Pima_Indian_Diabetes_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_YdLCFcw0wJ1"
   },
   "source": [
    "#**Industry Choice: Healthcare (Type 2 Diabetes)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wD53VOE-CqEr"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tLs7PonH2c6N"
   },
   "source": [
    "##**Scenario**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mqqjQG4p2ouz"
   },
   "source": [
    "A healthcare organization is working with a community of Pima Indians, who are\n",
    "known to have a high prevalence of Type 2 diabetes. The organization aims to\n",
    "identify individuals at high risk of developing Type 2 diabetes within the next five years to provide early intervention and prevention strategies. By leveraging historical health data, the organization seeks to implement a predictive analytics solution to achieve this goal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HQ-F4rem21yg"
   },
   "source": [
    "##**Problem Statement**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6OvHiHy93kBa"
   },
   "source": [
    "Type 2 diabetes is a significant health concern among the Pima Indian community, affecting a substantial portion of the population. Early identification and intervention are crucial to prevent the onset and complications associated with this chronic condition.\n",
    "The current challenge is the inability to accurately predict which individuals in the Pima Indian community are at high risk of developing Type 2 diabetes. This limitation hinders the ability to provide timely and targeted preventive care.\n",
    "\n",
    "Type 2 diabetes develops due to a combination of genetic, environmental, and lifestyle factors. The Pima Indians have a genetic predisposition to diabetes, and factors such as diet, physical activity, and body mass index (BMI) contribute to the disease's prevalence.\n",
    "\n",
    "Addressing this problem is critical for improving the health outcomes of the Pima Indian community. Early prediction and intervention can reduce the incidence of Type 2 diabetes, lower healthcare costs, and improve the quality of life for individuals at risk.\n",
    "The purpose of this initiative is to develop a machine learning model to predict the onset of Type 2 diabetes in the Pima Indian community. The vision is to use predictive analytics to enable healthcare providers to offer personalized preventive care. The scope includes analyzing historical health data, developing the predictive model, and integrating the solution into the healthcare system for real-time risk assessment.\n",
    "\n",
    "\n",
    "The objective is to create a predictive model using machine learning techniques to identify individuals at high risk of developing Type 2 diabetes. The proposed solution involves:\n",
    "1.\tData Collection: Gathering historical health data from the Pima Indian community, including demographic, clinical, and lifestyle information.\n",
    "2.\tData Preprocessing: Cleaning and preparing the data for analysis, including handling missing values and normalizing features.\n",
    "3.\tModel Development: Applying machine learning algorithms to develop a predictive model. Algorithms considered include logistic regression, random forest, and support vector machine (SVM).\n",
    "4.\tModel Evaluation: Assessing the model's performance using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC.\n",
    "5.\tModel Deployment: Integrating the predictive model into the healthcare system to enable real-time risk assessment and personalized intervention strategies.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TSWriCWP5HGJ"
   },
   "source": [
    "#**Review of Research Papers**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2NDkjkrS5Nln"
   },
   "source": [
    "**Paper 1**: \"A Comparative Study of Diabetes Detection Using The Pima Indian Diabetes Database\"\n",
    "\n",
    "Approach: This paper presents a comparative study of LSTM, Random Forest(RF) and CNN for diabetes detection on the widely used Pima Indians Diabetes dataset. The study aims to evaluate the performance  of  these  models  using  common  evaluation  metrics,  such  as  accuracy,  precision,recall, F1-score, and area under the receiver operating characteristic curve(AUC-ROC). In the comparative analysis, the strengths and weaknesses of each models are discussed.\n",
    "\n",
    "Findings:\n",
    "   Accuracy: In terms of accuracy, LSTM achieves a highest accuracy of 85%, followed by CNN with an accuracy of 82%, whereas RF performs with an accuracy of 78%.\n",
    "   Precision: Of all the models, LSTM showcased a highest precision of 82%, followed by CNN with a precision of 79% and RF with a precision of 75%.\n",
    "   Recall: LSTM attained a recall score of 78%, whereas CNN and RF obtained a recall score of 75% and 72% respectively.\n",
    "   AUC-ROC: LSTM obtained a highest AUC-ROC of 89%, whereas CNN and RF obtained an AUC-ROC of 86% and 81% respectively.\n",
    "   F1-Score: LSTM attained a top F1-Score of 80%, taking into account a balanced measure of precision and recall. CNN and RF achieved F1-Scores of 77% and 73% respectively.\n",
    "\n",
    "Comparison: LSTM has showcased highest performance across all metrics,suggesting its effectiveness in diabetes detection on the Pima Indians Diabetes Database because of it's capability in capturing temporal differences in sequential data. CNN and RF also performed reasonably well with scores closer to LSTM model. CNN's performance can be improved by proper design of convolutional layers and RF's performance was limited because of temporal dependencies and sequential data. \n",
    "   \n",
    "   Challenges: The  models' effectiveness can be impacted by variables like data accuracy, feature choice, and potential dataset biases. The Pima Indian Diabetes dataset used for analysis has  its  limitations, including  a  relatively small sample size and potential class imbalance. These  factors may affect the factors affecting the model performance.\n",
    "   Further Research: This includes optimizing RF and CNN models by hyperparameter tuning. Hybrid models can be explored by combining strengths of LSTM, RF and CNN to leverage their complementary capabilities and improve the model performance.\n",
    "\n",
    "**Paper 2**: \"A Machine Learning Approach to Predicting the Onset of Type II Diabetes in a Sample of Pima Indian Women\"\n",
    "\n",
    "Approach: In this paper, various supervised learning algorithms like Logistic Regression, SVM, Decision Tree Classifier and Random Forest Classifier have been used to evaluate the best performance in predicting type 2 diabetes using female Pima Indians diabetes dataset. Each algorithm was trained on unscaled data with an unbalanced and balanced dataset and on scaled data with unbalanced and balanced dataset. Best model was choosen based on highest recall and accuracy score.\n",
    "      \n",
    "Findings: In this paper, the metrics like accuracy, precision and recall were calculated for each algorithm (Logistic Regression, SVM, Decision Tree Classifier, Random Forest Classifier) based on various data measures which are:\n",
    "   1. Unbalanced and Unscaled data: Using these parameters, Logistic Regression has given the highest accuracy of 82%, followed by SVM with 81%. Decision Tree and Random Forest has given the accuracy of 72% and 79% respectively. In terms of precision, SVM has given the highest precision of 85%, followed by Randon Forest with 73%. Logistic Regression and Decision Tree have performed with a precision of 73% and 61% respectively. When it comes to Recall, Random Forest has given the highest recall of 66%, followed by Logistic Regression and Decision Tree with 60%. SVM provided the least recall of 53%.\n",
    "   2. Unbalanced and scaled data: The data has now been scaled to see if the models' performance can be improved. In terms of accuracy, Random Forest has given the highest accuracy of 75% (previously 79%), followed by Logistic Regression with 73% (previously 82%) and Decision Tree with 73%(previously 72%). SVM has given the lowest accuracy of 69% (previously the highest accuracy of 81%). In terms of precision, Decision Tree has given the highest precision of 73% (previously 61%), followed closely by Random Forest with 72% (previously 73%), and Logistic Regression with 71% (previously 70%). SVM has given the lowest precision of 68% (previously highest precision of 85%). In terms of Recall, Random Forest has given the highest recall of 76% (previously 66% and still leading), followed by Logistic Regression with 71% (previously 60%). Decision Tree has performed with a recall score of 65% (previously 60%) and SVM with a recall score of 63% (previously 53%).\n",
    "   3. Balanced and unscaled data: The data is now balanced in terms of class distribution but not scaled to see if the models' performance improved. All the metrics obtained are same as in case 2.\n",
    "   4. Balanced and scaled data: The data is now balanced in terms of class distribution and scaled so that all the features are given equal importance. In terms of accuracy, SVM has given the highest accuracy of 80% (previously 69%), followed by Random Forest with 76% (previously 75%) and Logistic Regression with 71%(previously 73%). Decision Tree has given the lowest accuracy of 70% (previously 73%). In terms of precision, SVM has given the highest precision of 75% (previously 68%), followed closely by Logistic Regression with 74% (previously 71%), and Random Forest with 71% (previously 72%). Decision Tree has given the lowest precision of 65% (previously highest precision of 73%). In terms of Recall, SVM has given the highest recall of 86% (previously 63%), followed closely by Random Forest with 84% (previously 76%). Decision Tree has performed with a recall score of 71% (previously 65%) and Logistic Regression with a recall score of 60% (previously 71%).\n",
    "\n",
    "In summary, SVM has given the average highest accuracy, precision and recall when data was unbalanced and unscaled and when data was balanced and scaled because of it's capability in supporting only binary classification.\n",
    "      \n",
    "  Comparison: In this project, recall is particularly important since it measures how accurately our model can identify the relevant data (True Positive). Imagine that our Pima female is diabetic, but there is no treatment given to her because our model predicted that she was not diabetic. That is a situation we would like to avoid. For this reason, optimal model was chosen based on highest recall and accuracy. As a result, the Support vector machine trained on balanced dataset with scaled data was the optimal model for predicting diabetes disease in the females of Pima Indians. Integrating Random Forest and SVM with additional features and optimized hyperparameters can improve our predictive model's performance.\n",
    "\n",
    "Further Research: The dataset currently has only 798 records which can be improved by gathering more data including men. Adding more features like daily exercise and dietary records can also help in training the model better improving the performance which can ultimately make better predictions.\n",
    "\n",
    "**Paper 3**: \"Prediction of Type 2 Diabetes Using Machine Learning Techniques\"\n",
    "\n",
    "Approach: In this paper, the author has used various machine learning classification algorithms like decision tree, naive bayes, logistic regression and SVM and compares the performance of all the algorithms using metrics like accuracy, precision, recall and f1-score. \n",
    "\n",
    "Findings: Various performances of various algorithms presented by different authors on different parameters like dataset, feature extraction and classifier are explored. Of all the algorithms considered, Genetic algorithm with features extracted like age, height, weight, smoking habit, alcohol habit and family history has performed well with an accuracy of 90% which was closely followed by Random Forest classifier on National Inpatient Sample(NIS) data with an accuracy of 89.55% using features extracted like age, sex, race. The lowest accuracy of 70% was obtained by KNN classifier with features extracted like body mass index, blood pressure and cholestrol level.\n",
    "\n",
    "Comparison: Research on diabetes detection has been done using Machine learning based on classification, feature selection and feature extraction. Results of these algorithms are not found at it's best because of limitation in solving object and speech recognition applications, where the dimension of data is very high. Utilizing neural networks alongside traditional machine learning algorithms can provide a robust predictive solution by capturing nonlinear patterns in the data.\n"
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    "#**Hypothesis/Proposal**"
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    "Based on the problem statement and the reviewed research papers, the proposed solution involves the following steps:\n",
    "1. Data Collection:\n",
    "   - Gather historical health data from the Pima Indian community, including demographic information, clinical measurements (e.g., glucose levels, blood pressure), and lifestyle factors (e.g., diet, physical activity).\n",
    "2. Data Preprocessing:\n",
    "   - Handle missing values using techniques like mean imputation or K-nearest neighbors (KNN) imputation.\n",
    "   - Normalize and standardize the data to ensure consistent feature scaling.\n",
    "3. Feature Engineering:\n",
    "   - Extract relevant features such as age, BMI, glucose levels, blood pressure, and family history of diabetes.\n",
    "   - Create new features that may indicate diabetes risk, such as the rate of change in glucose levels over time.\n",
    "4. Model Selection:\n",
    "   - Experiment with different machine learning algorithms, including logistic regression, random forest, SVM, LSTM, and neural networks.\n",
    "   - Evaluate the performance of each model using metrics like accuracy, precision, recall, F1-score, and AUC-ROC.\n",
    "5. Model Training:\n",
    "   - Train the selected models on the historical data.\n",
    "   - Perform cross-validation to ensure the models generalize well to unseen data.\n",
    "6. Model Deployment:\n",
    "   - Deploy the best-performing model in the healthcare system.\n",
    "   - Integrate the model with electronic health records to provide real-time risk assessments for patients.\n",
    "7. Evaluation and Iteration:\n",
    "   - Continuously monitor and evaluate the model's performance in a real-world setting.\n",
    "   - Iterate and improve the model based on new data and feedback from healthcare providers.\n",
    "\n"
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    "#**References in APA Format**\n",
    "\n",
    "1. Mousa, A., Mustafa, W., Marqas, R., & Mohammed, S. H. M. (2023). A Comparative Study of Diabetes Detection Using The Pima Indian Diabetes Database. *The Journal of The University of Duhok, 26*(2), 277-288. doi:10.26682/suod.2023.26.2.24\n",
    "2. Benarbia, M. (2022). A Machine Learning Approach to Predicting the Onset of Type II Diabetes in a Sample of Pima Indian Women. *City University of New York (CUNY) Academic Works*. Retrieved from https://academicworks.cuny.edu/gc_etds/4895\n",
    "3. Kumar, S., & Abhishek, P. A. (2021). Artificial Intelligence-Based Learning Techniques for Diabetes Prediction: Challenges and Systematic Review. *SN Computer Science, 1*(6), 322. doi:10.1007/s00521-020-05432-8\n",
    "\n"
   ]
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-1ab8ee8d-7cf7-4964-9f4c-4a97dfdd327d button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "diabetes = pd.read_csv(\"/content/diabetes.csv\")\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_fsKEUSw7ZXg"
   },
   "source": [
    "**The dataset contains the following columns:**\n",
    "\n",
    "Pregnancies: Number of times pregnant\n",
    "\n",
    "Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
    "\n",
    "BloodPressure: Diastolic blood pressure (mm Hg)\n",
    "\n",
    "SkinThickness: Triceps skin fold thickness (mm)\n",
    "\n",
    "Insulin: 2-Hour serum insulin (mu U/ml)\n",
    "\n",
    "BMI: Body mass index (weight in kg/(height in m)^2)\n",
    "\n",
    "DiabetesPedigreeFunction: Diabetes pedigree function\n",
    "\n",
    "Age: Age (years)\n",
    "\n",
    "Outcome: Class variable (0 or 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IKy7xc_k7pqS"
   },
   "source": [
    "#**Data Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "_2XHbPOw7WzI",
    "outputId": "6f1ce8f4-e0e9-417d-c634-6609190c437b"
   },
   "outputs": [
    {
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       "summary": "{\n  \"name\": \"X_scaled_df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -1.1418515161634994,\n        \"max\": 3.906578350084603,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0.6399472601593604,\n          -0.8448850534430228,\n          -0.2509521280020695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -2.5541306394868957,\n        \"max\": 2.54185012945309,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          0.9637399558458689,\n          -0.6801248083283201,\n          -0.31847456020999854\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -4.004244590729369,\n        \"max\": 4.102655043735656,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          1.1246102800546265,\n          -2.184328346257629,\n          1.04188681439682\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -2.5216702237571025,\n        \"max\": 7.950467479775526,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          -2.5216702237571025,\n          -1.9525323050868508,\n          2.14526070933896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -1.6659447394835167,\n        \"max\": 8.126237673294188,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          -1.2187056388999198,\n          -1.3481695890688556,\n          0.3230923131119544\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -2.075118680330338,\n        \"max\": 5.0420871425154425,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          -1.827690257041221,\n          -0.21212819909463243,\n          0.8212493334657982\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -1.1895531764897842,\n        \"max\": 5.88356476587794,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          3.802702997179665,\n          -0.13855187676990025,\n          -1.008346055848425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -1.0415494364835023,\n        \"max\": 4.063715751598595,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          2.276872935769861,\n          1.1707321450187398,\n          3.2979259733862807\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
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       "      <td>6.655021e-01</td>\n",
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       "      <td>0.166292</td>\n",
       "      <td>0.468492</td>\n",
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       "      <td>-1.041549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>-1.141852</td>\n",
       "      <td>0.503458</td>\n",
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       "      <td>6.655021e-01</td>\n",
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       "        document.querySelector('#df-d373d46c-7519-4775-ae84-23edab4d7e88 button.colab-df-convert');\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
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       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
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       "    width: 32px;\n",
       "  }\n",
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       "  .colab-df-quickchart:hover {\n",
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       "\n",
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       "    animation:\n",
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       "      border-color: transparent;\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-948acdd3-eac8-4979-9cb3-9574b2a45625 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   Pregnancies   Glucose  BloodPressure  SkinThickness       Insulin  \\\n",
       "0     0.639947  0.865108      -0.033518   6.655021e-01 -3.345079e-16   \n",
       "1    -0.844885 -1.206162      -0.529859  -1.746338e-02 -3.345079e-16   \n",
       "2     1.233880  2.015813      -0.695306   8.087936e-16 -3.345079e-16   \n",
       "3    -0.844885 -1.074652      -0.529859  -7.004289e-01 -7.243887e-01   \n",
       "4    -1.141852  0.503458      -2.680669   6.655021e-01  1.465506e-01   \n",
       "\n",
       "        BMI  DiabetesPedigreeFunction       Age  \n",
       "0  0.166292                  0.468492  1.425995  \n",
       "1 -0.852531                 -0.365061 -0.190672  \n",
       "2 -1.332833                  0.604397 -0.105584  \n",
       "3 -0.634212                 -0.920763 -1.041549  \n",
       "4  1.548980                  5.484909 -0.020496  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Replace zero values in 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', and 'BMI' with the mean of the respective column\n",
    "columns_with_zeros = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']\n",
    "imputer = SimpleImputer(missing_values=0, strategy='mean')\n",
    "diabetes[columns_with_zeros] = imputer.fit_transform(diabetes[columns_with_zeros])\n",
    "\n",
    "# Separate features and target variable\n",
    "X = diabetes.drop('Outcome', axis=1)\n",
    "y = diabetes['Outcome']\n",
    "\n",
    "# Normalize the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Convert the scaled features back to a DataFrame for better readability\n",
    "X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
    "\n",
    "\n",
    "\n",
    "X_scaled_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g0_vpTe88Guf"
   },
   "source": [
    "The data has been successfully preprocessed, with missing values handled and features normalized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iWkeffah8K8U"
   },
   "source": [
    "#**Model development and Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lMWsVmPs8SRM",
    "outputId": "87a87818-6988-4d2b-8c46-1615fdffdcf4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': {'Accuracy': 0.7532467532467533,\n",
       "  'Precision': 0.6666666666666666,\n",
       "  'Recall': 0.6181818181818182,\n",
       "  'F1-Score': 0.6415094339622642,\n",
       "  'AUC-ROC': 0.8242424242424242},\n",
       " 'Random Forest': {'Accuracy': 0.7532467532467533,\n",
       "  'Precision': 0.6545454545454545,\n",
       "  'Recall': 0.6545454545454545,\n",
       "  'F1-Score': 0.6545454545454545,\n",
       "  'AUC-ROC': 0.8312213039485767},\n",
       " 'Support Vector Machine': {'Accuracy': 0.7532467532467533,\n",
       "  'Precision': 0.6808510638297872,\n",
       "  'Recall': 0.5818181818181818,\n",
       "  'F1-Score': 0.627450980392157,\n",
       "  'AUC-ROC': 0.8102846648301193}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, classification_report\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the models\n",
    "log_reg = LogisticRegression(random_state=42)\n",
    "rand_forest = RandomForestClassifier(random_state=42)\n",
    "svm = SVC(probability=True, random_state=42)\n",
    "\n",
    "# Train the models\n",
    "log_reg.fit(X_train, y_train)\n",
    "rand_forest.fit(X_train, y_train)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred_log_reg = log_reg.predict(X_test)\n",
    "y_pred_rand_forest = rand_forest.predict(X_test)\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "\n",
    "# Calculate probabilities for AUC-ROC\n",
    "y_prob_log_reg = log_reg.predict_proba(X_test)[:, 1]\n",
    "y_prob_rand_forest = rand_forest.predict_proba(X_test)[:, 1]\n",
    "y_prob_svm = svm.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Evaluate the models\n",
    "metrics_df = {\n",
    "    \"Logistic Regression\": {\n",
    "        \"Accuracy\": accuracy_score(y_test, y_pred_log_reg),\n",
    "        \"Precision\": precision_score(y_test, y_pred_log_reg),\n",
    "        \"Recall\": recall_score(y_test, y_pred_log_reg),\n",
    "        \"F1-Score\": f1_score(y_test, y_pred_log_reg),\n",
    "        \"AUC-ROC\": roc_auc_score(y_test, y_prob_log_reg)\n",
    "    },\n",
    "    \"Random Forest\": {\n",
    "        \"Accuracy\": accuracy_score(y_test, y_pred_rand_forest),\n",
    "        \"Precision\": precision_score(y_test, y_pred_rand_forest),\n",
    "        \"Recall\": recall_score(y_test, y_pred_rand_forest),\n",
    "        \"F1-Score\": f1_score(y_test, y_pred_rand_forest),\n",
    "        \"AUC-ROC\": roc_auc_score(y_test, y_prob_rand_forest)\n",
    "    },\n",
    "    \"Support Vector Machine\": {\n",
    "        \"Accuracy\": accuracy_score(y_test, y_pred_svm),\n",
    "        \"Precision\": precision_score(y_test, y_pred_svm),\n",
    "        \"Recall\": recall_score(y_test, y_pred_svm),\n",
    "        \"F1-Score\": f1_score(y_test, y_pred_svm),\n",
    "        \"AUC-ROC\": roc_auc_score(y_test, y_prob_svm)\n",
    "    }\n",
    "}\n",
    "\n",
    "\n",
    "metrics_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JP2G_BWP8qKH"
   },
   "source": [
    "All three models perform similarly in terms of accuracy.\n",
    "\n",
    "The Random Forest model has a slightly better AUC-ROC, indicating it might have a better ability to distinguish between the classes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1NqKpePK9P6v"
   },
   "source": [
    "#**Visualization**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gZZLko2I9bLi"
   },
   "source": [
    "###**ROC_AUC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "YCvC2mPT9TIW",
    "outputId": "54d0fe82-4c3c-42e0-df4f-02cc50b55376"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "# Function to plot ROC curve\n",
    "def plot_roc_curve(y_test, y_prob, model_name):\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
    "    plt.plot(fpr, tpr, label=f'{model_name} (AUC = {roc_auc_score(y_test, y_prob):.2f})')\n",
    "\n",
    "# Plot ROC curves\n",
    "plt.figure(figsize=(10, 6))\n",
    "plot_roc_curve(y_test, y_prob_log_reg, \"Logistic Regression\")\n",
    "plot_roc_curve(y_test, y_prob_rand_forest, \"Random Forest\")\n",
    "plot_roc_curve(y_test, y_prob_svm, \"Support Vector Machine\")\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curves')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Eo3bksa79rI0"
   },
   "source": [
    "###**Confusion matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "z3rrUMyp93w0",
    "outputId": "656af069-355d-438b-eea3-ca767151ee39"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Fn58f1Go1JElCnz59tCZIv0hhE5fDhw9rkvKLFy/Cz8+vUPWp9GLiQ6WSg4MDypcvj9jYWNmxy5cvw8TEBO7u7gAAT09PXL16VVYuv335sbS0RO/evdG7d29kZWWhe/fu+PzzzzFlyhQolUqdhqjyVK1a1aBLnjt16oSWLVti9uzZGDp0KCwtLVG1alUcOHAATZs2fe4/5nk3v7t69arWb7v37t3T+Tf0vOEZR0dHTUL2ovIfffQRPvroI1y5cgW+vr5YsGABvv32W02ZJk2aoEmTJvj8888RGRmJ/v37Y+PGjfneu6YwnwN9lC9fHm3atMGhQ4cQHx//wnN6enriwIEDePjwoVZPxeXLlzXHDcXW1jbfoaT8hu7Mzc3RpUsXdOnSBbm5uRg+fDhWrVqFadOm5dsDmhdnbGysZqJxntjY2CK9gWK/fv0wePBg2NjY4M033yyw3NatWxEYGIgFCxZo9mVkZMhuKFqY7+mLJCQkYNSoUWjfvj3Mzc0xYcIEBAQElKkbShozDnVRqWRqaor27dvj+++/11q2mpSUhMjISDRr1kwzPBEQEIDo6Git2/vfv3+/wB6Rp927d0/rtbm5OWrWrAkhhGYeSt4qDl3u3NyjRw9cuHABO3bskB3T5bf7/EyePBn37t3D6tWrAQC9evVCTk4OZs2aJSv7+PFjTZxt27aFmZkZVqxYoVVm6dKlOrcdEBAAlUqF2bNn5zsvJ2+Z+KNHj2QrxapWrQpra2vNb80PHjyQvQd587IKGu4qzOdAX8HBwRBCYMCAAUhLS5MdP3PmjKbn5M0330ROTo7svVy0aBEkSULHjh0NEhPw5H28fPmy1pL8CxcuyIZkn/0sm5iYoE6dOgAKfn8bNmwIR0dHrFy5UqtMVFQUYmJi0KlTJ0NdhkzPnj0RHByM5cuXP/f+WqamprLPzZIlS2Q9XoX5nr7IkCFDkJubizVr1uCrr76CmZkZgoKCXvo7TKULe3yoRH3zzTf5zkMYM2YMPvvsM+zfvx/NmjXD8OHDYWZmhlWrViEzMxPz5s3TlJ00aRK+/fZbtGvXDqNGjdIsZ/fw8MD9+/ef+5tg+/bt4ezsjKZNm8LJyQkxMTFYunQpOnXqpPlNvkGDBgCAqVOnok+fPihXrhy6dOmS77LWiRMnYuvWrXjnnXcwaNAgNGjQAPfv38euXbuwcuVK1K1bt9DvUceOHfHaa69h4cKFGDFiBFq2bImhQ4ciNDQU58+fR/v27VGuXDlcuXIFW7ZswZdffomePXvCyckJY8aMwYIFC/DWW2+hQ4cOuHDhAqKiolChQgWdfkNWqVRYsWIFBgwYgPr166NPnz5wcHBAXFwcfvjhBzRt2hRLly7FX3/9hbZt26JXr16oWbMmzMzMsGPHDiQlJaFPnz4AgLVr12L58uV4++23UbVqVTx8+BCrV6+GSqV67m/8un4O9PXGG29g2bJlGD58OLy9vbXu3Hz48GHs2rULn332GQCgS5cuaN26NaZOnYqbN2+ibt26+Omnn/D9999j7NixWhOZ9TVo0CAsXLgQAQEBCAoKwt27d7Fy5UrUqlVLa2L34MGDcf/+fbRp0wZubm64desWlixZAl9fX61l3U8rV64c5s6di/fffx8tW7ZE3759NcvZK1WqhHHjxhnsOp6lVqt1eiZb586dsX79eqjVatSsWRPR0dE4cOAA7O3ttcr5+vrC1NQUc+fORUpKChQKBdq0aZPvEvnnCQ8Pxw8//ICIiAjNvayWLFmCd999FytWrJAtFqBXUEktJyPjlrf0uKAtPj5eCCHE2bNnRUBAgLCyshLly5cXrVu3FidOnJCd79y5c6J58+ZCoVAINzc3ERoaKsLCwgQAkZiYqCn37NLgVatWiRYtWgh7e3uhUChE1apVxcSJE0VKSorW+WfNmiUqVqwoTExMtJa257cU9t69e2LkyJGiYsWKwtzcXLi5uYnAwEDx77//Pvc98fT0FJ06dcr3WEREhAAgwsPDNfu++uor0aBBA2FhYSGsra1F7dq1xaRJk8SdO3c0ZR4/fiymTZsmnJ2dhYWFhWjTpo2IiYkR9vb2YtiwYbK/j4KWmv/8888iICBAqNVqoVQqRdWqVcXAgQPF6dOnhRBC/Pvvv2LEiBHC29tbWFpaCrVaLRo3biw2b96sOcfZs2dF3759hYeHh1AoFMLR0VF07txZc448eGY5e17dF30OCrqGZ5dwv8iZM2dEv379hKurqyhXrpywtbUVbdu2FWvXrhU5OTmacg8fPhTjxo3TlKtevbqYP3++7LYFeOZ2BHme/ewUtJxdCCG+/fZbUaVKFWFubi58fX3Fvn37ZMvZt27dKtq3by8cHR2Fubm58PDwEEOHDhUJCQkvfC82bdok6tWrJxQKhbCzsxP9+/cXt2/f1ioTGBgoLC0tZbEFBwcLXX6UPL2cvSD5vQcPHjwQ77//vqhQoYKwsrISAQEB4vLly/l+91avXi2qVKkiTE1Nta7zed+tp88THx8v1Gq16NKli6zc22+/LSwtLcX169dfeK1UuklCsO+OyqaxY8di1apVSEtLM9ikzLIgOTkZtra2+OyzzzB16tSSDoeIqFhxjg+VCf/995/W63v37mH9+vVo1qyZUSc9z74vALB48WIAyPcxCEREZR3n+FCZ4Ofnh1atWsHHxwdJSUlYs2YNUlNTMW3atJIOrURt2rQJERERePPNN2FlZYVjx47hu+++Q/v27dG0adOSDo+IqNgx8aEy4c0338TWrVvx1VdfQZIk1K9fH2vWrEGLFi1KOrQSVadOHZiZmWHevHlITU3VTHjOm6RLRGRsOMeHiIiIjAbn+BAREZHRYOJDRERERoNzfIxIbm4u7ty5A2tra4Pe3p2IiIqHEAIPHz6Eq6srTEyKru8iIyMDWVlZep/H3NwcSqXSABEZDhMfI3Lnzh2DPNeIiIhKVnx8vObO0oaWkZEBC2t74PEjvc/l7OyMGzdulKrkh4mPEcl7BIN5zUBIpgU/G4foVRZ3+IuSDoGoyDxMTUW1yu5aD8c1tKysLODxIyhqBgL6/KzIyULipbXIyspi4kMlI294SzI1Z+JDZZahHlpKVJoVy3QFM6VePyuEVDqnETPxISIiIjkJgD4JVimdSsrEh4iIiOQkkyebPvVLodIZFREREVERYI8PERERyUmSnkNdpXOsiz0+REREJJc31KXPVgg5OTmYNm0aKleuDAsLC1StWhWzZs3C00/WEkJg+vTpcHFxgYWFBfz9/XHlypVCtcPEh4iIiErc3LlzsWLFCixduhQxMTGYO3cu5s2bhyVLlmjKzJs3D2FhYVi5ciV+/fVXWFpaIiAgABkZGTq3w6EuIiIikivmoa4TJ06ga9eu6NSpEwCgUqVK+O677/Dbb78BeNLbs3jxYnz66afo2rUrAGDdunVwcnLCzp070adPH53aYY8PERER5UPfYa4nKUZqaqrWlpmZmW9rb7zxBg4ePIi//voLAHDhwgUcO3YMHTt2BADcuHEDiYmJ8Pf319RRq9Vo3LgxoqOjdb4q9vgQERFRkXn2UUnBwcEICQmRlfv444+RmpoKb29vmJqaIicnB59//jn69+8PAEhMTAQAODk5adVzcnLSHNMFEx8iIiKSM9BQV3x8vNYd1RUKRb7FN2/ejA0bNiAyMhK1atXC+fPnMXbsWLi6uiIwMPDl43gGEx8iIiKSM9ANDFUqlU6Pkpk4cSI+/vhjzVyd2rVr49atWwgNDUVgYCCcnZ0BAElJSXBxcdHUS0pKgq+vr85hcY4PERERlbhHjx7BxEQ7LTE1NUVubi4AoHLlynB2dsbBgwc1x1NTU/Hrr7/Cz89P53bY40NERERyxbyqq0uXLvj888/h4eGBWrVq4dy5c1i4cCEGDRr0v9NJGDt2LD777DNUr14dlStXxrRp0+Dq6opu3brp3A4THyIiIpIr5md1LVmyBNOmTcPw4cNx9+5duLq6YujQoZg+fbqmzKRJk5Ceno4PPvgAycnJaNasGfbu3QulUql7WOLpWyJSmZaamgq1Wg1F7SGQTM1LOhyiIvHg1NKSDoGoyKSmpsLJXo2UlBSd5s28bBtqtRqKJpMgmeU/EVkX4nEmMk/OK9JYXwbn+BAREZHR4FAXERERyRXzUFdxYeJDREREcpKkZ+LDp7MTERERlSj2+BAREZGcifRk06d+KcTEh4iIiOTK6Byf0hkVERERURFgjw8RERHJFfOdm4sLEx8iIiKS41AXERER0auNPT5EREQkx6EuIiIiMhpldKiLiQ8RERHJldEen9KZjhEREREVAfb4EBERkRyHuoiIiMhocKiLiIiI6NXGHh8iIiLKh55DXaW0b4WJDxEREclxqIuIiIjo1cYeHyIiIpKTJD1XdZXOHh8mPkRERCRXRpezl86oiIiIiIoAe3yIiIhIroxObmbiQ0RERHJldKiLiQ8RERHJldEen9KZjhEREREVAfb4EBERkRyHuoiIiMhocKiLiIiI6NXGHh8iIiKSkSQJUhns8WHiQ0RERDJlNfHhUBcREREZDfb4EBERkZz0v02f+qUQEx8iIiKS4VAXERER0SuOPT5EREQkU1Z7fJj4EBERkQwTHyIiIjIaZTXx4RwfIiIiMhrs8SEiIiI5LmcnIiIiY8GhLiIiIqJXHHt8iIiISEaSoGePj+FiMSQmPkRERCQjQc+hrlKa+XCoi4iIiIwGEx8iIiKSyZvcrM9WGJUqVcr3HCNGjAAAZGRkYMSIEbC3t4eVlRV69OiBpKSkQl8XEx8iIiKSkwywFcKpU6eQkJCg2fbv3w8AeOeddwAA48aNw+7du7FlyxYcOXIEd+7cQffu3Qt9WZzjQ0RERCXOwcFB6/WcOXNQtWpVtGzZEikpKVizZg0iIyPRpk0bAEB4eDh8fHxw8uRJNGnSROd22ONDREREcvoOc/1vqCs1NVVry8zMfGHTWVlZ+PbbbzFo0CBIkoQzZ84gOzsb/v7+mjLe3t7w8PBAdHR0oS6LiQ8RERHJGGqOj7u7O9RqtWYLDQ19Yds7d+5EcnIyBg4cCABITEyEubk5bGxstMo5OTkhMTGxUNfFoS4iIiKS0ffOzXl14+PjoVKpNPsVCsUL665ZswYdO3aEq6vrS7dfECY+REREVGRUKpVW4vMit27dwoEDB7B9+3bNPmdnZ2RlZSE5OVmr1ycpKQnOzs6FiodDXURERCRXzKu68oSHh8PR0RGdOnXS7GvQoAHKlSuHgwcPavbFxsYiLi4Ofn5+hTo/e3yIiIhIxlBDXYWRm5uL8PBwBAYGwszs/1MUtVqNoKAgjB8/HnZ2dlCpVBg1ahT8/PwKtaILYOJDREREpcSBAwcQFxeHQYMGyY4tWrQIJiYm6NGjBzIzMxEQEIDly5cXug0mPkRERCRTEj0+7du3hxAi32NKpRLLli3DsmXLXjomgIkPERER5aMkEp/iwMnNREREZDTY40NEREQyZbXHh4kPERERyemxJF1TvxTiUBcREREZDfb4EBERkQyHuoiIiMhoMPEhIiIio1FWEx/O8SEiIiKjwR4fIiIikiujq7qY+BAREZEMh7qIiIiIXnHs8QHQqlUr+Pr6YvHixTqVP3z4MFq3bo0HDx7AxsamSGOj0s/ERMLHH7yJXh0awdFehcR/UxC551d8sWYvAMDM1ASfftgF7ZrWgmdFe6SmZeDIb5cxY+kuJP6bUsLRE73Y8bNXsWT9AVy4HIfEf1Px7fwh6NSqrua4baOR+dabMbobRg/wL64wycDY41MEBg4cCEmSMGfOHK39O3fu1PsNi4iI0PylmZqawtbWFo0bN8bMmTORkqL9w2b79u2YNWuWXu29jEqVKumcbFHpNfa9dhjUozkmzd+Cxr0+Q8iS7zF6gD8+6N0SAFBeaY463u6YvyYKrQbMxXuTVqOapxMiFwwt4ciJdPPov0y8VqMi5k/qne/xy1Gztbal0/pDkiS81dq3eAMlg5IgaX6OvtRWSif5lHiPj1KpxNy5czF06FDY2toa9NwqlQqxsbEQQiA5ORknTpxAaGgowsPDcfz4cbi6ugIA7OzsDNouGZfX61TBj0d+x0/H/wQAxCfcR4+AhmhQyxMAkJqege4jl2rVmTR/Mw6tnQQ3J1vcTnpQ7DETFUa7prXQrmmtAo87VVBpvf7xl4to3qA6KrlVKOrQiAqtxOf4+Pv7w9nZGaGhoc8tt23bNtSqVQsKhQKVKlXCggULXnhuSZLg7OwMFxcX+Pj4ICgoCCdOnEBaWhomTZqkKdeqVSuMHTtW83r9+vVo2LAhrK2t4ezsjH79+uHu3buy8x8/fhx16tSBUqlEkyZN8Mcff2gdP3bsGJo3bw4LCwu4u7tj9OjRSE9P17R569YtjBs3Ttad+Lx6ALB8+XJUr14dSqUSTk5O6Nmz5wvfCyo6v/1+HS0beaGqhyMA4LXqFdGkbhUcOHGpwDoqKwvk5uYiJe2/4gqTqFjcvZeKn479gXe7+pV0KKQnvXp79BwmK0olnviYmppi9uzZWLJkCW7fvp1vmTNnzqBXr17o06cPLl68iJCQEEybNg0RERGFbs/R0RH9+/fHrl27kJOTk2+Z7OxszJo1CxcuXMDOnTtx8+ZNDBw4UFZu4sSJWLBgAU6dOgUHBwd06dIF2dnZAIBr166hQ4cO6NGjB37//Xds2rQJx44dw8iRT8bCt2/fDjc3N8ycORMJCQlISEjQqd7p06cxevRozJw5E7Gxsdi7dy9atGhR6PeBDGfR2v3Yvv8MftvyKe5Gf4kj307Gyo2HsWXv6XzLK8zNEDKyK7b9dAYP0zOKOVqiovXdD7/CylKJLhzmevVJBthKoRIf6gKAt99+G76+vggODsaaNWtkxxcuXIi2bdti2rRpAIAaNWrg0qVLmD9/fr4JyYt4e3vj4cOHuHfvHhwdHWXHBw0apPlzlSpVEBYWhkaNGiEtLQ1WVlaaY8HBwWjXrh0AYO3atXBzc8OOHTvQq1cvhIaGon///pqepOrVqyMsLAwtW7bEihUrYGdnB1NTU02vUp4X1YuLi4OlpSU6d+4Ma2treHp6ol69evleZ2ZmJjIzMzWvU1NTC/1e0Yu97V8f73RohCGfrsXl6wmoXaMiZo/viYR/UrDxh1+1ypqZmiA8NAiSJOGjOZtKKGKiorNh10m806EhlIpyJR0KUb5KvMcnz9y5c7F27VrExMTIjsXExKBp06Za+5o2bYorV64U2GvzPEIIAAXPOD9z5gy6dOkCDw8PWFtbo2XLJ5NU4+LitMr5+f1/V66dnR28vLw08V+4cAERERGwsrLSbAEBAcjNzcWNGzcKjO1F9dq1awdPT09UqVIFAwYMwIYNG/Do0aN8zxUaGgq1Wq3Z3N3ddX+TSGczx3TD4v/1+ly6dgebok5h+XeHMG5gO61yeUmPu7Mt3h65lL09VOacOHcVV24lYUDXN0o6FDIADnUVsRYtWiAgIABTpkwp8rZiYmKgUqlgb28vO5aeno6AgACoVCps2LABp06dwo4dOwAAWVlZOreRlpaGoUOH4vz585rtwoULuHLlCqpWrfrS9aytrXH27Fl89913cHFxwfTp01G3bl0kJyfLzjVlyhSkpKRotvj4eJ3jJ91ZKMyRm5urtS83V8BE+v+vV17SU9XDAd1GLMWDlPRnT0P0yvv2+2j4+rijdg23kg6FDKCsJj6lYqgrz5w5c+Dr6wsvLy+t/T4+Pjh+/LjWvuPHj6NGjRowNTUtVBt3795FZGQkunXrBhMTed53+fJl3Lt3D3PmzNH0kJw+nf9cjZMnT8LDwwMA8ODBA/z111/w8fEBANSvXx+XLl1CtWrVCozF3Nxc1mOlSz0zMzP4+/vD398fwcHBsLGxwaFDh9C9e3etcgqFAgqFosDzkGHsPXYR498PwO3EB4i5noA6Xm4Y3q81Nuw6CeBJ0rN27mDU9XZHn3ErYWoqwdHeGgDwIOURsh8XvteSqDilPcrEjfh/NK9v3bmHi7G3YaMuD3fnJ6tiU9P+w/cHz2HW2LdLKkwyMEl6sulTvzQqVYlP7dq10b9/f4SFhWnt/+ijj9CoUSPMmjULvXv3RnR0NJYuXYrly5c/93xCCCQmJmqWs0dHR2P27NlQq9Wyewfl8fDwgLm5OZYsWYJhw4bhjz/+KPAePzNnzoS9vT2cnJwwdepUVKhQAd26dQMATJ48GU2aNMHIkSMxePBgWFpa4tKlS9i/fz+WLn2ytLlSpUr45Zdf0KdPHygUClSoUOGF9fbs2YPr16+jRYsWsLW1xY8//ojc3FxZskjFZ/L8LfhkWGd8Mbk3KthaIfHfFERsP455X0cBAFwcbfBmyzoAgKOR2j2anYd+ieNnrxR7zESFcT7mFroM+/9/l6cu2g4A6NupMZaHDAAAbP/pDIQQ6BHQsERiJNJVqUp8gCfJxKZN2pM+69evj82bN2P69OmYNWsWXFxcMHPmzBdObE5NTYWLiwskSYJKpYKXlxcCAwMxZswYqFSqfOs4ODggIiICn3zyCcLCwlC/fn188cUXeOutt2Rl58yZgzFjxuDKlSvw9fXF7t27YW5uDgCoU6cOjhw5gqlTp6J58+YQQqBq1aro3fv/bwA2c+ZMDB06FFWrVkVmZiaEEC+sZ2Njg+3btyMkJAQZGRmoXr06vvvuO9SqVfA9NqhopT3KxCcLt+GThdvyPR6fcL/AO9sSvQqaNaiBB6eWPrfMwO7NMLB7s2KKiIrDkx4ffe7cbMBgDEgSeTN9qcxLTU2FWq2GovYQSKbmJR0OUZF40Q9ooldZamoqnOzVSElJKfAXeEO0oVarUWX0VpgqLF/6PDmZ6bge1rNIY30ZpWZyMxEREVFRK3VDXURERFTyyupDSpn4EBERkUxZXdXFoS4iIiIyGuzxISIiIhkTEwkmJi/fbSP0qFuUmPgQERGRDIe6iIiIiF5x7PEhIiIiGa7qIiIiIqNRVoe6mPgQERGRTFnt8eEcHyIiIjIa7PEhIiIimbLa48PEh4iIiGTK6hwfDnURERGR0WCPDxEREclI0HOoC6Wzy4eJDxEREclwqIuIiIjoFcceHyIiIpLhqi4iIiIyGhzqIiIiInrFsceHiIiIZDjURUREREaDQ11ERERkNPJ6fPTZCuvvv//Gu+++C3t7e1hYWKB27do4ffq05rgQAtOnT4eLiwssLCzg7++PK1euFKoNJj5ERERU4h48eICmTZuiXLlyiIqKwqVLl7BgwQLY2tpqysybNw9hYWFYuXIlfv31V1haWiIgIAAZGRk6t8OhLiIiIpLTc6irsDdunjt3Ltzd3REeHq7ZV7lyZc2fhRBYvHgxPv30U3Tt2hUAsG7dOjg5OWHnzp3o06ePTu2wx4eIiIhkDDXUlZqaqrVlZmbm296uXbvQsGFDvPPOO3B0dES9evWwevVqzfEbN24gMTER/v7+mn1qtRqNGzdGdHS0ztfFxIeIiIiKjLu7O9RqtWYLDQ3Nt9z169exYsUKVK9eHfv27cOHH36I0aNHY+3atQCAxMREAICTk5NWPScnJ80xXXCoi4iIiGQMtaorPj4eKpVKs1+hUORbPjc3Fw0bNsTs2bMBAPXq1cMff/yBlStXIjAw8OUDeQZ7fIiIiEjGUENdKpVKayso8XFxcUHNmjW19vn4+CAuLg4A4OzsDABISkrSKpOUlKQ5pgsmPkRERFTimjZtitjYWK19f/31Fzw9PQE8mejs7OyMgwcPao6npqbi119/hZ+fn87tcKiLiIiIZIr7Bobjxo3DG2+8gdmzZ6NXr1747bff8NVXX+Grr7763/kkjB07Fp999hmqV6+OypUrY9q0aXB1dUW3bt10boeJDxEREckU9yMrGjVqhB07dmDKlCmYOXMmKleujMWLF6N///6aMpMmTUJ6ejo++OADJCcno1mzZti7dy+USqXO7TDxISIiolKhc+fO6Ny5c4HHJUnCzJkzMXPmzJdug4kPERERyfAhpURERGQ0yupDSpn4EBERkUxZ7fHhcnYiIiIyGuzxISIiIhkOdREREZHR4FAXERER0SuOPT5EREQkI0HPoS6DRWJYTHyIiIhIxkSSYKJH5qNP3aLEoS4iIiIyGuzxISIiIhmu6iIiIiKjUVZXdTHxISIiIhkT6cmmT/3SiHN8iIiIyGiwx4eIiIjkJD2Hq0ppjw8THyIiIpIpq5ObOdRFRERERoM9PkRERCQj/e8/feqXRkx8iIiISIaruoiIiIhecezxISIiIhmjvoHhrl27dD7hW2+99dLBEBERUelQVld16ZT4dOvWTaeTSZKEnJwcfeIhIiIiKjI6JT65ublFHQcRERGVIiaSBBM9um30qVuU9Jrjk5GRAaVSaahYiIiIqJQoq0NdhV7VlZOTg1mzZqFixYqwsrLC9evXAQDTpk3DmjVrDB4gERERFb+8yc36bKVRoROfzz//HBEREZg3bx7Mzc01+1977TV8/fXXBg2OiIiIyJAKnfisW7cOX331Ffr37w9TU1PN/rp16+Ly5csGDY6IiIhKRt5Qlz5baVToOT5///03qlWrJtufm5uL7OxsgwRFREREJausTm4udI9PzZo1cfToUdn+rVu3ol69egYJioiIiKgoFLrHZ/r06QgMDMTff/+N3NxcbN++HbGxsVi3bh327NlTFDESERFRMZP+t+lTvzQqdI9P165dsXv3bhw4cACWlpaYPn06YmJisHv3brRr164oYiQiIqJiVlZXdb3UfXyaN2+O/fv3GzoWIiIioiL10jcwPH36NGJiYgA8mffToEEDgwVFREREJctEerLpU780KnTic/v2bfTt2xfHjx+HjY0NACA5ORlvvPEGNm7cCDc3N0PHSERERMWsrD6dvdBzfAYPHozs7GzExMTg/v37uH//PmJiYpCbm4vBgwcXRYxEREREBlHoHp8jR47gxIkT8PLy0uzz8vLCkiVL0Lx5c4MGR0RERCWnlHba6KXQiY+7u3u+NyrMycmBq6urQYIiIiKiksWhrv+ZP38+Ro0ahdOnT2v2nT59GmPGjMEXX3xh0OCIiIioZORNbtZnK4106vGxtbXVytzS09PRuHFjmJk9qf748WOYmZlh0KBB6NatW5EESkRERKQvnRKfxYsXF3EYREREVJqU1aEunRKfwMDAoo6DiIiISpGy+siKl76BIQBkZGQgKytLa59KpdIrICIiIqKiUujEJz09HZMnT8bmzZtx79492fGcnByDBEZEREQlx0SSYKLHcJU+dYtSoVd1TZo0CYcOHcKKFSugUCjw9ddfY8aMGXB1dcW6deuKIkYiIiIqZpKk/1YaFbrHZ/fu3Vi3bh1atWqF999/H82bN0e1atXg6emJDRs2oH///kURJxEREZHeCt3jc//+fVSpUgXAk/k89+/fBwA0a9YMv/zyi2GjIyIiohKRt6pLn60wQkJCZPW9vb01xzMyMjBixAjY29vDysoKPXr0QFJSUqGvq9CJT5UqVXDjxg0AgLe3NzZv3gzgSU9Q3kNLiYiI6NVWEkNdtWrVQkJCgmY7duyY5ti4ceOwe/dubNmyBUeOHMGdO3fQvXv3QrdR6KGu999/HxcuXEDLli3x8ccfo0uXLli6dCmys7OxcOHCQgdAREREBABmZmZwdnaW7U9JScGaNWsQGRmJNm3aAADCw8Ph4+ODkydPokmTJrq3Udigxo0bp/mzv78/Ll++jDNnzqBatWqoU6dOYU9HREREpZChVnWlpqZq7VcoFFAoFPnWuXLlClxdXaFUKuHn54fQ0FB4eHjgzJkzyM7Ohr+/v6ast7c3PDw8EB0dXbSJz7M8PT3h6emp72mIiIioFNF3ZVZeXXd3d639wcHBCAkJkZVv3LgxIiIi4OXlhYSEBMyYMQPNmzfHH3/8gcTERJibm8um1Dg5OSExMbFQcemU+ISFhel8wtGjRxcqACIiIip9DPXIivj4eK2bGxfU29OxY0fNn+vUqYPGjRvD09MTmzdvhoWFxUvH8SydEp9FixbpdDJJkpj4EBERkYZKpXqppzrY2NigRo0auHr1Ktq1a4esrCwkJydr9fokJSXlOyfoeXRKfPJWcVHZELN3Dqz5aBEqo05ff1DSIRAVmfS01BcXMhATvMTS72fq6yMtLQ3Xrl3DgAED0KBBA5QrVw4HDx5Ejx49AACxsbGIi4uDn59foc6r9xwfIiIiKnuK++nsEyZMQJcuXeDp6Yk7d+4gODgYpqam6Nu3L9RqNYKCgjB+/HjY2dlBpVJh1KhR8PPzK9TEZoCJDxEREZUCt2/fRt++fXHv3j04ODigWbNmOHnyJBwcHAA8mXZjYmKCHj16IDMzEwEBAVi+fHmh22HiQ0RERDKSBJgYYFWXrjZu3Pjc40qlEsuWLcOyZctePigw8SEiIqJ8mOiZ+OhTtyjpO/eIiIiI6JXxUonP0aNH8e6778LPzw9///03AGD9+vVaz9QgIiKiV1dxP6S0uBQ68dm2bRsCAgJgYWGBc+fOITMzE8CT52jMnj3b4AESERFR8csb6tJnK40Knfh89tlnWLlyJVavXo1y5cpp9jdt2hRnz541aHBEREREhlToyc2xsbFo0aKFbL9arUZycrIhYiIiIqISZqhndZU2he7xcXZ2xtWrV2X7jx07hipVqhgkKCIiIipZeU9n12crjQqd+AwZMgRjxozBr7/+CkmScOfOHWzYsAETJkzAhx9+WBQxEhERUTEzMcBWGhV6qOvjjz9Gbm4u2rZti0ePHqFFixZQKBSYMGECRo0aVRQxEhERERlEoRMfSZIwdepUTJw4EVevXkVaWhpq1qwJKyurooiPiIiISkBZnePz0nduNjc3R82aNQ0ZCxEREZUSJtBvno4JSmfmU+jEp3Xr1s+9KdGhQ4f0CoiIiIioqBQ68fH19dV6nZ2djfPnz+OPP/5AYGCgoeIiIiKiEsShrv9ZtGhRvvtDQkKQlpamd0BERERU8viQ0hd499138c033xjqdEREREQG99KTm58VHR0NpVJpqNMRERFRCZIk6DW5ucwMdXXv3l3rtRACCQkJOH36NKZNm2awwIiIiKjkcI7P/6jVaq3XJiYm8PLywsyZM9G+fXuDBUZERERkaIVKfHJycvD++++jdu3asLW1LaqYiIiIqIRxcjMAU1NTtG/fnk9hJyIiKuMkA/xXGhV6Vddrr72G69evF0UsREREVErk9fjos5VGhU58PvvsM0yYMAF79uxBQkICUlNTtTYiIiKi0krnOT4zZ87ERx99hDfffBMA8NZbb2k9ukIIAUmSkJOTY/goiYiIqFiV1Tk+Oic+M2bMwLBhw/Dzzz8XZTxERERUCkiS9Nxnc+pSvzTSOfERQgAAWrZsWWTBEBERERWlQi1nL63ZGxERERmW0Q91AUCNGjVemPzcv39fr4CIiIio5PHOzXgyz+fZOzcTERERvSoKlfj06dMHjo6ORRULERERlRImkqTXQ0r1qVuUdE58OL+HiIjIeJTVOT4638Awb1UXERER0atK5x6f3NzcooyDiIiIShM9JzeX0kd1FW6ODxERERkHE0gw0SN70aduUWLiQ0RERDJldTl7oR9SSkRERPSqYo8PERERyZTVVV1MfIiIiEimrN7Hh0NdREREZDTY40NEREQyZXVyMxMfIiIikjGBnkNdpXQ5O4e6iIiIyGiwx4eIiIhkONRFRERERsME+g0LldYhpdIaFxEREZHBsceHiIiIZCRJgqTHeJU+dYsSEx8iIiKSkaDfA9ZLZ9rDoS4iIiLKR96dm/XZ9DFnzhxIkoSxY8dq9mVkZGDEiBGwt7eHlZUVevTogaSkpMJdl15RERERERnYqVOnsGrVKtSpU0dr/7hx47B7925s2bIFR44cwZ07d9C9e/dCnZuJDxEREeVL0mN7WWlpaejfvz9Wr14NW1tbzf6UlBSsWbMGCxcuRJs2bdCgQQOEh4fjxIkTOHnypM7nZ+JDREREMnn38dFnA4DU1FStLTMz87ntjhgxAp06dYK/v7/W/jNnziA7O1trv7e3Nzw8PBAdHa3zdTHxISIioiLj7u4OtVqt2UJDQwssu3HjRpw9ezbfMomJiTA3N4eNjY3WficnJyQmJuocD1d1ERERkYyhlrPHx8dDpVJp9isUinzLx8fHY8yYMdi/fz+USuVLt/si7PEhIiIiGRMDbACgUqm0toISnzNnzuDu3buoX78+zMzMYGZmhiNHjiAsLAxmZmZwcnJCVlYWkpOTteolJSXB2dlZ5+tijw8RERGVuLZt2+LixYta+95//314e3tj8uTJcHd3R7ly5XDw4EH06NEDABAbG4u4uDj4+fnp3A4THyIiIpIp7js3W1tb47XXXtPaZ2lpCXt7e83+oKAgjB8/HnZ2dlCpVBg1ahT8/PzQpEkTndth4kNEREQypfHOzYsWLYKJiQl69OiBzMxMBAQEYPny5YU6BxMfIiIiKpUOHz6s9VqpVGLZsmVYtmzZS5+TiQ8RERHJ8CGlREREZDSeXpn1svVLIyY+REREJFNWe3xKa0JGREREZHDs8SEiIiKZ0riqyxCY+BAREZHM0w8afdn6pRGHuoiIiMhosMeHiIiIZEwgwUSPASt96hYlJj5EREQkw6EuIiIiolcce3yIiIhIRvrff/rUL42Y+BAREZEMh7qIiIiIXnHs8SEiIiIZSc9VXRzqIiIioldGWR3qYuJDREREMmU18eEcHyIiIjIa7PEhIiIiGS5nJyIiIqNhIj3Z9KlfGnGoi4iIiIwGe3yIiIhIhkNdREREZDS4qouIiIjoFcceHyIiIpKRoN9wVSnt8GHiQ0RERHJc1UVERET0ijP6Hh9JkrBjxw5069ZNp/IhISHYuXMnzp8/X6Rx0atj6fr9iPrld1y7dRdKRTk0eK0SPvmwC6p6OGnKbNh1Ajv3n8Eff91G2qNM/PHjbKity5dg1ES62/3Tb9i9/zck/ZMMAPB0c8S7PVrh9Xo1tMoJITB1znqcOn8FIRP6ommjmiUQLRlKWV3VVWZ7fAYOHAhJkiBJEsqVKwcnJye0a9cO33zzDXJzczXlEhIS0LFjx2KN7ebNm5AkiclTGXHy/DUEvt0M368ai8hFH+Lx41z0H78Sj/7L1JT5LyMLrRr7YOSAdiUYKdHLqWCvQlC/9lgW+iGWzR4G39cqI3h+JG7GJ2mV2/5jdAlFSEUhb1WXPltpVGYTHwDo0KEDEhIScPPmTURFRaF169YYM2YMOnfujMePHwMAnJ2doVAoSjhSepV9u2AYer3ZGF6VXVCzWkUs/KQf/k56gN9jb2vKDO7VCiPe9Uf9Wp4lGCnRy/Fr4I3G9WrAzcUebq4VMKhPO1gozRFz5f8/41dvJmDrnuOY8OHbJRgpGZJkgK00KtOJj0KhgLOzMypWrIj69evjk08+wffff4+oqChEREQAeDLUtXPnTk2dyZMno0aNGihfvjyqVKmCadOmITs7W3buVatWwd3dHeXLl0evXr2QkpKidfzrr7+Gj48PlEolvL29sXz5cs2xypUrAwDq1asHSZLQqlUrneplZWVh5MiRcHFxgVKphKenJ0JDQw3wTpEhpab/BwCwUXEoi8qenNxc/Hz8d2RkZqFmDXcAQEZmFkLDtmDUoM6ws7Eu4QiJns/o5vi0adMGdevWxfbt2zF48GDZcWtra0RERMDV1RUXL17EkCFDYG1tjUmTJmnKXL16FZs3b8bu3buRmpqKoKAgDB8+HBs2bAAAbNiwAdOnT8fSpUtRr149nDt3DkOGDIGlpSUCAwPx22+/4fXXX8eBAwdQq1YtmJub61QvLCwMu3btwubNm+Hh4YH4+HjEx8cXeK2ZmZnIzPz/4ZbU1FRDvY1UgNzcXMwI24FGtSvDu4pLSYdDZDA34hIx+tPVyMp+DAulOYIn9IOnmyMAYOXaKNSs4YE3GvmUcJRkSCaQYKLHeJVJKe3zMbrEBwC8vb3x+++/53vs008/1fy5UqVKmDBhAjZu3KiV+GRkZGDdunWoWLEiAGDJkiXo1KkTFixYAGdnZwQHB2PBggXo3r07gCc9PJcuXcKqVasQGBgIBwcHAIC9vT2cnZ01531Rvbi4OFSvXh3NmjWDJEnw9Hz+sEloaChmzJjxEu8QvaypC7ci9kYCti8bU9KhEBmUm2sFrJw3HOmPMnD05J+Yv2wbFoQE4e/Eezj353WsnDu8pEMkA9N3uKp0pj1GmvgIISAVkMVu2rQJYWFhuHbtGtLS0vD48WOoVCqtMh4eHpqkBwD8/PyQm5uL2NhYWFtb49q1awgKCsKQIUM0ZR4/fgy1Wl1gTOnp6S+sN3DgQLRr1w5eXl7o0KEDOnfujPbt2xd4zilTpmD8+PGa16mpqXB3dy+wPOnn00VbcTD6ErYuGQUXR5uSDofIoMqZmaGisz0AoEaVioi99jd2/BgNc/NySEh6gG7vz9YqP3PBRrzm44kFwUElES5RgYwy8YmJidHMs3ladHQ0+vfvjxkzZiAgIABqtRobN27EggULdD53WloaAGD16tVo3Lix1jFTU1O96tWvXx83btxAVFQUDhw4gF69esHf3x9bt27N95wKhYITt4uBEALTFm/D3l8uYkvYSHi42pd0SERFTgiBrMc5eK9XG3Rs00Dr2AcTl2JYYEc0aeBdQtGRQZTRLh+jS3wOHTqEixcvYty4cbJjJ06cgKenJ6ZOnarZd+vWLVm5uLg43LlzB66urgCAkydPwsTEBF5eXnBycoKrqyuuX7+O/v375xtD3pyenJwczT5d6gGASqVC79690bt3b/Ts2RMdOnTA/fv3YWdnp9sbQAY3deFWfH/gDL6ePRiW5RW4e+/JXCprKyUsFE/+ru/eS8U/91Nx8/a/AIDL1xNgVV4BVydb2KosSyx2Il2sifwJjXxrwLGCGv9lZOLQsd9x4dJNhH7yHuxsrPOd0OxYQQ0XR9sSiJYMpazex6dMJz6ZmZlITExETk4OkpKSsHfvXoSGhqJz58547733ZOWrV6+OuLg4bNy4EY0aNcIPP/yAHTt2yMoplUoEBgbiiy++QGpqKkaPHo1evXpp5uvMmDEDo0ePhlqtRocOHZCZmYnTp0/jwYMHGD9+PBwdHWFhYYG9e/fCzc0NSqUSarX6hfUWLlwIFxcX1KtXDyYmJtiyZQucnZ1hY2NT1G8lPcf6nccBAL1GL9Xav2BKX/R680nv3bffH8ei8H2aYz1HLpGVISqtklPTMW/5Ntx/8BCW5ZWo7OGE0E/eQ4M61Uo6NKJCK9OJz969e+Hi4gIzMzPY2tqibt26CAsLQ2BgIExM5Cv533rrLYwbNw4jR45EZmYmOnXqhGnTpiEkJESrXLVq1dC9e3e8+eabuH//Pjp37qy17Hzw4MEoX7485s+fj4kTJ8LS0hK1a9fG2LFjAQBmZmYICwvDzJkzMX36dDRv3hyHDx9+YT1ra2vMmzcPV65cgampKRo1aoQff/wx32uh4hN/dPELy4wf1BHjBxXvjTKJDOWjYYW7N8/+TbOKKBIqVvrehLB0dvhAEkKIkg6CikdqairUajWu/30P1s9M2CYqKy7feVjSIRAVmfS0VHRoUAkpKSmyhTeGkvez4tD5OFhZv3wbaQ9T0cbXo0hjfRnsKiAiIiKjUaaHuoiIiOglcVUXERERGQuu6iIiIiKjoe8T1vl0diIiIqISxh4fIiIikimjU3yY+BAREVE+ymjmw6EuIiIiMhpMfIiIiEhGMsB/hbFixQrUqVMHKpUKKpUKfn5+iIqK0hzPyMjAiBEjYG9vDysrK/To0QNJSUmFvi4mPkRERCSTt6pLn60w3NzcMGfOHJw5cwanT59GmzZt0LVrV/z5558AgHHjxmH37t3YsmULjhw5gjt37qB79+6Fvi7O8SEiIqIS16VLF63Xn3/+OVasWIGTJ0/Czc0Na9asQWRkJNq0aQMACA8Ph4+PD06ePIkmTZro3A57fIiIiEhGMsAGPHn219NbZmbmC9vOycnBxo0bkZ6eDj8/P5w5cwbZ2dnw9/fXlPH29oaHhweio6MLdV1MfIiIiEjOQJmPu7s71Gq1ZgsNDS2wyYsXL8LKygoKhQLDhg3Djh07ULNmTSQmJsLc3Bw2NjZa5Z2cnJCYmFioy+JQFxERERWZ+Ph4raezKxSKAst6eXnh/PnzSElJwdatWxEYGIgjR44YNB4mPkRERCRjqGd15a3S0oW5uTmqVasGAGjQoAFOnTqFL7/8Er1790ZWVhaSk5O1en2SkpLg7OxcqLg41EVEREQyxb2qKz+5ubnIzMxEgwYNUK5cORw8eFBzLDY2FnFxcfDz8yvUOdnjQ0RERDLFfePmKVOmoGPHjvDw8MDDhw8RGRmJw4cPY9++fVCr1QgKCsL48eNhZ2cHlUqFUaNGwc/Pr1ArugAmPkRERFQK3L17F++99x4SEhKgVqtRp04d7Nu3D+3atQMALFq0CCYmJujRowcyMzMREBCA5cuXF7odJj5EREQkV8xdPmvWrHnucaVSiWXLlmHZsmV6BMXEh4iIiPJhqMnNpQ0nNxMREZHRYI8PERERyei7MssQq7qKAhMfIiIikinuVV3FhUNdREREZDTY40NERERyZbTLh4kPERERyXBVFxEREdErjj0+REREJMNVXURERGQ0yugUHyY+RERElI8ymvlwjg8REREZDfb4EBERkUxZXdXFxIeIiIjk9JzcXErzHg51ERERkfFgjw8RERHJlNG5zUx8iIiIKB9lNPPhUBcREREZDfb4EBERkQxXdREREZHRKKuPrOBQFxERERkN9vgQERGRTBmd28zEh4iIiPJRRjMfJj5EREQkU1YnN3OODxERERkN9vgQERGRjAQ9V3UZLBLDYuJDREREMmV0ig+HuoiIiMh4sMeHiIiIZMrqDQyZ+BAREVE+yuZgF4e6iIiIyGiwx4eIiIhkONRFRERERqNsDnRxqIuIiIiMCHt8iIiISIZDXURERGQ0yuqzupj4EBERkVwZneTDOT5ERERkNNjjQ0RERDJltMOHiQ8RERHJldXJzRzqIiIiIqPBHh8iIiKS4aouIiIiMh5ldJIPh7qIiIjIaLDHh4iIiGTKaIcPe3yIiIhILm9Vlz5bYYSGhqJRo0awtraGo6MjunXrhtjYWK0yGRkZGDFiBOzt7WFlZYUePXogKSmpUO0w8SEiIqISd+TIEYwYMQInT57E/v37kZ2djfbt2yM9PV1TZty4cdi9eze2bNmCI0eO4M6dO+jevXuh2uFQFxEREeVDv1VdhR3s2rt3r9briIgIODo64syZM2jRogVSUlKwZs0aREZGok2bNgCA8PBw+Pj44OTJk2jSpIlO7bDHh4iIiGSKe6jrWSkpKQAAOzs7AMCZM2eQnZ0Nf39/TRlvb294eHggOjpa5/Oyx4eIiIiKTGpqqtZrhUIBhULx3Dq5ubkYO3YsmjZtitdeew0AkJiYCHNzc9jY2GiVdXJyQmJios7xsMeHiIiIioy7uzvUarVmCw0NfWGdESNG4I8//sDGjRsNHg97fIiIiEjGUM/qio+Ph0ql0ux/UW/PyJEjsWfPHvzyyy9wc3PT7Hd2dkZWVhaSk5O1en2SkpLg7Oysc1zs8SEiIiIZyQD/AYBKpdLaCkp8hBAYOXIkduzYgUOHDqFy5cpaxxs0aIBy5crh4MGDmn2xsbGIi4uDn5+fztfFHh8iIiIqcSNGjEBkZCS+//57WFtba+btqNVqWFhYQK1WIygoCOPHj4ednR1UKhVGjRoFPz8/nVd0AUx8iIiIKB+GGurS1YoVKwAArVq10tofHh6OgQMHAgAWLVoEExMT9OjRA5mZmQgICMDy5csL1Q4THyIiIpIp7kdWCCFeWEapVGLZsmVYtmzZywUFzvEhIiIiI8IeHyIiIpIro08pZeJDREREMk+vzHrZ+qURh7qIiIjIaLDHh4iIiGSKe1VXcWHiQ0RERDJldIoPEx8iIiLKRxnNfDjHh4iIiIwGe3yIiIhIpqyu6mLiQ0RERDKc3EyvvLzbgT98mFrCkRAVnfS0hyUdAlGRyft86/J4B32lpur3s0Lf+kWFiY8RefjwyRemrnflEo6EiIj08fDhQ6jV6iI5t7m5OZydnVG9srve53J2doa5ubkBojIcSRRH2kilQm5uLu7cuQNra2tIpbUPsoxJTU2Fu7s74uPjoVKpSjocIoPi57v4CSHw8OFDuLq6wsSk6NYnZWRkICsrS+/zmJubQ6lUGiAiw2GPjxExMTGBm5tbSYdhlFQqFX8wUJnFz3fxKqqenqcplcpSl7AYCpezExERkdFg4kNERERGg4kPURFSKBQIDg6GQqEo6VCIDI6fb3oVcXIzERERGQ32+BAREZHRYOJDRERERoOJDxERERkNJj5EemrVqhXGjh2rc/nDhw9DkiQkJycXWUxEzyNJEnbu3Klz+ZCQEPj6+hZZPETFiYkPvbIGDhwISZIwZ84crf07d+7U+87UERERkCQJkiTB1NQUtra2aNy4MWbOnImUlBStstu3b8esWbP0au9lVKpUCYsXLy72dqn0yvtOSJKEcuXKwcnJCe3atcM333yD3NxcTbmEhAR07NixWGO7efMmJEnC+fPni7Vdomcx8aFXmlKpxNy5c/HgwQODn1ulUiEhIQG3b9/GiRMn8MEHH2DdunXw9fXFnTt3NOXs7OxgbW1t8PaJXkaHDh2QkJCAmzdvIioqCq1bt8aYMWPQuXNnPH78GMCT5ydxCToZKyY+9Erz9/eHs7MzQkNDn1tu27ZtqFWrFhQKBSpVqoQFCxa88NySJMHZ2RkuLi7w8fFBUFAQTpw4gbS0NEyaNElT7tmhrvXr16Nhw4awtraGs7Mz+vXrh7t378rOf/z4cdSpUwdKpRJNmjTBH3/8oXX82LFjaN68OSwsLODu7o7Ro0cjPT1d0+atW7cwbtw4zW/4utQDgOXLl6N69epQKpVwcnJCz549X/he0KtDoVDA2dkZFStWRP369fHJJ5/g+++/R1RUFCIiIgDIh7omT56MGjVqoHz58qhSpQqmTZuG7Oxs2blXrVoFd3d3lC9fHr169ZL1fn799dfw8fGBUqmEt7c3li9frjlWufKThyPXq1cPkiShVatWOtXLysrCyJEj4eLiAqVSCU9Pzxd+34meSxC9ogIDA0XXrl3F9u3bhVKpFPHx8UIIIXbs2CGe/mifPn1amJiYiJkzZ4rY2FgRHh4uLCwsRHh4eIHnDg8PF2q1Ot9jY8aMEdbW1uLx48dCCCFatmwpxowZozm+Zs0a8eOPP4pr166J6Oho4efnJzp27Kg5/vPPPwsAwsfHR/z000/i999/F507dxaVKlUSWVlZQgghrl69KiwtLcWiRYvEX3/9JY4fPy7q1asnBg4cKIQQ4t69e8LNzU3MnDlTJCQkiISEBJ3qnTp1SpiamorIyEhx8+ZNcfbsWfHll18W7o2nUivvO5GfunXraj6HAMSOHTs0x2bNmiWOHz8ubty4IXbt2iWcnJzE3LlzNceDg4OFpaWlaNOmjTh37pw4cuSIqFatmujXr5+mzLfffitcXFzEtm3bxPXr18W2bduEnZ2diIiIEEII8dtvvwkA4sCBAyIhIUHcu3dPp3rz588X7u7u4pdffhE3b94UR48eFZGRkYZ828jIMPGhV9bT/8g3adJEDBo0SAghT3z69esn2rVrp1V34sSJombNmgWe+3mJz4oVKwQAkZSUJISQJz7POnXqlAAgHj58KIT4/8Rn48aNmjL37t0TFhYWYtOmTUIIIYKCgsQHH3ygdZ6jR48KExMT8d9//wkhhPD09BSLFi3SKvOietu2bRMqlUqkpqYWGC+9up6X+PTu3Vv4+PgIIeSJz7Pmz58vGjRooHkdHBwsTE1Nxe3btzX7oqKihImJiSbprlq1qiwhmTVrlvDz8xNCCHHjxg0BQJw7d06rzIvqjRo1SrRp00bk5uYWfOFEhcCns1OZMHfuXLRp0wYTJkyQHYuJiUHXrl219jVt2hSLFy9GTk4OTE1NC9WW+N/NzguaQH3mzBmEhITgwoULePDggWZSaVxcHGrWrKkp5+fnp/mznZ0dvLy8EBMTAwC4cOECfv/9d2zYsEGr3dzcXNy4cQM+Pj75tv2ieu3atYOnpyeqVKmCDh06oEOHDnj77bdRvnz5Qr0H9OoRQhT4md20aRPCwsJw7do1pKWl4fHjx7KnrXt4eKBixYqa135+fsjNzUVsbCysra1x7do1BAUFYciQIZoyjx8/fu6TxNPT019Yb+DAgWjXrh28vLzQoUMHdO7cGe3bt3+p94AIAJj4UJnQokULBAQEYMqUKRg4cGCRthUTEwOVSgV7e3vZsfT0dAQEBCAgIAAbNmyAg4MD4uLiEBAQgKysLJ3bSEtLw9ChQzF69GjZMQ8Pj5euZ25ujrNnz+Lw4cP46aefMH36dISEhODUqVOwsbHROT569cTExGjm2TwtOjoa/fv3x4wZMxAQEAC1Wo2NGzfqNA8uT1paGgBg9erVaNy4sdax5/1ioUu9+vXr48aNG4iKisKBAwfQq1cv+Pv7Y+vWrTrHR/Q0Jj5UZsyZMwe+vr7w8vLS2u/j44Pjx49r7Tt+/Dhq1KhR6N6eu3fvIjIyEt26dYOJiXxtwOXLl3Hv3j3MmTMH7u7uAIDTp0/ne66TJ09qkpgHDx7gr7/+0vTk1K9fH5cuXUK1atUKjMXc3Bw5OTla+3SpZ2ZmBn9/f/j7+yM4OBg2NjY4dOgQunfv/vyLp1fWoUOHcPHiRYwbN0527MSJE/D09MTUqVM1+27duiUrFxcXhzt37sDV1RXAk8+viYkJvLy84OTkBFdXV1y/fh39+/fPNwZzc3MA0PrM6lIPeLLCsnfv3ujduzd69uyJDh064P79+7Czs9PtDSB6ChMfKjNq166N/v37IywsTGv/Rx99hEaNGmHWrFno3bs3oqOjsXTpUq2VI/kRQiAxMRFCCCQnJyM6OhqzZ8+GWq2W3TsoT16vypIlSzBs2DD88ccfBd7jZ+bMmbC3t4eTkxOmTp2KChUqoFu3bgCerLJp0qQJRo4cicGDB8PS0hKXLl3C/v37sXTpUgBP7uPzyy+/oE+fPlAoFKhQocIL6+3ZswfXr19HixYtYGtrix9//BG5ubmyZJFeXZmZmUhMTEROTg6SkpKwd+9ehIaGonPnznjvvfdk5atXr464uDhs3LgRjRo1wg8//IAdO3bIyimVSgQGBuKLL75AamoqRo8ejV69esHZ2RkAMGPGDIwePRpqtRodOnRAZmYmTp8+jQcPHmD8+PFwdHSEhYUF9u7dCzc3NyiVSqjV6hfWW7hwIVxcXFCvXj2YmJhgy5YtcHZ2Zg8lvbySnGBEpI/8JnLeuHFDmJubi2c/2lu3bhU1a9YU5cqVEx4eHmL+/PnPPXd4eLgAIAAISZKEWq0Wr7/+upg5c6ZISUnRKvvs5ObIyEhRqVIloVAohJ+fn9i1a5fWpM68yc27d+8WtWrVEubm5uL1118XFy5c0Drvb7/9Jtq1ayesrKyEpaWlqFOnjvj88881x6Ojo0WdOnWEQqHQut7n1Tt69Kho2bKlsLW1FRYWFqJOnTqaCdX06gsMDNR8bs3MzISDg4Pw9/cX33zzjcjJydGUwzOTmydOnCjs7e2FlZWV6N27t1i0aJHW5P7g4GBRt25dsXz5cuHq6iqUSqXo2bOnuH//vlb7GzZsEL6+vsLc3FzY2tqKFi1aiO3bt2uOr169Wri7uwsTExPRsmVLnep99dVXwtfXV1haWgqVSiXatm0rzp49a9g3joyKJMT/ZmoSERERlXG8gSEREREZDSY+REREZDSY+BAREZHRYOJDRERERoOJDxERERkNJj5ERERkNJj4EBERkdFg4kNExWrgwIGaO1QDQKtWrTB27Nhij+Pw4cOQJAnJyckFlpEkCTt37tT5nCEhIfD19dUrrps3b0KSJJw/f16v8xBR/pj4EBEGDhwISZIgSRLMzc1RrVo1zJw5E48fPy7ytrdv317gYz2epUuyQkT0PHxWFxEBADp06IDw8HBkZmbixx9/xIgRI1CuXDlMmTJFVjYrK0vz0El98UGTRFSc2ONDRAAAhUIBZ2dneHp64sMPP4S/vz927doF4P+Hpz7//HO4urpqHmoaHx+PXr16wcbGBnZ2dujatStu3rypOWdOTg7Gjx8PGxsb2NvbY9KkSXj2KTnPDnVlZmZi8uTJcHd3h0KhQLVq1bBmzRrcvHkTrVu3BgDY2tpCkiQMHDgQAJCbm4vQ0FBUrlwZFhYWqFu3LrZu3arVzo8//ogaNWrAwsICrVu31opTV5MnT0aNGjVQvnx5VKlSBdOmTUN2dras3KpVq+Du7o7y5cujV69eSElJ0Tr+9ddfw8fHB0qlEt7e3i98YC4RGQ4THyLKl4WFBbKysjSvDx48iNjYWOzfvx979uxBdnY2AgICYG1tjaNHj+L48eOwsrJChw4dNPUWLFiAiIgIfPPNNzh27Bju37+f75O/n/bee+/hu+++Q1hYGGJiYrBq1SpYWVnB3d0d27ZtAwDExsYiISEBX375JQAgNDQU69atw8qVK/Hnn39i3LhxePfdd3HkyBEATxK07t27o0uXLjh//jwGDx6Mjz/+uNDvibW1NSIiInDp0iV8+eWXWL16NRYtWqRV5urVq9i8eTN2796NvXv34ty5cxg+fLjm+IYNGzB9+nR8/vnniImJwezZszFt2jSsXbu20PEQ0Uso4YekElEp8PST7nNzc8X+/fuFQqEQEyZM0Bx3cnISmZmZmjrr168XXl5eIjc3V7MvMzNTWFhYiH379gkhhHBxcRHz5s3THM/OzhZubm6atoTQfrp9bGysACD279+fb5x5T7Z/8OCBZl9GRoYoX768OHHihFbZoKAg0bdvXyGEEFOmTBE1a9bUOj558mTZuZ6FZ55i/qz58+eLBg0aaF4HBwcLU1NTcfv2bc2+qKgoYWJiIhISEoQQQlStWlVERkZqnWfWrFnCz89PCCHEjRs3BABx7ty5AtslopfHOT5EBADYs2cPrKyskJ2djdzcXPTr1w8hISGa47Vr19aa13PhwgVcvXoV1tbWWufJyMjAtWvXkJKSgoSEBDRu3FhzzMzMDA0bNpQNd+U5f/48TE1N0bJlS53jvnr1Kh49eoR27dpp7c/KykK9evUAADExMVpxAICfn5/ObeTZtGkTwsLCcO3aNaSlpeHx48dQqVRaZTw8PFCxYkWtdnJzcxEbGwtra2tcu3YNQUFBGDJkiKbM48ePoVarCx0PERUeEx8iAgC0bt0aK1asgLm5OVxdXWFmpv3Pg6WlpdbrtLQ0NGjQABs2bJCdy8HB4aVisLCwKHSdtLQ0AMAPP/yglXAAT+YtGUp0dDT69++PGTNmICAgAGq1Ghs3bsSCBQsKHevq1atliZipqanBYiWigjHxISIATxKbatWq6Vy+fv362LRpExwdHWW9HnlcXFzw66+/okWLFgCe9GycOXMG9evXz7d87dq1kZubiyNHjsDf3192PK/HKScnR7OvZs2aUCgUiIuLK7CnyMfHRzNRO8/JkydffJFPOXHiBDw9PTF16lTNvlu3bsnKxcXF4c6dO3B1ddW0Y2JiAi8vLzg5OcHV1RXXr19H//79C9U+ERkGJzcT0Uvp378/KlSogK5du+Lo0aO4ceMGDh8+jNGjR+P27dsAgDFjxmDOnDnYuXMnLl++jOHDhz/3HjyVKlVCYGAgBg0ahJ07d2rOuXnzZgCAp6cnJEnCnj178M8//yAtLQ3W1taYMGECxo0bh7Vr1+LatWs4e/YslixZopkwPGzYMFy5cgUTJ05EbGwsIiMjERERUajrrV69OuLi4rBx40Zcu3YNYWFh+U7UViqVCAwMxIULF3D06FGMHj0avXr1grOzMwBgxowZCA0NRVhYGP766y9cvHgR4eHhWLhwYaHiIaKXw8SHiF5K+fLl8csvv8DDwwPdu3eHj48PgoKCkJGRoekB+uijjzBgwAAEBgbCz88P1tbWePvtt5973hUrVqBnz54YPnw4vL29MWTIEKSnpwMAKlasiBkzZuDjjz+Gk5MTRo4cCQCYNWsWpk2bhtDQUPj4+KBDhw744YcfULlyZQBP5t1s27YNO3fuRN26dbFy5UrMnj27UNf71ltvYdy4cRg5ciR8fX1x4sQJTJs2TVauWrVq6N69O9588020b98ederU0VquPnjwYHz99dcIDw9H7dq10bJlS0RERGhiJaKiJYmCZhkSERERlTHs8SEiIiKjwcSHiIiIjAYTHyIiIjIaTHyIiIjIaDDxISIiIqPBxIeIiIiMBhMfIiIiMhpMfIiIiMhoMPEhIiIio8HEh4iIiIwGEx8iIiIyGkx8iIiIyGj8H74qpPPEmIp0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to plot confusion matrix\n",
    "def plot_confusion_matrix(y_test, y_pred, model_name):\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"No Diabetes\", \"Diabetes\"])\n",
    "    disp.plot(cmap=plt.cm.Blues)\n",
    "    plt.title(f'{model_name} Confusion Matrix')\n",
    "    plt.show()\n",
    "\n",
    "# Plot confusion matrices\n",
    "plot_confusion_matrix(y_test, y_pred_log_reg, \"Logistic Regression\")\n",
    "plot_confusion_matrix(y_test, y_pred_rand_forest, \"Random Forest\")\n",
    "plot_confusion_matrix(y_test, y_pred_svm, \"Support Vector Machine\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "maCv6Mtz9vG1"
   },
   "source": [
    "These matrices display the number of true positives, true negatives, false positives, and false negatives for each model."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3.10",
   "language": "python",
   "name": "py310_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}