LDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Shape of flattened images array: (50000, 3072)\n"
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       "      <td>6</td>\n",
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       "      <td>239</td>\n",
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       "      <td>176</td>\n",
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       "      <td>27</td>\n",
       "      <td>52</td>\n",
       "      <td>77</td>\n",
       "      <td>21</td>\n",
       "      <td>43</td>\n",
       "      <td>66</td>\n",
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       "      <td>239</td>\n",
       "      <td>236</td>\n",
       "      <td>237</td>\n",
       "      <td>247</td>\n",
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       "      <td>236</td>\n",
       "      <td>247</td>\n",
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      ],
      "text/plain": [
       "         0    1    2    3    4    5    6    7    8    9  ...  3063  3064  \\\n",
       "0       59   62   63   43   46   45   50   48   43   68  ...   216   184   \n",
       "1      154  177  187  126  137  136  105  104   95  102  ...   143   133   \n",
       "2      255  255  255  253  253  253  253  253  253  253  ...    78    85   \n",
       "3       28   25   10   37   34   19   38   35   20   42  ...    54    47   \n",
       "4      170  180  198  168  178  196  177  185  203  183  ...    75    79   \n",
       "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   \n",
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       "49997   35  178  235   40  176  239   42  176  241   39  ...    27    52   \n",
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       "49999  229  229  239  236  237  247  234  236  247  231  ...   179   177   \n",
       "\n",
       "       3065  3066  3067  3068  3069  3070  3071  Label  \n",
       "0       140   151   118    84   123    92    72      6  \n",
       "1       139   143   134   142   143   133   144      9  \n",
       "2        83    79    85    83    80    86    84      9  \n",
       "3        28    63    56    37    72    65    46      4  \n",
       "4        82    71    75    78    73    77    80      1  \n",
       "...     ...   ...   ...   ...   ...   ...   ...    ...  \n",
       "49995    56    36    38    53    37    39    54      2  \n",
       "49996    79   252   181    81   253   181    76      6  \n",
       "49997    77    21    43    66    12    31    50      9  \n",
       "49998   169   196   189   171   195   190   171      1  \n",
       "49999   173   164   164   162   163   163   161      1  \n",
       "\n",
       "[50000 rows x 3073 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# Load CIFAR-10 dataset using TensorFlow\n",
    "(train_images, train_labels), _ = tf.keras.datasets.cifar10.load_data()\n",
    "\n",
    "\n",
    "# Flatten images based on RGB channels\n",
    "num_samples = train_images.shape[0]  # Number of samples (images)\n",
    "num_pixels = train_images.shape[1] * train_images.shape[2] * train_images.shape[3]  # Number of pixels per image\n",
    "\n",
    "# Reshape the images to 1D arrays\n",
    "flattened_images = train_images.reshape(num_samples, num_pixels)\n",
    "\n",
    "# Print the shape of the flattened images\n",
    "print(\"Shape of flattened images array:\", flattened_images.shape)\n",
    "\n",
    "# Flatten images based on RGB channels\n",
    "num_samples = train_images.shape[0]  # Number of samples (images)\n",
    "num_pixels = train_images.shape[1] * train_images.shape[2] * train_images.shape[3]  # Number of pixels per image\n",
    "\n",
    "# Reshape the images to 1D arrays\n",
    "flattened_images = train_images.reshape(num_samples, num_pixels)\n",
    "\n",
    "# Create a DataFrame with pixel values and labels\n",
    "df_images = pd.DataFrame(flattened_images)\n",
    "\n",
    "# Add the label column to the DataFrame\n",
    "df_images['Label'] = train_labels.flatten()\n",
    "\n",
    "# Display the DataFrame\n",
    "df_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.395088</td>\n",
       "      <td>0.946620</td>\n",
       "      <td>0.403264</td>\n",
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       "      <td>-0.123197</td>\n",
       "      <td>-0.458600</td>\n",
       "      <td>-0.056050</td>\n",
       "      <td>-0.540320</td>\n",
       "      <td>-0.641402</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.317240</td>\n",
       "      <td>0.561588</td>\n",
       "      <td>0.676780</td>\n",
       "      <td>-0.057154</td>\n",
       "      <td>0.021664</td>\n",
       "      <td>0.052093</td>\n",
       "      <td>-0.360611</td>\n",
       "      <td>-0.449900</td>\n",
       "      <td>-0.473116</td>\n",
       "      <td>-0.410588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253645</td>\n",
       "      <td>0.118572</td>\n",
       "      <td>0.387855</td>\n",
       "      <td>0.256086</td>\n",
       "      <td>0.135572</td>\n",
       "      <td>0.430806</td>\n",
       "      <td>0.251995</td>\n",
       "      <td>0.114124</td>\n",
       "      <td>0.448238</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.693034</td>\n",
       "      <td>1.631441</td>\n",
       "      <td>1.522036</td>\n",
       "      <td>1.695976</td>\n",
       "      <td>1.635002</td>\n",
       "      <td>1.521992</td>\n",
       "      <td>1.688121</td>\n",
       "      <td>1.628957</td>\n",
       "      <td>1.515960</td>\n",
       "      <td>1.686173</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.762709</td>\n",
       "      <td>-0.660768</td>\n",
       "      <td>-0.475092</td>\n",
       "      <td>-0.741144</td>\n",
       "      <td>-0.656910</td>\n",
       "      <td>-0.473935</td>\n",
       "      <td>-0.718346</td>\n",
       "      <td>-0.636092</td>\n",
       "      <td>-0.459796</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.399097</td>\n",
       "      <td>-1.523252</td>\n",
       "      <td>-1.523374</td>\n",
       "      <td>-1.285726</td>\n",
       "      <td>-1.410870</td>\n",
       "      <td>-1.417807</td>\n",
       "      <td>-1.288077</td>\n",
       "      <td>-1.412593</td>\n",
       "      <td>-1.417298</td>\n",
       "      <td>-1.243738</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.137978</td>\n",
       "      <td>-1.277745</td>\n",
       "      <td>-1.322628</td>\n",
       "      <td>-0.990451</td>\n",
       "      <td>-1.125930</td>\n",
       "      <td>-1.179326</td>\n",
       "      <td>-0.841564</td>\n",
       "      <td>-0.971295</td>\n",
       "      <td>-1.034884</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.535188</td>\n",
       "      <td>0.602736</td>\n",
       "      <td>0.813512</td>\n",
       "      <td>0.522621</td>\n",
       "      <td>0.591895</td>\n",
       "      <td>0.805887</td>\n",
       "      <td>0.636070</td>\n",
       "      <td>0.680217</td>\n",
       "      <td>0.886505</td>\n",
       "      <td>0.714164</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.809617</td>\n",
       "      <td>-0.758185</td>\n",
       "      <td>-0.490501</td>\n",
       "      <td>-0.865797</td>\n",
       "      <td>-0.818641</td>\n",
       "      <td>-0.550608</td>\n",
       "      <td>-0.826162</td>\n",
       "      <td>-0.779750</td>\n",
       "      <td>-0.520331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>0.194644</td>\n",
       "      <td>0.342131</td>\n",
       "      <td>0.763791</td>\n",
       "      <td>0.287950</td>\n",
       "      <td>0.424998</td>\n",
       "      <td>0.843577</td>\n",
       "      <td>0.345371</td>\n",
       "      <td>0.484888</td>\n",
       "      <td>0.911684</td>\n",
       "      <td>0.325361</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.372521</td>\n",
       "      <td>-1.375162</td>\n",
       "      <td>-0.891155</td>\n",
       "      <td>-1.411157</td>\n",
       "      <td>-1.417046</td>\n",
       "      <td>-0.933973</td>\n",
       "      <td>-1.380642</td>\n",
       "      <td>-1.386307</td>\n",
       "      <td>-0.913812</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>1.693034</td>\n",
       "      <td>1.494280</td>\n",
       "      <td>-0.006884</td>\n",
       "      <td>1.709780</td>\n",
       "      <td>1.551553</td>\n",
       "      <td>0.026966</td>\n",
       "      <td>1.715807</td>\n",
       "      <td>1.573149</td>\n",
       "      <td>0.055626</td>\n",
       "      <td>1.700059</td>\n",
       "      <td>...</td>\n",
       "      <td>1.911083</td>\n",
       "      <td>0.784258</td>\n",
       "      <td>-0.536731</td>\n",
       "      <td>1.954493</td>\n",
       "      <td>0.895709</td>\n",
       "      <td>-0.504604</td>\n",
       "      <td>1.946241</td>\n",
       "      <td>0.880301</td>\n",
       "      <td>-0.580867</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>-1.303745</td>\n",
       "      <td>0.575304</td>\n",
       "      <td>1.273431</td>\n",
       "      <td>-1.244313</td>\n",
       "      <td>0.564079</td>\n",
       "      <td>1.346107</td>\n",
       "      <td>-1.232706</td>\n",
       "      <td>0.554648</td>\n",
       "      <td>1.364891</td>\n",
       "      <td>-1.285396</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.560155</td>\n",
       "      <td>-1.196563</td>\n",
       "      <td>-0.567550</td>\n",
       "      <td>-1.644883</td>\n",
       "      <td>-1.336181</td>\n",
       "      <td>-0.734623</td>\n",
       "      <td>-1.765698</td>\n",
       "      <td>-1.514004</td>\n",
       "      <td>-0.974348</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>0.794000</td>\n",
       "      <td>1.027934</td>\n",
       "      <td>1.335582</td>\n",
       "      <td>0.771096</td>\n",
       "      <td>1.009138</td>\n",
       "      <td>1.308417</td>\n",
       "      <td>0.746812</td>\n",
       "      <td>0.987162</td>\n",
       "      <td>1.289356</td>\n",
       "      <td>0.728050</td>\n",
       "      <td>...</td>\n",
       "      <td>1.066728</td>\n",
       "      <td>0.946620</td>\n",
       "      <td>0.850147</td>\n",
       "      <td>1.081917</td>\n",
       "      <td>1.025093</td>\n",
       "      <td>0.875510</td>\n",
       "      <td>1.052911</td>\n",
       "      <td>1.023960</td>\n",
       "      <td>0.856853</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>1.338869</td>\n",
       "      <td>1.274823</td>\n",
       "      <td>1.323152</td>\n",
       "      <td>1.461305</td>\n",
       "      <td>1.412473</td>\n",
       "      <td>1.446613</td>\n",
       "      <td>1.425108</td>\n",
       "      <td>1.391772</td>\n",
       "      <td>1.440425</td>\n",
       "      <td>1.380684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.816548</td>\n",
       "      <td>0.832966</td>\n",
       "      <td>0.911786</td>\n",
       "      <td>0.583302</td>\n",
       "      <td>0.620766</td>\n",
       "      <td>0.737498</td>\n",
       "      <td>0.560040</td>\n",
       "      <td>0.592985</td>\n",
       "      <td>0.705514</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 3073 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3         4         5         6  \\\n",
       "0     -0.976824 -1.015758 -0.864571 -1.202901 -1.243973 -1.091163 -1.121963   \n",
       "1      0.317240  0.561588  0.676780 -0.057154  0.021664  0.052093 -0.360611   \n",
       "2      1.693034  1.631441  1.522036  1.695976  1.635002  1.521992  1.688121   \n",
       "3     -1.399097 -1.523252 -1.523374 -1.285726 -1.410870 -1.417807 -1.288077   \n",
       "4      0.535188  0.602736  0.813512  0.522621  0.591895  0.805887  0.636070   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "49995  0.194644  0.342131  0.763791  0.287950  0.424998  0.843577  0.345371   \n",
       "49996  1.693034  1.494280 -0.006884  1.709780  1.551553  0.026966  1.715807   \n",
       "49997 -1.303745  0.575304  1.273431 -1.244313  0.564079  1.346107 -1.232706   \n",
       "49998  0.794000  1.027934  1.335582  0.771096  1.009138  1.308417  0.746812   \n",
       "49999  1.338869  1.274823  1.323152  1.461305  1.412473  1.446613  1.425108   \n",
       "\n",
       "              7         8         9  ...      3063      3064      3065  \\\n",
       "0     -1.231216 -1.127749 -0.882707  ...  1.395088  0.946620  0.403264   \n",
       "1     -0.449900 -0.473116 -0.410588  ...  0.253645  0.118572  0.387855   \n",
       "2      1.628957  1.515960  1.686173  ... -0.762709 -0.660768 -0.475092   \n",
       "3     -1.412593 -1.417298 -1.243738  ... -1.137978 -1.277745 -1.322628   \n",
       "4      0.680217  0.886505  0.714164  ... -0.809617 -0.758185 -0.490501   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "49995  0.484888  0.911684  0.325361  ... -1.372521 -1.375162 -0.891155   \n",
       "49996  1.573149  0.055626  1.700059  ...  1.911083  0.784258 -0.536731   \n",
       "49997  0.554648  1.364891 -1.285396  ... -1.560155 -1.196563 -0.567550   \n",
       "49998  0.987162  1.289356  0.728050  ...  1.066728  0.946620  0.850147   \n",
       "49999  1.391772  1.440425  1.380684  ...  0.816548  0.832966  0.911786   \n",
       "\n",
       "           3066      3067      3068      3069      3070      3071  Label  \n",
       "0      0.380740 -0.123197 -0.458600 -0.056050 -0.540320 -0.641402      6  \n",
       "1      0.256086  0.135572  0.430806  0.251995  0.114124  0.448238      9  \n",
       "2     -0.741144 -0.656910 -0.473935 -0.718346 -0.636092 -0.459796      9  \n",
       "3     -0.990451 -1.125930 -1.179326 -0.841564 -0.971295 -1.034884      4  \n",
       "4     -0.865797 -0.818641 -0.550608 -0.826162 -0.779750 -0.520331      1  \n",
       "...         ...       ...       ...       ...       ...       ...    ...  \n",
       "49995 -1.411157 -1.417046 -0.933973 -1.380642 -1.386307 -0.913812      2  \n",
       "49996  1.954493  0.895709 -0.504604  1.946241  0.880301 -0.580867      6  \n",
       "49997 -1.644883 -1.336181 -0.734623 -1.765698 -1.514004 -0.974348      9  \n",
       "49998  1.081917  1.025093  0.875510  1.052911  1.023960  0.856853      1  \n",
       "49999  0.583302  0.620766  0.737498  0.560040  0.592985  0.705514      1  \n",
       "\n",
       "[50000 rows x 3073 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Create a StandardScaler object\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler to the features (excluding the 'Label' column)\n",
    "scaler.fit(df_images.drop('Label', axis=1))\n",
    "\n",
    "# Transform the features\n",
    "scaled_features = scaler.transform(df_images.drop('Label', axis=1))\n",
    "\n",
    "# Create a new DataFrame with the scaled features\n",
    "df_images_scaled = pd.DataFrame(scaled_features, columns=df_images.columns[:-1])\n",
    "\n",
    "# Add the 'Label' column to the scaled DataFrame\n",
    "df_images_scaled['Label'] = df_images['Label']\n",
    "\n",
    "# Display the DataFrame\n",
    "df_images=df_images_scaled\n",
    "\n",
    "df_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Checking if the data if normally distributed or not...\n",
    "\n",
    "## ERROR: Out of Memory\n",
    "\n",
    "# from pingouin import multivariate_normality\n",
    "# # Example dataset (replace with your actual data)\n",
    "# data = df_images.iloc[:, :-1]  # Features (exclude the last column)\n",
    "\n",
    "# # Perform the Multivariate Normality Test\n",
    "# result = multivariate_normality(data, alpha=0.05)\n",
    "\n",
    "# # Extract relevant information\n",
    "# hz_statistic = result['hz']\n",
    "# p_value = result['pval']\n",
    "# is_normal = result['normal']\n",
    "\n",
    "# # Print the results\n",
    "# print(f\"HzResults (hz={hz_statistic:.6f}, pval={p_value:.6f}, normal={is_normal})\")\n",
    "\n",
    "# # Interpretation\n",
    "# if p_value > 0.05:\n",
    "#     print(\"The data follows a multivariate normal distribution.\")\n",
    "# else:\n",
    "#     print(\"The data does not follow a multivariate normal distribution.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Airplane DataFrame: (5000, 3073)\n",
      "              0         1         2         3         4         5         6  \\\n",
      "29     0.971083  0.931922  0.825942  0.991963  0.953506  0.843577  1.009825   \n",
      "30    -0.064168 -0.247659 -0.280349 -0.112371 -0.284314 -0.299678 -0.069912   \n",
      "35     1.638547  1.549144  1.459885  1.613151  1.523737  1.446613  1.605064   \n",
      "49     1.693034  1.631441  1.522036  1.723584  1.662818  1.547119  1.715807   \n",
      "77    -1.181150 -0.864882 -1.038594 -1.147684 -0.812822 -0.990657 -1.108121   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "49921  1.693034  1.631441  1.522036  1.337067  1.301208  1.207911  1.189781   \n",
      "49930  1.693034  1.631441  1.522036  1.723584  1.662818  1.547119  1.715807   \n",
      "49941  0.834865  0.657601  0.453035  0.619250  0.577987  0.642565  0.442271   \n",
      "49992  0.330862  0.259835  0.279012  0.329363  0.258101  0.278231  0.317686   \n",
      "49994  0.344483  0.314699  0.390884  0.343167  0.313733  0.391300  0.345371   \n",
      "\n",
      "              7         8         9  ...      3063      3064      3065  \\\n",
      "29     0.973210  0.861327  1.033539  ...  1.754721  1.823377  1.990469   \n",
      "30    -0.240619 -0.271691 -0.063442  ...  0.019102 -0.108735 -0.074438   \n",
      "35     1.517340  1.440425  1.616744  ... -0.137260  0.037391 -1.214760   \n",
      "49     1.656861  1.541138  0.700278  ...  2.004900  2.099393  2.175386   \n",
      "77    -0.770798 -0.951501 -1.077108  ... -1.137978 -0.839366 -0.968204   \n",
      "...         ...       ...       ...  ...       ...       ...       ...   \n",
      "49921  1.210395  1.113109  1.186283  ...  1.520178  1.677251  1.805552   \n",
      "49930  1.656861  1.541138  1.713944  ...  2.004900  2.099393  2.175386   \n",
      "49941  0.443032  0.685080  0.408676  ...  0.425643 -0.027554 -0.290175   \n",
      "49992  0.247703  0.269640  0.311475  ...  0.112919 -0.124972 -0.660009   \n",
      "49994  0.317463  0.382942  0.339247  ...  0.410007  0.573187  0.757689   \n",
      "\n",
      "           3066      3067      3068      3069      3070      3071  Label  \n",
      "29     1.751931  1.817576  1.979601  1.746012  1.790137  1.946493      0  \n",
      "30    -0.008803 -0.123197 -0.105904 -0.086854 -0.205117 -0.187386      0  \n",
      "35    -0.226947 -0.058505 -1.240665 -0.333290 -0.077421 -1.337561      0  \n",
      "49     2.001238  2.092519  2.163616  1.977045  2.061492  2.128099      0  \n",
      "77    -1.115105 -0.834814 -0.949307 -1.242022 -0.955332 -1.080285      0  \n",
      "...         ...       ...       ...       ...       ...       ...    ...  \n",
      "49921  1.518205  1.655845  1.780251  1.545783  1.678403  1.780020      0  \n",
      "49930  2.001238  2.092519  2.163616  1.977045  2.061492  2.128099      0  \n",
      "49941  0.396322 -0.009986 -0.243916  0.267397 -0.093383 -0.308457      0  \n",
      "49992  0.131432 -0.123197 -0.642615  0.128777 -0.125307 -0.641402      0  \n",
      "49994  0.396322  0.556073  0.737498  0.390615  0.545099  0.720648      0  \n",
      "\n",
      "[5000 rows x 3073 columns]\n"
     ]
    }
   ],
   "source": [
    "def dividingIntoClasses(df_images):\n",
    "    \"\"\"\n",
    "    Creates separate DataFrames for each class based on the 'Label' column in the input DataFrame using Dictionary as a Dataform to store.\n",
    "\n",
    "    Args:\n",
    "        df_images (pandas.DataFrame): The input DataFrame containing image data.\n",
    "\n",
    "    Returns:\n",
    "        dict: A dictionary where keys are class labels and values are DataFrames containing data for each class.\n",
    "    \"\"\"\n",
    "    class_dataframes = {}\n",
    "    for class_label in range(10):\n",
    "        class_dataframes[class_label] = df_images[df_images['Label'] == class_label]\n",
    "    return class_dataframes\n",
    "\n",
    "# Example usage:\n",
    "# Assuming df_images is your original DataFrame\n",
    "class_dataframes = dividingIntoClasses(df_images)\n",
    "# Example: Access the DataFrame for class 'Airplane' (label 0)\n",
    "airplane_df = class_dataframes[0]\n",
    "print(\"Shape of Airplane DataFrame:\", airplane_df.shape)\n",
    "print(airplane_df)  # Display the first few rows"
   ]
  }
 ],
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