diff --git a/GA2.ipynb b/GA2.ipynb
index 1131f014d40fec5b13be7fbe03364a726a3ac77e..587fcd981acdd70da969ddadec9d2d513b795514 100644
--- a/GA2.ipynb
+++ b/GA2.ipynb
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,43 +156,32 @@
     "\n",
     "        # Update population\n",
     "        population = toolbox.select(offspring, k=len(population))\n",
-    "\n",
-    "    # Use sortNondominated to select the best individual(s)\n",
+    "        \n",
     "    fronts = tools.sortNondominated(population, len(population))\n",
     "    best_front = fronts[0]  # Front 1 contains the best non-dominated solutions\n",
     "\n",
-    "    # Find the individual with the highest correlation in the best front\n",
+    "\n",
     "    #find individual with highest correclation\n",
     "    best_corr_individual = max(best_front, key=lambda ind: ind.fitness.values[0]) \n",
     "    #fitness.values[0]: Corr score\n",
-    "    # fitness.values[1]: Accuracy score\n",
-    "    #loops through best_corr_individual and selects the corresponding feature names from X_train.columns\n",
+    "    \n",
     "    best_corr_features = [X_train.columns[i] for i in range(len(best_corr_individual)) if best_corr_individual[i] == 1]\n",
     "    best_corr_value = best_corr_individual.fitness.values[0] #corr of the best individual\n",
     "\n",
-    "    # Find the individual with the highest accuracy in the best front\n",
+    "    # Find the individual with the highest accuracy \n",
     "    best_acc_individual = max(best_front, key=lambda ind: ind.fitness.values[1])\n",
     "    best_acc_features = [X_train.columns[i] for i in range(len(best_acc_individual)) if best_acc_individual[i] == 1]\n",
     "    best_acc_value = best_acc_individual.fitness.values[1]\n",
     "\n",
     "    combined_features = list(set(best_corr_features + best_acc_features))\n",
     "\n",
-    "    # Print results in the desired format\n",
-    "    #print(f\"Correlation: {best_corr_value},\\n Accuracy: {best_acc_value},\\n Best Features: {combined_features}\")\n",
-    "\n",
     "    return best_corr_value, best_acc_value, combined_features\n",
-    "    # # Get Best Individual\n",
-    "    # best_individual = tools.selBest(population, k=1)[0]\n",
-    "    # selected_features = [X.columns[i] for i in range(len(best_individual)) if best_individual[i] == 1]\n",
-    "\n",
-    "    # print(\"Selected Features using Pearson Correlation GA: \\n\", selected_features)\n",
-    "    # return best_individual.fitness.values[0], selected_features\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -307,9 +296,7 @@
     "    print(\"=\" * 50, \"\\n\")\n",
     "\n",
     "    corr_values.append(best_corr_value)\n",
-    "    acc_values.append(best_acc_value)\n",
-    "    \n",
-    " \n"
+    "    acc_values.append(best_acc_value)"
    ]
   },
   {
@@ -383,7 +370,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -397,7 +384,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/dashboard/__pycache__/filtered.cpython-312.pyc b/dashboard/__pycache__/filtered.cpython-312.pyc
index 1c3f806dd16e292cf1216e6b6f536e97ea0e5287..203d56b067ba67e82823fc2bdd4fdeaca7efb30f 100644
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diff --git a/dashboard/__pycache__/model_result.cpython-312.pyc b/dashboard/__pycache__/model_result.cpython-312.pyc
index d2b36416e0d41f58f557c242541c40c4568e8c82..9affc8edb754ce2c1217337c788eececfb8efde4 100644
Binary files a/dashboard/__pycache__/model_result.cpython-312.pyc and b/dashboard/__pycache__/model_result.cpython-312.pyc differ
diff --git a/dashboard/__pycache__/overview.cpython-312.pyc b/dashboard/__pycache__/overview.cpython-312.pyc
index 6a3c8a7fa4f9d18dc0887a8e0e1c917e88f13173..8f2f26704cc6e3bfa9550e84daca8bd42c99817e 100644
Binary files a/dashboard/__pycache__/overview.cpython-312.pyc and b/dashboard/__pycache__/overview.cpython-312.pyc differ
diff --git a/dashboard/__pycache__/prediction.cpython-312.pyc b/dashboard/__pycache__/prediction.cpython-312.pyc
index 2c97bfc1dfdac7190659cd859e4faa3795ff5e07..cf53ed87e59380c92f07845eca430f1803340a83 100644
Binary files a/dashboard/__pycache__/prediction.cpython-312.pyc and b/dashboard/__pycache__/prediction.cpython-312.pyc differ
diff --git a/dashboard/__pycache__/visuals.cpython-312.pyc b/dashboard/__pycache__/visuals.cpython-312.pyc
index bb606234b188a82a8c8f031487771e7597c1fc90..429913f67c3a2576b9f98e38f180de968ad44613 100644
Binary files a/dashboard/__pycache__/visuals.cpython-312.pyc and b/dashboard/__pycache__/visuals.cpython-312.pyc differ
diff --git a/dashboard/app.py b/dashboard/app.py
index d35a5fa316f18fd511d3bd8fdf4073af9b0c829b..231722e07bbb177d04382e1bd2b92faa61e31353 100644
--- a/dashboard/app.py
+++ b/dashboard/app.py
@@ -9,10 +9,6 @@ st.set_page_config(page_title="Alzheimer's Dashboard", layout="wide")
 # Load Data
 df_full = pd.read_csv("alzheimers_disease_data.csv")  
 
-# Sidebar Navigation
-# st.sidebar.title("Navigation")
-# page = st.sidebar.radio("Tabs", ["Dataset Overview", "Filtered Data Insight", "Model Results", "Make a Prediction"])
-
 # Set default page
 if "page" not in st.session_state:
     st.session_state.page = "Dataset Overview"
@@ -20,33 +16,20 @@ if "page" not in st.session_state:
 st.sidebar.markdown("## Tabs")
 if st.sidebar.button("Dataset Overview"):
     st.session_state.page = "Dataset Overview"
-if st.sidebar.button("Filtered Data Insight"):
-    st.session_state.page = "Filtered Data Insight"
+if st.sidebar.button("Selected Features"):
+    st.session_state.page = "Selected Features"
 if st.sidebar.button("Model Results"):
     st.session_state.page = "Model Results"
 if st.sidebar.button("Make a Prediction"):
     st.session_state.page = "Make a Prediction"
 
-# Display selected page
-st.title(f"🧠{st.session_state.page}")
-
-# if page == "Dataset Overview":
-#     data_overview()
-
-# elif page == "Filtered Data Insight":
-#     selected_data()
-
-# elif page == "Model Results":
-#     result()
-
-# elif page == "Make a Prediction":
-#     predict()
 
+st.title(f"💜🧠{st.session_state.page}")
 
 if st.session_state.page == "Dataset Overview":
     data_overview()
 
-elif st.session_state.page == "Filtered Data Insight":
+elif st.session_state.page == "Selected Features":
     selected_data()
 
 elif st.session_state.page == "Model Results":
diff --git a/dashboard/filtered.py b/dashboard/filtered.py
index 4026c58c8ae065e287528bb0920fb8dd40d4e448..d02fae01df1b8ab9ba253294b8b942ed4b4f2267 100644
--- a/dashboard/filtered.py
+++ b/dashboard/filtered.py
@@ -1,25 +1,21 @@
 import streamlit as st
 import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pickle
 from visuals import plot_corr_matrix
 
 df_filtered = pd.read_csv("filtered_selected_features.csv")
-df_full = pd.read_csv("alzheimers_disease_data.csv")  # assuming this is your original dataset
+df_full = pd.read_csv("alzheimers_disease_data.csv")
 feature_cols = df_filtered.columns[:-1]
 
 def selected_data():
-    st.title("Filtered Data Insight")
+    st.title("🎗️💜Selected Features Insight")
     st.subheader("Dataset View")
-    dataset_choice = st.radio("Select Dataset", ["Filtered Dataset (Selected Features)", "Full Dataset"])
+    dataset_choice = st.radio("Select Dataset", ["Selected Features", "Full Dataset"])
 
     st.write("Diagnosis Information: ")
     st.write("Diagnosis status for Alzheimer's Disease, where 0 indicates No and 1 indicates Yes.")
 
-    if dataset_choice == "Filtered Dataset (Selected Features)":
-        st.subheader("Filtered Dataset")
+    if dataset_choice == "Selected Features":
+        st.subheader("Selected Features Dataset")
         st.dataframe(df_filtered)
     else:
         st.subheader("Full Dataset")
diff --git a/dashboard/model_result.py b/dashboard/model_result.py
index 5758b500329ef64dd0525c708507fb61db5aac95..9d318d8fcad7b52ac40c0573af001969bdba7dcc 100644
--- a/dashboard/model_result.py
+++ b/dashboard/model_result.py
@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
 from PIL import Image
 
 def result():
-    st.title("📊 Model Performance Overview")
+    st.title("🎗️💜 Model Performance Overview")
 
     # Load and display model metrics
     model_metrics = pd.read_csv("metrics.csv")
@@ -20,28 +20,36 @@ def result():
         st.image(Image.open("Recall.png"))
     elif metric == "F1-Score":
         st.image(Image.open("F1-Score.png"))
-    # fig, ax = plt.subplots()
-    # ax.bar(model_metrics["Model"], model_metrics[metric], color="skyblue")
-    # ax.set_ylabel(metric)
-    # ax.set_title(f"{metric} Comparison")
-    # st.pyplot(fig)
 
     st.markdown("---")
     st.subheader("📉 Detailed Model Visualizations")
 
-    # Show saved plots
+    # Decision Tree Performance
     st.subheader("Decision Tree Performance")
-    st.image(Image.open("classification_dt.png"), caption="Decision Tree - Classification Report")
-    st.image(Image.open("confusion_dt.png"), caption="Decision Tree - Confusion Matrix")
+    img_dt_classification = Image.open("classification_dt.png")
+    img_dt_classification = img_dt_classification.resize((1000, 700))  
+    st.image(img_dt_classification, caption="Decision Tree - Classification Report")
 
+    img_dt_confusion = Image.open("confusion_dt.png")
+    img_dt_confusion = img_dt_confusion.resize((1000, 700))  
+    st.image(img_dt_confusion, caption="Decision Tree - Confusion Matrix")
+
+    #SVC Performance
     st.subheader("SVC Performance")
-    st.image(Image.open("classification_svc.png"), caption="SVC - Classification Report")
-    st.image(Image.open("confusion_svc.png"), caption="SVC - Confusion Matrix")
+    img_svc_classification = Image.open("classification_svc.png")
+    img_svc_classification = img_svc_classification.resize((1000, 700))  
+    st.image(img_svc_classification, caption="SVC - Classification Report")
+
+    img_svc_confusion = Image.open("confusion_svc.png")
+    img_svc_confusion = img_svc_confusion.resize((1000, 700))  
+    st.image(img_svc_confusion, caption="SVC - Confusion Matrix")
 
+    #Random Forest
     st.subheader("Random Forest Performance")
-    st.image(Image.open("classification_rf.png"), caption="Random Forest - Classification Report")
-    st.image(Image.open("confusion_rf.png"), caption="Random Forest - Confusion Matrix")
+    img_rf_classification = Image.open("classification_rf.png")
+    img_rf_classification = img_rf_classification.resize((1000, 700))  
+    st.image(img_rf_classification, caption="Random Forest - Classification Report")
 
-    # # Correlation Matrix
-    # st.subheader("📌 Correlation Matrix of Selected Features")
-    # st.image(Image.open("correlation_matrix.png"), caption="Feature Correlation Matrix")
\ No newline at end of file
+    img_rf_confusion = Image.open("confusion_rf.png")
+    img_rf_confusion = img_rf_confusion.resize((1000, 700)) 
+    st.image(img_rf_confusion, caption="Random Forest - Confusion Matrix")
diff --git a/dashboard/overview.py b/dashboard/overview.py
index 993f4e695205fa89ee2b59767e486f8090071961..53bedb6ccf9b492d412f8f27f420e605c7d9a2ec 100644
--- a/dashboard/overview.py
+++ b/dashboard/overview.py
@@ -1,26 +1,22 @@
 
 import streamlit as st
 import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pickle
 from visuals import load_full_data, create_grouped_features, plot_correlation_with_diagnosis, plot_distribution, plot_corr_matrix
 
 df_filtered = pd.read_csv("filtered_selected_features.csv")
-df_full = pd.read_csv("alzheimers_disease_data.csv")  # assuming this is your original dataset
+df_full = pd.read_csv("alzheimers_disease_data.csv")  
 feature_cols = df_filtered.columns[:-1]
 
 def data_overview():
-    st.title("📌 Dataset Description & Insights")
+    st.title("🎗️💜 Dataset Description & Insights")
     st.subheader("Dataset View")
-    dataset_choice = st.radio("Select Dataset", ["Filtered Dataset (Selected Features)", "Full Dataset"])
+    dataset_choice = st.radio("Select Dataset", ["Selected Features", "Full Dataset"])
 
     st.write("Diagnosis Information: ")
     st.write("Diagnosis status for Alzheimer's Disease, where 0 indicates No and 1 indicates Yes.")
 
-    if dataset_choice == "Filtered Dataset (Selected Features)":
-        st.subheader("Filtered Dataset")
+    if dataset_choice == "Selected Features":
+        st.subheader("Selected Features Dataset")
         st.dataframe(df_filtered)
     else:
         st.subheader("Full Dataset")
@@ -28,6 +24,7 @@ def data_overview():
 
     st.subheader("Diagnosis Distribution")
     fig = plot_distribution(df_full)
+    fig.set_size_inches(10, 4)
     st.pyplot(fig)
 
     st.subheader("Feature distribution")
@@ -42,7 +39,7 @@ def data_overview():
     for group_name, group_df in groups.items():
         st.markdown(f"## 📌 {group_name} Features")
 
-        col1, col2 = st.columns([1, 2])  # [text column, graph column]
+        col1, col2 = st.columns([1, 2])  
 
         with col1:
             st.markdown(f"""
diff --git a/dashboard/prediction.py b/dashboard/prediction.py
index c48035eb4d1c83d3153c1aca73027010ab3c2e69..8f2b1b8f1238d7702b6ef8cf348c7cbb5bac53b6 100644
--- a/dashboard/prediction.py
+++ b/dashboard/prediction.py
@@ -1,8 +1,6 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
 import pickle
 
 # Load models & scaler
@@ -24,26 +22,98 @@ models = {
 df_filtered = pd.read_csv("filtered_selected_features.csv")
 feature_cols = df_filtered.columns[:-1]
 
+# Defining the features and their ranges
+feature_info = {
+    'BehavioralProblems': {
+        'Range': '0 to 1',
+        'Description': 'Indicates whether the patient has behavioural problems. 0 = No, 1 = Yes'
+    },
+    'Gender': {
+        'Range': '0 to 1',
+        'Description': 'Gender of the patient. 0 = Male, 1 = Female'
+    },
+    'MemoryComplaints': {
+        'Range': '0 to 1',
+        'Description': 'Indicates whether the patient has memory complaints. 0 = No, 1 = Yes'
+    },
+    'FunctionalAssessment': {
+        'Range': '0 to 10',
+        'Description': 'Functional assessment score, ranging from 0 to 10. Lower scores indicate greater impairment.'
+    },
+    'ADL': {
+        'Range': '0 to 10',
+        'Description': 'Activities of Daily Living (ADL) score, ranging from 0 to 10. Lower scores indicate greater impairment.'
+    },
+    'MMSE': {
+        'Range': '0 to 30',
+        'Description': 'Mini-Mental State Examination (MMSE) score, ranging from 0 to 30. Lower scores indicate cognitive impairment.'
+    }
+}
+
+# Clean up feature names to match the keys in feature_info
+def feature_name(feature):
+    feature_clean = feature.strip().replace(" ", "")
+    return feature_clean
+
+# Convert the feature into a table
+feature_table = pd.DataFrame.from_dict(feature_info, orient='index')
+
+def validate_input(feature, value):
+    """Validate that input value is within the valid range."""
+    feature_match = feature_name(feature) 
+    
+    if feature_match not in feature_info:
+        return f"Error: The feature '{feature}' is not recognized."
+
+    range_str = feature_info[feature_match]['Range']
+    min_val, max_val = map(float, range_str.split(' to '))
+    
+    if not (min_val <= value <= max_val):
+        return f"Error: {feature} must be between {min_val} and {max_val}."
+    return None
+
 def predict():
-    st.title("Predict Alzheimer's Diagnosis")
+    st.title("🎗️💜Predict Alzheimer's Diagnosis")
 
     model_name = st.selectbox("Choose a Model", list(models.keys()))
     selected_model = models[model_name]
-    st.subheader("Helful tip to input data ;)")
-    st.write("Behavioural Problems is between 0 and 1, 0 indicates No and 1 indicates Yes")
-    st.write("Gender is between 0 and 1, 0 indicates Male and 1 indicates Female")
-    st.write("Presence of memory complaints, where 0 indicates No and 1 indicates Yes")
-    st.write("Functional assessment score, ranging from 0 to 10. Lower scores indicate greater impairment")
-    st.write("Activities of Daily Living score, ranging from 0 to 10. Lower scores indicate greater impairment")
-    st.write("Mini-Mental State Examination score, ranging from 0 to 30. Lower scores indicate cognitive impairment")
-    
+    #display feature information
+    st.subheader("Helpful tip to input data ;)")
+    st.table(feature_table)
+
     user_input = []
+    error_messages = []
+
     for col in feature_cols:
         val = st.number_input(f"{col}", step=0.1)
         user_input.append(val)
-
+        
+        # Validate input value for each feature
+        error_message = validate_input(col, val)
+        if error_message:
+            error_messages.append(error_message)
+    
     if st.button("Predict"):
-        input_array = np.array(user_input).reshape(1, -1)
-        scaled_input = scaler.transform(input_array)
-        prediction = selected_model.predict(scaled_input)[0]
-        st.success(f"Prediction with {model_name}: {"🟢 Positive (Alzheimers Present)" if prediction == 1 else "🔴 Negative (No Alzhemeirs)"}")
\ No newline at end of file
+        # Show error messages if any invalid input
+        if error_messages:
+            for msg in error_messages:
+                st.error(msg)
+        else:
+            # If inputs are valid, proceed with prediction
+            input_array = np.array(user_input).reshape(1, -1)
+            scaled_input = scaler.transform(input_array)
+            
+            prob = selected_model.predict_proba(scaled_input)
+            # Probability for class 1 (Alzheimer's present)
+            alz_prob = prob[0][1] * 100  
+            no_alz_prob = prob[0][0] * 100  
+            
+            # Display probabilities
+            st.write(f"Prediction with {model_name}:")
+            #st.write(f"🟢 {alz_prob:.2f}% chance of Alzheimer's")
+            #st.write(f"🔴 {no_alz_prob:.2f}% chance of No Alzheimer's")
+            
+            if alz_prob > no_alz_prob:
+                st.success(f"🟢 Positive Alzheimer's diagnosis ({alz_prob:.2f}% chance of Alzheimer's)")
+            else:
+                st.success(f"🔴 Negative Alzheimer's diagnosis ({no_alz_prob:.2f}% chance of no Alzheimer's)")
diff --git a/dashboard/visuals.py b/dashboard/visuals.py
index 97a85c0aa9e3a69b101821abea062a19abd9e12c..cf3c7db89d062584157aea3cd0499e2f890c1b4c 100644
--- a/dashboard/visuals.py
+++ b/dashboard/visuals.py
@@ -31,7 +31,6 @@ def plot_correlation_with_diagnosis(group_df, y, group_name):
     plt.xticks(rotation=45)
     return fig
 
-
 def plot_distribution(df):
     fig, ax = plt.subplots()
     sns.countplot(x="Diagnosis", data=df, ax=ax)
@@ -41,7 +40,7 @@ def plot_distribution(df):
     return fig
 
 def plot_corr_matrix(df):
-    # Keep only numeric columns
+    
     df_numeric = df.select_dtypes(include=[np.number])
 
     correlation_matrix = df_numeric.corr()
diff --git a/dt_model.pkl b/dt_model.pkl
deleted file mode 100644
index c2b9e831ad114a0ec53f3b80054c192d7ab577d0..0000000000000000000000000000000000000000
Binary files a/dt_model.pkl and /dev/null differ
diff --git a/metrics.csv b/metrics.csv
index 449c78f6a94bc4055c383f1627ebf5c2072f7d84..a357847465a41e59d13fc40dc07e6369289e32ae 100644
--- a/metrics.csv
+++ b/metrics.csv
@@ -1,3 +1,4 @@
 Model,Accuracy,Precision,Recall,F1-Score
 Random Forest,0.9534883720930233,0.9538906924045891,0.9534883720930233,0.9531150398295376
 SVC,0.9279069767441861,0.927771792161327,0.9279069767441861,0.9273908901898491
+Decision Tree,0.9465116279069767,0.9466651081476662,0.9465116279069767,0.9461287249795657
diff --git a/models.ipynb b/models.ipynb
index 1f6421d8fb5e91b1edf8fab34111ef1405a5e8a8..8231fd691a9458a37147bef7bc0764e8beead97f 100644
--- a/models.ipynb
+++ b/models.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -156,7 +156,7 @@
        "9  ['BehavioralProblems', 'DifficultyCompletingTa...  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -182,7 +182,7 @@
        " 'MMSE']"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
@@ -582,7 +582,7 @@
        "[2149 rows x 35 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -594,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -773,7 +773,7 @@
        "[2149 rows x 7 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -786,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
@@ -834,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
@@ -858,13 +858,13 @@
     "    plt.title(f'{col} by Diagnosis')\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.savefig(\"numerical_f.png\", dpi=300)\n",
+    "#plt.savefig(\"numerical_f.png\", dpi=300)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
@@ -888,7 +888,7 @@
     "    plt.title(f'{col} vs Diagnosis')\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.savefig(\"categorical_f.png\", dpi=300)\n",
+    "#plt.savefig(\"categorical_f.png\", dpi=300)\n",
     "plt.show()"
    ]
   },
@@ -901,18 +901,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
     "X = df_filtered.drop('Diagnosis', axis= 1)\n",
     "y = df_filtered['Diagnosis']\n",
     "\n",
-    "#split the data into test and train\n",
-    "\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
     "\n",
-    "#apply scaler\n",
     "scaler = StandardScaler()\n",
     "X_train_scaled = scaler.fit_transform(X_train)\n",
     "X_test_scaled = scaler.transform(X_test)\n",
@@ -923,12 +920,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Building the models"
+    "# Building the models"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -963,9 +960,16 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Grid Serach"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -994,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1030,15 +1034,6 @@
       "[0.9538906924045891]\n"
      ]
     },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "image/png": 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",
@@ -1061,8 +1056,6 @@
     }
    ],
    "source": [
-    "plt.savefig(\"classification_rf.png\")    \n",
-    "plt.savefig(\"confusion_rf.png\")          \n",
     "\n",
     "accuracy_scores = [] \n",
     "metrics = {\n",
@@ -1135,22 +1128,21 @@
     "ax.annotate(support_text, xy=(1, 0), xycoords='axes fraction', fontsize=10, color='black', ha='right', va='bottom')\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.savefig(\"classification_rf.png\", dpi=300)\n",
+    "#plt.savefig(\"classification_rf.png\", dpi=300)\n",
     "plt.show()\n",
     "\n",
-    "# Plot Confusion Matrix\n",
     "conf_matrix = confusion_matrix(y_test, y_predr)\n",
     "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='coolwarm')\n",
     "plt.xlabel('Predicted')\n",
     "plt.ylabel('Actual')\n",
     "plt.title('Confusion Matrix - Random Forest')\n",
-    "plt.savefig(\"confusion_rf.png\", dpi=300)\n",
+    "#plt.savefig(\"confusion_rf.png\", dpi=300)\n",
     "plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
@@ -1186,15 +1178,6 @@
       "[0.9538906924045891, 0.927771792161327]\n"
      ]
     },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "image/png": 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/GXHx4kUZhmH6+3Xt2rUp3ntpXcv/T/7+/nrttdc0Y8YMHTlyRAsWLNCtW7c0cOBAm3pdunSRnZ2dZs2aJUn6+uuvFR8fn+LSg4IFC+rs2bM2azgAwJOEa7wBIIvZ29vr+eef108//aQ///wzzYWF7mX+/PlydHTU8uXL5eLiYi1fsmRJiro1atRQjRo1lJSUpO3bt2vSpEnq37+/fHx81LZtW0m3/1ju0qWLrl+/rt9++03Dhw/XCy+8oMOHD1tHDu9X7ty5lZSUpNjY2DQD3+rVq3X69GmtWbPGOsot3V6o60HkypVLMTExKcrvLEDl7e39QPu/W+HCha0LqtWsWVOurq56++23NWnSJL355pvKkSOHdQQzrRkAAQEBNo9jY2NT1ImNjbWGrjv/Tes87ezslCNHDpvyf47cPoiQkBAFBgZq1qxZev311zV37lw999xzKc4hNc2aNVOzZs0UFxenzZs3a8yYMWrXrp0KFSqk4OBg6/v6zpcWd9z9xcS+ffu0e/duzZ49W506dbKWHz169L7P68qVK2rUqJGSk5MVHh6e4vr+jPz+pVeOHDlkZ2dn+vv1zi0M/+lOsM+I1q1ba8yYMdq3b59NeYECBRQaGqpvvvlGH374oWbNmqWiRYuqZs2aNvXq16+vVatWadmyZdb/DwHAk4QRbwB4BAwZMkSGYah79+6Kj49P8XxCQoKWLVuWZnuLxSIHBwebadE3b960jjymxt7eXlWrVrWuXv3777+nqOPu7q6GDRtq6NChio+P1/79+zNyWqlq2LChpNsrp6flThC8e+Gpzz//PEXdjIxCP//88zpw4ECKc/3yyy9lsVhUp06df93Hg3jrrbdUtGhRvf/++7p69arc3NxUp04d7dy5U+XKlVPlypVTbHePYs6bN89mpeyTJ09q48aNql27tiSpRIkSyp8/v7755hubetevX9f3339vXen832R0dP+OV155RQcOHNDbb7+ts2fP2qxMnh7Ozs6qVauWPvjgA0myrth+Z/XvPXv22NT/50r4UsbeO+kRHx+vFi1a6MSJE/rpp59S/WIsI79/zs7O6XpN3d3dVbVqVS1atMimfnJysubOnasCBQrYLNJ3v7Jly5biPffP0fy7pfZFgHR7lkp0dHSqob1r1666ePGi3nnnHe3atUtdunRJ8WVP165d5evrq7feeivNSw3uzHgBgMcRI94A8AgIDg7W1KlT1atXLwUFBem1115T6dKllZCQoJ07d+qLL75QmTJl0lzxt3HjxpowYYLatWunV199VefPn9f48eNThI/PPvtMq1evVuPGjVWwYEHdunVLM2fOlCTVrVtXktS9e3e5urqqevXqyps3r2JjYzVmzBh5enpar8V9EDVq1FCHDh00atQo/fXXX3rhhRfk7OysnTt3ys3NTX379lVISIhy5Mihnj17avjw4XJ0dNTXX3+t3bt3p9jfndsRffDBB2rYsKHs7e1Vrly5VMPDgAED9OWXX6px48YaOXKk/P399eOPP2rKlCl67bXXMiXI3Iujo6NGjx6t1q1b6+OPP9bbb7+tjz/+WM8++6xq1Kih1157TYUKFdLVq1d19OhRLVu2LMV152fOnFGLFi3UvXt3Xb58WcOHD5eLi4uGDBki6fbtnMaOHav27dvrhRdeUI8ePRQXF6dx48bp0qVLev/999PV14y8rv/UsWNH/fe//9W4cePk5eWlli1b/uux3nnnHf355596/vnnVaBAAV26dEkff/yxzXX9zzzzjEqUKKE333xTiYmJypEjhxYvXqz169fb7KtkyZIqUqSIBg8eLMMwlDNnTi1btuy+L5UYMGCAVq9erdGjR+vatWs219znzp1bRYoUSffvn3T7dZ0/f74WLFigwoULy8XFxfpa323MmDGqV6+e6tSpozfffFNOTk6aMmWK9u3bp3nz5mXaTIWMeO+997Rhwwa1adPGejvAyMhITZ48WefPn9e4ceNStGnatKm8vb01btw42dvb28xEuMPT01M//PCDXnjhBVWsWFF9+vRRcHCwnJycdOTIEc2dO1e7d+9O1/sJAB5JWbiwGwDgLrt27TI6depkFCxY0HBycjLc3d2NihUrGu+8845x5swZa73UVjWfOXOmUaJECcPZ2dkoXLiwMWbMGGPGjBk2qzZv2rTJaNGiheHv7284OzsbuXLlMmrVqmUsXbrUup85c+YYderUMXx8fAwnJycjX758RuvWrY09e/ZY6zzIquaGcXvV548++sgoU6aM4eTkZHh6ehrBwcHGsmXLrHU2btxoBAcHG25ubkbu3LmNbt26Gb///nuKlZ/j4uKMbt26Gblz5zYsFovN+d69qrlhGMbJkyeNdu3aGbly5TIcHR2NEiVKGOPGjbNZdfvOCtPjxo1L8TOSZAwfPjxF+T/deX2+++67VJ+vWrWqkSNHDusq3pGRkcYrr7xi5M+f33B0dDRy585thISEGKNGjUqxz6+++sro16+fkTt3bsPZ2dmoUaOGsX379hTHWLJkiVG1alXDxcXFcHd3N55//nljw4YNNnXurGp+9uzZFO3v9br+mxYtWqS6yvYdd78nli9fbjRs2NDInz+/4eTkZOTJk8do1KiRsW7dOpt2hw8fNkJDQ43s2bMbuXPnNvr27Wv8+OOPKd6LBw4cMOrVq2dky5bNyJEjh/HSSy8ZUVFRKX526VnVvFatWoakVLd/vrfS8/tnGLdXoQ8NDTWyZctmSLIeK7VVzQ3j9irqzz33nOHu7m64uroa1apVs/k9+ed53P27928rvt+PzZs3G7179zbKly9v5MyZ07C3tzdy585tNGjQwAgPD0+z3YABAwxJRqNGje65/9jYWGPQoEFG6dKlDTc3N8PZ2dkoWrSo0aNHD2Pv3r2Zdh4A8LBZDOMf89AAAMAjac2aNapTp46+++47vfjii1ndHQAAkAFc4w0AAAAAgIkI3gAAAAAAmIip5gAAAAAAmIgRbwAAAAAATETwBgAAAADARARvAAAAAABM5JDVHXjYkpOTdfr0aWXLlk0WiyWruwMAAAAAeAwZhqGrV68qX758srP7lzHtrLyJ+Nq1a40XXnjByJs3ryHJWLx48b+2WbNmjVGpUiXD2dnZCAgIMKZOnZqhY0ZHRxuS2NjY2NjY2NjY2NjY2NgeeIuOjv7XHJqlI97Xr19X+fLl1aVLF7Vq1epf60dGRqpRo0bq3r275s6dqw0bNqhXr17KnTt3utpLUrZs2SRJ0dHRyp49+wP1HwAAAADwdLpy5Yr8/PysGfNesjR4N2zYUA0bNkx3/c8++0wFCxbUxIkTJUmBgYHavn27xo8fn+7gfWd6efbs2QneAAAAAIAHkp5LmB+rxdU2bdqk0NBQm7L69etr+/btSkhIyKJeAQAAAACQtsdqcbXY2Fj5+PjYlPn4+CgxMVHnzp1T3rx5U7SJi4tTXFyc9fGVK1dM7ycAAAAAAHc8ViPeUsphfMMwUi2/Y8yYMfL09LRufn5+pvcRAAAAAIA7Hqvg7evrq9jYWJuyM2fOyMHBQbly5Uq1zZAhQ3T58mXrFh0d/TC6CgAAAACApMcseAcHBysiIsKmbNWqVapcubIcHR1TbePs7GxdSI0F1YAHN2XKFAUEBMjFxUVBQUFat27dPet/+umnCgwMlKurq0qUKKEvv/wyRZ1Lly6pd+/eyps3r1xcXBQYGKjw8HCzTgEAAAB4qLL0Gu9r167p6NGj1seRkZHatWuXcubMqYIFC2rIkCE6deqU9Q/1nj17avLkyQoLC1P37t21adMmzZgxQ/PmzcuqUwCeKgsWLFD//v01ZcoUVa9eXZ9//rkaNmyoAwcOqGDBginqT506VUOGDNG0adP0zDPPaOvWrerevbty5MihJk2aSJLi4+NVr1495cmTRwsXLlSBAgUUHR2drtsyAAAAAI8Di3HnIukssGbNGtWpUydFeadOnTR79mx17txZJ06c0Jo1a6zPrV27VgMGDND+/fuVL18+DRo0SD179kz3Ma9cuSJPT09dvnyZ0W8gg6pWrapKlSpp6tSp1rLAwEA1b95cY8aMSVE/JCRE1atX17hx46xl/fv31/bt27V+/XpJt28TOG7cOP3xxx9pzlwBAAAAHjUZyZZZOuJdu3Zt3Sv3z549O0VZrVq19Pvvv5vYKwCpiY+P144dOzR48GCb8tDQUG3cuDHVNnFxcXJxcbEpc3V11datW5WQkCBHR0ctXbpUwcHB6t27t3744Qflzp1b7dq106BBg2Rvb2/a+QAAAAAPy2N1jTeArHPu3DklJSWleku/uxc9vKN+/fqaPn26duzYIcMwtH37ds2cOVMJCQk6d+6cJOn48eNauHChkpKSFB4errffflsffvih3nvvPdPPCQAAAHgYHqv7eAPIeqnd0i+t2/kNGzZMsbGxqlatmgzDkI+Pjzp37qyxY8daR7OTk5OVJ08effHFF7K3t1dQUJBOnz6tcePG6Z133jH9fAAAAACzMeINIF28vb1lb2+f6i397h4Fv8PV1VUzZ87UjRs3dOLECUVFRalQoULKli2bvL29JUl58+ZV8eLFbaaVBwYGKjY2VvHx8eadEAAAAPCQELwBpIuTk5OCgoJS3NIvIiJCISEh92zr6OioAgUKyN7eXvPnz9cLL7wgO7vb//upXr26jh49quTkZGv9w4cPK2/evHJycsr8EwEAAAAeMoI3gHQLCwvT9OnTNXPmTB08eFADBgxQVFSU9c4CQ4YMUceOHa31Dx8+rLlz5+rIkSPaunWr2rZtq3379mn06NHWOq+99prOnz+v119/XYcPH9aPP/6o0aNHq3fv3g/9/GDOfdrvmD9/viwWi5o3b57JvQYAAHi0cY03gHRr06aNzp8/r5EjRyomJkZlypRReHi4/P39JUkxMTGKioqy1k9KStKHH36oQ4cOydHRUXXq1NHGjRtVqFAhax0/Pz+tWrVKAwYMULly5ZQ/f369/vrrGjRo0MM+vaeeGfdpv+PkyZN68803VaNGjYd1OgAAAI+MLL2Pd1bgPt4AkDoz7tMu3f4CplatWurSpYvWrVunS5cuacmSJaaeCwAAgNkyki2Zag4AsN6nPTQ01Kb8Qe7TfsfIkSOVO3dude3aNfM7DgAA8BggeAMATLtP+4YNGzRjxgxNmzbN9HMAAAB4VBG8AQBWGb1Pe8OGDVWtWjU5OjqqWbNm6ty5syTJ3t5eV69e1X/+8x9NmzbNevs4AACApxHBGwBgyn3ajx07phMnTqhJkyZycHCQg4ODvvzySy1dulQODg46duzYwzg1AACALEfwhiky+5ZEixYtUuXKleXl5SV3d3dVqFBBX331lZmnADxVzLhPe8mSJbV3717t2rXLujVt2lR16tTRrl275OfnZ+YpAQAAPDK4nRgynRm3JMqZM6eGDh2qkiVLysnJScuXL1eXLl2UJ08e1a9f/2GfIvBECgsLU4cOHVS5cmUFBwfriy++SHGf9lOnTlm/GDt8+LC2bt2qqlWr6uLFi5owYYL27dunOXPmSJJcXFxUpkwZm2N4eXlJUopyAACAJxnBG5luwoQJ6tq1q7p16yZJmjhxolauXKmpU6emekuir776Sj169FCbNm0kSYULF9bmzZv1wQcfWIN37dq1bdq8/vrrmjNnjtavX/9YBu8903dldRdgonLdKmR1F+6LGfdpBwAAAMEbmezOLYkGDx5sU/4gtyRydHS0ec4wDK1evVqHDh3SBx98kLknADzlevXqpV69eqX63OzZs20eBwYGaufOnRna/937AAAAeBpwjTcylVm3JJKky5cvy8PDQ05OTmrcuLEmTZqkevXqmXo+AAA86jJ7XZVp06apRo0aypEjh3LkyKG6detq69atZp4CADzxCN4wRWbekuiObNmyadeuXdq2bZvee+89hYWFac2aNWadAgAAj7w766oMHTpUO3fuVI0aNdSwYUOby0L+6c66KiNGjND+/fv17rvvqnfv3lq2bJm1zpo1a/Tyyy/r119/1aZNm1SwYEGFhobq1KlTD+u0AOCJYzEMw8jqTjxMV65ckaenpy5fvqzs2bNndXeeOPHx8XJzc9N3332nFi1aWMtff/117dq1S2vXrk2zbUJCgv766y/lzZtXX3zxhQYNGqRLly7Jzi7174e6deum6OhorVy5MtPPw2xc4/1ke1yv8Qbw+KlataoqVaqkqVOnWssCAwPVvHnzVNdVCQkJUfXq1TVu3DhrWf/+/bV9+3atX78+1WMkJSUpR44cmjx5sjp27Jj5JwEAj6mMZEtGvJGpzLglUVoMw1BcXFym9BsAgMfNnXVVQkNDbcofZF2V1Ny4cUMJCQnKmTNn5nQcAJ5CLK6GTJfZtySSpDFjxqhy5coqUqSI4uPjFR4eri+//NLmG34AAJ4mD7KuSvPmzVWpUiXt2LHDZl2VvHnzpmgzePBg5c+fX3Xr1jXlPADgaUDwRqYz45ZE169fV69evfTnn3/K1dVVJUuW1Ny5c623IAMA4GmV0XVVYmNjVa1aNRmGIR8fH3Xu3Fljx461WVfljrFjx2revHlas2ZNipFyAED6cY03kAW4xvvJNtmxZFZ3ASb5ohPBA48Os9dVGT9+vEaNGqWff/5ZlStXNvVcAOBxxDXeAAAATzgz11UZN26c/ve//2nFihWEbgDIBEw1BwAAeEyZsa7K2LFjNWzYMH3zzTcqVKiQ9XpxDw8PeXh4PPyTBIAnAMEbAADgMWXGuipTpkxRfHy8XnzxRZtjDR8+XCNGjHgYpwUATxyu8QayANd4P9m4xvvJxTXeAADgDq7xBgAAAADgEUHwBgAAAADARARvAAAAAABMxOJqj7ARDklZ3QWYpOVnWd0DAAAAZKUpU6Zo3LhxiomJUenSpTVx4kTVqFEjzfqffvqpJk+erBMnTqhgwYIaOnSoOnbsaFPn+++/17Bhw3Ts2DEVKVJE7733nlq0aGH2qSAdGPEGAAAAgIdowYIF6t+/v4YOHaqdO3eqRo0aatiwoc1dCP5p6tSpGjJkiEaMGKH9+/fr3XffVe/evbVs2TJrnU2bNqlNmzbq0KGDdu/erQ4dOqh169basmXLwzot3AOrmj/CGPF+crX8bG9WdwEmYlXzJxermgMAMkPVqlVVqVIlTZ061VoWGBio5s2ba8yYMSnqh4SEqHr16ho3bpy1rH///tq+fbvWr18v6fbtBa9cuaKffvrJWqdBgwbKkSOH5s2bZ+LZPL0yki2Zag4AAJ56fNn9ZBuRaJ/VXQCs4uPjtWPHDg0ePNimPDQ0VBs3bky1TVxcnFxcbL/8dXV11datW5WQkCBHR0dt2rRJAwYMsKlTv359TZw4MVP7j/vDVHMAAAAAeEjOnTunpKQk+fj42JT7+PgoNjY21Tb169fX9OnTtWPHDhmGoe3bt2vmzJlKSEjQuXPnJEmxsbEZ2iceLoI3AAAAADxkFovF5rFhGCnK7hg2bJgaNmyoatWqydHRUc2aNVPnzp0lSfb2f8/oyMg+8XARvAEAAADgIfH29pa9vX2KkegzZ86kGLG+w9XVVTNnztSNGzd04sQJRUVFqVChQsqWLZu8vb0lSb6+vhnaJx4ugjcAAAAAPCROTk4KCgpSRESETXlERIRCQkLu2dbR0VEFChSQvb295s+frxdeeEF2drcjXXBwcIp9rlq16l/3iYeDxdUAAAAA4CEKCwtThw4dVLlyZQUHB+uLL75QVFSUevbsKUkaMmSITp06pS+//FKSdPjwYW3dulVVq1bVxYsXNWHCBO3bt09z5syx7vP1119XzZo19cEHH6hZs2b64Ycf9PPPP1tXPUfWIngDAAAAwEPUpk0bnT9/XiNHjlRMTIzKlCmj8PBw+fv7S5JiYmJs7umdlJSkDz/8UIcOHZKjo6Pq1KmjjRs3qlChQtY6ISEhmj9/vt5++20NGzZMRYoU0YIFC1S1atWHfXpIBffxfoRxa5MnF/fxfrJxH+8nF/fxfnLxmftk43ZiAMyQkWzJNd4AAAAAAJiI4A0AAAAAgIkI3gAAAMAjasqUKQoICJCLi4uCgoK0bt26e9b/+uuvVb58ebm5uSlv3rzq0qWLzp8/b32+du3aslgsKbbGjRubfSrAU43gDQAAADyCFixYoP79+2vo0KHauXOnatSooYYNG9osuvVP69evV8eOHdW1a1ft379f3333nbZt26Zu3bpZ6yxatEgxMTHWbd++fbK3t9dLL730sE4LeCoRvAEAAIBH0IQJE9S1a1d169ZNgYGBmjhxovz8/DR16tRU62/evFmFChVSv379FBAQoGeffVY9evTQ9u3brXVy5swpX19f6xYRESE3NzeCN2AygjcAAADwiImPj9eOHTsUGhpqUx4aGqqNGzem2iYkJER//vmnwsPDZRiG/vrrLy1cuPCe08hnzJihtm3byt3dPVP7D8AW9/EGAAAAHjHnzp1TUlKSfHx8bMp9fHwUGxubapuQkBB9/fXXatOmjW7duqXExEQ1bdpUkyZNSrX+1q1btW/fPs2YMSPT+/+wvDrnVlZ3ASZ50m7hyYg3AAAA8IiyWCw2jw3DSFF2x4EDB9SvXz+988472rFjh1asWKHIyEj17Nkz1fozZsxQmTJlVKVKlUzvNwBbjHgDAAAAjxhvb2/Z29unGN0+c+ZMilHwO8aMGaPq1atr4MCBkqRy5crJ3d1dNWrU0KhRo5Q3b15r3Rs3bmj+/PkaOXKkeScBwIoRbwAAAOAR4+TkpKCgIEVERNiUR0REKCQkJNU2N27ckJ2d7Z/39vb2km6PlP/Tt99+q7i4OP3nP//JxF4DSAvBGwAAAHgEhYWFafr06Zo5c6YOHjyoAQMGKCoqyjp1fMiQIerYsaO1fpMmTbRo0SJNnTpVx48f14YNG9SvXz9VqVJF+fLls9n3jBkz1Lx5c+XKleuhnhPwtGKqOQAAAPAIatOmjc6fP6+RI0cqJiZGZcqUUXh4uPz9/SVJMTExNvf07ty5s65evarJkyfrjTfekJeXl5577jl98MEHNvs9fPiw1q9fr1WrVj3U8wGeZgRvAAAA4BHVq1cv9erVK9XnZs+enaKsb9++6tu37z33Wbx48RRTzwGYi6nmAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAibidGAAAAJ5oe6bvyuouwCyOJbO6B0C6MOINAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAibI8eE+ZMkUBAQFycXFRUFCQ1q1bd8/6X3/9tcqXLy83NzflzZtXXbp00fnz5x9SbwEAAAAAyJgsDd4LFixQ//79NXToUO3cuVM1atRQw4YNFRUVlWr99evXq2PHjuratav279+v7777Ttu2bVO3bt0ecs8BAAAAAEifLA3eEyZMUNeuXdWtWzcFBgZq4sSJ8vPz09SpU1Otv3nzZhUqVEj9+vVTQECAnn32WfXo0UPbt29/yD0HAAAAACB9six4x8fHa8eOHQoNDbUpDw0N1caNG1NtExISoj///FPh4eEyDEN//fWXFi5cqMaNGz+MLgMAAAAAkGFZFrzPnTunpKQk+fj42JT7+PgoNjY21TYhISH6+uuv1aZNGzk5OcnX11deXl6aNGlSmseJi4vTlStXbDYAAAAAAB6WLF9czWKx2Dw2DCNF2R0HDhxQv3799M4772jHjh1asWKFIiMj1bNnzzT3P2bMGHl6elo3Pz+/TO0/AAAAAAD3kmXB29vbW/b29ilGt8+cOZNiFPyOMWPGqHr16ho4cKDKlSun+vXra8qUKZo5c6ZiYmJSbTNkyBBdvnzZukVHR2f6uQAAAAAAkJYsC95OTk4KCgpSRESETXlERIRCQkJSbXPjxg3Z2dl22d7eXtLtkfLUODs7K3v27DYbAAAAAAAPS5ZONQ8LC9P06dM1c+ZMHTx4UAMGDFBUVJR16viQIUPUsWNHa/0mTZpo0aJFmjp1qo4fP64NGzaoX79+qlKlivLly5dVpwEAAAAAQJocsvLgbdq00fnz5zVy5EjFxMSoTJkyCg8Pl7+/vyQpJibG5p7enTt31tWrVzV58mS98cYb8vLy0nPPPacPPvggq04BAAAAAIB7ytLgLUm9evVSr169Un1u9uzZKcr69u2rvn37mtwrAAAAAAAyR5avag4AAAAAwJOM4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJsjx4T5kyRQEBAXJxcVFQUJDWrVt3z/pxcXEaOnSo/P395ezsrCJFimjmzJkPqbcAAAAAAGSMQ1YefMGCBerfv7+mTJmi6tWr6/PPP1fDhg114MABFSxYMNU2rVu31l9//aUZM2aoaNGiOnPmjBITEx9yzwEAAAAASJ8sDd4TJkxQ165d1a1bN0nSxIkTtXLlSk2dOlVjxoxJUX/FihVau3atjh8/rpw5c0qSChUq9DC7DAAAAABAhmTZVPP4+Hjt2LFDoaGhNuWhoaHauHFjqm2WLl2qypUra+zYscqfP7+KFy+uN998Uzdv3kzzOHFxcbpy5YrNBgAAAADAw5JlI97nzp1TUlKSfHx8bMp9fHwUGxubapvjx49r/fr1cnFx0eLFi3Xu3Dn16tVLFy5cSPM67zFjxujdd9/N9P4DAAAAAJAeWb64msVisXlsGEaKsjuSk5NlsVj09ddfq0qVKmrUqJEmTJig2bNnpznqPWTIEF2+fNm6RUdHZ/o5AAAAAACQliwb8fb29pa9vX2K0e0zZ86kGAW/I2/evMqfP788PT2tZYGBgTIMQ3/++aeKFSuWoo2zs7OcnZ0zt/MAAAAAAKRTlo14Ozk5KSgoSBERETblERERCgkJSbVN9erVdfr0aV27ds1advjwYdnZ2alAgQKm9hcAAAAAgPuRpVPNw8LCNH36dM2cOVMHDx7UgAEDFBUVpZ49e0q6PU28Y8eO1vrt2rVTrly51KVLFx04cEC//fabBg4cqFdeeUWurq5ZdRoAAAAAAKQpS28n1qZNG50/f14jR45UTEyMypQpo/DwcPn7+0uSYmJiFBUVZa3v4eGhiIgI9e3bV5UrV1auXLnUunVrjRo1KqtOAQAAAACAe8rS4C1JvXr1Uq9evVJ9bvbs2SnKSpYsmWJ6OgAAAAAAj6osX9UcAAAAAIAnGcEbAAAAAAATEbwBAAAAADARwRsAAAAAABMRvAEAAAAAMBHBGwAAAAAAExG8AQAAAAAwEcEbAAAAAAATEbwBAAAAADARwRsAAAAAABMRvAEAAAAAMBHBGwAAAAAAExG8AQAAAAAwEcEbAAAAAAATEbwBAAAAADARwRsAAAAAABMRvAEAAAAAMBHBGwAAAAAAExG8AQAAAAAwEcEbAAAAAAATEbwBAAAAADBRpgTvK1euaMmSJTp48GBm7A4AAAAAgCfGfQXv1q1ba/LkyZKkmzdvqnLlymrdurXKlSun77//PlM7CAAAAADA4+y+gvdvv/2mGjVqSJIWL14swzB06dIlffLJJxo1alSmdhAAAAAAgMfZfQXvy5cvK2fOnJKkFStWqFWrVnJzc1Pjxo115MiRTO0gAAAAAACPs/sK3n5+ftq0aZOuX7+uFStWKDQ0VJJ08eJFubi4ZGoHAQAAAAB4nDncT6P+/furffv28vDwUMGCBVW7dm1Jt6egly1bNjP7BwAAAADAY+2+gnevXr1UpUoVRUdHq169erKzuz1wXrhwYa7xBgAAAADgH+4reEtS5cqVVa5cOUVGRqpIkSJycHBQ48aNM7NvAAAAAAA89u7rGu8bN26oa9eucnNzU+nSpRUVFSVJ6tevn95///1M7SAAAAAAAI+z+wreQ4YM0e7du7VmzRqbxdTq1q2rBQsWZFrnAAAAAAB43N3XVPMlS5ZowYIFqlatmiwWi7W8VKlSOnbsWKZ1DgAAAACAx919jXifPXtWefLkSVF+/fp1myAOAAAAAMDT7r6C9zPPPKMff/zR+vhO2J42bZqCg4Mzp2cAAAAAADwB7muq+ZgxY9SgQQMdOHBAiYmJ+vjjj7V//35t2rRJa9euzew+AgAAAADw2LqvEe+QkBBt3LhRN27cUJEiRbRq1Sr5+Pho06ZNCgoKyuw+AgAAAADw2MrwiHdCQoJeffVVDRs2THPmzDGjTwAAAAAAPDEyPOLt6OioxYsXm9EXAAAAAACeOPc11bxFixZasmRJJncFAAAAAIAnz30trla0aFH973//08aNGxUUFCR3d3eb5/v165cpnQMAAAAA4HF3X8F7+vTp8vLy0o4dO7Rjxw6b5ywWC8EbAAAAAID/d1/BOzIyMrP7AQAAAADAE+m+rvH+J8MwZBhGZvQFAAAAAIAnzn0H7y+//FJly5aVq6urXF1dVa5cOX311VeZ2TcAAAAAAB579zXVfMKECRo2bJj69Omj6tWryzAMbdiwQT179tS5c+c0YMCAzO4nAAAAAACPpfsK3pMmTdLUqVPVsWNHa1mzZs1UunRpjRgxguANAAAAAMD/u6+p5jExMQoJCUlRHhISopiYmAfuFAAAAAAAT4r7Ct5FixbVt99+m6J8wYIFKlas2AN3CgAAAACAJ8V9TTV/99131aZNG/3222+qXr26LBaL1q9fr19++SXVQA4AAAAAwNPqvka8W7VqpS1btsjb21tLlizRokWL5O3tra1bt6pFixaZ3UcAAAAAAB5b9zXiLUlBQUGaO3duZvYFAAAAAIAnzn2NeIeHh2vlypUpyleuXKmffvrpgTsFAAAAAMCT4r6C9+DBg5WUlJSi3DAMDR48+IE7BQAAAADAk+K+gveRI0dUqlSpFOUlS5bU0aNHH7hTAAAAAAA8Ke4reHt6eur48eMpyo8ePSp3d/cH7hQAAAAAAE+K+wreTZs2Vf/+/XXs2DFr2dGjR/XGG2+oadOmmdY5AAAAAAAed/cVvMeNGyd3d3eVLFlSAQEBCggIUMmSJZUrVy6NHz8+s/sIAAAAAMBj675uJ+bp6amNGzcqIiJCu3fvlqurq8qXL68aNWpkdv8AAAAAAHisZWjEe8uWLdbbhVksFoWGhipPnjwaP368WrVqpVdffVVxcXGmdBQAAAAAgMdRhoL3iBEjtGfPHuvjvXv3qnv37qpXr54GDx6sZcuWacyYMZneSQAAAAAAHlcZCt67du3S888/b308f/58ValSRdOmTVNYWJg++eQTffvtt5neSQAAAAAAHlcZCt4XL16Uj4+P9fHatWvVoEED6+NnnnlG0dHRmdc7AAAAAAAecxkK3j4+PoqMjJQkxcfH6/fff1dwcLD1+atXr8rR0TFzewgAAAAAwGMsQ8G7QYMGGjx4sNatW6chQ4bIzc3NZiXzPXv2qEiRIpneSQAAAAAAHlcZup3YqFGj1LJlS9WqVUseHh6aM2eOnJycrM/PnDlToaGhmd5JAAAAAAAeVxkK3rlz59a6det0+fJleXh4yN7e3ub57777Th4eHpnaQQAAAAAAHmcZCt53eHp6plqeM2fOB+oMAAAAAABPmgxd4w0AAAAAADKG4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAibI8eE+ZMkUBAQFycXFRUFCQ1q1bl652GzZskIODgypUqGBuBwEAAAAAeABZGrwXLFig/v37a+jQodq5c6dq1Kihhg0bKioq6p7tLl++rI4dO+r5559/SD0FAAAAAOD+ZGnwnjBhgrp27apu3bopMDBQEydOlJ+fn6ZOnXrPdj169FC7du0UHBz8kHoKAAAAAMD9ybLgHR8frx07dig0NNSmPDQ0VBs3bkyz3axZs3Ts2DENHz7c7C4CAAAAAPDAHLLqwOfOnVNSUpJ8fHxsyn18fBQbG5tqmyNHjmjw4MFat26dHBzS1/W4uDjFxcVZH1+5cuX+Ow0AAAAAQAZl+eJqFovF5rFhGCnKJCkpKUnt2rXTu+++q+LFi6d7/2PGjJGnp6d18/Pze+A+AwAAAACQXlkWvL29vWVvb59idPvMmTMpRsEl6erVq9q+fbv69OkjBwcHOTg4aOTIkdq9e7ccHBy0evXqVI8zZMgQXb582bpFR0ebcj4AAAAAAKQmy6aaOzk5KSgoSBEREWrRooW1PCIiQs2aNUtRP3v27Nq7d69N2ZQpU7R69WotXLhQAQEBqR7H2dlZzs7Omdt5AAAAAADSKcuCtySFhYWpQ4cOqly5soKDg/XFF18oKipKPXv2lHR7tPrUqVP68ssvZWdnpzJlyti0z5Mnj1xcXFKUAwAAAADwqMjS4N2mTRudP39eI0eOVExMjMqUKaPw8HD5+/tLkmJiYv71nt4AAAAAADzKsjR4S1KvXr3Uq1evVJ+bPXv2PduOGDFCI0aMyPxOAQAAAACQSbJ8VXMAAAAAAJ5kBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMlOXBe8qUKQoICJCLi4uCgoK0bt26NOsuWrRI9erVU+7cuZU9e3YFBwdr5cqVD7G3AAAAAABkTJYG7wULFqh///4aOnSodu7cqRo1aqhhw4aKiopKtf5vv/2mevXqKTw8XDt27FCdOnXUpEkT7dy58yH3HAAAAACA9MnS4D1hwgR17dpV3bp1U2BgoCZOnCg/Pz9NnTo11foTJ07UW2+9pWeeeUbFihXT6NGjVaxYMS1btuwh9xwAAAAAgPTJsuAdHx+vHTt2KDQ01KY8NDRUGzduTNc+kpOTdfXqVeXMmTPNOnFxcbpy5YrNBgAAAADAw5JlwfvcuXNKSkqSj4+PTbmPj49iY2PTtY8PP/xQ169fV+vWrdOsM2bMGHl6elo3Pz+/B+o3AAAAAAAZkeWLq1ksFpvHhmGkKEvNvHnzNGLECC1YsEB58uRJs96QIUN0+fJl6xYdHf3AfQYAAAAAIL0csurA3t7esre3TzG6febMmRSj4HdbsGCBunbtqu+++05169a9Z11nZ2c5Ozs/cH8BAAAAALgfWTbi7eTkpKCgIEVERNiUR0REKCQkJM128+bNU+fOnfXNN9+ocePGZncTAAAAAIAHkmUj3pIUFhamDh06qHLlygoODtYXX3yhqKgo9ezZU9LtaeKnTp3Sl19+Kel26O7YsaM+/vhjVatWzTpa7urqKk9Pzyw7DwAAAAAA0pKlwbtNmzY6f/68Ro4cqZiYGJUpU0bh4eHy9/eXJMXExNjc0/vzzz9XYmKievfurd69e1vLO3XqpNmzZz/s7gMAAAAA8K+yNHhLUq9evdSrV69Un7s7TK9Zs8b8DgEAAAAAkImyfFVzAAAAAACeZARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARARvAAAAAABMRPAGAAAAAMBEBG8AAAAAAExE8AYAAAAAwEQEbwAAAAAATETwBgAAAADARA5Z3QEAAB5nycnJio+Pz+puIAMcHR1lb2+f1d0AADxFCN4AANyn+Ph4RUZGKjk5Oau7ggzy8vKSr6+vLBZLVncFAPAUIHgDAHAfDMNQTEyM7O3t5efnJzs7rt56HBiGoRs3bujMmTOSpLx582ZxjwAATwOCNwAA9yExMVE3btxQvnz55ObmltXdQQa4urpKks6cOaM8efIw7RwAYDq+ngcA4D4kJSVJkpycnLK4J7gfd74sSUhIyOKeAACeBgRvAAAeANcIP574uQEAHiaCNwAAMF2hQoU0ceLETK8LAMDjgGu8AQB4ynTu3Flz5syRJDk4OMjPz08tW7bUu+++K3d3d1OOuW3btnTvOyN1AQB4HBC8AQDIRCMckh7u8RLvb2GwBg0aaNasWUpISNC6devUrVs3Xb9+XVOnTrWpl5CQIEdHxwfuZ+7cuU2pCwDA44Cp5gAAPIWcnZ3l6+srPz8/tWvXTu3bt9eSJUs0YsQIVahQQTNnzlThwoXl7OwswzB0+fJlvfrqq8qTJ4+yZ8+u5557Trt377bZ59KlS1W5cmW5uLjI29tbLVu2tD539/TxESNGqGDBgnJ2dla+fPnUr1+/NOtGRUWpWbNm8vDwUPbs2dW6dWv99ddfNvuqUKGCvvrqKxUqVEienp5q27atrl69mvkvHAAA94HgDQAA5Orqal3h++jRo/r222/1/fffa9euXZKkxo0bKzY2VuHh4dqxY4cqVaqk559/XhcuXJAk/fjjj2rZsqUaN26snTt36pdfflHlypVTPdbChQv10Ucf6fPPP9eRI0e0ZMkSlS1bNtW6hmGoefPmunDhgtauXauIiAgdO3ZMbdq0sal37NgxLVmyRMuXL9fy5cu1du1avf/++5n06gAA8GCYag4AwFNu69at+uabb/T8889LkuLj4/XVV19Zp3yvXr1ae/fu1ZkzZ+Ts7CxJGj9+vJYsWaKFCxfq1Vdf1Xvvvae2bdvq3Xffte63fPnyqR4vKipKvr6+qlu3rhwdHVWwYEFVqVIl1bo///yz9uzZo8jISPn5+UmSvvrqK5UuXVrbtm3TM888I0lKTk7W7NmzlS1bNklShw4d9Msvv+i9997LhFcIAIAHw4g3AABPoeXLl8vDw0MuLi4KDg5WzZo1NWnSJEmSv7+/zXXWO3bs0LVr15QrVy55eHhYt8jISB07dkyStGvXLmtw/zcvvfSSbt68qcKFC6t79+5avHixEhMTU6178OBB+fn5WUO3JJUqVUpeXl46ePCgtaxQoULW0C1JefPm1ZkzZ9L/ggAAYCJGvAEAeArVqVNHU6dOlaOjo/Lly2ezgNrdK4onJycrb968WrNmTYr9eHl5Sbo9VT29/Pz8dOjQIUVEROjnn39Wr169NG7cOK1duzbFQm6GYaR6z+27y+9uZ7FYlJycnO4+AQBgJka8AQB4Crm7u6to0aLy9/f/11XLK1WqpNjYWDk4OKho0aI2m7e3tySpXLly+uWXX9J9fFdXVzVt2lSffPKJ1qxZo02bNmnv3r0p6pUqVUpRUVGKjo62lh04cECXL19WYGBguo8HAEBWYsQbAADcU926dRUcHKzmzZvrgw8+UIkSJXT69GmFh4erefPmqly5soYPH67nn39eRYoUUdu2bZWYmKiffvpJb731Vor9zZ49W0lJSapatarc3Nz01VdfydXVVf7+/qkeu1y5cmrfvr0mTpyoxMRE9erVS7Vq1Upz8TYAAB41jHgDAIB7slgsCg8PV82aNfXKK6+oePHiatu2rU6cOCEfHx9JUu3atfXdd99p6dKlqlChgp577jlt2bIl1f15eXlp2rRpql69unWkfNmyZcqVK1eqx16yZIly5MihmjVrqm7duipcuLAWLFhg6jkDAJCZLIZhGFndiYfpypUr8vT01OXLl5U9e/as7s49jXBIyuouwCQtP0s5nRJPjsmOJbO6CzDJF51crP++deuWIiMjFRAQIBcXl3u0wqPo7p8fn7lPNj53n1x85j65/vmZ+6jKSLZkxBsAAAAAABMRvAEAAAAAMBHBGwAAAAAAExG8AQAAAAAwEcEbAAAAAAATEbwBAAAAADARwRsAAAAAABMRvAEAAAAAMBHBGwAAAAAAExG8AQDAQ1eoUCFNnDjR+thisWjJkiVZ1h8AAMxE8AYA4CnTuXNnWSwWWSwWOTg4qGDBgnrttdd08eLFrO4aAABPJIes7gAAAE+SPdN3PdTjletW4b7aNWjQQLNmzVJiYqIOHDigV155RZcuXdK8efMyt4MAAIARbwAAnkbOzs7y9fVVgQIFFBoaqjZt2mjVqlXW52fNmqXAwEC5uLioZMmSmjJlik37P//8U23btlXOnDnl7u6uypUra8uWLZKkY8eOqVmzZvLx8ZGHh4eeeeYZ/fzzzw/1/AAAeJQw4g0AwFPu+PHjWrFihRwdHSVJ06ZN0/DhwzV58mRVrFhRO3fuVPfu3eXu7q5OnTrp2rVrqlWrlvLnz6+lS5fK19dXv//+u5KTkyVJ165dU6NGjTRq1Ci5uLhozpw5atKkiQ4dOqSCBQtm5akCAJAlCN4AADyFli9fLg8PDyUlJenWrVuSpAkTJkiS/ve//+nDDz9Uy5YtJUkBAQE6cOCAPv/8c3Xq1EnffPONzp49q23btilnzpySpKJFi1r3Xb58eZUvX976eNSoUVq8eLGWLl2qPn36PKxTBADgkUHwBgDgKVSnTh1NnTpVN27c0PTp03X48GH17dtXZ8+eVXR0tLp27aru3btb6ycmJsrT01OStGvXLlWsWNEauu92/fp1vfvuu1q+fLlOnz6txMRE3bx5U1FRUQ/l3AAAeNQQvAEAeAq5u7tbR6k/+eQT1alTR++++651RHratGmqWrWqTRt7e3tJkqur6z33PXDgQK1cuVLjx49X0aJF5erqqhdffFHx8fEmnAkAAI8+gjcAANDw4cPVsGFDvfbaa8qfP7+OHz+u9u3bp1q3XLlymj59ui5cuJDqqPe6devUuXNntWjRQtLta75PnDhhZvcBAHiksao5AABQ7dq1Vbp0aY0ePVojRozQmDFj9PHHH+vw4cPau3evZs2aZb0G/OWXX5avr6+aN2+uDRs26Pjx4/r++++1adMmSbev9160aJF27dql3bt3q127dtaF1wAAeBoRvAEAgCQpLCxM06ZNU/369TV9+nTNnj1bZcuWVa1atTR79mwFBARIkpycnLRq1SrlyZNHjRo1UtmyZfX+++9bp6J/9NFHypEjh0JCQtSkSRPVr19flSpVyspTAwAgS1kMwzCyuhMP05UrV+Tp6anLly8re/bsWd2dexrhkJTVXYBJWn62N6u7ABNNdiyZ1V2ASb7o5GL9961btxQZGamAgAC5uLjcoxUeRXf//PjMfbLxufvk4jP3yfXPz9xHVUayJSPeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkI3gAAAAAAmIjgDQAAAACAiQjeAAAAAACYiOANAAAAAICJCN4AAAAAAJiI4A0AAAAAgIkcsroDAAA8SV6dc+uhHu+LTi4ZbtO5c2fNmTMnRfmRI0d0+vRpjRs3Tjt27FBMTIwWL16s5s2b/+s+d+7cqWHDhmnr1q26cuWKfH19VbVqVX366afy9vbOcB8BAHiSMOINAMBTqEGDBoqJibHZAgICdP36dZUvX16TJ09O977OnDmjunXrytvbWytXrtTBgwc1c+ZM5c2bVzdu3DDtHBISEkzbNwAAmYngDQDAU8jZ2Vm+vr42m729vRo2bKhRo0apZcuW6d7Xxo0bdeXKFU2fPl0VK1ZUQECAnnvuOU2cOFEFCxa01tu/f78aN26s7NmzK1u2bKpRo4aOHTsmSUpOTtbIkSNVoEABOTs7q0KFClqxYoW17YkTJ2SxWPTtt9+qdu3acnFx0dy5cyVJs2bNUmBgoFxcXFSyZElNmTIlk14lAAAyB8EbAAA8EF9fXyUmJmrx4sUyDCPVOqdOnVLNmjXl4uKi1atXa8eOHXrllVeUmJgoSfr444/14Ycfavz48dqzZ4/q16+vpk2b6siRIzb7GTRokPr166eDBw+qfv36mjZtmoYOHar33ntPBw8e1OjRozVs2LBUp9IDAJBVuMYbAICn0PLly+Xh4WF93LBhQ3333Xf3ta9q1arpv//9r9q1a6eePXuqSpUqeu6559SxY0f5+PhIkj799FN5enpq/vz5cnR0lCQVL17cuo/x48dr0KBBatu2rSTpgw8+0K+//qqJEyfq008/tdbr37+/zWj8//73P3344YfWsoCAAB04cECff/65OnXqdF/nAwBAZmPEGwCAp1CdOnW0a9cu6/bJJ5+kq93o0aPl4eFh3aKioiRJ7733nmJjY/XZZ5+pVKlS+uyzz1SyZEnt3btXkrRr1y7VqFHDGrr/6cqVKzp9+rSqV69uU169enUdPHjQpqxy5crWf589e1bR0dHq2rWrTZ9GjRplncIOAMCjgBFvAACeQu7u7ipatGiG2/Xs2VOtW7e2Ps6XL5/137ly5dJLL72kl156SWPGjFHFihU1fvx4zZkzR66urv+6b4vFYvPYMIwUZe7u7tZ/JycnS5KmTZumqlWr2tSzt7dP/0kBAGAygjcAAEi3nDlzKmfOnP9az8nJSUWKFNH169clSeXKldOcOXOUkJCQYtQ7e/bsypcvn9avX6+aNWtayzdu3KgqVaqkeQwfHx/lz59fx48fV/v27e/zjAAAMB/BGwAAWF27dk1Hjx61Po6MjNSuXbuUM2dOmxXK/2n58uWaP3++2rZtq+LFi8swDC1btkzh4eGaNWuWJKlPnz6aNGmS2rZtqyFDhsjT01ObN29WlSpVVKJECQ0cOFDDhw9XkSJFVKFCBc2aNUu7du3S119/fc/+jhgxQv369VP27NnVsGFDxcXFafv27bp48aLCwsIy74UBAOABELwBAIDV9u3bVadOHevjO+G1U6dOmj17dqptSpUqJTc3N73xxhuKjo6Ws7OzihUrpunTp6tDhw6Sbk9DX716tQYOHKhatWrJ3t5eFSpUsF7X3a9fP125ckVvvPGGzpw5o1KlSmnp0qUqVqzYPfvbrVs3ubm5ady4cXrrrbfk7u6usmXLqn///g/+YgAAkEksRlr3/XhCXblyRZ6enrp8+bKyZ8+e1d25pxEOSVndBZik5Wd7s7oLMNFkx5JZ3QWY5ItOLtZ/37p1S5GRkQoICJCLi8s9WuFRdPfPj8/cJxufu08uPnOfXP/8zH1UZSRbsqo5AAAAAAAmIngDAAAgU7yb5KA/jB+yuht4gjUc3EjbDm1/4P2smdZdqz5+KRN6BKQP13gDAADgX10zYvWbMUZHjHBd1Sm5K498VV5V7fqpsOX5rO6eDMPQWmOkdhjTdUsXlV9V1MhukvJYSqfZ5kbcTX2+7AtF7IjQ2Utn5e7ipiL5iqhjaEfVKl8zzXaPklPnTqvRkMZaMGy+ShYskaG23/+2SMs2LdfR07cXVCzlH6i+LfqqbEAZa52Ggxvp9PmYFG3b1G6t/7YfIkkq371iqvsf8GJ/da7fKc3j/7zjZ336wxRFn/1TfrkLqE/zPnq+0nMZOofUGIahP9bO1KHfZuviqYOys3NQdp8iKhrcVoG1u8rB2e2Bj/EgblyK0eb5g3XuxE5d/uuoytTtpeD24/+1XeS2xdr90wRdijksGclyz+knv7L1VO3lDx5CrzPH8jGhylWwXLrO926vv/661q9fr3379ikwMFC7du2yef7EiRMKCAhI0e6nn35SgwYNJEnr16/XoEGD9Mcff+jGjRvy9/dXjx49NGDAgHseOyoqSr1799bq1avl6uqqdu3aafz4jJ0DwRsAAAD3dMk4oZnJNeUiL9Wze18+KqskJeiYsUrhyf3Ux35/VndRG4xx2mRMVHO7mcqlYvrNGK2vkhuoj92BNNuMmvue9kXu05B2g1Q4bxFdvn5Ju47u1uXrlx5exx9AQmLCA7Xffmi7GlZpoPJFysvZ0UmzVs7Rax+9pu/f/V4+OfJIkr4eOlfJycnWNkdPHVWPj15Tvcr1rGW/jI+w2e/6fRs0Ys67qlsp7S9kdh/brbe+GKzezV7TcxWf0+qdq/XWF4M0662ZKle47AOd15ovXlHkjh9UsclgVf/PR3LJllvno/do36rJyubtr0JBTR9o/w8qKSFeLtm8VaHJIO1bOSldbU7tX61fPuuoZ1q9K/+KL0gWiy6dOqhTB341ubeZIzkxQXYOjv9e8R4Mw9Arr7yiLVu2aM+ePWnW+/nnn1W69N9fuP3zFpju7u7q06ePypUrJ3d3d61fv149evSQu7u7Xn311VT3l5SUpMaNGyt37txav369zp8/r06dOskwDL333nvp7j/BGwAAAPf0Y3IfSRZ1s9skJ4u7tTyPpbQqGl3SbBeRPFh/GD/oiv6Uh3xV1vKyalmGyd5y+w/wWGO3ViaH6bR2SLIol4rpBbspymeprEvGSf2U3E9R2qAkxctLhVTP7n0VszRKcRzDMLTF+EQ1LEMUaGkhSWquWRpv5NNeY55eVnCq/ftt91q91XagapStIUnK751PpfxL2dQp372iPuo1Qc9V/Hu1/2f71dDANgPVrHpT64jz+93HaN7qeTp48g8VyF1A/20/RM+UqCxJ2nZou7qN765JfT/RpMWTdCL2pIr7FdeIju+oWIG/V+6/PQI8VdFno5Xb01ttn2urTqEdrc83HNxILZ5toegz0Vq961fVqVBbyzYtlyS1+V9bSVLl4kGaMXB6mj+TfxrTfbTN4+Edh+nnHT9r68EtahLSRJKUM1tOmzozf5olv9x+qlw8yFrm7eltU2fNrjV6psQzKpC7QJrHnvvzN6pWqqq6NuoqSeqat6u2H/5dX//8tcq9+n66+p+aY1sX6uim+arX71sVqtTEWp4tt7/8K76ghJtXUm0XvWeVdi57Xxf/PCCLnb3yFK2qkPbjlT1PYUlSUmK8Ns97S5Hbf1D89Yty9fRRYJ1uqvDCQEnSjsWjdGjdHN28ckYuHjkVULmFQv4zIdVjZcvtr5D2H0qSDv82J13nFbUrXL7FQlS+0d+3SfTyLWbzJcKaad0Vf+OSQl//zlq26es3dT5qj14YskrS7RHnHAVuv8ePbpwvi529Ap/rrsoth8tisUiS5r1RQiVqdtalmD90cuePcnLNpvKNB6pMvV7W/V47H6WNc9/QqQO/ymKxU4Gy9RTynwly8/Sxvh4nfl+mMvV6aeey93X13EkVC2mnmEPrFHNonfZFfCpJajvuD2XL7Z+u1+CTTz6RJJ09e/aewTtXrlzy9fVN9bmKFSuqYsW/Z2gUKlRIixYt0rp169IM3qtWrdKBAwcUHR2tfPnySZI+/PBDde7cWYMGDUpX3yWu8QYA4IE8ZTcHeWLwc0u/m8YFHdVKPWN5zSZ03+Fi8UqzrbOyqbndDPW226sGdhP0uzFDm42J1ucXJXdUdksBdbfbrFfttqq63Vuy0+1QHp7cV4mKU2e7X/Wa3S7VtRstJ3mkepxLitQ1xaqI5e9RWAeLswqppv7UpjT7l8vTW+v2btD1W9f/5VX4dx8tnKiO9TpowTvzVKFoeb0+ub8uXbt0V52PFPZSmL4ZOlc5s+VUv8n9raPWB04e0MDPB6lBlfpaOPw79WzSU1N+mKofNiy12cecVV+qaP6imvf213r1he76+r9zJUlfhH2mX8ZHaEKv24Fu26HtKt+9ok6dO53uc7gVf0uJSYnK7u6Z6vMJiQn6cUu4mldvZg1pdzt/5bzW7V2vFs82v+ex9hzfo+BStl+IhJQO1u5ju9Pd39Qc27RAnr7FbUL3HRaLRU5uqZ9bYtx1la3fT82Hr1fjt8Jlsdgp4pM2Mv5/tH9/xKc6ufNHPd/rK730/m7V6TFTHt4FJUnHty3S3lWTVKPzZLX5YK/q9ftWOQuUSfU498vV00cXTx3UhT8ffHbJkQ1fy87OQc3e+U0h7cdr38pJOrR2lk2dPT99pJwFyqjFu5tUvvFAbZ73lv7c94uk2///XPVJG926fkEvDFmlRgOX6+qZSK2e0sFmH1fOHNPxrd+rbp95ajlyi0Laf6g8RauqZK1X1H5ipNpPjJR7rttfzsx7o4R2LB71wOcmSU2bNlWePHlUvXp1LVy48J51d+7cqY0bN6pWrVpp1tm0aZPKlCljDd2SVL9+fcXFxaWY7n4vjHgDAHAf7O3tJUnx8fFydXXN4t4go27cuCFJcnR8sKmPT4MLOirJkLcl47dtqmk31PpvLxXSOcsh7Te+U3XdHiW8rCiF6A3rvnPp79Hfy4pWoKWFfCy3px3nUOE0j3NNsZIkD/nYlLtbfHTZOJlmu3c6vK0h04eqZv/aKlGguCoWq6i6QXVVsWiFjJ2opLZ12qhuUF1J0tD2/9WGfRu1eP0SdWnQ2VqnR5MeCi5VTZI06pWRCn2rgVbv/FX1nwnVV6vmqkpgFfV44faoWyFffx2POa7ZK+eoWfW/RzWfKfGMOtX/exT8TrD2dPeyGXl2cXJRId9CcrBP/5/7H3//ifJ45VG1UlVTfX71zl919cZVNa2eMtTesXTjMrk5u/3rtdrnLp9Truy5bMpyZc+lc1fOp7u/qbn811F55S2e4XYBz7SweVzzlama26+gLp4+qJwFSuva+Wh5+hSVb/Hqslgsyub99yjttfPRcvP0Uf5Sz8nOwVEeuQoqT+FnHug87la6Xi/FHt6o79+ufHv/RaqoQJm6KhrcVvaOzhnal3vOAqrWbpwsFou88hbXhT/3a++qSSpZ+xVrHZ9iwdbRfC/fYvrryCbtWzVJBco8r1P7V+tC9F61HXdQHrn8JEm1X52hhUMr6ezx7cpd+PZMj+TEeNV+dYZcs+e27tfe3kkOTq5y87Idkc6ep7Bcstm+HzLKw8NDEyZMUPXq1WVnZ6elS5eqTZs2mjNnjv7zn//Y1C1QoIDOnj2rxMREjRgxQt26dUtzv7GxsfLxsf1/S44cOeTk5KQzZ86ku38EbwAA7oODg4Pc3Nx09uxZOTo6ys6OSWSPA8MwdOPGDZ05c0ZeXl7WL1CQNkO3ZwdYlPoI570cML7X5uSPdUHHFK9rSlainPX3vW6DLf21zHhVe5LmqrDleZWyvKicliKSpKqWPvrR6K3jSREKsDyvUpaW8rGU+5cj2vbxdt/T7ndQ8SD9OHqZ9h7fq53HdmnrH9v09S/f6LWmPa0BOL3KF/m7bw72DirlX0rHYyJt6xT+u46nu6f8ffwV+f91jsdGqk752jb1KxQtr7k/f62k5CTZ291+r5YuZDsVPi1lA8roh/8tTnf/Z62YrZ+2rtCMgdPknEaQW7x+iaqXqa48XnnS3M+SDT+oUdWGae7jn+7+yRiGcV/vs7t2cl/Nrpw5ru2L3tWZY1t16+p5Gcbtke5r56OVs0BpFX+2g8LHvaBvB5eTX9l6KlihkQqUuf1FS+FnWmrfqsma/1agCpQJlV/5+vKv0Fh2GfjS4984OrurQdhiXTlzXKcPrtWZY1u1ef5g7Yv4VM3eXpOhBePyFKliM2MhT9Gq2rPyYyUnJ8nu/99nPkVtv3zxKVpVe1dNliRdivlD7jkLWEO3JOXIHygnNy9djDlkDd4e3gVtQve9NB70U7r7nxZvb2+bRdIqV66sixcvauzYsSmC97p163Tt2jVt3rxZgwcPVtGiRfXyyy+nue/UZnhkdOYUwRsAgPtgsViUN29eRUZG6uTJtEfU8Gjy8vJK8xpA2Lo9Cm3RWeOgSlqapbvdn8ZmLUxup9qW4SpqCZWzPLXPWKBNxkfWOrXthqus8bIOG+E6aqzQGuNdtbL7RoGW5qpk11VFjFAdMcJ1zIjQeuMDhVrGqapdnxTH8tDtn+U1xSqb8lrLbxhn5GHxSVH/nxwdHFWpeCVVKl5JXRu+oi+WT9Pny7/QKw26yNHBURaLJcUf2AlJiel6DdKajm1b6fZ/DMNIkURT+7ve1TnzZ9jMWfmlZoTP0Odhn6l4gdRHi0+fP60tB7doQq+0V3L+/fDvOhF7QmPTcY22t6d3itHtC1cvKFf2nGm0SB9P32K6FHMow+1WTmwl95wFVKPLFLl55ZVhJOv7oUFKToy/3d9CFdV2/EFF71mpU/t/1S+f/kf5S9dR3T7z5JHLT63f36NT+3/Rqf2rteHL/trz00dqMjjigRcUu1v2PIWVPU9hlazVRRWaDNK3g8vq2NaFKlGjoyypfAGcnM73anpY38+Gkfp7+64vThycUl6a8rBVq1ZN06enXPPgzurnZcuW1V9//aURI0akGbx9fX21ZcsWm7KLFy8qISFBefKk/SXU3QjeAADcJycnJxUrVkzx8fFZ3RVkgKOjIyPdGeBqyamiCtU2Y6qqGn1TXOd9y7iU6nXeUcZGeclfNe3+ay27bESlqJfLUlzBluIKVn99n9xeu5JnK9C+uSTJ0+KnypYeqqwe+jn5v/rdmKGqShm8vRQgD/nquPGz8lpuL5yUZMTrhH5TXY3J0PkWzldYSclJikuIk6ODo3J45NC5y+esz5/866Ruxd9K0W7P8b0K+v8FxxKTEnUw6qDa1mmTok7eXLe/GLhy/YpO/nVSAb63A0CRvIW18+gum/q7j+2Wv4+/dbQ7NY4Ot/+cTzaSMnSed8xeOUfTfpyuqa9/qtKF0r712g8blipn9pzWhehSs3j9EpXyD1QJv3+/rVm5wuW0+cBmdaj390jkpgObVL5I+YydwF2KVGut1VM76sTvy1Jc520YhhJuXklxnfeta+d16fQferbTJOUt8awkKfbwhhT7dnLNriJVX1KRqi8p4JkWWvFhU926dkEuHjnl4OQq/4ovyL/iCyr1fE99N6S8Lvy5T96FUr/VWmbI5u0vByc3JcbdXqPAJZu3Lt51Dfj5qN2ys7cN/2eObU3x2NOnqHW0O606Xnlv/1y98gXq2vloXTsfbR31vnjqoOJvXpZXvnv/7O0cnJScfH/v1fuxc+dO5c2b9551DMNQXFxcms8HBwfrvffeU0xMjHVfq1atkrOzsypUqJDuvmR58J4yZYrGjRunmJgYlS5dWhMnTlSNGmn/Qq9du1ZhYWHav3+/8uXLp7feeks9e/Z8iD0GAOBvdnZ2cnFxyepuAKZqZDdZM5NraHpysGrbjZCPyipZiTpu/Kztxufqbb8vRZuclqK6bERpX/IC5bNU1hEjXH8YS6zPJxg3FWEMUilLS3kpQFf0p04Z262rkq9IDlNRS33lUnHd0kWdMH5V7jSuM7dYLKpq6ad1xvvKaRRTLhXVOuN9OcpNZS0vSzqRaruu47qpQZUGKl2olDzdvXQ85pgmLZ6sZ0o8Iw/X2wu5VSn5jOb/Ol/lCpdVspGsid9/kup10wt+XaCCeQqqcN4AffXz17py/YqaV7edIfD58i/k5eGpnNlzafLiycrh4WVdLb1jaAe1e+8/+nz5F6pfub72HN+t+b8u0H/bDbnnzyZntpxycXLRhn0b5ZPDR04OTsrmlk17I/fp7ZnD9EXY59Zbg91t1orZ+vSHKXq/22jl885n/YLBzdlNbi5/T11OTk7WDxt+UJPgF9K8ZvzazWtatSNCb7wUlurzQ2e8rTw58uj1lv0kSe2ff1mvjOummT/NUp0KtfXrrjXacnCrZr01857n+28KV3lRJ3Ys1erPOqlS0yHKX/o5uWTz1oU/92vfykkqXfe1FLcTc3bLIWePXPpjzUy5eeXVtfPR2vbd2zZ19q78RG6evspVsLxkZ6fIbYvk6ukrZzcvHV73lZKTk5SnyDNycHLTkY3fyN7J1br4WmrOn7y9iFxC3HXdvHpO50/ulp2Dk3LkD0y1/o7Fo5QYf0N+5RrIw7ug4m9c0v6IKUpOSlD+0rdv25YvsLb2/PSRDm/4Wj5Fquropnm6cOqAvAvafplx/cKf2jTvLQXW7qZzJ3dq/89TVa2t7SyF2CObtDv8Q/lXaqpT+37R8W2LVH/A7UsX8pd+Tjn9yurXz7souN04JScnasOX/ZW3RA3lDgjSvWTz9tfZ49t09exJObq4y9k9pyx2dvrxg4YqFNRUpeu+lmbbo0eP6tq1a4qNjdXNmzetC5uVKlVKTk5OmjNnjhwdHVWxYkXZ2dlp2bJl+uSTT/TBB3/f5/zTTz9VwYIFVbLk7f+XrF+/XuPHj1ffvn2tdRYvXqwhQ4bojz/+kCSFhoaqVKlS6tChg8aNG6cLFy7ozTffVPfu3ZU9+9+XzvybLA3eCxYsUP/+/TVlyhRVr15dn3/+uRo2bKgDBw6oYMGUb9TIyEg1atRI3bt319y5c7Vhwwb16tVLuXPnVqtWrbLgDAAAAJ58OSwBetVum9YZY7QqeaCuKUZuyq18qqTGdpNTbVPS0lTVLK8r3OinJCNOxSyNVNMyVGuMkZIkO9nrps5rcXIXXddfcpO3SlpaqI5lhCQpWUkKT+6nK/pTzsquopb6qm/5MM0+VrcMVKJuKjy5j27qogqoijrY/SRnS7Y024SUDtayTcs0afFk3Yq/pdxeuVWzXA2b67vfaB2md2aNUJdxXZXbM7cGtR2ogydT3hv89Zb9NGvFbP0R/Yf8chfQxN4fKUe2HCnqfDB/nKLORKl4geL6uM9EOf7/VORA/0CN6/GBPv1hqr5YPk25Pb3Vq+lrNgurpcbB3kGD2g7U58unacoPU1WpWEXNGDhdt+Jv6UTsCSXeY6rxt2u+VUJigt74bKBNec8mPfRa078HtjYf3KKYC7FqXr15mvtasW2lJKlhlQapPh97IVZ2lr+nQlcoWkEfvDpGk5dM0ac/TJFfbj998Or7D3wPb4vFoud6ztHBNTN0eN0c7Vz6viz2DvL0Kapi1dupQNl6KdvY2en5177Uxq/f0PdDg+SZt7hC2n+o5e+HWus4Ontod/gEXf7rqCx29sodEKQGYYtlsbOTk5undv84XpvnD5KRnKScBUqrfv/v5eKR9mJhi4ZXs/773InfdWzzAnnkKqiXP0x9mnzekjV04JfPtGZaV928ckbObjmUy7+8Gr25zLqYnF/ZeqrUdIi2LhiqpIRbKl6zo4qFtEsxCl4spL2S4m9pycgasrOzV+m6r6lk7a42dco1eF3nTuzU70tGy9Elm6q1fV9+///aWSwWhfZboI1z39CyMfVsbif2b8o27K+107rpu6EVlRR/03o7sStnjuvW1XsvrNetWzetXbvW+vjObcEiIyNVqFAhSdKoUaN08uRJ2dvbq3jx4po5c6bN9d3JyckaMmSIIiMj5eDgoCJFiuj9999Xjx49rHUuX76sQ4f+/jnY29vrxx9/VK9evVS9enW5urqqXbt2Gj9+/D1Hyu9mMbLwfhpVq1ZVpUqVNHXqVGtZYGCgmjdvrjFjUk4LGjRokJYuXaqDBw9ay3r27Kndu3dr06a0bxXxT1euXJGnp6cuX76coW8ossIIh4c3DQMPV8vP9mZ1F2CiyY4ZX/kXj4cvOjGy/aTiM/fJZubn7p37eC8YNl8lC6Y+zfbOfbzXffybsrul/UUA/l3DwY00sstI6z3S+czNmOVjQpWrYDkFt0/7Wv15b5RQmdA+Klu/b5p1HobH4TM3I9kyy5ZgjY+P144dOxQaGmpTHhoaqo0bN6baZtOmTSnq169fX9u3b1dCQoJpfQUAAAAA4H5l2VTzc+fOKSkpKcU90Xx8fBQbG5tqm9Tuoebj46PExESdO3cu1Qvn4+LibKYAXL58WdLtbycedXH3uUgGHn3Xbl7L6i7ARPGJj/7/X3B/rlxhEbUnFZ+5TzYzP3dv3Lq9sNWNuBtpHudm3E1J0vWb12SXnpXOkaZkw9DNuJvW15rP3IxJTk5SUmK84m+m/boZhqGkhFv3rPMwPA6fuXcyZbomkRtZ5NSpU4YkY+PGjTblo0aNMkqUKJFqm2LFihmjR4+2KVu/fr0hyYiJiUm1zfDhww1JbGxsbGxsbGxsbGxsbGyZvkVHR/9r/s2yEW9vb2/Z29unGN0+c+ZMilHtO3x9fVOt7+DgoFy5Ul+8YMiQIQoL+3t1xeTkZF24cEG5cuVK370VATyQK1euyM/PT9HR0Y/8ugoAADzO+MwFHi7DMHT16lXly5fvX+tmWfB2cnJSUFCQIiIi1KJFC2t5RESEmjVrlmqb4OBgLVu2zKZs1apVqly5shwdU785vbOzs5ydnW3KvLy8HqzzADIse/bs/BEAAMBDwGcu8PB4enqmq16WLa4mSWFhYZo+fbpmzpypgwcPasCAAYqKirLel3vIkCHq2LGjtX7Pnj118uRJhYWF6eDBg5o5c6ZmzJihN998M6tOAQAAAACAe8rS+3i3adNG58+f18iRIxUTE6MyZcooPDxc/v7+kqSYmBhFRUVZ6wcEBCg8PFwDBgzQp59+qnz58umTTz7hHt4AAAAAgEdWlt7HG8CTLy4uTmPGjNGQIUNSXPYBAAAyD5+5wKOL4A0AAAAAgImy9BpvAAAAAACedARvAAAAAABMRPAGkG4Wi0VLlizJ6m4AAPDE4zMXeLIQvAFIkmJjY9W3b18VLlxYzs7O8vPzU5MmTfTLL79kddckSYZhaMSIEcqXL59cXV1Vu3Zt7d+/P6u7BQBAhj3qn7mLFi1S/fr15e3tLYvFol27dmV1l4DHHsEbgE6cOKGgoCCtXr1aY8eO1d69e7VixQrVqVNHvXv3zuruSZLGjh2rCRMmaPLkydq2bZt8fX1Vr149Xb16Nau7BgBAuj0On7nXr19X9erV9f7772d1V4AnBsEbgHr16iWLxaKtW7fqxRdfVPHixVW6dGmFhYVp8+bNabYbNGiQihcvLjc3NxUuXFjDhg1TQkKC9fndu3erTp06ypYtm7Jnz66goCBt375dknTy5Ek1adJEOXLkkLu7u0qXLq3w8PBUj2MYhiZOnKihQ4eqZcuWKlOmjObMmaMbN27om2++ydwXAwAAEz3qn7mS1KFDB73zzjuqW7du5p048JRzyOoOAMhaFy5c0IoVK/Tee+/J3d09xfNeXl5pts2WLZtmz56tfPnyae/everevbuyZcumt956S5LUvn17VaxYUVOnTpW9vb127dolR0dHSVLv3r0VHx+v3377Te7u7jpw4IA8PDxSPU5kZKRiY2MVGhpqLXN2dlatWrW0ceNG9ejR4wFeAQAAHo7H4TMXgDkI3sBT7ujRozIMQyVLlsxw27ffftv670KFCumNN97QggULrH8EREVFaeDAgdZ9FytWzFo/KipKrVq1UtmyZSVJhQsXTvM4sbGxkiQfHx+bch8fH508eTLD/QYAICs8Dp+5AMzBVHPgKWcYhqTbq6dm1MKFC/Xss8/K19dXHh4eGjZsmKKioqzPh4WFqVu3bqpbt67ef/99HTt2zPpcv379NGrUKFWvXl3Dhw/Xnj17/vV4d/fRMIz76jcAAFnhcfrMBZC5CN7AU65YsWKyWCw6ePBghtpt3rxZbdu2VcOGDbV8+XLt3LlTQ4cOVXx8vLXOiBEjtH//fjVu3FirV69WqVKltHjxYklSt27ddPz4cXXo0EF79+5V5cqVNWnSpFSP5evrK+nvke87zpw5k2IUHACAR9Xj8JkLwBwEb+AplzNnTtWvX1+ffvqprl+/nuL5S5cupdpuw4YN8vf319ChQ1W5cmUVK1Ys1WnfxYsX14ABA7Rq1Sq1bNlSs2bNsj7n5+ennj17atGiRXrjjTc0bdq0VI8VEBAgX19fRUREWMvi4+O1du1ahYSEZPCMAQDIGo/DZy4AcxC8AWjKlClKSkpSlSpV9P333+vIkSM6ePCgPvnkEwUHB6fapmjRooqKitL8+fN17NgxffLJJ9Zv1iXp5s2b6tOnj9asWaOTJ09qw4YN2rZtmwIDAyVJ/fv318qVKxUZGanff/9dq1evtj53N4vFov79+2v06NFavHix9u3bp86dO8vNzU3t2rXL/BcEAACTPOqfudLtReB27dqlAwcOSJIOHTqkXbt2pZh5BiADDAAwDOP06dNG7969DX9/f8PJycnInz+/0bRpU+PXX3+11pFkLF682Pp44MCBRq5cuQwPDw+jTZs2xkcffWR4enoahmEYcXFxRtu2bQ0/Pz/DycnJyJcvn9GnTx/j5s2bhmEYRp8+fYwiRYoYzs7ORu7cuY0OHToY586dS7N/ycnJxvDhww1fX1/D2dnZqFmzprF3714zXgoAAEz1qH/mzpo1y5CUYhs+fLgJrwbwdLAYxv+v8gAAAAAAADIdU80BAAAAADARwRsAAAAAABMRvAEAAAAAMBHBGwAAAAAAExG8AQAAAAAwEcEbAAAAAAATEbwBAAAAADARwRsAAAAAABMRvAEAeEJZLBYtWbIkq7sBAMBTj+ANAMBjKjY2Vn379lXhwoXl7OwsPz8/NWnSRL/88ktWdw0AAPyDQ1Z3AAAAZNyJEydUvXp1eXl5aezYsSpXrpwSEhK0cuVK9e7dW3/88UdWdxEAAPw/RrwBAHgM9erVSxaLRVu3btWLL76o4sWLq3Tp0goLC9PmzZtTbTNo0CAVL15cbm5uKly4sIYNG6aEhATr87t371adOnWULVs2Zc+eXUFBQdq+fbsk6eTJk2rSpIly5Mghd3d3lS5dWuHh4da2Bw4cUKNGjeTh4SEfHx916NBB586dsz6/cOFClS1bVq6ursqVK5fq1q2r69evm/TqAADwaGHEGwCAx8yFCxe0YsUKvffee3J3d0/xvJeXV6rtsmXLptmzZytfvnzau3evunfvrmzZsumtt96SJLVv314VK1bU1KlTZW9vr127dsnR0VGS1Lt3b8XHx+u3336Tu7u7Dhw4IA8PD0lSTEyMatWqpe7du2vChAm6efOmBg0apNatW2v16tWKiYnRyy+/rLFjx6pFixa6evWq1q1bJ8MwzHmBAAB4xBC8AQB4zBw9elSGYahkyZIZavf2229b/12oUCG98cYbWrBggTV4R0VFaeDAgdb9FitWzFo/KipKrVq1UtmyZSVJhQsXtj43depUVapUSaNHj7aWzZw5U35+fjp8+LCuXbumxMREtWzZUv7+/pJk3Q8AAE8DgjcAAI+ZOyPFFoslQ+0WLlyoiRMn6ujRo9YwnD17duvzYWFh6tatm7766ivVrVtXL730kooUKSJJ6tevn1577TWtWrVKdevWVatWrVSuXDlJ0o4dO/Trr79aR8D/6dixYwoNDdXzzz+vsmXLqn79+goNDdWLL76oHDly3O9LAADAY4VrvAEAeMwUK1ZMFotFBw8eTHebzZs3q23btmrYsKGWL1+unTt3aujQoYqPj7fWGTFihPbv36/GjRtr9erVKlWqlBYvXixJ6tatm44fP64OHTpo7969qly5siZNmiRJSk5OVpMmTbRr1y6b7ciRI6pZs6bs7e0VERGhn376SaVKldKkSZNUokQJRUZGZu4LAwDAI8picIEVAACPnYYNG2rv3r06dOhQiuu8L126JC8vL1ksFi1evFjNmzfXhx9+qClTpujYsWPWet26ddPChQt16dKlVI/x8ssv6/r161q6dGmK54YMGaIff/xRe/bs0dChQ/X9999r3759cnD498l0SUlJ8vf3V1hYmMLCwjJ24gAAPIYY8QYA4DE0ZcoUJSUlqUqVKvr+++915MgRHTx4UJ988omCg4NT1C9atKiioqI0f/58HTt2TJ988ol1NFuSbt68qT59+mjNmjU6efKkNmzYoG3btikwMFCS1L9/f61cuVKRkZH6/ffftXr1autzvXv31oULF/Tyyy9r69atOn78uFatWqVXXnlFSUlJ2rJli0aPHq3t27crKipKixYt0tmzZ63tAQB40nGNNwAAj6GAgAD9/vvveu+99/TGG28oJiZGuXPnVlBQkKZOnZqifrNmzTRgwAD16dNHcXFxaty4sYYNG6YRI0ZIkuzt7XX+/Hl17NhRf/31l7y9vdWyZUu9++67km6PUvfu3Vt//vmnsmfPrgYNGuijjz6SJOXLl08bNmzQoEGDVL9+ff1fu3ZMAzEMBFF0FQCmsHQccJaMKZDSuQuKuWvegzDdl+acU91dc866rqvGGPU8T+29633f6u5aa9V93z/bCwD+ydUcAAAAglzNAQAAIEh4AwAAQJDwBgAAgCDhDQAAAEHCGwAAAIKENwAAAAQJbwAAAAgS3gAAABAkvAEAACBIeAMAAECQ8AYAAIAg4Q0AAABBHx2/L+wm7WzBAAAAAElFTkSuQmCC",
@@ -1217,8 +1200,6 @@
     }
    ],
    "source": [
-    "plt.savefig(\"classification_svc.png\")    \n",
-    "plt.savefig(\"confusion_svc.png\") \n",
     "\n",
     "modelsvc = SVC(kernel='rbf', gamma='auto', C=10)\n",
     "modelsvc.fit(X_train_scaled, y_train)\n",
@@ -1284,7 +1265,7 @@
     "ax.annotate(support_text, xy=(1, 0), xycoords='axes fraction', fontsize=10, color='black', ha='right', va='bottom')\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.savefig(\"classification_svc.png\", dpi=300)\n",
+    "#plt.savefig(\"classification_svc.png\", dpi=300)\n",
     "plt.show()\n",
     "\n",
     "# Plot Confusion Matrix\n",
@@ -1293,13 +1274,13 @@
     "plt.xlabel('Predicted')\n",
     "plt.ylabel('Actual')\n",
     "plt.title('Confusion Matrix - SVC')\n",
-    "plt.savefig(\"confusion_svc.png\", dpi=300)\n",
+    "#plt.savefig(\"confusion_svc.png\", dpi=300)\n",
     "plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1413,16 +1394,16 @@
     "    for rect in bar:\n",
     "        height = rect.get_height()\n",
     "        ax.annotate(f'{height:.2f}',\n",
-    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
-    "                    xytext=(0, 3),\n",
-    "                    textcoords='offset points',\n",
-    "                    ha='center', va='bottom')\n",
+    "        xy=(rect.get_x() + rect.get_width() / 2, height),\n",
+    "        xytext=(0, 3),\n",
+    "        textcoords='offset points',\n",
+    "        ha='center', va='bottom')\n",
     "\n",
     "support_text = f'Class 0 Support: {support[0]} | Class 1 Support: {support[1]}'\n",
     "ax.annotate(support_text, xy=(1, 0), xycoords='axes fraction', fontsize=10, color='black', ha='right', va='bottom')\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.savefig(\"classification_dt.png\", dpi=300)\n",
+    "#plt.savefig(\"classification_dt.png\", dpi=300)\n",
     "plt.show()\n",
     "\n",
     "# Plot Confusion Matrix\n",
@@ -1431,13 +1412,13 @@
     "plt.xlabel('Predicted')\n",
     "plt.ylabel('Actual')\n",
     "plt.title('Confusion Matrix - Decision Tree')\n",
-    "plt.savefig(\"confusion_dt.png\", dpi=300)\n",
+    "#plt.savefig(\"confusion_dt.png\", dpi=300)\n",
     "plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
@@ -1450,7 +1431,7 @@
        " 'F1-Score': [0.9531150398295376, 0.9273908901898491, 0.9461287249795657]}"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1461,7 +1442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [
     {
@@ -1523,7 +1504,7 @@
     "    # Adding values on top of bars\n",
     "    for i, value in enumerate(data):\n",
     "        plt.text(i, value + 0.01, f'{value:.2f}', ha='center', fontsize=10)\n",
-    "    plt.savefig(f\"{title}.png\", dpi=300)\n",
+    "    #plt.savefig(f\"{title}.png\", dpi=300)\n",
     "    plt.show()\n",
     "\n",
     "# Plot each metric separately\n",
@@ -1535,7 +1516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1551,36 +1532,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pickle\n",
-    "# Save models\n",
-    "with open(\"rf_model.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(modelr, f)\n",
+    "# import pickle\n",
+    "# # Save models\n",
+    "# with open(\"rf_model.pkl\", \"wb\") as f:\n",
+    "#     pickle.dump(modelr, f)\n",
     "\n",
-    "with open(\"svc_model.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(modelsvc, f)\n",
+    "# with open(\"svc_model.pkl\", \"wb\") as f:\n",
+    "#     pickle.dump(modelsvc, f)\n",
     "\n",
-    "with open(\"dt_model.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(modeld, f)\n"
+    "# with open(\"dt_model.pkl\", \"wb\") as f:\n",
+    "#     pickle.dump(modeld, f)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [],
    "source": [
     "\n",
-    "with open(\"scaler.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(scaler, f)"
+    "# with open(\"scaler.pkl\", \"wb\") as f:\n",
+    "#     pickle.dump(scaler, f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [
     {
@@ -1593,7 +1574,7 @@
        " 'F1-Score': [0.9531150398295376, 0.9273908901898491, 0.9461287249795657]}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/models2.ipynb b/models2.ipynb
index 645e399453ef4e96967f0fd4d26b8fd0be08b75e..46c5579b6b50637499dd3552d512297c2bb3ed9b 100644
--- a/models2.ipynb
+++ b/models2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,7 +10,6 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
-    "import ast\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
@@ -25,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -411,7 +410,7 @@
        "[2149 rows x 35 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -423,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -809,7 +808,7 @@
        "[2149 rows x 33 columns]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -829,7 +828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -1215,7 +1214,7 @@
        "[1719 rows x 32 columns]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1224,11 +1223,9 @@
     "X = df.drop('Diagnosis', axis= 1)\n",
     "y = df['Diagnosis']\n",
     "\n",
-    "#split the data into test and train\n",
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
     "\n",
-    "#apply scaler\n",
     "scaler = StandardScaler()\n",
     "X_train_scaled = scaler.fit_transform(X_train)\n",
     "X_test_scaled = scaler.transform(X_test)\n",
@@ -1238,7 +1235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1258,7 +1255,7 @@
        "Name: Diagnosis, Length: 1719, dtype: int64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1271,12 +1268,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Building the models"
+    "# Building the models"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1286,7 +1283,7 @@
     "    'DecisionTree': DecisionTreeClassifier(), 'RandomForest': RandomForestClassifier(), 'svc': SVC()\n",
     "}\n",
     "\n",
-    "# Define parameters to test using the randomized grid search\n",
+    "# Define parametersh\n",
     "param_grids = {\n",
     "    'DecisionTree': {\n",
     "        'criterion': ['gini', 'entropy'],\n",
@@ -1311,9 +1308,16 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Randomised grid dearch"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1341,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1483,7 +1487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1618,7 +1622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -1628,8 +1632,8 @@
       "Classification Report:\n",
       "               precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.91      0.97      0.94       277\n",
-      "           1       0.93      0.83      0.88       153\n",
+      "           0       0.92      0.96      0.94       277\n",
+      "           1       0.93      0.84      0.88       153\n",
       "\n",
       "    accuracy                           0.92       430\n",
       "   macro avg       0.92      0.90      0.91       430\n",
@@ -1639,24 +1643,24 @@
       "=================================================\n",
       "\n",
       "Confusion Matrix:\n",
-      " [[268   9]\n",
-      " [ 26 127]]\n",
+      " [[267  10]\n",
+      " [ 24 129]]\n",
       "\n",
       "=================================================\n",
       "\n",
       "Accuracy Score:\n",
-      " 0.9186046511627907\n",
-      "[0.9348837209302325, 0.8372093023255814, 0.9186046511627907]\n",
+      " 0.9209302325581395\n",
+      "[0.9348837209302325, 0.8372093023255814, 0.9209302325581395]\n",
       "\n",
       "=================================================\n",
       "\n",
-      "[0.9338733940478126, 0.835612739631147, 0.9174223348770127]\n",
-      "[0.9365970306135856, 0.835428690788246, 0.919484654326847]\n"
+      "[0.9338733940478126, 0.835612739631147, 0.920009175793852]\n",
+      "[0.9365970306135856, 0.835428690788246, 0.9212731277457056]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1666,7 +1670,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1753,12 +1757,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1768,7 +1772,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1778,7 +1782,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1788,7 +1792,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1827,10 +1831,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Model': ['Random Forest', 'SVC', 'Decision Tree'],\n",
+       " 'Accuracy': [0.9348837209302325, 0.8372093023255814, 0.9209302325581395],\n",
+       " 'Precision': [0.9365970306135856, 0.835428690788246, 0.9212731277457056],\n",
+       " 'Recall': [0.9348837209302325, 0.8372093023255814, 0.9209302325581395],\n",
+       " 'F1-Score': [0.9338733940478126, 0.835612739631147, 0.920009175793852]}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics"
+   ]
   }
  ],
  "metadata": {
diff --git a/output.png b/output.png
deleted file mode 100644
index c15673e611bdac1d6365405d243f8468870fee99..0000000000000000000000000000000000000000
Binary files a/output.png and /dev/null differ
diff --git a/preprocessing.ipynb b/preprocessing.ipynb
index cdd23294ceb3c83f33688cea657a8bf9b93ea1ab..90b7dbe8c936213b3f9ef2eea7f3224fefbcc623 100644
--- a/preprocessing.ipynb
+++ b/preprocessing.ipynb
@@ -1278,6 +1278,37 @@
     "3: Higher"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Diagnosis', ylabel='count'>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#0 indicates No and 1 indicates Yes\n",
+    "sns.countplot(x=\"Diagnosis\", data=a_df)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 31,
@@ -2509,37 +2540,6 @@
     "sns.heatmap(cognitivedf == 0, yticklabels=False)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Diagnosis', ylabel='count'>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#0 indicates No and 1 indicates Yes\n",
-    "sns.countplot(x=\"Diagnosis\", data=a_df)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 42,
diff --git a/rf_model.pkl b/rf_model.pkl
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diff --git a/svc_model.pkl b/svc_model.pkl
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