From de26e32d1c65910e4562d071be67172bf3c4dad8 Mon Sep 17 00:00:00 2001
From: Arpit <arpitmahapatraofficial@gmail.com>
Date: Thu, 22 May 2025 00:54:14 +0100
Subject: [PATCH] Adding the SVR Model for Garment dataset
---
models/SVM_Regression(small dataset).ipynb | 122 +++++++++++++++++++++
1 file changed, 122 insertions(+)
create mode 100644 models/SVM_Regression(small dataset).ipynb
diff --git a/models/SVM_Regression(small dataset).ipynb b/models/SVM_Regression(small dataset).ipynb
new file mode 100644
index 0000000..f560a3f
--- /dev/null
+++ b/models/SVM_Regression(small dataset).ipynb
@@ -0,0 +1,122 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "02242d2f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best SVR Model Parameters: SVR(C=1, epsilon=0.01)\n",
+ "Root Mean Squared Error (RMSE): 0.13203110041677338\n",
+ "Mean Absolute Error (MAE): 0.08847032288463337\n",
+ "R² Score: 0.343480521413826\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\Arpit Mahapatra\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\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",
+ "\n",
+ "# Load preprocessed dataset\n",
+ "df = pd.read_csv(\"C:/Users/Arpit Mahapatra/Desktop/MLDM Coursework 2025/mlmavericks_coursework/data/processed/tree_model_ready.csv\")\n",
+ "\n",
+ "# Define features and target\n",
+ "X = df.drop(columns=[\"actual_productivity\", \"date\"])\n",
+ "y = df[\"actual_productivity\"]\n",
+ "\n",
+ "# Encode categorical features\n",
+ "bool_cols = X.select_dtypes(include=[\"bool\"]).columns\n",
+ "X[bool_cols] = X[bool_cols].astype(int)\n",
+ "\n",
+ "if 'overtime_bin' in X.columns:\n",
+ " X = pd.get_dummies(X, columns=[\"overtime_bin\"], drop_first=True)\n",
+ "\n",
+ "# Train-test split\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+ "\n",
+ "# Feature scaling\n",
+ "scaler = StandardScaler()\n",
+ "X_train_scaled = scaler.fit_transform(X_train)\n",
+ "X_test_scaled = scaler.transform(X_test)\n",
+ "\n",
+ "# Define and tune SVR\n",
+ "param_grid = {\n",
+ " \"C\": [0.1, 1, 10],\n",
+ " \"epsilon\": [0.01, 0.1, 0.2],\n",
+ " \"kernel\": [\"rbf\"]\n",
+ "}\n",
+ "svr = SVR()\n",
+ "grid_search = GridSearchCV(svr, param_grid, cv=5, scoring=\"r2\", n_jobs=-1)\n",
+ "grid_search.fit(X_train_scaled, y_train)\n",
+ "\n",
+ "# Evaluate best model\n",
+ "best_model = grid_search.best_estimator_\n",
+ "y_pred = best_model.predict(X_test_scaled)\n",
+ "\n",
+ "# Performance metrics\n",
+ "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+ "mae = mean_absolute_error(y_test, y_pred)\n",
+ "r2 = r2_score(y_test, y_pred)\n",
+ "\n",
+ "# Print results\n",
+ "print(\"Best SVR Model Parameters:\", best_model)\n",
+ "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
+ "print(\"Mean Absolute Error (MAE):\", mae)\n",
+ "print(\"R² Score:\", r2)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4e0f7d9d-ee69-476b-93d4-d42d10110001",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2a9796b5-d6ad-49e0-9c89-2d8220b2e61f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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