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Submissions/heart_failure_analysis.ipynb b/Major Project Submissions/heart_failure_analysis.ipynb new file mode 100644 index 0000000..1a53e0e --- /dev/null +++ b/Major Project Submissions/heart_failure_analysis.ipynb @@ -0,0 +1,1008 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4fc1abab-c6fc-42ca-bbcd-de8f6e23d8c1", + "metadata": {}, + "outputs": [], + "source": [ + "# data manipulation libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# data visualiation\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns \n", + "import matplotlib\n", + "import plotly.graph_objects as go\n", + "import plotly.express as px\n", + "\n", + "from sklearn.metrics import classification_report,confusion_matrix\n", + "import itertools\n", + "\n", + "from sklearn import preprocessing\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + 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ageanaemiacreatinine_phosphokinasediabetesejection_fractionhigh_blood_pressureplateletsserum_creatinineserum_sodiumsexsmokingtimeDEATH_EVENT
count299.000000299.000000299.000000299.000000299.000000299.000000299.000000299.00000299.000000299.000000299.00000299.000000299.00000
mean60.8338930.431438581.8394650.41806038.0836120.351171263358.0292641.39388136.6254180.6488290.32107130.2608700.32107
std11.8948090.496107970.2878810.49406711.8348410.47813697804.2368691.034514.4124770.4781360.4676777.6142080.46767
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50%60.0000000.000000250.0000000.00000038.0000000.000000262000.0000001.10000137.0000001.0000000.00000115.0000000.00000
75%70.0000001.000000582.0000001.00000045.0000001.000000303500.0000001.40000140.0000001.0000001.00000203.0000001.00000
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" + ], + "text/plain": [ + " age anaemia creatinine_phosphokinase diabetes \\\n", + "count 299.000000 299.000000 299.000000 299.000000 \n", + "mean 60.833893 0.431438 581.839465 0.418060 \n", + "std 11.894809 0.496107 970.287881 0.494067 \n", + "min 40.000000 0.000000 23.000000 0.000000 \n", + "25% 51.000000 0.000000 116.500000 0.000000 \n", + "50% 60.000000 0.000000 250.000000 0.000000 \n", + "75% 70.000000 1.000000 582.000000 1.000000 \n", + "max 95.000000 1.000000 7861.000000 1.000000 \n", + "\n", + " ejection_fraction high_blood_pressure platelets \\\n", + "count 299.000000 299.000000 299.000000 \n", + "mean 38.083612 0.351171 263358.029264 \n", + "std 11.834841 0.478136 97804.236869 \n", + "min 14.000000 0.000000 25100.000000 \n", + "25% 30.000000 0.000000 212500.000000 \n", + "50% 38.000000 0.000000 262000.000000 \n", + "75% 45.000000 1.000000 303500.000000 \n", + "max 80.000000 1.000000 850000.000000 \n", + "\n", + " serum_creatinine serum_sodium sex smoking time \\\n", + "count 299.00000 299.000000 299.000000 299.00000 299.000000 \n", + "mean 1.39388 136.625418 0.648829 0.32107 130.260870 \n", + "std 1.03451 4.412477 0.478136 0.46767 77.614208 \n", + "min 0.50000 113.000000 0.000000 0.00000 4.000000 \n", + "25% 0.90000 134.000000 0.000000 0.00000 73.000000 \n", + "50% 1.10000 137.000000 1.000000 0.00000 115.000000 \n", + "75% 1.40000 140.000000 1.000000 1.00000 203.000000 \n", + "max 9.40000 148.000000 1.000000 1.00000 285.000000 \n", + "\n", + " DEATH_EVENT \n", + "count 299.00000 \n", + "mean 0.32107 \n", + "std 0.46767 \n", + "min 0.00000 \n", + "25% 0.00000 \n", + "50% 0.00000 \n", + "75% 1.00000 \n", + "max 1.00000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dad8ab09-99d6-47b2-8156-a8242e8f8547", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().values.any()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "011b4382-1a13-41d8-bf4a-5bad80e0171d", + "metadata": {}, + "outputs": [], + "source": [ + "df.fillna(0,inplace=True) # inplace filling missing data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "40332e9b-86ef-4b2f-a009-1d2f05f3acbf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 203\n", + "1 96\n", + "Name: DEATH_EVENT, dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['DEATH_EVENT'].value_counts() # counting the number of deaths" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ce0080af-86d9-4ce0-8fd4-406fbb8258ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 299 entries, 0 to 298\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 age 299 non-null float64\n", + " 1 anaemia 299 non-null int64 \n", + " 2 creatinine_phosphokinase 299 non-null int64 \n", + " 3 diabetes 299 non-null int64 \n", + " 4 ejection_fraction 299 non-null int64 \n", + " 5 high_blood_pressure 299 non-null int64 \n", + " 6 platelets 299 non-null float64\n", + " 7 serum_creatinine 299 non-null float64\n", + " 8 serum_sodium 299 non-null int64 \n", + " 9 sex 299 non-null int64 \n", + " 10 smoking 299 non-null int64 \n", + " 11 time 299 non-null int64 \n", + " 12 DEATH_EVENT 299 non-null int64 \n", + "dtypes: float64(3), int64(10)\n", + "memory usage: 30.5 KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1d115339-fd12-4ed9-97ca-aaf9d3741ba8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n", + " 'ejection_fraction', 'high_blood_pressure', 'platelets',\n", + " 'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n", + " 'DEATH_EVENT'],\n", + " dtype='object')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "be90ae16-324d-41a0-b786-448e2aa8fe32", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# calculating the percentage of deaths and plotting a pie-chart\n", + "df.DEATH_EVENT.value_counts().plot.pie(explode=[0.1,0],autopct='%1.2f%%',shadow=True)\n", + "plt.title('Percentage of deaths')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "685ab7ad-df5b-4c44-8d19-12d107da2cca", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting a bar-grape of age distribution\n", + "\n", + "plt.figure(figsize=(25,10))\n", + "sns.countplot(x='age',data=df)\n", + "plt.xticks(rotation=90)\n", + "plt.title('Age Distribution')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c30ce4f-a4f3-4ef6-a735-b5bbb12e2459", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ebbd0cf4-7150-4acf-bf71-fd9f348f7b30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[7.5000000e+01, 0.0000000e+00, 5.8200000e+02, 0.0000000e+00,\n", + " 2.0000000e+01, 1.0000000e+00, 2.6500000e+05, 1.9000000e+00,\n", + " 1.3000000e+02, 1.0000000e+00, 0.0000000e+00, 4.0000000e+00,\n", + " 1.0000000e+00],\n", + " [5.5000000e+01, 0.0000000e+00, 7.8610000e+03, 0.0000000e+00,\n", + " 3.8000000e+01, 0.0000000e+00, 2.6335803e+05, 1.1000000e+00,\n", + " 1.3600000e+02, 1.0000000e+00, 0.0000000e+00, 6.0000000e+00,\n", + " 1.0000000e+00]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = df.values\n", + "X[0:2]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "07abcf7c-4204-4539-b17a-be029c21a58f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[7.50e+01, 0.00e+00, 5.82e+02, 0.00e+00, 2.00e+01, 1.00e+00,\n", + " 2.65e+05, 1.90e+00, 1.30e+02, 1.00e+00, 0.00e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xt=X[:,0:11]\n", + "xt[0:1]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "de23533c-bd52-476d-ad82-c643c35b8fd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1., 1.])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y=X[:,12]\n", + "y[0:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "185c411a-bb74-4efb-81a7-4f08e40a5760", + "metadata": {}, + "outputs": [], + "source": [ + "# Standardising data\n", + "xt=preprocessing.StandardScaler().fit(xt).transform(xt)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "059be8e1-6619-44ac-b4d7-51f8c21ef8e2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "xtrain,xtest,ytrain,ytest=train_test_split(xt,y,test_size=0.85,random_state=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e1870d8-26a1-4e88-b96f-535af1cf2888", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ed2e6584-8f63-4550-94ac-5b254c38c600", + "metadata": {}, + "outputs": [], + "source": [ + "# displaying any confusion matrix\n", + "def plot_confusion_matrix(cm, classes,\n", + " normalize=False,\n", + " title='Confusion matrix',\n", + " cmap=plt.cm.Greys):\n", + " \n", + " if normalize:\n", + " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + " print(\"Normalized confusion matrix\")\n", + " else:\n", + " print('Confusion matrix, without normalization')\n", + "\n", + " print(cm)\n", + "\n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(classes))\n", + " plt.xticks(tick_marks, classes, rotation=45)\n", + " plt.yticks(tick_marks, classes)\n", + "\n", + " fmt = '.2f' if normalize else 'd'\n", + " thresh = cm.max() / 2.\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j, i, format(cm[i, j], fmt),\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + "\n", + " plt.tight_layout()\n", + " plt.ylabel('Actual label')\n", + "\n", + " plt.xlabel('Predicted label')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e82cd257-9d9b-48f7-9fd8-c80a03856651", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized confusion matrix\n", + "[[0.23 0.77]\n", + " [0.06 0.94]]\n", + " precision recall f1-score support\n", + "\n", + " 0.0 0.73 0.94 0.82 176\n", + " 1.0 0.62 0.23 0.33 79\n", + "\n", + " accuracy 0.72 255\n", + " macro avg 0.68 0.58 0.58 255\n", + "weighted avg 0.70 0.72 0.67 255\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Algorithm 1: K-Nearest Neighbour\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "kn=KNeighborsClassifier(n_neighbors=6).fit(xtrain,ytrain)\n", + "yknn=kn.predict(xtest)\n", + "cnf_matrix=confusion_matrix(ytest,yknn,labels=[1,0])\n", + "np.set_printoptions(precision=2)\n", + "plt.figure()\n", + "plot_confusion_matrix(cnf_matrix,classes=['DeathEvent=1','DeathEvent=0'],normalize='False',title='LOGISTIC REGRESSION LIBLINEAR',cmap=plt.cm.Oranges)\n", + "\n", + "print(classification_report(ytest,yknn))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e0c14fed-c190-4a8f-af50-1a0c1f389a40", + "metadata": {}, + "outputs": [], + "source": [ + "# Algorithm 2: Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "72d0a0d5-94c2-41cd-8674-53d64aeb24c9", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "02654d59-131b-4b6b-80b0-04d02c1923c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized confusion matrix\n", + "[[0.57 0.43]\n", + " [0.23 0.77]]\n", + " precision recall f1-score support\n", + "\n", + " 0.0 0.80 0.77 0.79 176\n", + " 1.0 0.53 0.57 0.55 79\n", + "\n", + " accuracy 0.71 255\n", + " macro avg 0.66 0.67 0.67 255\n", + "weighted avg 0.72 0.71 0.71 255\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "LR=LogisticRegression(C=0.01,solver=\"liblinear\").fit(xtrain,ytrain)\n", + "ylr=LR.predict(xtest)\n", + "\n", + "cnf_matrix=confusion_matrix(ytest,ylr,labels=[1,0])\n", + "np.set_printoptions(precision=2)\n", + "plt.figure()\n", + "plot_confusion_matrix(cnf_matrix,classes=['DeathEvent=1','DeathEvent=0'],normalize='False',title='LOGISTIC REGRESSION LIBLINEAR',cmap=plt.cm.Blues)\n", + "\n", + "print(classification_report(ytest,ylr))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6b046fcb-bd64-4e26-b760-f998f52bc686", + "metadata": {}, + "outputs": [], + "source": [ + "# Algorithm 3: Support Vector Machine (SVM)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "56dc4eed-98e1-4bae-889b-22373ff239a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized confusion matrix\n", + "[[0.43 0.57]\n", + " [0.24 0.76]]\n", + " precision recall f1-score support\n", + "\n", + " 0.0 0.75 0.76 0.75 176\n", + " 1.0 0.45 0.43 0.44 79\n", + "\n", + " accuracy 0.66 255\n", + " macro avg 0.60 0.60 0.60 255\n", + "weighted avg 0.66 0.66 0.66 255\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import svm\n", + "\n", + "\n", + "svm=svm.SVC(kernel='linear')\n", + "svm.fit(xtrain,ytrain)\n", + "ysvm=svm.predict(xtest)\n", + "\n", + "cnf_matrix=confusion_matrix(ytest,ysvm,labels=[1,0])\n", + "np.set_printoptions(precision=2)\n", + "plt.figure()\n", + "plot_confusion_matrix(cnf_matrix,classes=['DeathEvent=1','DeathEvent=0'],normalize='False',title='SVM linear',cmap=plt.cm.Reds)\n", + "\n", + "print(classification_report(ytest,ysvm))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "da8bce7f-c351-49cc-8a21-62d4aae992af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNN Accuracy : 0.72\n", + "Logistic Regression Accuracy : 0.71\n", + "SVM Accuracy : 0.66\n" + ] + } + ], + "source": [ + "#Comparing the accuracy of each algorithm\n", + "\n", + "print(f\"KNN Accuracy : {metrics.accuracy_score(ytest,yknn):.2f}\")\n", + "print(f\"Logistic Regression Accuracy : {metrics.accuracy_score(ytest,ylr):.2f}\")\n", + "print(f\"SVM Accuracy : {metrics.accuracy_score(ytest,ysvm):.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f998493-86b4-41fc-bec8-8208154abeb2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}