UDAY_KIRAN_AI_ML.ipynb

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              "   ROLL NO         NAME      BRANCH            COURSE NAME    PRICE\n",
              "0       103        RAJU         CSE                    DBMS  4000.0\n",
              "1       105  UDAY KIRAN         CSE              MERN STACK  4500.0\n",
              "2       106      CHERRY         ECE                   AI ML  3000.0\n",
              "3       110      GANESH         EEE       ELECTRICAL DESIGN  2000.0\n",
              "4       111      MADHAV       CIVIL              NETWORKING  1000.0\n",
              "5       132    PRANEETH  MECHANICAL  MECHANICAL SERVICE ENG  1200.0\n",
              "6       113    SANTHOSH         CSE                     AWS  5000.0\n",
              "7       121      VISHNU         ECE             VLSI DESIGN     NaN\n",
              "8       124    KIRANMAI         EEE          CIRCUIT DESIGN  2500.0\n",
              "9       125      SHADIQ         CSE         JAVA FULL STACK  8000.0"
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              "      <th>ROLL NO</th>\n",
              "      <th>NAME</th>\n",
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              "      <th>COURSE NAME</th>\n",
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              "      <th>0</th>\n",
              "      <td>103</td>\n",
              "      <td>RAJU</td>\n",
              "      <td>CSE</td>\n",
              "      <td>DBMS</td>\n",
              "      <td>4000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>105</td>\n",
              "      <td>UDAY KIRAN</td>\n",
              "      <td>CSE</td>\n",
              "      <td>MERN STACK</td>\n",
              "      <td>4500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>106</td>\n",
              "      <td>CHERRY</td>\n",
              "      <td>ECE</td>\n",
              "      <td>AI ML</td>\n",
              "      <td>3000.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>110</td>\n",
              "      <td>GANESH</td>\n",
              "      <td>EEE</td>\n",
              "      <td>ELECTRICAL DESIGN</td>\n",
              "      <td>2000.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>111</td>\n",
              "      <td>MADHAV</td>\n",
              "      <td>CIVIL</td>\n",
              "      <td>NETWORKING</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
              "      <td>132</td>\n",
              "      <td>PRANEETH</td>\n",
              "      <td>MECHANICAL</td>\n",
              "      <td>MECHANICAL SERVICE ENG</td>\n",
              "      <td>1200.0</td>\n",
              "    </tr>\n",
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              "      <th>6</th>\n",
              "      <td>113</td>\n",
              "      <td>SANTHOSH</td>\n",
              "      <td>CSE</td>\n",
              "      <td>AWS</td>\n",
              "      <td>5000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>121</td>\n",
              "      <td>VISHNU</td>\n",
              "      <td>ECE</td>\n",
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              "      <th>8</th>\n",
              "      <td>124</td>\n",
              "      <td>KIRANMAI</td>\n",
              "      <td>EEE</td>\n",
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              "      <th>9</th>\n",
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              "      <td>SHADIQ</td>\n",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "data ={\n",
        "'ROLL NO ':[103,105,106,110,111,132,113,121,124,125],\n",
        "'NAME': ['RAJU','UDAY KIRAN','CHERRY','GANESH','MADHAV','PRANEETH','SANTHOSH','VISHNU','KIRANMAI','SHADIQ'],\n",
        "'BRANCH':['CSE','CSE','ECE','EEE','CIVIL','MECHANICAL','CSE','ECE','EEE','CSE'],\n",
        "'COURSE NAME ': ['DBMS','MERN STACK','AI ML','ELECTRICAL DESIGN','NETWORKING','MECHANICAL SERVICE ENG','AWS','VLSI DESIGN','CIRCUIT DESIGN','JAVA FULL STACK'],\n",
        "'PRICE': [4000,4500,3000,2000,1000,1200,5000,np.nan,2500,8000]\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "df"
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10 entries, 0 to 9\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   ROLL NO       10 non-null     int64  \n",
            " 1   NAME          10 non-null     object \n",
            " 2   BRANCH        10 non-null     object \n",
            " 3   COURSE NAME   10 non-null     object \n",
            " 4   PRICE         9 non-null      float64\n",
            "dtypes: float64(1), int64(1), object(3)\n",
            "memory usage: 528.0+ bytes\n"
          ]
        }
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              "         ROLL NO         PRICE\n",
              "count   10.000000     9.000000\n",
              "mean   115.000000  3466.666667\n",
              "std      9.865766  2201.703886\n",
              "min    103.000000  1000.000000\n",
              "25%    107.000000  2000.000000\n",
              "50%    112.000000  3000.000000\n",
              "75%    123.250000  4500.000000\n",
              "max    132.000000  8000.000000"
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              "      <th>mean</th>\n",
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              "      <th>min</th>\n",
              "      <td>103.000000</td>\n",
              "      <td>1000.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <th>50%</th>\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-55061d61-aa11-498c-845f-d45863512e8e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[3]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5bymYbDAp0F",
        "outputId": "d468de05-f0f4-4c0d-bab1-fcb14785f239"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ROLL NO                       110\n",
              "NAME                       GANESH\n",
              "BRANCH                        EEE\n",
              "COURSE NAME     ELECTRICAL DESIGN\n",
              "PRICE                      2000.0\n",
              "Name: 3, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cRGTju8nA2hL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To check the null value\n",
        "df.isnull().any()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frTd88ZJBIUi",
        "outputId": "7138042b-9083-4475-8209-7aac43201d91"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ROLL NO         False\n",
              "NAME            False\n",
              "BRANCH          False\n",
              "COURSE NAME     False\n",
              "PRICE            True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum() # To check the number of null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkWtTWerBPAj",
        "outputId": "60eaea3c-7db1-4960-bb9b-3c9b88a7f9ca"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ROLL NO         0\n",
              "NAME            0\n",
              "BRANCH          0\n",
              "COURSE NAME     0\n",
              "PRICE           1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "4lHyr1QPA3RU"
      }
    }
  ]
}