Identifying outliers.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset= [10,12,12, 13,12,11,14,13,15,10,10, 10, 100,12, 14,13, 12,10, 10,11,12,15,12,13,12,11,14,13,15,10, 15,12,10,14,13,15,10]"
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   "execution_count": 28,
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      "text/plain": [
       "36"
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     "execution_count": 28,
     "metadata": {},
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   ],
   "source": [
    "len(data_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "outliers=[]\n",
    "def detect_outlier(data_1):\n",
    "    \n",
    "    threshold=3\n",
    "    mean_1 = np.mean(data_1)\n",
    "    std_1 =np.std(data_1)\n",
    "    \n",
    "    \n",
    "    for y in data_1:\n",
    "        z_score= (y - mean_1)/std_1 \n",
    "        if np.abs(z_score) > threshold:\n",
    "            outliers.append(y)\n",
    "    return outliers\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\n"
     ]
    }
   ],
   "source": [
    "outlier_datapoints = detect_outlier(dataset)\n",
    "print(outlier_datapoints)"
   ]
  },
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   "execution_count": 29,
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       " 100]"
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     },
     "execution_count": 29,
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   ],
   "source": [
    "sorted(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1, q3= np.percentile(dataset,[25,75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "11.0"
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     "execution_count": 30,
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   ],
   "source": [
    "q1"
   ]
  },
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       "14.0"
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     "execution_count": 31,
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   "source": [
    "q3"
   ]
  },
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     "execution_count": 32,
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   "source": [
    "iqr = q3 - q1\n",
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bound = q1 - (1.5 * q1)\n",
    "upper_bound = q3 + (1.5 * q3)"
   ]
  },
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       "-5.5"
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     "execution_count": 14,
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    "lower_bound"
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     "execution_count": 15,
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   "source": [
    "upper_bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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