outlier_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.datasets import load_diabetes\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetics = load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019907</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068332</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005670</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031988</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  \n",
       "0 -0.002592  0.019907 -0.017646  \n",
       "1 -0.039493 -0.068332 -0.092204  \n",
       "2 -0.002592  0.002861 -0.025930  \n",
       "3  0.034309  0.022688 -0.009362  \n",
       "4 -0.002592 -0.031988 -0.046641  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_name = diabetics.feature_names\n",
    "df_diabetics = pd.DataFrame(diabetics.data)\n",
    "df_diabetics.columns = column_name\n",
    "df_diabetics.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outliers can be detected using visualization, implementing mathematical formulas on the dataset or using the statistical approach"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "\n",
    "### Visualizing outliers using Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.boxplot(df_diabetics['bmi'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Values above 10 are outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 32, 114, 138, 145, 256, 262, 327, 332, 362, 366, 367, 405]),)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.where(df_diabetics['bmi']> 0.10))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing outliers using scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax.scatter(df_diabetics['bmi'], df_diabetics['bp'])\n",
    "ax.set_xlabel(\"body mass index of people\")\n",
    "ax.set_ylabel(\"bp of the people\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the graph we can infer that most of the data is at the bottom left of the graph,\n",
    "so the data at the top right corner are definitely outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 32, 145, 256, 262, 366, 367, 405]),)\n"
     ]
    }
   ],
   "source": [
    "print(np.where((df_diabetics['bmi']>0.12) & (df_diabetics['bp'] < 0.8)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Z-Score\n",
    "\n",
    "also called `standard score`\n",
    "This score helps to understand how far the data point is from the mean\n",
    "After setting a threshold value one can utilize `z-score` values of data points to define the outliers\n",
    "\n",
    "zscore = (data_point - mean) / std.deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0.800500\n",
      "1      0.039567\n",
      "2      1.793307\n",
      "3      1.872441\n",
      "4      0.113172\n",
      "         ...   \n",
      "437    0.876870\n",
      "438    0.115937\n",
      "439    0.876870\n",
      "440    0.956004\n",
      "441    0.956004\n",
      "Name: age, Length: 442, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "\n",
    "z = np.abs(stats.zscore(df_diabetics['age']))\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 10,  26,  41,  77,  79, 106, 131, 204, 223, 226, 242, 311, 321,\n",
      "       344, 374, 402]),)\n"
     ]
    }
   ],
   "source": [
    "threshold = 2\n",
    "print(np.where(z > 2))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inter-quartile Range (IQR)\n",
    "\n",
    "IQR = Quartile 3 - Quartile 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**syntax**\n",
    "\n",
    "numpy.percentile(arr, n, axis=None, out=None)\n",
    "\n",
    "- arr : input array\n",
    "- n : percentile value\n",
    "\n",
    "To define the outlier base value is defined above and below the dataset's normal range( Upper, Lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06520763046978838\n"
     ]
    }
   ],
   "source": [
    "Q1 = np.percentile(df_diabetics['bmi'], 25, method='midpoint')\n",
    "Q3 = np.percentile(df_diabetics['bmi'], 75, method='midpoint')\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To define the outlier base value is defined above & below dataset's normal range (Upper and Lower bounds),\n",
    "define the upper and the lower bond\n",
    "\n",
    "- upper = Q3 + 1.5 * IQR\n",
    "- lower = Q1 - 1.5 * IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper Bound:  0.12879000811776306\n",
      "3\n",
      "Lower Bound:  -0.13204051376139045\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "upper = Q3 + 1.5 * IQR\n",
    "upper_array = np.array(df_diabetics['bmi'] >=upper)\n",
    "print(\"Upper Bound: \", upper)\n",
    "print(upper_array.sum())\n",
    "\n",
    "lower = Q1 - 1.5 * IQR\n",
    "lower_array = np.array(df_diabetics['bmi']<= lower)\n",
    "print(\"Lower Bound: \", lower)\n",
    "print(lower_array.sum())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing the Outliers\n",
    "\n",
    "dataframe.drop(row_index, inplace=True)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old Shape:  (442, 10)\n",
      "New Shape:  (439, 10)\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.datasets import load_diabetes\n",
    "\n",
    "# load the dataset\n",
    "diabetes = load_diabetes()\n",
    "\n",
    "# create the dataframe\n",
    "column_name = diabetes.feature_names\n",
    "df_diabetes = pd.DataFrame(diabetes.data)\n",
    "df_diabetes.columns = column_name\n",
    "df_diabetes.head()\n",
    "print(\"Old Shape: \", df_diabetes.shape)\n",
    "\n",
    "\"\"\"Outlier Detection\"\"\"\n",
    "\n",
    "# IQR\n",
    "Q1 = df_diabetes['bmi'].quantile(0.25)\n",
    "Q3 = df_diabetes['bmi'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "lower = Q1 - 1.5 * IQR\n",
    "upper = Q3 + 1.5 * IQR\n",
    "\n",
    "# create arrays of boolean values indicating the outlier rows\n",
    "upper_array = np.where(df_diabetes['bmi']>= upper)[0]\n",
    "lower_array = np.where(df_diabetes['bmi']<=lower)[0]\n",
    "\n",
    "# Removing the outliers\n",
    "df_diabetes.drop(index=upper_array, inplace=True)\n",
    "df_diabetes.drop(index=lower_array, inplace=True)\n",
    "\n",
    "print(\"New Shape: \", df_diabetes.shape)"
   ]
  }
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