diff --git a/Scripts/Miscellaneous/Prograamming Quiz GUI/Dashboard.jpg b/Scripts/Miscellaneous/Prograamming Quiz GUI/Dashboard.jpg new file mode 100644 index 000000000..99995a203 Binary files /dev/null and b/Scripts/Miscellaneous/Prograamming Quiz GUI/Dashboard.jpg differ diff --git a/Scripts/Miscellaneous/Prograamming Quiz GUI/Quiz.py b/Scripts/Miscellaneous/Prograamming Quiz GUI/Quiz.py new file mode 100644 index 000000000..996b70ef9 --- /dev/null +++ b/Scripts/Miscellaneous/Prograamming Quiz GUI/Quiz.py @@ -0,0 +1,101 @@ +from tkinter import * +from tkinter import messagebox +from questions import * + +class Project: + def __init__(self): + self.que="" + self.ans="" + self.correct=0 + self.count=-1 + self.__correct_answer=None + self.__answer=None + self.__question=None + + def course(self): + self.correct=0 + self.count=-1 + B.config(text="Start") + if s.get()=="Python": + self.que=python_que + self.ans=python_ans + elif s.get()=="C": + self.que=c_que + self.ans=c_ans + + + def set_(self): + if self.que=="": + messagebox.showinfo("Error","Please select course first...") + return + self.count+=1 + + if self.count==len(self.que): + messagebox.showinfo("Result","You have answered {0} out of {1} questions correctly".format(self.correct,self.count)) + sys.exit() + + if(self.count==len(self.que)-1): + B.config(text="Finish") + else: + B.config(text="Next") + + self.__question=self.que[self.count] + self.__answer=self.ans[self.__question] + l.config(text=self.__question) + for i in range(0,4): + v[i].set(self.__answer[i]) + self.__correct_answer=self.__answer[4] + + def fun(self,y,n): + if y==self.__correct_answer: + self.correct+=1 + n.config(activebackground="green") + else: + n.config(activebackground="red") + self.set_() + + + +obj=Project() +scr=Tk(className="quiz") +s=StringVar() +main_menu=Menu(scr) +file_menu=Menu(main_menu,tearoff=0) + +course_menu=Menu(file_menu,tearoff=0) +course_menu.add_radiobutton(label="Python",value="Python",variable=s,command=obj.course) +course_menu.add_radiobutton(label="C",value="C",variable=s,command=obj.course) + +file_menu.add_cascade(label="Course",menu=course_menu) +file_menu.add_command(label="Exit",command=exit) +main_menu.add_cascade(label="file",menu=file_menu) +scr.config(menu=main_menu) + +l=Label(scr,font=("consolas",20),relief="groove",width=40,height=2,wraplength=600,bg="steel blue") +l.grid(row=0,column=0,columnspan=20,sticky="news") + +b=[] +for i in range(0,4): + b.append(Button()) +v=[] +for i in range(0,4): + v.append(StringVar()) + + +b[0]=Button(scr,textvariable=v[0],font=("consolas",20),anchor="w",width=20,activebackground="light blue",command=lambda :obj.fun(v[0].get(),b[0])) +b[0].grid(row=2,column=0,sticky=W) + +b[1]=Button(scr,textvariable=v[1],font=("consolas",20),anchor="w",width=20,command=lambda :obj.fun(v[1].get(),b[1])) +b[1].grid(row=2,column=1,sticky=S) + +b[2]=Button(scr,textvariable=v[2],font=("consolas",20),anchor="w",width=20,command=lambda :obj.fun(v[2].get(),b[2])) +b[2].grid(row=3,column=0,sticky=W) + +b[3]=Button(scr,textvariable=v[3],font=("consolas",20),anchor="w",width=20,command=lambda :obj.fun(v[3].get(),b[3])) +b[3].grid(row=3,column=1,sticky=S) + +B=Button(scr,text="Start",font=("consolas",20),width=40,height=1,bg="black",fg="white",command=obj.set_) +B.grid(row=10,column=0,columnspan=2,sticky="news") +#B.geometry('{0}x{1}+0+0'.format(scr.winfo_screenwidth(),200)) + +scr.mainloop() diff --git a/Scripts/Miscellaneous/Prograamming Quiz GUI/README.md b/Scripts/Miscellaneous/Prograamming Quiz GUI/README.md new file mode 100644 index 000000000..24d858d02 --- /dev/null +++ b/Scripts/Miscellaneous/Prograamming Quiz GUI/README.md @@ -0,0 +1,43 @@ +# Programming Quiz Project +The project is about developing a quiz of 10 questions using python programming language on graphical user interphase. + +### Prerequisites +1. Tkinter + + +### How to run the script +
  • Download the prerequisited library using `pip install tkinter` command on the terminal/command prompt +
  • Run the Graphical user Interphase using `python Quiz.py` +
  • Go to file menu to select the programming language for the quiz. +
  • Click on start button to proceed with the quiz + +![script execution](Dashboard.jpg) + +
  • After successful submission, a popup will display the result + + ![script execution](popup.jpg) + +### Quiz Enhancement + +
  • Changes in questions.py-:
    + 1. Add questions que dictonary (eg java_que={0 : ...,1 : ...}) +
    + 2. Add option and answer to the answer dictonary ( eg java_ans={question : [options , correct answer]}) +
    +
  • Changes in Quiz.py
    + 1. Add th course to the course() function (Line 15) +
    + elif s.get()=="Java": +
    + self.que=java_que + self.ans=java_ans +
    + 2. Add course to the file menu (Line 65) +
    + course_menu.add_radiobutton(label="Java",value="Java",variable=s,command=obj.course) +
    +
  • Save and run the script using the instructions given above + + + ## *Author Name* +[Pulkit Dhingra](https://github.com/Pulkit12dhingra) diff --git a/Scripts/Miscellaneous/Prograamming Quiz GUI/__pycache__/questions.cpython-36.pyc b/Scripts/Miscellaneous/Prograamming Quiz GUI/__pycache__/questions.cpython-36.pyc new file mode 100644 index 000000000..897a7e290 Binary files /dev/null and b/Scripts/Miscellaneous/Prograamming Quiz GUI/__pycache__/questions.cpython-36.pyc differ diff --git a/Scripts/Miscellaneous/Prograamming Quiz GUI/__pycache__/questions.cpython-39.pyc b/Scripts/Miscellaneous/Prograamming Quiz GUI/__pycache__/questions.cpython-39.pyc new file mode 100644 index 000000000..3e03ac108 Binary files /dev/null and b/Scripts/Miscellaneous/Prograamming Quiz GUI/__pycache__/questions.cpython-39.pyc differ diff --git a/Scripts/Miscellaneous/Prograamming Quiz GUI/popup.jpg b/Scripts/Miscellaneous/Prograamming Quiz GUI/popup.jpg new file mode 100644 index 000000000..100ea24a2 Binary files /dev/null and b/Scripts/Miscellaneous/Prograamming Quiz GUI/popup.jpg differ diff --git a/Scripts/Miscellaneous/Prograamming Quiz GUI/questions.py b/Scripts/Miscellaneous/Prograamming Quiz GUI/questions.py new file mode 100644 index 000000000..7766ebde1 --- /dev/null +++ b/Scripts/Miscellaneous/Prograamming Quiz GUI/questions.py @@ -0,0 +1,49 @@ +python_que={ + 0:"What is python", + 1:"Who is founder of Python", + 2:"What is variable", + 3:"What is symbol of List", + 4:"What is symbol of tuple", + 5:"What is symbol of Set", + 6:"What is symbol of dictionary", + 7:"Python was launched in..", + 8:"Latest Version of python is..", + 9:"Python is suitable for.." + } +python_ans={ + "What is variable":["Container","Object","Class","Structure","Object"], + "What is symbol of List":["( )","{ }","[ ]","None of the above","[ ]"], + "What is symbol of tuple":["( )","{ }","[ ]","None of the above","( )"], + "What is symbol of Set":["( )","{ }","[ ]","None of the above","{ }"], + "What is symbol of dictionary":["( )","{ }","[ ]","None of the above","{ }"], + "Who is founder of Python":["Steve Jobs","James Gosling","Guido von Rossum","Dennis Richie","Guido von Rossum"], + "Python was launched in..":["1990","2003","1980","1996","1990"], + "What is python":["Scripting language","Programming language","Markup Language","Snake","Programming language"], + "Latest Version of python is..":["3.8.4","3.9.5","3.8.9","3.9.0","3.9.0"], + "Python is suitable for..":["GUI Application","Web application","Android","All of these","All of these"]} + +c_que={ + 0:"What is c", + 1:"Who is father of c", + 2:"What is strength of C language", + 3:"C was launched in", + 4:"What is format specifier of char", + 5:"what is size of int in 64 bit os", + 6:"what is format specifier of double", + 7:"what is size of double", + 8:"output of for(;;)", + 9:"what is symbol of function" + } +c_ans={ + "What is c":["Scripting language","Programming language","Markup Language","Snake","Programming language"], + "Who is father of c":["Steve Jobs","James Gosling","Guido von Rossum","Dennis Richie","Dennis Richie"], + "What is strength of C language":["data structure","Pointer","Speed","B and C both","B and C both"], + "C was launched in":["1970","1972","1980","1982","1972"], + "What is format specifier of char":["%ch","%c","%char","%chr","%c"], + "what is size of int in 64 bit os":["1 byte","8 bit","4 bit","32 bit","4 bit"], + "what is format specifier of double":["%d","%l","%lf","%Lf","%d"], + "what is size of double":["1 byte","8 bit","8 byte","32 bit","8 byte"], + "output of for(;;)":["syntax error","0 iteration","infinite iteration","runtime error","infinite iteration"], + "what is symbol of function":["( )","{ }","[ ]","< >","( )"] + } + diff --git a/Scripts/Miscellaneous/heart attack analysis/README.md b/Scripts/Miscellaneous/heart attack analysis/README.md new file mode 100644 index 000000000..b99fb001b --- /dev/null +++ b/Scripts/Miscellaneous/heart attack analysis/README.md @@ -0,0 +1,54 @@ +# Heart Attack Analysis & Prediction + +# Dataset Information + +Heart attacks result in severe medical conditions that may be fatal if not handled correctly. Due to the current lifestyle, heart attacks are much more often than in the past. With the help of modern-day technologies and the capacity of storing the data, we can create a heart attack prediction mechanism that would allow us to measure the chances of having a heart attack on a person. + +### Attribute Information: + +Input variables: \ +1 - Age : Age of the patient \ +2 - Sex : Sex of the patient \ +3 - exang: exercise induced angina (1 = yes; 0 = no) \ +4 - ca: number of major vessels (0-3) \ +5 - cp : Chest Pain type chest pain type \ + + +6 - trtbps : resting blood pressure (in mm Hg) \ +7 - chol : cholestoral in mg/dl fetched via BMI sensor \ +8 - Trihalomethanes-> Amount of Trihalomethanes in μg/L \ +9 - fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) \ +10 - est_ecg : resting electrocardiographic results + +11 - thalach : maximum heart rate achieved + +Output variable (based on sensory data): \ +10 - target : 0= less chance of heart attack 1= more chance of heart attack + + +# Libraries + + +
  • pandas +
  • matplotlib +
  • seaborn +
  • plotly +
  • scikit-learn +
  • xgboost + +# Algorithm +
  • XGBoost
    • + +
    + +**Model Accuracy:** 80.00 + diff --git a/Scripts/Miscellaneous/heart attack analysis/heart-attack-analysis-xg-boost.ipynb b/Scripts/Miscellaneous/heart attack analysis/heart-attack-analysis-xg-boost.ipynb new file mode 100644 index 000000000..dab7e1440 --- /dev/null +++ b/Scripts/Miscellaneous/heart attack analysis/heart-attack-analysis-xg-boost.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\nimport seaborn as sns\nimport plotly.express as px\nimport matplotlib.pyplot as plt\nimport missingno as msno\nimport plotly.graph_objects as go\n\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nplt.style.use('fivethirtyeight')\n%matplotlib inline\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-08-24T17:50:38.074908Z","iopub.execute_input":"2021-08-24T17:50:38.075253Z","iopub.status.idle":"2021-08-24T17:50:41.104332Z","shell.execute_reply.started":"2021-08-24T17:50:38.075223Z","shell.execute_reply":"2021-08-24T17:50:41.103401Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"/kaggle/input/heart-attack-analysis-prediction-dataset/o2Saturation.csv\n/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Reading the data","metadata":{}},{"cell_type":"code","source":"data=pd.read_csv('/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv')\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.106138Z","iopub.execute_input":"2021-08-24T17:50:41.106792Z","iopub.status.idle":"2021-08-24T17:50:41.157251Z","shell.execute_reply.started":"2021-08-24T17:50:41.106741Z","shell.execute_reply":"2021-08-24T17:50:41.156071Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp \\\n0 63 1 3 145 233 1 0 150 0 2.3 0 \n1 37 1 2 130 250 0 1 187 0 3.5 0 \n2 41 0 1 130 204 0 0 172 0 1.4 2 \n3 56 1 1 120 236 0 1 178 0 0.8 2 \n4 57 0 0 120 354 0 1 163 1 0.6 2 \n\n caa thall output \n0 0 1 1 \n1 0 2 1 \n2 0 2 1 \n3 0 2 1 \n4 0 2 1 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    063131452331015002.30011
    137121302500118703.50021
    241011302040017201.42021
    356111202360117800.82021
    457001203540116310.62021
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"# Exploratory data Analysis","metadata":{}},{"cell_type":"markdown","source":"### Basic information about Data","metadata":{}},{"cell_type":"code","source":"print('There are {} data points and {} features in the data'.format(data.shape[0],data.shape[1]))","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.158928Z","iopub.execute_input":"2021-08-24T17:50:41.159265Z","iopub.status.idle":"2021-08-24T17:50:41.165647Z","shell.execute_reply.started":"2021-08-24T17:50:41.159235Z","shell.execute_reply":"2021-08-24T17:50:41.164523Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"There are 303 data points and 14 features in the data\n","output_type":"stream"}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.167558Z","iopub.execute_input":"2021-08-24T17:50:41.168076Z","iopub.status.idle":"2021-08-24T17:50:41.197775Z","shell.execute_reply.started":"2021-08-24T17:50:41.168040Z","shell.execute_reply":"2021-08-24T17:50:41.196295Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"\nRangeIndex: 303 entries, 0 to 302\nData columns (total 14 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 age 303 non-null int64 \n 1 sex 303 non-null int64 \n 2 cp 303 non-null int64 \n 3 trtbps 303 non-null int64 \n 4 chol 303 non-null int64 \n 5 fbs 303 non-null int64 \n 6 restecg 303 non-null int64 \n 7 thalachh 303 non-null int64 \n 8 exng 303 non-null int64 \n 9 oldpeak 303 non-null float64\n 10 slp 303 non-null int64 \n 11 caa 303 non-null int64 \n 12 thall 303 non-null int64 \n 13 output 303 non-null int64 \ndtypes: float64(1), int64(13)\nmemory usage: 33.3 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"data.describe()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.199238Z","iopub.execute_input":"2021-08-24T17:50:41.199601Z","iopub.status.idle":"2021-08-24T17:50:41.266325Z","shell.execute_reply.started":"2021-08-24T17:50:41.199551Z","shell.execute_reply":"2021-08-24T17:50:41.264847Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs \\\ncount 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \nmean 54.366337 0.683168 0.966997 131.623762 246.264026 0.148515 \nstd 9.082101 0.466011 1.032052 17.538143 51.830751 0.356198 \nmin 29.000000 0.000000 0.000000 94.000000 126.000000 0.000000 \n25% 47.500000 0.000000 0.000000 120.000000 211.000000 0.000000 \n50% 55.000000 1.000000 1.000000 130.000000 240.000000 0.000000 \n75% 61.000000 1.000000 2.000000 140.000000 274.500000 0.000000 \nmax 77.000000 1.000000 3.000000 200.000000 564.000000 1.000000 \n\n restecg thalachh exng oldpeak slp caa \\\ncount 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \nmean 0.528053 149.646865 0.326733 1.039604 1.399340 0.729373 \nstd 0.525860 22.905161 0.469794 1.161075 0.616226 1.022606 \nmin 0.000000 71.000000 0.000000 0.000000 0.000000 0.000000 \n25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n50% 1.000000 153.000000 0.000000 0.800000 1.000000 0.000000 \n75% 1.000000 166.000000 1.000000 1.600000 2.000000 1.000000 \nmax 2.000000 202.000000 1.000000 6.200000 2.000000 4.000000 \n\n thall output \ncount 303.000000 303.000000 \nmean 2.313531 0.544554 \nstd 0.612277 0.498835 \nmin 0.000000 0.000000 \n25% 2.000000 0.000000 \n50% 2.000000 1.000000 \n75% 3.000000 1.000000 \nmax 3.000000 1.000000 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    count303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000
    mean54.3663370.6831680.966997131.623762246.2640260.1485150.528053149.6468650.3267331.0396041.3993400.7293732.3135310.544554
    std9.0821010.4660111.03205217.53814351.8307510.3561980.52586022.9051610.4697941.1610750.6162261.0226060.6122770.498835
    min29.0000000.0000000.00000094.000000126.0000000.0000000.00000071.0000000.0000000.0000000.0000000.0000000.0000000.000000
    25%47.5000000.0000000.000000120.000000211.0000000.0000000.000000133.5000000.0000000.0000001.0000000.0000002.0000000.000000
    50%55.0000001.0000001.000000130.000000240.0000000.0000001.000000153.0000000.0000000.8000001.0000000.0000002.0000001.000000
    75%61.0000001.0000002.000000140.000000274.5000000.0000001.000000166.0000001.0000001.6000002.0000001.0000003.0000001.000000
    max77.0000001.0000003.000000200.000000564.0000001.0000002.000000202.0000001.0000006.2000002.0000004.0000003.0000001.000000
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### checking for null values","metadata":{}},{"cell_type":"code","source":"\nmsno.bar(data)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.267947Z","iopub.execute_input":"2021-08-24T17:50:41.268348Z","iopub.status.idle":"2021-08-24T17:50:42.260698Z","shell.execute_reply.started":"2021-08-24T17:50:41.268305Z","shell.execute_reply":"2021-08-24T17:50:42.259641Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"
    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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='viridis')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:42.262103Z","iopub.execute_input":"2021-08-24T17:50:42.262510Z","iopub.status.idle":"2021-08-24T17:50:42.444523Z","shell.execute_reply.started":"2021-08-24T17:50:42.262472Z","shell.execute_reply":"2021-08-24T17:50:42.443356Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
    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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"There are no missing values present in the dataset","metadata":{}},{"cell_type":"markdown","source":"### Checking correlation","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (15, 8))\n\nsns.heatmap(data.corr(), annot = True, linewidths = 1)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:42.448228Z","iopub.execute_input":"2021-08-24T17:50:42.448692Z","iopub.status.idle":"2021-08-24T17:50:43.571416Z","shell.execute_reply.started":"2021-08-24T17:50:42.448638Z","shell.execute_reply":"2021-08-24T17:50:43.570496Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"
    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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"There are no correlated columns presebt in the data ","metadata":{}},{"cell_type":"markdown","source":"### Analysis of Features","metadata":{}},{"cell_type":"markdown","source":"### Age","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['age'])\nplt.title('Distribution Plot of Ages\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:43.573210Z","iopub.execute_input":"2021-08-24T17:50:43.573693Z","iopub.status.idle":"2021-08-24T17:50:43.962739Z","shell.execute_reply.started":"2021-08-24T17:50:43.573636Z","shell.execute_reply":"2021-08-24T17:50:43.961890Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"
    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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"Age_18_25 = data.age[(data.age >= 18) & (data.age <= 25)]\nAge_26_35 = data.age[(data.age >= 26) & (data.age <= 35)]\nAge_36_45 = data.age[(data.age >= 36) & (data.age <= 45)]\nAge_46_55 = data.age[(data.age >= 46) & (data.age <= 55)]\nAge_56_65 = data.age[(data.age >= 56) & (data.age <= 65)]\nAge_66_75 = data.age[(data.age >= 66) & (data.age <= 75)]\nAge_75above = data.age[data.age >= 76]\nx_Age = [ '18-25','26-35', '36-45', '46-55', '56-65','66-75','75+']\ny_Age = [len(Age_18_25.values), len(Age_26_35.values), len(Age_36_45.values), len(Age_46_55.values), len(Age_56_65.values),\n len(Age_66_75.values), len(Age_75above.values)]\n\npx.bar(data_frame = data, x = x_Age, y = y_Age, color = x_Age, template = 'plotly_dark',\n labels={\n 'x': \"Age\",\n 'y': \"Number\",\n 'color':'Age group'\n \n },\n title = 'Number of patients per Age group')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:43.964009Z","iopub.execute_input":"2021-08-24T17:50:43.964456Z","iopub.status.idle":"2021-08-24T17:50:45.569476Z","shell.execute_reply.started":"2021-08-24T17:50:43.964408Z","shell.execute_reply":"2021-08-24T17:50:45.568343Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/html":" \n "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"We can see the cases are more of age group from 56 to 65","metadata":{}},{"cell_type":"markdown","source":"### Gender","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.sex.value_counts().keys()), y = list(data.sex.value_counts()), \n color = list(data.sex.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"Gender\",\n 'y': \"Number\",\n 'color':'Gender group'\n \n },\n title = 'Number of patients per Gender group')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.571029Z","iopub.execute_input":"2021-08-24T17:50:45.571358Z","iopub.status.idle":"2021-08-24T17:50:45.672438Z","shell.execute_reply.started":"2021-08-24T17:50:45.571324Z","shell.execute_reply":"2021-08-24T17:50:45.671211Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"Assigning labels for one hot encoding","metadata":{}},{"cell_type":"code","source":"# since we don't know 0 is male or female and vice versa we are assigning with the same label \ndata['sex'] = data['sex'].map({0:\"0_gender\", 1: \"1_gender\"}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.676611Z","iopub.execute_input":"2021-08-24T17:50:45.677195Z","iopub.status.idle":"2021-08-24T17:50:45.700136Z","shell.execute_reply.started":"2021-08-24T17:50:45.677143Z","shell.execute_reply":"2021-08-24T17:50:45.698873Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng oldpeak \\\n0 63 1_gender 3 145 233 1 0 150 0 2.3 \n1 37 1_gender 2 130 250 0 1 187 0 3.5 \n2 41 0_gender 1 130 204 0 0 172 0 1.4 \n3 56 1_gender 1 120 236 0 1 178 0 0.8 \n4 57 0_gender 0 120 354 0 1 163 1 0.6 \n\n slp caa thall output \n0 0 0 1 1 \n1 0 0 2 1 \n2 2 0 2 1 \n3 2 0 2 1 \n4 2 0 2 1 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    0631_gender31452331015002.30011
    1371_gender21302500118703.50021
    2410_gender11302040017201.42021
    3561_gender11202360117800.82021
    4570_gender01203540116310.62021
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### cp\nChest Pain type chest pain type","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.cp.value_counts().keys()), y = list(data.cp.value_counts()), \n color = list(data.cp.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"Chest Pain intnsity\",\n 'y': \"Count\",\n 'color':'Chest Pain intnsity'\n \n },\n title = 'Number of patients per Chest Pain intnsity')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.701850Z","iopub.execute_input":"2021-08-24T17:50:45.702224Z","iopub.status.idle":"2021-08-24T17:50:45.788604Z","shell.execute_reply.started":"2021-08-24T17:50:45.702174Z","shell.execute_reply":"2021-08-24T17:50:45.787314Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"\ncp_0_1 = data.cp[(data.cp == 0) & (data.output == 1)]\ncp_0_0 = data.cp[(data.cp == 0) & (data.output == 0)]\ncp_1_1 = data.cp[(data.cp == 1) & (data.output == 1)]\ncp_1_0 = data.cp[(data.cp == 1) & (data.output == 0)]\ncp_2_1 = data.cp[(data.cp == 2) & (data.output == 1)]\ncp_2_0 = data.cp[(data.cp == 2) & (data.output == 0)]\ncp_3_1 = data.cp[(data.cp == 3) & (data.output == 1)]\ncp_3_0 = data.cp[(data.cp == 3) & (data.output == 0)]\n\ny_cp_1 = [len(cp_0_1.values), len(cp_1_1.values), len(cp_2_1.values), len(cp_3_1.values)]\ny_cp_0 = [len(cp_0_0.values), len(cp_1_0.values), len(cp_2_0.values),len(cp_3_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2,3],\n y=y_cp_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2,3],\n y=y_cp_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.790389Z","iopub.execute_input":"2021-08-24T17:50:45.790948Z","iopub.status.idle":"2021-08-24T17:50:45.825684Z","shell.execute_reply.started":"2021-08-24T17:50:45.790891Z","shell.execute_reply":"2021-08-24T17:50:45.824258Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"Though Chest pain is represented as numeric data but it is categorical in nature. We can convert the data to categorical to get dummies. LabelEncoding will not work here as we can see that there is not such relation among the categories that resembles an ordinal relationship.","metadata":{}},{"cell_type":"code","source":"data['cp'] = data['cp'].map({0:\"Intensity_0\", 1: \"Intensity_1\", 2: 'Intensity_2',3:'Intensity_3'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.827498Z","iopub.execute_input":"2021-08-24T17:50:45.827861Z","iopub.status.idle":"2021-08-24T17:50:45.855474Z","shell.execute_reply.started":"2021-08-24T17:50:45.827829Z","shell.execute_reply":"2021-08-24T17:50:45.853873Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 0 150 0 \n1 37 1_gender Intensity_2 130 250 0 1 187 0 \n2 41 0_gender Intensity_1 130 204 0 0 172 0 \n3 56 1_gender Intensity_1 120 236 0 1 178 0 \n4 57 0_gender Intensity_0 120 354 0 1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 0 0 1 1 \n1 3.5 0 0 2 1 \n2 1.4 2 0 2 1 \n3 0.8 2 0 2 1 \n4 0.6 2 0 2 1 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    0631_genderIntensity_31452331015002.30011
    1371_genderIntensity_21302500118703.50021
    2410_genderIntensity_11302040017201.42021
    3561_genderIntensity_11202360117800.82021
    4570_genderIntensity_01203540116310.62021
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### trtbps\nresting blood pressure (in mm Hg)","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['trtbps'])\nplt.title('Distribution Plot of Resting blood pressure (in mm Hg)\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.857331Z","iopub.execute_input":"2021-08-24T17:50:45.857744Z","iopub.status.idle":"2021-08-24T17:50:46.138074Z","shell.execute_reply.started":"2021-08-24T17:50:45.857710Z","shell.execute_reply":"2021-08-24T17:50:46.136810Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"
    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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### chol\ncholestoral in mg/dl fetched via BMI sensor","metadata":{}},{"cell_type":"code","source":"px.box(x = 'trtbps', data_frame = data, template = 'plotly_dark')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.140799Z","iopub.execute_input":"2021-08-24T17:50:46.141265Z","iopub.status.idle":"2021-08-24T17:50:46.253118Z","shell.execute_reply.started":"2021-08-24T17:50:46.141223Z","shell.execute_reply":"2021-08-24T17:50:46.251780Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### chol\ncholestoral in mg/dl fetched via BMI sensor","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['chol'])\nplt.title('Distribution Plot of cholestoral in mg/dl\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.255249Z","iopub.execute_input":"2021-08-24T17:50:46.255722Z","iopub.status.idle":"2021-08-24T17:50:46.518442Z","shell.execute_reply.started":"2021-08-24T17:50:46.255670Z","shell.execute_reply":"2021-08-24T17:50:46.517205Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"px.box(x = 'chol', data_frame = data, template = 'plotly_dark')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.520438Z","iopub.execute_input":"2021-08-24T17:50:46.520820Z","iopub.status.idle":"2021-08-24T17:50:46.598670Z","shell.execute_reply.started":"2021-08-24T17:50:46.520782Z","shell.execute_reply":"2021-08-24T17:50:46.597348Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"lets see trtbps and chol has similar outliers ","metadata":{}},{"cell_type":"code","source":" data.chol[data.trtbps >= 171]","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.600272Z","iopub.execute_input":"2021-08-24T17:50:46.600615Z","iopub.status.idle":"2021-08-24T17:50:46.610604Z","shell.execute_reply.started":"2021-08-24T17:50:46.600559Z","shell.execute_reply":"2021-08-24T17:50:46.609116Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"8 199\n101 270\n110 325\n203 274\n223 288\n241 249\n248 283\n260 228\n266 327\nName: chol, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"the values of trtbps outliers are well in range of cholestrol level","metadata":{}},{"cell_type":"markdown","source":"### fbs\n(fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.fbs.value_counts().keys()), y = list(data.fbs.value_counts()), \n color = list(data.fbs.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"fasting blood sugar > 120 mg/dl\",\n 'y': \"Count\",\n 'color':'fasting blood sugar > 120 mg/dl'\n \n },\n title = 'Number of patients having fasting blood sugar > 120 mg/dl')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.612405Z","iopub.execute_input":"2021-08-24T17:50:46.612784Z","iopub.status.idle":"2021-08-24T17:50:46.703375Z","shell.execute_reply.started":"2021-08-24T17:50:46.612750Z","shell.execute_reply":"2021-08-24T17:50:46.702342Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nfbs_0_1 = data.fbs[(data.fbs == 0) & (data.output == 1)]\nfbs_0_0 = data.fbs[(data.fbs == 0) & (data.output == 0)]\nfbs_1_1 = data.fbs[(data.fbs == 1) & (data.output == 1)]\nfbs_1_0 = data.fbs[(data.fbs == 1) & (data.output == 0)]\n\ny_fbs_1 = [len(fbs_0_1.values), len(fbs_1_1.values)]\ny_fbs_0 = [len(fbs_0_0.values), len(fbs_1_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1],\n y=y_fbs_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1],\n y=y_fbs_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.704849Z","iopub.execute_input":"2021-08-24T17:50:46.705159Z","iopub.status.idle":"2021-08-24T17:50:46.734276Z","shell.execute_reply.started":"2021-08-24T17:50:46.705128Z","shell.execute_reply":"2021-08-24T17:50:46.732958Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"Variation in sugar level is not the sole cause of a heart attack","metadata":{}},{"cell_type":"markdown","source":"### restecg\nresting electrocardiographic results","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.restecg.value_counts().keys()), y = list(data.restecg.value_counts()), \n color = list(data.restecg.value_counts().keys()),\n labels={\n 'x': \"resting electrocardiographic results\",\n 'y': \"Count\",\n 'color':'resting electrocardiographic results'\n \n },\n title = 'Number of patients per resting electrocardiographic results')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.737761Z","iopub.execute_input":"2021-08-24T17:50:46.738162Z","iopub.status.idle":"2021-08-24T17:50:46.843963Z","shell.execute_reply.started":"2021-08-24T17:50:46.738124Z","shell.execute_reply":"2021-08-24T17:50:46.842720Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nrestecg_0_1 = data.restecg[(data.restecg == 0) & (data.output == 1)]\nrestecg_0_0 = data.restecg[(data.restecg == 0) & (data.output == 0)]\nrestecg_1_1 = data.restecg[(data.restecg == 1) & (data.output == 1)]\nrestecg_1_0 = data.restecg[(data.restecg == 1) & (data.output == 0)]\nrestecg_2_1 = data.restecg[(data.restecg == 2) & (data.output == 1)]\nrestecg_2_0 = data.restecg[(data.restecg == 2) & (data.output == 0)]\n\ny_restecg_1 = [len(restecg_0_1.values), len(restecg_1_1.values), len(restecg_2_1.values)]\ny_restecg_0 = [len(restecg_0_0.values), len(restecg_1_0.values), len(restecg_2_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2],\n y=y_restecg_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2],\n y=y_restecg_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.846248Z","iopub.execute_input":"2021-08-24T17:50:46.846644Z","iopub.status.idle":"2021-08-24T17:50:46.876938Z","shell.execute_reply.started":"2021-08-24T17:50:46.846609Z","shell.execute_reply":"2021-08-24T17:50:46.875449Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"raw","source":"We can see that if the result is 1 we have more chances of survival compared to 0 and 2 \nsince we have least data points which has category of '2' it the results for label 2 are ambiguous. Thus it is better we use one hot encoding for the feature","metadata":{}},{"cell_type":"code","source":"data['restecg'] = data['restecg'].map({0:\"restecg_0\", 1: \"restecg_1\", 2: 'restecg_2'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.878693Z","iopub.execute_input":"2021-08-24T17:50:46.879177Z","iopub.status.idle":"2021-08-24T17:50:46.909257Z","shell.execute_reply.started":"2021-08-24T17:50:46.879145Z","shell.execute_reply":"2021-08-24T17:50:46.907704Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 0 0 1 1 \n1 3.5 0 0 2 1 \n2 1.4 2 0 2 1 \n3 0.8 2 0 2 1 \n4 0.6 2 0 2 1 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    0631_genderIntensity_31452331restecg_015002.30011
    1371_genderIntensity_21302500restecg_118703.50021
    2410_genderIntensity_11302040restecg_017201.42021
    3561_genderIntensity_11202360restecg_117800.82021
    4570_genderIntensity_01203540restecg_116310.62021
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### thalachh\nmaximum heart rate achieved","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['thalachh'])\nplt.title('Distribution Plot of maximum heart rate achieved\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.911126Z","iopub.execute_input":"2021-08-24T17:50:46.911798Z","iopub.status.idle":"2021-08-24T17:50:47.181288Z","shell.execute_reply.started":"2021-08-24T17:50:46.911727Z","shell.execute_reply":"2021-08-24T17:50:47.179948Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"thalachh_50_85 = data.thalachh[(data.thalachh >= 50) & (data.thalachh <= 85)]\nthalachh_86_110 = data.thalachh[(data.thalachh >= 86) & (data.thalachh <= 110)]\nthalachh_111_135 = data.thalachh[(data.thalachh >= 111) & (data.thalachh <= 135)]\nthalachh_136_160 = data.thalachh[(data.thalachh >= 136) & (data.thalachh <= 160)]\nthalachh_161_185 = data.thalachh[(data.thalachh >= 161) & (data.thalachh <= 185)]\nthalachh_185above = data.thalachh[data.thalachh >= 186]\nx_thalachh = [ '50-85','86-110', '111-135', '136-160', '161-185','185+']\ny_thalachh = [len(thalachh_50_85.values), len(thalachh_86_110.values), len(thalachh_111_135.values), len(thalachh_136_160.values)\n , len(thalachh_161_185.values), len(thalachh_185above.values)]\n\npx.bar(data_frame = data, x = x_thalachh, y = y_thalachh, color = x_thalachh, template = 'plotly_dark',\n labels={\n 'x': \"maximum heart rate achieved\",\n 'y': \"Count\",\n 'color':'maximum heart rate achieved'\n \n })","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:47.183126Z","iopub.execute_input":"2021-08-24T17:50:47.183871Z","iopub.status.idle":"2021-08-24T17:50:47.301138Z","shell.execute_reply.started":"2021-08-24T17:50:47.183814Z","shell.execute_reply":"2021-08-24T17:50:47.300088Z"},"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"px.bar(data_frame = data, x = 'age', y = 'thalachh', color = 'age', template = 'plotly_dark',\n labels={\n 'x': \"Age\",\n 'y': \"maximum heart beat\",\n 'color':'Age'},\n title = 'Age to maximum heart beat(sum)')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:47.302751Z","iopub.execute_input":"2021-08-24T17:50:47.303354Z","iopub.status.idle":"2021-08-24T17:50:47.381054Z","shell.execute_reply.started":"2021-08-24T17:50:47.303308Z","shell.execute_reply":"2021-08-24T17:50:47.380069Z"},"trusted":true},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"px.box(x = 'thalachh', data_frame = data, template = 'plotly_dark')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:47.382459Z","iopub.execute_input":"2021-08-24T17:50:47.383049Z","iopub.status.idle":"2021-08-24T17:50:47.454247Z","shell.execute_reply.started":"2021-08-24T17:50:47.383006Z","shell.execute_reply":"2021-08-24T17:50:47.453044Z"},"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### exng\nexercise induced angina (1 = yes; 0 = no)","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.exng.value_counts().keys()), y = list(data.exng.value_counts()), \n color = list(data.exng.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"exercise induced angina\",\n 'y': \"Count\",\n 'color':'exercise induced angina'\n \n },\n title = 'Number of patients having exercise induced angina')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:59.714687Z","iopub.execute_input":"2021-08-24T17:50:59.715274Z","iopub.status.idle":"2021-08-24T17:50:59.797785Z","shell.execute_reply.started":"2021-08-24T17:50:59.715236Z","shell.execute_reply":"2021-08-24T17:50:59.796554Z"},"trusted":true},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nexng_0_1 = data.exng[(data.exng == 0) & (data.output == 1)]\nexng_0_0 = data.exng[(data.exng == 0) & (data.output == 0)]\nexng_1_1 = data.exng[(data.exng == 1) & (data.output == 1)]\nexng_1_0 = data.exng[(data.exng == 1) & (data.output == 0)]\n\ny_exng_1 = [len(exng_0_1.values), len(exng_1_1.values)]\ny_exng_0 = [len(exng_0_0.values), len(exng_1_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1],\n y=y_exng_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1],\n y=y_exng_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:00.387656Z","iopub.execute_input":"2021-08-24T17:51:00.388234Z","iopub.status.idle":"2021-08-24T17:51:00.412075Z","shell.execute_reply.started":"2021-08-24T17:51:00.388183Z","shell.execute_reply":"2021-08-24T17:51:00.411098Z"},"trusted":true},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"We can see that not getting exercise induced angina may have a greater chance of heart attack","metadata":{}},{"cell_type":"markdown","source":"### oldpeak\nPrevious peak","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['oldpeak'])\nplt.title('Distribution Plot of Previous peak achieved\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:02.145990Z","iopub.execute_input":"2021-08-24T17:51:02.146632Z","iopub.status.idle":"2021-08-24T17:51:02.366983Z","shell.execute_reply.started":"2021-08-24T17:51:02.146546Z","shell.execute_reply":"2021-08-24T17:51:02.365646Z"},"trusted":true},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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= 'oldpeak', data_frame = data, template = 'plotly_dark')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:02.482004Z","iopub.execute_input":"2021-08-24T17:51:02.482607Z","iopub.status.idle":"2021-08-24T17:51:02.555813Z","shell.execute_reply.started":"2021-08-24T17:51:02.482541Z","shell.execute_reply":"2021-08-24T17:51:02.554713Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### slp","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.slp.value_counts().keys()), y = list(data.slp.value_counts()), \n color = list(data.slp.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"slp\",\n 'y': \"Count\",\n 'color':'slp'\n \n },\n title = 'slp plot')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:03.090528Z","iopub.execute_input":"2021-08-24T17:51:03.091171Z","iopub.status.idle":"2021-08-24T17:51:03.176137Z","shell.execute_reply.started":"2021-08-24T17:51:03.091125Z","shell.execute_reply":"2021-08-24T17:51:03.174996Z"},"trusted":true},"execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nslp_0_1 = data.slp[(data.slp == 0) & (data.output == 1)]\nslp_0_0 = data.slp[(data.slp == 0) & (data.output == 0)]\nslp_1_1 = data.slp[(data.slp == 1) & (data.output == 1)]\nslp_1_0 = data.slp[(data.slp == 1) & (data.output == 0)]\nslp_2_1 = data.slp[(data.slp == 2) & (data.output == 1)]\nslp_2_0 = data.slp[(data.slp == 2) & (data.output == 0)]\n\ny_slp_1 = [len(slp_0_1.values), len(slp_1_1.values), len(slp_2_1.values)]\ny_slp_0 = [len(slp_0_0.values), len(slp_1_0.values), len(slp_2_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2],\n y=y_slp_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2],\n y=y_slp_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:03.565664Z","iopub.execute_input":"2021-08-24T17:51:03.566237Z","iopub.status.idle":"2021-08-24T17:51:03.594014Z","shell.execute_reply.started":"2021-08-24T17:51:03.566201Z","shell.execute_reply":"2021-08-24T17:51:03.591703Z"},"trusted":true},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"data['slp'] = data['slp'].map({0:\"slp_0\", 1: \"slp_1\", 2: 'slp_2'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:04.165074Z","iopub.execute_input":"2021-08-24T17:51:04.165445Z","iopub.status.idle":"2021-08-24T17:51:04.187353Z","shell.execute_reply.started":"2021-08-24T17:51:04.165413Z","shell.execute_reply":"2021-08-24T17:51:04.186565Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 0 1 1 \n1 3.5 slp_0 0 2 1 \n2 1.4 slp_2 0 2 1 \n3 0.8 slp_2 0 2 1 \n4 0.6 slp_2 0 2 1 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    0631_genderIntensity_31452331restecg_015002.3slp_0011
    1371_genderIntensity_21302500restecg_118703.5slp_0021
    2410_genderIntensity_11302040restecg_017201.4slp_2021
    3561_genderIntensity_11202360restecg_117800.8slp_2021
    4570_genderIntensity_01203540restecg_116310.6slp_2021
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### caa","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.caa.value_counts().keys()), y = list(data.caa.value_counts()), \n color = list(data.caa.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"caa\",\n 'y': \"Count\",\n 'color':'caa'\n \n },\n title = 'caa plot')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:05.260930Z","iopub.execute_input":"2021-08-24T17:51:05.261477Z","iopub.status.idle":"2021-08-24T17:51:05.336171Z","shell.execute_reply.started":"2021-08-24T17:51:05.261416Z","shell.execute_reply":"2021-08-24T17:51:05.335209Z"},"trusted":true},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\ncaa_0_1 = data.caa[(data.caa == 0) & (data.output == 1)]\ncaa_0_0 = data.caa[(data.caa == 0) & (data.output == 0)]\ncaa_1_1 = data.caa[(data.caa == 1) & (data.output == 1)]\ncaa_1_0 = data.caa[(data.caa == 1) & (data.output == 0)]\ncaa_2_1 = data.caa[(data.caa == 2) & (data.output == 1)]\ncaa_2_0 = data.caa[(data.caa == 2) & (data.output == 0)]\n\ncaa_3_1 = data.caa[(data.caa == 3) & (data.output == 1)]\ncaa_3_0 = data.caa[(data.caa == 3) & (data.output == 0)]\n\ncaa_4_1 = data.caa[(data.caa == 4) & (data.output == 1)]\ncaa_4_0 = data.caa[(data.caa == 4) & (data.output == 0)]\n\ny_caa_1 = [len(caa_0_1.values), len(caa_1_1.values), len(caa_2_1.values), len(caa_3_1.values), len(caa_4_1.values)]\ny_caa_0 = [len(caa_0_0.values), len(caa_1_0.values), len(caa_2_0.values), len(caa_3_0.values), len(caa_4_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2,3,4],\n y=y_caa_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2,3,4],\n y=y_caa_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:05.785608Z","iopub.execute_input":"2021-08-24T17:51:05.786009Z","iopub.status.idle":"2021-08-24T17:51:05.822881Z","shell.execute_reply.started":"2021-08-24T17:51:05.785972Z","shell.execute_reply":"2021-08-24T17:51:05.821271Z"},"trusted":true},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"data['caa'] = data['caa'].map({0:\"caa_0\", 1: \"caa_1\", 2: 'caa_2', 3: 'caa_3', 4: 'caa_4'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:06.350241Z","iopub.execute_input":"2021-08-24T17:51:06.350697Z","iopub.status.idle":"2021-08-24T17:51:06.374248Z","shell.execute_reply.started":"2021-08-24T17:51:06.350657Z","shell.execute_reply":"2021-08-24T17:51:06.372939Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 1 1 \n1 3.5 slp_0 caa_0 2 1 \n2 1.4 slp_2 caa_0 2 1 \n3 0.8 slp_2 caa_0 2 1 \n4 0.6 slp_2 caa_0 2 1 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    0631_genderIntensity_31452331restecg_015002.3slp_0caa_011
    1371_genderIntensity_21302500restecg_118703.5slp_0caa_021
    2410_genderIntensity_11302040restecg_017201.4slp_2caa_021
    3561_genderIntensity_11202360restecg_117800.8slp_2caa_021
    4570_genderIntensity_01203540restecg_116310.6slp_2caa_021
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### thall","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.thall.value_counts().keys()), y = list(data.thall.value_counts()), \n color = list(data.thall.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"thall\",\n 'y': \"Count\",\n 'color':'thall'\n \n },\n title = 'caa plot',barmode='group')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:07.226753Z","iopub.execute_input":"2021-08-24T17:51:07.227144Z","iopub.status.idle":"2021-08-24T17:51:07.308313Z","shell.execute_reply.started":"2021-08-24T17:51:07.227112Z","shell.execute_reply":"2021-08-24T17:51:07.307176Z"},"trusted":true},"execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nthall_0_1 = data.thall[(data.thall == 0) & (data.output == 1)]\nthall_0_0 = data.thall[(data.thall == 0) & (data.output == 0)]\nthall_1_1 = data.thall[(data.thall == 1) & (data.output == 1)]\nthall_1_0 = data.thall[(data.thall == 1) & (data.output == 0)]\nthall_2_1 = data.thall[(data.thall == 2) & (data.output == 1)]\nthall_2_0 = data.thall[(data.thall == 2) & (data.output == 0)]\nthall_3_1 = data.thall[(data.thall == 3) & (data.output == 1)]\nthall_3_0 = data.thall[(data.thall == 3) & (data.output == 0)]\n\ny_thall_1 = [len(thall_0_1.values), len(thall_1_1.values), len(thall_2_1.values), len(thall_3_1.values)]\ny_thall_0 = [len(thall_0_0.values), len(thall_1_0.values), len(thall_2_0.values), len(thall_3_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2,3],\n y=y_thall_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2,3],\n y=y_thall_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:07.832719Z","iopub.execute_input":"2021-08-24T17:51:07.833127Z","iopub.status.idle":"2021-08-24T17:51:07.864557Z","shell.execute_reply.started":"2021-08-24T17:51:07.833095Z","shell.execute_reply":"2021-08-24T17:51:07.863067Z"},"trusted":true},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/html":"
    "},"metadata":{}}]},{"cell_type":"code","source":"data['thall'] = data['thall'].map({0:\"thall_0\", 1: \"thall_1\", 2: 'thall_2', 3: 'thall_3'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:14.268121Z","iopub.execute_input":"2021-08-24T17:51:14.268562Z","iopub.status.idle":"2021-08-24T17:51:14.291975Z","shell.execute_reply.started":"2021-08-24T17:51:14.268526Z","shell.execute_reply":"2021-08-24T17:51:14.291132Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 thall_1 1 \n1 3.5 slp_0 caa_0 thall_2 1 \n2 1.4 slp_2 caa_0 thall_2 1 \n3 0.8 slp_2 caa_0 thall_2 1 \n4 0.6 slp_2 caa_0 thall_2 1 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    0631_genderIntensity_31452331restecg_015002.3slp_0caa_0thall_11
    1371_genderIntensity_21302500restecg_118703.5slp_0caa_0thall_21
    2410_genderIntensity_11302040restecg_017201.4slp_2caa_0thall_21
    3561_genderIntensity_11202360restecg_117800.8slp_2caa_0thall_21
    4570_genderIntensity_01203540restecg_116310.6slp_2caa_0thall_21
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### Feature engineering","metadata":{}},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:15.404868Z","iopub.execute_input":"2021-08-24T17:51:15.405450Z","iopub.status.idle":"2021-08-24T17:51:15.425204Z","shell.execute_reply.started":"2021-08-24T17:51:15.405413Z","shell.execute_reply":"2021-08-24T17:51:15.424037Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 thall_1 1 \n1 3.5 slp_0 caa_0 thall_2 1 \n2 1.4 slp_2 caa_0 thall_2 1 \n3 0.8 slp_2 caa_0 thall_2 1 \n4 0.6 slp_2 caa_0 thall_2 1 ","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
    0631_genderIntensity_31452331restecg_015002.3slp_0caa_0thall_11
    1371_genderIntensity_21302500restecg_118703.5slp_0caa_0thall_21
    2410_genderIntensity_11302040restecg_017201.4slp_2caa_0thall_21
    3561_genderIntensity_11202360restecg_117800.8slp_2caa_0thall_21
    4570_genderIntensity_01203540restecg_116310.6slp_2caa_0thall_21
    \n
    "},"metadata":{}}]},{"cell_type":"markdown","source":"### One hot encoding","metadata":{}},{"cell_type":"code","source":"data=pd.get_dummies(data)\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:43.662879Z","iopub.execute_input":"2021-08-24T17:51:43.663284Z","iopub.status.idle":"2021-08-24T17:51:43.703889Z","shell.execute_reply.started":"2021-08-24T17:51:43.663250Z","shell.execute_reply":"2021-08-24T17:51:43.702491Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":" age trtbps chol fbs thalachh exng oldpeak output sex_0_gender \\\n0 63 145 233 1 150 0 2.3 1 0 \n1 37 130 250 0 187 0 3.5 1 0 \n2 41 130 204 0 172 0 1.4 1 1 \n3 56 120 236 0 178 0 0.8 1 0 \n4 57 120 354 0 163 1 0.6 1 1 \n\n sex_1_gender ... slp_slp_2 caa_caa_0 caa_caa_1 caa_caa_2 caa_caa_3 \\\n0 1 ... 0 1 0 0 0 \n1 1 ... 0 1 0 0 0 \n2 0 ... 1 1 0 0 0 \n3 1 ... 1 1 0 0 0 \n4 0 ... 1 1 0 0 0 \n\n caa_caa_4 thall_thall_0 thall_thall_1 thall_thall_2 thall_thall_3 \n0 0 0 1 0 0 \n1 0 0 0 1 0 \n2 0 0 0 1 0 \n3 0 0 0 1 0 \n4 0 0 0 1 0 \n\n[5 rows x 29 columns]","text/html":"
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    agetrtbpscholfbsthalachhexngoldpeakoutputsex_0_gendersex_1_gender...slp_slp_2caa_caa_0caa_caa_1caa_caa_2caa_caa_3caa_caa_4thall_thall_0thall_thall_1thall_thall_2thall_thall_3
    063145233115002.3101...0100000100
    137130250018703.5101...0100000010
    241130204017201.4110...1100000010
    356120236017800.8101...1100000010
    457120354016310.6110...1100000010
    \n

    5 rows × 29 columns

    \n
    "},"metadata":{}}]},{"cell_type":"code","source":"X= data.drop(['output'],axis=1)\nY= data[\"output\"]","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:44.409492Z","iopub.execute_input":"2021-08-24T17:51:44.409935Z","iopub.status.idle":"2021-08-24T17:51:44.418220Z","shell.execute_reply.started":"2021-08-24T17:51:44.409899Z","shell.execute_reply":"2021-08-24T17:51:44.416782Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n# split the data to train and test set\nx_train,x_test,y_train,y_test = train_test_split(X,Y,train_size=0.85,random_state=42)\n\n\nprint(\"training data shape:- {} labels {} \".format(x_train.shape[0],x_train.shape[1]))\nprint(\"testing data shape:- {} labels {} \".format(x_test.shape[0],x_test.shape[1]))","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:46.397641Z","iopub.execute_input":"2021-08-24T17:51:46.398020Z","iopub.status.idle":"2021-08-24T17:51:46.571787Z","shell.execute_reply.started":"2021-08-24T17:51:46.397986Z","shell.execute_reply":"2021-08-24T17:51:46.571031Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"training data shape:- 257 labels 28 \ntesting data shape:- 46 labels 28 \n","output_type":"stream"}]},{"cell_type":"code","source":"from xgboost import XGBClassifier\nfrom sklearn.metrics import r2_score\n\nxgb = XGBClassifier(colsample_bylevel= 0.9,\n colsample_bytree = 0.8, \n gamma=0.99,\n max_depth= 5,\n min_child_weight= 1,\n n_estimators= 8,\n nthread= 5,\n random_state= 0,\n )\nxgb.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:46.758774Z","iopub.execute_input":"2021-08-24T17:51:46.759413Z","iopub.status.idle":"2021-08-24T17:51:46.898773Z","shell.execute_reply.started":"2021-08-24T17:51:46.759379Z","shell.execute_reply":"2021-08-24T17:51:46.897333Z"},"trusted":true},"execution_count":48,"outputs":[{"name":"stdout","text":"[17:51:46] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n","output_type":"stream"},{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.9,\n colsample_bynode=1, colsample_bytree=0.8, gamma=0.99, gpu_id=-1,\n importance_type='gain', interaction_constraints='',\n learning_rate=0.300000012, max_delta_step=0, max_depth=5,\n min_child_weight=1, missing=nan, monotone_constraints='()',\n n_estimators=8, n_jobs=5, nthread=5, num_parallel_tree=1,\n random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n subsample=1, tree_method='exact', validate_parameters=1,\n verbosity=None)"},"metadata":{}}]},{"cell_type":"code","source":"print('Accuracy of XGBoost classifier on training set: {:.2f}'\n .format(xgb.score(x_train, y_train)))\nprint('Accuracy of XGBoost classifier on test set: {:.2f}'\n .format(xgb.score(x_test, y_test)))","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:47.196831Z","iopub.execute_input":"2021-08-24T17:51:47.197210Z","iopub.status.idle":"2021-08-24T17:51:47.210886Z","shell.execute_reply.started":"2021-08-24T17:51:47.197179Z","shell.execute_reply":"2021-08-24T17:51:47.209783Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stdout","text":"Accuracy of XGBoost classifier on training set: 0.95\nAccuracy of XGBoost classifier on test set: 0.80\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn import metrics\n\ny_pred=xgb.predict(x_test)\nprint(\"Accuracy of XG Boost model is:\",\nmetrics.accuracy_score(y_test, y_pred)*100)","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:47.731717Z","iopub.execute_input":"2021-08-24T17:51:47.732172Z","iopub.status.idle":"2021-08-24T17:51:47.743918Z","shell.execute_reply.started":"2021-08-24T17:51:47.732130Z","shell.execute_reply":"2021-08-24T17:51:47.742668Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"Accuracy of XG Boost model is: 80.43478260869566\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\n\nconf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred)\nplt.figure(figsize = (15, 8))\nsns.set(font_scale=1.4) # for label size\nsns.heatmap(conf_matrix, annot=True, annot_kws={\"size\": 16},cbar=False, linewidths = 1) # font size\nplt.title(\"Test Confusion Matrix\")\nplt.xlabel(\"Predicted class\")\nplt.ylabel(\"Actual class\")\nplt.savefig('conf_test.png')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:48.291984Z","iopub.execute_input":"2021-08-24T17:51:48.292444Z","iopub.status.idle":"2021-08-24T17:51:48.527485Z","shell.execute_reply.started":"2021-08-24T17:51:48.292408Z","shell.execute_reply":"2021-08-24T17:51:48.526197Z"},"trusted":true},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":"
    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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\ny_pred_t=xgb.predict(x_train)\nconf_matrix = confusion_matrix(y_true=y_train, y_pred=y_pred_t)\nplt.figure(figsize = (15, 8))\nsns.set(font_scale=1.4) # for label size\nsns.heatmap(conf_matrix, annot=True, annot_kws={\"size\": 16},cbar=False, linewidths = 1) # font size\nplt.title(\"Train Confusion Matrix\")\nplt.xlabel(\"Predicted class\")\nplt.ylabel(\"Actual class\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:48.818203Z","iopub.execute_input":"2021-08-24T17:51:48.818597Z","iopub.status.idle":"2021-08-24T17:51:49.011291Z","shell.execute_reply.started":"2021-08-24T17:51:48.818549Z","shell.execute_reply":"2021-08-24T17:51:49.009691Z"},"trusted":true},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":"
    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\n"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import precision_score, recall_score, f1_score\nprint(\"For testing data\")\nprint('Precision: %.3f' % precision_score(y_test, y_pred,average='micro'))\nprint('Recall: %.3f' % recall_score(y_test, y_pred,average='micro'))\nprint('F1 Score: %.3f' % f1_score(y_test, y_pred,average='micro'))\n\nprint()\n\nprint(\"For training data\")\ny_pred_t=xgb.predict(x_train)\nprint('Precision: %.3f' % precision_score(y_train, y_pred_t,average='micro'))\nprint('Recall: %.3f' % recall_score(y_train, y_pred_t,average='micro'))\nprint('F1 Score: %.3f' % f1_score(y_train, y_pred_t,average='micro'))\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:49.480900Z","iopub.execute_input":"2021-08-24T17:51:49.481316Z","iopub.status.idle":"2021-08-24T17:51:49.510782Z","shell.execute_reply.started":"2021-08-24T17:51:49.481282Z","shell.execute_reply":"2021-08-24T17:51:49.509418Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"For testing data\nPrecision: 0.804\nRecall: 0.804\nF1 Score: 0.804\n\nFor training data\nPrecision: 0.949\nRecall: 0.949\nF1 Score: 0.949\n","output_type":"stream"}]},{"cell_type":"markdown","source":"For fine tuning our main aim should be to reduce true negative","metadata":{}}]} \ No newline at end of file diff --git a/Scripts/Miscellaneous/heart attack analysis/heart.csv b/Scripts/Miscellaneous/heart attack analysis/heart.csv new file mode 100644 index 000000000..0966e67b5 --- /dev/null +++ b/Scripts/Miscellaneous/heart attack analysis/heart.csv @@ -0,0 +1,304 @@ +age,sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall,output +63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 +37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 +41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 +56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 +57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 +57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 +56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 +44,1,1,120,263,0,1,173,0,0,2,0,3,1 +52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 +57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 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