analyzing-diamond-prices.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
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   "source": [
    "data = pd.read_csv('../input/diamonds/diamonds.csv')"
   ]
  },
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       "       Unnamed: 0  carat        cut color clarity  depth  table  price     x  \\\n",
       "0               1   0.23      Ideal     E     SI2   61.5   55.0    326  3.95   \n",
       "1               2   0.21    Premium     E     SI1   59.8   61.0    326  3.89   \n",
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       "4               5   0.31       Good     J     SI2   63.3   58.0    335  4.34   \n",
       "...           ...    ...        ...   ...     ...    ...    ...    ...   ...   \n",
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       "53937       53938   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66   \n",
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       "53935  5.76  3.50  \n",
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       "53937  5.68  3.56  \n",
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       "\n",
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   "source": [
    "data.drop(data.columns[0], axis=1, inplace=True)"
   ]
  },
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   "source": [
    "y = data['price']\n",
    "X = data.drop('price', axis=1)"
   ]
  },
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "Cuts: 5\n",
      "Colors: 7\n",
      "Clarities: 8\n"
     ]
    }
   ],
   "source": [
    "print(f\"Cuts: {len(X['cut'].unique())}\")\n",
    "print(f\"Colors: {len(X['color'].unique())}\")\n",
    "print(f\"Clarities: {len(X['clarity'].unique())}\")"
   ]
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   "source": [
    "encoder = LabelEncoder()\n",
    "\n",
    "X['cut'] = encoder.fit_transform(X['cut'])\n",
    "cut_mappings = {index: label for index, label in enumerate(encoder.classes_)}\n",
    "\n",
    "X['color'] = encoder.fit_transform(X['color'])\n",
    "color_mappings = {index: label for index, label in enumerate(encoder.classes_)}\n",
    "\n",
    "X['clarity'] = encoder.fit_transform(X['clarity'])\n",
    "clarity_mappings = {index: label for index, label in enumerate(encoder.classes_)}"
   ]
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   "outputs": [
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     "text": [
      "{0: 'Fair', 1: 'Good', 2: 'Ideal', 3: 'Premium', 4: 'Very Good'}\n",
      "{0: 'D', 1: 'E', 2: 'F', 3: 'G', 4: 'H', 5: 'I', 6: 'J'}\n",
      "{0: 'I1', 1: 'IF', 2: 'SI1', 3: 'SI2', 4: 'VS1', 5: 'VS2', 6: 'VVS1', 7: 'VVS2'}\n"
     ]
    }
   ],
   "source": [
    "print(cut_mappings)\n",
    "print(color_mappings)\n",
    "print(clarity_mappings)"
   ]
  },
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat  cut  color  clarity  depth  table     x     y     z\n",
       "0       0.23    2      1        3   61.5   55.0  3.95  3.98  2.43\n",
       "1       0.21    3      1        2   59.8   61.0  3.89  3.84  2.31\n",
       "2       0.23    1      1        4   56.9   65.0  4.05  4.07  2.31\n",
       "3       0.29    3      5        5   62.4   58.0  4.20  4.23  2.63\n",
       "4       0.31    1      6        3   63.3   58.0  4.34  4.35  2.75\n",
       "...      ...  ...    ...      ...    ...    ...   ...   ...   ...\n",
       "53935   0.72    2      0        2   60.8   57.0  5.75  5.76  3.50\n",
       "53936   0.72    1      0        2   63.1   55.0  5.69  5.75  3.61\n",
       "53937   0.70    4      0        2   62.8   60.0  5.66  5.68  3.56\n",
       "53938   0.86    3      4        3   61.0   58.0  6.15  6.12  3.74\n",
       "53939   0.75    2      0        3   62.2   55.0  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-16T00:35:12.740422Z",
     "iopub.status.busy": "2020-08-16T00:35:12.739096Z",
     "iopub.status.idle": "2020-08-16T00:35:12.771309Z",
     "shell.execute_reply": "2020-08-16T00:35:12.770483Z"
    },
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     "end_time": "2020-08-16T00:35:12.771457",
     "exception": false,
     "start_time": "2020-08-16T00:35:12.716019",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-16T00:35:12.809971Z",
     "iopub.status.busy": "2020-08-16T00:35:12.807988Z",
     "iopub.status.idle": "2020-08-16T00:35:12.820349Z",
     "shell.execute_reply": "2020-08-16T00:35:12.819495Z"
    },
    "papermill": {
     "duration": 0.033568,
     "end_time": "2020-08-16T00:35:12.820492",
     "exception": false,
     "start_time": "2020-08-16T00:35:12.786924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-16T00:35:12.860661Z",
     "iopub.status.busy": "2020-08-16T00:35:12.859408Z",
     "iopub.status.idle": "2020-08-16T00:35:13.047397Z",
     "shell.execute_reply": "2020-08-16T00:35:13.046527Z"
    },
    "papermill": {
     "duration": 0.211269,
     "end_time": "2020-08-16T00:35:13.047545",
     "exception": false,
     "start_time": "2020-08-16T00:35:12.836276",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_model = LinearRegression()\n",
    "l1_model = Lasso(alpha=1)\n",
    "l2_model = Ridge(alpha=1)\n",
    "\n",
    "std_model.fit(X_train, y_train)\n",
    "l1_model.fit(X_train, y_train)\n",
    "l2_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-16T00:35:13.089936Z",
     "iopub.status.busy": "2020-08-16T00:35:13.088601Z",
     "iopub.status.idle": "2020-08-16T00:35:13.100134Z",
     "shell.execute_reply": "2020-08-16T00:35:13.099271Z"
    },
    "papermill": {
     "duration": 0.034494,
     "end_time": "2020-08-16T00:35:13.100278",
     "exception": false,
     "start_time": "2020-08-16T00:35:13.065784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Without regularization: 0.8842138957246679\n",
      "Lasso (L1) regularization: 0.8845004379822026\n",
      "Ridge (L2) regularization: 0.8849502891496184\n"
     ]
    }
   ],
   "source": [
    "print(f\"---Without regularization: {std_model.score(X_test, y_test)}\")\n",
    "print(f\"Lasso (L1) regularization: {l1_model.score(X_test, y_test)}\")\n",
    "print(f\"Ridge (L2) regularization: {l2_model.score(X_test, y_test)}\")"
   ]
  }
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