Age_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Age detection.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Shibu Mohapatra\n",
        "\n",
        "MSC CS Artificial Intelligence"
      ],
      "metadata": {
        "id": "XYlXus3blUB2"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDi2qbpjPiOt"
      },
      "source": [
        "**Age detection using neural network procedure with Python.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNIruC3QPmzX"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "v1YU4v6fPswx",
        "outputId": "34bebff2-4b3b-4bf2-9d39-fd94ae676746"
      },
      "source": [
        "df=pd.read_csv(\"/content/drive/MyDrive/age_gender.csv\")\n",
        "df1= pd.DataFrame(df)\n",
        "\n",
        "# 1 = Female\n",
        "# 0 = Male\n",
        "\n",
        "plt.xlabel = 'Gender (1= Female, 0= Male)'\n",
        "plt.figure(figsize=(4,5))\n",
        "ax=df1.gender.value_counts().plot.bar(x='Gender (1= Female, 0= Male)', y='Count', title='Gender', legend = (1,0, ('Female', 'Male')))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(4,5))\n",
        "labels =['White','Black','Indian','Asian','Hispanic']\n",
        "ax=df1.ethnicity.value_counts().plot.bar()\n",
        "ax.set_xticklabels(labels)\n",
        "ax.set_title('Ethinicity')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "jcK2C38v5JJ2",
        "outputId": "d9c71245-3224-4184-d99f-d757ae687c80"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ethinicity')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LZfcFPgDcKunm0vYp4DTgAknHAvcB7yvzLgcOApYBTwHHANheJekU4IbS72Tbq7qoKyJaMO4wsf1TQCPM3m+Y/gaOG2FdZwNnj7eWiGhf7oCNiCoSJhFRRTfnTCKih2afcNmErfve0w7ueh3ZM4mIKhImEVFFwiQiqkiYREQVCZOIqCJhEhFVJEwiooqESURUkTCJiCoSJhFRRcIkIqpImEREFQmTiKgiYRIRVSRMIqKKhElEVJEwiYgqEiYRUUXCJCKqSJhERBUJk4ioImESEVUkTCKiioRJRFSRMImIKhImEVFFwiQiqkiYREQVCZOIqCJhEhFVJEwiooqESURUkTCJiCoSJhFRRcIkIqpImEREFQmTiKgiYRIRVSRMIqKKvgkTSfMk3SlpmaQT2q4nIjZMX4SJpCnA14EDgV2BIyTt2m5VEbEh+iJMgD2BZbbvtv074Hxgfss1RcQGkO22a0DSYcA82x8q0x8A9rL9kXX6LQQWlsk3AHdOQDnbA49MwHon0qDVPGj1wuDVPJH17mR7xrqNUydoYxPC9iJg0URuQ9JS23Mnchu1DVrNg1YvDF7NbdTbL4c5K4AdO6ZnlbaIGBD9EiY3AHMk7SxpM+Bw4NKWa4qIDdAXhzm210r6CHAlMAU42/ZtLZUzoYdRE2TQah60emHwau55vX1xAjYiBl+/HOZExIBLmEREFQmTiKgiYRIRVfTF1Zw2SRJwJLCL7ZMlvRZ4le3rWy5tVJJOtv23HdNTgHNsH9liWaMqNe5Ax++d7V+3V9HIJM0APgzM5qX1frCtmsYiaWvgt7afL9ObAFvYfqoX28+eCXwD2Ac4okw/QfPQYb/bUdKJAJI2By4C7mq3pJFJ+mvgIWAJcFl5/ajVokZ3CbAt8C+8WO9lrVY0tquArTqmt6Kpvycm/Z4JzTNAe0i6CcD26nLjXL/7IHBeCZR3Apfb/krLNY3meOANth9tu5D1tJXtT7ZdxAbawvZvhiZs/0bSVqMtUFP2TODZsvtteGH39vl2SxqZpD0k7QHsDpwOvJ9mj+Qnpb1f3Q881nYRG+BHkg5qu4gN9GTn74CktwK/7dXGJ/1Na5KOpPmD3ANYDBwG/E/bF7Ra2AgkXTPKbNt+V8+K2QCSzqJ50vsy4Jmhdttfaq2oUUh6AtiaptZnAdH8fF/RamGjkPQnNMN3/DtNva8C3m/7xp5sf7KHCYCkNwL70fwDXGX7jpZL2uhIOmm4dtuf7nUtGzNJm9KENsCdtp/t2bYne5hIOtf2B8Zq6zeS/g74vO01ZXo68HHb/6PdyjYe5Wc6B9hiqM32T9qraHiS3mX7aknvGW6+7Yt6UUdOwMJunRPl/MlbW6plQxxo+1NDE+XE8UFAX4ZJORf1CZqfd+cfZ78eln2I5qTxLOBmYG/g50A/1vsO4GrgL4aZZ5orfRNu0oZJuQryKWBLSY/THOIA/I7BeEJ0iqTNbT8DIGlLYPOWaxrNecD3gEOAvwIWACtbrWh0xwN/Alxr+53lUPjvWq5pWLZPKl+PabOOSRsmtj8LfFbSZ22f2HY943AecJWkb5XpY2hOIPerV9o+S9Lxtv8V+FdJN7Rd1Cietv20JEpo/0rSG8ZerD3lfqO/5OU32p3ci+1P2jCR9EbbvwK+P9wlVdu/aKGs9Wb7c5JuoTlxDHCK7SvbrGkMQycCH5B0MM0Vh+1arGcsyyVNAy4GlkhaDdzXck1juYTm8vuNdFwx65VJewJW0iLbC0e41Nq3l1gHlaRDgH+jGZ7zq8ArgE/b7vsR9SS9g+Zu2CvKpyf0JUm/tP3m1rY/WcNk0Enam+aP8k3AZjQj1D3Zz/dBDAJJr7D9uKRh95psr+p1TetL0iLgq7ZvbWX7CROQ9DZefpx5TmsFrQdJS2nGyv0+MBc4GviDfjv/I+kTtj8v6auUu4w72f5oC2WNSNKPbB8i6R6aetUx27Z3aam0MUm6HXg9cA/NYc7QjXZ/1IvtT9pzJkMknQu8juby33Ol2UBfhwmA7WWSpth+DvhWeb6or8IEGLoBcGmrVawn24eUrzu3Xcs4HNjmxid9mND8r76rB28X7anyQOLNkj4PPEAfPmtl+4flaz9faXoZSfsCN9t+UtJRNI9bfKVfh0wAsH0fgKTfp+Nenl6Z9Ic5kr4PfNT2A23XsiEk7QQ8DGwKfIzmBOE3bC9rtbB1SPohwxzeDLH97h6Ws97KlbK3AH8EfBv4JvA+2+9os67RSHo38EXgNTS/GzsBd9jebdQFK5m0eyYdv+S/B9wu6Xpe+gBaX/6SDxn6X4jmqdB+fr7lC+Xre2gePPtOmT6CZnyTfrXWtiXNB75W7pE5tu2ixnAKzZ26/2J7d0nvBI7q1cYnbZjQ3H68KfALXrwHou9JupXR/6fvycm29VVuUEPSF9f5uMoflpPI/eqJcpf0B4A/LaOW9fvfy7O2H5W0iaRNbF8jqWdj3PT7D2cizQTeRnPC8hbg/wI/A37Wz5f/aG5HH0RbS9rF9t0AknamecS/X70f+A/AMbYflPR2+rtegDWStqG5n+c8SQ8DT/Zq4zln0pzEnEsTLPuU1xrbu7Za2AaQtD3waD+fRJY0j+aZp7tpLlnuBPynfr5rV9LuNIHyXprLrRfZ/mq7VY2sjAH7NM3P90ia82jn9Wp0u8m8ZzJkS5q7Mbctr38HWrnpZ32Um9VOA1bRHCOfC2wPbCLpaNtXtFnfSGxfIWkO8MbS9KuhhxT7iaQ/oDmfcwTwCM3DibL9zlYLWw/lytOrgD1pfj+u7OUwmZN2z6TcLbgbzQDS1wHX0jwhurrVwsZQzjN8iib4FtEMRXBtear1u7Z3b7XAUQzCzYGSnqc5TDh26MqYpLv7+Wa1IWXYhL+lOR8omqEJTrZ9di+2P5n3TF5L88j+XcAKYDmwptWK1s9U2/8HXvi4i2sBylOt7VY2igG6OfA9NHcWXyPpCpphEPv3B/tS/w3YfWhvRNIrac4DJkwmku15av76dqM5X/Jx4M2SVgE/Hxojog91Dna97mDB/bybORA3B9q+GLi4nH+YD/wN8PuSzgR+MBTkfepRmj3tIU+Utp6YtIc5nSTNAvalCZVDaMbemNZuVcOT9BzNGXrRnO8Z+oAl0XzUwaZt1TaaQb05EF4YvvG9NIMz7zdW/7ZIOgf4Q5qhCEwThreU14QP3j1pw0TSR2nC420095n8rON1q8unokUdZaiHPwYG6ubAQTLSoN1DJnrw7skcJl+i3FsyiP9bDpoyJsjLDN3UFnWVm+y2sf14z7Y5WcMkYmMj6R9pxtd9DriB5paH023/fS+233dPmcbGRdITkh4f5vVEGcg76tm17IkcCvwzsDPN4wA9MWmv5kRv2P69tmuYRDYtH8J1KM3Dic9K6tmhR/ZMIjYe/xu4l+YZop+UYSpyziQiuidpqu21vdhWDnMiBpyko2x/R9J/GaFLTz4cPmESMfiGhkZo9fxUDnMioorsmUQMOElnjDa/Vx8nkjCJGHw3drz/NNDKQ6o5zInYiEi6qa0xbXKfScTGpbW9g4RJRFSRw5yIASfpCV7cI9mKl45x4159mH3CJCKqyGFORFSRMImIKhImEVFFwiQiqvj/mB2AzvZpQxgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PdLxcEigP5JZ",
        "outputId": "22d541c9-905d-45a5-bcba-34b5bd89aa42"
      },
      "source": [
        "# Converting pixels into numpy array\n",
        "df1['pixels'] = df1['pixels'].apply(lambda x:  np.reshape(np.array(x.split(), dtype=\"float32\"), (48,48)))\n",
        "df1.head(5)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c57ff4f1-a4bc-4d04-9c01-19674f187c1a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>gender</th>\n",
              "      <th>img_name</th>\n",
              "      <th>pixels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>20161219203650636.jpg.chip.jpg</td>\n",
              "      <td>[[129.0, 128.0, 128.0, 126.0, 127.0, 130.0, 13...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>20161219222752047.jpg.chip.jpg</td>\n",
              "      <td>[[164.0, 74.0, 111.0, 168.0, 169.0, 171.0, 175...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>20161219222832191.jpg.chip.jpg</td>\n",
              "      <td>[[67.0, 70.0, 71.0, 70.0, 69.0, 67.0, 70.0, 79...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>20161220144911423.jpg.chip.jpg</td>\n",
              "      <td>[[193.0, 197.0, 198.0, 200.0, 199.0, 200.0, 20...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>20161220144914327.jpg.chip.jpg</td>\n",
              "      <td>[[202.0, 205.0, 209.0, 210.0, 209.0, 209.0, 21...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c57ff4f1-a4bc-4d04-9c01-19674f187c1a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c57ff4f1-a4bc-4d04-9c01-19674f187c1a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c57ff4f1-a4bc-4d04-9c01-19674f187c1a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age  ...                                             pixels\n",
              "0    1  ...  [[129.0, 128.0, 128.0, 126.0, 127.0, 130.0, 13...\n",
              "1    1  ...  [[164.0, 74.0, 111.0, 168.0, 169.0, 171.0, 175...\n",
              "2    1  ...  [[67.0, 70.0, 71.0, 70.0, 69.0, 67.0, 70.0, 79...\n",
              "3    1  ...  [[193.0, 197.0, 198.0, 200.0, 199.0, 200.0, 20...\n",
              "4    1  ...  [[202.0, 205.0, 209.0, 210.0, 209.0, 209.0, 21...\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8UgRbYsP7hx"
      },
      "source": [
        "def plot_data(rows, cols, lower_value, upper_value):\n",
        "    fig = plt.figure(figsize=(cols*3,rows*4))\n",
        "    for i in range(1, cols*rows + 1):\n",
        "        k = np.random.randint(lower_value,upper_value)\n",
        "        fig.add_subplot(rows, cols, i) # adding sub plot\n",
        "        gender = df.gender[k]\n",
        "        ethnicity = df.ethnicity[k]\n",
        "        age = df.age[k]\n",
        "        im = df.pixels[k]\n",
        "        plt.imshow(im, cmap='gray')\n",
        "        plt.axis('off')\n",
        "        plt.title(f'Gender:{gender} \\nAge:{age} \\nEthnicity:{ethnicity}')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "        plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "V6F7FKQgjzFX",
        "outputId": "33837ddb-80fc-4bab-db0d-cd4a1b3f79d4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "        plt.show()"
      ],
      "metadata": {
        "id": "tfMzKBUOj5OY"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_data(rows=1, cols=5, lower_value=0, upper_value=len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "Fzw3zVKlj9yj",
        "outputId": "48d9354e-cfcd-4ffe-859c-a7e8c8648a6b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}