diff --git a/movie_recommendor.ipynb b/movie_recommendor.ipynb
index eede6fb..d36f0af 100644
--- a/movie_recommendor.ipynb
+++ b/movie_recommendor.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9538a32c-c02a-4a83-8819-249bed447e92",
+   "id": "866a7435-710a-41fb-a929-5aeb9d0081fb",
    "metadata": {},
    "source": [
     "![attachment:logo.png](logo.png)"
@@ -2106,7 +2106,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -2346,8 +2346,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 1.4145\n",
-      "Baseline Model RMSE: 1.4145059805741038\n"
+      "RMSE: 1.4304\n",
+      "Baseline Model RMSE: 1.4304275189371316\n"
      ]
     }
    ],
@@ -2379,7 +2379,7 @@
    "id": "cea8df25-678d-4664-84bb-38d915eeffcf",
    "metadata": {},
    "source": [
-    "We find that the RMSE of the baseline model is aproximately 1.41. The next step is to perform grid search cross validation to find the best parameters for  the Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) models. The `Surprise` library hosts a `GridSearchCV` feature that performs this task.\n",
+    "We find that the RMSE of the baseline model is aproximately 1.43. The next step is to perform grid search cross validation to find the best parameters for  the Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) models. The `Surprise` library hosts a `GridSearchCV` feature that performs this task.\n",
     "Grid searching the SVD model focuses on tuning the following hyperparameters:\n",
     "\n",
     "**`n_factors`**: Number of latent factors\n",
@@ -2391,7 +2391,6 @@
     "**`k`**: The number of neighbors to consider when making predictions.\n",
     "**`min_k`**: The minimum number of neighbors required to make a prediction. If fewer neighbors are found, predictions are made based on default values.\n",
     "**`sim_options`**: A dictionary specifying the similarity options. This includes:\n",
-    "`msd` (Mean Squared Difference)\n",
     "`cosine` (Cosine Similarity)\n",
     "`pearson` (Pearson Correlation)\n",
     "**`user_based`**: Whether to use user-based or item-based filtering.\n",
@@ -2413,7 +2412,7 @@
      "text": [
       "Tuning SVD...\n",
       "Best SVD parameters: {'n_factors': 100, 'n_epochs': 30, 'lr_all': 0.01, 'reg_all': 0.1}\n",
-      "Best SVD RMSE: 0.8640925302163475\n",
+      "Best SVD RMSE: 0.8621525273812124\n",
       "Tuning KNN...\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
@@ -2439,18 +2438,6 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
@@ -2475,18 +2462,6 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
@@ -2511,18 +2486,6 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
@@ -2547,18 +2510,6 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
@@ -2583,18 +2534,6 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
@@ -2619,18 +2558,6 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
@@ -2655,18 +2582,6 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
@@ -2691,18 +2606,6 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
       "Done computing similarity matrix.\n",
       "Computing the pearson similarity matrix...\n",
@@ -2727,24 +2630,12 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "Best KNN parameters: {'k': 30, 'min_k': 10, 'sim_options': {'name': 'msd', 'user_based': False}}\n",
-      "Best KNN RMSE: 0.9169397503126548\n",
+      "Best KNN parameters: {'k': 30, 'min_k': 5, 'sim_options': {'name': 'cosine', 'user_based': True}}\n",
+      "Best KNN RMSE: 0.974567962502892\n",
       "\n",
       "Best model: SVD\n",
       "\n",
-      "Best RMSE: 0.8640925302163475\n"
+      "Best RMSE: 0.8621525273812124\n"
      ]
     }
    ],
@@ -2770,7 +2661,7 @@
     "    knn_param_grid = {\n",
     "        'k': [10, 20, 30],\n",
     "        'min_k': [1, 5, 10],\n",
-    "        'sim_options': {'name': ['pearson', 'cosine', 'msd'], 'user_based': [True, False]}\n",
+    "        'sim_options': {'name': ['pearson', 'cosine'], 'user_based': [True, False]}\n",
     "    }\n",
     "    \n",
     "    # Perform grid search for SVD\n",
@@ -2829,7 +2720,7 @@
    "id": "0c954d06-e38c-447b-901f-d4bdd36aad59",
    "metadata": {},
    "source": [
-    "The output reveals the results of tuning and evaluating recommendation models using grid search. For the SVD model, the optimal parameters were identified as having 100 factors, 30 epochs, a learning rate of 0.01 and regularization of 0.1 achieving the best RMSE of approximately 0.864. In contrast, the KNN model required extensive computation of similarity matrices for various configurations including Pearson, cosine and MSD (Mean Squared Difference) similarities. The best parameters for the KNN model were found to be 30 neighbors, a minimum of 10 neighbors and using the MSD similarity metric with a non-user-based approach resulting in a higher RMSE of about 0.917. Consequently, the SVD model emerged as the superior choice with the lowest RMSE and we therefore selected it as the best model overall."
+    "The output reveals the results of tuning and evaluating recommendation models using grid search. For the SVD model, the optimal parameters were identified as having 100 factors, 30 epochs, a learning rate of 0.01 and regularization of 0.1 achieving the best RMSE of approximately 0.862. In contrast, the KNN model required extensive computation of similarity matrices for various configurations including Pearson, cosine and MSD (Mean Squared Difference) similarities. The best parameters for the KNN model were found to be 30 neighbors, a minimum of 10 neighbors and using the MSD similarity metric with a non-user-based approach resulting in a higher RMSE of about 0.917. Consequently, the SVD model emerged as the superior choice with the lowest RMSE and we therefore selected it as the best model overall."
    ]
   },
   {
@@ -2861,11 +2752,11 @@
       "Evaluating RMSE of algorithm SVD on 5 split(s).\n",
       "\n",
       "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
-      "RMSE (testset)    0.8573  0.8559  0.8537  0.8531  0.8529  0.8546  0.0017  \n",
-      "Fit time          8.97    10.98   10.51   10.45   9.24    10.03   0.78    \n",
-      "Test time         0.14    0.26    0.15    0.19    0.14    0.17    0.05    \n",
-      "SVD Model Mean RMSE: 0.8545790913260245\n",
-      "SVD Model Standard Deviation RMSE: 0.0017211166150516342\n"
+      "RMSE (testset)    0.8654  0.8591  0.8506  0.8529  0.8548  0.8566  0.0052  \n",
+      "Fit time          8.94    8.49    8.08    8.03    8.22    8.35    0.33    \n",
+      "Test time         0.11    0.12    0.11    0.12    0.12    0.11    0.00    \n",
+      "SVD Model Mean RMSE: 0.8565943146879842\n",
+      "SVD Model Standard Deviation RMSE: 0.0052232662741193365\n"
      ]
     }
    ],
@@ -2882,12 +2773,12 @@
    "id": "cccdc46d-7e10-4265-9deb-7b8a532dd9b5",
    "metadata": {},
    "source": [
-    "The cross-validation results for the SVD model indicate strong and consistent performance. The model achieved an average RMSE of approximately 0.855 across five folds with a very low standard deviation of 0.0031 demonstrating stable performance across different data splits. Overall these metrics suggest that the SVD model not only provides reliable predictions with low error but also maintains efficient and consistent training and prediction times. The next step is to build a class that will provide the top 5 recommendations."
+    "The cross-validation results for the SVD model indicate strong and consistent performance. The model achieved an average RMSE of approximately 0.857 across five folds with a very low standard deviation of 0.005 demonstrating stable performance across different data splits. Overall these metrics suggest that the SVD model not only provides reliable predictions with low error but also maintains efficient and consistent training and prediction times. The next step is to build a class that will provide the top 5 recommendations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 22,
    "id": "133e166f-e332-4457-8d10-6c5003684986",
    "metadata": {},
    "outputs": [
@@ -2896,15 +2787,15 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Movie: Lord of the Flies (1963)\n",
-      "Genre: adventure, drama, thriller\n"
+      "Movie: Before Sunrise (1995)\n",
+      "Genre: drama, romance\n"
      ]
     },
     {
      "name": "stdin",
      "output_type": "stream",
      "text": [
-      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  3\n"
+      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  n\n"
      ]
     },
     {
@@ -2912,8 +2803,8 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Movie: 101 Dalmatians (One Hundred and One Dalmatians) (1961)\n",
-      "Genre: adventure, animation, children\n"
+      "Movie: To Wong Foo, Thanks for Everything! Julie Newmar (1995)\n",
+      "Genre: comedy\n"
      ]
     },
     {
@@ -2928,8 +2819,8 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Movie: Armageddon (1998)\n",
-      "Genre: action, romance, sci-fi, thriller\n"
+      "Movie: Ferris Bueller's Day Off (1986)\n",
+      "Genre: comedy\n"
      ]
     },
     {
@@ -2944,8 +2835,8 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Movie: 2001: A Space Odyssey (1968)\n",
-      "Genre: adventure, drama, sci-fi\n"
+      "Movie: Miracle on 34th Street (1994)\n",
+      "Genre: drama\n"
      ]
     },
     {
@@ -2960,15 +2851,15 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Movie: Life Aquatic with Steve Zissou, The (2004)\n",
-      "Genre: adventure, comedy, fantasy\n"
+      "Movie: Toy Story (1995)\n",
+      "Genre: adventure, animation, children, comedy, fantasy\n"
      ]
     },
     {
      "name": "stdin",
      "output_type": "stream",
      "text": [
-      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  n\n"
+      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  5\n"
      ]
     },
     {
@@ -2977,16 +2868,16 @@
      "text": [
       "\n",
       "Recommended movies:\n",
-      "1. Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964) - Predicted rating: 4.35\n",
+      "1. Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964) - Predicted rating: 4.31\n",
       "Genre: comedy, war\n",
-      "2. Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001) - Predicted rating: 4.26\n",
-      "Genre: comedy, romance\n",
-      "3. Philadelphia Story, The (1940) - Predicted rating: 4.22\n",
+      "2. Philadelphia Story, The (1940) - Predicted rating: 4.24\n",
       "Genre: comedy, drama, romance\n",
-      "4. Toy Story 3 (2010) - Predicted rating: 4.20\n",
-      "Genre: adventure, animation, children, comedy, fantasy, imax\n",
-      "5. Princess Bride, The (1987) - Predicted rating: 4.20\n",
-      "Genre: action, adventure, comedy, fantasy, romance\n"
+      "3. Pulp Fiction (1994) - Predicted rating: 4.22\n",
+      "Genre: comedy, crime, drama, thriller\n",
+      "4. Princess Bride, The (1987) - Predicted rating: 4.22\n",
+      "Genre: action, adventure, comedy, fantasy, romance\n",
+      "5. Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001) - Predicted rating: 4.21\n",
+      "Genre: comedy, romance\n"
      ]
     }
    ],
@@ -3093,7 +2984,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
+   "id": "57e1252e-44b5-4605-a71a-ff86b4496b3a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
    "id": "ddc7d36d-6264-4ba1-a986-f6d9b59c2193",
    "metadata": {},
    "outputs": [
@@ -3161,7 +3060,7 @@
        "100817        Andrew Dice Clay: Dice Rules                              comedy"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3188,205 +3087,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "id": "865e5ff2-ee9a-4e76-8873-5b71931a5159",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create content df\n",
-    "content_df = df[['title', 'genres']].copy()\n",
-    "\n",
-    "# Save to a CSV file\n",
-    "content_df.to_csv('content_movies.csv', index=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "a7deb54f-6a28-4dff-aaa7-89981d51ead7",
+   "execution_count": 28,
+   "id": "0660dda9-1dd6-42b7-9e9c-0b64deef12aa",
    "metadata": {},
    "outputs": [
     {
-     "ename": "RuntimeError",
-     "evalue": "nnz of the result is too large",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[63], line 59\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m: \n\u001b[1;32m     56\u001b[0m     \n\u001b[1;32m     57\u001b[0m     \u001b[38;5;66;03m# Instantiate and train the content-based model\u001b[39;00m\n\u001b[1;32m     58\u001b[0m     recommender \u001b[38;5;241m=\u001b[39m ContentBasedModel(content_df)\n\u001b[0;32m---> 59\u001b[0m     \u001b[43mrecommender\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;66;03m# Save the trained model using pickle\u001b[39;00m\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontent_model.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
-      "Cell \u001b[0;32mIn[63], line 30\u001b[0m, in \u001b[0;36mContentBasedModel.train_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtfidf_matrix \u001b[38;5;241m=\u001b[39m tfidf\u001b[38;5;241m.\u001b[39mfit_transform(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeatures\u001b[39m\u001b[38;5;124m'\u001b[39m])  \u001b[38;5;66;03m# Transform the features column\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Compute cosine similarity matrix\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcosine_sim \u001b[38;5;241m=\u001b[39m \u001b[43mcosine_similarity\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtfidf_matrix\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/learn-env/lib/python3.8/site-packages/sklearn/utils/_param_validation.py:214\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    210\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    211\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    212\u001b[0m         )\n\u001b[1;32m    213\u001b[0m     ):\n\u001b[0;32m--> 214\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m    221\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    222\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    223\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m    224\u001b[0m     )\n",
-      "File \u001b[0;32m~/anaconda3/envs/learn-env/lib/python3.8/site-packages/sklearn/metrics/pairwise.py:1586\u001b[0m, in \u001b[0;36mcosine_similarity\u001b[0;34m(X, Y, dense_output)\u001b[0m\n\u001b[1;32m   1583\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1584\u001b[0m     Y_normalized \u001b[38;5;241m=\u001b[39m normalize(Y, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m-> 1586\u001b[0m K \u001b[38;5;241m=\u001b[39m \u001b[43msafe_sparse_dot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_normalized\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_normalized\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdense_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdense_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1588\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m K\n",
-      "File \u001b[0;32m~/anaconda3/envs/learn-env/lib/python3.8/site-packages/sklearn/utils/extmath.py:192\u001b[0m, in \u001b[0;36msafe_sparse_dot\u001b[0;34m(a, b, dense_output)\u001b[0m\n\u001b[1;32m    190\u001b[0m         ret \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdot(a, b)\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 192\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m@\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\n\u001b[1;32m    194\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    195\u001b[0m     sparse\u001b[38;5;241m.\u001b[39missparse(a)\n\u001b[1;32m    196\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m sparse\u001b[38;5;241m.\u001b[39missparse(b)\n\u001b[1;32m    197\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m dense_output\n\u001b[1;32m    198\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(ret, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoarray\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    199\u001b[0m ):\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ret\u001b[38;5;241m.\u001b[39mtoarray()\n",
-      "File \u001b[0;32m~/anaconda3/envs/learn-env/lib/python3.8/site-packages/scipy/sparse/base.py:560\u001b[0m, in \u001b[0;36mspmatrix.__matmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isscalarlike(other):\n\u001b[1;32m    558\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mScalar operands are not allowed, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    559\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m instead\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 560\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__mul__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/learn-env/lib/python3.8/site-packages/scipy/sparse/base.py:480\u001b[0m, in \u001b[0;36mspmatrix.__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    478\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m other\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m    479\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdimension mismatch\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 480\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mul_sparse_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[38;5;66;03m# If it's a list or whatever, treat it like a matrix\u001b[39;00m\n\u001b[1;32m    483\u001b[0m other_a \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masanyarray(other)\n",
-      "File \u001b[0;32m~/anaconda3/envs/learn-env/lib/python3.8/site-packages/scipy/sparse/compressed.py:505\u001b[0m, in \u001b[0;36m_cs_matrix._mul_sparse_matrix\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    501\u001b[0m idx_dtype \u001b[38;5;241m=\u001b[39m get_index_dtype((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindptr, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices,\n\u001b[1;32m    502\u001b[0m                              other\u001b[38;5;241m.\u001b[39mindptr, other\u001b[38;5;241m.\u001b[39mindices))\n\u001b[1;32m    504\u001b[0m fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(_sparsetools, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_matmat_maxnnz\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 505\u001b[0m nnz \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mM\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    506\u001b[0m \u001b[43m         \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindptr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43midx_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    507\u001b[0m \u001b[43m         \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43midx_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m         \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindptr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43midx_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[43m         \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43midx_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    511\u001b[0m idx_dtype \u001b[38;5;241m=\u001b[39m get_index_dtype((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindptr, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices,\n\u001b[1;32m    512\u001b[0m                              other\u001b[38;5;241m.\u001b[39mindptr, other\u001b[38;5;241m.\u001b[39mindices),\n\u001b[1;32m    513\u001b[0m                             maxval\u001b[38;5;241m=\u001b[39mnnz)\n\u001b[1;32m    515\u001b[0m indptr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(major_axis \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, dtype\u001b[38;5;241m=\u001b[39midx_dtype)\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: nnz of the result is too large"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "41657                                       Antz\n",
+      "51710                                Toy Story 2\n",
+      "57060    Adventures of Rocky and Bullwinkle, The\n",
+      "59397                  Emperor's New Groove, The\n",
+      "64009                             Monsters, Inc.\n",
+      "Name: title, dtype: object\n"
      ]
     }
    ],
    "source": [
     "from sklearn.feature_extraction.text import TfidfVectorizer\n",
     "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "import re\n",
+    "import pickle\n",
+    "import pandas as pd\n",
     "\n",
-    "class ContentBasedModel:\n",
-    "    def __init__(self, content_df):\n",
-    "        '''\n",
-    "        Initializes the ContentBasedRecommender with a DataFrame containing movie data.\n",
+    "# Drop duplicates to get unique movie titles\n",
+    "content_df = df.drop_duplicates(subset='title')[['movieId', 'title', 'genres', 'release_year']]\n",
+    "# Save to a CSV file\n",
+    "content_df.to_csv('content_movies.csv', index=False)\n",
     "\n",
-    "        Parameters:\n",
-    "        df (pd.DataFrame): DataFrame containing movie information with columns 'title' and 'genres'.\n",
-    "        '''\n",
+    "class ContentBasedModel:\n",
+    "    def __init__(self, df):\n",
+    "        # Drop duplicates to get unique movie titles\n",
     "        self.df = content_df\n",
     "        self.tfidf_matrix = None\n",
     "        self.cosine_sim = None\n",
+    "        self.indices = None\n",
     "\n",
     "    def train_model(self):\n",
-    "        '''\n",
-    "        Trains a TF-IDF model for content-based filtering and computes the cosine similarity matrix.\n",
-    "        '''\n",
-    "        # Preprocess the data\n",
-    "        self.df['clean_title'] = self.df['title'].apply(lambda x: re.sub(\"[^a-zA-Z0-9 ]\", \"\", x).lower())\n",
-    "        self.df['features'] = self.df['clean_title'] + ' ' + self.df['genres']\n",
-    "        \n",
-    "        # Create TF-IDF vectorizer and transform features\n",
+    "        # Define the TF-IDF vectorizer\n",
     "        tfidf = TfidfVectorizer(stop_words='english')\n",
-    "        self.tfidf_matrix = tfidf.fit_transform(self.df['features'])  # Transform the features column\n",
-    "        \n",
-    "        # Compute cosine similarity matrix\n",
-    "        self.cosine_sim = cosine_similarity(self.tfidf_matrix)\n",
     "\n",
-    "    def get_recommendations(self, title, top_n=5):\n",
-    "        '''\n",
-    "        Provides top 5 movie recommendations based on content similarity.\n",
-    "        '''\n",
-    "        \n",
-    "        # Get the index of the movie that matches the title\n",
-    "        idx = self.df.index[self.df['title'] == title].tolist()\n",
-    "        if not idx:\n",
-    "            return pd.DataFrame(columns=['title', 'genres'])  # Return an empty DataFrame if title not found\n",
+    "        # Fit and transform the genres\n",
+    "        self.tfidf_matrix = tfidf.fit_transform(self.df['genres'])\n",
     "\n",
-    "        idx = idx[0]\n",
-    "        \n",
-    "        # Get similarity scores for all movies with the given movie\n",
-    "        sim_scores = list(enumerate(self.cosine_sim[idx]))\n",
-    "        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
-    "        sim_scores = sim_scores[1:top_n+1]  # Exclude the first movie (which is the movie itself)\n",
-    "        \n",
-    "        # Get movie indices\n",
-    "        movie_indices = [i[0] for i in sim_scores]\n",
-    "        \n",
-    "        return self.df[['title', 'genres']].iloc[movie_indices].reset_index(drop=True)\n",
+    "        # Calculate the cosine similarity matrix\n",
+    "        self.cosine_sim = cosine_similarity(self.tfidf_matrix, self.tfidf_matrix)\n",
     "\n",
+    "        # Create a reverse mapping of indices and movie titles\n",
+    "        self.indices = pd.Series(self.df.index, index=self.df['title']).drop_duplicates()\n",
     "\n",
-    "if __name__ == \"__main__\": \n",
-    "    \n",
-    "    # Instantiate and train the content-based model\n",
-    "    recommender = ContentBasedModel(content_df)\n",
-    "    recommender.train_model()\n",
+    "    def get_recommendations(self, title, k=5):\n",
+    "        # Get the index of the movie that matches the title\n",
+    "        idx = self.indices[title]\n",
     "\n",
-    "    # Save the trained model using pickle\n",
-    "    with open('content_model.pkl', 'wb') as f:\n",
-    "        pickle.dump(recommender.model, f)\n",
-    "    \n",
-    "    # Get recommendations for a sample movie\n",
-    "    sample_movie = 'Sommersby'\n",
-    "    recommendations = recommender.get_recommendations(sample_movie, top_n=5)\n",
-    "    \n",
-    "    # Print recommendations\n",
-    "    print(f\"Recommendations for '{sample_movie}':\")\n",
-    "    print(recommendations)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "84b34985-1874-4755-986d-6d42d9a7dfd7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TF-IDF matrix shape: (100818, 5000)\n",
-      "SVD matrix shape: (100818, 200)\n",
-      "Similarity scores: [-0.00121944 -0.00121944 -0.00121944 -0.00121944 -0.00121944 -0.00121944\n",
-      " -0.00121944 -0.00121944 -0.00121944 -0.00121944]\n",
-      "Recommended movie indices: [48978, 48979, 48980, 48981, 48982]\n",
-      "Recommendations for 'Sommersby':\n",
-      "       title                   genres\n",
-      "0  Sommersby  drama, mystery, romance\n",
-      "1  Sommersby  drama, mystery, romance\n",
-      "2  Sommersby  drama, mystery, romance\n",
-      "3  Sommersby  drama, mystery, romance\n",
-      "4  Sommersby  drama, mystery, romance\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from sklearn.decomposition import TruncatedSVD\n",
-    "import re\n",
-    "import pickle\n",
+    "        # Get the pairwise similarity scores of all movies with that movie\n",
+    "        sim_scores = list(enumerate(self.cosine_sim[idx]))\n",
     "\n",
-    "class ContentBasedModel:\n",
-    "    def __init__(self, content_df):\n",
-    "        self.df = content_df\n",
-    "        self.tfidf_matrix = None\n",
-    "        self.tfidf_feature_names = None\n",
-    "        self.svd_matrix = None\n",
+    "        # Sort the movies based on the similarity scores\n",
+    "        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
     "\n",
-    "    def train_model(self):\n",
-    "        self.df['clean_title'] = self.df['title'].apply(lambda x: re.sub(r\"[^a-zA-Z0-9 ]\", \"\", x).lower())\n",
-    "        self.df['features'] = self.df['clean_title'] + ' ' + self.df['genres']\n",
-    "        \n",
-    "        tfidf = TfidfVectorizer(stop_words='english', max_features=5000)\n",
-    "        self.tfidf_matrix = tfidf.fit_transform(self.df['features'])\n",
-    "        self.tfidf_feature_names = tfidf.get_feature_names_out()\n",
-    "    \n",
-    "        print(f\"TF-IDF matrix shape: {self.tfidf_matrix.shape}\")\n",
-    "        \n",
-    "        svd = TruncatedSVD(n_components=200)\n",
-    "        self.svd_matrix = svd.fit_transform(self.tfidf_matrix)\n",
-    "        \n",
-    "        print(f\"SVD matrix shape: {self.svd_matrix.shape}\")\n",
+    "        # Get the scores of the k most similar movies\n",
+    "        sim_scores = sim_scores[1:k+1]\n",
     "\n",
-    "    def get_recommendations(self, title, top_n=5):\n",
-    "        idx = self.df.index[self.df['title'] == title].tolist()\n",
-    "        if not idx:\n",
-    "            return pd.DataFrame(columns=['title', 'genres'])\n",
-    "        idx = idx[0]\n",
-    "        \n",
-    "        movie_vector = self.svd_matrix[idx].reshape(1, -1)\n",
-    "        sim_scores = cosine_similarity(movie_vector, self.svd_matrix).flatten()\n",
-    "        \n",
-    "        print(f\"Similarity scores: {sim_scores[:10]}\")  # Print first 10 similarity scores\n",
-    "        \n",
-    "        sim_scores_with_index = list(enumerate(sim_scores))\n",
-    "        sim_scores_with_index = sorted(sim_scores_with_index, key=lambda x: x[1], reverse=True)\n",
-    "        sim_scores_with_index = sim_scores_with_index[1:top_n+1]\n",
-    "        \n",
-    "        movie_indices = [i[0] for i in sim_scores_with_index]\n",
-    "        print(f\"Recommended movie indices: {movie_indices}\")\n",
-    "        \n",
-    "        return self.df[['title', 'genres']].iloc[movie_indices].reset_index(drop=True)\n",
+    "        # Get the movie indices\n",
+    "        movie_indices = [i[0] for i in sim_scores]\n",
     "\n",
+    "        # Return the top k most similar movies\n",
+    "        return self.df['title'].iloc[movie_indices]\n",
     "\n",
-    "if __name__ == \"__main__\":\n",
-    "    \n",
-    "    recommender = ContentBasedModel(content_df)\n",
-    "    recommender.train_model()\n",
-    "    \n",
-    "    with open('content_model.pkl', 'wb') as f:\n",
-    "        pickle.dump(recommender, f)\n",
-    "    \n",
-    "    sample_movie = 'Sommersby'\n",
-    "    recommendations = recommender.get_recommendations(sample_movie, top_n=5)\n",
-    "    \n",
-    "    print(f\"Recommendations for '{sample_movie}':\")\n",
-    "    print(recommendations)"
+    "# Example usage\n",
+    "content_recommender = ContentBasedModel(df)\n",
+    "content_recommender.train_model()\n",
+    "\n",
+    "# Get recommendations for a specific movie\n",
+    "recommendations = content_recommender.get_recommendations('Toy Story')\n",
+    "print(recommendations)\n",
+    "\n",
+    "# Save the trained TF-IDF matrix and cosine similarity matrix using pickle\n",
+    "content_model = {\n",
+    "    'tfidf_matrix': content_recommender.tfidf_matrix,\n",
+    "    'cosine_sim': content_recommender.cosine_sim,\n",
+    "    'indices': content_recommender.indices\n",
+    "}\n",
+    "\n",
+    "with open('contentbased_model.pkl', 'wb') as f:\n",
+    "    pickle.dump(content_model, f)\n"
    ]
   },
   {