diff --git a/Project.ipynb b/Project.ipynb
index 0f5ffc7..a780a43 100644
--- a/Project.ipynb
+++ b/Project.ipynb
@@ -1815,429 +1815,24 @@
    "cell_type": "code",
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 1/300\n",
-      "1050/1050 [==============================] - 2s 1ms/step - loss: 1.0956 - accuracy: 0.6962 - val_loss: 0.6570 - val_accuracy: 0.8210\n",
-      "Epoch 2/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.6116 - accuracy: 0.8071 - val_loss: 0.5299 - val_accuracy: 0.8264\n",
-      "Epoch 3/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5482 - accuracy: 0.8136 - val_loss: 0.5036 - val_accuracy: 0.8305\n",
-      "Epoch 4/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5365 - accuracy: 0.8175 - val_loss: 0.5118 - val_accuracy: 0.8240\n",
-      "Epoch 5/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5329 - accuracy: 0.8183 - val_loss: 0.5042 - val_accuracy: 0.8270\n",
-      "Epoch 6/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5337 - accuracy: 0.8162 - val_loss: 0.5141 - val_accuracy: 0.8206\n",
-      "Epoch 7/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5291 - accuracy: 0.8184 - val_loss: 0.4960 - val_accuracy: 0.8279\n",
-      "Epoch 8/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5273 - accuracy: 0.8188 - val_loss: 0.4946 - val_accuracy: 0.8301\n",
-      "Epoch 9/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5245 - accuracy: 0.8167 - val_loss: 0.4972 - val_accuracy: 0.8238\n",
-      "Epoch 10/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5216 - accuracy: 0.8185 - val_loss: 0.4997 - val_accuracy: 0.8230\n",
-      "Epoch 11/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5208 - accuracy: 0.8175 - val_loss: 0.4927 - val_accuracy: 0.8240\n",
-      "Epoch 12/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5185 - accuracy: 0.8215 - val_loss: 0.4870 - val_accuracy: 0.8287\n",
-      "Epoch 13/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5203 - accuracy: 0.8204 - val_loss: 0.4905 - val_accuracy: 0.8225\n",
-      "Epoch 14/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5167 - accuracy: 0.8185 - val_loss: 0.4948 - val_accuracy: 0.8199\n",
-      "Epoch 15/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5148 - accuracy: 0.8211 - val_loss: 0.4854 - val_accuracy: 0.8277\n",
-      "Epoch 16/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5121 - accuracy: 0.8228 - val_loss: 0.4869 - val_accuracy: 0.8282\n",
-      "Epoch 17/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5130 - accuracy: 0.8209 - val_loss: 0.4826 - val_accuracy: 0.8273\n",
-      "Epoch 18/300\n",
-      "1050/1050 [==============================] - 1s 1000us/step - loss: 0.5134 - accuracy: 0.8199 - val_loss: 0.4910 - val_accuracy: 0.8249\n",
-      "Epoch 19/300\n",
-      "1050/1050 [==============================] - 1s 998us/step - loss: 0.5083 - accuracy: 0.8208 - val_loss: 0.4715 - val_accuracy: 0.8290\n",
-      "Epoch 20/300\n",
-      "1050/1050 [==============================] - 1s 998us/step - loss: 0.5074 - accuracy: 0.8198 - val_loss: 0.4844 - val_accuracy: 0.8243\n",
-      "Epoch 21/300\n",
-      "1050/1050 [==============================] - 1s 996us/step - loss: 0.5079 - accuracy: 0.8215 - val_loss: 0.4783 - val_accuracy: 0.8273\n",
-      "Epoch 22/300\n",
-      "1050/1050 [==============================] - 1s 997us/step - loss: 0.5040 - accuracy: 0.8204 - val_loss: 0.4733 - val_accuracy: 0.8311\n",
-      "Epoch 23/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5019 - accuracy: 0.8219 - val_loss: 0.4747 - val_accuracy: 0.8307\n",
-      "Epoch 24/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5041 - accuracy: 0.8201 - val_loss: 0.4817 - val_accuracy: 0.8229\n",
-      "Epoch 25/300\n",
-      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4994 - accuracy: 0.8217 - val_loss: 0.4717 - val_accuracy: 0.8336\n",
-      "Epoch 26/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5013 - accuracy: 0.8205 - val_loss: 0.4728 - val_accuracy: 0.8265\n",
-      "Epoch 27/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5016 - accuracy: 0.8210 - val_loss: 0.4667 - val_accuracy: 0.8279\n",
-      "Epoch 28/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5018 - accuracy: 0.8220 - val_loss: 0.4721 - val_accuracy: 0.8269\n",
-      "Epoch 29/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5008 - accuracy: 0.8222 - val_loss: 0.4767 - val_accuracy: 0.8224\n",
-      "Epoch 30/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5017 - accuracy: 0.8202 - val_loss: 0.4666 - val_accuracy: 0.8354\n",
-      "Epoch 31/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5003 - accuracy: 0.8212 - val_loss: 0.4795 - val_accuracy: 0.8235\n",
-      "Epoch 32/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5006 - accuracy: 0.8193 - val_loss: 0.4645 - val_accuracy: 0.8301\n",
-      "Epoch 33/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4962 - accuracy: 0.8206 - val_loss: 0.4684 - val_accuracy: 0.8280\n",
-      "Epoch 34/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5007 - accuracy: 0.8189 - val_loss: 0.4653 - val_accuracy: 0.8302\n",
-      "Epoch 35/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4968 - accuracy: 0.8207 - val_loss: 0.4710 - val_accuracy: 0.8251\n",
-      "Epoch 36/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4957 - accuracy: 0.8205 - val_loss: 0.4684 - val_accuracy: 0.8283\n",
-      "Epoch 37/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4966 - accuracy: 0.8224 - val_loss: 0.4685 - val_accuracy: 0.8277\n",
-      "Epoch 38/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5009 - accuracy: 0.8216 - val_loss: 0.4652 - val_accuracy: 0.8311\n",
-      "Epoch 39/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4959 - accuracy: 0.8243 - val_loss: 0.4724 - val_accuracy: 0.8220\n",
-      "Epoch 40/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4974 - accuracy: 0.8204 - val_loss: 0.4732 - val_accuracy: 0.8235\n",
-      "Epoch 41/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4987 - accuracy: 0.8192 - val_loss: 0.4731 - val_accuracy: 0.8255\n",
-      "Epoch 42/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4979 - accuracy: 0.8211 - val_loss: 0.4656 - val_accuracy: 0.8304\n",
-      "Epoch 43/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4927 - accuracy: 0.8222 - val_loss: 0.4676 - val_accuracy: 0.8269\n",
-      "Epoch 44/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4977 - accuracy: 0.8233 - val_loss: 0.4701 - val_accuracy: 0.8281\n",
-      "Epoch 45/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4962 - accuracy: 0.8214 - val_loss: 0.4675 - val_accuracy: 0.8273\n",
-      "Epoch 46/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4981 - accuracy: 0.8190 - val_loss: 0.4682 - val_accuracy: 0.8252\n",
-      "Epoch 47/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4971 - accuracy: 0.8234 - val_loss: 0.4628 - val_accuracy: 0.8295\n",
-      "Epoch 48/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4988 - accuracy: 0.8225 - val_loss: 0.4684 - val_accuracy: 0.8275\n",
-      "Epoch 49/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4964 - accuracy: 0.8214 - val_loss: 0.4683 - val_accuracy: 0.8252\n",
-      "Epoch 50/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4977 - accuracy: 0.8211 - val_loss: 0.4656 - val_accuracy: 0.8308\n",
-      "Epoch 51/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4965 - accuracy: 0.8211 - val_loss: 0.4622 - val_accuracy: 0.8285\n",
-      "Epoch 52/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4957 - accuracy: 0.8218 - val_loss: 0.4791 - val_accuracy: 0.8200\n",
-      "Epoch 53/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4968 - accuracy: 0.8205 - val_loss: 0.4682 - val_accuracy: 0.8292\n",
-      "Epoch 54/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.8206 - val_loss: 0.4623 - val_accuracy: 0.8306\n",
-      "Epoch 55/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8197 - val_loss: 0.4681 - val_accuracy: 0.8286\n",
-      "Epoch 56/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4958 - accuracy: 0.8193 - val_loss: 0.4640 - val_accuracy: 0.8273\n",
-      "Epoch 57/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8215 - val_loss: 0.4642 - val_accuracy: 0.8318\n",
-      "Epoch 58/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4947 - accuracy: 0.8204 - val_loss: 0.4724 - val_accuracy: 0.8256\n",
-      "Epoch 59/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4958 - accuracy: 0.8201 - val_loss: 0.4665 - val_accuracy: 0.8299\n",
-      "Epoch 60/300\n",
-      "1050/1050 [==============================] - 1s 994us/step - loss: 0.4952 - accuracy: 0.8205 - val_loss: 0.4602 - val_accuracy: 0.8311\n",
-      "Epoch 61/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4959 - accuracy: 0.8226 - val_loss: 0.4657 - val_accuracy: 0.8313\n",
-      "Epoch 62/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4996 - accuracy: 0.8192 - val_loss: 0.4655 - val_accuracy: 0.8268\n",
-      "Epoch 63/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8224 - val_loss: 0.4598 - val_accuracy: 0.8295\n",
-      "Epoch 64/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4939 - accuracy: 0.8222 - val_loss: 0.4628 - val_accuracy: 0.8318\n",
-      "Epoch 65/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4956 - accuracy: 0.8213 - val_loss: 0.4615 - val_accuracy: 0.8307\n",
-      "Epoch 66/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4972 - accuracy: 0.8201 - val_loss: 0.4602 - val_accuracy: 0.8288\n",
-      "Epoch 67/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.8211 - val_loss: 0.4642 - val_accuracy: 0.8289\n",
-      "Epoch 68/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.8204 - val_loss: 0.4684 - val_accuracy: 0.8292\n",
-      "Epoch 69/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4969 - accuracy: 0.8226 - val_loss: 0.4706 - val_accuracy: 0.8231\n",
-      "Epoch 70/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4976 - accuracy: 0.8214 - val_loss: 0.4666 - val_accuracy: 0.8315\n",
-      "Epoch 71/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4950 - accuracy: 0.8225 - val_loss: 0.4672 - val_accuracy: 0.8264\n",
-      "Epoch 72/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4935 - accuracy: 0.8214 - val_loss: 0.4621 - val_accuracy: 0.8279\n",
-      "Epoch 73/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4972 - accuracy: 0.8200 - val_loss: 0.4637 - val_accuracy: 0.8289\n",
-      "Epoch 74/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4931 - accuracy: 0.8222 - val_loss: 0.4616 - val_accuracy: 0.8351\n",
-      "Epoch 75/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4965 - accuracy: 0.8214 - val_loss: 0.4627 - val_accuracy: 0.8307\n",
-      "Epoch 76/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4961 - accuracy: 0.8215 - val_loss: 0.4649 - val_accuracy: 0.8282\n",
-      "Epoch 77/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4924 - accuracy: 0.8208 - val_loss: 0.4584 - val_accuracy: 0.8331\n",
-      "Epoch 78/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4933 - accuracy: 0.8221 - val_loss: 0.4696 - val_accuracy: 0.8277\n",
-      "Epoch 79/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4948 - accuracy: 0.8203 - val_loss: 0.4619 - val_accuracy: 0.8281\n",
-      "Epoch 80/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4904 - accuracy: 0.8227 - val_loss: 0.4715 - val_accuracy: 0.8249\n",
-      "Epoch 81/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4937 - accuracy: 0.8207 - val_loss: 0.4615 - val_accuracy: 0.8323\n",
-      "Epoch 82/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4953 - accuracy: 0.8208 - val_loss: 0.4607 - val_accuracy: 0.8280\n",
-      "Epoch 83/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4944 - accuracy: 0.8217 - val_loss: 0.4641 - val_accuracy: 0.8280\n",
-      "Epoch 84/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4961 - accuracy: 0.8208 - val_loss: 0.4715 - val_accuracy: 0.8269\n",
-      "Epoch 85/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4969 - accuracy: 0.8201 - val_loss: 0.4704 - val_accuracy: 0.8254\n",
-      "Epoch 86/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4919 - accuracy: 0.8203 - val_loss: 0.4649 - val_accuracy: 0.8264\n",
-      "Epoch 87/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4937 - accuracy: 0.8223 - val_loss: 0.4593 - val_accuracy: 0.8323\n",
-      "Epoch 88/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4942 - accuracy: 0.8201 - val_loss: 0.4631 - val_accuracy: 0.8302\n",
-      "Epoch 89/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4953 - accuracy: 0.8204 - val_loss: 0.4592 - val_accuracy: 0.8295\n",
-      "Epoch 90/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4923 - accuracy: 0.8212 - val_loss: 0.4647 - val_accuracy: 0.8262\n",
-      "Epoch 91/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8197 - val_loss: 0.4600 - val_accuracy: 0.8315\n",
-      "Epoch 92/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.8192 - val_loss: 0.4663 - val_accuracy: 0.8270\n",
-      "Epoch 93/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4963 - accuracy: 0.8194 - val_loss: 0.4556 - val_accuracy: 0.8332\n",
-      "Epoch 94/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4931 - accuracy: 0.8224 - val_loss: 0.4615 - val_accuracy: 0.8293\n",
-      "Epoch 95/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.8211 - val_loss: 0.4575 - val_accuracy: 0.8344\n",
-      "Epoch 96/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4961 - accuracy: 0.8220 - val_loss: 0.4600 - val_accuracy: 0.8304\n",
-      "Epoch 97/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.8215 - val_loss: 0.4620 - val_accuracy: 0.8289\n",
-      "Epoch 98/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8214 - val_loss: 0.4631 - val_accuracy: 0.8261\n",
-      "Epoch 99/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4951 - accuracy: 0.8210 - val_loss: 0.4611 - val_accuracy: 0.8270\n",
-      "Epoch 100/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4962 - accuracy: 0.8192 - val_loss: 0.4660 - val_accuracy: 0.8268\n",
-      "Epoch 101/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4940 - accuracy: 0.8227 - val_loss: 0.4659 - val_accuracy: 0.8305\n",
-      "Epoch 102/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8211 - val_loss: 0.4666 - val_accuracy: 0.8294\n",
-      "Epoch 103/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4934 - accuracy: 0.8221 - val_loss: 0.4595 - val_accuracy: 0.8352\n",
-      "Epoch 104/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8203 - val_loss: 0.4606 - val_accuracy: 0.8265\n",
-      "Epoch 105/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4951 - accuracy: 0.8211 - val_loss: 0.4587 - val_accuracy: 0.8320\n",
-      "Epoch 106/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4969 - accuracy: 0.8198 - val_loss: 0.4635 - val_accuracy: 0.8296\n",
-      "Epoch 107/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4932 - accuracy: 0.8205 - val_loss: 0.4584 - val_accuracy: 0.8312\n",
-      "Epoch 108/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4938 - accuracy: 0.8217 - val_loss: 0.4611 - val_accuracy: 0.8290\n",
-      "Epoch 109/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4928 - accuracy: 0.8209 - val_loss: 0.4626 - val_accuracy: 0.8288\n",
-      "Epoch 110/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4929 - accuracy: 0.8216 - val_loss: 0.4573 - val_accuracy: 0.8344\n",
-      "Epoch 111/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4933 - accuracy: 0.8214 - val_loss: 0.4553 - val_accuracy: 0.8321\n",
-      "Epoch 112/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4953 - accuracy: 0.8210 - val_loss: 0.4614 - val_accuracy: 0.8317\n",
-      "Epoch 113/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4921 - accuracy: 0.8221 - val_loss: 0.4611 - val_accuracy: 0.8298\n",
-      "Epoch 114/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4954 - accuracy: 0.8202 - val_loss: 0.4634 - val_accuracy: 0.8287\n",
-      "Epoch 115/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4918 - accuracy: 0.8224 - val_loss: 0.4540 - val_accuracy: 0.8317\n",
-      "Epoch 116/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4948 - accuracy: 0.8197 - val_loss: 0.4601 - val_accuracy: 0.8308\n",
-      "Epoch 117/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4937 - accuracy: 0.8208 - val_loss: 0.4622 - val_accuracy: 0.8269\n",
-      "Epoch 118/300\n",
-      "1050/1050 [==============================] - 1s 999us/step - loss: 0.4936 - accuracy: 0.8222 - val_loss: 0.4583 - val_accuracy: 0.8330\n",
-      "Epoch 119/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8189 - val_loss: 0.4669 - val_accuracy: 0.8294\n",
-      "Epoch 120/300\n",
-      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4937 - accuracy: 0.8227 - val_loss: 0.4673 - val_accuracy: 0.8255\n",
-      "Epoch 121/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4951 - accuracy: 0.8206 - val_loss: 0.4651 - val_accuracy: 0.8300\n",
-      "Epoch 122/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4922 - accuracy: 0.8219 - val_loss: 0.4688 - val_accuracy: 0.8254\n",
-      "Epoch 123/300\n",
-      "1050/1050 [==============================] - 1s 989us/step - loss: 0.4952 - accuracy: 0.8207 - val_loss: 0.4623 - val_accuracy: 0.8296\n",
-      "Epoch 124/300\n",
-      "1050/1050 [==============================] - 1s 998us/step - loss: 0.4947 - accuracy: 0.8211 - val_loss: 0.4664 - val_accuracy: 0.8235\n",
-      "Epoch 125/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4952 - accuracy: 0.8214 - val_loss: 0.4617 - val_accuracy: 0.8319\n",
-      "Epoch 126/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4932 - accuracy: 0.8203 - val_loss: 0.4657 - val_accuracy: 0.8289\n",
-      "Epoch 127/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8211 - val_loss: 0.4649 - val_accuracy: 0.8257\n",
-      "Epoch 128/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.8208 - val_loss: 0.4594 - val_accuracy: 0.8305\n",
-      "Epoch 129/300\n",
-      "1050/1050 [==============================] - 1s 994us/step - loss: 0.4967 - accuracy: 0.8209 - val_loss: 0.4592 - val_accuracy: 0.8326\n",
-      "Epoch 130/300\n",
-      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4911 - accuracy: 0.8233 - val_loss: 0.4719 - val_accuracy: 0.8262\n",
-      "Epoch 131/300\n",
-      "1050/1050 [==============================] - 1s 989us/step - loss: 0.4924 - accuracy: 0.8214 - val_loss: 0.4650 - val_accuracy: 0.8273\n",
-      "Epoch 132/300\n",
-      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4906 - accuracy: 0.8228 - val_loss: 0.4628 - val_accuracy: 0.8308\n",
-      "Epoch 133/300\n",
-      "1050/1050 [==============================] - 1s 992us/step - loss: 0.4967 - accuracy: 0.8206 - val_loss: 0.4600 - val_accuracy: 0.8302\n",
-      "Epoch 134/300\n",
-      "1050/1050 [==============================] - 1s 999us/step - loss: 0.4904 - accuracy: 0.8226 - val_loss: 0.4644 - val_accuracy: 0.8299\n",
-      "Epoch 135/300\n",
-      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4939 - accuracy: 0.8216 - val_loss: 0.4589 - val_accuracy: 0.8319\n",
-      "Epoch 136/300\n",
-      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4940 - accuracy: 0.8226 - val_loss: 0.4638 - val_accuracy: 0.8285\n",
-      "Epoch 137/300\n",
-      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4950 - accuracy: 0.8191 - val_loss: 0.4680 - val_accuracy: 0.8239\n",
-      "Epoch 138/300\n",
-      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4930 - accuracy: 0.8226 - val_loss: 0.4584 - val_accuracy: 0.8312\n",
-      "Epoch 139/300\n",
-      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4946 - accuracy: 0.8214 - val_loss: 0.4660 - val_accuracy: 0.8235\n",
-      "Epoch 140/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4952 - accuracy: 0.8221 - val_loss: 0.4641 - val_accuracy: 0.8292\n",
-      "Epoch 141/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4956 - accuracy: 0.8213 - val_loss: 0.4589 - val_accuracy: 0.8289\n",
-      "Epoch 142/300\n",
-      "1050/1050 [==============================] - 1s 991us/step - loss: 0.4946 - accuracy: 0.8207 - val_loss: 0.4641 - val_accuracy: 0.8298\n",
-      "Epoch 143/300\n",
-      "1050/1050 [==============================] - 1s 991us/step - loss: 0.4940 - accuracy: 0.8214 - val_loss: 0.4514 - val_accuracy: 0.8358\n",
-      "Epoch 144/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4957 - accuracy: 0.8201 - val_loss: 0.4621 - val_accuracy: 0.8285\n",
-      "Epoch 145/300\n",
-      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4945 - accuracy: 0.8201 - val_loss: 0.4601 - val_accuracy: 0.8301\n",
-      "Epoch 146/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4922 - accuracy: 0.8208 - val_loss: 0.4706 - val_accuracy: 0.8265\n",
-      "Epoch 147/300\n",
-      "1050/1050 [==============================] - 1s 994us/step - loss: 0.4914 - accuracy: 0.8214 - val_loss: 0.4681 - val_accuracy: 0.8271\n",
-      "Epoch 148/300\n",
-      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4920 - accuracy: 0.8211 - val_loss: 0.4694 - val_accuracy: 0.8236\n",
-      "Epoch 149/300\n",
-      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4892 - accuracy: 0.8240 - val_loss: 0.4599 - val_accuracy: 0.8304\n",
-      "Epoch 150/300\n",
-      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4930 - accuracy: 0.8211 - val_loss: 0.4570 - val_accuracy: 0.8336\n",
-      "Epoch 151/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4929 - accuracy: 0.8202 - val_loss: 0.4702 - val_accuracy: 0.8248\n",
-      "Epoch 152/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4923 - accuracy: 0.8206 - val_loss: 0.4688 - val_accuracy: 0.8299\n",
-      "Epoch 153/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4929 - accuracy: 0.8222 - val_loss: 0.4604 - val_accuracy: 0.8300\n",
-      "Epoch 154/300\n",
-      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4919 - accuracy: 0.8223 - val_loss: 0.4634 - val_accuracy: 0.8276\n",
-      "Epoch 155/300\n",
-      "1050/1050 [==============================] - 1s 989us/step - loss: 0.4917 - accuracy: 0.8209 - val_loss: 0.4657 - val_accuracy: 0.8263\n",
-      "Epoch 156/300\n",
-      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4931 - accuracy: 0.8208 - val_loss: 0.4601 - val_accuracy: 0.8243\n",
-      "Epoch 157/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4919 - accuracy: 0.8203 - val_loss: 0.4656 - val_accuracy: 0.8248\n",
-      "Epoch 158/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4922 - accuracy: 0.8189 - val_loss: 0.4562 - val_accuracy: 0.8282\n",
-      "Epoch 159/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4932 - accuracy: 0.8188 - val_loss: 0.4595 - val_accuracy: 0.8307\n",
-      "Epoch 160/300\n",
-      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4912 - accuracy: 0.8224 - val_loss: 0.4602 - val_accuracy: 0.8321\n",
-      "Epoch 161/300\n",
-      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4912 - accuracy: 0.8217 - val_loss: 0.4568 - val_accuracy: 0.8304\n",
-      "Epoch 162/300\n",
-      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4909 - accuracy: 0.8197 - val_loss: 0.4709 - val_accuracy: 0.8214\n",
-      "Epoch 163/300\n",
-      "1050/1050 [==============================] - 1s 988us/step - loss: 0.4901 - accuracy: 0.8238 - val_loss: 0.4730 - val_accuracy: 0.8230\n",
-      "Epoch 164/300\n",
-      "1050/1050 [==============================] - 1s 991us/step - loss: 0.4874 - accuracy: 0.8222 - val_loss: 0.4667 - val_accuracy: 0.8249\n",
-      "Epoch 165/300\n",
-      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4896 - accuracy: 0.8230 - val_loss: 0.4592 - val_accuracy: 0.8308\n",
-      "Epoch 166/300\n",
-      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4911 - accuracy: 0.8227 - val_loss: 0.4584 - val_accuracy: 0.8310\n",
-      "Epoch 167/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4920 - accuracy: 0.8224 - val_loss: 0.4582 - val_accuracy: 0.8270\n",
-      "Epoch 168/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4899 - accuracy: 0.8218 - val_loss: 0.4565 - val_accuracy: 0.8323\n",
-      "Epoch 169/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8223 - val_loss: 0.4591 - val_accuracy: 0.8299\n",
-      "Epoch 170/300\n",
-      "1050/1050 [==============================] - 1s 992us/step - loss: 0.4923 - accuracy: 0.8221 - val_loss: 0.4587 - val_accuracy: 0.8340\n",
-      "Epoch 171/300\n",
-      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4913 - accuracy: 0.8230 - val_loss: 0.4605 - val_accuracy: 0.8315\n",
-      "Epoch 172/300\n",
-      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4925 - accuracy: 0.8213 - val_loss: 0.4646 - val_accuracy: 0.8280\n",
-      "Epoch 173/300\n",
-      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4938 - accuracy: 0.8216 - val_loss: 0.4582 - val_accuracy: 0.8293\n",
-      "Epoch 174/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4921 - accuracy: 0.8206 - val_loss: 0.4641 - val_accuracy: 0.8268\n",
-      "Epoch 175/300\n",
-      "1050/1050 [==============================] - 1s 992us/step - loss: 0.4907 - accuracy: 0.8199 - val_loss: 0.4547 - val_accuracy: 0.8333\n",
-      "Epoch 176/300\n",
-      "1050/1050 [==============================] - 1s 991us/step - loss: 0.4937 - accuracy: 0.8217 - val_loss: 0.4668 - val_accuracy: 0.8245\n",
-      "Epoch 177/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4913 - accuracy: 0.8216 - val_loss: 0.4639 - val_accuracy: 0.8260\n",
-      "Epoch 178/300\n",
-      "1050/1050 [==============================] - 1s 998us/step - loss: 0.4914 - accuracy: 0.8203 - val_loss: 0.4647 - val_accuracy: 0.8239\n",
-      "Epoch 179/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4919 - accuracy: 0.8200 - val_loss: 0.4575 - val_accuracy: 0.8357\n",
-      "Epoch 180/300\n",
-      "1050/1050 [==============================] - 1s 992us/step - loss: 0.4903 - accuracy: 0.8228 - val_loss: 0.4610 - val_accuracy: 0.8270\n",
-      "Epoch 181/300\n",
-      "1050/1050 [==============================] - 1s 998us/step - loss: 0.4926 - accuracy: 0.8199 - val_loss: 0.4600 - val_accuracy: 0.8290\n",
-      "Epoch 182/300\n",
-      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4939 - accuracy: 0.8216 - val_loss: 0.4732 - val_accuracy: 0.8188\n",
-      "Epoch 183/300\n",
-      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4914 - accuracy: 0.8199 - val_loss: 0.4595 - val_accuracy: 0.8294\n",
-      "Epoch 184/300\n",
-      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4923 - accuracy: 0.8210 - val_loss: 0.4598 - val_accuracy: 0.8298\n",
-      "Epoch 185/300\n",
-      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4940 - accuracy: 0.8200 - val_loss: 0.4643 - val_accuracy: 0.8285\n",
-      "Epoch 186/300\n",
-      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4896 - accuracy: 0.8220 - val_loss: 0.4605 - val_accuracy: 0.8308\n",
-      "Epoch 187/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4909 - accuracy: 0.8207 - val_loss: 0.4629 - val_accuracy: 0.8254\n",
-      "Epoch 188/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4891 - accuracy: 0.8234 - val_loss: 0.4626 - val_accuracy: 0.8255\n",
-      "Epoch 189/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4905 - accuracy: 0.8220 - val_loss: 0.4555 - val_accuracy: 0.8308\n",
-      "Epoch 190/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4909 - accuracy: 0.8203 - val_loss: 0.4583 - val_accuracy: 0.8305\n",
-      "Epoch 191/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4897 - accuracy: 0.8233 - val_loss: 0.4738 - val_accuracy: 0.8192\n",
-      "Epoch 192/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4904 - accuracy: 0.8228 - val_loss: 0.4718 - val_accuracy: 0.8223\n",
-      "Epoch 193/300\n",
-      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4898 - accuracy: 0.8228 - val_loss: 0.4642 - val_accuracy: 0.8273\n",
-      "263/263 [==============================] - 0s 522us/step\n"
+      "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
      ]
     },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 1200x600 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test loss: 0.4641617238521576\n",
-      "Test accuracy: 0.8272619247436523\n",
-      "Classification Report: \n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.78      0.91      0.84      4192\n",
-      "           1       0.89      0.74      0.81      4208\n",
-      "\n",
-      "    accuracy                           0.83      8400\n",
-      "   macro avg       0.84      0.83      0.83      8400\n",
-      "weighted avg       0.84      0.83      0.83      8400\n"
+      "Epoch 1/10\n",
+      "1050/1050 [==============================] - 2s 1ms/step - loss: 0.8294 - accuracy: 0.6602 - val_loss: 0.6055 - val_accuracy: 0.8129\n",
+      "Epoch 2/10\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.6153 - accuracy: 0.7962 - val_loss: 0.5363 - val_accuracy: 0.8283\n",
+      "Epoch 3/10\n",
+      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.5471 - accuracy: 0.8205 - val_loss: 0.5016 - val_accuracy: 0.8326\n",
+      "Epoch 4/10\n",
+      " 244/1050 [=====>........................] - ETA: 6s - loss: 0.5188 - accuracy: 0.8300"
      ]
     }
    ],
@@ -2249,6 +1844,7 @@
     "from sklearn.preprocessing import StandardScaler\n",
     "from tensorflow.keras.layers import BatchNormalization\n",
     "from tensorflow.keras import regularizers\n",
+    "from tensorflow.keras.optimizers import Adam\n",
     "\n",
     "# Set random seed for reproducibility\n",
     "tf.random.set_seed(42)\n",
@@ -2265,24 +1861,27 @@
     "X_train = scaler.fit_transform(X_train)\n",
     "X_test = scaler.transform(X_test)\n",
     "\n",
-    "# Define the model with batch normalization and weight regularization\n",
+    "# Define the model with adjusted learning rate and regularization strength\n",
     "model = Sequential()\n",
-    "model.add(Dense(32, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
+    "model.add(Dense(64, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
     "model.add(BatchNormalization())\n",
     "model.add(Dropout(0.5))\n",
-    "model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
+    "model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
     "model.add(BatchNormalization())\n",
     "model.add(Dropout(0.5))\n",
     "model.add(Dense(1, activation='sigmoid'))\n",
     "\n",
-    "# Compile the model\n",
-    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
-    "\n",
-    "# Define early stopping with a higher patience value\n",
+    "# Define early stopping\n",
     "early_stopping = EarlyStopping(monitor='val_loss', patience=50)\n",
     "\n",
-    "# Train the model with increased epochs\n",
-    "history = model.fit(X_train, y_train, epochs=300, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
+    "# Compile the model with a lower learning rate\n",
+    "model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001), metrics=['accuracy'])\n",
+    "\n",
+    "# Train the model with reduced epochs\n",
+    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
     "\n",
     "# Evaluate the model\n",
     "scores = model.evaluate(X_test, y_test, verbose=0)\n",
@@ -2317,13 +1916,13 @@
    ],
    "metadata": {
     "collapsed": false,
+    "is_executing": true,
     "ExecuteTime": {
-     "end_time": "2024-03-15T15:35:44.337303Z",
-     "start_time": "2024-03-15T15:32:16.255784Z"
+     "start_time": "2024-03-15T15:52:13.445525Z"
     }
    },
    "id": "c8745832a585d5ec",
-   "execution_count": 39
+   "execution_count": null
   }
  ],
  "metadata": {