Assignment1_ML_BOOTCAMP.ipynb

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      "name": "python"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dheeraj0000/2216177_AIML_Bootcamp_2023/blob/main/Assignment1_ML_BOOTCAMP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": null,
      "metadata": {
        "id": "Dm9CUJPfuYDj"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"/content/Employee_StudyHours.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
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        "outputId": "3f711a0f-ba1d-415b-8e76-5dd976bb5898"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Hours  Scores\n",
              "0    2.5    21.0\n",
              "1    5.1    47.0\n",
              "2    3.2    27.0\n",
              "3    8.5    75.0\n",
              "4    3.5    30.0"
            ],
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              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.1</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>30.0</td>\n",
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      "cell_type": "code",
      "source": [
        "df.info()\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "He2k6EzEucqR",
        "outputId": "7f4dc3ce-d8ec-4f3e-d58b-c269029031b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32 entries, 0 to 31\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Hours   30 non-null     float64\n",
            " 1   Scores  30 non-null     float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 640.0 bytes\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def heat_map(df):\n",
        "  plt.figure(figsize=(6,4))\n",
        "  sns.heatmap(df.isnull()) #for adding use this code yticklabels=False,cbar=False,cmap='viridis'\n",
        "  return plt.show()\n",
        "\n",
        "heat_map(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "edTBWlCKufwM",
        "outputId": "66a4d87a-22bc-4173-c55d-d3f71d2864ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math as m\n",
        "hours_median=m.floor(df['Hours'].median())\n",
        "print(hours_median)\n",
        "\n",
        "scores_mean=m.floor(df['Scores'].mean())\n",
        "print(scores_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REcr902nuoS1",
        "outputId": "86dc1158-81b9-42a4-daf2-0acb8122cf54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n",
            "51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Hours']=df['Hours'].fillna(hours_median)\n",
        "df['Scores']=df['Scores'].fillna(scores_mean)"
      ],
      "metadata": {
        "id": "mtxGZa-VuqWC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heat_map(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "v0Y14HNrusZz",
        "outputId": "e30ce3bd-e0fc-4e9d-d497-0ec64062091d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.iloc[:,:-1].values\n",
        "Y=df.iloc[:,1:].values"
      ],
      "metadata": {
        "id": "oXXW1o-euuWz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split as tts\n",
        "X_train,X_test,Y_train,Y_test=tts(X,Y,test_size=0.3,random_state=0)"
      ],
      "metadata": {
        "id": "Tkr90h3zuwYw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model=LinearRegression()"
      ],
      "metadata": {
        "id": "jhrr838QuyP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1lvtXImu0mq",
        "outputId": "f2be0e36-faea-40b9-de94-0fa43fd0bc31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_test,Y_test,color='red')\n",
        "plt.plot(X_test,model.predict(X_test),color='green')   # plt.plot(X_test,Y_pred,color='blue') \n",
        "plt.xlabel('Hours')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('Case Study 2 Model Evaluation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "ELqcHTxUu2in",
        "outputId": "795a7a34-cba8-4cc9-c264-1403e179c88b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Case Study 2 Model Evaluation')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test,Y_test)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OqTlvLOSu4a9",
        "outputId": "9fc93bea-304c-4906-f9b4-ff8f26182671"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67.10267948989208"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h=int(input(\"Enter NO of hours \"))\n",
        "print(\"Predicted Score is \",model.predict([[h]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eB7U6VqTu6Ye",
        "outputId": "be27e34c-5a32-41be-d3b5-2da623ef209f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter NO of hours 4\n",
            "Predicted Score is  [[42.61978307]]\n"
          ]
        }
      ]
    }
  ]
}