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diff --git a/notebooks/regression/SVM_Regression(small dataset).ipynb b/notebooks/regression/SVM_Regression(small dataset).ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4f83ca22-626c-4fe3-b623-f71a4965131e",
+ "metadata": {},
+ "source": [
+ "# Support Vector Machine Model for a Regression \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81837ffb-565c-4656-a76e-af897047fe1e",
+ "metadata": {},
+ "source": [
+ "### Problem statement -\"Develop a regression model to predict garment employee productivity. Accurate predictions will optimize workforce planning and enhance production efficiency. \""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "02242d2f",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# Importing the required libraries\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+ "from sklearn.svm import SVR\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+ "from sklearn.inspection import permutation_importance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "13be1eb2-8d75-488d-bd04-bd7fcceb9021",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter path to CSV file: C:/Users/Arpit Mahapatra/Desktop/MLDM Coursework 2025/mlmavericks_coursework/data/processed/tree_model_ready.csv\n"
+ ]
+ },
+ {
+ "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>date</th>\n",
+ " <th>team</th>\n",
+ " <th>targeted_productivity</th>\n",
+ " <th>smv</th>\n",
+ " <th>wip</th>\n",
+ " <th>over_time</th>\n",
+ " <th>incentive</th>\n",
+ " <th>no_of_style_change</th>\n",
+ " <th>no_of_workers</th>\n",
+ " <th>actual_productivity</th>\n",
+ " <th>...</th>\n",
+ " <th>idle_ratio</th>\n",
+ " <th>day_num</th>\n",
+ " <th>day_Monday</th>\n",
+ " <th>day_Saturday</th>\n",
+ " <th>day_Sunday</th>\n",
+ " <th>day_Thursday</th>\n",
+ " <th>day_Tuesday</th>\n",
+ " <th>day_Wednesday</th>\n",
+ " <th>department_label</th>\n",
+ " <th>quarter_label</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2015-01-01</td>\n",
+ " <td>8</td>\n",
+ " <td>0.80</td>\n",
+ " <td>26.16</td>\n",
+ " <td>1108.0</td>\n",
+ " <td>7080</td>\n",
+ " <td>98</td>\n",
+ " <td>0</td>\n",
+ " <td>59.0</td>\n",
+ " <td>0.940725</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2015-01-01</td>\n",
+ " <td>1</td>\n",
+ " <td>0.75</td>\n",
+ " <td>3.94</td>\n",
+ " <td>1039.0</td>\n",
+ " <td>960</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.886500</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2015-01-01</td>\n",
+ " <td>11</td>\n",
+ " <td>0.80</td>\n",
+ " <td>11.41</td>\n",
+ " <td>968.0</td>\n",
+ " <td>3660</td>\n",
+ " <td>50</td>\n",
+ " <td>0</td>\n",
+ " <td>30.5</td>\n",
+ " <td>0.800570</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2015-01-01</td>\n",
+ " <td>12</td>\n",
+ " <td>0.80</td>\n",
+ " <td>11.41</td>\n",
+ " <td>968.0</td>\n",
+ " <td>3660</td>\n",
+ " <td>50</td>\n",
+ " <td>0</td>\n",
+ " <td>30.5</td>\n",
+ " <td>0.800570</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2015-01-01</td>\n",
+ " <td>6</td>\n",
+ " <td>0.80</td>\n",
+ " <td>25.90</td>\n",
+ " <td>1170.0</td>\n",
+ " <td>1920</td>\n",
+ " <td>50</td>\n",
+ " <td>0</td>\n",
+ " <td>56.0</td>\n",
+ " <td>0.800382</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 23 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " date team targeted_productivity smv wip over_time \\\n",
+ "0 2015-01-01 8 0.80 26.16 1108.0 7080 \n",
+ "1 2015-01-01 1 0.75 3.94 1039.0 960 \n",
+ "2 2015-01-01 11 0.80 11.41 968.0 3660 \n",
+ "3 2015-01-01 12 0.80 11.41 968.0 3660 \n",
+ "4 2015-01-01 6 0.80 25.90 1170.0 1920 \n",
+ "\n",
+ " incentive no_of_style_change no_of_workers actual_productivity ... \\\n",
+ "0 98 0 59.0 0.940725 ... \n",
+ "1 0 0 8.0 0.886500 ... \n",
+ "2 50 0 30.5 0.800570 ... \n",
+ "3 50 0 30.5 0.800570 ... \n",
+ "4 50 0 56.0 0.800382 ... \n",
+ "\n",
+ " idle_ratio day_num day_Monday day_Saturday day_Sunday day_Thursday \\\n",
+ "0 0.0 3 False False False True \n",
+ "1 0.0 3 False False False True \n",
+ "2 0.0 3 False False False True \n",
+ "3 0.0 3 False False False True \n",
+ "4 0.0 3 False False False True \n",
+ "\n",
+ " day_Tuesday day_Wednesday department_label quarter_label \n",
+ "0 False False 1 0 \n",
+ "1 False False 0 0 \n",
+ "2 False False 1 0 \n",
+ "3 False False 1 0 \n",
+ "4 False False 1 0 \n",
+ "\n",
+ "[5 rows x 23 columns]"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ " # Defining a flexible file path for the ease of new user\n",
+ "file_path = input(\"Enter path to CSV file: \")\n",
+ "df = pd.read_csv(file_path)\n",
+ "\n",
+ "df.head() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "73c1f4c7-6c12-43f2-90a5-8497aa2cc5bc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.int64(0)"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Checking for missing values in target variable \n",
+ "df[\"actual_productivity\"].isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0503b4a0-be36-4968-9ae0-7e2996c4a14a",
+ "metadata": {},
+ "source": [
+ "### Preprocessing for better results of SVR\n",
+ "\n",
+ "* As we can clearly see that the date column is a time stamp and doesnt carry any importance for our problem statement as we arent doing time-series analysis\n",
+ "\n",
+ "* Also as per the dataset we can see alot of boolean values which the SVR cannot understand hence we need to convert the boolenan into numeric"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "a58df25e-73db-400a-b449-f454dab77c0b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " # Droping the date column \n",
+ "df.drop(columns=['date'], inplace=True)\n",
+ "\n",
+ "# Encoding categorical features\n",
+ "bool_cols = df.select_dtypes(include=[\"bool\"]).columns\n",
+ "df[bool_cols] = df[bool_cols].astype(int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "876bb8f3-5ac0-4907-8a3b-5de4c34cc587",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "team int64\n",
+ "targeted_productivity float64\n",
+ "smv float64\n",
+ "wip float64\n",
+ "over_time int64\n",
+ "incentive int64\n",
+ "no_of_style_change int64\n",
+ "no_of_workers float64\n",
+ "actual_productivity float64\n",
+ "overtime_bin object\n",
+ "wip_log float64\n",
+ "idle_men_ratio float64\n",
+ "idle_ratio float64\n",
+ "day_num int64\n",
+ "day_Monday int64\n",
+ "day_Saturday int64\n",
+ "day_Sunday int64\n",
+ "day_Thursday int64\n",
+ "day_Tuesday int64\n",
+ "day_Wednesday int64\n",
+ "department_label int64\n",
+ "quarter_label int64\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Checking that all the data types to spot out whether there are any non numeric features\n",
+ "print(df.dtypes)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c354cc2-edb3-413f-9eae-b6baeeb373b6",
+ "metadata": {},
+ "source": [
+ "**As we can see that there is a object datatype of overtime bin column lets go and see how does the column look like**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "a52d6c38-3dfd-4a0f-ae9a-4378feeccdef",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "overtime_bin\n",
+ "5001-10000 386\n",
+ "1001-2000 245\n",
+ "2001-5000 238\n",
+ "10001-20000 145\n",
+ "501-1000 141\n",
+ "0-500 41\n",
+ "20001+ 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ " # Checking for different types of values in overtime bin\n",
+ "print(df['overtime_bin'].value_counts())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ed276bd5-80e2-4fdf-a61f-90a0cff5bb02",
+ "metadata": {},
+ "source": [
+ "These ranges are taken as objects by the python kernel so we need to one hot encode all these objects so that we can get best results with SVR which only deals with numeric columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "e033117c-6911-4f65-a646-f8dc7d9df3e8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " # One-hot encoding 'overtime_bin' \n",
+ "if \"overtime_bin\" in df.columns:\n",
+ " df = pd.get_dummies(df, columns=[\"overtime_bin\"], drop_first=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "eb688363-11e2-4012-bfe3-aafacd08a16d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Summary statistics:\n",
+ "count 1197.000000\n",
+ "mean 0.735091\n",
+ "std 0.174488\n",
+ "min 0.233705\n",
+ "25% 0.650307\n",
+ "50% 0.773333\n",
+ "75% 0.850253\n",
+ "max 1.120437\n",
+ "Name: actual_productivity, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ " # Displaying the summery of the target variable \n",
+ "print(\"Summary statistics:\")\n",
+ "print(df['actual_productivity'].describe())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "4c0a6b92-4ed5-40dd-a986-917f3dec4435",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Checking the distribution of target variable \n",
+ "plt.figure(figsize=(8, 5))\n",
+ "sns.histplot(df['actual_productivity'], bins=50, kde=True, color='lightblue')\n",
+ "plt.title(\"Distribution of actual_productivity\")\n",
+ "plt.xlabel(\"Actual_productivity\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ac473929-f671-47d6-a66f-2d452033c5ae",
+ "metadata": {},
+ "source": [
+ "### Target Variable Exploration: actual_productivity\n",
+ "\n",
+ "* The distribution is centered around 0.8, with a clear dominant peak, suggesting many tickets achieved similar productivity levels.\n",
+ "\n",
+ "* The distribution exhibits a slight right skew, but remains overall reasonably symmetric, which is favorable for regression models.\n",
+ "\n",
+ "* A distinct spike suggests a standard productivity threshold or automation benchmark being met across numerous incidents.\n",
+ "\n",
+ "* The values range between approximately 0.2 and 1.1, indicating a narrow, controlled spread with minimal outlier influence.\n",
+ "\n",
+ "* The smooth KDE curve over the histogram suggests a continuous and predictable variable, making it suitable as a model feature.\n",
+ "\n",
+ "* No transformation needed — the scale and shape of actual_productivity are already model-friendly."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3fdc34d1-2bfe-4c55-a4e2-173b3ac304ce",
+ "metadata": {},
+ "source": [
+ " **Lets check the corelation of the variables to that of the target variable**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "e4d838ad-beda-4fb8-9e2d-89b82344d415",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1000x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ " # Creating a correlation matrix for this data set \n",
+ "correlation_matrix = df.corr(numeric_only=True)\n",
+ "target_corr = correlation_matrix['actual_productivity'].drop('actual_productivity')\n",
+ "target_corr_sorted = target_corr.sort_values(ascending=False)\n",
+ "\n",
+ "# Plotting the figure for correlation of features\n",
+ "plt.figure(figsize=(10, 8))\n",
+ "target_corr_sorted.plot(kind='barh', color='skyblue')\n",
+ "plt.title(\"Correlation of Features with Target: actual_productivity\")\n",
+ "plt.xlabel(\"Correlation Coefficient\")\n",
+ "plt.grid(True)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "73b975f5-450c-4bc0-a33b-083de7752efa",
+ "metadata": {},
+ "source": [
+ "### Correlation Analysis with Target Variable (time_to_resolution_log)\n",
+ "\n",
+ "* targeted_productivity shows the strongest positive correlation (~0.41) with actual_productivity, suggesting it is a key predictive feature and aligns well with operational expectations.\n",
+ "\n",
+ "Other features with mild positive correlation include:\n",
+ "\n",
+ "* wip_log (~0.18)\n",
+ "\n",
+ "* overtime_bin_1001-2000 and wip (~0.15–0.17)\n",
+ "\n",
+ "* incentive, day_Saturday, and overtime_bin_10001-20000 show weak but positive associations, suggesting potential minor contributions to productivity.\n",
+ "\n",
+ "* Features like day_Tuesday, day_Monday, day_num, and other weekday indicators appear negligibly correlated, indicating low linear impact from daily cycles.\n",
+ "\n",
+ "* Features such as over_time, no_of_workers, and department_label are very weakly or negatively correlated, implying limited standalone predictive power in a linear model.\n",
+ "\n",
+ "* Notably, variables like:\n",
+ "\n",
+ " * team, idle_men_ratio, and no_of_style_change show moderate negative correlations (up to -0.21), possibly suggesting efficiency loss due to idle time, task switching, or organizational factors."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "338a4460-fa1c-43b8-9334-f16e1831d1de",
+ "metadata": {},
+ "source": [
+ "#### Now our target variable is ready for the SVR modeling "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "id": "5f75bc4a-2060-4fd7-85f9-3534d5b6aa57",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Defining features and target\n",
+ "X = df.drop(columns=[\"actual_productivity\"])\n",
+ "y = df[\"actual_productivity\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "17aa6f76-1172-4995-8133-e7f9addd125a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train-test spliting\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "53988cbf-18fe-40dd-9dd4-808d982fe273",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-4 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: #000;\n",
+ " --sklearn-color-text-muted: #666;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-4 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: flex;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ " align-items: start;\n",
+ " justify-content: space-between;\n",
+ " gap: 0.5em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+ " font-size: 0.6rem;\n",
+ " font-weight: lighter;\n",
+ " color: var(--sklearn-color-text-muted);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-4 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-4 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-4 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 0.5em;\n",
+ " text-align: center;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVR(), n_jobs=-1,\n",
+ " param_grid={'C': [0.1, 1, 10, 50],\n",
+ " 'epsilon': [0.01, 0.05, 0.1, 0.2], 'kernel': ['rbf']},\n",
+ " scoring='neg_root_mean_squared_error')</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=SVR(), n_jobs=-1,\n",
+ " param_grid={'C': [0.1, 1, 10, 50],\n",
+ " 'epsilon': [0.01, 0.05, 0.1, 0.2], 'kernel': ['rbf']},\n",
+ " scoring='neg_root_mean_squared_error')</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: SVR</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=50)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=50)</pre></div> </div></div></div></div></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "GridSearchCV(cv=5, estimator=SVR(), n_jobs=-1,\n",
+ " param_grid={'C': [0.1, 1, 10, 50],\n",
+ " 'epsilon': [0.01, 0.05, 0.1, 0.2], 'kernel': ['rbf']},\n",
+ " scoring='neg_root_mean_squared_error')"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Defining and tuning the SVR\n",
+ "param_grid = {\n",
+ " \"C\": [0.1, 1, 10, 50],\n",
+ " \"epsilon\": [0.01, 0.05, 0.1, 0.2],\n",
+ " \"kernel\": [\"rbf\"]\n",
+ "}\n",
+ "\n",
+ "grid_search = GridSearchCV(\n",
+ " estimator=SVR(),\n",
+ " param_grid=param_grid,\n",
+ " scoring=\"neg_root_mean_squared_error\",\n",
+ " cv=5,\n",
+ " n_jobs=-1\n",
+ ")\n",
+ "grid_search.fit(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "4bb1233c-c914-49e0-8832-4440a0c5f788",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best Parameters Found:\n",
+ "{'C': 50, 'epsilon': 0.1, 'kernel': 'rbf'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Showing best parameters\n",
+ "print(\"Best Parameters Found:\")\n",
+ "print(grid_search.best_params_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "48311589-1ca1-457f-ace9-9f91bc43e2bc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-5 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: #000;\n",
+ " --sklearn-color-text-muted: #666;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-5 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-5 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-5 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-5 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: flex;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ " align-items: start;\n",
+ " justify-content: space-between;\n",
+ " gap: 0.5em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+ " font-size: 0.6rem;\n",
+ " font-weight: lighter;\n",
+ " color: var(--sklearn-color-text-muted);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-5 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-5 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-5 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-5 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-5 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 0.5em;\n",
+ " text-align: center;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-5 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=50)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=50)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVR(C=50)"
+ ]
+ },
+ "execution_count": 79,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Defining and training final SVR model with best parameters\n",
+ "final_svr = SVR(\n",
+ " C=50,\n",
+ " epsilon=0.1,\n",
+ " kernel='rbf'\n",
+ ")\n",
+ "\n",
+ "final_svr.fit(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "f70c7319-6fa4-4984-981c-f6a8ce8019fb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Model Evaluation Metrics (SVR):\n",
+ "MAE : 0.1113\n",
+ "RMSE : 0.1463\n",
+ "R² : 0.1942\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Making predictions on test set\n",
+ "y_pred = final_svr.predict(X_test)\n",
+ "\n",
+ "# Evaluating model performance\n",
+ "mae = mean_absolute_error(y_test, y_pred)\n",
+ "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+ "r2 = r2_score(y_test, y_pred)\n",
+ "\n",
+ "# 4. Print evaluation metrics\n",
+ "print(\"\\nModel Evaluation Metrics (SVR):\")\n",
+ "print(f\"MAE : {mae:.4f}\")\n",
+ "print(f\"RMSE : {rmse:.4f}\")\n",
+ "print(f\"R² : {r2:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "59cfb59c-4363-4d2d-bcc0-593a4eeedb98",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 600x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Defining metrics\n",
+ "metrics = ['MAE', 'RMSE', 'R²']\n",
+ "values = [0.1113, 0.1463, 0.1942]\n",
+ "\n",
+ "# Creating bar plot\n",
+ "plt.figure(figsize=(6, 4))\n",
+ "bars = plt.bar(metrics, values, color=['steelblue', 'slateblue', 'seagreen'])\n",
+ "\n",
+ "# Annotation of values on bars\n",
+ "for bar in bars:\n",
+ " height = bar.get_height()\n",
+ " plt.text(bar.get_x() + bar.get_width()/2, height + 0.03, f'{height:.4f}', ha='center', fontsize=10)\n",
+ "\n",
+ "plt.title(\"Model Evaluation Metrics (SVR)\")\n",
+ "plt.ylabel(\"Score\")\n",
+ "plt.ylim(0, max(values) + 0.5)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "54bf09bc-2964-40ca-a407-ff6530fbf7d8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ " # Calculating residuals\n",
+ "residuals = y_test - y_pred\n",
+ "\n",
+ "# Ploting histogram of residuals\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "sns.histplot(residuals, bins=40, kde=True, color='darkblue')\n",
+ "plt.axvline(0, color='red', linestyle='--', label='Zero Error')\n",
+ "plt.title(\"Residuals Distribution (Actual - Predicted)\")\n",
+ "plt.xlabel(\"Residuals\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.legend()\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "b08719a1-a4d4-4185-8de3-5b9085e18d07",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Creating the importance DataFrame\n",
+ "perm_result = permutation_importance(final_svr, X_test, y_test, n_repeats=10, random_state=42, n_jobs=-1)\n",
+ "perm_importances = pd.Series(perm_result.importances_mean, index=X_test.columns).sort_values(ascending=False)\n",
+ "\n",
+ "# Ploting top 20 important features \n",
+ "plt.figure(figsize=(10, 6))\n",
+ "perm_importances[:20].plot(kind='barh', color='teal')\n",
+ "plt.title(\"Top 20 Permutation Importances (SVR)\")\n",
+ "plt.xlabel(\"Importance Score\")\n",
+ "plt.gca().invert_yaxis()\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
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