decisionreg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "05a22afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "from sklearn.tree import DecisionTreeRegressor,export_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "628c93b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\",header=None,sep=\"\\s+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ea83aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
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       "      <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",
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       "      <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",
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       "      <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>3433.0</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>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "      0  1      2      3       4     5   6  7                          8\n",
       "0  18.0  8  307.0  130.0  3504.0  12.0  70  1  chevrolet chevelle malibu\n",
       "1  15.0  8  350.0  165.0  3693.0  11.5  70  1          buick skylark 320\n",
       "2  18.0  8  318.0  150.0  3436.0  11.0  70  1         plymouth satellite\n",
       "3  16.0  8  304.0  150.0  3433.0  12.0  70  1              amc rebel sst\n",
       "4  17.0  8  302.0  140.0  3449.0  10.5  70  1                ford torino"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0fc761ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['mpg',\n",
    "'cylinders',\n",
    "'displacement',\n",
    "'horsepower',\n",
    "'weight',\n",
    "'acceleration',\n",
    "'model',\n",
    "'origin',\n",
    "'car'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "48d30eb4",
   "metadata": {},
   "outputs": [
    {
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       "  <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</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <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</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>3693.0</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>3436.0</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>3433.0</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>3449.0</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",
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      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0  18.0          8         307.0      130.0  3504.0          12.0     70   \n",
       "1  15.0          8         350.0      165.0  3693.0          11.5     70   \n",
       "2  18.0          8         318.0      150.0  3436.0          11.0     70   \n",
       "3  16.0          8         304.0      150.0  3433.0          12.0     70   \n",
       "4  17.0          8         302.0      140.0  3449.0          10.5     70   \n",
       "\n",
       "   origin                        car  \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  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "809e53f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower       object\n",
       "weight          float64\n",
       "acceleration    float64\n",
       "model             int64\n",
       "origin            int64\n",
       "car              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "06b82781",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c8202c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "df['car'] = le.fit_transform(df['car'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c5e2bdc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-32-89e467dd2fcb>:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  df['horsepower']=df['horsepower'].str.replace(\"?\",\"0\")\n"
     ]
    }
   ],
   "source": [
    "df['horsepower']=df['horsepower'].str.replace(\"?\",\"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "967a39c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['horsepower'] = df['horsepower'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fe8cfa35",
   "metadata": {},
   "outputs": [
    {
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       "  <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</th>\n",
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       "      <th>car</th>\n",
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       "      <td>1</td>\n",
       "      <td>49</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>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>36</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>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>231</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>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>14</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>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  model  \\\n",
       "0  18.0          8         307.0       130.0  3504.0          12.0     70   \n",
       "1  15.0          8         350.0       165.0  3693.0          11.5     70   \n",
       "2  18.0          8         318.0       150.0  3436.0          11.0     70   \n",
       "3  16.0          8         304.0       150.0  3433.0          12.0     70   \n",
       "4  17.0          8         302.0       140.0  3449.0          10.5     70   \n",
       "\n",
       "   origin  car  \n",
       "0       1   49  \n",
       "1       1   36  \n",
       "2       1  231  \n",
       "3       1   14  \n",
       "4       1  161  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f15f499b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('mpg',axis=1)\n",
    "Y= df['mpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b4ea5050",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c1cf0206",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeRegressor()\n",
    "model.fit(X_train,Y_train)\n",
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ed90d40c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.516416666666668\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "49c0af2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7654146086632551\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17d2f111",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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