AI1703HW_50startups.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thanhtruongphan/HomeworkAI/blob/main/AI1703HW_50startups.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **50 startups**"
      ],
      "metadata": {
        "id": "rRAULoZNehTq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Linear bậc 1**"
      ],
      "metadata": {
        "id": "Od186RVwOoqz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "Rb2lSyh7Gdqm"
      },
      "outputs": [],
      "source": [
        "import pandas as pd "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = '/content/drive/MyDrive/A_HOC TAP/Nam 4, Hoc ky 2/Artificial Intelligence/Code_AI/files/50_Startups.csv'\n",
        "data = pd.read_csv(url)\n",
        "data.head()"
      ],
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          "height": 206
        },
        "id": "rt7G8RtZGx8U",
        "outputId": "edf1eff1-65d4-4ae0-886a-de0e269f009e"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
              "0  165349.20       136897.80        471784.10    New York  192261.83\n",
              "1  162597.70       151377.59        443898.53  California  191792.06\n",
              "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
              "3  144372.41       118671.85        383199.62    New York  182901.99\n",
              "4  142107.34        91391.77        366168.42     Florida  166187.94"
            ],
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              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
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              "      <td>165349.20</td>\n",
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              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
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              "      <th>1</th>\n",
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              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
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              "      <td>191792.06</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "data.hist()\n",
        "plt.show()"
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        "colab": {
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      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/Folder')"
      ],
      "metadata": {
        "id": "woAoW15BHMxP"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = data.corr()\n",
        "print(correlation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9J5W_TAHayx",
        "outputId": "cffc8380-a225-48aa-bf67-2cf6664f676b"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 R&D Spend  Administration  Marketing Spend    Profit\n",
            "R&D Spend         1.000000        0.241955         0.724248  0.972900\n",
            "Administration    0.241955        1.000000        -0.032154  0.200717\n",
            "Marketing Spend   0.724248       -0.032154         1.000000  0.747766\n",
            "Profit            0.972900        0.200717         0.747766  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "sns.heatmap(correlation, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "MXWzEmJ3H4xV",
        "outputId": "8e16075d-038d-4ef8-f1a3-71999c3dbf1b"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "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": [],
      "metadata": {
        "id": "qNubdsxrKKl0"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "6fsWtxiSJZ_0",
        "outputId": "2b237606-a60a-4d61-c86b-c9580caa9d86"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f89c4969460>"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "no7hWj1AJg_M"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#1 import library \n",
        "from sklearn import linear_model"
      ],
      "metadata": {
        "id": "oPJWwlTzKLlL"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2 fit-model, find intercept & coef\n",
        "model = linear_model.LinearRegression()\n",
        "model.fit(X=pd.DataFrame(data['Administration']),y= data['Profit'])\n",
        "print(\"He so intercept la : \", model.intercept_)\n",
        "print(\"He so coef la : \", model.coef_)"
      ],
      "metadata": {
        "id": "If92E7dgKSsQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6ed38f8-e85d-48d5-d500-dd26c33c7fe6"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "He so intercept la :  76974.47130541757\n",
            "He so coef la :  [0.2887492]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X=pd.DataFrame(data['Administration']),y= data['Profit'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4-9cjAbLPjp",
        "outputId": "b6a91f69-1489-4685-c0df-d36ce3ceb447"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04028714077757223"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j-sufbKqMNRw"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X=pd.DataFrame(data['Administration']))\n",
        "data.plot(kind='scatter',x='Administration',y='Profit',figsize=(7,7),color='orange', xlim=(40000,200000))\n",
        "plt.plot(data['Administration'],y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "J-wioYrTNJCw",
        "outputId": "65107da5-2f11-4228-b21d-ca4d0d9e5a49"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f89c4969670>]"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5xBVaveYNkZh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "zAsrRFFTcQ76"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data['Administration'],data['Administration']**2,\n",
        "                data['Administration']**3,data['Administration']**4,\n",
        "                data['Administration']**5,data['Administration']**6,\n",
        "                data['Administration']**7,data['Administration']**8,\n",
        "                data['Administration']**9,data['Administration']**10,\n",
        "                data['Administration']**11,data['Administration']**12,\n",
        "                data['Administration']**13,data['Administration']**14,\n",
        "                data['Administration']**15\n",
        "                ]).T \n",
        "y=data['Profit']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjGNWO_UcRPv",
        "outputId": "7b2a09cf-5462-4ccc-e20b-7fe1af3d3729"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  102091.97134986722\n",
            "Coef cua mo hinh la :  [ 1.75942875e-122 -5.98123898e-065 -3.59383880e-072  2.89597723e-119\n",
            "  1.45335790e-101  1.38335656e-096  1.70455712e-091  2.01376046e-086\n",
            "  2.26002413e-081  2.36602135e-076  2.23095183e-071  1.75579872e-066\n",
            "  9.23232712e-062 -1.08043541e-066  3.14801559e-072]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPHlurMscRSb",
        "outputId": "97211377-a5d0-4fde-9aef-2710d3edb1f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03863989118580535"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## bậc 18\n",
        "\n"
      ],
      "metadata": {
        "id": "GauTZk2wi9kC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data['Administration'],data['Administration']**2,\n",
        "                data['Administration']**3,data['Administration']**4,\n",
        "                data['Administration']**5,data['Administration']**6,\n",
        "                data['Administration']**7,data['Administration']**8,\n",
        "                data['Administration']**9,data['Administration']**10,\n",
        "                data['Administration']**11,data['Administration']**12,\n",
        "                data['Administration']**13,data['Administration']**14,\n",
        "                data['Administration']**15,data['Administration']**16,\n",
        "                data['Administration']**17,data['Administration']**18]).T \n",
        "y=data['Profit']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTwSzq_hjCrs",
        "outputId": "507b0b87-a02d-4b21-80ab-d815b2f0d7a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  102889.88197732197\n",
            "Coef cua mo hinh la :  [ 2.65495278e-153 -1.33768093e-080 -1.39289402e-086  7.67045854e-093\n",
            "  3.09626887e-132  2.74512434e-127  1.18969681e-117  4.74337017e-117\n",
            "  6.00922251e-112  7.37972144e-107  8.73682851e-102  9.85748960e-097\n",
            "  1.03939457e-091  9.88088142e-087  7.84377959e-082  4.16061053e-077\n",
            " -4.91661622e-082  1.44468518e-087]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(50000,190000,10)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7,range**8,range**9,range**10,range**11,range**12,range**13,range**14,range**15,range**16,range**17,range**18]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data.plot(kind='scatter',x='Administration',y='Profit',figsize=(9,9),color='black',xlim=(40000,200000))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "bynKK3hNjKg1",
        "outputId": "976992a6-852f-4595-d8b4-1a826685a052"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f89d2638910>]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLW_LVzfbHsk",
        "outputId": "983f01e1-e649-4bfe-8cd5-4927291213c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04993008981449465"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## bậc 25 "
      ],
      "metadata": {
        "id": "WaWldbwsdzp0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data['Administration'],data['Administration']**2,\n",
        "                data['Administration']**3,data['Administration']**4,\n",
        "                data['Administration']**5,data['Administration']**6,\n",
        "                data['Administration']**7,data['Administration']**8,\n",
        "                data['Administration']**9,data['Administration']**10,\n",
        "                data['Administration']**11,data['Administration']**12,\n",
        "                data['Administration']**13,data['Administration']**14,\n",
        "                data['Administration']**15,data['Administration']**16,\n",
        "                data['Administration']**17,data['Administration']**18,\n",
        "                data['Administration']**19,data['Administration']**20,\n",
        "                data['Administration']**21,data['Administration']**22,\n",
        "                data['Administration']**23,data['Administration']**24,\n",
        "                data['Administration']**25]).T \n",
        "y=data['Profit']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQyDqIHQdz5-",
        "outputId": "d6be00ee-cfd1-4ade-f4c3-8e14a52bbb95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  105293.40470036298\n",
            "Coef cua mo hinh la :  [ 1.41204637e-225  8.73083095e-116 -1.52078341e-122  5.83874118e-223\n",
            "  2.78989192e-204  2.08742978e-199 -1.71840170e-151  1.49166815e-154\n",
            " -3.64176794e-158  8.51420211e-179 -1.63928458e-161 -1.08373385e-165\n",
            "  2.14731414e-163  2.84283761e-158  3.69847527e-153  4.70972323e-148\n",
            "  5.83843604e-143  6.98948882e-138  7.98196413e-133  8.52248257e-128\n",
            "  8.20467199e-123  6.59485144e-118  3.54091399e-113 -4.20620577e-118\n",
            "  1.24148361e-123]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXdkr4jYdz8f",
        "outputId": "67910f65-1e69-42a1-f356-f503fcf5fbd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06584647170865243"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oCM8J6jBdz_H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## bậc 26 (độ chính xác cao nhất) "
      ],
      "metadata": {
        "id": "DtTNnNJ2e1oX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data['Administration'],data['Administration']**2,\n",
        "                data['Administration']**3,data['Administration']**4,\n",
        "                data['Administration']**5,data['Administration']**6,\n",
        "                data['Administration']**7,data['Administration']**8,\n",
        "                data['Administration']**9,data['Administration']**10,\n",
        "                data['Administration']**11,data['Administration']**12,\n",
        "                data['Administration']**13,data['Administration']**14,\n",
        "                data['Administration']**15,data['Administration']**16,\n",
        "                data['Administration']**17,data['Administration']**18,\n",
        "                data['Administration']**19,data['Administration']**20,\n",
        "                data['Administration']**21,data['Administration']**22,\n",
        "                data['Administration']**23,data['Administration']**24,\n",
        "                data['Administration']**25,data['Administration']**26]).T \n",
        "y=data['Profit']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "id": "a7N1HnrXkNx2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "488ccd53-9049-48a3-95e9-965eec51146f"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  105618.55589973919\n",
            "Coef cua mo hinh la :  [ 2.11292936e-120  2.45698811e-120 -3.26922903e-127  2.41282724e-233\n",
            "  1.31588477e-214  1.80838491e-209  2.45882410e-204  3.31955787e-199\n",
            "  4.45687479e-194  5.95353181e-189  7.91042542e-184  1.04462626e-178\n",
            "  1.36932762e-173  1.77858409e-168  2.28372315e-163  2.88977936e-158\n",
            "  3.58859278e-153  4.34828534e-148  5.09863909e-143  5.71353583e-138\n",
            "  5.99603095e-133  5.68206007e-128  4.50171430e-123  2.38529981e-118\n",
            " -2.83393236e-123  8.36567666e-129]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTQ2RYboc5rE",
        "outputId": "e4aceb3b-f686-4815-b817-883a4ec426fa"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06593192356420341"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(50000,190000,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7,range**8,range**9,range**10,range**11,range**12,range**13,range**14,range**15,range**16,range**17,range**18,range**19,range**20,range**21,range**22,range**23,range**24,range**25,range**26]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data.plot(kind='scatter',x='Administration',y='Profit',figsize=(9,9),color='black',xlim=(40000,200000))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "OnVGbLTNewYp",
        "outputId": "d2012bfe-d9d4-40df-822d-c52bbcd481e5"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f89d146dee0>]"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## bậc 27 "
      ],
      "metadata": {
        "id": "Z7yrqv8gdk_j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data['Administration'],data['Administration']**2,\n",
        "                data['Administration']**3,data['Administration']**4,\n",
        "                data['Administration']**5,data['Administration']**6,\n",
        "                data['Administration']**7,data['Administration']**8,\n",
        "                data['Administration']**9,data['Administration']**10,\n",
        "                data['Administration']**11,data['Administration']**12,\n",
        "                data['Administration']**13,data['Administration']**14,\n",
        "                data['Administration']**15,data['Administration']**16,\n",
        "                data['Administration']**17,data['Administration']**18,\n",
        "                data['Administration']**19,data['Administration']**20,\n",
        "                data['Administration']**21,data['Administration']**22,\n",
        "                data['Administration']**23,data['Administration']**24,\n",
        "                data['Administration']**25,data['Administration']**26,\n",
        "                data['Administration']**27]).T \n",
        "y=data['Profit']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3AlFwJ5dlSz",
        "outputId": "6cd6b0ca-fd74-4659-b349-dd3c879d1124"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  105929.41446407877\n",
            "Coef cua mo hinh la :  [ 1.71857833e-125  3.22580872e-125 -7.20701435e-133  1.00923537e-243\n",
            "  6.12596430e-225  8.46247759e-220  1.15709686e-214  1.57173240e-209\n",
            "  2.12446415e-204  2.85913195e-199  3.83080921e-194  5.10698842e-189\n",
            "  6.76748035e-184  8.90171349e-179  1.16013200e-173  1.49447975e-168\n",
            "  1.89696891e-163  2.36264273e-158  2.87076253e-153  3.37493830e-148\n",
            "  3.79120526e-143  3.98774920e-138  3.78699936e-133  3.00628289e-128\n",
            "  1.59587116e-123 -1.89623506e-128  5.59815311e-134]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uR-GJaHidlVh",
        "outputId": "af0c3b68-bcb1-4ce4-f78f-bf367b023514"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06554071207957435"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jonTHgNBdleY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}