ML Model for dyscalculia

final_model_deploy.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted Improvement Score: 78.01%\n"
+     ]
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "\n",
+    "# Load the saved model and scalers\n",
+    "rf_model = joblib.load(\"./fun1/rf_model.pkl\")\n",
+    "scaler_X = joblib.load(\"./fun1/scaler_X.pkl\")\n",
+    "scaler_y = joblib.load(\"./fun1/scaler_y.pkl\")\n",
+    "\n",
+    "def predict_improvement_score(success_count, attempt_count, game_score_xp, game_level, engagement_time_mins):\n",
+    "    # Prepare the user inputs as a numpy array\n",
+    "    user_inputs = np.array([success_count, attempt_count, game_score_xp, game_level, engagement_time_mins]).reshape(1, -1)\n",
+    "    \n",
+    "    # Disable warnings\n",
+    "    warnings.filterwarnings(\"ignore\")\n",
+    "    \n",
+    "    # Scale the user inputs using the loaded scalers\n",
+    "    user_inputs_scaled = scaler_X.transform(user_inputs)\n",
+    "    \n",
+    "    # Enable warnings again\n",
+    "    warnings.filterwarnings(\"default\")\n",
+    "    \n",
+    "    # Make the prediction using the loaded model\n",
+    "    predicted_score_scaled = rf_model.predict(user_inputs_scaled)\n",
+    "    \n",
+    "    # Inverse transform to get the predicted score back to the original scale\n",
+    "    predicted_score = scaler_y.inverse_transform(predicted_score_scaled.reshape(1, -1))\n",
+    "    \n",
+    "    return predicted_score[0][0] * 10\n",
+    "\n",
+    "# Example usage:\n",
+    "success_count = 8\n",
+    "attempt_count = 10\n",
+    "game_score_xp = 73\n",
+    "game_level = 9\n",
+    "engagement_time_mins = 5\n",
+    "\n",
+    "predicted_improvement_score = predict_improvement_score(success_count, attempt_count, game_score_xp, game_level, engagement_time_mins)\n",
+    "\n",
+    "print(f\"Predicted Improvement Score: {predicted_improvement_score:.2f}%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "learn-joy",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}