Lecture_20_Visualisation_HTS.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"jIZzoZHDzFw0"},"source":["# **0.Hope to Skills - Free AI Course**\n","This note book cover the following concepts\n","\n","1. Visualization\n","2. Sea born"]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import sklearn\n","import seaborn as sns\n","import warnings\n","warnings.filterwarnings('ignore')\n","plt.rcParams[\"figure.figsize\"] = [10,5]\n","# Ignore warnings\n","\n","import warnings\n","# Set the warning filter to ignore FutureWarning\n","warnings.simplefilter(action = \"ignore\", category = FutureWarning)"],"metadata":{"id":"iDTtOurHR-dn"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":143,"metadata":{"id":"KLwKdmlhIzks","executionInfo":{"status":"ok","timestamp":1691510777313,"user_tz":-300,"elapsed":382,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}}},"outputs":[],"source":["full_data = pd.read_csv('/content/titanic_cleaned.csv')"]},{"cell_type":"markdown","metadata":{"id":"0a28ZbGoLTva"},"source":["**Data Shape**  \n","After loading the dataset, I examine its shape to get a better sense of the data and the information it contains.  "]},{"cell_type":"code","execution_count":144,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1691510778538,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"},"user_tz":-300},"id":"JVXGbVuJLWWE","outputId":"ff3d2dff-063f-47ab-8c3c-0481914e440f"},"outputs":[{"output_type":"stream","name":"stdout","text":["train data: (889, 9)\n"]}],"source":["# Data shape\n","print('train data:',full_data.shape)"]},{"cell_type":"code","execution_count":145,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":400,"status":"ok","timestamp":1691510779427,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"},"user_tz":-300},"id":"Aw9H7w2QLXXa","outputId":"54f14128-0770-4384-e8d1-c4e6105b4bbc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n","0         0       3  22.0      1      0   7.2500     1  0  1\n","1         1       1  38.0      1      0  71.2833     0  0  0\n","2         1       3  26.0      0      0   7.9250     0  0  1\n","3         1       1  35.0      1      0  53.1000     0  0  1\n","4         0       3  35.0      0      0   8.0500     1  0  1"],"text/html":["\n","\n","  <div id=\"df-ec0a88e3-0817-49d2-b8af-3769d4d188f0\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>male</th>\n","      <th>Q</th>\n","      <th>S</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec0a88e3-0817-49d2-b8af-3769d4d188f0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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everything is concatenated together"]},{"cell_type":"markdown","metadata":{"id":"PL98j5CbgzYz"},"source":["## **OBJECTIVE 2: MACHINE LEARNING**\n","Next, I will feed these features into various classification algorithms to determine the best performance using a simple framework: **Split, Fit, Predict, Score It.**"]},{"cell_type":"markdown","source":["## **Target Variable Splitting**\n","We will spilt the Full dataset into **Input** and **target** variables\n","\n","Input is also called **Feature Variables**\n","Output referes to Target **variables**"],"metadata":{"id":"8fv_AVme8vs2"}},{"cell_type":"code","execution_count":146,"metadata":{"id":"3R4SJeDPg2wq","executionInfo":{"status":"ok","timestamp":1691510789074,"user_tz":-300,"elapsed":565,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}}},"outputs":[],"source":["# Split data to be used in the models\n","# Create matrix of features\n","x = full_data.drop('Survived', axis = 1) # grabs everything else but 'Survived'\n","\n","# Create target variable\n","y = full_data['Survived'] # y is the column we're trying to predict\n"]},{"cell_type":"markdown","source":["![1_train-test-split_0 - 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)"],"metadata":{"id":"q2Pa1LnC5Qdz"}},{"cell_type":"code","source":["# Use x and y variables to split the training data into train and test set\n","from sklearn.model_selection import train_test_split\n","x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = .20, random_state = 101)"],"metadata":{"id":"GvBfh3TUyC41","executionInfo":{"status":"ok","timestamp":1691510791864,"user_tz":-300,"elapsed":380,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}}},"execution_count":147,"outputs":[]},{"cell_type":"markdown","source":["![1_train-test-split_0 - Copy 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)"],"metadata":{"id":"FXrQ1y8R5pDN"}},{"cell_type":"code","source":["print(\"Shape of x_train: \",x_train.shape)\n","print(\"Shape of y_train: \",y_train.shape)\n","print(\"---\"*10)\n","print(\"Shape of x_test: \",x_test.shape)\n","print(\"Shape of y_test: \",y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lLql7Qnd1beR","executionInfo":{"status":"ok","timestamp":1691510796333,"user_tz":-300,"elapsed":377,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"c8770e51-8965-41f7-846e-913a6e1ed898"},"execution_count":148,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of x_train:  (711, 8)\n","Shape of y_train:  (711,)\n","------------------------------\n","Shape of x_test:  (178, 8)\n","Shape of y_test:  (178,)\n"]}]},{"cell_type":"markdown","source":["![1_train-test-split_0 - Copy 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)"],"metadata":{"id":"2z4jNubC8eTO"}},{"cell_type":"markdown","metadata":{"id":"PzAOyNq8iPgf"},"source":["# **1. LOGISTIC REGRESSION**"]},{"cell_type":"markdown","source":["## **Model Training**"],"metadata":{"id":"Okaoq5Js9x67"}},{"cell_type":"code","execution_count":149,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":91},"executionInfo":{"elapsed":382,"status":"ok","timestamp":1691510807580,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"},"user_tz":-300},"id":"Xl52pA-8iSI0","outputId":"ac3fba35-2233-4993-addb-78b7b0d81a6a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Logistic Regression\n"]},{"output_type":"execute_result","data":{"text/plain":["LogisticRegression()"],"text/html":["<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":149}],"source":["# Import model\n","from sklearn.linear_model import LogisticRegression\n","\n","print('Logistic Regression')\n","# Create instance of model\n","log_reg = LogisticRegression()\n","\n","# Pass training data into model\n","log_reg.fit(x_train, y_train)"]},{"cell_type":"markdown","source":["## **Model Evaluation**\n","**Logistic regression**"],"metadata":{"id":"ya0etsyGyJzA"}},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","# prediction from the model\n","y_pred_log_reg = log_reg.predict(x_test)\n","# Score It\n","\n","print('Logistic Regression')\n","# Accuracy\n","print('--'*30)\n","log_reg_accuracy = round(accuracy_score(y_test, y_pred_log_reg) * 100,2)\n","print('Accuracy', log_reg_accuracy,'%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jCbC2_t_t2vw","executionInfo":{"status":"ok","timestamp":1691510816867,"user_tz":-300,"elapsed":397,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"22a3a4d0-0ab8-4ac0-bad2-beb661c7491e"},"execution_count":150,"outputs":[{"output_type":"stream","name":"stdout","text":["Logistic Regression\n","------------------------------------------------------------\n","Accuracy 82.02 %\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import precision_score, recall_score, confusion_matrix\n","# Calculate precision and recall\n","precision = precision_score(y_test, y_pred_log_reg)\n","recall = recall_score(y_test, y_pred_log_reg)\n","\n","# Print the results\n","print(f'Precision: {precision:.2f}')\n","print(f'Recall: {recall:.2f}')\n","print(\"--\"*30)\n","# Calculate confusion matrix\n","confusion = confusion_matrix(y_test, y_pred_log_reg)\n","print(confusion)\n","sns.heatmap(confusion, annot=True, fmt=\"d\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":549},"id":"6XbBzqfEgn5A","executionInfo":{"status":"ok","timestamp":1691510939668,"user_tz":-300,"elapsed":996,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"d4bac2fc-9b93-416c-a206-a1ac7d961222"},"execution_count":151,"outputs":[{"output_type":"stream","name":"stdout","text":["Precision: 0.85\n","Recall: 0.66\n","------------------------------------------------------------\n","[[99  8]\n"," [24 47]]\n"]},{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":151},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# **2. Decision Tree**"],"metadata":{"id":"8FBIgW1vvBDv"}},{"cell_type":"markdown","source":["## **Model Training**"],"metadata":{"id":"qFjzpB_yvIKJ"}},{"cell_type":"code","source":["from sklearn.tree import DecisionTreeClassifier\n","\n","print('Decision Tree Classifier')\n","# Create instance of model\n","Dtree = DecisionTreeClassifier()\n","\n","# Pass training data into model\n","Dtree.fit(x_train, y_train)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":91},"id":"biuHD_9XvFK3","executionInfo":{"status":"ok","timestamp":1691511059798,"user_tz":-300,"elapsed":398,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"32f40607-6b81-4da0-e45c-c78e447d5713"},"execution_count":152,"outputs":[{"output_type":"stream","name":"stdout","text":["Decision Tree Classifier\n"]},{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier()"],"text/html":["<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":152}]},{"cell_type":"markdown","source":["## **Model Evalution**\n","**Decision Tree Classifier**"],"metadata":{"id":"I7eT-R2RyRw9"}},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","# prediction from the model\n","y_pred_Dtree = Dtree.predict(x_test)\n","# Score It\n","\n","print('Decision Tree Classifier')\n","# Accuracy\n","print('--'*30)\n","Dtree_accuracy = round(accuracy_score(y_test, y_pred_Dtree) * 100,2)\n","print('Accuracy', Dtree_accuracy,'%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zMcZucONwF0n","executionInfo":{"status":"ok","timestamp":1691511063203,"user_tz":-300,"elapsed":390,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"976a6cbb-f33a-4a79-ffe6-5129d035831f"},"execution_count":153,"outputs":[{"output_type":"stream","name":"stdout","text":["Decision Tree Classifier\n","------------------------------------------------------------\n","Accuracy 79.21 %\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import precision_score, recall_score, confusion_matrix\n","# Calculate precision and recall\n","precision = precision_score(y_test, y_pred_Dtree)\n","recall = recall_score(y_test, y_pred_Dtree)\n","Dtree_accuracy = round(accuracy_score(y_test, y_pred_Dtree) * 100,2)\n","\n","# Print the results\n","print(f'Precision: {precision:.2f}')\n","print(f'Recall: {recall:.2f}')\n","\n","print(\"--\"*30)\n","# Calculate confusion matrix\n","confusion = confusion_matrix(y_test, y_pred_Dtree)\n","\n","sns.heatmap(confusion, annot=True, fmt=\"d\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":514},"id":"NXxKxYSZxN2I","executionInfo":{"status":"ok","timestamp":1691511070955,"user_tz":-300,"elapsed":406,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"19998bdc-b466-43a9-e729-7e1ff853eea5"},"execution_count":154,"outputs":[{"output_type":"stream","name":"stdout","text":["Precision: 0.72\n","Recall: 0.77\n","------------------------------------------------------------\n"]},{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":154},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# **3. Random Forest**"],"metadata":{"id":"4a6du7PfwgcG"}},{"cell_type":"markdown","source":["## **Model Training**"],"metadata":{"id":"rZ2Q3X5mwkHQ"}},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","\n","print('Random Forest Classifier')\n","# Create instance of model\n","rfc = RandomForestClassifier()\n","\n","# Pass training data into model\n","rfc.fit(x_train, y_train)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":91},"id":"XztLXPDzwnie","executionInfo":{"status":"ok","timestamp":1691511104667,"user_tz":-300,"elapsed":449,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"c8f7367a-7eeb-4b08-fb1f-6c9289fbe218"},"execution_count":155,"outputs":[{"output_type":"stream","name":"stdout","text":["Random Forest Classifier\n"]},{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier()"],"text/html":["<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":155}]},{"cell_type":"markdown","source":["## **Model Evaluation**\n","**Random Forest**"],"metadata":{"id":"pfdyE95CyZqW"}},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","# prediction from the model\n","y_pred_rfc = rfc.predict(x_test)\n","# Score It\n","\n","print('Random Forest Classifier')\n","# Accuracy\n","print('--'*30)\n","rfc_accuracy = round(accuracy_score(y_test, y_pred_rfc) * 100,2)\n","print('Accuracy', rfc_accuracy,'%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vOB2xeV3w2cK","executionInfo":{"status":"ok","timestamp":1691511108041,"user_tz":-300,"elapsed":606,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"df890339-d124-461e-b3ce-181551c8d40e"},"execution_count":156,"outputs":[{"output_type":"stream","name":"stdout","text":["Random Forest Classifier\n","------------------------------------------------------------\n","Accuracy 82.02 %\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import precision_score, recall_score, confusion_matrix\n","# Calculate precision and recall\n","precision = precision_score(y_test, y_pred_rfc)\n","recall = recall_score(y_test, y_pred_rfc)\n","\n","# Print the results\n","print(f'Precision: {precision:.2f}')\n","print(f'Recall: {recall:.2f}')\n","print(\"--\"*30)\n","# Calculate confusion matrix\n","confusion = confusion_matrix(y_test, y_pred_rfc)\n","sns.heatmap(confusion, annot=True, fmt=\"d\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":514},"id":"iLWjynT4xvTZ","executionInfo":{"status":"ok","timestamp":1691511110721,"user_tz":-300,"elapsed":399,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"c5d495cf-3738-4075-f2e9-9043e387222f"},"execution_count":157,"outputs":[{"output_type":"stream","name":"stdout","text":["Precision: 0.79\n","Recall: 0.75\n","------------------------------------------------------------\n"]},{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":157},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# **4. Gradient Bossting Classifier**"],"metadata":{"id":"FzvrST_MxP03"}},{"cell_type":"markdown","source":["## **Model Training**"],"metadata":{"id":"EfBhKP3-xTUO"}},{"cell_type":"code","source":["from sklearn.ensemble import GradientBoostingClassifier\n","\n","print('Gradient Boosting Classifier')\n","# Create instance of model\n","gbc = GradientBoostingClassifier()\n","\n","# Pass training data into model\n","gbc.fit(x_train, y_train)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":91},"id":"_t4FrQ5RxVO2","executionInfo":{"status":"ok","timestamp":1691511144455,"user_tz":-300,"elapsed":502,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"728cacc9-b8af-4a8e-8acb-b867285bcc68"},"execution_count":158,"outputs":[{"output_type":"stream","name":"stdout","text":["Gradient Boosting Classifier\n"]},{"output_type":"execute_result","data":{"text/plain":["GradientBoostingClassifier()"],"text/html":["<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":158}]},{"cell_type":"markdown","source":["## **Model Evalution**\n","**Gradient Boosting**"],"metadata":{"id":"pCU77gQtyfZl"}},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","# prediction from the model\n","y_pred_gbc = gbc.predict(x_test)\n","# Score It\n","\n","print('Gradient Boosting Classifier')\n","# Accuracy\n","print('--'*30)\n","gbc_accuracy = round(accuracy_score(y_test, y_pred_gbc) * 100,2)\n","print('Accuracy', gbc_accuracy,'%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"M_4x9HY3xXtu","executionInfo":{"status":"ok","timestamp":1691511147914,"user_tz":-300,"elapsed":463,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"6b7920ca-628c-4266-cc9c-c1cdb3bb2be6"},"execution_count":159,"outputs":[{"output_type":"stream","name":"stdout","text":["Gradient Boosting Classifier\n","------------------------------------------------------------\n","Accuracy 84.27 %\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import precision_score, recall_score, confusion_matrix\n","# Calculate precision and recall\n","precision = precision_score(y_test, y_pred_gbc)\n","recall = recall_score(y_test, y_pred_gbc)\n","\n","# Print the results\n","print(f'Precision: {precision:.2f}')\n","print(f'Recall: {recall:.2f}')\n","print(\"--\"*30)\n","# Calculate confusion matrix\n","confusion = confusion_matrix(y_test, y_pred_gbc)\n","sns.heatmap(confusion, annot=True, fmt=\"d\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":514},"id":"WTq7mao-zs2h","executionInfo":{"status":"ok","timestamp":1691511165342,"user_tz":-300,"elapsed":398,"user":{"displayName":"Muhammad Haris","userId":"13172555328013255752"}},"outputId":"57f2b8c0-fa6a-43ab-8d30-7b13359fa47b"},"execution_count":160,"outputs":[{"output_type":"stream","name":"stdout","text":["Precision: 0.83\n","Recall: 0.76\n","------------------------------------------------------------\n"]},{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":160},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["sns.countplot(x=\"Survived\", data=full_data, palette=\"Blues\");\n","plt.show()"],"metadata":{"id":"L-p1Qcot_quY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Sample model scores (replace these with your actual model scores)\n","model_scores = {\n","    \"Logistic Regression\": 82.02,\n","    \"Decision Tree Classifier\": 80.34,\n","    \"Random Forest Classifier\": 82.58,\n","    \"Gradient Boosting Classifier\": 84.27\n","}\n","\n","# Sort the model scores in descending order based on their values (higher values first)\n","sorted_scores = sorted(model_scores.items(), key=lambda x: x[1], reverse=True)\n","\n","# Display the ranking of the models\n","print(\"Model Rankings (Greater Values are better):\")\n","for rank, (model_name, score) in enumerate(sorted_scores, start=1):\n","    print(f\"{rank}. {model_name}: {score}\")\n"],"metadata":{"id":"XSiIimLf1rO1"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"}},"nbformat":4,"nbformat_minor":0}