{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "hvn1t3fB94IS"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.preprocessing import KBinsDiscretizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 415
    },
    "id": "3RatXS8B_E-Q",
    "outputId": "30fe42a0-bb38-4223-8118-2dfcc8bcf546"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0    18.0          8         307.0       130.0    3504          12.0   \n",
       "1    15.0          8         350.0       165.0    3693          11.5   \n",
       "2    18.0          8         318.0       150.0    3436          11.0   \n",
       "3    16.0          8         304.0       150.0    3433          12.0   \n",
       "4    17.0          8         302.0       140.0    3449          10.5   \n",
       "..    ...        ...           ...         ...     ...           ...   \n",
       "393  27.0          4         140.0        86.0    2790          15.6   \n",
       "394  44.0          4          97.0        52.0    2130          24.6   \n",
       "395  32.0          4         135.0        84.0    2295          11.6   \n",
       "396  28.0          4         120.0        79.0    2625          18.6   \n",
       "397  31.0          4         119.0        82.0    2720          19.4   \n",
       "\n",
       "     model year  origin                   car name  \n",
       "0            70       1  chevrolet chevelle malibu  \n",
       "1            70       1          buick skylark 320  \n",
       "2            70       1         plymouth satellite  \n",
       "3            70       1              amc rebel sst  \n",
       "4            70       1                ford torino  \n",
       "..          ...     ...                        ...  \n",
       "393          82       1            ford mustang gl  \n",
       "394          82       2                  vw pickup  \n",
       "395          82       1              dodge rampage  \n",
       "396          82       1                ford ranger  \n",
       "397          82       1                 chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"auto-mpg.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "BcC6Y8tS_SMY"
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import MissingIndicator\n",
    "indicator = MissingIndicator(missing_values=np.NaN)\n",
    "indicator = indicator.fit_transform(df)\n",
    "indicator = pd.DataFrame(indicator, columns=['horsepower'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "id": "6_zl5F0VCOj1",
    "outputId": "bb0dbe42-d9c1-470f-d3a1-cbe2aeb1caa3"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
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       "      <td>18.0</td>\n",
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       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
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       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0    18.0        8.0         307.0       130.0  3504.0          12.0   \n",
       "1    15.0        8.0         350.0       165.0  3693.0          11.5   \n",
       "2    18.0        8.0         318.0       150.0  3436.0          11.0   \n",
       "3    16.0        8.0         304.0       150.0  3433.0          12.0   \n",
       "4    17.0        8.0         302.0       140.0  3449.0          10.5   \n",
       "..    ...        ...           ...         ...     ...           ...   \n",
       "393  27.0        4.0         140.0        86.0  2790.0          15.6   \n",
       "394  44.0        4.0          97.0        52.0  2130.0          24.6   \n",
       "395  32.0        4.0         135.0        84.0  2295.0          11.6   \n",
       "396  28.0        4.0         120.0        79.0  2625.0          18.6   \n",
       "397  31.0        4.0         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     model year  origin                   car name  \n",
       "0          70.0       1  chevrolet chevelle malibu  \n",
       "1          70.0       1          buick skylark 320  \n",
       "2          70.0       1         plymouth satellite  \n",
       "3          70.0       1              amc rebel sst  \n",
       "4          70.0       1                ford torino  \n",
       "..          ...     ...                        ...  \n",
       "393        82.0       1            ford mustang gl  \n",
       "394        82.0       2                  vw pickup  \n",
       "395        82.0       1              dodge rampage  \n",
       "396        82.0       1                ford ranger  \n",
       "397        82.0       1                 chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replacing the missing values by their mean \n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean') \n",
    "imputer = imputer.fit(df.iloc[:, 1:7])\n",
    "df.iloc[:, 1:7] = imputer.transform(df.iloc[:, 1:7])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "id": "L_iJCWXxGitn",
    "outputId": "726891eb-1007-4244-a040-305e14e34b80"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
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       "      <th>weight</th>\n",
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       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>70.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
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       "      <td>12.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
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       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>4.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>4.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>4.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cylinders  displacement  horsepower  weight  acceleration  model year  \\\n",
       "0          8.0         307.0       130.0  3504.0          12.0        70.0   \n",
       "1          8.0         350.0       165.0  3693.0          11.5        70.0   \n",
       "2          8.0         318.0       150.0  3436.0          11.0        70.0   \n",
       "3          8.0         304.0       150.0  3433.0          12.0        70.0   \n",
       "4          8.0         302.0       140.0  3449.0          10.5        70.0   \n",
       "..         ...           ...         ...     ...           ...         ...   \n",
       "393        4.0         140.0        86.0  2790.0          15.6        82.0   \n",
       "394        4.0          97.0        52.0  2130.0          24.6        82.0   \n",
       "395        4.0         135.0        84.0  2295.0          11.6        82.0   \n",
       "396        4.0         120.0        79.0  2625.0          18.6        82.0   \n",
       "397        4.0         119.0        82.0  2720.0          19.4        82.0   \n",
       "\n",
       "     origin                   car name  \n",
       "0         1  chevrolet chevelle malibu  \n",
       "1         1          buick skylark 320  \n",
       "2         1         plymouth satellite  \n",
       "3         1              amc rebel sst  \n",
       "4         1                ford torino  \n",
       "..      ...                        ...  \n",
       "393       1            ford mustang gl  \n",
       "394       2                  vw pickup  \n",
       "395       1              dodge rampage  \n",
       "396       1                ford ranger  \n",
       "397       1                 chevy s-10  \n",
       "\n",
       "[398 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,1:]\n",
    "Y = df.iloc[:,0]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MO1WYelyJ9nu",
    "outputId": "3ecbb3b3-9d03-42f3-a88b-c5321a8f7590"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.70902366,  2.94798968,  3.40749723, ...,  4.35694676,\n",
       "        18.95488984,  1.24836677],\n",
       "       [ 4.70902366,  3.36090028,  4.32490032, ...,  4.17540731,\n",
       "        18.95488984,  1.24836677],\n",
       "       [ 4.70902366,  3.05361797,  3.93172757, ...,  3.99386786,\n",
       "        18.95488984,  1.24836677],\n",
       "       ...,\n",
       "       [ 2.35451183,  1.29634725,  2.20176744, ...,  4.2117152 ,\n",
       "        22.20429953,  1.24836677],\n",
       "       [ 2.35451183,  1.15230867,  2.07070985, ...,  6.75326748,\n",
       "        22.20429953,  1.24836677],\n",
       "       [ 2.35451183,  1.1427061 ,  2.1493444 , ...,  7.04373059,\n",
       "        22.20429953,  1.24836677]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#standardisaation, Feature scaling\n",
    "#x_scaled = (x — u) / s\n",
    "\n",
    "sc_X = StandardScaler(with_mean=False)\n",
    "X = sc_X.fit_transform(X.drop(['car name'], axis=1))\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "8Py_CmGKW1VH"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "nm = Normalizer()\n",
    "x_sc = nm.fit_transform(X)\n",
    "X=pd.DataFrame(x_sc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 434
    },
    "id": "Gxn8BV79XDPU",
    "outputId": "8952fc72-b2e4-4504-ae6b-d13bc44a4025"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(398, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.224629</td>\n",
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       "      <td>0.207835</td>\n",
       "      <td>0.904184</td>\n",
       "      <td>0.059549</td>\n",
       "      <td>chevroletchevellemalibu</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>buickskylark320</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>0.190344</td>\n",
       "      <td>0.903373</td>\n",
       "      <td>0.059496</td>\n",
       "      <td>plymouthsatellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.223869</td>\n",
       "      <td>0.138779</td>\n",
       "      <td>0.186916</td>\n",
       "      <td>0.192966</td>\n",
       "      <td>0.207131</td>\n",
       "      <td>0.901124</td>\n",
       "      <td>0.059348</td>\n",
       "      <td>amcrebelsst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.225506</td>\n",
       "      <td>0.138874</td>\n",
       "      <td>0.175730</td>\n",
       "      <td>0.195283</td>\n",
       "      <td>0.182565</td>\n",
       "      <td>0.907712</td>\n",
       "      <td>0.059782</td>\n",
       "      <td>fordtorino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0.100398</td>\n",
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       "      <td>0.096120</td>\n",
       "      <td>0.140661</td>\n",
       "      <td>0.241518</td>\n",
       "      <td>0.946808</td>\n",
       "      <td>0.053231</td>\n",
       "      <td>fordmustanggl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0.096635</td>\n",
       "      <td>0.038229</td>\n",
       "      <td>0.055941</td>\n",
       "      <td>0.103361</td>\n",
       "      <td>0.366581</td>\n",
       "      <td>0.911320</td>\n",
       "      <td>0.102472</td>\n",
       "      <td>vwpickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>0.102104</td>\n",
       "      <td>0.056216</td>\n",
       "      <td>0.095480</td>\n",
       "      <td>0.117671</td>\n",
       "      <td>0.182642</td>\n",
       "      <td>0.962893</td>\n",
       "      <td>0.054136</td>\n",
       "      <td>dodgerampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.099409</td>\n",
       "      <td>0.048651</td>\n",
       "      <td>0.087426</td>\n",
       "      <td>0.131038</td>\n",
       "      <td>0.285126</td>\n",
       "      <td>0.937476</td>\n",
       "      <td>0.052707</td>\n",
       "      <td>fordranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
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       "      <td>0.048031</td>\n",
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       "      <td>0.135176</td>\n",
       "      <td>0.296066</td>\n",
       "      <td>0.933304</td>\n",
       "      <td>0.052472</td>\n",
       "      <td>chevys-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "0    0.224629  0.140625  0.162544  0.197626  0.207835  0.904184  0.059549   \n",
       "1    0.222104  0.158519  0.203986  0.205944  0.196935  0.894018  0.058880   \n",
       "2    0.224428  0.145533  0.187383  0.193617  0.190344  0.903373  0.059496   \n",
       "3    0.223869  0.138779  0.186916  0.192966  0.207131  0.901124  0.059348   \n",
       "4    0.225506  0.138874  0.175730  0.195283  0.182565  0.907712  0.059782   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "393  0.100398  0.057325  0.096120  0.140661  0.241518  0.946808  0.053231   \n",
       "394  0.096635  0.038229  0.055941  0.103361  0.366581  0.911320  0.102472   \n",
       "395  0.102104  0.056216  0.095480  0.117671  0.182642  0.962893  0.054136   \n",
       "396  0.099409  0.048651  0.087426  0.131038  0.285126  0.937476  0.052707   \n",
       "397  0.098966  0.048031  0.090342  0.135176  0.296066  0.933304  0.052472   \n",
       "\n",
       "                    car name  \n",
       "0    chevroletchevellemalibu  \n",
       "1            buickskylark320  \n",
       "2          plymouthsatellite  \n",
       "3                amcrebelsst  \n",
       "4                 fordtorino  \n",
       "..                       ...  \n",
       "393            fordmustanggl  \n",
       "394                 vwpickup  \n",
       "395             dodgerampage  \n",
       "396               fordranger  \n",
       "397                chevys-10  \n",
       "\n",
       "[398 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['car name'] = df.iloc[:,-1]\n",
    "X['car name'] = X['car name'].str.replace(\" \",\"\")\n",
    "\n",
    "print(X.shape)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "id": "eRJ_GIQhQuj4",
    "outputId": "894cbc23-0378-4258-c08c-b85285df137e",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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       "      <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>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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9    ...  294  295  296  \\\n",
       "0      0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "1      0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "2      0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "3      0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "4      0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "393    0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "394    0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "395    0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "396    0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "397    0    0    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "\n",
       "     297  298  299  300  301  302  303  \n",
       "0      0    0    0    0    0    0    0  \n",
       "1      0    0    0    0    0    0    0  \n",
       "2      0    0    0    0    0    0    0  \n",
       "3      0    0    0    0    0    0    0  \n",
       "4      0    0    0    0    0    0    0  \n",
       "..   ...  ...  ...  ...  ...  ...  ...  \n",
       "393    0    0    0    0    0    0    0  \n",
       "394    0    0    0    1    0    0    0  \n",
       "395    0    0    0    0    0    0    0  \n",
       "396    0    0    0    0    0    0    0  \n",
       "397    0    0    0    0    0    0    0  \n",
       "\n",
       "[398 rows x 304 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TEST ENCODER\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "onehot = OneHotEncoder(dtype=np.int, sparse=True)\n",
    "nominals = pd.DataFrame(\n",
    "    onehot.fit_transform(X[['car name']])\\\n",
    "    .toarray())\n",
    "nominals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'x^2')"
      ]
     },
     "execution_count": 25,
     "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": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "x = [1,2,3,4,5]\n",
    "\n",
    "y = [1,4,9,16,25]\n",
    "\n",
    "# plotting the points\n",
    "plt.plot(x, y, color='green', linestyle='dashed', linewidth = 3,\n",
    "         marker='o', markerfacecolor='red', markersize=12)\n",
    "\n",
    "\n",
    "plt.xlabel('x - axis: numbers')\n",
    "\n",
    "plt.ylabel('y - axis: Square(x)')\n",
    "\n",
    "\n",
    "plt.title('x^2')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# x-coordinates of left sides of bars\n",
    "left = [1, 2, 3, 4, 5]\n",
    "\n",
    "# heights of bars\n",
    "height = [1,4,9,16,25]\n",
    "\n",
    "# labels for bars\n",
    "tick_label = ['one', 'two', 'three', 'four', 'five']\n",
    "\n",
    "# plotting a bar chart\n",
    "plt.bar(left, height, tick_label = tick_label,\n",
    "\t\twidth = 0.8, color = ['red', 'green'])\n",
    "\n",
    "# naming the x-axis\n",
    "plt.xlabel('x - axis')\n",
    "# naming the y-axis\n",
    "plt.ylabel('y - axis')\n",
    "# plot title\n",
    "plt.title('My bar chart!')\n",
    "\n",
    "# function to show the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# frequencies\n",
    "ages = [2,5,70,40,30,45,50,45,43,40,44,\n",
    "\t\t60,7,13,57,18,90,77,32,21,20,40]\n",
    "\n",
    "# setting the ranges and no. of intervals\n",
    "range = (0, 100)\n",
    "bins = 10\n",
    "\n",
    "# plotting a histogram\n",
    "plt.hist(ages, bins, range, color = 'green',\n",
    "\t\thisttype = 'bar', rwidth = 0.8)\n",
    "\n",
    "# x-axis label\n",
    "plt.xlabel('age')\n",
    "# frequency label\n",
    "plt.ylabel('No. of people')\n",
    "# plot title\n",
    "plt.title('My histogram')\n",
    "\n",
    "# function to show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# x-axis values\n",
    "x = [1,2,3,4,5,6,7,8,9,10]\n",
    "# y-axis values\n",
    "y = [1,4,9,16,25,36,49,64,81,100]\n",
    "\n",
    "# plotting points as a scatter plot\n",
    "plt.scatter(x, y, label= \"stars\", color= \"green\",\n",
    "\t\t\tmarker= \"o\", s=30)\n",
    "\n",
    "# x-axis label\n",
    "plt.xlabel('x - axis')\n",
    "# frequency label\n",
    "plt.ylabel('y - axis')\n",
    "# plot title\n",
    "plt.title('My scatter plot!')\n",
    "# showing legend\n",
    "plt.legend()\n",
    "\n",
    "# function to show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# defining labels\n",
    "activities = ['php', 'java', 'python', 'c']\n",
    "\n",
    "# portion covered by each label\n",
    "slices = [3, 7, 8, 6]\n",
    "\n",
    "# color for each label\n",
    "colors = ['r', 'y', 'g', 'b']\n",
    "\n",
    "# plotting the pie chart\n",
    "plt.pie(slices, labels = activities, colors=colors,\n",
    "\t\tstartangle=90, shadow = True, explode = (0, 0, 0.1, 0),\n",
    "\t\tradius = 1.2, autopct = '%1.1f%%')\n",
    "\n",
    "# plotting legend\n",
    "plt.legend()\n",
    "\n",
    "# showing the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0    3504          12.0   \n",
       "1  15.0          8         350.0       165.0    3693          11.5   \n",
       "2  18.0          8         318.0       150.0    3436          11.0   \n",
       "3  16.0          8         304.0       150.0    3433          12.0   \n",
       "4  17.0          8         302.0       140.0    3449          10.5   \n",
       "\n",
       "   model year  origin                   car name  \n",
       "0          70       1  chevrolet chevelle malibu  \n",
       "1          70       1          buick skylark 320  \n",
       "2          70       1         plymouth satellite  \n",
       "3          70       1              amc rebel sst  \n",
       "4          70       1                ford torino  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"auto-mpg.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by in Data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py:239: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x125da1ba8>]"
      ]
     },
     "execution_count": 50,
     "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": [
    "x=data.groupby(\"cylinders\")['horsepower'].mean()\n",
    "y=set(data['cylinders'])\n",
    "y=list(y)\n",
    "plt.plot(y, x, color='green', linestyle='dashed', linewidth = 3,marker='o', markerfacecolor='red', markersize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering a Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 103 artists>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WbQtwe1ve2dZp2z/e5jAnUdcXk7wGuBS4ur14z/X/6bn52yQXMhh331jtupaobV1rC4N55uGxdk27IuIi4PGqOjjJ2trmKxl8mPe/Q/0ndtyq6vqqOruqZhl8uPrxqvodpjzWjlLX7x4PY22R2qY+1hYqdhKfSv8yg7coDwD3t9sm4B+AL7T2ncC6ocf8JYOz5YeAyyddWx359Pz35/X/beBBBlcF3Qf8xhhr+3ngc622PRy58ufZDF749gH/CJzS2p/e1ve17c+ecF1PtL/Z3HGca79u6JjdDfzSFI7Zx9tY2wO8lyNXv4TBf5LzpbZ946Rra9s+yeCdx3D/iR23efu9mCNXkEx1rC1S19TH2iK1TX2szb/5zVhJ6pzfjJWkzhn0ktQ5g16SOmfQS1LnDHpJ6pxBL0mdM+glqXMGvSR17v8B1vJdBEr84DAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data[data['cylinders']==8]['displacement']\n",
    "y=data[data['cylinders']==8]['horsepower']\n",
    "#plt.plot(y, x, color='green', linestyle='dashed', linewidth = 3,marker='o', markerfacecolor='red', markersize=12)\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "DS_PRAC-2_18it089.ipynb",
   "provenance": []
  },
  "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": 1
}