Chapter 3 - Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.preprocessing as preprocessing\n",
    "from sklearn.impute import SimpleImputer\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Price</th>\n",
       "      <th>Color</th>\n",
       "      <th>Location</th>\n",
       "      <th>Model</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994</td>\n",
       "      <td>94000.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>white</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>GL</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>white</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>2495.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>GL</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Mileage   Price  Color Location Model  Age\n",
       "0  1990      NaN  1600.0    NaN  Phoenix   NaN   19\n",
       "1  1994  94000.0  1988.0  white  Phoenix    GL   15\n",
       "2  1995      NaN  2288.0  white  Phoenix   NaN   14\n",
       "3  1995  68000.0  2495.0    NaN  Phoenix   NaN   14\n",
       "4  1995      NaN  1995.0    NaN  Phoenix    GL   14"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fords=pd.read_csv('fords.csv')\n",
    "fords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 635 entries, 0 to 634\n",
      "Data columns (total 7 columns):\n",
      "Year        635 non-null int64\n",
      "Mileage     616 non-null float64\n",
      "Price       629 non-null float64\n",
      "Color       625 non-null object\n",
      "Location    635 non-null object\n",
      "Model       627 non-null object\n",
      "Age         635 non-null int64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 34.8+ KB\n"
     ]
    }
   ],
   "source": [
    "fords.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>635.000000</td>\n",
       "      <td>616.000000</td>\n",
       "      <td>6.290000e+02</td>\n",
       "      <td>635.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2004.886614</td>\n",
       "      <td>56015.571429</td>\n",
       "      <td>2.531353e+04</td>\n",
       "      <td>4.280315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.989553</td>\n",
       "      <td>34145.828077</td>\n",
       "      <td>3.983778e+05</td>\n",
       "      <td>3.284152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1990.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.200000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>31772.750000</td>\n",
       "      <td>5.995000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>48897.500000</td>\n",
       "      <td>8.950000e+03</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>74503.250000</td>\n",
       "      <td>1.169000e+04</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2100.000000</td>\n",
       "      <td>181484.000000</td>\n",
       "      <td>9.999999e+06</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year        Mileage         Price         Age\n",
       "count   635.000000     616.000000  6.290000e+02  635.000000\n",
       "mean   2004.886614   56015.571429  2.531353e+04    4.280315\n",
       "std       4.989553   34145.828077  3.983778e+05    3.284152\n",
       "min    1990.000000      42.000000  1.200000e+03    0.000000\n",
       "25%    2003.000000   31772.750000  5.995000e+03    2.000000\n",
       "50%    2006.000000   48897.500000  8.950000e+03    3.000000\n",
       "75%    2007.000000   74503.250000  1.169000e+04    6.000000\n",
       "max    2100.000000  181484.000000  9.999999e+06   19.000000"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fords.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>Color_beige</th>\n",
       "      <th>Color_black</th>\n",
       "      <th>Color_blue</th>\n",
       "      <th>Color_brown</th>\n",
       "      <th>Color_burgundy</th>\n",
       "      <th>Color_gold</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_Fresno</th>\n",
       "      <th>Location_Philadelphia</th>\n",
       "      <th>Location_Phoenix</th>\n",
       "      <th>Location_St Paul</th>\n",
       "      <th>Model_GL</th>\n",
       "      <th>Model_LX</th>\n",
       "      <th>Model_Limited</th>\n",
       "      <th>Model_SE</th>\n",
       "      <th>Model_SEL</th>\n",
       "      <th>Model_SES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>19</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994</td>\n",
       "      <td>94000.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>15</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>14</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>2495.0</td>\n",
       "      <td>14</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>14</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Mileage   Price  Age  Color_beige  Color_black  Color_blue  \\\n",
       "0  1990      NaN  1600.0   19            0            0           0   \n",
       "1  1994  94000.0  1988.0   15            0            0           0   \n",
       "2  1995      NaN  2288.0   14            0            0           0   \n",
       "3  1995  68000.0  2495.0   14            0            0           0   \n",
       "4  1995      NaN  1995.0   14            0            0           0   \n",
       "\n",
       "   Color_brown  Color_burgundy  Color_gold  ...  Location_Fresno  \\\n",
       "0            0               0           0  ...                0   \n",
       "1            0               0           0  ...                0   \n",
       "2            0               0           0  ...                0   \n",
       "3            0               0           0  ...                0   \n",
       "4            0               0           0  ...                0   \n",
       "\n",
       "   Location_Philadelphia  Location_Phoenix  Location_St Paul  Model_GL  \\\n",
       "0                      0                 1                 0         0   \n",
       "1                      0                 1                 0         1   \n",
       "2                      0                 1                 0         0   \n",
       "3                      0                 1                 0         0   \n",
       "4                      0                 1                 0         1   \n",
       "\n",
       "   Model_LX  Model_Limited  Model_SE  Model_SEL  Model_SES  \n",
       "0         0              0         0          0          0  \n",
       "1         0              0         0          0          0  \n",
       "2         0              0         0          0          0  \n",
       "3         0              0         0          0          0  \n",
       "4         0              0         0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding\n",
    "fords= pd.get_dummies(fords)\n",
    "fords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 635 entries, 0 to 634\n",
      "Data columns (total 26 columns):\n",
      "Year                     635 non-null float64\n",
      "Mileage                  635 non-null float64\n",
      "Price                    635 non-null float64\n",
      "Age                      635 non-null float64\n",
      "Color_beige              635 non-null uint8\n",
      "Color_black              635 non-null uint8\n",
      "Color_blue               635 non-null uint8\n",
      "Color_brown              635 non-null uint8\n",
      "Color_burgundy           635 non-null uint8\n",
      "Color_gold               635 non-null uint8\n",
      "Color_gray               635 non-null uint8\n",
      "Color_green              635 non-null uint8\n",
      "Color_red                635 non-null uint8\n",
      "Color_white              635 non-null uint8\n",
      "Location_Cambridge       635 non-null uint8\n",
      "Location_Dallas          635 non-null uint8\n",
      "Location_Fresno          635 non-null uint8\n",
      "Location_Philadelphia    635 non-null uint8\n",
      "Location_Phoenix         635 non-null uint8\n",
      "Location_St Paul         635 non-null uint8\n",
      "Model_GL                 635 non-null uint8\n",
      "Model_LX                 635 non-null uint8\n",
      "Model_Limited            635 non-null uint8\n",
      "Model_SE                 635 non-null uint8\n",
      "Model_SEL                635 non-null uint8\n",
      "Model_SES                635 non-null uint8\n",
      "dtypes: float64(4), uint8(22)\n",
      "memory usage: 33.6 KB\n"
     ]
    }
   ],
   "source": [
    "#missing\n",
    "rows=['Year','Mileage','Price','Age']\n",
    "for numerical_row in rows:\n",
    "    imr_mean = SimpleImputer(strategy='mean')\n",
    "    imr_mean = imr_mean.fit(fords[[numerical_row]])\n",
    "    fords[numerical_row] = imr_mean.transform(fords[[numerical_row]]).ravel()\n",
    "fords.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#outliers\n",
    "fords.Year.hist()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fords.Mileage.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fords.Price.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#outliers\n",
    "fords.Age.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "fords=fords.loc[~((fords.Price>8000000)|\n",
    "                  (fords.Year>2080)),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>Color_beige</th>\n",
       "      <th>Color_black</th>\n",
       "      <th>Color_blue</th>\n",
       "      <th>Color_brown</th>\n",
       "      <th>Color_burgundy</th>\n",
       "      <th>Color_gold</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_Fresno</th>\n",
       "      <th>Location_Philadelphia</th>\n",
       "      <th>Location_Phoenix</th>\n",
       "      <th>Location_St Paul</th>\n",
       "      <th>Model_GL</th>\n",
       "      <th>Model_LX</th>\n",
       "      <th>Model_Limited</th>\n",
       "      <th>Model_SE</th>\n",
       "      <th>Model_SEL</th>\n",
       "      <th>Model_SES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.308493</td>\n",
       "      <td>0.016588</td>\n",
       "      <td>1.000000</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.517840</td>\n",
       "      <td>0.032679</td>\n",
       "      <td>0.789474</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.263158</td>\n",
       "      <td>0.308493</td>\n",
       "      <td>0.045120</td>\n",
       "      <td>0.736842</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.374544</td>\n",
       "      <td>0.053704</td>\n",
       "      <td>0.736842</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.308493</td>\n",
       "      <td>0.032969</td>\n",
       "      <td>0.736842</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year   Mileage     Price       Age  Color_beige  Color_black  \\\n",
       "0  0.000000  0.308493  0.016588  1.000000            0            0   \n",
       "1  0.210526  0.517840  0.032679  0.789474            0            0   \n",
       "2  0.263158  0.308493  0.045120  0.736842            0            0   \n",
       "3  0.263158  0.374544  0.053704  0.736842            0            0   \n",
       "4  0.263158  0.308493  0.032969  0.736842            0            0   \n",
       "\n",
       "   Color_blue  Color_brown  Color_burgundy  Color_gold  ...  Location_Fresno  \\\n",
       "0           0            0               0           0  ...                0   \n",
       "1           0            0               0           0  ...                0   \n",
       "2           0            0               0           0  ...                0   \n",
       "3           0            0               0           0  ...                0   \n",
       "4           0            0               0           0  ...                0   \n",
       "\n",
       "   Location_Philadelphia  Location_Phoenix  Location_St Paul  Model_GL  \\\n",
       "0                      0                 1                 0         0   \n",
       "1                      0                 1                 0         1   \n",
       "2                      0                 1                 0         0   \n",
       "3                      0                 1                 0         0   \n",
       "4                      0                 1                 0         1   \n",
       "\n",
       "   Model_LX  Model_Limited  Model_SE  Model_SEL  Model_SES  \n",
       "0         0              0         0          0          0  \n",
       "1         0              0         0          0          0  \n",
       "2         0              0         0          0          0  \n",
       "3         0              0         0          0          0  \n",
       "4         0              0         0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardization\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "fords[['Year','Mileage','Price','Age']] = scaler.fit_transform(fords[['Year','Mileage','Price','Age']])\n",
    "fords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>Color_beige</th>\n",
       "      <th>Color_black</th>\n",
       "      <th>Color_blue</th>\n",
       "      <th>Color_brown</th>\n",
       "      <th>Color_burgundy</th>\n",
       "      <th>Color_gold</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_Fresno</th>\n",
       "      <th>Location_Philadelphia</th>\n",
       "      <th>Location_Phoenix</th>\n",
       "      <th>Location_St Paul</th>\n",
       "      <th>Model_GL</th>\n",
       "      <th>Model_LX</th>\n",
       "      <th>Model_Limited</th>\n",
       "      <th>Model_SE</th>\n",
       "      <th>Model_SEL</th>\n",
       "      <th>Model_SES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.294748</td>\n",
       "      <td>0.015849</td>\n",
       "      <td>0.955444</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.217510</td>\n",
       "      <td>0.535018</td>\n",
       "      <td>0.033763</td>\n",
       "      <td>0.815662</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.312445</td>\n",
       "      <td>0.366271</td>\n",
       "      <td>0.053570</td>\n",
       "      <td>0.874845</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.302789</td>\n",
       "      <td>0.430950</td>\n",
       "      <td>0.061792</td>\n",
       "      <td>0.847810</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.312654</td>\n",
       "      <td>0.366516</td>\n",
       "      <td>0.039170</td>\n",
       "      <td>0.875431</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year   Mileage     Price       Age  Color_beige  Color_black  \\\n",
       "0  0.000000  0.294748  0.015849  0.955444            0            0   \n",
       "1  0.217510  0.535018  0.033763  0.815662            0            0   \n",
       "2  0.312445  0.366271  0.053570  0.874845            0            0   \n",
       "3  0.302789  0.430950  0.061792  0.847810            0            0   \n",
       "4  0.312654  0.366516  0.039170  0.875431            0            0   \n",
       "\n",
       "   Color_blue  Color_brown  Color_burgundy  Color_gold  ...  Location_Fresno  \\\n",
       "0           0            0               0           0  ...                0   \n",
       "1           0            0               0           0  ...                0   \n",
       "2           0            0               0           0  ...                0   \n",
       "3           0            0               0           0  ...                0   \n",
       "4           0            0               0           0  ...                0   \n",
       "\n",
       "   Location_Philadelphia  Location_Phoenix  Location_St Paul  Model_GL  \\\n",
       "0                      0                 1                 0         0   \n",
       "1                      0                 1                 0         1   \n",
       "2                      0                 1                 0         0   \n",
       "3                      0                 1                 0         0   \n",
       "4                      0                 1                 0         1   \n",
       "\n",
       "   Model_LX  Model_Limited  Model_SE  Model_SEL  Model_SES  \n",
       "0         0              0         0          0          0  \n",
       "1         0              0         0          0          0  \n",
       "2         0              0         0          0          0  \n",
       "3         0              0         0          0          0  \n",
       "4         0              0         0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalization\n",
    "fords[['Year','Mileage','Price','Age']] = preprocessing.normalize(fords[[\n",
    "    'Year','Mileage','Price','Age']])\n",
    "fords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>Color_beige</th>\n",
       "      <th>Color_black</th>\n",
       "      <th>Color_blue</th>\n",
       "      <th>Color_brown</th>\n",
       "      <th>Color_burgundy</th>\n",
       "      <th>Color_gold</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_Fresno</th>\n",
       "      <th>Location_Philadelphia</th>\n",
       "      <th>Location_Phoenix</th>\n",
       "      <th>Location_St Paul</th>\n",
       "      <th>Model_GL</th>\n",
       "      <th>Model_LX</th>\n",
       "      <th>Model_Limited</th>\n",
       "      <th>Model_SE</th>\n",
       "      <th>Model_SEL</th>\n",
       "      <th>Model_SES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Mileage  Price  Age  Color_beige  Color_black  Color_blue  \\\n",
       "0   0.0      5.0    0.0  9.0            0            0           0   \n",
       "1   0.0      5.0    0.0  9.0            0            0           0   \n",
       "2   0.0      5.0    0.0  9.0            0            0           0   \n",
       "3   0.0      5.0    0.0  9.0            0            0           0   \n",
       "4   0.0      5.0    0.0  9.0            0            0           0   \n",
       "\n",
       "   Color_brown  Color_burgundy  Color_gold  ...  Location_Fresno  \\\n",
       "0            0               0           0  ...                0   \n",
       "1            0               0           0  ...                0   \n",
       "2            0               0           0  ...                0   \n",
       "3            0               0           0  ...                0   \n",
       "4            0               0           0  ...                0   \n",
       "\n",
       "   Location_Philadelphia  Location_Phoenix  Location_St Paul  Model_GL  \\\n",
       "0                      0                 1                 0         0   \n",
       "1                      0                 1                 0         1   \n",
       "2                      0                 1                 0         0   \n",
       "3                      0                 1                 0         0   \n",
       "4                      0                 1                 0         1   \n",
       "\n",
       "   Model_LX  Model_Limited  Model_SE  Model_SEL  Model_SES  \n",
       "0         0              0         0          0          0  \n",
       "1         0              0         0          0          0  \n",
       "2         0              0         0          0          0  \n",
       "3         0              0         0          0          0  \n",
       "4         0              0         0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#binning\n",
    "est = preprocessing.KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')\n",
    "est.fit(fords[['Year','Mileage','Price','Age']])\n",
    "fords[['Year','Mileage','Price','Age']] = est.transform(fords[['Year','Mileage','Price','Age']])\n",
    "fords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}