Merge pull request Eva-Claire#1 from Eva-Claire/claire_branch

Add deployment section

movie_recommendor.ipynb

@@ -2692,7 +2692,8 @@
     "        best_model_params = knn_best_params\n",
     "        best_model_name = 'KNN'\n",
     "        best_score = knn_best_score\n",
-    "    \n",
+    "        \n",
+    "    # Print the best model and best RMSE score\n",
     "    print(f\"\\nBest model: {best_model_name}\")\n",
     "    print(f\"\\nBest RMSE: {best_score}\")\n",
     "    \n",
@@ -3676,6 +3677,22 @@
     "plot_rmse_vs_weight(weights, rmse_scores)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "53328243-2794-41fc-aff0-0899b13c0652",
+   "metadata": {},
+   "source": [
+    "## Deployment on Streamlit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c0548a9-2cec-43b9-9cb1-03523fa09506",
+   "metadata": {},
+   "source": [
+    "Streamlit is a Python library that simplifies the creation of web applications for data science and machine learning projects. In this deployment, Streamlit is used to create an interactive movie recommendation system. The app features the collaborative filtering model using the SVD algorithm which was trained on user-movie ratings data. The model was chosen due to its better accuracy score as continuous improvement is performed on the hybrid model. \n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "986c0d7d-209c-4860-83fa-cec0add9783d",