Dummy Variables & One Hot Encoding(Exercise).ipynb

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       "      <th>Car Model</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Sell Price($)</th>\n",
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       "      <td>12000</td>\n",
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      "text/plain": [
       "                Car Model  Mileage  Sell Price($)  Age(yrs)\n",
       "0                  BMW X5    69000          18000         6\n",
       "1                  BMW X5    35000          34000         3\n",
       "2                  BMW X5    57000          26100         5\n",
       "3                  BMW X5    22500          40000         2\n",
       "4                  BMW X5    46000          31500         4\n",
       "5                 Audi A5    59000          29400         5\n",
       "6                 Audi A5    52000          32000         5\n",
       "7                 Audi A5    72000          19300         6\n",
       "8                 Audi A5    91000          12000         8\n",
       "9   Mercedez Benz C class    67000          22000         6\n",
       "10  Mercedez Benz C class    83000          20000         7\n",
       "11  Mercedez Benz C class    79000          21000         7\n",
       "12  Mercedez Benz C class    59000          33000         5"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"/Users/souravghoshhansda/Library/CloudStorage/OneDrive-UniversityofEssex/MA336/Practice/linear_regression/Dummy Variables & One Hot Encoding/carprices.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5419f9e",
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   "outputs": [
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      "text/plain": [
       "    Audi A5  BMW X5  Mercedez Benz C class\n",
       "0         0       1                      0\n",
       "1         0       1                      0\n",
       "2         0       1                      0\n",
       "3         0       1                      0\n",
       "4         0       1                      0\n",
       "5         1       0                      0\n",
       "6         1       0                      0\n",
       "7         1       0                      0\n",
       "8         1       0                      0\n",
       "9         0       0                      1\n",
       "10        0       0                      1\n",
       "11        0       0                      1\n",
       "12        0       0                      1"
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     "execution_count": 2,
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   ],
   "source": [
    "dummies=pd.get_dummies(df['Car Model'])\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a0d8221",
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      "text/plain": [
       "                Car Model  Mileage  Sell Price($)  Age(yrs)  Audi A5  BMW X5  \\\n",
       "0                  BMW X5    69000          18000         6        0       1   \n",
       "1                  BMW X5    35000          34000         3        0       1   \n",
       "2                  BMW X5    57000          26100         5        0       1   \n",
       "3                  BMW X5    22500          40000         2        0       1   \n",
       "4                  BMW X5    46000          31500         4        0       1   \n",
       "5                 Audi A5    59000          29400         5        1       0   \n",
       "6                 Audi A5    52000          32000         5        1       0   \n",
       "7                 Audi A5    72000          19300         6        1       0   \n",
       "8                 Audi A5    91000          12000         8        1       0   \n",
       "9   Mercedez Benz C class    67000          22000         6        0       0   \n",
       "10  Mercedez Benz C class    83000          20000         7        0       0   \n",
       "11  Mercedez Benz C class    79000          21000         7        0       0   \n",
       "12  Mercedez Benz C class    59000          33000         5        0       0   \n",
       "\n",
       "    Mercedez Benz C class  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  \n",
       "6                       0  \n",
       "7                       0  \n",
       "8                       0  \n",
       "9                       1  \n",
       "10                      1  \n",
       "11                      1  \n",
       "12                      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged=pd.concat([df,dummies],axis='columns')\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
       "      <td>20000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79000</td>\n",
       "      <td>21000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59000</td>\n",
       "      <td>33000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Sell Price($)  Age(yrs)  Audi A5  BMW X5\n",
       "0     69000          18000         6        0       1\n",
       "1     35000          34000         3        0       1\n",
       "2     57000          26100         5        0       1\n",
       "3     22500          40000         2        0       1\n",
       "4     46000          31500         4        0       1\n",
       "5     59000          29400         5        1       0\n",
       "6     52000          32000         5        1       0\n",
       "7     72000          19300         6        1       0\n",
       "8     91000          12000         8        1       0\n",
       "9     67000          22000         6        0       0\n",
       "10    83000          20000         7        0       0\n",
       "11    79000          21000         7        0       0\n",
       "12    59000          33000         5        0       0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final=merged.drop(['Car Model','Mercedez Benz C class'],axis='columns')\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b268588",
   "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>Mileage</th>\n",
       "      <th>Age(yrs)</th>\n",
       "      <th>Audi A5</th>\n",
       "      <th>BMW X5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22500</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>91000</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Age(yrs)  Audi A5  BMW X5\n",
       "0     69000         6        0       1\n",
       "1     35000         3        0       1\n",
       "2     57000         5        0       1\n",
       "3     22500         2        0       1\n",
       "4     46000         4        0       1\n",
       "5     59000         5        1       0\n",
       "6     52000         5        1       0\n",
       "7     72000         6        1       0\n",
       "8     91000         8        1       0\n",
       "9     67000         6        0       0\n",
       "10    83000         7        0       0\n",
       "11    79000         7        0       0\n",
       "12    59000         5        0       0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = final.drop('Sell Price($)',axis='columns')\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f60b75f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     18000\n",
       "1     34000\n",
       "2     26100\n",
       "3     40000\n",
       "4     31500\n",
       "5     29400\n",
       "6     32000\n",
       "7     19300\n",
       "8     12000\n",
       "9     22000\n",
       "10    20000\n",
       "11    21000\n",
       "12    33000\n",
       "Name: Sell Price($), dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = final['Sell Price($)']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87c43429",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f50b6da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6acac748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9417050937281082"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7a72489",
   "metadata": {},
   "source": [
    "Price of mercedez benz that is 4 yr old with mileage 45000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "39b2ca74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([36991.31721061])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[45000,4,0,0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de818645",
   "metadata": {},
   "source": [
    "Price of BMW X5 that is 7 yr old with mileage 86000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0288b076",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([11080.74313219])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[86000,7,0,1]])"
   ]
  }
 ],
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