Manipulation_of_Image_Data_and_Category_Determination_using_Text_Data.ipynb

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   "cell_type": "markdown",
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    "[1](The_Problem_Statement.ipynb)\n",
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tropical Cyclone Intensity Estimation using a Deep  Convolutional Neural Network - Part 2 \n",
    "\n",
    "**Contents of this notebook:**\n",
    "\n",
    "- [Understand the Model Requirements](#Understand-the-Model-requirements)\n",
    "    - [Exploring Resizing Options](#Exploring-different-types-of-resizing-options)\n",
    "    - [Choosing a Random Patch](#Step-2-:-Choosing-a-Random-Patch-from-the-Image)\n",
    "- [Annotating Our Dataset ](#Annotating-our-dataset) \n",
    "- [Wrapping Things Up](#Wrapping-Things-Up-:)\n",
    "    - [Preparing the Dataset](#Preparing-the-Dataset)\n",
    "    - [Defining our Model](#Defining-our-Model)\n",
    "    - [Compiling and Training our Model](#Compiling-and-Training-our-Model)\n",
    "    - [Visualisations](#Visualisations)\n",
    "\n",
    "**By the end of this Notebook you will:**\n",
    "\n",
    "- Understand the Model Requirements.\n",
    "- Annotation of Dataset.\n",
    "- Train your Model."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understand the Model requirements\n",
    "\n",
    "### We have seen the model to which our image will be fed\n",
    "\n",
    "- The model described in the paper \n",
    "![alt text](images/model.png)\n",
    "\n",
    "We can see that the images need to be ( 232, 232, 3) in shape to be fed into our model.\n",
    "\n",
    "So, we will do the following steps before feeding the image into our model.\n",
    "\n",
    "- Step 1 : Resize Image from ( 1024, 1024 ,3) to ( 256 , 256 ,3 ) \n",
    "- Step 2 : Choose a random ( 232 , 232 , 3 ) patch from the ( 256 , 256 , 3 ) and feed into our model.\n",
    "\n",
    "**Alternate Approach** : We can modify the model's input shape to be ( 256 x 256 x 3 ) and train it on the scaled images, but we take a ( 232 x 232 x 3 ) random patch so that our model does not expect the cyclone to be in the center and learn to understanding the mapping even with the cyclones in the sides of the images.\n",
    "\n",
    "### Step 1 :\n",
    "Let's now start with Step 1 and understand all the resizing methods available to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024, 1024, 3)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "#Read the Image Using cv2.imread()\n",
    "img = cv2.imread('images/image_shape.jpg',1)\n",
    "#Changing the Color Spaces\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "# Print the Shape of the Image\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f578816bf40>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "#Plot the image\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring different types of resizing options \n",
    "\n",
    "The Images can be resized in different ways. Some methods are as follows (as stated in OpenCV documentation) : \n",
    "\n",
    "<h3>Scaling</h3>\n",
    "<p>Scaling is just resizing of the image. OpenCV comes with a function <b>cv2.resize()</a></b> for this purpose. The size of the image can be specified manually, or you can specify the scaling factor. Different interpolation methods are used. Preferable interpolation methods are <b>cv2.INTER_AREA</b> for shrinking and <b>cv2.INTER_CUBIC</b> (slow) &amp; <b>cv2.INTER_LINEAR</b> for zooming. By default, interpolation method used is <b>cv2.INTER_LINEAR</b> for all resizing purposes.\n",
    "\n",
    "* cv2.INTER_AREA    ( Preferable for Shrinking ) \n",
    "* cv2.INTER_CUBIC   ( Preferable for Zooming but slow )\n",
    "* cv2.INTER_LINEAR  ( Preferable for Zooming and the default option )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2 : Choosing a Random Patch from the Image\n",
    "\n",
    "We will use the `np.random.randint()` function from the Numpy toolbox to generate random numbers. The parameters of this function are the upper limits and size of the Output array as mentioned in the [Numpy Documentation](https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapping things up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f57787b6d30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import numpy to Generate Random Numbers \n",
    "import numpy as np\n",
    "#Generate random number from [0,0] to [23,23] and define start and end points \n",
    "start_pt= np.random.randint(24,size=2)\n",
    "end_pt = start_pt + [232,232]\n",
    "# Scale Image and Take a Random patch from it\n",
    "img  = cv2.resize(img,(256,256))\n",
    "rand = img[start_pt[0]:end_pt[0],start_pt[1]:end_pt[1]]\n",
    "plt.imshow(rand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(232, 232, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output Of the final images is obtained as (232, 232, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Annotating our dataset \n",
    "\n",
    "Let us start by taking an example of Katrina Hurricane from 2005 and scaling it for all the Cyclones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Read the CSV we saved earlier\n",
    "df = pd.read_csv('atlantic_storms.csv')\n",
    "# Create a Mask to Filter our Katrina Cyclone (2005)\n",
    "mask = (df['date'] > '2005-01-01') & (df['date'] <= '2006-01-01') & ( df['name'] == 'KATRINA')\n",
    "# Apply the Mask to the Original Data Frame and Extract the new Dataframe\n",
    "new_df = df.loc[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>record_identifier</th>\n",
       "      <th>status_of_system</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>maximum_sustained_wind_knots</th>\n",
       "      <th>...</th>\n",
       "      <th>34_kt_sw</th>\n",
       "      <th>34_kt_nw</th>\n",
       "      <th>50_kt_ne</th>\n",
       "      <th>50_kt_se</th>\n",
       "      <th>50_kt_sw</th>\n",
       "      <th>50_kt_nw</th>\n",
       "      <th>64_kt_ne</th>\n",
       "      <th>64_kt_se</th>\n",
       "      <th>64_kt_sw</th>\n",
       "      <th>64_kt_nw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44500</th>\n",
       "      <td>44500</td>\n",
       "      <td>44500</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-23 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD</td>\n",
       "      <td>23.1N</td>\n",
       "      <td>75.1W</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44501</th>\n",
       "      <td>44501</td>\n",
       "      <td>44501</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-24 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD</td>\n",
       "      <td>23.4N</td>\n",
       "      <td>75.7W</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44502</th>\n",
       "      <td>44502</td>\n",
       "      <td>44502</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-24 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD</td>\n",
       "      <td>23.8N</td>\n",
       "      <td>76.2W</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44503</th>\n",
       "      <td>44503</td>\n",
       "      <td>44503</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-24 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TS</td>\n",
       "      <td>24.5N</td>\n",
       "      <td>76.5W</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44504</th>\n",
       "      <td>44504</td>\n",
       "      <td>44504</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-24 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TS</td>\n",
       "      <td>25.4N</td>\n",
       "      <td>76.9W</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44505</th>\n",
       "      <td>44505</td>\n",
       "      <td>44505</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-25 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TS</td>\n",
       "      <td>26.0N</td>\n",
       "      <td>77.7W</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44506</th>\n",
       "      <td>44506</td>\n",
       "      <td>44506</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-25 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TS</td>\n",
       "      <td>26.1N</td>\n",
       "      <td>78.4W</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44507</th>\n",
       "      <td>44507</td>\n",
       "      <td>44507</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-25 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TS</td>\n",
       "      <td>26.2N</td>\n",
       "      <td>79.0W</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44508</th>\n",
       "      <td>44508</td>\n",
       "      <td>44508</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-25 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TS</td>\n",
       "      <td>26.2N</td>\n",
       "      <td>79.6W</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44509</th>\n",
       "      <td>44509</td>\n",
       "      <td>44509</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-25 22:30:00</td>\n",
       "      <td>L</td>\n",
       "      <td>HU</td>\n",
       "      <td>26.0N</td>\n",
       "      <td>80.1W</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44510</th>\n",
       "      <td>44510</td>\n",
       "      <td>44510</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-26 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>25.9N</td>\n",
       "      <td>80.3W</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44511</th>\n",
       "      <td>44511</td>\n",
       "      <td>44511</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-26 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>25.4N</td>\n",
       "      <td>81.3W</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44512</th>\n",
       "      <td>44512</td>\n",
       "      <td>44512</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-26 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>25.1N</td>\n",
       "      <td>82.0W</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44513</th>\n",
       "      <td>44513</td>\n",
       "      <td>44513</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-26 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>24.9N</td>\n",
       "      <td>82.6W</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44514</th>\n",
       "      <td>44514</td>\n",
       "      <td>44514</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-27 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>24.6N</td>\n",
       "      <td>83.3W</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44515</th>\n",
       "      <td>44515</td>\n",
       "      <td>44515</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-27 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>24.4N</td>\n",
       "      <td>84.0W</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44516</th>\n",
       "      <td>44516</td>\n",
       "      <td>44516</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-27 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>24.4N</td>\n",
       "      <td>84.7W</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44517</th>\n",
       "      <td>44517</td>\n",
       "      <td>44517</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-27 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>24.5N</td>\n",
       "      <td>85.3W</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44518</th>\n",
       "      <td>44518</td>\n",
       "      <td>44518</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-28 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>24.8N</td>\n",
       "      <td>85.9W</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44519</th>\n",
       "      <td>44519</td>\n",
       "      <td>44519</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-28 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>25.2N</td>\n",
       "      <td>86.7W</td>\n",
       "      <td>125</td>\n",
       "      <td>...</td>\n",
       "      <td>125.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44520</th>\n",
       "      <td>44520</td>\n",
       "      <td>44520</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-28 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>25.7N</td>\n",
       "      <td>87.7W</td>\n",
       "      <td>145</td>\n",
       "      <td>...</td>\n",
       "      <td>125.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44521</th>\n",
       "      <td>44521</td>\n",
       "      <td>44521</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-28 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>26.3N</td>\n",
       "      <td>88.6W</td>\n",
       "      <td>150</td>\n",
       "      <td>...</td>\n",
       "      <td>125.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44522</th>\n",
       "      <td>44522</td>\n",
       "      <td>44522</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-29 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>27.2N</td>\n",
       "      <td>89.2W</td>\n",
       "      <td>140</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44523</th>\n",
       "      <td>44523</td>\n",
       "      <td>44523</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-29 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>28.2N</td>\n",
       "      <td>89.6W</td>\n",
       "      <td>125</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44524</th>\n",
       "      <td>44524</td>\n",
       "      <td>44524</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-29 11:10:00</td>\n",
       "      <td>L</td>\n",
       "      <td>HU</td>\n",
       "      <td>29.3N</td>\n",
       "      <td>89.6W</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44525</th>\n",
       "      <td>44525</td>\n",
       "      <td>44525</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-29 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>29.5N</td>\n",
       "      <td>89.6W</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44526</th>\n",
       "      <td>44526</td>\n",
       "      <td>44526</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-29 14:45:00</td>\n",
       "      <td>L</td>\n",
       "      <td>HU</td>\n",
       "      <td>30.2N</td>\n",
       "      <td>89.6W</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44527</th>\n",
       "      <td>44527</td>\n",
       "      <td>44527</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-29 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HU</td>\n",
       "      <td>31.1N</td>\n",
       "      <td>89.6W</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44528</th>\n",
       "      <td>44528</td>\n",
       "      <td>44528</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-30 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TS</td>\n",
       "      <td>32.6N</td>\n",
       "      <td>89.1W</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44529</th>\n",
       "      <td>44529</td>\n",
       "      <td>44529</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-30 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TS</td>\n",
       "      <td>34.1N</td>\n",
       "      <td>88.6W</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44530</th>\n",
       "      <td>44530</td>\n",
       "      <td>44530</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-30 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD</td>\n",
       "      <td>35.6N</td>\n",
       "      <td>88.0W</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44531</th>\n",
       "      <td>44531</td>\n",
       "      <td>44531</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-30 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD</td>\n",
       "      <td>37.0N</td>\n",
       "      <td>87.0W</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44532</th>\n",
       "      <td>44532</td>\n",
       "      <td>44532</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-31 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EX</td>\n",
       "      <td>38.6N</td>\n",
       "      <td>85.3W</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44533</th>\n",
       "      <td>44533</td>\n",
       "      <td>44533</td>\n",
       "      <td>AL122005</td>\n",
       "      <td>KATRINA</td>\n",
       "      <td>2005-08-31 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EX</td>\n",
       "      <td>40.1N</td>\n",
       "      <td>82.9W</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  index        id     name                 date  \\\n",
       "44500       44500  44500  AL122005  KATRINA  2005-08-23 18:00:00   \n",
       "44501       44501  44501  AL122005  KATRINA  2005-08-24 00:00:00   \n",
       "44502       44502  44502  AL122005  KATRINA  2005-08-24 06:00:00   \n",
       "44503       44503  44503  AL122005  KATRINA  2005-08-24 12:00:00   \n",
       "44504       44504  44504  AL122005  KATRINA  2005-08-24 18:00:00   \n",
       "44505       44505  44505  AL122005  KATRINA  2005-08-25 00:00:00   \n",
       "44506       44506  44506  AL122005  KATRINA  2005-08-25 06:00:00   \n",
       "44507       44507  44507  AL122005  KATRINA  2005-08-25 12:00:00   \n",
       "44508       44508  44508  AL122005  KATRINA  2005-08-25 18:00:00   \n",
       "44509       44509  44509  AL122005  KATRINA  2005-08-25 22:30:00   \n",
       "44510       44510  44510  AL122005  KATRINA  2005-08-26 00:00:00   \n",
       "44511       44511  44511  AL122005  KATRINA  2005-08-26 06:00:00   \n",
       "44512       44512  44512  AL122005  KATRINA  2005-08-26 12:00:00   \n",
       "44513       44513  44513  AL122005  KATRINA  2005-08-26 18:00:00   \n",
       "44514       44514  44514  AL122005  KATRINA  2005-08-27 00:00:00   \n",
       "44515       44515  44515  AL122005  KATRINA  2005-08-27 06:00:00   \n",
       "44516       44516  44516  AL122005  KATRINA  2005-08-27 12:00:00   \n",
       "44517       44517  44517  AL122005  KATRINA  2005-08-27 18:00:00   \n",
       "44518       44518  44518  AL122005  KATRINA  2005-08-28 00:00:00   \n",
       "44519       44519  44519  AL122005  KATRINA  2005-08-28 06:00:00   \n",
       "44520       44520  44520  AL122005  KATRINA  2005-08-28 12:00:00   \n",
       "44521       44521  44521  AL122005  KATRINA  2005-08-28 18:00:00   \n",
       "44522       44522  44522  AL122005  KATRINA  2005-08-29 00:00:00   \n",
       "44523       44523  44523  AL122005  KATRINA  2005-08-29 06:00:00   \n",
       "44524       44524  44524  AL122005  KATRINA  2005-08-29 11:10:00   \n",
       "44525       44525  44525  AL122005  KATRINA  2005-08-29 12:00:00   \n",
       "44526       44526  44526  AL122005  KATRINA  2005-08-29 14:45:00   \n",
       "44527       44527  44527  AL122005  KATRINA  2005-08-29 18:00:00   \n",
       "44528       44528  44528  AL122005  KATRINA  2005-08-30 00:00:00   \n",
       "44529       44529  44529  AL122005  KATRINA  2005-08-30 06:00:00   \n",
       "44530       44530  44530  AL122005  KATRINA  2005-08-30 12:00:00   \n",
       "44531       44531  44531  AL122005  KATRINA  2005-08-30 18:00:00   \n",
       "44532       44532  44532  AL122005  KATRINA  2005-08-31 00:00:00   \n",
       "44533       44533  44533  AL122005  KATRINA  2005-08-31 06:00:00   \n",
       "\n",
       "      record_identifier status_of_system latitude longitude  \\\n",
       "44500               NaN               TD    23.1N     75.1W   \n",
       "44501               NaN               TD    23.4N     75.7W   \n",
       "44502               NaN               TD    23.8N     76.2W   \n",
       "44503               NaN               TS    24.5N     76.5W   \n",
       "44504               NaN               TS    25.4N     76.9W   \n",
       "44505               NaN               TS    26.0N     77.7W   \n",
       "44506               NaN               TS    26.1N     78.4W   \n",
       "44507               NaN               TS    26.2N     79.0W   \n",
       "44508               NaN               TS    26.2N     79.6W   \n",
       "44509                 L               HU    26.0N     80.1W   \n",
       "44510               NaN               HU    25.9N     80.3W   \n",
       "44511               NaN               HU    25.4N     81.3W   \n",
       "44512               NaN               HU    25.1N     82.0W   \n",
       "44513               NaN               HU    24.9N     82.6W   \n",
       "44514               NaN               HU    24.6N     83.3W   \n",
       "44515               NaN               HU    24.4N     84.0W   \n",
       "44516               NaN               HU    24.4N     84.7W   \n",
       "44517               NaN               HU    24.5N     85.3W   \n",
       "44518               NaN               HU    24.8N     85.9W   \n",
       "44519               NaN               HU    25.2N     86.7W   \n",
       "44520               NaN               HU    25.7N     87.7W   \n",
       "44521               NaN               HU    26.3N     88.6W   \n",
       "44522               NaN               HU    27.2N     89.2W   \n",
       "44523               NaN               HU    28.2N     89.6W   \n",
       "44524                 L               HU    29.3N     89.6W   \n",
       "44525               NaN               HU    29.5N     89.6W   \n",
       "44526                 L               HU    30.2N     89.6W   \n",
       "44527               NaN               HU    31.1N     89.6W   \n",
       "44528               NaN               TS    32.6N     89.1W   \n",
       "44529               NaN               TS    34.1N     88.6W   \n",
       "44530               NaN               TD    35.6N     88.0W   \n",
       "44531               NaN               TD    37.0N     87.0W   \n",
       "44532               NaN               EX    38.6N     85.3W   \n",
       "44533               NaN               EX    40.1N     82.9W   \n",
       "\n",
       "       maximum_sustained_wind_knots  ...  34_kt_sw  34_kt_nw  50_kt_ne  \\\n",
       "44500                            30  ...       0.0       0.0       0.0   \n",
       "44501                            30  ...       0.0       0.0       0.0   \n",
       "44502                            30  ...       0.0       0.0       0.0   \n",
       "44503                            35  ...       0.0       0.0       0.0   \n",
       "44504                            40  ...       0.0       0.0       0.0   \n",
       "44505                            45  ...       0.0       0.0       0.0   \n",
       "44506                            50  ...       0.0       0.0      15.0   \n",
       "44507                            55  ...      30.0      50.0      20.0   \n",
       "44508                            60  ...      50.0      60.0      25.0   \n",
       "44509                            70  ...       NaN       NaN       NaN   \n",
       "44510                            70  ...      50.0      40.0      20.0   \n",
       "44511                            65  ...      40.0      30.0      60.0   \n",
       "44512                            75  ...      45.0      25.0      60.0   \n",
       "44513                            85  ...      55.0      35.0      60.0   \n",
       "44514                            90  ...      75.0      75.0      60.0   \n",
       "44515                            95  ...      90.0     130.0      60.0   \n",
       "44516                           100  ...      90.0     130.0      60.0   \n",
       "44517                           100  ...      90.0     130.0      70.0   \n",
       "44518                           100  ...     100.0     140.0      80.0   \n",
       "44519                           125  ...     125.0     140.0     100.0   \n",
       "44520                           145  ...     125.0     140.0     120.0   \n",
       "44521                           150  ...     125.0     180.0     120.0   \n",
       "44522                           140  ...     150.0     180.0     120.0   \n",
       "44523                           125  ...     150.0     150.0     120.0   \n",
       "44524                           110  ...       NaN       NaN       NaN   \n",
       "44525                           110  ...     150.0     100.0     120.0   \n",
       "44526                           105  ...       NaN       NaN       NaN   \n",
       "44527                            80  ...     100.0     100.0      75.0   \n",
       "44528                            50  ...      90.0      50.0      50.0   \n",
       "44529                            40  ...      75.0      50.0       0.0   \n",
       "44530                            30  ...       0.0       0.0       0.0   \n",
       "44531                            30  ...       0.0       0.0       0.0   \n",
       "44532                            30  ...       0.0       0.0       0.0   \n",
       "44533                            25  ...       0.0       0.0       0.0   \n",
       "\n",
       "       50_kt_se  50_kt_sw  50_kt_nw  64_kt_ne  64_kt_se  64_kt_sw  64_kt_nw  \n",
       "44500       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44501       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44502       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44503       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44504       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44505       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44506       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44507      20.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44508      25.0      20.0      20.0       0.0       0.0       0.0       0.0  \n",
       "44509       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "44510      20.0      20.0      20.0      10.0      10.0      10.0      10.0  \n",
       "44511      60.0      20.0      20.0      10.0      10.0      10.0      10.0  \n",
       "44512      60.0      25.0      20.0      20.0      20.0      10.0      10.0  \n",
       "44513      60.0      35.0      20.0      20.0      20.0      15.0      10.0  \n",
       "44514      60.0      40.0      30.0      25.0      25.0      20.0      15.0  \n",
       "44515      60.0      45.0      60.0      35.0      30.0      30.0      25.0  \n",
       "44516      60.0      45.0      60.0      35.0      30.0      30.0      25.0  \n",
       "44517      70.0      60.0      70.0      45.0      35.0      35.0      35.0  \n",
       "44518      80.0      65.0      80.0      60.0      45.0      45.0      50.0  \n",
       "44519     100.0      75.0     100.0      75.0      75.0      50.0      75.0  \n",
       "44520     120.0      75.0     100.0      90.0      90.0      50.0      75.0  \n",
       "44521     120.0      75.0     120.0      90.0      90.0      50.0      90.0  \n",
       "44522     120.0      75.0     100.0      90.0      90.0      60.0      80.0  \n",
       "44523     120.0      75.0     100.0      90.0      90.0      60.0      70.0  \n",
       "44524       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "44525     120.0      75.0      75.0      90.0      90.0      60.0      60.0  \n",
       "44526       NaN       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "44527     100.0      75.0      75.0      50.0      50.0      30.0      30.0  \n",
       "44528      60.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44529       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44530       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44531       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44532       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "44533       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "\n",
       "[34 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20050823.1245.goes12.x.ir1km.99LINVEST.30kts-1008mb-229N-752W.jpg',\n",
       " '20050823.1315.goes12.x.ir1km.99LINVEST.30kts-1008mb-229N-752W.jpg',\n",
       " '20050823.1345.goes12.x.ir1km.99LINVEST.25kts-1009mb-221N-747W.jpg',\n",
       " '20050823.1415.goes12.x.ir1km.99LINVEST.25kts-1009mb-221N-747W.jpg',\n",
       " '20050823.1445.goes12.x.ir1km.99LINVEST.25kts-1009mb-221N-747W.jpg']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the list of Images from Our Dataset for Katrina\n",
    "import os\n",
    "e = os.listdir('Dataset/tcdat/tc05/ATL/12L.KATRINA/ir/geo/1km')\n",
    "e.sort()\n",
    "#Show First five images\n",
    "e[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can observe, the images are taken once 30 minutes, but the text data is available once every 6 hours.  So we will be interpolating the text data to fit the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get list of Dates and Velocity from the New Dataframe\n",
    "date_list = new_df['date'].tolist()\n",
    "velocity_list = new_df['maximum_sustained_wind_knots'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2005-08-23 18:00:00', '2005-08-24 00:00:00', '2005-08-24 06:00:00', '2005-08-24 12:00:00', '2005-08-24 18:00:00']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(date_list[:5])\n",
    "type(date_list[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Dates are in STR Format which we will be converting now to datetime format to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2005-08-23 18:00:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "# Get and Convert to Datetime format for the First Last recorded time of Text Data.\n",
    "first = (datetime.strptime(date_list[0], \"%Y-%m-%d %H:%M:%S\"))\n",
    "last = (datetime.strptime(date_list[-1], \"%Y-%m-%d %H:%M:%S\"))\n",
    "print(first)\n",
    "type(first)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "21600.0\n",
      "43200.0\n",
      "64800.0\n",
      "86400.0\n",
      "108000.0\n",
      "129600.0\n",
      "151200.0\n",
      "172800.0\n",
      "189000.0\n",
      "194400.0\n",
      "216000.0\n",
      "237600.0\n",
      "259200.0\n",
      "280800.0\n",
      "302400.0\n",
      "324000.0\n",
      "345600.0\n",
      "367200.0\n",
      "388800.0\n",
      "410400.0\n",
      "432000.0\n",
      "453600.0\n",
      "475200.0\n",
      "493800.0\n",
      "496800.0\n",
      "506700.0\n",
      "518400.0\n",
      "540000.0\n",
      "561600.0\n",
      "583200.0\n",
      "604800.0\n",
      "626400.0\n",
      "648000.0\n"
     ]
    }
   ],
   "source": [
    "#Changes the list from Convert everything to seconds from the first image to interpolate the data\n",
    "for i in range(len(date_list)):\n",
    "    date_list[i]=( (datetime.strptime(date_list[i], \"%Y-%m-%d %H:%M:%S\")) - first ).total_seconds()\n",
    "    print(date_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpolate using the Scipy Library Funciton\n",
    "from scipy import interpolate\n",
    "func = interpolate.splrep(date_list,velocity_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting List of Katrina Images \n",
    "import os\n",
    "e = os.listdir('Dataset/tcdat/tc05/ATL/12L.KATRINA/ir/geo/1km')\n",
    "# Sort images by time\n",
    "e.sort()\n",
    "x=[]\n",
    "y=[]\n",
    "for m in e :\n",
    "    try :\n",
    "        #Strip the Time Data from image and convert it the a datetime type.\n",
    "        time_img=(datetime.strptime(m[:13], \"%Y%m%d.%H%M\"))\n",
    "        # If the Image is taken between the available text data\n",
    "        if(time_img>=first and time_img <= last):\n",
    "            # Get Interpolated Value for that time and Save It \n",
    "            value = int(interpolate.splev((time_img-first).total_seconds(),func))\n",
    "            x.append((time_img-first).total_seconds())\n",
    "            y.append(value)\n",
    "    except :\n",
    "       pass   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f564013ebb0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Plot All the Saved Data Points\n",
    "f = plt.figure(figsize=(24,10))\n",
    "ax = f.add_subplot(121)\n",
    "ax2 = f.add_subplot(122)\n",
    "ax.title.set_text('Datapoints frm csv file')\n",
    "ax2.title.set_text('Interpolated from CSV file to images')\n",
    "ax.plot(date_list,velocity_list,'-o')\n",
    "ax2.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we have interpolated and found relevant velocity for all images between the recorded text timeframe. Let us now use it for training our Model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrapping Things Up :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Dataset\n",
    "\n",
    "#####  All the above modules are joined together and make It into a single function to load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "................................\n",
      "27582\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('/workspace/python/source_code')\n",
    "# Import Utlility functions\n",
    "from utils import * \n",
    "# Load dataset\n",
    "filenames,labels = load_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[344, 344, 344, 344, 344, 344, 344, 344]\n",
      "Counter({2: 7936, 3: 5339, 1: 3803, 4: 2934, 5: 2336, 6: 2178, 7: 204, 0: 100})\n"
     ]
    }
   ],
   "source": [
    "val_filenames , val_labels = make_test_set(filenames,labels,val=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understand our dataset :\n",
    "\n",
    "We can see the following lines from the Output : \n",
    "\n",
    "`[344, 344, 344, 344, 344, 344, 344, 344]` and `{2: 7936, 3: 5339, 1: 3803, 4: 2934, 5: 2336, 6: 2178, 7: 204, 0: 100}`\n",
    "\n",
    "This is the distribution of our validation set and training set over it's classes. \n",
    "\n",
    "For the validation set we use *Stratified Validation* set so that our validation set nearly respresent the whole class. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make train test set\n",
    "test = 0.2\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(filenames, labels, test_size=test, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One-Hot Encoding \n",
    "\n",
    "`y_train` is a list containing data from 0-7 such as [ 2,4,5,....] but our Model Needs an Input of Array for Each Output as as 1D vector  :\n",
    "\n",
    "2 --- > [ 0 , 0 , 1 , 0 , 0 , 0 , 0 , 0] \n",
    "\n",
    "4 --- > [ 0 , 0 , 0 , 0 , 1 , 0 , 0 , 0] \n",
    "\n",
    "\n",
    "This is encoded as such because keeping the other values 0 is necessary for the model to find the model Loss and use backpropagation for making it learn the _Weight Matrix_.\n",
    "\n",
    "The below given image is an example of One-Hot Encoding :\n",
    "\n",
    "![alt text](images/one_hot.jfif)\n",
    "\n",
    "Reference : [What is One Hot Encoding and How to Do It](https://medium.com/@michaeldelsole/what-is-one-hot-encoding-and-how-to-do-it-f0ae272f1179)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "y_train = tf.one_hot(y_train,depth=8)\n",
    "y_test = tf.one_hot(y_test,depth=8)\n",
    "val_labels = tf.one_hot(val_labels,depth=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test,val = make_dataset((x_train,y_train,128),(x_test,y_test,32),(val_filenames,val_labels,32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining our Model\n",
    "\n",
    "![alt_text](images/model.png)\n",
    "\n",
    "We will be Implementing this model in Keras using the following code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 75, 75, 64)        19264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 37, 37, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 33, 33, 256)       409856    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 288)       663840    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 15, 15, 288)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 272)       705296    \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 256)       626944    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 6, 6, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 3584)              33033728  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2048)              7342080   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 8)                 16392     \n",
      "=================================================================\n",
      "Total params: 42,817,400\n",
      "Trainable params: 42,817,400\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "tf.random.set_seed(1337)\n",
    "\n",
    "import tensorflow.keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten ,Dropout, MaxPooling2D\n",
    "from tensorflow.keras import backend as K \n",
    "\n",
    "#Reset Graphs and Create Sequential model\n",
    "K.clear_session()\n",
    "model = Sequential()\n",
    "#Convolution Layers\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=10,strides=3, activation='relu', input_shape=(232,232,3)))\n",
    "model.add(MaxPooling2D(pool_size=(3, 3),strides=2))\n",
    "model.add(Conv2D(256, kernel_size=5,strides=1,activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(3, 3),strides=2))\n",
    "model.add(Conv2D(288, kernel_size=3,strides=1,padding='same',activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2),strides=1))\n",
    "model.add(Conv2D(272, kernel_size=3,strides=1,padding='same',activation='relu'))\n",
    "model.add(Conv2D(256, kernel_size=3,strides=1,activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(3, 3),strides=2))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Flatten())\n",
    "\n",
    "#Linear Layers \n",
    "\n",
    "model.add(Dense(3584,activation='relu'))\n",
    "model.add(Dense(2048,activation='relu'))\n",
    "model.add(Dense(8, activation='softmax'))\n",
    "\n",
    "# Print Model Summary\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compiling and Training our Model\n",
    "\n",
    "We will be using the following : \n",
    "\n",
    "- Optimizer : SGD ( Stochastic Gradient Descent ) with parameters mentioned in the research paper.\n",
    "    - Learning Rate : 0.001\n",
    "    - Momentum : 0.9\n",
    "- Loss Function : Categorical Cross Entropy ( Used in Multi-class classification ) \n",
    "- Metrics : We will be using two metrics to determine how our model performs \n",
    "    - Accuracy  : Number of Predictions correct / Total number of Predictions\n",
    "    - Top -2 Accuracy : Top-2 accuracy means that any of your model 2 highest probability answers must match the expected answer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "156/156 [==============================] - 58s 285ms/step - loss: 0.6027 - accuracy: 0.7825 - top2_acc: 0.9255 - val_loss: 0.6667 - val_accuracy: 0.7736 - val_top2_acc: 0.9121\n",
      "Epoch 2/4\n",
      "156/156 [==============================] - 11s 73ms/step - loss: 0.5462 - accuracy: 0.8001 - top2_acc: 0.9341 - val_loss: 0.5880 - val_accuracy: 0.8012 - val_top2_acc: 0.9273\n",
      "Epoch 3/4\n",
      "156/156 [==============================] - 11s 73ms/step - loss: 0.4933 - accuracy: 0.8211 - top2_acc: 0.9450 - val_loss: 0.6152 - val_accuracy: 0.7958 - val_top2_acc: 0.9339\n",
      "Epoch 4/4\n",
      "156/156 [==============================] - 12s 74ms/step - loss: 0.4526 - accuracy: 0.8351 - top2_acc: 0.9521 - val_loss: 0.5021 - val_accuracy: 0.8325 - val_top2_acc: 0.9411\n",
      "Test loss: 0.3461168706417084\n",
      "Test accuracy: 0.8793798089027405\n"
     ]
    }
   ],
   "source": [
    "import functools\n",
    "\n",
    "# Include Top-2 Accuracy Metrics \n",
    "top2_acc = functools.partial(tensorflow.keras.metrics.top_k_categorical_accuracy, k=2)\n",
    "top2_acc.__name__ = 'top2_acc'\n",
    "\n",
    "#Define Number of Epochs\n",
    "epochs = 4\n",
    "\n",
    "#But Training our model from scratch will take a long time\n",
    "#So we will load a partially trained model to speedup the process \n",
    "model.load_weights(\"trained_16.h5\")\n",
    "\n",
    "# Optimizer\n",
    "sgd = tensorflow.keras.optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9)\n",
    "\n",
    "\n",
    "#Compile Model with Loss Function , Optimizer and Metrics\n",
    "model.compile(loss=tensorflow.keras.losses.categorical_crossentropy, \n",
    "              optimizer=sgd,\n",
    "              metrics=['accuracy',top2_acc])\n",
    "\n",
    "# Train the Model \n",
    "trained_model = model.fit(train,\n",
    "          epochs=epochs,\n",
    "          verbose=1,\n",
    "          validation_data=val)\n",
    "\n",
    "# Test Model Aganist Validation Set\n",
    "score = model.evaluate(test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisations\n",
    "\n",
    "Let us now visualise how our model perfromed during the training process : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "f = plt.figure(figsize=(15,5))\n",
    "ax = f.add_subplot(121)\n",
    "ax.plot(trained_model.history['accuracy'])\n",
    "ax.plot(trained_model.history['val_accuracy'])\n",
    "ax.set_title('Model Accuracy')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.legend(['Train', 'Val'])\n",
    "\n",
    "ax2 = f.add_subplot(122)\n",
    "ax2.plot(trained_model.history['loss'])\n",
    "ax2.plot(trained_model.history['val_loss'])\n",
    "ax2.set_title('Model Loss')\n",
    "ax2.set_ylabel('Loss')\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.legend(['Train', 'Val'],loc= 'upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix :\n",
    "\n",
    "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. \n",
    "\n",
    "Here , the rows display the predicted class and the columns are the truth value of the classes.From this we can estimate how our model performs over different classes which would in turn help us determine how our data should be fed into our model.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "#Plotting a heatmap using the confusion matrix\n",
    "pred = model.predict(val)\n",
    "p = np.argmax(pred, axis=1)\n",
    "y_valid = np.argmax(val_labels, axis=1, out=None)\n",
    "results = confusion_matrix(y_valid, p) \n",
    "classes=['NC','TD','TC','H1','H3','H3','H4','H5']\n",
    "df_cm = pd.DataFrame(results, index = [i for i in classes], columns = [i for i in classes])\n",
    "plt.figure(figsize = (15,15))\n",
    "\n",
    "sn.heatmap(df_cm, annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Congralutions on running your first model. Now In the next notebook , let us try to understand the drawbacks of this model and make it better :\n",
    "\n",
    "\n",
    "We can notice that the validation accuracy is lesser than the training accuracy. This is because the model is not properly Regularized and the possible reasons are : \n",
    "\n",
    "**Not enough data-points / Imbalanced classes**\n",
    "\n",
    "Using different techniques we will be regulating and normalising the model in the upcoming notebook.\n",
    "\n",
    "## Important:\n",
    "<mark>Shutdown the kernel before clicking on “Next Notebook” to free up the GPU memory.</mark>\n",
    "\n",
    "## Licensing\n",
    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Previous Notebook](Approach_to_the_Problem_&_Inspecting_and_Cleaning_the_Required_Data.ipynb)\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "[1](The_Problem_Statement.ipynb)\n",
    "[2](Approach_to_the_Problem_&_Inspecting_and_Cleaning_the_Required_Data.ipynb)\n",
    "[3]\n",
    "[4](Countering_Data_Imbalance.ipynb)\n",
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