Data_Normilty_Cheeking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Basic libs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "## Data Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "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>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>likeness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>170.688</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>165.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>171.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>173.000</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>164.000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   height  weight gender likeness\n",
       "0   27  170.688    76.0   Male  Biryani\n",
       "1   41  165.000    70.0   Male  Biryani\n",
       "2   29  171.000    80.0   Male  Biryani\n",
       "3   27  173.000   102.0   Male  Biryani\n",
       "4   29  164.000    67.0   Male  Biryani"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import your data\n",
    "df = pd.read_csv(\"mldata.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.5817128419876099, pvalue=6.201376782044408e-24)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normality check for data\n",
    "from scipy.stats import shapiro\n",
    "shapiro(df[\"height\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value from the test Anderson-Darling test below 0.05 generally means non-normal: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ali Jin\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residuals are not normally distributed\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Normality\n",
    "from statsmodels.stats.diagnostic import normal_ad\n",
    "\n",
    "# Performing the test on the residuals\n",
    "p_value = normal_ad(df['height'])[1]\n",
    "print('p-value from the test Anderson-Darling test below 0.05 generally means non-normal:', p_value)\n",
    "\n",
    "# Plotting the residuals distribution\n",
    "plt.subplots(figsize=(8, 4))\n",
    "plt.title('Distribution of Residuals', fontsize=18)\n",
    "sns.distplot(df['height'])\n",
    "plt.show()\n",
    "\n",
    "# Reporting the normality of the residuals\n",
    "if p_value < 0.05:\n",
    "    print('Residuals are not normally distributed')\n",
    "else:\n",
    "    print('Residuals are normally distributed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for the removel of outliyers\n",
    "dfclean = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value from the test Anderson-Darling test below 0.05 generally means non-normal: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Ali Jin\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residuals are not normally distributed\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Normality\n",
    "from statsmodels.stats.diagnostic import normal_ad\n",
    "\n",
    "# Performing the test on the residuals\n",
    "p_value = normal_ad(dfclean['height'])[1]\n",
    "print('p-value from the test Anderson-Darling test below 0.05 generally means non-normal:', p_value)\n",
    "\n",
    "# Plotting the residuals distribution\n",
    "plt.subplots(figsize=(8, 4))\n",
    "plt.title('Distribution of Residuals', fontsize=18)\n",
    "sns.distplot(dfclean['height'])\n",
    "plt.show()\n",
    "\n",
    "# Reporting the normality of the residuals\n",
    "if p_value < 0.05:\n",
    "    print('Residuals are not normally distributed')\n",
    "else:\n",
    "    print('Residuals are normally distributed')"
   ]
  }
 ],
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