Feature Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# feature Selection\n",
    "Selecting the appropriate feature for data analysis requires the knowledge of feature selection methods and some domain knowledge. Combining both allows us to choose the optimum features necessary for tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector #wrapper method for feature selection\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import seaborn as sns\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "HousingP = pd.read_csv(\"HousingData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90    NaN  36.2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HousingP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     486 non-null    float64\n",
      " 1   ZN       486 non-null    float64\n",
      " 2   INDUS    486 non-null    float64\n",
      " 3   CHAS     486 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      486 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    486 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "#for frnaming coloumns in pd we can use rename or set-axis to rename all\n",
    "HousingP.info()\n",
    "HousingP.isnull().any()#checking null values and removing them(**)\n",
    "HousingP.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrapper Method\n",
    "The wrapper method requires a machine-learning algorithm to find the optimum features. This method generally has a high computational time. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequential feature selector\n",
    "\n",
    "This is a greedy algorithm that sequentially checks the model's performance with each feature and sequentially adds to the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(12,)</td>\n",
       "      <td>[0.21013813325242514, 0.54190683621638, -0.112...</td>\n",
       "      <td>0.22293</td>\n",
       "      <td>(LSTAT,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(10, 12)</td>\n",
       "      <td>[0.4661273389667262, 0.5088911614358224, 0.179...</td>\n",
       "      <td>0.396472</td>\n",
       "      <td>(PTRATIO, LSTAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(3, 10, 12)</td>\n",
       "      <td>[0.5137676819294879, 0.48362796380553885, 0.22...</td>\n",
       "      <td>0.419265</td>\n",
       "      <td>(CHAS, PTRATIO, LSTAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(1, 3, 10, 12)</td>\n",
       "      <td>[0.5133327595225368, 0.48847614737790557, 0.23...</td>\n",
       "      <td>0.424254</td>\n",
       "      <td>(ZN, CHAS, PTRATIO, LSTAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 1, 3, 10, 12)</td>\n",
       "      <td>[0.5086971431210954, 0.490100682842474, 0.2213...</td>\n",
       "      <td>0.418466</td>\n",
       "      <td>(CRIM, ZN, CHAS, PTRATIO, LSTAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 1, 3, 5, 10, 12)</td>\n",
       "      <td>[0.7340323137403271, 0.679120884276981, 0.4995...</td>\n",
       "      <td>0.427932</td>\n",
       "      <td>(CRIM, ZN, CHAS, RM, PTRATIO, LSTAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 1, 3, 5, 10, 11, 12)</td>\n",
       "      <td>[0.7254995454770403, 0.7036527875577943, 0.523...</td>\n",
       "      <td>0.45501</td>\n",
       "      <td>(CRIM, ZN, CHAS, RM, PTRATIO, B, LSTAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(0, 1, 3, 5, 7, 10, 11, 12)</td>\n",
       "      <td>[0.6748889716339588, 0.704018167068265, 0.5779...</td>\n",
       "      <td>0.45146</td>\n",
       "      <td>(CRIM, ZN, CHAS, RM, DIS, PTRATIO, B, LSTAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 12)</td>\n",
       "      <td>[0.6346947724509273, 0.7667936819045665, 0.561...</td>\n",
       "      <td>0.477801</td>\n",
       "      <td>(CRIM, ZN, CHAS, NOX, RM, DIS, PTRATIO, B, LSTAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 7, 10, 11, 12)</td>\n",
       "      <td>[0.6345413486323541, 0.7701713195149424, 0.567...</td>\n",
       "      <td>0.481953</td>\n",
       "      <td>(CRIM, ZN, INDUS, CHAS, NOX, RM, DIS, PTRATIO,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          feature_idx  \\\n",
       "1                               (12,)   \n",
       "2                            (10, 12)   \n",
       "3                         (3, 10, 12)   \n",
       "4                      (1, 3, 10, 12)   \n",
       "5                   (0, 1, 3, 10, 12)   \n",
       "6                (0, 1, 3, 5, 10, 12)   \n",
       "7            (0, 1, 3, 5, 10, 11, 12)   \n",
       "8         (0, 1, 3, 5, 7, 10, 11, 12)   \n",
       "9      (0, 1, 3, 4, 5, 7, 10, 11, 12)   \n",
       "10  (0, 1, 2, 3, 4, 5, 7, 10, 11, 12)   \n",
       "\n",
       "                                            cv_scores avg_score  \\\n",
       "1   [0.21013813325242514, 0.54190683621638, -0.112...   0.22293   \n",
       "2   [0.4661273389667262, 0.5088911614358224, 0.179...  0.396472   \n",
       "3   [0.5137676819294879, 0.48362796380553885, 0.22...  0.419265   \n",
       "4   [0.5133327595225368, 0.48847614737790557, 0.23...  0.424254   \n",
       "5   [0.5086971431210954, 0.490100682842474, 0.2213...  0.418466   \n",
       "6   [0.7340323137403271, 0.679120884276981, 0.4995...  0.427932   \n",
       "7   [0.7254995454770403, 0.7036527875577943, 0.523...   0.45501   \n",
       "8   [0.6748889716339588, 0.704018167068265, 0.5779...   0.45146   \n",
       "9   [0.6346947724509273, 0.7667936819045665, 0.561...  0.477801   \n",
       "10  [0.6345413486323541, 0.7701713195149424, 0.567...  0.481953   \n",
       "\n",
       "                                        feature_names  \n",
       "1                                            (LSTAT,)  \n",
       "2                                    (PTRATIO, LSTAT)  \n",
       "3                              (CHAS, PTRATIO, LSTAT)  \n",
       "4                          (ZN, CHAS, PTRATIO, LSTAT)  \n",
       "5                    (CRIM, ZN, CHAS, PTRATIO, LSTAT)  \n",
       "6                (CRIM, ZN, CHAS, RM, PTRATIO, LSTAT)  \n",
       "7             (CRIM, ZN, CHAS, RM, PTRATIO, B, LSTAT)  \n",
       "8        (CRIM, ZN, CHAS, RM, DIS, PTRATIO, B, LSTAT)  \n",
       "9   (CRIM, ZN, CHAS, NOX, RM, DIS, PTRATIO, B, LSTAT)  \n",
       "10  (CRIM, ZN, INDUS, CHAS, NOX, RM, DIS, PTRATIO,...  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Attribute = HousingP.iloc[:,:13]\n",
    "target = HousingP.iloc[:,-1]\n",
    "Sfs = SequentialFeatureSelector(LinearRegression(),\n",
    "                                  k_features = 10,\n",
    "                                  forward = True,\n",
    "                                  floating = False,\n",
    "                                  scoring = 'r2')\n",
    "Sfs.fit(Attribute,target)\n",
    "sfs_result = pd.DataFrame(Sfs.subsets_).transpose()\n",
    "sfs_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this shows that best seven features are 0,1,3,5,10,11,12\n",
    "\n",
    "[Sequential feature selector documentation](http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/#:~:text=Sequential%20feature%20selection%20algorithms%20are%20a%20family%20of,features%20that%20is%20most%20relevant%20to%20the%20problem.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter Methods\n",
    "\n",
    "Filter methods are a set of statistical techniques required for measuring the importance of features in a dataset. It includes Correlation, Chi-Square test, information gain, etc. These methods are comparatively faster in terms of time complexity. \n",
    ">    · **Pearson’s correlation:** this method is used to calculate the correlation between continuous variables.\n",
    "\n",
    ">   · **Linear Discriminant Analysis**: measures the correlation between continuous and categorical features.\n",
    "\n",
    ">    · **Chi-square:** calculates the correlation between two or more categorical features.\n",
    "\n",
    " >   · **ANOVA:** is the same as Linear Discriminant Analysis apart from the fact that there are two independent categorical features and one continuous dependent feature.\n",
    "\n",
    "* Computationally very fast;\n",
    "* Avoids overfitting;\n",
    "* Do not depend on the models, but only features;\n",
    "* Based on different statistical methods\n",
    "----\n",
    "** These dont remove multi collinearity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "correlation = HousingP.corr()\n",
    "\n",
    "plt.figure(figsize= (15,12))\n",
    "sns.heatmap(correlation, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RAD and TAX are highly correlated, and therefore, we can remove one with a low value of absolute correlation with the target variable (MEDV). \"RAD\" can be removed. Also, we can set a threshold value of the absolute correlation to remove the less informative features. For instance, if we select 0.40 correlation as a feature selection criteria for the modeling, we are left with 'INDUS,' 'NOX,' 'RM,' 'TAX,' 'PTRATIO,' 'LSTAT.' as the final set of features.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embedded Methods\n",
    "\n",
    "* **Combine the qualities of Filter and Wrapper methods.** \n",
    "* **Works  iteratively** (`building the models while also evaluating their performance`). \n",
    "* **Based on performance, the best set of parameters is chosen.** \n",
    "#### **Random Forest Feature Importance** \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfr = RandomForestRegressor(n_estimators=1300)\n",
    "rfr.fit(Attribute, target)\n",
    "\n",
    "importance = rfr.feature_importances_\n",
    "\n",
    "importance_df = pd.DataFrame({\"Features\": Attribute.columns, \"Importance\":importance})\n",
    "importance_df = importance_df.sort_values(\"Importance\")\n",
    "\n",
    "plt.bar(importance_df[\"Features\"], importance_df[\"Importance\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Quality Assessment\n",
    "This section deals with the quality of data. Data is prone to noise, human errors, scaling errors, and reading errors. Such errors induce inconsistency in the collected data. It is recommended to process the data to remove these inconsistencies, and we will do the same on the Advanced Regression house price dataset. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Values\n",
    "* Row Elimination\n",
    "* Imputation\n",
    "    * numerical\n",
    "        * mean/median()\n",
    "        * forward and backward fill\n",
    "    * Catagorical\n",
    "        * frquent catogery \n",
    "        * missing\n",
    "\n",
    "* Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean Imputation\n",
    "HousingP['MEDV']=HousingP['MEDV'].fillna(HousingP['MEDV'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forward Fill\n",
    "HousingP[\"MEDV\"].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backward Fill\n",
    "HousingP[\"MEDV\"].fillna(method='bfill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear Interpolation\n",
    "HousingP[\"MDEV\"]= HousingP[\"MDEV\"].interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HousingP['Payment Method'] = HousingP['Payment Method'].fillna(HousingP['Payment Method'].mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HousingP['Payment Method'] = HousingP['Payment Method'].fillna('Missing')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Outliers\n",
    "* Statistical\n",
    "    * IQR\n",
    "    * Standard Deveation\n",
    "* ML-ALgorithms\n",
    "\n",
    "\n",
    "#### Duplicate items\n",
    "\n",
    "#### Sampiling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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   "language": "python",
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   "file_extension": ".py",
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