Regression(linear,plynomial, logistic).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XgNbxYo5aLTH"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as mtp\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "_q_l2eF2bm8A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"Position_Salaries.csv\")"
      ],
      "metadata": {
        "id": "qKs8sKGHb2_2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.iloc[:,1:2].values\n",
        "y=data.iloc[:,2].values"
      ],
      "metadata": {
        "id": "H_WkiedbcRSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_regs=LinearRegression()\n",
        "lin_regs.fit(x,y)\n",
        "LinearRegression()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaX_NLv8dWvB",
        "outputId": "9eb7e17c-a9f4-450d-e3b2-8afee74d5640"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_regs=PolynomialFeatures(degree=2)\n",
        "x_poly=poly_regs.fit_transform(x)\n",
        "lin_reg_2=LinearRegression()\n",
        "lin_reg_2.fit(x_poly,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rV34D594d3pW",
        "outputId": "0194ff0f-fc8a-4941-e30c-36a2cddb8db1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mtp.scatter(x,y,color=\"blue\")\n",
        "mtp.plot(x,lin_regs.predict(x),color=\"red\")\n",
        "mtp.title(\"Bluff detection model(Linear Regresion)\")\n",
        "mtp.xlabel(\"position Levels\")\n",
        "mtp.ylabel(\"salary\")\n",
        "mtp.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "P-KtjV1xehAW",
        "outputId": "b80e49e5-e1ad-4599-aaed-69dab48ba365"
      },
      "execution_count": null,
      "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.preprocessing import PolynomialFeatures\n",
        "poly_regs=PolynomialFeatures(degree=4)\n",
        "x_poly=poly_regs.fit_transform(x)\n",
        "lin_reg_2=LinearRegression()\n",
        "lin_reg_2.fit(x_poly,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bu-TygJpfFo0",
        "outputId": "9a2c77f4-f9a5-4748-a90d-1cba011f6987"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mtp.scatter(x,y,color=\"blue\")\n",
        "mtp.plot(x,lin_reg_2.predict(poly_regs.fit_transform(x)),color=\"red\")\n",
        "mtp.title(\"Bluff detection model(Linear Regresion)\")\n",
        "mtp.xlabel(\"position Levels\")\n",
        "mtp.ylabel(\"salary\")\n",
        "mtp.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "exfQlugQfn5s",
        "outputId": "2ccf367d-35f8-43a0-a60a-026e55f67736"
      },
      "execution_count": null,
      "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": [
        "lin_pred=lin_regs.predict([[6.5]])\n",
        "print(lin_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsF2ks9bfs_7",
        "outputId": "c868e241-81b9-4af5-b0b7-6cb30e0f8237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[330378.78787879]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "poly_pred=lin_reg_2.predict(poly_regs.fit_transform([[6.5]]))\n",
        "print(poly_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWNTO0I5gVl6",
        "outputId": "0dfcb59b-90fa-40c0-e62c-41bce94e10e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[158862.45265155]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as mtp\n",
        "import pandas as pd\n",
        "dataset=pd.read_csv(\"Social_Network_Ads.csv\")\n"
      ],
      "metadata": {
        "id": "bOw1O3-3gpqo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=dataset.iloc[:,[2,3]].values\n",
        "y=dataset.iloc[:,4].values"
      ],
      "metadata": {
        "id": "n3Bswy0ajeqF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain, xtest, ytrain, ytest=train_test_split(x,y,test_size=0.25, random_state=0)"
      ],
      "metadata": {
        "id": "rmBxjq9hjnnM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x=StandardScaler()\n",
        "xtrain=sc_x.fit_transform(xtrain)\n",
        "xtest=sc_x.fit_transform(xtest)\n",
        "print(xtrain[0:10,:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqOpPuwqnCMG",
        "outputId": "79733fe1-4683-4a62-84a3-c8b8d54b3b70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.58164944 -0.88670699]\n",
            " [-0.60673761  1.46173768]\n",
            " [-0.01254409 -0.5677824 ]\n",
            " [-0.60673761  1.89663484]\n",
            " [ 1.37390747 -1.40858358]\n",
            " [ 1.47293972  0.99784738]\n",
            " [ 0.08648817 -0.79972756]\n",
            " [-0.01254409 -0.24885782]\n",
            " [-0.21060859 -0.5677824 ]\n",
            " [-0.21060859 -0.19087153]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier=LogisticRegression(random_state=0)\n",
        "classifier.fit(xtrain,ytrain)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Co5PSwyjnbsM",
        "outputId": "416da933-bbcf-4981-aaeb-4238a69f790b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=classifier.predict(xtest)"
      ],
      "metadata": {
        "id": "xPXLJglOnzME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(ytest,y_pred)\n",
        "print(\"Confusion Matrix: \\n\",cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2bFIi5qn6iL",
        "outputId": "22d911d5-0c5b-44ab-9046-d38e83659d1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix: \n",
            " [[63  5]\n",
            " [ 8 24]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(\"Accuracy: \",accuracy_score(ytest,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fy_HfRV7oN5b",
        "outputId": "077e2836-37e5-42e2-eadb-2a0a448756d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.87\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "x_set,y_set=xtest,ytest\n",
        "x1,x2=np.meshgrid(np.arange(start=x_set[:,0].min()-1, \n",
        "                             stop=x_set[:,0].max()+1,step=0.01),\n",
        "                  np.arange(start=x_set[:,1].min()-1, \n",
        "                             stop=x_set[:,1].max()+1,step=0.01))\n",
        "plt.contourf(x1,x2, classifier.predict(np.array([x1.ravel(),x2.ravel()]).T).reshape(\n",
        "\n",
        "x1.shape), alpha=0.75, cmap=ListedColormap(('red', 'green')))\n",
        "\n",
        "plt.xlim(x1.min(),x1.max())\n",
        "\n",
        "plt.ylim(x2.min(),x2.max())\n",
        "\n",
        "for i,j in enumerate (np.unique (y_set)):\n",
        "  plt.scatter(x_set[y_set==j, 0],x_set[y_set==j,1],c=ListedColormap(('red', 'green')) (i), label=j)\n",
        "\n",
        "plt.title('Classifier (Test set)')\n",
        "\n",
        "plt.xlabel('Age')\n",
        "\n",
        "plt.ylabel('Estimated Salary')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "WuD5m04LpAoq",
        "outputId": "0a472a74-dd7c-4c48-f5f2-86cd55cda398"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ]
        },
        {
          "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": [],
      "metadata": {
        "id": "VRIz6OXopH_z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}