Feature Selection (Mean Absolute Difference ).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cfcec125",
   "metadata": {},
   "source": [
    "<b>‘The mean absolute difference (MAD) computes the absolute difference from the mean value. The main difference between the variance and MAD measures is the absence of the square in the latter. The MAD, like the variance, is also a scale variant.’ This means that higher the MAD, higher the discriminatory power.<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8cfe62e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>price</th>\n",
       "      <th>lotsize</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrms</th>\n",
       "      <th>stories</th>\n",
       "      <th>driveway</th>\n",
       "      <th>recroom</th>\n",
       "      <th>fullbase</th>\n",
       "      <th>gashw</th>\n",
       "      <th>airco</th>\n",
       "      <th>garagepl</th>\n",
       "      <th>prefarea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>5850</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>38500.0</td>\n",
       "      <td>4000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>49500.0</td>\n",
       "      <td>3060</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60500.0</td>\n",
       "      <td>6650</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>6360</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0    price  lotsize  bedrooms  bathrms  stories  driveway  \\\n",
       "0           1  42000.0     5850         3        1        2         1   \n",
       "1           2  38500.0     4000         2        1        1         1   \n",
       "2           3  49500.0     3060         3        1        1         1   \n",
       "3           4  60500.0     6650         3        1        2         1   \n",
       "4           5  61000.0     6360         2        1        1         1   \n",
       "\n",
       "   recroom  fullbase  gashw  airco  garagepl  prefarea  \n",
       "0        0         1      0      0         1         0  \n",
       "1        0         0      0      0         0         0  \n",
       "2        0         0      0      0         0         0  \n",
       "3        1         0      0      0         0         0  \n",
       "4        0         0      0      0         0         0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "df=pd.read_csv('https://raw.githubusercontent.com/chandanverma07/DataSets/master/Housing_Data.csv')\n",
    "for feature in df[['driveway','recroom','fullbase','gashw','airco','prefarea']]:\n",
    "    df[feature]=np.where(df[feature]=='no',0,1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37fcfb7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(['price','Unnamed: 0'],axis=1)\n",
    "y=df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19a242ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 11 artists>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mad=np.sum(np.abs(X-np.mean(X,axis=0)),axis=0)/X.shape[0]\n",
    "\n",
    "#plot the bar chart\n",
    "plt.bar(np.arange(X.shape[1]),mad,color='teal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48db22b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lotsize     1698.717654\n",
       "bedrooms       0.495230\n",
       "bathrms        0.420722\n",
       "stories        0.671598\n",
       "driveway       0.242275\n",
       "recroom        0.292188\n",
       "fullbase       0.454890\n",
       "gashw          0.087382\n",
       "airco          0.432912\n",
       "garagepl       0.760778\n",
       "prefarea       0.358948\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d147f2d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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