estimasi_mobil.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deskripsi library yang di gunakan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fungsi memanggil data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('merc.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deskripsi data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SLK</td>\n",
       "      <td>2005</td>\n",
       "      <td>5200</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>63000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>325</td>\n",
       "      <td>32.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Class</td>\n",
       "      <td>2017</td>\n",
       "      <td>34948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>27000</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>61.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SL CLASS</td>\n",
       "      <td>2016</td>\n",
       "      <td>49948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>6200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>555</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G Class</td>\n",
       "      <td>2016</td>\n",
       "      <td>61948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>16000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>325</td>\n",
       "      <td>30.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G Class</td>\n",
       "      <td>2016</td>\n",
       "      <td>73948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>325</td>\n",
       "      <td>30.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "0        SLK  2005   5200    Automatic    63000   Petrol  325  32.1   \n",
       "1    S Class  2017  34948    Automatic    27000   Hybrid   20  61.4   \n",
       "2   SL CLASS  2016  49948    Automatic     6200   Petrol  555  28.0   \n",
       "3    G Class  2016  61948    Automatic    16000   Petrol  325  30.4   \n",
       "4    G Class  2016  73948    Automatic     4000   Petrol  325  30.1   \n",
       "\n",
       "   engineSize  \n",
       "0         1.8  \n",
       "1         2.1  \n",
       "2         5.5  \n",
       "3         4.0  \n",
       "4         4.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13119 entries, 0 to 13118\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   model         13119 non-null  object \n",
      " 1   year          13119 non-null  int64  \n",
      " 2   price         13119 non-null  int64  \n",
      " 3   transmission  13119 non-null  object \n",
      " 4   mileage       13119 non-null  int64  \n",
      " 5   fuelType      13119 non-null  object \n",
      " 6   tax           13119 non-null  int64  \n",
      " 7   mpg           13119 non-null  float64\n",
      " 8   engineSize    13119 non-null  float64\n",
      "dtypes: float64(2), int64(4), object(3)\n",
      "memory usage: 922.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.296288</td>\n",
       "      <td>24698.596920</td>\n",
       "      <td>21949.559037</td>\n",
       "      <td>129.972178</td>\n",
       "      <td>55.155843</td>\n",
       "      <td>2.071530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.224709</td>\n",
       "      <td>11842.675542</td>\n",
       "      <td>21176.512267</td>\n",
       "      <td>65.260286</td>\n",
       "      <td>15.220082</td>\n",
       "      <td>0.572426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>17450.000000</td>\n",
       "      <td>6097.500000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>22480.000000</td>\n",
       "      <td>15189.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>28980.000000</td>\n",
       "      <td>31779.500000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>159999.000000</td>\n",
       "      <td>259000.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>217.300000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year          price        mileage           tax           mpg  \\\n",
       "count  13119.000000   13119.000000   13119.000000  13119.000000  13119.000000   \n",
       "mean    2017.296288   24698.596920   21949.559037    129.972178     55.155843   \n",
       "std        2.224709   11842.675542   21176.512267     65.260286     15.220082   \n",
       "min     1970.000000     650.000000       1.000000      0.000000      1.100000   \n",
       "25%     2016.000000   17450.000000    6097.500000    125.000000     45.600000   \n",
       "50%     2018.000000   22480.000000   15189.000000    145.000000     56.500000   \n",
       "75%     2019.000000   28980.000000   31779.500000    145.000000     64.200000   \n",
       "max     2020.000000  159999.000000  259000.000000    580.000000    217.300000   \n",
       "\n",
       "         engineSize  \n",
       "count  13119.000000  \n",
       "mean       2.071530  \n",
       "std        0.572426  \n",
       "min        0.000000  \n",
       "25%        1.800000  \n",
       "50%        2.000000  \n",
       "75%        2.100000  \n",
       "max        6.200000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualisasi Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric = df.select_dtypes(include=[np.number])\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(numeric.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jumlah Mobil berdasakar Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = df.groupby('model').count()[['tax']].sort_values(by='tax',ascending=True).reset_index()\n",
    "model = model.rename(columns={'tax':'numberOfCars'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0,\n",
       "  1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26],\n",
       " [Text(0, 0, '230'),\n",
       "  Text(1, 0, '200'),\n",
       "  Text(2, 0, '220'),\n",
       "  Text(3, 0, '180'),\n",
       "  Text(4, 0, ' R Class'),\n",
       "  Text(5, 0, ' CLC Class'),\n",
       "  Text(6, 0, ' CLK'),\n",
       "  Text(7, 0, ' G Class'),\n",
       "  Text(8, 0, ' GLB Class'),\n",
       "  Text(9, 0, ' GLS Class'),\n",
       "  Text(10, 0, ' M Class'),\n",
       "  Text(11, 0, ' X-CLASS'),\n",
       "  Text(12, 0, ' CLA Class'),\n",
       "  Text(13, 0, ' SLK'),\n",
       "  Text(14, 0, ' GL Class'),\n",
       "  Text(15, 0, ' S Class'),\n",
       "  Text(16, 0, ' V Class'),\n",
       "  Text(17, 0, ' CLS Class'),\n",
       "  Text(18, 0, ' SL CLASS'),\n",
       "  Text(19, 0, ' GLE Class'),\n",
       "  Text(20, 0, ' CL Class'),\n",
       "  Text(21, 0, ' B Class'),\n",
       "  Text(22, 0, ' GLA Class'),\n",
       "  Text(23, 0, ' GLC Class'),\n",
       "  Text(24, 0, ' E Class'),\n",
       "  Text(25, 0, ' A Class'),\n",
       "  Text(26, 0, ' C Class')])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=model['model'],y=model['numberOfCars'],color='royalblue')\n",
    "plt.xticks(rotation=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ukuran mesin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng = df.groupby('engineSize').count()[['tax']].sort_values(by='tax').reset_index()\n",
    "eng = eng.rename(columns={'tax':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='engineSize', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=eng['engineSize'],y=eng['count'],color='royalblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribusi mileage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_4796\\148899218.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['mileage'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='mileage', ylabel='Density'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.distplot(df['mileage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribusi Harga mobil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_4796\\3048279456.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['price'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='Density'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.distplot(df['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# seleksi fitur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13119, 5), (13119,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['year','mileage','tax','mpg','engineSize']\n",
    "x=df[features]\n",
    "y=df['price']\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# split data training dan data testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3280,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test,y_train, y_test = train_test_split(x, y, random_state=70)\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# regresi linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "pred= lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasu model regresi linear =  0.689668140479532\n"
     ]
    }
   ],
   "source": [
    "score = lr.score(x_test, y_test)\n",
    "print('Akurasu model regresi linear = ',score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# membuat inputan model regresi linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimasi [34301.38871233]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "input_data = np.array([[2019,500,145,30.2,2]])\n",
    "\n",
    "prediction = lr.predict(input_data)\n",
    "print('estimasi', prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# membuat saving model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "filename = 'estimasi_mobil.sav'\n",
    "\n",
    "pickle.dump(lr,open(filename,'wb'))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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