Adding Experiment 1

notebooks/Experiment-Naive_Bayes.ipynb

@@ -0,0 +1,142 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7c7072e-41a8-4a68-816a-f138dfe2f713",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.metrics import f1_score\n",
+    "from sklearn.naive_bayes import BernoulliNB\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2007cdd-38d5-40a5-9ed7-4f593d081a2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = pd.read_csv(\"train.csv\")\n",
+    "val_df = pd.read_csv(\"validation.csv\")\n",
+    "test_df = pd.read_csv(\"test.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa429827-8d41-44eb-a206-a5e5531ef105",
+   "metadata": {},
+   "source": [
+    "### Evaluating Naive Bayes on Validation Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2c29088-90a7-4084-8b2c-be0651c36c20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BNB = make_pipeline(CountVectorizer(), BernoulliNB())\n",
+    "BNB.fit(train_df[\"Stemmed\"], train_df[\"Result_Bin\"])\n",
+    "\n",
+    "# Predict on the test set\n",
+    "y_pred = BNB.predict(val_df[\"Stemmed\"])\n",
+    "y_val = val_df[\"Result_Bin\"]\n",
+    "# Calculate F1\n",
+    "f1 = f1_score(y_val, y_pred)\n",
+    "print(\"F1 Score:\", f1)\n",
+    "# Calculate Precision\n",
+    "precision = precision_score(y_val, y_pred)\n",
+    "print(\"Precision:\", round(precision, 3))\n",
+    "# Calculate recall\n",
+    "recall = recall_score(y_val, y_pred)\n",
+    "print(\"Recall:\", round(recall, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48c88dff-14a5-4975-9670-1cc33cc611df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conf_matrix = confusion_matrix(y_val, y_pred)\n",
+    "\n",
+    "cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels = [\"Loss\",\"Win\"])\n",
+    "cm_display.plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae96b678-a0ba-46be-9759-b3ecb78c37fc",
+   "metadata": {},
+   "source": [
+    "### Evaluating Naive Bayes on Testing Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "649a1ffa-5717-4efa-ba97-6d6109e7cac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predict on the test set\n",
+    "y_pred = BNB.predict(test_df[\"Stemmed\"])\n",
+    "y_test = test_df[\"Result_Bin\"]\n",
+    "# Calculate F1\n",
+    "f1 = f1_score(y_test, y_pred)\n",
+    "print(\"F1 Score:\", f1)\n",
+    "# Calculate Precision\n",
+    "precision = precision_score(y_test, y_pred)\n",
+    "print(\"Precision:\", round(precision, 3))\n",
+    "# Calculate Recall\n",
+    "recall = recall_score(y_test, y_pred)\n",
+    "print(\"Recall:\", round(recall, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcf8fd82-2256-465c-bf87-fffefdf87e6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels = [0,1])\n",
+    "cm_display.plot()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}