Gradient_Descent_Linear_Regression_KNN_Sklearn.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Adarsh-Saurabh/AI-Champ-/blob/main/Gradient_Descent_Linear_Regression_KNN_Sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q65zyrTd12Oy"
      },
      "source": [
        "## Easy Partial differentiation with sympy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kaUINguS0jWP"
      },
      "outputs": [],
      "source": [
        "from sympy import symbols, diff"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XmuoVVs80nHn"
      },
      "outputs": [],
      "source": [
        "x, y, z, m, c = symbols('x y z m c', real=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mk06Vx9D0vM0",
        "outputId": "89838882-32ed-424d-e508-f0574ee91482"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-2*x*(-c - m*x + y)\n",
            "2*c + 2*m*x - 2*y\n"
          ]
        }
      ],
      "source": [
        "f = (y - (m*x + c)) ** 2\n",
        "print(diff(f, m))\n",
        "print(diff(f, c))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KAZgcVst1V_A"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "grTRF8Pr25sn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "DnSUgDeL2-zt",
        "outputId": "2d768e50-f859-402f-d0da-b6f5b5b493e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f95d6a234d0>"
            ]
          },
          "execution_count": 35,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "m = 4\n",
        "c = np.random.randn(100) * 40\n",
        "x = np.random.randint(0, 100, size=(100))\n",
        "y = m * x + c\n",
        "\n",
        "\n",
        "plt.scatter(x, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sciUMzb63Xu5",
        "outputId": "da8cbbc8-fe93-4a89-fc77-57eab1ae99d9"
      },
      "outputs": [],
      "source": [
        "# Gradient descent for linear regression\n",
        "\n",
        "n = y.shape[0]\n",
        "epochs = 100\n",
        "search_m = 0\n",
        "search_c = 0\n",
        "learning_rate = 1e-5\n",
        "\n",
        "def cost_function(y, y_hat):\n",
        "  return np.square(y - y_hat).mean()\n",
        "\n",
        "def hypothesis(m, c, x):\n",
        "  return m*x + c\n",
        "\n",
        "for i in range(epochs):\n",
        "\n",
        "  y_hat = hypothesis(search_m, search_c, x)\n",
        "\n",
        "  cost = cost_function(y, y_hat)\n",
        "\n",
        "  diff_cost_wrt_m = (-2/n) * (np.sum(x * (y - y_hat)))\n",
        "  diff_cost_wrt_c = (-2/n) * (np.sum(y-y_hat))\n",
        "\n",
        "  search_m = search_m - learning_rate * diff_cost_wrt_m\n",
        "  search_c= search_c - learning_rate * diff_cost_wrt_c\n",
        "\n",
        "  print(f\"Epoch: {i+1}\\nCost: {cost}\\nm: {search_m}\\nc: {search_c}\\n\", \"-\"*70)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Ba8jILDL5M0T",
        "outputId": "1ca11a63-4eeb-4752-c868-2e9fea792f6f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f95d639b210>]"
            ]
          },
          "execution_count": 38,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "y_hat = search_m * x + search_c\n",
        "\n",
        "plt.scatter(x, y)\n",
        "plt.plot(x, y_hat, color='r')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vSX6zx1q9AYo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnUGk5_VMBQ0"
      },
      "source": [
        "## KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0BNijG0EMCPF"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from scipy.stats import mode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FuqCmFBqMN55"
      },
      "outputs": [],
      "source": [
        "iris = load_iris()\n",
        " \n",
        "X = iris.data\n",
        "y = iris.target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zywBt7RuMRA_",
        "outputId": "619d4c58-e4e7-4c65-ef2b-5ee03b8a8ce4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "execution_count": 41,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Zo-0pJ3MxGZ"
      },
      "outputs": [],
      "source": [
        "def eucledian(p1,p2):\n",
        "    dist = np.sqrt(np.sum((p1-p2)**2))\n",
        "    return dist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xfv9t5LBMRcl"
      },
      "outputs": [],
      "source": [
        "y_hat = []\n",
        "k = 5\n",
        "     \n",
        "for item in X: \n",
        "    point_dist = []\n",
        "      \n",
        "    for j in range(len(X)): \n",
        "        distances = eucledian(np.array(X[j,:]), item) \n",
        "        point_dist.append(distances) \n",
        "\n",
        "    point_dist = np.array(point_dist) \n",
        "      \n",
        "    dist = np.argsort(point_dist)[:k] \n",
        "      \n",
        "    labels = y[dist]\n",
        "      \n",
        "    lab = mode(labels) \n",
        "    lab = lab.mode[0]\n",
        "    y_hat.append(lab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdGACS3mNAzE",
        "outputId": "36a4f3d0-8f01-41ab-e4fa-7d30cce5c06b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "execution_count": 43,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y, y_hat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t057-5jPNnEs",
        "outputId": "faa7f825-9ba5-434a-a857-bdf69e16b9b0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[50,  0,  0],\n",
              "       [ 0, 47,  3],\n",
              "       [ 0,  2, 48]])"
            ]
          },
          "execution_count": 44,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "confusion_matrix(y, y_hat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UjXzNFd-Nysq"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPrKYoCRPxvL"
      },
      "source": [
        "## Using Sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mpf2mNRkPz52"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.tree import ExtraTreeClassifier, DecisionTreeClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.neighbors import RadiusNeighborsClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fhPc7iJoP5Ca"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kyhosYbQVYU",
        "outputId": "dc95879d-7d5d-4d73-fd9b-bbf2f7c5371f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9333333333333333"
            ]
          },
          "execution_count": 53,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classifier = SVC()\n",
        "classifier.fit(X_train, y_train) # training\n",
        "classifier.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "01SaJB4zQnSh"
      },
      "outputs": [],
      "source": [
        "y_hat = classifier.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ql-LYG26Q9ZC",
        "outputId": "9289e3c2-36a7-49e8-afe4-1951c2dc4fe3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 9,  0,  0],\n",
              "       [ 0, 10,  1],\n",
              "       [ 0,  1,  9]])"
            ]
          },
          "execution_count": 55,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "confusion_matrix(y_test, y_hat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_aayRPF5Q_Ab"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "include_colab_link": true,
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