decision_boundary_binary_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sn\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing IRIS Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris = pd.DataFrame(iris['data'],columns = iris['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris['target'] = iris['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, I have selected only two classes 0 and 1\n",
    "df_iris = df_iris[df_iris.target != 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                 5.1               3.5                1.4               0.2   \n",
       "1                 4.9               3.0                1.4               0.2   \n",
       "2                 4.7               3.2                1.3               0.2   \n",
       "3                 4.6               3.1                1.5               0.2   \n",
       "4                 5.0               3.6                1.4               0.2   \n",
       "..                ...               ...                ...               ...   \n",
       "95                5.7               3.0                4.2               1.2   \n",
       "96                5.7               2.9                4.2               1.3   \n",
       "97                6.2               2.9                4.3               1.3   \n",
       "98                5.1               2.5                3.0               1.1   \n",
       "99                5.7               2.8                4.1               1.3   \n",
       "\n",
       "    target  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "..     ...  \n",
       "95       1  \n",
       "96       1  \n",
       "97       1  \n",
       "98       1  \n",
       "99       1  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_model = LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_iris.iloc[:,[0,1]]\n",
    "y = df_iris['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  sepal width (cm)\n",
       "0                 5.1               3.5\n",
       "1                 4.9               3.0\n",
       "2                 4.7               3.2\n",
       "3                 4.6               3.1\n",
       "4                 5.0               3.6\n",
       "..                ...               ...\n",
       "95                5.7               3.0\n",
       "96                5.7               2.9\n",
       "97                6.2               2.9\n",
       "98                5.1               2.5\n",
       "99                5.7               2.8\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-4.54328413]), array([[ 2.47133976, -2.844877  ]]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_model.intercept_, iris_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = [iris_model.intercept_[0],iris_model.coef_[0,0],iris_model.coef_[0,1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Decision Boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines._AxLine at 0x244c9f0d400>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "g =sn.scatterplot(x=\"sepal length (cm)\",y=\"sepal width (cm)\",\n",
    "              hue=\"target\",\n",
    "              data=df_iris,palette=['green','red']);\n",
    "\n",
    "x1 = (-weights[0] - weights[2] * df_iris.iloc[0,1])/weights[1]\n",
    "x2 = (-weights[0] - weights[2] * df_iris.iloc[4,1])/weights[1]\n",
    "\n",
    "plt.axline((x1, df_iris.iloc[0,1]), (x2, df_iris.iloc[4,1]), color = \"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}