notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "95c8c703",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from matplotlib.colors import ListedColormap  \n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ff47c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>estimated_salary</th>\n",
       "      <th>purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  gender  age  estimated_salary  purchased\n",
       "0  15624510    Male   19             19000          0\n",
       "1  15810944    Male   35             20000          0\n",
       "2  15668575  Female   26             43000          0\n",
       "3  15603246  Female   27             57000          0\n",
       "4  15804002    Male   19             76000          0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "user_id              int64\n",
       "gender              object\n",
       "age                  int64\n",
       "estimated_salary     int64\n",
       "purchased            int64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing data and creating a dataframe.\n",
    "df = pd.read_csv(\"data/user-data.csv\")\n",
    "display(df.head())\n",
    "display(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a25d7ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   19 19000]\n",
      " [   35 20000]\n",
      " [   26 43000]\n",
      " [   27 57000]\n",
      " [   19 76000]]\n",
      "[0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Extracting independent and dependent variables.\n",
    "x = df.iloc[:, [2, 3]].values\n",
    "y = df.iloc[:, 4].values\n",
    "\n",
    "# Output first five values in both the lists.\n",
    "print(x[:5])\n",
    "print(y[:5])\n",
    "\n",
    "# Independent variables are age and estimated_salary.\n",
    "# Dependent variable is purchased because it depends on a person's age as well as his/her estimated salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4253f7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into training and testing data.\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e4382be3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: [[ 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",
      "\n",
      "x_test: [[-0.80480212  0.50496393]\n",
      " [-0.01254409 -0.5677824 ]\n",
      " [-0.30964085  0.1570462 ]\n",
      " [-0.80480212  0.27301877]\n",
      " [-0.30964085 -0.5677824 ]] ...\n"
     ]
    }
   ],
   "source": [
    "std_x = StandardScaler()\n",
    "x_train = std_x.fit_transform(x_train)\n",
    "x_test = std_x.transform(x_test)\n",
    "\n",
    "print(\"x_train:\", x_train[:5], \"...\")\n",
    "print(\"\")\n",
    "print(\"x_test:\", x_test[:5], \"...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f0007da",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "SVC(kernel='linear', random_state=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(kernel=\"linear\", random_state=0)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d1ffab26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 0] ...\n"
     ]
    }
   ],
   "source": [
    "# Predicting the testing dataset results\n",
    "y_pred = model.predict(x_test)\n",
    "print(y_pred[:10], \"...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c7f6624",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "df = pd.DataFrame(y_test, y_pred)\n",
    "df.head()"
   ]
  },
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   "id": "d09c9518",
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      "[[66  2]\n",
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating the Confusion matrix and heatmap\n",
    "from sklearn.metrics import confusion_matrix  \n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56571df1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "debe4934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_set, y_set = x_train, y_train\n",
    "\n",
    "x1, x2 = np.meshgrid(np.arange(start=x_set[:, 0].min() - 1, stop=x_set[:, 0].max() + 1, step=0.01), np.arange(start=x_set[:, 1].min() - 1, stop=x_set[:, 1].max() + 1, step=0.01)) \n",
    "\n",
    "plt.contourf(x1, x2, model.predict(np.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape), alpha = 0.75, cmap = ListedColormap(('orange', 'dodgerblue')))  \n",
    "plt.xlim(x1.min(), x1.max())  \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', 'white'))(i)], label = j)  \n",
    "\n",
    "plt.title('SVM classifier (Training set)')  \n",
    "\n",
    "plt.xlabel('Age')  \n",
    "plt.ylabel('Estimated Salary')  \n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2bc26e1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visulaizing the test set result  \n",
    "x_set, y_set = x_test, y_test  \n",
    "x1, x2 = np.meshgrid(np.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step  =0.01), np.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01))  \n",
    "\n",
    "plt.contourf(x1, x2, model.predict(np.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape), alpha = 0.75, cmap = ListedColormap(('orange','dodgerblue' )))  \n",
    "plt.xlim(x1.min(), x1.max())  \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', 'white'))(i)], label=j)\n",
    "\n",
    "plt.title('SVM classifier (Test set)')  \n",
    "plt.xlabel('Age')  \n",
    "plt.ylabel('Estimated Salary')  \n",
    "plt.legend()  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fb6b903",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}