Week6_logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "O2aFmyeFh0Bg"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "8FobAfthiIrh"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"Social_Network_Ads (2).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "vBly9b22iSl1"
   },
   "outputs": [],
   "source": [
    "y = dataset.iloc[:,-1]\n",
    "x = dataset.iloc[:,:-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "qFQkGoidi9IQ"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X, X_test, Y, Y_test = train_test_split(x, y, test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "4GFrlpQezqmr"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "2fqrEFEJjCZs",
    "outputId": "d8ed9509-e11d-489f-d539-130da26d2de2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "9BMbT7BikCnd"
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uoG01NMEkUrh",
    "outputId": "c78995f6-4a03-4c7c-8196-c24448abf481"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[43  5]\n",
      " [10 22]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8125"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(Y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy_score(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q5VXy1Y2k5LW",
    "outputId": "4eccd7cc-64b4-482f-d377-53bcdb93ef7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict([[30,87000]])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 384
    },
    "id": "YB-02oMIlTi5",
    "outputId": "c4214cc7-9636-4317-d648-855d184d0ab6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*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 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*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 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set =sc.inverse_transform(X), Y\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
    "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red','green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\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",
    "plt.title('Logistic Regression (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}