From 780f5e91ffe9a7b55a05d29420e76995a75c5a23 Mon Sep 17 00:00:00 2001
From: Harminder Singh <Harminder2.Singh@live.uwe.ac.uk>
Date: Wed, 2 Apr 2025 10:49:50 +0100
Subject: [PATCH] Updated Version

---
 Model_training/Feeding Dashboard data.csv   | 5387 +++++++++++++++++++
 Model_training/Model_Training_Process.ipynb |  938 ++++
 README.md                                   |  125 +-
 3 files changed, 6370 insertions(+), 80 deletions(-)
 create mode 100644 Model_training/Feeding Dashboard data.csv
 create mode 100644 Model_training/Model_Training_Process.ipynb

diff --git a/Model_training/Feeding Dashboard data.csv b/Model_training/Feeding Dashboard data.csv
new file mode 100644
index 0000000..cb50e3a
--- /dev/null
+++ b/Model_training/Feeding Dashboard data.csv	
@@ -0,0 +1,5387 @@
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diff --git a/Model_training/Model_Training_Process.ipynb b/Model_training/Model_Training_Process.ipynb
new file mode 100644
index 0000000..0c568f3
--- /dev/null
+++ b/Model_training/Model_Training_Process.ipynb
@@ -0,0 +1,938 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c507bd45-d572-4a49-9194-d7b204100f30",
+   "metadata": {},
+   "source": [
+    "# Random Forest For Medical Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d15916b2-cd96-4876-9315-8dbd2e00a030",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#import libraries\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns #for statistics ploting\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split, RandomizedSearchCV, GridSearchCV\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import accuracy_score, f1_score,ConfusionMatrixDisplay\n",
+    "from scipy.stats import randint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "670ddbc2-7d17-4ac9-accd-391eca01b9ee",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "med_data=pd.read_csv(\"./Feeding Dashboard data.csv\") #Import Medical Data\n",
+    "type(med_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "3c04f847-f896-442e-a23a-eeb8e207eb72",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# med_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "422d1fe8-b168-436e-9b7d-30106cc042bb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "encounterId            0\n",
+       "end_tidal_co2       2766\n",
+       "feed_vol            1979\n",
+       "feed_vol_adm        2047\n",
+       "fio2                1932\n",
+       "fio2_ratio          2015\n",
+       "insp_time           2841\n",
+       "oxygen_flow_rate    1099\n",
+       "peep                2593\n",
+       "pip                 2771\n",
+       "resp_rate           2400\n",
+       "sip                 5050\n",
+       "tidal_vol           2817\n",
+       "tidal_vol_actual    2640\n",
+       "tidal_vol_kg        2653\n",
+       "tidal_vol_spon      3147\n",
+       "bmi                  313\n",
+       "referral               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "med_data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "22f2dc3e-c72e-4e40-ab28-d1d269c06c84",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0    3785\n",
+       "1.0    1601\n",
+       "Name: referral, dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find Null Values in Data\n",
+    "\n",
+    "med_data[\"referral\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "39896d39-fdcd-4434-9279-b0b6e086eb14",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(med_data.isnull(), yticklabels=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "c516884b-8804-4f00-be4a-5a9540e81470",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "med_data.drop(\"sip\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "acc6c05f-8b7e-4234-a4c5-a85e25828443",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# med_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "6fa10b7a-a3ce-4a1e-bd18-c5f94da94bf1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Remove Rows With null values\n",
+    "# med_data=pd.concat([med_data,end_tidal_co2],axis=1)\n",
+    "# med_data.dropna(inplace=True)\n",
+    "med_data['feed_vol'].fillna(med_data['feed_vol'].mean(), inplace=True)\n",
+    "med_data['oxygen_flow_rate'].fillna(med_data['oxygen_flow_rate'].mean(), inplace=True)\n",
+    "med_data['peep'].fillna(med_data['peep'].mean(), inplace=True)\n",
+    "med_data['resp_rate'].fillna(med_data['resp_rate'].mean(), inplace=True)\n",
+    "med_data['tidal_vol_spon'].fillna(med_data['tidal_vol_spon'].mean(), inplace=True)\n",
+    "\n",
+    "# med_data['feed_vol'].fillna(0, inplace=True)\n",
+    "# med_data['oxygen_flow_rate'].fillna(0, inplace=True)\n",
+    "# med_data['peep'].fillna(0, inplace=True)\n",
+    "# med_data['resp_rate'].fillna(0, inplace=True)\n",
+    "# med_data['tidal_vol_spon'].fillna(0, inplace=True)\n",
+    "\n",
+    "\n",
+    "# med_data.head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "63132bd7-c18f-4355-8c23-6a9ebe65abca",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ids = med_data['encounterId']\n",
+    "# Remove Less important Features\n",
+    "# med_data.drop(['encounterId'], axis=1,inplace=True)\n",
+    "med_data.drop(['encounterId','end_tidal_co2','insp_time','pip','tidal_vol','tidal_vol_actual','tidal_vol_kg','bmi','feed_vol_adm','fio2','fio2_ratio'], axis=1,inplace=True)\n",
+    "# med_data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "8e24e786-efce-4a53-91d6-ad1cfd67b9e8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(med_data.isnull(), yticklabels=False, cbar=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "3aafd76b-fa8e-46aa-a442-3907cb09883c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5386"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "med_data.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "fca5d6d8-2de7-4f2d-8500-462587dffb4f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n",
+    "    # Get the current Axes instance on the current figure matching the given keyword args, or create one.\n",
+    "    ax = ax or plt.gca() \n",
+    "    \n",
+    "    \n",
+    "    # ✅ Convert Pandas DataFrame to NumPy array\n",
+    "    X = X.to_numpy() if hasattr(X, \"to_numpy\") else np.array(X)\n",
+    "    y = np.array(y)  \n",
+    "    \n",
+    "     # ✅ Get the expected number of features\n",
+    "    n_features = X.shape[1]  # e.g., 5 features\n",
+    "    \n",
+    "    # Plot the training points\n",
+    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n",
+    "               clim=(y.min(), y.max()), zorder=3) \n",
+    "    ax.axis('tight')\n",
+    "    ax.axis('off')\n",
+    "    xlim = ax.get_xlim()\n",
+    "    ylim = ax.get_ylim()\n",
+    "    \n",
+    "    # fit the estimator\n",
+    "    model.fit(X, y)\n",
+    "    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n",
+    "                         np.linspace(*ylim, num=200))\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "     # ✅ Fix: Create a full feature set for pipeline compatibility\n",
+    "    X_test_grid = np.c_[xx.ravel(), yy.ravel()]\n",
+    "\n",
+    "    # ✅ Expand grid to 5 features (by filling missing features with mean values)\n",
+    "    if n_features > 2:\n",
+    "        extra_features = np.full((X_test_grid.shape[0], n_features - 2), X[:, 2:].mean(axis=0))\n",
+    "        X_test_grid = np.hstack([X_test_grid, extra_features])\n",
+    "\n",
+    "    # ✅ Predict using the full feature set\n",
+    "    Z = model.predict(X_test_grid).reshape(xx.shape)\n",
+    "\n",
+    "    # Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
+    "\n",
+    "    # Create a color plot with the results\n",
+    "    n_classes = len(np.unique(y))\n",
+    "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
+    "                           levels=np.arange(n_classes + 1) - 0.5,\n",
+    "                           cmap=cmap, clim=(y.min(), y.max()),\n",
+    "                           zorder=1)\n",
+    "\n",
+    "    ax.set(xlim=xlim, ylim=ylim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "784cc6d9-fcba-4c6b-b91a-6ec8e68bbf8c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "X=med_data.drop(['referral'],axis=1)\n",
+    "y=med_data['referral']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "dff5a896-4799-43f6-ad7a-2ccd852bc4dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test,ids_train, ids_test = train_test_split(X,y,ids,test_size=0.2,random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "629cc6b4-17e1-4c89-bf61-f6aec2d79d88",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.73\n",
+      "F1 Score: 0.72\n",
+      "\n",
+      "Sample Predictions:\n",
+      "         ID  Actual  Predicted\n",
+      "5087  22020     0.0        0.0\n",
+      "2575  12624     0.0        0.0\n",
+      "3334  15458     0.0        0.0\n",
+      "84     2078     1.0        0.0\n",
+      "4651  20436     0.0        0.0\n",
+      "[[634 128]\n",
+      " [164 152]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC  # Import Support Vector Classifier (SVC)\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "# Step 1: Load the data\n",
+    "# file_path = \"/Users/naz/Desktop/sys dev/Feeding Dashboard data.csv\"\n",
+    "file_path = 'Feeding Dashboard data.csv'  # Replace with your CSV file path\n",
+    "data = pd.read_csv(file_path)\n",
+    "\n",
+    "# Step 2: Split the data into features (X), target (y), and ids\n",
+    "ids = data[\"encounterId\"]  # Store the 'id' column for tracking purposes\n",
+    "X = data.drop(columns=[\"encounterId\", 'referral'])  # Drop 'id' and 'target' to get features\n",
+    "y = data['referral']  # Target column\n",
+    "\n",
+    "# Step 3: Handle missing values (impute with median)\n",
+    "imputer = SimpleImputer(strategy=\"median\")\n",
+    "X = imputer.fit_transform(X)\n",
+    "\n",
+    "# Step 4: Split into training and testing sets (80% train, 20% test)\n",
+    "X_train, X_test, y_train, y_test, ids_train, ids_test = train_test_split(X, y, ids, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Step 5: Train an SVC model\n",
+    "model = SVC(kernel='rbf', class_weight='balanced', random_state=42)  # Using RBF kernel and balanced class weights\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 6: Make predictions on the test set\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Step 7: Evaluate the model\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "f1 = f1_score(y_test, y_pred, average=\"weighted\")  # Weighted for multi-class scenarios\n",
+    "\n",
+    "# Step 8: Print evaluation metrics\n",
+    "print(f\"Accuracy: {accuracy:.2f}\")\n",
+    "print(f\"F1 Score: {f1:.2f}\")\n",
+    "\n",
+    "# Step 9: Combine results with IDs for tracking\n",
+    "results = pd.DataFrame({\n",
+    "    \"ID\": ids_test,\n",
+    "    \"Actual\": y_test,\n",
+    "    \"Predicted\": y_pred\n",
+    "})\n",
+    "\n",
+    "# Step 10: Display sample results\n",
+    "print(\"\\nSample Predictions:\")\n",
+    "print(results.head())\n",
+    "confusion_matrix(y_test,y_pred)\n",
+    "print(confusion_matrix(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "b07eac8a-0dcc-47b7-8d1d-c9f10bada3c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n",
+      "Best parameters found:  {'max_depth': 15, 'max_features': 3, 'max_samples': 0.8, 'min_samples_split': 10, 'n_estimators': 100}\n",
+      "Best cross-validation score:  0.8430867361374972\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Base classifier with initial parameters\n",
+    "base_rf = RandomForestClassifier(\n",
+    "    n_estimators=200,\n",
+    "    max_features=3,\n",
+    "    max_samples=0.7,\n",
+    "    bootstrap=True,\n",
+    "    criterion='gini',\n",
+    "    max_depth=15,\n",
+    "    min_samples_split=5,\n",
+    "    class_weight={0: 3, 1: 4},\n",
+    "    random_state=42  # Ensures reproducibility\n",
+    ")\n",
+    "\n",
+    "# Define a grid of hyperparameters to explore\n",
+    "param_grid = {\n",
+    "    'n_estimators': [100, 200, 300],\n",
+    "    'max_features': [2, 3, 4],\n",
+    "    'max_samples': [0.6, 0.7, 0.8],\n",
+    "    'max_depth': [10, 15, 20, None],\n",
+    "    'min_samples_split': [2, 5, 10]\n",
+    "}\n",
+    "\n",
+    "# Set up the grid search with 5-fold cross-validation\n",
+    "grid_search = GridSearchCV(\n",
+    "    estimator=base_rf,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=5,\n",
+    "    scoring='accuracy',  # Adjust scoring if needed\n",
+    "    n_jobs=-1,\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "# Fit the grid search on your training data\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "# Output the best parameters and best cross-validation score\n",
+    "print(\"Best parameters found: \", grid_search.best_params_)\n",
+    "print(\"Best cross-validation score: \", grid_search.best_score_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "9f0259c2-5ed2-4674-a497-a31545e8b58d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy : [0.8895078922934077, 0.8895078922934077, 0.8899721448467967, 0.8957753017641596, 0.8929897864438254, 0.8934540389972145, 0.8929897864438254, 0.8948467966573816, 0.8913649025069638, 0.8939182915506035, 0.893686165273909, 0.8934540389972145, 0.8927576601671309]\n",
+      "Validation Accuracy : [0.8237476808905381, 0.8413729128014842, 0.839517625231911, 0.8432282003710575, 0.8413729128014842, 0.8404452690166976, 0.8441558441558441, 0.8413729128014842, 0.8376623376623377, 0.8432282003710575, 0.839517625231911, 0.8413729128014842, 0.8423005565862709]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize lists to store metrics\n",
+    "train_accuracies = []\n",
+    "validation_accuracies = []\n",
+    "\n",
+    "# Train Random Forest with varying hyperparameters (e.g., n_estimators)\n",
+    "for n_trees in range(10, 400, 30):\n",
+    "    # Initialize the model\n",
+    "    # rf_model = RandomForestClassifier(n_estimators=n_trees, random_state=42)\n",
+    "    rf_model = Pipeline([\n",
+    "    ('scaler', StandardScaler()),  # Step 1: Feature scaling\n",
+    "    ('classifier', RandomForestClassifier(n_estimators=n_trees, min_samples_split = 10, max_features=2, max_depth=10, max_samples = 0.8))  # Step 2: Random Forest\n",
+    "])\n",
+    "    # Train the model on the training data\n",
+    "    rf_model.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Compute accuracy on the training set\n",
+    "    train_pred = rf_model.predict(X_train)\n",
+    "    train_accuracy = accuracy_score(y_train, train_pred)\n",
+    "    train_accuracies.append(train_accuracy)\n",
+    "    \n",
+    "    # Compute accuracy on the validation set\n",
+    "    valid_pred = rf_model.predict(X_test)\n",
+    "    valid_accuracy = accuracy_score(y_test, valid_pred)\n",
+    "    validation_accuracies.append(valid_accuracy)\n",
+    "\n",
+    "# Plot Training vs Validation Accuracy\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(range(10, 400, 30), train_accuracies, label='Training Accuracy', marker='o')\n",
+    "plt.plot(range(10, 400, 30), validation_accuracies, label='Validation Accuracy', marker='o')\n",
+    "plt.xlabel('Number of Trees (n_estimators)', fontsize=12)\n",
+    "plt.ylabel('Accuracy', fontsize=12)\n",
+    "plt.title('Training vs Validation Accuracy', fontsize=14)\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()\n",
+    "print('Training Accuracy :',train_accuracies)\n",
+    "print('Validation Accuracy :',validation_accuracies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "0e424204-9c5d-417e-9dec-1d548db749a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('dropper',\n",
+       "                 DropColumns(columns=['encounterId', 'end_tidal_co2',\n",
+       "                                      'insp_time', 'pip', 'tidal_vol',\n",
+       "                                      'tidal_vol_actual', 'tidal_vol_kg', 'bmi',\n",
+       "                                      'feed_vol_adm', 'fio2', 'fio2_ratio',\n",
+       "                                      'referral', 'sip'])),\n",
+       "                ('imputer', SimpleImputer()), ('scaler', StandardScaler()),\n",
+       "                ('classifier',\n",
+       "                 RandomForestClassifier(class_weight={0: 3, 1: 4}, max_depth=15,\n",
+       "                                        max_features=3, max_samples=0.7,\n",
+       "                                        min_samples_split=5,\n",
+       "                                        n_estimators=200))])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "import joblib\n",
+    "\n",
+    "# Custom Transformer for Dropping Columns\n",
+    "class DropColumns(BaseEstimator, TransformerMixin):\n",
+    "    def __init__(self, columns):\n",
+    "        self.columns = columns\n",
+    "    \n",
+    "    def fit(self, X, y=None):\n",
+    "        return self  # No fitting needed\n",
+    "\n",
+    "    def transform(self, X):\n",
+    "        return X.drop(columns=self.columns, errors='ignore')\n",
+    "\n",
+    "# Define columns to drop and impute\n",
+    "drop_columns = ['encounterId', 'end_tidal_co2', 'insp_time', 'pip', 'tidal_vol', \n",
+    "                'tidal_vol_actual', 'tidal_vol_kg', 'bmi', 'feed_vol_adm', 'fio2', 'fio2_ratio','referral','sip']\n",
+    "columns_to_impute = ['feed_vol', 'oxygen_flow_rate', 'peep', 'resp_rate', 'tidal_vol_spon']\n",
+    "\n",
+    "# Load or preprocess your dataset\n",
+    "# med_data = pd.read_csv(\"your_data.csv\")  # Uncomment if loading data\n",
+    "\n",
+    "# Store encounter IDs separately before dropping\n",
+    "# Separate features and target\n",
+    "X = med_data.drop(columns=['referral'])\n",
+    "y = med_data['referral']\n",
+    "\n",
+    "# Define the full pipeline\n",
+    "full_pipeline = Pipeline([\n",
+    "    ('dropper', DropColumns(drop_columns)),  # Drop unnecessary columns\n",
+    "    ('imputer', SimpleImputer(strategy='mean')),  # Fill missing values with mean\n",
+    "    ('scaler', StandardScaler()),  # Standardize numerical features\n",
+    "        ('classifier', RandomForestClassifier(\n",
+    "            n_estimators=200, max_features=3, max_samples=0.7, bootstrap=True,\n",
+    "            criterion='gini', max_depth=15, min_samples_split=5, class_weight={0: 3, 1: 4}\n",
+    "        ))\n",
+    "])\n",
+    "\n",
+    "# Split dataset\n",
+    "X_train, X_test, y_train, y_test, ids_train, ids_test = train_test_split(X, y, ids, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train model\n",
+    "full_pipeline.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "251ebad3-39a3-41c5-b1b2-cfaf9247d322",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[717  45]\n",
+      " [138 178]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.84      0.94      0.89       762\n",
+      "         1.0       0.80      0.56      0.66       316\n",
+      "\n",
+      "    accuracy                           0.83      1078\n",
+      "   macro avg       0.82      0.75      0.77      1078\n",
+      "weighted avg       0.83      0.83      0.82      1078\n",
+      "\n",
+      "Accuracy: 0.83\n",
+      "F1 Score: 0.82\n",
+      "         ID  Actual  Predicted\n",
+      "5087  22020     0.0        0.0\n",
+      "2575  12624     0.0        0.0\n",
+      "3334  15458     0.0        0.0\n",
+      "84     2078     1.0        1.0\n",
+      "4651  20436     0.0        0.0\n",
+      "3692  16784     0.0        0.0\n",
+      "5142  22225     1.0        1.0\n",
+      "589    4783     0.0        0.0\n",
+      "2726  13156     0.0        0.0\n",
+      "4860  21171     1.0        1.0\n",
+      "3782  17147     1.0        0.0\n",
+      "2503  12351     0.0        0.0\n",
+      "3839  17364     1.0        1.0\n",
+      "2561  12560     0.0        0.0\n",
+      "497    4464     0.0        0.0\n",
+      "33     1473     1.0        1.0\n",
+      "3641  16599     0.0        0.0\n",
+      "4649  20434     0.0        0.0\n",
+      "315    3919     1.0        0.0\n",
+      "3388  15658     0.0        1.0\n",
+      "Model saved as random_forest_pipeline.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Make predictions\n",
+    "y_pred = full_pipeline.predict(X_test)\n",
+    "\n",
+    "\n",
+    "cm = confusion_matrix(y_test,y_pred)\n",
+    "print(confusion_matrix(y_test,y_pred))\n",
+    "\n",
+    "print(classification_report(y_test,y_pred))\n",
+    "\n",
+    "# Step 6: Evaluate the model\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "f1 = f1_score(y_test, y_pred, average=\"weighted\")  # Weighted for multi-class scenarios\n",
+    "\n",
+    "# Step 7: Print evaluation metrics\n",
+    "print(f\"Accuracy: {accuracy:.2f}\")\n",
+    "print(f\"F1 Score: {f1:.2f}\")\n",
+    "\n",
+    "# Step 8: Combine results with IDs for tracking\n",
+    "results = pd.DataFrame({\n",
+    "    \"ID\": ids_test,\n",
+    "    \"Actual\": y_test,\n",
+    "    \"Predicted\": y_pred\n",
+    "})\n",
+    "\n",
+    "# Display or save results\n",
+    "print(results.head(20))  # Print first few rows\n",
+    "results.to_csv(\"predictions.csv\", index=False)  # Save results to a file\n",
+    "\n",
+    "# Export the trained pipeline\n",
+    "joblib.dump(full_pipeline, 'random_forest_pipeline.pkl')\n",
+    "print(\"Model saved as random_forest_pipeline.pkl\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c56ce220-1669-4fae-afc6-b5ede797f63c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    ID  Predicted\n",
+      "0  666        1.0\n",
+      "1  795        1.0\n",
+      "2  822        1.0\n",
+      "3  832        1.0\n",
+      "4  837        1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import joblib\n",
+    "\n",
+    "# Load the trained model\n",
+    "model = joblib.load(\"random_forest_pipeline.pkl\")\n",
+    "\n",
+    "# Load new real-world data (replace 'new_data.csv' with your actual data file)\n",
+    "new_data = pd.read_csv(\"Feeding Dashboard data.csv\")\n",
+    "\n",
+    "# Store encounter IDs separately (if needed)\n",
+    "ids_new = new_data['encounterId']\n",
+    "\n",
+    "# Ensure consistency by dropping unnecessary columns (if they exist in new data)\n",
+    "# drop_columns = ['encounterId', 'end_tidal_co2', 'insp_time', 'pip', 'tidal_vol', \n",
+    "#                 'tidal_vol_actual', 'tidal_vol_kg', 'bmi', 'feed_vol_adm', 'fio2', 'fio2_ratio']\n",
+    "\n",
+    "# new_data = new_data.drop(columns=drop_columns, errors='ignore')\n",
+    "\n",
+    "# Make predictions using the trained pipeline\n",
+    "y_pred_new = model.predict(new_data)\n",
+    "\n",
+    "new_data['Prediction'] = y_pred_new\n",
+    "\n",
+    "# Create results DataFrame\n",
+    "results_new = pd.DataFrame({\n",
+    "    \"ID\": ids_new,\n",
+    "    \"Predicted\": y_pred_new\n",
+    "})\n",
+    "\n",
+    "# Display or save results\n",
+    "print(results_new.head())  # Print first few rows\n",
+    "new_data.to_csv(\"real_predictions_demo.csv\", index=False)  # Save results to CSV\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3d0591df-f086-4e1a-8a61-12ffba498bb4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test,y_pred)\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
+    "plt.title(\"Confusion Matrix\")\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a1eba6de-2bf0-4047-a3dc-5989326bfbb9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Accuracy: 0.9094\n",
+      "\n",
+      "Confusion Matrix:\n",
+      " [[3683  102]\n",
+      " [ 386 1215]]\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.91      0.97      0.94      3785\n",
+      "         1.0       0.92      0.76      0.83      1601\n",
+      "\n",
+      "    accuracy                           0.91      5386\n",
+      "   macro avg       0.91      0.87      0.89      5386\n",
+      "weighted avg       0.91      0.91      0.91      5386\n",
+      "\n",
+      "\n",
+      "Download Comparison Results: [Comparison_Results.csv](sandbox:Comparison_Results.csv)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"real_predictions_demo.csv\")\n",
+    "\n",
+    "# Ensure both columns exist before comparing\n",
+    "if 'referral' in df.columns and 'Prediction' in df.columns:\n",
+    "    # Compare actual vs. predicted\n",
+    "    comparison = df[['encounterId', 'referral', 'Prediction']]\n",
+    "    \n",
+    "    # Calculate accuracy\n",
+    "    accuracy = accuracy_score(df['referral'], df['Prediction'])\n",
+    "    conf_matrix = confusion_matrix(df['referral'], df['Prediction'])\n",
+    "    class_report = classification_report(df['referral'], df['Prediction'])\n",
+    "\n",
+    "    # Save comparison results\n",
+    "    output_path = \"Comparison_Results.csv\"\n",
+    "    comparison.to_csv(output_path, index=False)\n",
+    "\n",
+    "    # Display results\n",
+    "    print(f\"Model Accuracy: {accuracy:.4f}\")\n",
+    "    print(\"\\nConfusion Matrix:\\n\", conf_matrix)\n",
+    "    print(\"\\nClassification Report:\\n\", class_report)\n",
+    "\n",
+    "    # Provide download link\n",
+    "    print(f\"\\nDownload Comparison Results: [Comparison_Results.csv](sandbox:{output_path})\")\n",
+    "else:\n",
+    "    print(\"Error: Required columns 'referral' and 'Prediction' not found in dataset.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a3f96e8-e238-4472-ab35-a60dd6e320f9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Machine Learning",
+   "language": "python",
+   "name": "mlenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index 34b4a13..91e3ad6 100644
--- a/README.md
+++ b/README.md
@@ -1,93 +1,58 @@
-# Systems Development Group Project 2025
+# System Development Group Project 2025
 
+Welcome to **System Development Group Project 2025**! This repository contains the code and trained model files for our desktop application.
 
+## 📂 Project Structure
 
-## Getting started
+- **`Feeding_Dashboard.py`** – The main desktop application script.
+- **`random_forest_pipeline.pkl`** – The trained machine learning model.
+- **`Model_training/`** – Contains Jupyter Notebook files used for model processing and training.
 
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
+## Getting Started
 
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+### Prerequisites
+Ensure you have Python installed along with the required dependencies. You may install them using:
 
+```bash
+pip install -r requirements.txt
 ```
-cd existing_repo
-git remote add origin https://gitlab.uwe.ac.uk/h39-singh/systems-development-group-project-2025.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.uwe.ac.uk/h39-singh/systems-development-group-project-2025/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
 
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
+### Required Imports
+Ensure the following libraries are available in your environment:
+
+```python
+import customtkinter as ctk
+import tkinter as tk
+from tkinter import filedialog, messagebox
+import pandas as pd
+import joblib
+import os
+from tkinter import ttk
+from sklearn.base import BaseEstimator, TransformerMixin
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+from reportlab.lib import colors
+from reportlab.lib.pagesizes import letter
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image, Table, TableStyle
+from reportlab.lib.styles import getSampleStyleSheet
+from reportlab.lib.units import inch
+import matplotlib.pyplot as plt
+from reportlab.pdfgen import canvas
+from reportlab.lib.utils import ImageReader
+import io
+```
 
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
+### Running the Application
+1. Place the `random_forest_pipeline.pkl` file in the same directory as `Feeding_Dashboard.py`.
+2. Run the application using:
 
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+```bash
+python Feeding_Dashboard.py
+```
 
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+## Important Notes
+- The **model file** (`random_forest_pipeline.pkl`) **must be in the same folder** as `Feeding_Dashboard.py` for the application to work correctly.
+- The **Model_training folder** contains Jupyter Notebook files detailing the model training process.
 
-## License
-For open source projects, say how it is licensed.
+-------
 
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
-- 
GitLab