SMOTE - Handling Imbalance Dataset.ipynb

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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>outcome</th>\n",
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       "      <td>med</td>\n",
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      ],
      "text/plain": [
       "  buying  maint  doors  persons lug_boot safety outcome\n",
       "0  vhigh  vhigh      2        2    small    low   unacc\n",
       "1  vhigh  vhigh      2        2    small    med   unacc\n",
       "2  vhigh  vhigh      2        2    small   high   unacc\n",
       "3  vhigh  vhigh      2        2      med    low   unacc\n",
       "4  vhigh  vhigh      2        2      med    med   unacc"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('car_evaluation.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1728, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unacc    1210\n",
       "acc       384\n",
       "good       69\n",
       "vgood      65\n",
       "Name: outcome, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.outcome.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vhigh</td>\n",
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       "      <th>4</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
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      "text/plain": [
       "  buying  maint  doors  persons lug_boot safety\n",
       "0  vhigh  vhigh      2        2    small    low\n",
       "1  vhigh  vhigh      2        2    small    med\n",
       "2  vhigh  vhigh      2        2    small   high\n",
       "3  vhigh  vhigh      2        2      med    low\n",
       "4  vhigh  vhigh      2        2      med    med"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.iloc[:,:-1]\n",
    "y = data.outcome\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   buying  maint  doors  persons  lug_boot  safety\n",
       "0       3      3      2        2         2       1\n",
       "1       3      3      2        2         2       2\n",
       "2       3      3      2        2         2       0\n",
       "3       3      3      2        2         1       1\n",
       "4       3      3      2        2         1       2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "enc = LabelEncoder()\n",
    "X.loc[:,['buying','maint','lug_boot','safety']] = \\\n",
    "X.loc[:,['buying','maint','lug_boot','safety']].apply(enc.fit_transform)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test, y_train,y_test = \\\n",
    "train_test_split(X,y,test_size=0.3,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "y_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.928709055876686\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>acc</th>\n",
       "      <th>good</th>\n",
       "      <th>unacc</th>\n",
       "      <th>vgood</th>\n",
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       "    <tr>\n",
       "      <th>outcome</th>\n",
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       "      <th>acc</th>\n",
       "      <td>89</td>\n",
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       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "      <th>good</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>unacc</th>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
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       "      <th>vgood</th>\n",
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      "text/plain": [
       "col_0    acc  good  unacc  vgood\n",
       "outcome                         \n",
       "acc       89     2     11      0\n",
       "good      10    10      1      0\n",
       "unacc      5     0    366      0\n",
       "vgood      5     2      1     17"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test,y_predict))\n",
    "pd.crosstab(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "17/25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imblearn in /Users/ashokveda/anaconda3/lib/python3.7/site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in /Users/ashokveda/anaconda3/lib/python3.7/site-packages (from imblearn) (0.5.0)\n",
      "Requirement already satisfied: numpy>=1.11 in /Users/ashokveda/anaconda3/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.16.2)\n",
      "Requirement already satisfied: scipy>=0.17 in /Users/ashokveda/anaconda3/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.2.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /Users/ashokveda/anaconda3/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.14.0)\n",
      "Requirement already satisfied: scikit-learn>=0.21 in /Users/ashokveda/anaconda3/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.21.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_smote, y_train_smote = smote.fit_sample(X_train.astype('float'),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before SMOTE : Counter({'unacc': 839, 'acc': 282, 'good': 48, 'vgood': 40})\n",
      "After SMOTE : Counter({'acc': 839, 'unacc': 839, 'vgood': 839, 'good': 839})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print(\"Before SMOTE :\" , Counter(y_train))\n",
    "print(\"After SMOTE :\" , Counter(y_train_smote))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9441233140655106\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>acc</th>\n",
       "      <th>good</th>\n",
       "      <th>unacc</th>\n",
       "      <th>vgood</th>\n",
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       "    <tr>\n",
       "      <th>outcome</th>\n",
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       "      <th>unacc</th>\n",
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       "      <th>vgood</th>\n",
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      "text/plain": [
       "col_0    acc  good  unacc  vgood\n",
       "outcome                         \n",
       "acc       92     6      2      2\n",
       "good       0    21      0      0\n",
       "unacc     15     1    354      1\n",
       "vgood      0     2      0     23"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_smote,y_train_smote)\n",
    "y_predict = model.predict(X_test)\n",
    "print(accuracy_score(y_test,y_predict))\n",
    "pd.crosstab(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92"
      ]
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "23/25"
   ]
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   "execution_count": null,
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   "execution_count": null,
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