8.1 Feature Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.1 Feature Engineering\n",
    "## Feature Scaling, Encoding, Inputing\n",
    "Boise State University\\\n",
    "Department of Computer Science\\\n",
    "CS 334 Algorithms of Machine Learning\\\n",
    "Conrad Kennington\\\n",
    "Fall 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine learning algorithm just sees number — if there is a vast difference in the range say few ranging in thousands and few ranging in the tens, and it makes the underlying assumption that higher ranging numbers have superiority of some sort. So these more significant number starts playing a more decisive role while training the model.\n",
    "\n",
    "Feature scaling in machine learning is one of the most critical steps during the pre-processing of data before creating a machine learning model.\\\n",
    "Scaling can make a difference between a weak machine learning model and a better one.\n",
    "\n",
    "Like choosing a model algorithm, which feature scaling approach works best is a matter of experimentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        WEIGHT  PRICE\n",
      "Orange      15      1\n",
      "Apple       18      3\n",
      "Banana      12      2\n",
      "Grape       10      5\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'WEIGHT': [15, 18, 12,10],\n",
    "                   'PRICE': [1,3,2,5]},\n",
    "                   index = ['Orange','Apple','Banana','Grape'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Min-max scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g., between zero and one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Scaler responds well if the standard deviation is small and when a distribution is not Gaussian. This Scaler is sensitive to outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x129a1a370>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "df1 = pd.DataFrame(scaler.fit_transform(df),\n",
    "                   columns=['WEIGHT','PRICE'],\n",
    "                   index = ['Orange','Apple','Banana','Grape'])\n",
    "ax = df.plot.scatter(x='WEIGHT', y='PRICE',color=['red','green','blue','yellow'], \n",
    "                     marker = '*',s=80, label='BREFORE SCALING');\n",
    "df1.plot.scatter(x='WEIGHT', y='PRICE', color=['red','green','blue','yellow'],\n",
    "                 marker = 'o',s=60,label='AFTER SCALING', ax = ax)\n",
    "plt.axhline(0, color='red',alpha=0.2)\n",
    "plt.axvline(0, color='red',alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard Scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Standard Scaler assumes data is normally distributed within each feature and scales them such that the distribution centered around 0, with a standard deviation of 1.\\\n",
    "Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set.\\\n",
    "If data is not normally distributed, this is not the best Scaler to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x129b8ca90>"
      ]
     },
     "execution_count": 9,
     "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": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "df2 = pd.DataFrame(scaler.fit_transform(df),\n",
    "                   columns=['WEIGHT','PRICE'],\n",
    "                   index = ['Orange','Apple','Banana','Grape'])\n",
    "ax = df.plot.scatter(x='WEIGHT', y='PRICE',color=['red','green','blue','yellow'], \n",
    "                     marker = '*',s=80, label='BREFORE SCALING')\n",
    "df2.plot.scatter(x='WEIGHT', y='PRICE', color=['red','green','blue','yellow'],\n",
    "                 marker = 'o',s=60,label='AFTER SCALING', ax = ax)\n",
    "plt.axhline(0, color='red',alpha=0.2)\n",
    "plt.axvline(0, color='red',alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Max Abs Scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale each feature by its maximum absolute value.\\\n",
    "This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set is 1.0.\\\n",
    "It does not shift/center the data and thus does not destroy any sparsity.\\\n",
    "On positive-only data, this Scaler behaves similarly to Min Max Scaler and, therefore, also suffers from the presence of significant outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x129c696a0>"
      ]
     },
     "execution_count": 12,
     "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": [
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "scaler = MaxAbsScaler()\n",
    "df4 = pd.DataFrame(scaler.fit_transform(df),\n",
    "                   columns=['WEIGHT','PRICE'],\n",
    "                   index = ['Orange','Apple','Banana','Grape'])\n",
    "ax = df.plot.scatter(x='WEIGHT', y='PRICE',color=['red','green','blue','yellow'], \n",
    "                     marker = '*',s=80, label='BREFORE SCALING');\n",
    "df4.plot.scatter(x='WEIGHT', y='PRICE', color=['red','green','blue','yellow'],\n",
    "                 marker = 'o',s=60,label='AFTER SCALING', ax = ax)\n",
    "plt.axhline(0, color='red',alpha=0.2)\n",
    "plt.axvline(0, color='red',alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Robust Scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the name suggests, this Scaler is robust to outliers. If our data contains many outliers, scaling using the mean and standard deviation of the data won’t work well.\\\n",
    "\\\n",
    "This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). The centering and scaling statistics of this Scaler are based on percentiles and are therefore not influenced by a few numbers of huge marginal outliers. Note that the outliers themselves are still present in the transformed data. If a separate outlier clipping is desirable, a non-linear transformation is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x129cda910>"
      ]
     },
     "execution_count": 13,
     "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": [
    "from sklearn.preprocessing import RobustScaler\n",
    "scaler = RobustScaler()\n",
    "df3 = pd.DataFrame(scaler.fit_transform(df),\n",
    "                   columns=['WEIGHT','PRICE'],\n",
    "                   index = ['Orange','Apple','Banana','Grape'])\n",
    "ax = df.plot.scatter(x='WEIGHT', y='PRICE',color=['red','green','blue','yellow'], \n",
    "                     marker = '*',s=80, label='BREFORE SCALING');\n",
    "df3.plot.scatter(x='WEIGHT', y='PRICE', color=['red','green','blue','yellow'],\n",
    "                 marker = 'o',s=60,label='AFTER SCALING', ax = ax)\n",
    "plt.axhline(0, color='red',alpha=0.2)\n",
    "plt.axvline(0, color='red',alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantile Transformer Scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform features using quantiles information.\\\n",
    "\\\n",
    "This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this is, therefore, a robust pre-processing scheme.\\\n",
    "\\\n",
    "The cumulative distribution function of a feature is used to project the original values. Note that this transform is non-linear and may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable. This is also sometimes called as Rank scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/opt/python@3.8/Frameworks/Python.framework/Versions/3.8/lib/python3.8/sklearn/preprocessing/_data.py:2367: UserWarning: n_quantiles (1000) is greater than the total number of samples (4). n_quantiles is set to n_samples.\n",
      "  warnings.warn(\"n_quantiles (%s) is greater than the total number \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1299f50d0>"
      ]
     },
     "execution_count": 22,
     "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": [
    "from sklearn.preprocessing import QuantileTransformer\n",
    "scaler = QuantileTransformer()\n",
    "df6 = pd.DataFrame(scaler.fit_transform(df),\n",
    "                   columns=['WEIGHT','PRICE'],\n",
    "                   index = ['Orange','Apple','Banana','Grape'])\n",
    "ax = df.plot.scatter(x='WEIGHT', y='PRICE',color=['red','green','blue','yellow'], \n",
    "                     marker = '*',s=80, label='BREFORE SCALING');\n",
    "df6.plot.scatter(x='WEIGHT', y='PRICE', color=['red','green','blue','yellow'],\n",
    "                 marker = 'o',s=60,label='AFTER SCALING', ax = ax,figsize=(6,4))\n",
    "plt.axhline(0, color='red',alpha=0.2)\n",
    "plt.axvline(0, color='red',alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical to Numeric Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many Machine-learning or Data Science activities, the data set might contain text or categorical values (basically non-numerical values).\\\n",
    "\\\n",
    "For example, color feature having values like red, orange, blue, white etc. Meal plan having values like breakfast, lunch, snacks, dinner, tea etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many ways to convert categorical values into numerical values. Each approach has its own trade-offs and impact on the feature set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label-Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Bridge_Types</th>\n",
       "      <th>Bridge_Types_Cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arch</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beam</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Truss</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cantilever</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tied Arch</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Suspension</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cable</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bridge_Types  Bridge_Types_Cat\n",
       "0         Arch                 0\n",
       "1         Beam                 1\n",
       "2        Truss                 6\n",
       "3   Cantilever                 3\n",
       "4    Tied Arch                 5\n",
       "5   Suspension                 4\n",
       "6        Cable                 2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# creating initial dataframe\n",
    "bridge_types = ('Arch','Beam','Truss','Cantilever','Tied Arch','Suspension','Cable')\n",
    "bridge_df = pd.DataFrame(bridge_types, columns=['Bridge_Types'])\n",
    "\n",
    "# creating instance of labelencoder\n",
    "labelencoder = LabelEncoder()\n",
    "\n",
    "# Assigning numerical values and storing in another column\n",
    "bridge_df['Bridge_Types_Cat'] = labelencoder.fit_transform(bridge_df['Bridge_Types'])\n",
    "bridge_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Bridge_Types</th>\n",
       "      <th>Bridge_Types_Cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arch</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beam</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Truss</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cantilever</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tied Arch</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Suspension</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cable</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bridge_Types  Bridge_Types_Cat\n",
       "0         Arch                 0\n",
       "1         Beam                 1\n",
       "2        Truss                 6\n",
       "3   Cantilever                 3\n",
       "4    Tied Arch                 5\n",
       "5   Suspension                 4\n",
       "6        Cable                 2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# creating initial dataframe\n",
    "bridge_types = ('Arch','Beam','Truss','Cantilever','Tied Arch','Suspension','Cable')\n",
    "bridge_df = pd.DataFrame(bridge_types, columns=['Bridge_Types'])\n",
    "\n",
    "# converting type of columns to 'category'\n",
    "bridge_df['Bridge_Types'] = bridge_df['Bridge_Types'].astype('category')\n",
    "\n",
    "# Assigning numerical values and storing in another column\n",
    "bridge_df['Bridge_Types_Cat'] = bridge_df['Bridge_Types'].cat.codes\n",
    "bridge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Bridge_Types</th>\n",
       "      <th>Bridge_Types_Cat</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arch</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beam</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Truss</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cantilever</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tied Arch</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Suspension</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bridge_Types  Bridge_Types_Cat    0    1    2    3    4    5    6\n",
       "0         Arch                 0  1.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "1         Beam                 1  0.0  1.0  0.0  0.0  0.0  0.0  0.0\n",
       "2        Truss                 6  0.0  0.0  0.0  0.0  0.0  0.0  1.0\n",
       "3   Cantilever                 3  0.0  0.0  0.0  1.0  0.0  0.0  0.0\n",
       "4    Tied Arch                 5  0.0  0.0  0.0  0.0  0.0  1.0  0.0\n",
       "5   Suspension                 4  0.0  0.0  0.0  0.0  1.0  0.0  0.0\n",
       "6        Cable                 2  0.0  0.0  1.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# creating instance of one-hot-encoder\n",
    "enc = OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "# passing bridge-types-cat column (label encoded values of bridge_types)\n",
    "enc_df = pd.DataFrame(enc.fit_transform(bridge_df[['Bridge_Types_Cat']]).toarray())\n",
    "\n",
    "# merge with main df bridge_df on key values\n",
    "bridge_df = bridge_df.join(enc_df)\n",
    "bridge_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-hot using dummy values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Bridge_Types</th>\n",
       "      <th>Type_is_Arch</th>\n",
       "      <th>Type_is_Beam</th>\n",
       "      <th>Type_is_Cable</th>\n",
       "      <th>Type_is_Cantilever</th>\n",
       "      <th>Type_is_Suspension</th>\n",
       "      <th>Type_is_Tied Arch</th>\n",
       "      <th>Type_is_Truss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arch</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beam</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Truss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cantilever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tied Arch</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Suspension</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cable</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bridge_Types  Type_is_Arch  Type_is_Beam  Type_is_Cable  Type_is_Cantilever  \\\n",
       "0         Arch             1             0              0                   0   \n",
       "1         Beam             0             1              0                   0   \n",
       "2        Truss             0             0              0                   0   \n",
       "3   Cantilever             0             0              0                   1   \n",
       "4    Tied Arch             0             0              0                   0   \n",
       "5   Suspension             0             0              0                   0   \n",
       "6        Cable             0             0              1                   0   \n",
       "\n",
       "   Type_is_Suspension  Type_is_Tied Arch  Type_is_Truss  \n",
       "0                   0                  0              0  \n",
       "1                   0                  0              0  \n",
       "2                   0                  0              1  \n",
       "3                   0                  0              0  \n",
       "4                   0                  1              0  \n",
       "5                   1                  0              0  \n",
       "6                   0                  0              0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating initial dataframe\n",
    "bridge_types = ('Arch','Beam','Truss','Cantilever','Tied Arch','Suspension','Cable')\n",
    "bridge_df = pd.DataFrame(bridge_types, columns=['Bridge_Types'])\n",
    "\n",
    "# generate binary values using get_dummies\n",
    "dum_df = pd.get_dummies(bridge_df, columns=[\"Bridge_Types\"], prefix=[\"Type_is\"])\n",
    "\n",
    "# merge with main df bridge_df on key values\n",
    "bridge_df = bridge_df.join(dum_df)\n",
    "bridge_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inputing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation is identifying and replacing missing values from a dataset using appropriate values. \n",
    "\n",
    "Presence of missing values in a dataset can be caused by many possible factors such as: privacy concerns, technical faults when recording data using sensors, human errors, etc…"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two main types of Imputation:\n",
    "\n",
    "**Numerical Imputation**: missing numbers in numerical features can be imputed using many different techniques. Some of the main methods used, are to replace missing values with the overall mean or mode of the affected column. If you are interested in learning more about advanced techniques, you can find more information here.\n",
    "\n",
    "**Categorical Imputation**: for categorical features, missing values are commonly replaced using the overall column mode. In some particular case, if the categorical column structure is not well defined, it might be better instead to replace the missing values creating a new category and naming it “Unknown” or “Other”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "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>Date</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.655155</td>\n",
       "      <td>7.865133</td>\n",
       "      <td>Car</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>5.187388</td>\n",
       "      <td>19.388020</td>\n",
       "      <td>7.261167</td>\n",
       "      <td>Car</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>8.700367</td>\n",
       "      <td>22.861708</td>\n",
       "      <td>7.408588</td>\n",
       "      <td>Car</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.761383</td>\n",
       "      <td>7.176629</td>\n",
       "      <td>Car</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>8.344623</td>\n",
       "      <td>22.867737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date        X1         X2        X3   X4     Y\n",
       "0 2016-01-01       NaN  18.655155  7.865133  Car  True\n",
       "1 2016-01-02  5.187388  19.388020  7.261167  Car  True\n",
       "2 2016-01-03  8.700367  22.861708  7.408588  Car  True\n",
       "3 2016-01-04       NaN  22.761383  7.176629  Car  True\n",
       "4 2016-01-05  8.344623  22.867737       NaN  Car  True"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "\n",
    "# Generate 8000 rows of random data with missing samples.\n",
    "dataset_len = 8000\n",
    "dlen = int(dataset_len/2)\n",
    "X_11 = pd.Series(np.random.normal(8,2,dlen))\n",
    "X_12 = pd.Series(np.random.randint(low=0, high=100, size=dlen))\n",
    "X_1 = pd.concat([X_11, X_12]).reset_index(drop=True)\n",
    "X_21 = pd.Series(np.random.normal(20,3,dlen))\n",
    "X_22 = pd.Series(np.random.normal(9,3,dlen))\n",
    "X_2 = pd.concat([X_21, X_22]).reset_index(drop=True)\n",
    "X_31 = pd.Series(np.random.normal(8,2,dlen))\n",
    "X_32 = pd.Series(np.random.randint(low=0, high=300, size=dlen))\n",
    "X_3 = pd.concat([X_31, X_32]).reset_index(drop=True)\n",
    "X_4 = pd.Series(np.repeat(['Car', 'Bus', 'Bike', 'Scooter'],\n",
    "                          dlen/2))\n",
    "Y = pd.Series(np.repeat(['True','False'],dlen))\n",
    "df = pd.concat([X_1, X_2, X_3, X_4, Y], axis=1)\n",
    "df.columns = ['X1', 'X2', 'X3', 'X4', 'Y']\n",
    "df['X1'] = df['X1'].mask(np.random.random(df['X1'].shape) < 0.22)\n",
    "df[['X3', 'X4']] = df[['X3', 'X4']].mask(np.random.random(df[['X3', \n",
    "                        'X4']].shape) < 0.10)\n",
    "dates = pd.date_range(start='1/1/2016', periods=len(df), freq='D')\n",
    "df.insert(loc= 0, column='Date', value= dates)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>NaNs percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>22.3625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>10.3875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X4</th>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      NaNs percentage\n",
       "X1            22.3625\n",
       "X3            10.3875\n",
       "X4             9.5000\n",
       "Date           0.0000\n",
       "X2             0.0000\n",
       "Y              0.0000"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate missing data percentages\n",
    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
    "missing_values = pd.DataFrame({'NaNs percentage': percent_missing})\n",
    "missing_values.sort_values(by ='NaNs percentage' , ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove rows with nulls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes this is an acceptable option, if there's enough data in rows without nulls."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replace nulls with a median, max, aand most common class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>NaNs percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X4</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      NaNs percentage\n",
       "Date              0.0\n",
       "X1                0.0\n",
       "X2                0.0\n",
       "X3                0.0\n",
       "X4                0.0\n",
       "Y                 0.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['X1'].fillna(df['X1'].value_counts().min(), inplace=True)    \n",
    "df['X2'].fillna(df['X3'].value_counts().median(), inplace=True)   \n",
    "df['X3'].fillna(df['X3'].value_counts().max(), inplace=True)\n",
    "df['X4'].fillna(df['X4'].value_counts().idxmax(), inplace=True)\n",
    "   \n",
    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
    "missing_values = pd.DataFrame({'NaNs percentage': percent_missing})\n",
    "missing_values.sort_values(by ='NaNs percentage' , ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Class Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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