src/logistic-regression-low-accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "419eb8f2",
   "metadata": {},
   "source": [
    "# Logistic Regression - Low Accuracy Case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4378ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "wine = pd.read_csv('https://bit.ly/wine_csv_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5666d472",
   "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>alcohol</th>\n",
       "      <th>sugar</th>\n",
       "      <th>pH</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  sugar    pH  class\n",
       "0      9.4    1.9  3.51    0.0\n",
       "1      9.8    2.6  3.20    0.0\n",
       "2      9.8    2.3  3.26    0.0\n",
       "3      9.8    1.9  3.16    0.0\n",
       "4      9.4    1.9  3.51    0.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abbfa84c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6497 entries, 0 to 6496\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   alcohol  6497 non-null   float64\n",
      " 1   sugar    6497 non-null   float64\n",
      " 2   pH       6497 non-null   float64\n",
      " 3   class    6497 non-null   float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 203.2 KB\n"
     ]
    }
   ],
   "source": [
    "wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6352de10",
   "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>alcohol</th>\n",
       "      <th>sugar</th>\n",
       "      <th>pH</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.491801</td>\n",
       "      <td>5.443235</td>\n",
       "      <td>3.218501</td>\n",
       "      <td>0.753886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.192712</td>\n",
       "      <td>4.757804</td>\n",
       "      <td>0.160787</td>\n",
       "      <td>0.430779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.500000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.300000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.900000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           alcohol        sugar           pH        class\n",
       "count  6497.000000  6497.000000  6497.000000  6497.000000\n",
       "mean     10.491801     5.443235     3.218501     0.753886\n",
       "std       1.192712     4.757804     0.160787     0.430779\n",
       "min       8.000000     0.600000     2.720000     0.000000\n",
       "25%       9.500000     1.800000     3.110000     1.000000\n",
       "50%      10.300000     3.000000     3.210000     1.000000\n",
       "75%      11.300000     8.100000     3.320000     1.000000\n",
       "max      14.900000    65.800000     4.010000     1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3326470",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5197, 3) (1300, 3)\n"
     ]
    }
   ],
   "source": [
    "data = wine[['alcohol', 'sugar', 'pH']].to_numpy()\n",
    "target = wine['class'].to_numpy()\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_input, test_input, train_target, test_target = train_test_split(\n",
    "    data, target, test_size=0.2, random_state=42)\n",
    "\n",
    "print(train_input.shape, test_input.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c5dadce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss = StandardScaler()\n",
    "ss.fit(train_input)\n",
    "\n",
    "train_scaled = ss.transform(train_input)\n",
    "test_scaled = ss.transform(test_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fefec0a",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d88f9061",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7808350971714451\n",
      "0.7776923076923077\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(train_scaled, train_target)\n",
    "\n",
    "print(lr.score(train_scaled, train_target))\n",
    "print(lr.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4568e54b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.51270274  1.6733911  -0.68767781]] [1.81777902]\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_, lr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b6e2d92",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b992dfa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
      "Collecting xgboost\n",
      "  Using cached xgboost-1.6.2-py3-none-manylinux2014_x86_64.whl (255.9 MB)\n",
      "Requirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from xgboost) (1.20.3)\n",
      "Requirement already satisfied: scipy in /home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages (from xgboost) (1.5.3)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.6.2\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.2.2 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/python3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52989836",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60026ffd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8885895709062921\n",
      "0.8684615384615385\n"
     ]
    }
   ],
   "source": [
    "xgb = XGBClassifier(n_estimators=5)\n",
    "xgb.fit(train_scaled, train_target)\n",
    "\n",
    "print(xgb.score(train_scaled, train_target))\n",
    "print(xgb.score(test_scaled, test_target))"
   ]
  }
 ],
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