images, and the desired name of the destination directory. \n",
+    "metadata, dest_dir = load_sort_data('HAM10000_metadata', 'ISIC2018_Task3_Training_Input', 'Training_Images_')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7e9702c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'h:\\\\School\\\\Winter 2022\\\\DS Projects\\\\2018\\\\hvm-image-clf/data/Training_Images_'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The path of our training image folders sorted by label\n",
+    "dest_dir"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6601df9a-0323-40e3-96f1-96c92895e4c9",
+   "id": "20b8415f",
    "metadata": {},
    "source": [
-    "### Sample conversion from a single image\n"
+    "#### Step 2. Tidy Metadata\n",
+    "We will now take steps to tidy our metadata.\n",
+    "First, subset the variables we intend on using, next analyze missingness and finally we will correct our expected datatypes."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "35352bf4-6670-4de9-afe2-7efc51c2b151",
+   "id": "0ba9148a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10015 entries, 0 to 10014\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   image_id      10015 non-null  object \n",
+      " 1   dx            10015 non-null  object \n",
+      " 2   age           9958 non-null   float64\n",
+      " 3   sex           10015 non-null  object \n",
+      " 4   localization  10015 non-null  object \n",
+      "dtypes: float64(1), object(4)\n",
+      "memory usage: 391.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Subsetting into variables we will use. \n",
+    "metadata = metadata[['image_id', 'dx', 'age', 'sex', 'localization']]\n",
+    "# We will need to change the Dtypes of the columns into the expected types\n",
+    "metadata.info()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f4a3578",
+   "metadata": {},
+   "source": [
+    "As we can see below, we have a total of 57 NA values in age.  When looking at the distribution of NA values, only our largest quantity of labels have NA's. The age variable is only useful in providing context to our problem and will not be used as a feature in our model.  Therefore it is not necessary to do anything further to the NA values.  During exploratory analysis we can deal with the NA values as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e6d378d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of unique labels\n",
+      " nv       6705\n",
+      "mel      1113\n",
+      "bkl      1099\n",
+      "bcc       514\n",
+      "akiec     327\n",
+      "vasc      142\n",
+      "df        115\n",
+      "Name: dx, dtype: int64 \n",
+      "Number of NaN values within each label\n",
+      "       dx  image_id  age  sex  localization\n",
+      "0  akiec         0    0    0             0\n",
+      "1    bcc         0    0    0             0\n",
+      "2    bkl         0   10    0             0\n",
+      "3     df         0    0    0             0\n",
+      "4    mel         0    2    0             0\n",
+      "5     nv         0   45    0             0\n",
+      "6   vasc         0    0    0             0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-5-4c0ddd092066>:5: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
+      "  metadata.drop('dx',1).isna().groupby(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sum the na values contained within each label\n",
+    "print(\"Total number of unique labels\\n\",\n",
+    "      metadata['dx'].value_counts(), \n",
+    "      \"\\nNumber of NaN values within each label\\n\",\n",
+    "      metadata.drop('dx',1).isna().groupby(\n",
+    "          metadata.dx, \n",
+    "          dropna=False, \n",
+    "          observed = True\n",
+    "          ).sum().reset_index()\n",
+    "      )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "91aa284b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10015 entries, 0 to 10014\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count  Dtype   \n",
+      "---  ------        --------------  -----   \n",
+      " 0   image_id      10015 non-null  string  \n",
+      " 1   dx            10015 non-null  category\n",
+      " 2   age           9958 non-null   float64 \n",
+      " 3   sex           10015 non-null  category\n",
+      " 4   localization  10015 non-null  category\n",
+      "dtypes: category(3), float64(1), string(1)\n",
+      "memory usage: 187.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Changing datatypes\n",
+    "dtypes = {'image_id':'string', \n",
+    "          'dx':'category', \n",
+    "          'sex':'category',\n",
+    "          'localization': 'category'\n",
+    "          }\n",
+    "metadata = metadata.astype(dtypes)\n",
+    "metadata.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41f467b5",
+   "metadata": {},
+   "source": [
+    "#### Step 3. Image Processing\n",
+    "\n",
+    "In this step we will construct an NxM matrix where N is an image and M is the number of pixels in the image. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "05398a91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assign vectorized images to variables\n",
+    "akiec_images = transform(dest_dir + 'akiec')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e8642d8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bcc_images = transform(dest_dir + 'bcc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "5312b5de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bkl_images = transform(dest_dir + 'bkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "49338970",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_images = transform(dest_dir + 'df')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "784d69cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mel_images = transform(dest_dir + 'mel')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6cd167a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-35-c19654a1d8a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnv_images\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdest_dir\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'nv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m150\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mh:\\School\\Winter 2022\\DS Projects\\2018\\hvm-image-clf\\data_processing.py\u001b[0m in \u001b[0;36mtransform\u001b[1;34m(path, size)\u001b[0m\n\u001b[0;32m     50\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m             \u001b[1;31m# concatenate different images\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m             \u001b[0mfull_mat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_mat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg_ts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     53\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mUnboundLocalError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m             \u001b[1;31m# if not assigned yet, assign one\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "#This takes a really long time to run even when cutting down the images size.\n",
+    "nv_images = transform(dest_dir + 'nv', size=(200, 150))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4de5cec3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vasc_images = transform(dest_dir + 'vasc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "d92158fa",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "(1113, 67500)"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.imshow(training_data[0], cmap='gray')\n",
-    "plt.show()"
+    "mel_images.shape"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4936bf4f-422e-44cd-9a0a-d95bf7a1a9fa",
+   "id": "3fb5f03e",
    "metadata": {},
    "source": [
-    "Comparison of the first reconstructed image and original image in the dataset.\n",
+    "# 3. Exploritory Data Analysis\n",
     "\n",
-    "<img src=\"./data/ISIC2018_Task3_Training_Input/ISIC_0024306.jpg\" width=300>"
+    "Exploritory analysis will be conducted on in two major steps.  First we will complete analysis on the metadata then the image dataset.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ca3d6bf",
+   "metadata": {},
+   "source": [
+    "#### Step 1: Metadata EDA\n",
+    "We will perform the following analysis on the metadata:\n",
+    "- Summary Statistics\n",
+    "- Class label distributions\n",
+    "- Correlation"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2fd91bc2",
+   "id": "686965dd",
    "metadata": {},
    "source": [
-    "### Flatten image"
+    "Summary Statistics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "561e73a8-6c36-4baf-b1e6-3d2e05b0004f",
+   "execution_count": null,
+   "id": "6e579e93",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e031ebf0",
+   "metadata": {},
+   "source": [
+    "Class Label Distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf51add8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8858805d",
+   "metadata": {},
+   "source": [
+    "Correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0602660",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "864a5b70",
+   "metadata": {},
+   "source": [
+    "#### Step 2: Image EDA\n",
+    "We will perform the following analysis on the metadata:\n",
+    "- Average image of each label.\n",
+    "- Contrast between the average images.\n",
+    "- Principal component analysis (PCA) on each label."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b80a2f2b",
+   "metadata": {},
+   "source": [
+    "Average Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "971b33b7",
    "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>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>269990</th>\n",
-       "      <th>269991</th>\n",
-       "      <th>269992</th>\n",
-       "      <th>269993</th>\n",
-       "      <th>269994</th>\n",
-       "      <th>269995</th>\n",
-       "      <th>269996</th>\n",
-       "      <th>269997</th>\n",
-       "      <th>269998</th>\n",
-       "      <th>269999</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.239216</td>\n",
-       "      <td>0.223529</td>\n",
-       "      <td>0.231373</td>\n",
-       "      <td>0.239216</td>\n",
-       "      <td>0.247059</td>\n",
-       "      <td>0.247059</td>\n",
-       "      <td>0.231373</td>\n",
-       "      <td>0.278431</td>\n",
-       "      <td>0.309804</td>\n",
-       "      <td>0.278431</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.317647</td>\n",
-       "      <td>0.270588</td>\n",
-       "      <td>0.247059</td>\n",
-       "      <td>0.239216</td>\n",
-       "      <td>0.254902</td>\n",
-       "      <td>0.231373</td>\n",
-       "      <td>0.231373</td>\n",
-       "      <td>0.254902</td>\n",
-       "      <td>0.278431</td>\n",
-       "      <td>0.262745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.184314</td>\n",
-       "      <td>0.184314</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.192157</td>\n",
-       "      <td>0.223529</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.176471</td>\n",
-       "      <td>0.184314</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.207843</td>\n",
-       "      <td>0.176471</td>\n",
-       "      <td>0.239216</td>\n",
-       "      <td>0.239216</td>\n",
-       "      <td>0.247059</td>\n",
-       "      <td>0.231373</td>\n",
-       "      <td>0.223529</td>\n",
-       "      <td>0.215686</td>\n",
-       "      <td>0.254902</td>\n",
-       "      <td>0.262745</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2 rows × 270000 columns</p>\n",
-       "</div>"
-      ],
+      "image/png": "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",
       "text/plain": [
-       "     0         1         2         3         4         5         6       \\\n",
-       "0  0.239216  0.223529  0.231373  0.239216  0.247059  0.247059  0.231373   \n",
-       "1  0.184314  0.184314  0.200000  0.200000  0.192157  0.223529  0.200000   \n",
-       "\n",
-       "     7         8         9       ...    269990    269991    269992    269993  \\\n",
-       "0  0.278431  0.309804  0.278431  ...  0.317647  0.270588  0.247059  0.239216   \n",
-       "1  0.200000  0.176471  0.184314  ...  0.207843  0.176471  0.239216  0.239216   \n",
-       "\n",
-       "     269994    269995    269996    269997    269998    269999  \n",
-       "0  0.254902  0.231373  0.231373  0.254902  0.278431  0.262745  \n",
-       "1  0.247059  0.231373  0.223529  0.215686  0.254902  0.262745  \n",
-       "\n",
-       "[2 rows x 270000 columns]"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 5,
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[121.11101 , 122.14468 , 122.84349 , ..., 119.93995 , 118.71974 ,\n",
+       "        117.54959 ],\n",
+       "       [122.58053 , 123.72703 , 124.56142 , ..., 121.46952 , 119.99272 ,\n",
+       "        119.01729 ],\n",
+       "       [122.78344 , 124.01911 , 124.747955, ..., 121.76524 , 120.1738  ,\n",
+       "        119.262054],\n",
+       "       ...,\n",
+       "       [121.69427 , 122.42311 , 123.208374, ..., 116.711555, 115.545044,\n",
+       "        114.91993 ],\n",
+       "       [121.50591 , 122.17652 , 123.054596, ..., 116.464966, 115.33849 ,\n",
+       "        114.60964 ],\n",
+       "       [120.98453 , 121.68972 , 122.56961 , ..., 115.86988 , 114.70428 ,\n",
+       "        113.95268 ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = transform(training_data[0:2])\n",
-    "df"
+    "EDA.find_mean_img(bkl_images, \"Vasc Images\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3acaca8b",
+   "metadata": {},
+   "source": [
+    "Contrast Between Images"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1e29869a",
+   "id": "39497a39",
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e327218b",
+   "metadata": {},
+   "source": [
+    "Principal Component Analysis. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "ee96ea64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of PC:  6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 576x576 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EDA.plot_pca(EDA.eigenimages(bkl_images, \"bkl images\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3157d03a",
+   "metadata": {},
+   "source": [
+    "# 4. Data Processing for Model Ingestion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64adf033",
+   "metadata": {},
+   "source": [
+    "# 5. Model Creation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3114115",
+   "metadata": {},
+   "source": [
+    "# 6. Model Scoring & Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38d22b6e",
+   "metadata": {},
+   "source": [
+    "# 7. Interpretation of Results"
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "ee39f17eb82bb6ae04362652a1189337f31e2d08025196c7b18744ffeacb3697"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "R",
    "language": "python",
    "name": "python3"
   },