diff --git a/Mideval code/kuldeep_mideval_code/mideval_ps_kuldeep (2).ipynb b/Mideval code/kuldeep_mideval_code/mideval_ps_kuldeep (2).ipynb
new file mode 100644
index 0000000..06925a0
--- /dev/null
+++ b/Mideval code/kuldeep_mideval_code/mideval_ps_kuldeep (2).ipynb	
@@ -0,0 +1,711 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "zeoKusfsajl-",
+        "outputId": "20c5383e-ac56-47ad-b6db-2a4ae096e6b7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   lookback_days  high_volatility  trend_continuation  technical_score  \\\n",
+              "0             48                0                   1            59.99   \n",
+              "1             38                1                   1            78.54   \n",
+              "2             24                1                   0            56.03   \n",
+              "3             52                0                   0            66.51   \n",
+              "4             17                1                   1            61.21   \n",
+              "\n",
+              "   edge_density  slope_strength  candlestick_variance  pattern_symmetry  \\\n",
+              "0         0.504           0.298                 1.572             0.768   \n",
+              "1         0.559           0.037                 0.692             0.538   \n",
+              "2         0.617           0.212                 1.419             0.301   \n",
+              "3         0.360           0.347                 0.699             0.498   \n",
+              "4         0.492           0.144                 2.520             0.828   \n",
+              "\n",
+              "   future_trend  asset_type_equity  asset_type_index  market_regime_bullish  \\\n",
+              "0             1                  1                 0                      1   \n",
+              "1             1                  0                 1                      1   \n",
+              "2             1                  1                 0                      1   \n",
+              "3             1                  1                 0                      1   \n",
+              "4             1                  1                 0                      1   \n",
+              "\n",
+              "   market_regime_sideways  \n",
+              "0                       0  \n",
+              "1                       0  \n",
+              "2                       0  \n",
+              "3                       0  \n",
+              "4                       0  "
+            ],
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>lookback_days</th>\n",
+              "      <th>high_volatility</th>\n",
+              "      <th>trend_continuation</th>\n",
+              "      <th>technical_score</th>\n",
+              "      <th>edge_density</th>\n",
+              "      <th>slope_strength</th>\n",
+              "      <th>candlestick_variance</th>\n",
+              "      <th>pattern_symmetry</th>\n",
+              "      <th>future_trend</th>\n",
+              "      <th>asset_type_equity</th>\n",
+              "      <th>asset_type_index</th>\n",
+              "      <th>market_regime_bullish</th>\n",
+              "      <th>market_regime_sideways</th>\n",
+              "    </tr>\n",
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+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>48</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>59.99</td>\n",
+              "      <td>0.504</td>\n",
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+              "      <td>1.572</td>\n",
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+              "      <td>78.54</td>\n",
+              "      <td>0.559</td>\n",
+              "      <td>0.037</td>\n",
+              "      <td>0.692</td>\n",
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+              "      <td>1</td>\n",
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+              "    <tr>\n",
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+              "      <td>24</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>56.03</td>\n",
+              "      <td>0.617</td>\n",
+              "      <td>0.212</td>\n",
+              "      <td>1.419</td>\n",
+              "      <td>0.301</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>52</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>66.51</td>\n",
+              "      <td>0.360</td>\n",
+              "      <td>0.347</td>\n",
+              "      <td>0.699</td>\n",
+              "      <td>0.498</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>61.21</td>\n",
+              "      <td>0.492</td>\n",
+              "      <td>0.144</td>\n",
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+              "      <td>0.828</td>\n",
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+              "                                                    [key], {});\n",
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+              "\n",
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+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"lookback_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 10,\n        \"max\": 60,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          31,\n          15,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high_volatility\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trend_continuation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"technical_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.028468080337015,\n        \"min\": 24.2,\n        \"max\": 100.0,\n        \"num_unique_values\": 195,\n        \"samples\": [\n          71.25,\n          69.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"edge_density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11605043079384576,\n        \"min\": 0.191,\n        \"max\": 0.798,\n        \"num_unique_values\": 159,\n        \"samples\": [\n          0.384,\n          0.695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope_strength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6199660874985351,\n        \"min\": -1.217,\n        \"max\": 1.833,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          -0.285,\n          0.217\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"candlestick_variance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5755348790123836,\n        \"min\": 0.028,\n        \"max\": 2.52,\n        \"num_unique_values\": 195,\n        \"samples\": [\n          2.085,\n          0.634\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pattern_symmetry\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17357671352283097,\n        \"min\": 0.21,\n        \"max\": 1.0,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          0.919,\n          0.318\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"future_trend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asset_type_equity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asset_type_index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"market_regime_bullish\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"market_regime_sideways\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "old_df = pd.read_csv('quantvision_financial_dataset_200.csv')\n",
+        "\n",
+        "df = pd.get_dummies(old_df, columns=['asset_type' , 'market_regime'] , drop_first=True  )\n",
+        "bool_columns = df.select_dtypes(include=[bool]).columns\n",
+        "df[bool_columns] = df[bool_columns].astype(int)\n",
+        "df.head()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = df.drop('future_trend' , axis = 1)\n",
+        "y = df['future_trend']\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "acDFzMvqavA5"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "x_train , x_test , y_train , y_test = train_test_split(\n",
+        "    x , y , test_size = 0.2 , random_state = 40 , stratify = y\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "scaler = StandardScaler()\n",
+        "x_train = scaler.fit_transform(x_train)\n",
+        "\n",
+        "x_test = scaler.transform(x_test)\n",
+        ""
+      ],
+      "metadata": {
+        "id": "-xetJO5LmyuV"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "\n",
+        "from imblearn.over_sampling import SMOTE\n",
+        "smote = SMOTE(random_state=42)\n",
+        "x_train_smote , y_train_smote = smote.fit_resample(x_train, y_train)\n",
+        "\n",
+        "\n",
+        "\n",
+        "LR = LogisticRegression(penalty = 'l2' , C = 1.6 , solver='liblinear' , random_state= 42\n",
+        ")\n",
+        "LR.fit(x_train_smote , y_train_smote)\n",
+        "\n",
+        "y_test_pred = LR.predict(x_test)\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "score = accuracy_score(y_valid , y_test_pred)\n",
+        "print(score)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rfCb6q-Opy9Q",
+        "outputId": "4f4885fd-e4be-462b-e91b-97e49b801a4c"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense, Dropout\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import classification_report, confusion_matrix\n"
+      ],
+      "metadata": {
+        "id": "5KX7QyExq2Bi"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = Sequential()\n",
+        "\n",
+        "model.add(Dense(64, activation='relu', input_shape=(x_train.shape[1],)))\n",
+        "model.add(Dropout(0.3))\n",
+        "\n",
+        "model.add(Dense(32, activation='relu'))\n",
+        "model.add(Dropout(0.2))\n",
+        "\n",
+        "model.add(Dense(1, activation='sigmoid'))  # Binary output\n"
+      ],
+      "metadata": {
+        "id": "oaI1jdUEGjhL"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(\n",
+        "    optimizer=Adam(learning_rate=0.001),\n",
+        "    loss='binary_crossentropy',\n",
+        "    metrics=['accuracy']\n",
+        ")\n"
+      ],
+      "metadata": {
+        "id": "0OFCG5QzHNNa"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = model.fit(\n",
+        "    x_train, y_train,\n",
+        "    epochs=40,\n",
+        "    batch_size=32,\n",
+        "    validation_data=(x_test, y_test),\n",
+        "    verbose=1\n",
+        ")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GcnOQJyvHQnS",
+        "outputId": "4ee51da8-cab0-40fe-aa05-1a3bcc431956"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.9422 - loss: 0.1399 - val_accuracy: 0.9000 - val_loss: 0.4047\n",
+            "Epoch 2/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9392 - loss: 0.1207 - val_accuracy: 0.9000 - val_loss: 0.4096\n",
+            "Epoch 3/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9561 - loss: 0.0972 - val_accuracy: 0.9000 - val_loss: 0.4148\n",
+            "Epoch 4/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9434 - loss: 0.0911 - val_accuracy: 0.8500 - val_loss: 0.4188\n",
+            "Epoch 5/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9526 - loss: 0.0867 - val_accuracy: 0.8500 - val_loss: 0.4224\n",
+            "Epoch 6/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - accuracy: 0.9690 - loss: 0.0901 - val_accuracy: 0.8500 - val_loss: 0.4267\n",
+            "Epoch 7/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - accuracy: 0.9422 - loss: 0.1073 - val_accuracy: 0.8500 - val_loss: 0.4304\n",
+            "Epoch 8/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9494 - loss: 0.1083 - val_accuracy: 0.8500 - val_loss: 0.4354\n",
+            "Epoch 9/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.9681 - loss: 0.0693 - val_accuracy: 0.8500 - val_loss: 0.4407\n",
+            "Epoch 10/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9403 - loss: 0.1112 - val_accuracy: 0.8500 - val_loss: 0.4449\n",
+            "Epoch 11/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.9485 - loss: 0.1111 - val_accuracy: 0.8500 - val_loss: 0.4487\n",
+            "Epoch 12/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9584 - loss: 0.0795 - val_accuracy: 0.8500 - val_loss: 0.4531\n",
+            "Epoch 13/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9386 - loss: 0.1042 - val_accuracy: 0.8500 - val_loss: 0.4585\n",
+            "Epoch 14/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9778 - loss: 0.0705 - val_accuracy: 0.8500 - val_loss: 0.4645\n",
+            "Epoch 15/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - accuracy: 0.9569 - loss: 0.0814 - val_accuracy: 0.8500 - val_loss: 0.4716\n",
+            "Epoch 16/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9412 - loss: 0.1285 - val_accuracy: 0.8500 - val_loss: 0.4768\n",
+            "Epoch 17/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - accuracy: 0.9357 - loss: 0.0985 - val_accuracy: 0.8500 - val_loss: 0.4812\n",
+            "Epoch 18/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9741 - loss: 0.0745 - val_accuracy: 0.8500 - val_loss: 0.4869\n",
+            "Epoch 19/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9672 - loss: 0.0859 - val_accuracy: 0.8500 - val_loss: 0.4903\n",
+            "Epoch 20/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9795 - loss: 0.0570 - val_accuracy: 0.8500 - val_loss: 0.4937\n",
+            "Epoch 21/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.9569 - loss: 0.0890 - val_accuracy: 0.8500 - val_loss: 0.4960\n",
+            "Epoch 22/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9714 - loss: 0.0851 - val_accuracy: 0.8500 - val_loss: 0.4990\n",
+            "Epoch 23/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9718 - loss: 0.0735 - val_accuracy: 0.8500 - val_loss: 0.5011\n",
+            "Epoch 24/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9545 - loss: 0.0896 - val_accuracy: 0.8500 - val_loss: 0.5033\n",
+            "Epoch 25/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9698 - loss: 0.0639 - val_accuracy: 0.8500 - val_loss: 0.5060\n",
+            "Epoch 26/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9351 - loss: 0.1141 - val_accuracy: 0.8500 - val_loss: 0.5069\n",
+            "Epoch 27/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9464 - loss: 0.0903 - val_accuracy: 0.8500 - val_loss: 0.5107\n",
+            "Epoch 28/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9630 - loss: 0.0855 - val_accuracy: 0.8500 - val_loss: 0.5140\n",
+            "Epoch 29/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9843 - loss: 0.0648 - val_accuracy: 0.8500 - val_loss: 0.5192\n",
+            "Epoch 30/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9558 - loss: 0.0834 - val_accuracy: 0.8500 - val_loss: 0.5239\n",
+            "Epoch 31/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.9799 - loss: 0.0829 - val_accuracy: 0.8500 - val_loss: 0.5291\n",
+            "Epoch 32/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - accuracy: 0.9745 - loss: 0.0615 - val_accuracy: 0.8500 - val_loss: 0.5320\n",
+            "Epoch 33/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9644 - loss: 0.0746 - val_accuracy: 0.8500 - val_loss: 0.5351\n",
+            "Epoch 34/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9688 - loss: 0.0714 - val_accuracy: 0.8500 - val_loss: 0.5415\n",
+            "Epoch 35/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.9868 - loss: 0.0644 - val_accuracy: 0.8500 - val_loss: 0.5477\n",
+            "Epoch 36/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9649 - loss: 0.0718 - val_accuracy: 0.8500 - val_loss: 0.5538\n",
+            "Epoch 37/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9727 - loss: 0.0713 - val_accuracy: 0.8500 - val_loss: 0.5594\n",
+            "Epoch 38/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.9581 - loss: 0.0787 - val_accuracy: 0.8500 - val_loss: 0.5645\n",
+            "Epoch 39/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9615 - loss: 0.0820 - val_accuracy: 0.8500 - val_loss: 0.5699\n",
+            "Epoch 40/40\n",
+            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.9742 - loss: 0.0627 - val_accuracy: 0.8500 - val_loss: 0.5744\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_loss, test_accuracy = model.evaluate(x_test, y_test)\n",
+        "print(\"Test Accuracy:\", test_accuracy)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N3FMlzKLHvWC",
+        "outputId": "31ba2f20-860a-41a2-c52a-b46e84862225"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.8500 - loss: 0.5744\n",
+            "Test Accuracy: 0.8500000238418579\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test_prob = model.predict(x_test)\n",
+        "y_test_pred = (y_test_prob > 0.5).astype(int)\n",
+        "\n",
+        "print(confusion_matrix(y_test, y_test_pred))\n",
+        "print(classification_report(y_test, y_test_pred))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EDbvJ0JNH4pI",
+        "outputId": "539ef226-b047-4012-d930-42b27a184b5f"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step\n",
+            "[[ 0  2]\n",
+            " [ 1 17]]\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.00      0.00      0.00         2\n",
+            "           1       0.89      0.94      0.92        18\n",
+            "\n",
+            "    accuracy                           0.85        20\n",
+            "   macro avg       0.45      0.47      0.46        20\n",
+            "weighted avg       0.81      0.85      0.83        20\n",
+            "\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Mideval code/kuldeep_mideval_code/readme.md b/Mideval code/kuldeep_mideval_code/readme.md
new file mode 100644
index 0000000..cf29aee
--- /dev/null
+++ b/Mideval code/kuldeep_mideval_code/readme.md	
@@ -0,0 +1 @@
+kuldeep