Stochastic Gradient Descent Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "1831674b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Required packages\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "import pandas  as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e818fe0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e03ce79b",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
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       "      <td>2</td>\n",
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       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
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       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
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       "      <td>11</td>\n",
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       "      <td>2</td>\n",
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       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2  ...         20       756  2549     9     7         19   \n",
       "1        136        3  ...        905      1988  2631    17     3          7   \n",
       "2        145        5  ...       1263      1716  2603    11     2          9   \n",
       "3        131        6  ...       1216      1786  2769    16     8         11   \n",
       "4        141        2  ...       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('mobile_price_train.csv')\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2984e285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   battery_power  2000 non-null   int64  \n",
      " 1   blue           2000 non-null   int64  \n",
      " 2   clock_speed    2000 non-null   float64\n",
      " 3   dual_sim       2000 non-null   int64  \n",
      " 4   fc             2000 non-null   int64  \n",
      " 5   four_g         2000 non-null   int64  \n",
      " 6   int_memory     2000 non-null   int64  \n",
      " 7   m_dep          2000 non-null   float64\n",
      " 8   mobile_wt      2000 non-null   int64  \n",
      " 9   n_cores        2000 non-null   int64  \n",
      " 10  pc             2000 non-null   int64  \n",
      " 11  px_height      2000 non-null   int64  \n",
      " 12  px_width       2000 non-null   int64  \n",
      " 13  ram            2000 non-null   int64  \n",
      " 14  sc_h           2000 non-null   int64  \n",
      " 15  sc_w           2000 non-null   int64  \n",
      " 16  talk_time      2000 non-null   int64  \n",
      " 17  three_g        2000 non-null   int64  \n",
      " 18  touch_screen   2000 non-null   int64  \n",
      " 19  wifi           2000 non-null   int64  \n",
      " 20  price_range    2000 non-null   int64  \n",
      "dtypes: float64(2), int64(19)\n",
      "memory usage: 328.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "590b3c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, df.columns != 'price_range']\n",
    "y = df['price_range']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a978bbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 52.67\n"
     ]
    }
   ],
   "source": [
    "sgdc = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=5)\n",
    "sgdc.fit(X_train, y_train)\n",
    "y_pred = sgdc.predict(X_test)\n",
    "print('Accuracy: {:.2f}'.format(sgdc.score(X_test, y_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b676397a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.97      0.71       159\n",
      "           1       0.00      0.00      0.00       156\n",
      "           2       0.40      0.38      0.39       151\n",
      "           3       0.57      0.78      0.66       134\n",
      "\n",
      "    accuracy                           0.53       600\n",
      "   macro avg       0.38      0.53      0.44       600\n",
      "weighted avg       0.38      0.53      0.43       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fb2fe94",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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