Session3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNGR3UjBPK6mGXEjp09xRAP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ahmad162539/ML-class-excercise-codes/blob/main/Session3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9sKb5kCXAEw",
        "outputId": "f547696c-93d0-4d96-ab00-dd02f5a991ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[39.02690982],\n",
              "       [56.31732887],\n",
              "       [39.66868375],\n",
              "       [43.4206367 ],\n",
              "       [46.44986695],\n",
              "       [65.06981218],\n",
              "       [58.31315746],\n",
              "       [53.94067743],\n",
              "       [36.01548385],\n",
              "       [60.68119037],\n",
              "       [48.82325065],\n",
              "       [63.07164915],\n",
              "       [43.90473941],\n",
              "       [36.99097278],\n",
              "       [65.33270243],\n",
              "       [42.73601486],\n",
              "       [49.17463068],\n",
              "       [46.06337649],\n",
              "       [37.6831945 ],\n",
              "       [64.5128643 ],\n",
              "       [64.71287017],\n",
              "       [69.0012782 ],\n",
              "       [48.488192  ],\n",
              "       [68.40708438],\n",
              "       [50.60154263],\n",
              "       [58.44036281],\n",
              "       [37.88750174],\n",
              "       [66.3984503 ],\n",
              "       [45.43012252],\n",
              "       [44.18066879],\n",
              "       [35.17953422],\n",
              "       [54.01208803],\n",
              "       [51.64587285],\n",
              "       [57.27307872],\n",
              "       [69.23714469],\n",
              "       [66.80319652],\n",
              "       [66.85535765],\n",
              "       [53.38394849],\n",
              "       [38.64066324],\n",
              "       [41.33201083],\n",
              "       [68.35640773],\n",
              "       [49.41835442],\n",
              "       [65.27559922],\n",
              "       [58.52620478],\n",
              "       [57.00750286],\n",
              "       [44.64455718],\n",
              "       [66.38615444],\n",
              "       [42.241198  ],\n",
              "       [49.15418351],\n",
              "       [69.77503723],\n",
              "       [60.75044785],\n",
              "       [50.57714944],\n",
              "       [54.62321093],\n",
              "       [49.39394212],\n",
              "       [60.44457972],\n",
              "       [48.97189101],\n",
              "       [58.45508076],\n",
              "       [59.66504628],\n",
              "       [56.33459549],\n",
              "       [53.90120625],\n",
              "       [42.21286478],\n",
              "       [41.97065168],\n",
              "       [62.85086027],\n",
              "       [45.16164722],\n",
              "       [57.95869897],\n",
              "       [45.48658715],\n",
              "       [40.05674324],\n",
              "       [49.13848334],\n",
              "       [45.85943344],\n",
              "       [43.51893057],\n",
              "       [55.58364149],\n",
              "       [43.58701385],\n",
              "       [61.17197141],\n",
              "       [60.20513267],\n",
              "       [59.33413034],\n",
              "       [38.59599725],\n",
              "       [68.02748499],\n",
              "       [52.61688715],\n",
              "       [66.48857668],\n",
              "       [41.95029596],\n",
              "       [55.80572154],\n",
              "       [68.78930036],\n",
              "       [69.95443861],\n",
              "       [35.84590187],\n",
              "       [51.84561667],\n",
              "       [45.19979414],\n",
              "       [37.23021989],\n",
              "       [54.93685396],\n",
              "       [35.17791496],\n",
              "       [56.39471564],\n",
              "       [65.4563517 ],\n",
              "       [65.92605127],\n",
              "       [68.40118191],\n",
              "       [60.89523369],\n",
              "       [41.46495351],\n",
              "       [50.21374124],\n",
              "       [66.00648234],\n",
              "       [43.92661981],\n",
              "       [50.51594432],\n",
              "       [56.59279415],\n",
              "       [38.61733788],\n",
              "       [52.15383814],\n",
              "       [36.56465409],\n",
              "       [45.90696143],\n",
              "       [61.32138902],\n",
              "       [60.04246803],\n",
              "       [67.95439853],\n",
              "       [62.19133783],\n",
              "       [42.53299802],\n",
              "       [66.67399132],\n",
              "       [54.32924515],\n",
              "       [64.57142607],\n",
              "       [67.28713193],\n",
              "       [64.01364476],\n",
              "       [48.79692993],\n",
              "       [55.93658478],\n",
              "       [50.14443278],\n",
              "       [59.29514975],\n",
              "       [51.96402714],\n",
              "       [39.48911196],\n",
              "       [57.55296738],\n",
              "       [42.16471757],\n",
              "       [61.07349939],\n",
              "       [38.69660222],\n",
              "       [35.25583228],\n",
              "       [46.7071147 ],\n",
              "       [68.92394263],\n",
              "       [59.53414913],\n",
              "       [49.47899372],\n",
              "       [39.22632597],\n",
              "       [37.11272405],\n",
              "       [60.41740858],\n",
              "       [66.69078445],\n",
              "       [35.56090713],\n",
              "       [55.44492907],\n",
              "       [60.50114176],\n",
              "       [45.22691405],\n",
              "       [53.29791657],\n",
              "       [63.51981602],\n",
              "       [55.87144431],\n",
              "       [48.24943688],\n",
              "       [37.71452319],\n",
              "       [52.04171617],\n",
              "       [51.38678671],\n",
              "       [41.35371649],\n",
              "       [37.45475771],\n",
              "       [52.79403263],\n",
              "       [46.61481072],\n",
              "       [42.50742371],\n",
              "       [38.3129913 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "import numpy as np\n",
        "def Func1 (n= 150, n_test= 150):\n",
        "  np.random.seed(43)\n",
        "  def Func2(x):\n",
        "      g = (x - 10) / 30\n",
        "      return 3 * (0.25 + g**4.)\n",
        "  x = (35) * np.random.rand(n) + 35\n",
        "  eps = np.random.rand(n) * Func2(x)\n",
        "  y = (6 * x * (1. + np.sin(x)) + 0.6) + eps\n",
        "  x = x[..., np.newaxis]\n",
        "  #x_tst = np.linspace(*2, num=n_test).astype(np.float32)\n",
        "  #x_tst = x_tst[..., np.newaxis]\n",
        "  return x, y\n",
        "\n",
        "x, y = Func1() \n",
        "x # # this x will not compiled because it is in of block of statement. \n",
        "#x_tst # this x will be compiled because it is out of block of statement. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=[12,7])\n",
        "plt.scatter(x, y, label = 'data') # this command for plotting the x and y data\n",
        "plt.xlabel('data') \n",
        "plt.ylabel('target')\n",
        "plt.legend() # this command is for inserting legend which defined in plt.scatter (x, y, label = 'data')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "5RwGOYyXfkiM",
        "outputId": "9808a3fb-1d5d-4a28-cacc-a3dcdc990030"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "what is SVM or SVR in sklearn \n",
        "\n",
        "  SVM stands for Support Vector Machine, which is a powerful machine learning algorithm used for classification and regression analysis. In Scikit-learn (sklearn), SVM is implemented in the svm module. \n",
        "\n",
        "  In Scikit-learn, there are several types of SVM classifiers available, including:\n",
        "\n",
        "1- svm.SVC: This is the standard SVM classifier for classification problems.\n",
        "\n",
        "2- svm.SVR: This is the SVM regressor for regression problems.\n",
        "\n",
        "\n",
        "Here's an example of how to use SVM for classification with Scikit-learn:"
      ],
      "metadata": {
        "id": "hDmU9CSKk1Z0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# this code coped from chat GPT\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn import metrics\n",
        "\n",
        "# Load the iris dataset\n",
        "iris = datasets.load_iris()\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=0)\n",
        "\n",
        "# Create an SVM classifier with a linear kernel\n",
        "clf = svm.SVC(kernel='linear')\n",
        "\n",
        "# Fit the model to the training data\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict the classes of the test data\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Print the classification accuracy\n",
        "print(\"Accuracy:\", metrics.accuracy_score(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eFmTFee7LeY9",
        "outputId": "ceca2a9e-57dc-45c3-c598-9b6eaabcf677"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9777777777777777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import Breast_cancer_dataset from sklearn.datasets => train data => use svm algorithm => discuss about accuracy\n",
        "\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn import metrics\n",
        "\n",
        "# Load the iris dataset\n",
        "Breast_cancer_dataset = load_breast_cancer()\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(Breast_cancer_dataset.data, Breast_cancer_dataset.target, test_size=0.3, random_state=0)\n",
        "\n",
        "# Create an SVM classifier with a linear kernel\n",
        "clf = svm.SVC(kernel='linear')\n",
        "\n",
        "# Fit the model to the training data\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict the classes of the test data\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Print the classification accuracy\n",
        "print(\"Accuracy:\", metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3VglcuqJMrH3",
        "outputId": "a7c0df58-d010-4791-fce8-348740a8c09a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9590643274853801\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import  California housing dataset from sklearn.datasets => train data => use svm algorithm => discuss about accuracy\n",
        "\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn import metrics\n",
        "\n",
        "# Load the iris dataset\n",
        "fetch_california_housing = fetch_california_housing()\n",
        "\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(fetch_california_housing.data, fetch_california_housing.target, test_size=0.3, random_state=0)\n",
        "\n",
        "# Create an SVM classifier with a linear kernel\n",
        "clf = svm.SVC(kernel='linear')\n",
        "\n",
        "# Fit the model to the training data\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict the classes of the test data\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Print the classification accuracy\n",
        "print(\"Accuracy:\", metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "EQFyOW8lOkpx",
        "outputId": "52feedc0-1b8e-4ac6-ff60-6f4a712ac92c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-a1555d64d6ed>\u001b[0m in \u001b[0;36m<cell line: 19>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# Fit the model to the training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# Predict the classes of the test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/svm/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    199\u001b[0m             )\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         sample_weight = np.asarray(\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/svm/_base.py\u001b[0m in \u001b[0;36m_validate_targets\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    743\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    744\u001b[0m         \u001b[0my_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 745\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    746\u001b[0m         \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    747\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_weight_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_class_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;34m\"multilabel-sequences\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m     ]:\n\u001b[0;32m--> 218\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
          ]
        }
      ]
    }
  ]
}