Untitled10.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "%matplotlib inline"
      ]
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      "source": [
        "raw_df = pd.read_csv('Salary_Data.csv')"
      ],
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        "raw_df.head()"
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              "   YearsExperience   Salary\n",
              "0              1.1  39343.0\n",
              "1              1.3  46205.0\n",
              "2              1.5  37731.0\n",
              "3              2.0  43525.0\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30 entries, 0 to 29\n",
            "Data columns (total 2 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   YearsExperience  30 non-null     float64\n",
            " 1   Salary           30 non-null     float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 608.0 bytes\n"
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        "raw_df.shape"
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              "(30, 2)"
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            "text/plain": [
              "       YearsExperience         Salary\n",
              "count        30.000000      30.000000\n",
              "mean          5.313333   76003.000000\n",
              "std           2.837888   27414.429785\n",
              "min           1.100000   37731.000000\n",
              "25%           3.200000   56720.750000\n",
              "50%           4.700000   65237.000000\n",
              "75%           7.700000  100544.750000\n",
              "max          10.500000  122391.000000"
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              "      <td>30.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>5.313333</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
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        "plt.figure(figsize=(12, 6))\n",
        "sns.scatterplot(x = raw_df['YearsExperience'], y = raw_df['Salary'], data = raw_df)\n",
        "plt.xlabel('YearsExperience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show() \n"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = raw_df['YearsExperience']\n",
        "y = raw_df['Salary']"
      ],
      "metadata": {
        "id": "KznLMz9zt_Vy"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g94XK1qZvzaQ",
        "outputId": "85058342-d738-4d8d-b7d2-3e6f30785660"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.1\n",
              "1    1.3\n",
              "2    1.5\n",
              "3    2.0\n",
              "4    2.2\n",
              "Name: YearsExperience, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h79e5Yl7v1jL",
        "outputId": "894f7293-196a-45d0-db5e-1d9dd4f915ba"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    39343.0\n",
              "1    46205.0\n",
              "2    37731.0\n",
              "3    43525.0\n",
              "4    39891.0\n",
              "Name: Salary, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "gd8y5Awhv5WB"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=42)"
      ],
      "metadata": {
        "id": "LmDvTC60wGRk"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qc97-bzGwWnx",
        "outputId": "51a6f5e0-7568-4a5d-a69e-506953a18d05"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21,)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "xpxlfkP7wY69"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "GHo9CITLwmEp"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train[:,np.newaxis]\n",
        "x_test = x_test[:,np.newaxis]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwx_UALFwyrz",
        "outputId": "fba77c41-2397-427d-f3b8-fc32f1ce178b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2WFQ30_w3On",
        "outputId": "6c141123-a290-4242-b33c-92659e02893d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "TzlzKuoSxL4_"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "difference = []\n",
        "for i, j in zip(y_test, y_pred):\n",
        "  difference.append(i-j)"
      ],
      "metadata": {
        "id": "gHtOtnYExekN"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = model.predict([[16]])"
      ],
      "metadata": {
        "id": "AsQ6PAIixjpE"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33Csp63tx0Wi",
        "outputId": "941df842-ef3e-4ae3-d969-97000bc2c1ec"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([175343.74591594])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6SHNkmplyBpz",
        "outputId": "18ba6288-4b9e-48c5-941f-b6f1488ad35d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9339.08172382])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gzsFqwPDyHmC",
        "outputId": "0512858c-1c38-4de5-fdc9-f2bdcebf0c43"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25918.438334893202"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x=\"YearsExperience\", y=\"Salary\", data=raw_df);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "BjI9ndzZyJeJ",
        "outputId": "5693a8c9-d224-4066-f5b8-b256943ef59b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "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": [
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "_VNZY_W5yuct"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "id": "prW-WIPEz6BB"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRj7Hqgs0FB3",
        "outputId": "d78e873d-6968-4a4d-ecee-e6f8f335ac64"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37784662.46621314"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rsq = r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "Lu5T5e7l0fKC"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rsq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJ-LKvds0z-2",
        "outputId": "ca88fa50-a8fc-4372-a925-cfe831f26c14"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9414466227178214"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "vrgy6WsV01Mz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}