heart-disease-prediction.ipynb

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    "# ***Heart Disease Prediction Using Machine Learning***\n",
    "\n",
    "***Using Python's Data Science and Machine Learning Libraries, building a machine learning model capable to classify whether or not a person is having a heart disease based upon his/her medical attributes.***\n",
    "\n",
    "***Our approach will be :*** \n",
    "  * ***1. Defining the problem***\n",
    "  * ***2. Data***\n",
    "  * ***3. Evaluation***\n",
    "  * ***4. Features***\n",
    "  * ***5. Modelling***\n",
    "  * ***6. Experimentation***"
   ]
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    "## ***What is classification?***\n",
    "\n",
    "Classification involves deciding whether a sample is part of one class or another (**single-class classification**). If there are multiple class options, it's referred to as **multi-class classification**.\n",
    "\n",
    "\n",
    "## ***What we'll end up with***\n",
    "\n",
    "Since we already have a dataset, we'll approach the problem with the following machine learning modelling framework.\n",
    "\n",
    "| <img src=\"./images/ml101-6-step-ml-framework.png\" width=500/> | \n",
    "|:--:| \n",
    "| 6 Step Machine Learning Modelling Framework |\n",
    "\n",
    "***More specifically, we'll look at the following topics.***\n",
    "\n",
    "* **Exploratory data analysis (EDA)** - the process of going through a dataset and finding out more about it.\n",
    "* **Model training** - create model(s) to learn to predict a target variable based on other variables.\n",
    "* **Model evaluation** - evaluating a models predictions using problem-specific evaluation metrics. \n",
    "* **Model comparison** - comparing several different models to find the best one.\n",
    "* **Model fine-tuning** - once we've found a good model, how can we improve it?\n",
    "* **Feature importance** - since we're predicting the presence of heart disease, are there some things which are more important for prediction?\n",
    "* **Cross-validation** - if we do build a good model, can we be sure it will work on unseen data?\n",
    "* **Reporting what we've found** - if we had to present our work, what would we show someone?\n",
    "\n",
    "To work through these topics, we'll use pandas, Matplotlib and NumPy for data anaylsis, as well as, Scikit-Learn for machine learning and modelling tasks.\n",
    "\n",
    "| <img src=\"./images/numpy-6-step-ml-framework-tools-numpy-highlight.png\" width=500/> | \n",
    "|:--:| \n",
    "| Tools which can be used for each step of the machine learning modelling process. |\n",
    "\n",
    "We'll work through each step and by the end of the notebook, we'll have a handful of models, all which can predict whether or not a person has heart disease based on a number of different parameters at a considerable accuracy. \n",
    "\n",
    "We'll also be able to describe which parameters are more indicative than others, for example, sex may be more important than age."
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    "### ***1. Defining the problem 🤔***\n",
    "\n",
    "***Problem Statement : Using given medical attributes about a patient, predicting whether or not a person have heart disease.***"
   ]
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    "### ***2. Data***\n",
    "\n",
    "***Originally the data is take from Clevland Data from the UCI Machine Learning Repository.***\n",
    "***https://archive.ics.uci.edu/ml/datasets/Heart+Disease***\n",
    "\n",
    "***It is also available on Kaggle.***\n",
    "***https://www.kaggle.com/ronitf/heart-disease-uci***\n"
   ]
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    "### ***3. Evaluation***\n",
    "\n",
    "***We'll carry out this project only if 95% accuracy is achievable in classifying whether or not a person has heart disease.***"
   ]
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    "### ***4. Features***\n",
    "\n",
    "***Data Atrributes (Information about each section of data)***\n",
    "\n",
    "***1. age - age in years***\n",
    "\n",
    "***2. sex - (1 = male; 0 = female)***\n",
    "\n",
    "***3. cp - chest pain type***\n",
    "  * ***0: Typical angina: chest pain related decrease blood supply to the heart***\n",
    "  * ***1: Atypical angina: chest pain not related to heart***\n",
    "  * ***2: Non-anginal pain: typically esophageal spasms (non heart related)***\n",
    "  * ***3: Asymptomatic: chest pain not showing signs of disease***\n",
    "    \n",
    "***4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)***\n",
    "   * ***anything above 130-140 is typically cause for concern***\n",
    "    \n",
    "***5. chol - serum cholestoral in mg/dl*** \n",
    "   * ***serum = LDL + HDL + .2 * triglycerides***\n",
    "   * ***above 200 is cause for concern***\n",
    "    \n",
    "***6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)*** \n",
    "   * ***'>126' mg/dL signals diabetes***\n",
    "    \n",
    "***7. restecg - resting electrocardiographic results***\n",
    "   * ***0: Nothing to note***\n",
    "   * ***1: ST-T Wave abnormality***\n",
    "     * ***can range from mild symptoms to severe problems***\n",
    "     * ***signals non-normal heart beat***\n",
    "   * ***2: Possible or definite left ventricular hypertrophy***\n",
    "     * ***Enlarged heart's main pumping chamber***\n",
    "        \n",
    "***8. thalach - maximum heart rate achieved*** \n",
    "\n",
    "***9. exang - exercise induced angina (1 = yes; 0 = no)*** \n",
    "\n",
    "***10. oldpeak - ST depression induced by exercise relative to rest*** \n",
    "  * ***looks at stress of heart during excercise***\n",
    "  * ***unhealthy heart will stress more***\n",
    "    \n",
    "***11. slope - the slope of the peak exercise ST segment***\n",
    "   * ***0: Upsloping: better heart rate with excercise (uncommon)***\n",
    "   * ***1: Flatsloping: minimal change (typical healthy heart)***\n",
    "   * ***2: Downslopins: signs of unhealthy heart***\n",
    "    \n",
    "***12. ca - number of major vessels (0-3) colored by flourosopy***\n",
    "   * ***colored vessel means the doctor can see the blood passing through***\n",
    "   * ***the more blood movement the better (no clots)***\n",
    "    \n",
    "***13. thal - thalium stress result***\n",
    "   * ***1,3: normal***\n",
    "   * ***6: fixed defect: used to be defect but ok now***\n",
    "   * ***7: reversable defect: no proper blood movement when excercising***\n",
    "    \n",
    "***14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)***\n",
    "\n",
    "### ***Getting the tools ready😎*** \n",
    "\n",
    "***We'll be working with Pandas, Matplotlib and NumPy for data analysis and manipulation.*** "
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    "### ***Preparing the tools***\n",
    "\n",
    "***At the start of any project, it's custom to see the required libraries imported in a big chunk like you can see below.***\n",
    "\n",
    "***However, in practice, your projects may import libraries as you go. After you've spent a couple of hours working on your problem, you'll probably want to do some tidying up. This is where you may want to consolidate every library you've used at the top of your notebook (like the cell below).***\n",
    "\n",
    "***The libraries you use will differ from project to project. But there are a few which will you'll likely take advantage of during almost every structured data project.*** \n",
    "\n",
    "* ***[pandas](https://pandas.pydata.org/) for data analysis.***\n",
    "* ***[NumPy](https://numpy.org/) for numerical operations.***\n",
    "* ***[Matplotlib](https://matplotlib.org/)/[seaborn](https://seaborn.pydata.org/) for plotting or data visualization.***\n",
    "* ***[Scikit-Learn](https://scikit-learn.org/stable/) for machine learning modelling and evaluation.***"
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       " color: #a6e22e;\n",
       " background-color: transparent;\n",
       " border-color: transparent;\n",
       " border-bottom: 2px solid transparent;\n",
       "}\n",
       ".nav > li.disabled > a,\n",
       ".nav > li.disabled > a:hover {\n",
       " color: #75715e;\n",
       "}\n",
       ".nav-tabs > li > a:before {\n",
       " content: \"\";\n",
       " position: absolute;\n",
       " width: 100%;\n",
       " height: 2px;\n",
       " bottom: -2px;\n",
       " left: 0;\n",
       " background-color: #a6e22e;\n",
       " visibility: hidden;\n",
       " -webkit-transform: perspective(0)scaleX(0);\n",
       " transform: perspective(0)scaleX(0);\n",
       " -webkit-transition: ease 220ms;\n",
       " transition: ease 220ms;\n",
       " -webkit-font-smoothing: antialiased !important;\n",
       "}\n",
       ".nav-tabs > li > a:hover:before {\n",
       " visibility: visible;\n",
       " -webkit-transform: perspective(1)scaleX(1);\n",
       " transform: perspective(1)scaleX(1);\n",
       "}\n",
       ".nav-tabs > li.active > a:before {\n",
       " content: \"\";\n",
       " position: absolute;\n",
       " width: 100%;\n",
       " height: 2px;\n",
       " bottom: -2px;\n",
       " left: 0;\n",
       " background-color: #a6e22e;\n",
       " visibility: visible;\n",
       " -webkit-transform: perspective(1)scaleX(1);\n",
       " transform: perspective(1)scaleX(1);\n",
       " -webkit-font-smoothing: subpixel-antialiased !important;\n",
       "}\n",
       "div#notebook {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " padding-top: 4px;\n",
       "}\n",
       ".notebook_app {\n",
       " background-color: #1e1e1e;\n",
       "}\n",
       "#notebook-container {\n",
       " padding: 13px 2px;\n",
       " background-color: #1e1e1e;\n",
       " min-height: 0px;\n",
       " box-shadow: none;\n",
       " width: 980px;\n",
       " margin-right: auto;\n",
       " margin-left: auto;\n",
       "}\n",
       "div#ipython-main-app.container {\n",
       " width: 980px;\n",
       " margin-right: auto;\n",
       " margin-left: auto;\n",
       " margin-right: auto;\n",
       " margin-left: auto;\n",
       "}\n",
       ".container {\n",
       " width: 980px;\n",
       " margin-right: auto;\n",
       " margin-left: auto;\n",
       "}\n",
       "div#menubar-container {\n",
       " width: 100%;\n",
       " width: 980px;\n",
       "}\n",
       "div#header-container {\n",
       " width: 980px;\n",
       "}\n",
       ".notebook_app #header,\n",
       ".edit_app #header {\n",
       " box-shadow: none !important;\n",
       " background-color: #1e1e1e;\n",
       " border-bottom: 2px solid rgba(93,92,82,.25);\n",
       "}\n",
       "#header,\n",
       ".edit_app #header {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " box-shadow: none;\n",
       " background-color: #1e1e1e;\n",
       "}\n",
       "#header .header-bar,\n",
       ".edit_app #header .header-bar {\n",
       " background: #1e1e1e;\n",
       " background-color: #1e1e1e;\n",
       "}\n",
       "body > #header .header-bar {\n",
       " width: 100%;\n",
       " background: #1e1e1e;\n",
       "}\n",
       "span.checkpoint_status,\n",
       "span.autosave_status {\n",
       " font-size: small;\n",
       " display: none;\n",
       "}\n",
       "#menubar,\n",
       "div#menubar {\n",
       " background-color: #1e1e1e;\n",
       " padding-top: 0px !important;\n",
       "}\n",
       "#menubar .navbar,\n",
       ".navbar-default {\n",
       " background-color: #1e1e1e;\n",
       " margin-bottom: 0px;\n",
       " margin-top: 0px;\n",
       "}\n",
       ".navbar {\n",
       " border: none;\n",
       "}\n",
       "div.navbar-text,\n",
       ".navbar-text,\n",
       ".navbar-text.indicator_area,\n",
       "p.navbar-text.indicator_area {\n",
       " margin-top: 8px !important;\n",
       " margin-bottom: 0px;\n",
       " color: #a6e22e;\n",
       "}\n",
       ".navbar-default {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " background-color: #1e1e1e;\n",
       " border-color: rgba(93,92,82,.25);\n",
       " line-height: 1.5em;\n",
       " padding-bottom: 0px;\n",
       "}\n",
       ".navbar-default .navbar-nav > li > a {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " color: #f8f8f0;\n",
       " display: block;\n",
       " line-height: 1.5em;\n",
       " padding-top: 14px;\n",
       " padding-bottom: 11px;\n",
       "}\n",
       ".navbar-default .navbar-nav > li > a:hover,\n",
       ".navbar-default .navbar-nav > li > a:focus {\n",
       " color: #f8f8f0 !important;\n",
       " background-color: rgba(93,92,82,.25) !important;\n",
       " border-color: rgba(93,92,82,.25) !important;\n",
       " line-height: 1.5em;\n",
       " transition: 80ms ease;\n",
       "}\n",
       ".navbar-default .navbar-nav > .open > a,\n",
       ".navbar-default .navbar-nav > .open > a:hover,\n",
       ".navbar-default .navbar-nav > .open > a:focus {\n",
       " color: #f8f8f2;\n",
       " background-color: #383838;\n",
       " border-color: #383838;\n",
       " line-height: 1.5em;\n",
       "}\n",
       ".navbar-nav > li > .dropdown-menu {\n",
       " margin-top: 0px;\n",
       "}\n",
       ".navbar-nav {\n",
       " margin: 0;\n",
       "}\n",
       "div.notification_widget.info,\n",
       ".notification_widget.info,\n",
       ".notification_widget:active:hover,\n",
       ".notification_widget.active:hover,\n",
       ".open > .dropdown-toggle.notification_widget:hover,\n",
       ".notification_widget:active:focus,\n",
       ".notification_widget.active:focus,\n",
       ".open > .dropdown-toggle.notification_widget:focus,\n",
       ".notification_widget:active.focus,\n",
       ".notification_widget.active.focus,\n",
       ".open > .dropdown-toggle.notification_widget.focus,\n",
       "div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn,\n",
       "div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:hover,\n",
       "div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:focus {\n",
       " color: #f8f8f0 !important;\n",
       " background-color: transparent !important;\n",
       " border-color: transparent !important;\n",
       " padding-bottom: 0px !important;\n",
       " margin-bottom: 0px !important;\n",
       " font-size: 9pt !important;\n",
       " z-index: 0;\n",
       "}\n",
       "div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn {\n",
       " font-size: 9pt !important;\n",
       " z-index: 0;\n",
       "}\n",
       ".notification_widget {\n",
       " color: #ae81ff;\n",
       " z-index: -500;\n",
       " font-size: 9pt;\n",
       " background: transparent;\n",
       " background-color: transparent;\n",
       " margin-right: 3px;\n",
       " border: none;\n",
       "}\n",
       ".notification_widget,\n",
       "div.notification_widget {\n",
       " margin-right: 0px;\n",
       " margin-left: 0px;\n",
       " padding-right: 0px;\n",
       " vertical-align: text-top !important;\n",
       " margin-top: 6px !important;\n",
       " background: transparent !important;\n",
       " background-color: transparent !important;\n",
       " font-size: 9pt !important;\n",
       " border: none;\n",
       "}\n",
       ".navbar-btn.btn-xs:hover {\n",
       " border: none !important;\n",
       " background: transparent !important;\n",
       " background-color: transparent !important;\n",
       " color: #f8f8f0 !important;\n",
       "}\n",
       "div.notification_widget.info,\n",
       ".notification_widget.info {\n",
       " display: none !important;\n",
       "}\n",
       ".edit_mode .modal_indicator:before {\n",
       " display: none;\n",
       "}\n",
       ".command_mode .modal_indicator:before {\n",
       " display: none;\n",
       "}\n",
       ".item_icon {\n",
       " color: #ae81ff;\n",
       "}\n",
       ".item_buttons .kernel-name {\n",
       " font-size: 13pt;\n",
       " color: #ae81ff;\n",
       "}\n",
       ".running_notebook_icon:before {\n",
       " color: #a6e22e !important;\n",
       " font: normal normal normal 15px/1 FontAwesome;\n",
       " font-size: 15px;\n",
       " text-rendering: auto;\n",
       " -webkit-font-smoothing: antialiased;\n",
       " -moz-osx-font-smoothing: grayscale;\n",
       " content: \"\\f10c\";\n",
       " vertical-align: middle;\n",
       " position: static;\n",
       " display: inherit;\n",
       "}\n",
       ".item_buttons .running-indicator {\n",
       " padding-top: 4px;\n",
       " color: #a6e22e;\n",
       " font-family: sans-serif;\n",
       " text-rendering: auto;\n",
       " -webkit-font-smoothing: antialiased;\n",
       "}\n",
       "#notification_trusted {\n",
       " font-family: sans-serif;\n",
       " border: none;\n",
       " background: transparent;\n",
       " background-color: transparent;\n",
       " margin-bottom: 0px !important;\n",
       " vertical-align: bottom !important;\n",
       " color: #75715e !important;\n",
       " cursor: default !important;\n",
       "}\n",
       "#notification_area,\n",
       "div.notification_area {\n",
       " float: right !important;\n",
       " position: static;\n",
       " cursor: pointer;\n",
       " padding-top: 6px;\n",
       " padding-right: 4px;\n",
       "}\n",
       "div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn {\n",
       " font-size: 9pt !important;\n",
       " z-index: 0;\n",
       " margin-top: -5px !important;\n",
       "}\n",
       "#modal_indicator {\n",
       " float: right !important;\n",
       " color: #4c8be2;\n",
       " background: #1e1e1e;\n",
       " background-color: #1e1e1e;\n",
       " margin-top: 8px !important;\n",
       " margin-left: 0px;\n",
       "}\n",
       "#kernel_indicator {\n",
       " float: right !important;\n",
       " color: #a6e22e;\n",
       " background: #1e1e1e;\n",
       " background-color: #1e1e1e;\n",
       " border-left: 2px solid #a6e22e;\n",
       " padding-top: 0px;\n",
       " padding-bottom: 4px;\n",
       " margin-top: 10px !important;\n",
       " margin-left: -2px;\n",
       " padding-left: 5px !important;\n",
       "}\n",
       "#kernel_indicator .kernel_indicator_name {\n",
       " font-size: 17px;\n",
       " color: #a6e22e;\n",
       " background: #1e1e1e;\n",
       " background-color: #1e1e1e;\n",
       " padding-left: 5px;\n",
       " padding-right: 5px;\n",
       " margin-top: 4px;\n",
       " vertical-align: text-top;\n",
       " padding-bottom: 0px;\n",
       "}\n",
       ".kernel_idle_icon:before {\n",
       " display: inline-block;\n",
       " font: normal normal normal 22px/1 FontAwesome;\n",
       " font-size: 22px;\n",
       " text-rendering: auto;\n",
       " -webkit-font-smoothing: antialiased;\n",
       " cursor: pointer;\n",
       " margin-left: 0px !important;\n",
       " opacity: 0.7;\n",
       " vertical-align: bottom;\n",
       " margin-top: 1px;\n",
       " content: \"\\f1db\";\n",
       "}\n",
       ".kernel_busy_icon:before {\n",
       " display: inline-block;\n",
       " font: normal normal normal 22px/1 FontAwesome;\n",
       " font-size: 22px;\n",
       " -webkit-animation: pulsate 2s infinite ease-out;\n",
       " animation: pulsate 2s infinite ease-out;\n",
       " text-rendering: auto;\n",
       " -webkit-font-smoothing: antialiased;\n",
       " cursor: pointer;\n",
       " margin-left: 0px !important;\n",
       " vertical-align: bottom;\n",
       " margin-top: 1px;\n",
       " content: \"\\f111\";\n",
       "}\n",
       "@-webkit-keyframes pulsate {\n",
       " 0% {\n",
       "  -webkit-transform: scale(1.0,1.0);\n",
       "  opacity: 0.8;\n",
       " }\n",
       " 8% {\n",
       "  -webkit-transform: scale(1.0,1.0);\n",
       "  opacity: 0.8;\n",
       " }\n",
       " 50% {\n",
       "  -webkit-transform: scale(0.75,0.75);\n",
       "  opacity: 0.3;\n",
       " }\n",
       " 92% {\n",
       "  -webkit-transform: scale(1.0,1.0);\n",
       "  opacity: 0.8;\n",
       " }\n",
       " 100% {\n",
       "  -webkit-transform: scale(1.0,1.0);\n",
       "  opacity: 0.8;\n",
       " }\n",
       "}\n",
       "div.notification_widget.info,\n",
       ".notification_widget.info,\n",
       ".notification_widget:active:hover,\n",
       ".notification_widget.active:hover,\n",
       ".open > .dropdown-toggle.notification_widget:hover,\n",
       ".notification_widget:active:focus,\n",
       ".notification_widget.active:focus,\n",
       ".open > .dropdown-toggle.notification_widget:focus,\n",
       ".notification_widget:active.focus,\n",
       ".notification_widget.active.focus,\n",
       ".open > .dropdown-toggle.notification_widget.focus,\n",
       "div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn,\n",
       "div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:hover,\n",
       "div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:focus {\n",
       " color: #f8f8f0;\n",
       " background-color: #1e1e1e;\n",
       " border-color: #1e1e1e;\n",
       "}\n",
       "#notification_area,\n",
       "div.notification_area {\n",
       " float: right !important;\n",
       " position: static;\n",
       "}\n",
       ".notification_widget,\n",
       "div.notification_widget {\n",
       " margin-right: 0px;\n",
       " margin-left: 0px;\n",
       " padding-right: 0px;\n",
       " vertical-align: text-top !important;\n",
       " margin-top: 6px !important;\n",
       " z-index: 1000;\n",
       "}\n",
       "#kernel_logo_widget,\n",
       "#kernel_logo_widget .current_kernel_logo {\n",
       " display: none;\n",
       "}\n",
       "div#ipython_notebook {\n",
       " display: none;\n",
       "}\n",
       "i.fa.fa-icon {\n",
       " -webkit-font-smoothing: antialiased;\n",
       " -moz-osx-font-smoothing: grayscale;\n",
       " text-rendering: auto;\n",
       "}\n",
       ".fa {\n",
       " display: inline-block;\n",
       " font: normal normal normal 10pt/1 \"FontAwesome\", sans-serif;\n",
       " text-rendering: auto;\n",
       " -webkit-font-smoothing: antialiased;\n",
       " -moz-osx-font-smoothing: grayscale;\n",
       "}\n",
       ".dropdown-menu {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " box-shadow: none;\n",
       " padding: 0px;\n",
       " text-align: left;\n",
       " border: none;\n",
       " background-color: #383838;\n",
       " background: #383838;\n",
       " line-height: 1;\n",
       "}\n",
       ".dropdown-menu:hover {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " box-shadow: none;\n",
       " padding: 0px;\n",
       " text-align: left;\n",
       " border: none;\n",
       " background-color: #383838;\n",
       " box-shadow: none;\n",
       " line-height: 1;\n",
       "}\n",
       ".dropdown-menu > li > a {\n",
       " font-family: sans-serif;\n",
       " font-size: 12.0pt;\n",
       " display: block;\n",
       " padding: 10px 20px 9px 10px;\n",
       " color: #f8f8f0;\n",
       " background-color: #383838;\n",
       " background: #383838;\n",
       "}\n",
       ".dropdown-menu > li > a:hover,\n",
       ".dropdown-menu > li > a:focus {\n",
       " color: #f8f8f0;\n",
       " background-color: rgba(93,92,82,.25);\n",
       " background: rgba(93,92,82,.25);\n",
       " border-color: rgba(93,92,82,.25);\n",
       " transition: 200ms ease;\n",
       "}\n",
       ".dropdown-menu .divider {\n",
       " height: 1px;\n",
       " margin: 0px 0px;\n",
       " overflow: hidden;\n",
       " background-color: rgba(93,92,82,.5);\n",
       "}\n",
       ".dropdown-submenu > .dropdown-menu {\n",
       " display: none;\n",
       " top: 2px !important;\n",
       " left: 100%;\n",
       " margin-top: -2px;\n",
       " margin-left: 0px;\n",
       " padding-top: 0px;\n",
       " transition: 200ms ease;\n",
       "}\n",
       ".dropdown-menu > .disabled > a,\n",
       ".dropdown-menu > .disabled > a:hover,\n",
       ".dropdown-menu > .disabled > a:focus {\n",
       " font-family: sans-serif;\n",
       " font-size: 12.0pt;\n",
       " font-weight: normal;\n",
       " color: #75715e;\n",
       " padding: none;\n",
       " display: block;\n",
       " clear: both;\n",
       " white-space: nowrap;\n",
       "}\n",
       ".dropdown-submenu > a:after {\n",
       " color: #f8f8f0;\n",
       " margin-right: -16px;\n",
       " margin-top: 0px;\n",
       " display: inline-block;\n",
       "}\n",
       ".dropdown-submenu:hover > a:after,\n",
       ".dropdown-submenu:active > a:after,\n",
       ".dropdown-submenu:focus > a:after,\n",
       ".dropdown-submenu:visited > a:after {\n",
       " color: #a6e22e;\n",
       " margin-right: -16px;\n",
       " display: inline-block !important;\n",
       "}\n",
       "div.kse-dropdown > .dropdown-menu,\n",
       ".kse-dropdown > .dropdown-menu {\n",
       " min-width: 0;\n",
       " top: 94%;\n",
       "}\n",
       ".btn,\n",
       ".btn-default {\n",
       " font-family: sans-serif;\n",
       " color: #f8f8f0;\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border: 2px solid #2f2f2f;\n",
       " font-weight: normal;\n",
       " box-shadow: none;\n",
       " text-shadow: none;\n",
       " border-radius: 3px;\n",
       " font-size: initial;\n",
       "}\n",
       ".btn:hover,\n",
       ".btn:active:hover,\n",
       ".btn.active:hover,\n",
       ".btn-default:hover,\n",
       ".open > .dropdown-toggle.btn-default:hover,\n",
       ".open > .dropdown-toggle.btn:hover {\n",
       " color: #a6e22e;\n",
       " border: 2px solid #2a2a2a;\n",
       " background-color: #2a2a2a;\n",
       " background: #2a2a2a;\n",
       " background-image: none;\n",
       " box-shadow: none !important;\n",
       " border-radius: 3px;\n",
       "}\n",
       ".btn:active,\n",
       ".btn.active,\n",
       ".btn:active:focus,\n",
       ".btn.active:focus,\n",
       ".btn:active.focus,\n",
       ".btn.active.focus,\n",
       ".btn-default:focus,\n",
       ".btn-default.focus,\n",
       ".btn-default:active,\n",
       ".btn-default.active,\n",
       ".btn-default:active:hover,\n",
       ".btn-default.active:hover,\n",
       ".btn-default:active:focus,\n",
       ".btn-default.active:focus,\n",
       ".btn-default:active.focus,\n",
       ".btn-default.active.focus,\n",
       ".open > .dropdown-toggle.btn:focus,\n",
       ".open > .dropdown-toggle.btn.focus,\n",
       ".open > .dropdown-toggle.btn-default:hover,\n",
       ".open > .dropdown-toggle.btn-default:focus,\n",
       ".open > .dropdown-toggle.btn-default.hover,\n",
       ".open > .dropdown-toggle.btn-default.focus {\n",
       " color: #a6e22e;\n",
       " border: 2px solid #2a2a2a;\n",
       " background-color: #2a2a2a !important;\n",
       " background: #2a2a2a !important;\n",
       " background-image: none;\n",
       " box-shadow: none !important;\n",
       " border-radius: 3px;\n",
       "}\n",
       ".btn-default:active:hover,\n",
       ".btn-default.active:hover,\n",
       ".btn-default:active:focus,\n",
       ".btn-default.active:focus,\n",
       ".btn-default:active.focus,\n",
       ".btn-default.active.focus {\n",
       " color: #a6e22e !important;\n",
       " background-color: #2f2f2f;\n",
       " border-color: #546745 !important;\n",
       " transition: 2000ms ease;\n",
       "}\n",
       ".btn:focus,\n",
       ".btn.focus,\n",
       ".btn:active:focus,\n",
       ".btn.active:focus,\n",
       ".btn:active,\n",
       ".btn.active,\n",
       ".btn:active.focus,\n",
       ".btn.active.focus {\n",
       " color: #a6e22e !important;\n",
       " outline: none !important;\n",
       " outline-width: 0px !important;\n",
       " background: #546745 !important;\n",
       " background-color: #546745 !important;\n",
       " border-color: #546745 !important;\n",
       " transition: 200ms ease !important;\n",
       "}\n",
       ".item_buttons > .btn,\n",
       ".item_buttons > .btn-group,\n",
       ".item_buttons > .input-group {\n",
       " font-size: 13pt;\n",
       " background: transparent;\n",
       " background-color: transparent;\n",
       " border: 0px solid #2f2f2f;\n",
       " border-bottom: 2px solid transparent;\n",
       " margin-left: 5px;\n",
       " padding-top: 4px !important;\n",
       "}\n",
       ".item_buttons > .btn:hover,\n",
       ".item_buttons > .btn-group:hover,\n",
       ".item_buttons > .input-group:hover,\n",
       ".item_buttons > .btn.active,\n",
       ".item_buttons > .btn-group.active,\n",
       ".item_buttons > .input-group.active,\n",
       ".item_buttons > .btn.focus {\n",
       " margin-left: 5px;\n",
       " background: #2a2a2a;\n",
       " padding-top: 4px !important;\n",
       " background-color: transparent;\n",
       " border: 0px solid transparent;\n",
       " border-bottom: 2px solid #a6e22e;\n",
       " border-radius: 0px;\n",
       " transition: none;\n",
       "}\n",
       ".item_buttons {\n",
       " line-height: 1.5em !important;\n",
       "}\n",
       ".item_buttons .btn {\n",
       " min-width: 11ex;\n",
       "}\n",
       ".btn-group > .btn:first-child {\n",
       " margin-left: 3px;\n",
       "}\n",
       ".btn-group > .btn-mini,\n",
       ".btn-sm,\n",
       ".btn-group-sm > .btn,\n",
       ".btn-xs,\n",
       ".btn-group-xs > .btn,\n",
       ".alternate_upload .btn-upload,\n",
       ".btn-group,\n",
       ".btn-group-vertical {\n",
       " font-size: inherit;\n",
       " font-weight: normal;\n",
       " height: inherit;\n",
       " line-height: inherit;\n",
       "}\n",
       ".btn-xs,\n",
       ".btn-group-xs > .btn {\n",
       " font-size: initial !important;\n",
       " background-image: none;\n",
       " font-weight: normal;\n",
       " text-shadow: none;\n",
       " display: inline-table;\n",
       " padding: 2px 5px;\n",
       " line-height: 1.45;\n",
       "}\n",
       ".btn-group > .btn:first-child {\n",
       " margin-left: 3px;\n",
       "}\n",
       "div#new-buttons > button,\n",
       "#new-buttons > button,\n",
       "div#refresh_notebook_list,\n",
       "#refresh_notebook_list {\n",
       " background: transparent;\n",
       " background-color: transparent;\n",
       " border: none;\n",
       "}\n",
       "div#new-buttons > button:hover,\n",
       "#new-buttons > button:hover,\n",
       "div#refresh_notebook_list,\n",
       "#refresh_notebook_list,\n",
       "div.alternate_upload .btn-upload,\n",
       ".alternate_upload .btn-upload,\n",
       "div.dynamic-buttons > button,\n",
       ".dynamic-buttons > button,\n",
       ".dynamic-buttons > button:focus,\n",
       ".dynamic-buttons > button:active:focus,\n",
       ".dynamic-buttons > button.active:focus,\n",
       ".dynamic-buttons > button.focus,\n",
       ".dynamic-buttons > button:active.focus,\n",
       ".dynamic-buttons > button.active.focus,\n",
       "#new-buttons > button:focus,\n",
       "#new-buttons > button:active:focus,\n",
       "#new-buttons > button.active:focus,\n",
       "#new-buttons > button.focus,\n",
       "#new-buttons > button:active.focus,\n",
       "#new-buttons > button.active.focus,\n",
       ".alternate_upload .btn-upload:focus,\n",
       ".alternate_upload .btn-upload:active:focus,\n",
       ".alternate_upload .btn-upload.active:focus,\n",
       ".alternate_upload .btn-upload.focus,\n",
       ".alternate_upload .btn-upload:active.focus,\n",
       ".alternate_upload .btn-upload.active.focus {\n",
       " background: transparent !important;\n",
       " background-color: transparent !important;\n",
       " border: none !important;\n",
       "}\n",
       ".alternate_upload input.fileinput {\n",
       " text-align: center;\n",
       " vertical-align: bottom;\n",
       " margin-left: -.5ex;\n",
       " display: inline-table;\n",
       " border: solid 0px #2f2f2f;\n",
       " margin-bottom: -1ex;\n",
       "}\n",
       ".alternate_upload .btn-upload {\n",
       " display: inline-table;\n",
       " background: transparent;\n",
       " border: none;\n",
       "}\n",
       ".btn-group .btn + .btn,\n",
       ".btn-group .btn + .btn-group,\n",
       ".btn-group .btn-group + .btn,\n",
       ".btn-group .btn-group + .btn-group {\n",
       " margin-left: -2px;\n",
       "}\n",
       ".btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n",
       " border-bottom-right-radius: 0;\n",
       " border-top-right-radius: 0;\n",
       " z-index: 2;\n",
       "}\n",
       ".dropdown-header {\n",
       " font-family: sans-serif !important;\n",
       " font-size: 13pt !important;\n",
       " color: #a6e22e !important;\n",
       " border-bottom: none !important;\n",
       " padding: 0px !important;\n",
       " margin: 6px 6px 0px !important;\n",
       "}\n",
       "span#last-modified.btn.btn-xs.btn-default.sort-action,\n",
       "span#sort-name.btn.btn-xs.btn-default.sort-action,\n",
       "span#file-size.btn.btn-xs.btn-default.sort-action {\n",
       " font-family: sans-serif;\n",
       " font-size: 16px;\n",
       " background-color: transparent;\n",
       " background: transparent;\n",
       " border: none;\n",
       " color: #f8f8f0;\n",
       " padding-bottom: 0px;\n",
       " margin-bottom: 0px;\n",
       " vertical-align: sub;\n",
       "}\n",
       "span#last-modified.btn.btn-xs.btn-default.sort-action {\n",
       " margin-left: 19px;\n",
       "}\n",
       "button.close {\n",
       " border: 0px none;\n",
       " font-family: sans-serif;\n",
       " font-size: 20pt;\n",
       " font-weight: normal;\n",
       "}\n",
       ".dynamic-buttons {\n",
       " padding-top: 0px;\n",
       " display: inline-block;\n",
       "}\n",
       ".close {\n",
       " color: #f92672;\n",
       " opacity: .5;\n",
       " text-shadow: none;\n",
       " font-weight: normal;\n",
       "}\n",
       ".close:hover {\n",
       " color: #f92672;\n",
       " opacity: 1;\n",
       " font-weight: normal;\n",
       "}\n",
       "div.nbext-enable-btns .btn[disabled],\n",
       "div.nbext-enable-btns .btn[disabled]:hover,\n",
       ".btn-default.disabled,\n",
       ".btn-default[disabled],\n",
       ".btn-default.disabled:hover,\n",
       ".btn-default[disabled]:hover,\n",
       "fieldset[disabled] .btn-default:hover,\n",
       ".btn-default.disabled:focus,\n",
       ".btn-default[disabled]:focus,\n",
       "fieldset[disabled] .btn-default:focus,\n",
       ".btn-default.disabled.focus,\n",
       ".btn-default[disabled].focus,\n",
       "fieldset[disabled] .btn-default.focus {\n",
       " color: #888888;\n",
       " background: #2c2c2c;\n",
       " background-color: #2c2c2c;\n",
       " border-color: #2c2c2c;\n",
       " transition: 200ms ease;\n",
       "}\n",
       ".input-group-addon {\n",
       " padding: 2px 5px;\n",
       " font-size: 13pt;\n",
       " font-weight: normal;\n",
       " height: auto;\n",
       " color: #f8f8f0;\n",
       " text-align: center;\n",
       " background-color: transparent;\n",
       " border: 2px solid transparent !important;\n",
       " text-transform: capitalize;\n",
       "}\n",
       "a.btn.btn-default.input-group-addon:hover {\n",
       " background: transparent !important;\n",
       " background-color: transparent !important;\n",
       "}\n",
       ".btn-group > .btn + .dropdown-toggle {\n",
       " padding-left: 8px;\n",
       " padding-right: 8px;\n",
       " height: 100%;\n",
       "}\n",
       ".btn-group > .btn + .dropdown-toggle:hover {\n",
       " background: #2a2a2a !important;\n",
       "}\n",
       ".input-group-btn {\n",
       " position: relative;\n",
       " font-size: inherit;\n",
       " white-space: nowrap;\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border: none;\n",
       "}\n",
       ".input-group-btn:hover {\n",
       " background: #2a2a2a;\n",
       " background-color: #2a2a2a;\n",
       " border: none;\n",
       "}\n",
       ".input-group-btn:first-child > .btn,\n",
       ".input-group-btn:first-child > .btn-group {\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border: none;\n",
       " margin-left: 2px;\n",
       " margin-right: -1px;\n",
       " font-size: inherit;\n",
       "}\n",
       ".input-group-btn:first-child > .btn:hover,\n",
       ".input-group-btn:first-child > .btn-group:hover {\n",
       " background: #2a2a2a;\n",
       " background-color: #2a2a2a;\n",
       " border: none;\n",
       " font-size: inherit;\n",
       " transition: 200ms ease;\n",
       "}\n",
       "div.modal .btn-group > .btn:first-child {\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border: 1px solid #2c2c2c;\n",
       " margin-top: 0px !important;\n",
       " margin-left: 0px;\n",
       " margin-bottom: 2px;\n",
       "}\n",
       "div.modal .btn-group > .btn:first-child:hover {\n",
       " background: #2a2a2a;\n",
       " background-color: #2a2a2a;\n",
       " border: 1px solid #2a2a2a;\n",
       " transition: 200ms ease;\n",
       "}\n",
       "div.modal > button,\n",
       "div.modal-footer > button {\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border-color: #2f2f2f;\n",
       "}\n",
       "div.modal > button:hover,\n",
       "div.modal-footer > button:hover {\n",
       " background: #2a2a2a;\n",
       " background-color: #2a2a2a;\n",
       " border-color: #2a2a2a;\n",
       " transition: 200ms ease;\n",
       "}\n",
       ".modal-content {\n",
       " font-family: sans-serif;\n",
       " font-size: 12.0pt;\n",
       " position: relative;\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border: none;\n",
       " border-radius: 1px;\n",
       " background-clip: padding-box;\n",
       " outline: none;\n",
       "}\n",
       ".modal-header {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " color: #f8f8f0;\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border-color: rgba(93,92,82,.25);\n",
       " padding: 12px;\n",
       " min-height: 16.4286px;\n",
       "}\n",
       ".modal-content h4 {\n",
       " font-family: sans-serif;\n",
       " font-size: 16pt;\n",
       " color: #f8f8f0;\n",
       " padding: 5px;\n",
       "}\n",
       ".modal-body {\n",
       " background-color: #232323;\n",
       " position: relative;\n",
       " padding: 15px;\n",
       "}\n",
       ".modal-footer {\n",
       " padding: 8px;\n",
       " text-align: right;\n",
       " background-color: #232323;\n",
       " border-top: none;\n",
       "}\n",
       ".alert-info {\n",
       " background-color: #2f2f2f;\n",
       " border-color: rgba(93,92,82,.25);\n",
       " color: #f8f8f0;\n",
       "}\n",
       ".modal-header .close {\n",
       " margin-top: -5px;\n",
       " font-size: 25pt;\n",
       "}\n",
       ".modal-backdrop,\n",
       ".modal-backdrop.in {\n",
       " opacity: 0.85;\n",
       " background-color: notebook-bg;\n",
       "}\n",
       "div.panel,\n",
       "div.panel-default,\n",
       ".panel,\n",
       ".panel-default {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " background-color: #232323;\n",
       " color: #f8f8f0;\n",
       " margin-bottom: 14px;\n",
       " border: 0;\n",
       " box-shadow: none;\n",
       "}\n",
       "div.panel > .panel-heading,\n",
       "div.panel-default > .panel-heading {\n",
       " font-size: 14pt;\n",
       " color: #f8f8f0;\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border: 0;\n",
       "}\n",
       ".modal .modal-dialog {\n",
       " min-width: 950px;\n",
       " margin: 50px auto;\n",
       "}\n",
       "div.container-fluid {\n",
       " margin-right: auto;\n",
       " margin-left: auto;\n",
       " padding-left: 0px;\n",
       " padding-right: 5px;\n",
       "}\n",
       "div.form-control,\n",
       ".form-control {\n",
       " font-family: sans-serif;\n",
       " font-size: initial;\n",
       " color: #f8f8f0;\n",
       " background-color: #282828;\n",
       " border: 1px solid #282828 !important;\n",
       " margin-left: 2px;\n",
       " box-shadow: none;\n",
       " transition: border-color 0.15s ease-in-out 0s, box-shadow 0.15s ease-in-out 0s;\n",
       "}\n",
       ".form-control-static {\n",
       " min-height: inherit;\n",
       " height: inherit;\n",
       "}\n",
       ".form-group.list-group-item {\n",
       " color: #f8f8f0;\n",
       " background-color: #232323;\n",
       " border-color: rgba(93,92,82,.25);\n",
       " margin-bottom: 0px;\n",
       "}\n",
       ".form-group .input-group {\n",
       " float: left;\n",
       "}\n",
       "input,\n",
       "button,\n",
       "select,\n",
       "textarea {\n",
       " background-color: #282828;\n",
       " font-weight: normal;\n",
       " border: 1px solid rgba(93,92,82,.25);\n",
       "}\n",
       "select.form-control.select-xs {\n",
       " height: 33px;\n",
       " font-size: 13pt;\n",
       "}\n",
       ".toolbar select,\n",
       ".toolbar label {\n",
       " width: auto;\n",
       " vertical-align: middle;\n",
       " margin-right: 0px;\n",
       " margin-bottom: 0px;\n",
       " display: inline;\n",
       " font-size: 92%;\n",
       " margin-left: 10px;\n",
       " padding: 0px;\n",
       " background: #2f2f2f !important;\n",
       " background-color: #2f2f2f !important;\n",
       " border: 2px solid #2f2f2f !important;\n",
       "}\n",
       ".form-control:focus {\n",
       " border-color: #a6e22e;\n",
       " outline: 2px solid #49483e;\n",
       " -webkit-box-shadow: none;\n",
       "}\n",
       "::-webkit-input-placeholder {\n",
       " color: #75715e;\n",
       "}\n",
       "::-moz-placeholder {\n",
       " color: #75715e;\n",
       "}\n",
       ":-ms-input-placeholder {\n",
       " color: #75715e;\n",
       "}\n",
       ":-moz-placeholder {\n",
       " color: #75715e;\n",
       "}\n",
       "[dir=\"ltr\"] #find-and-replace .input-group-btn + .form-control {\n",
       " border: 2px solid rgba(93,92,82,.25) !important;\n",
       "}\n",
       "[dir=\"ltr\"] #find-and-replace .input-group-btn + .form-control:focus {\n",
       " border-color: #a6e22e;\n",
       " outline: 2px solid #49483e;\n",
       " -webkit-box-shadow: none;\n",
       " box-shadow: none;\n",
       "}\n",
       "div.output.output_scroll {\n",
       " box-shadow: none;\n",
       "}\n",
       "::-webkit-scrollbar {\n",
       " width: 11px;\n",
       " max-height: 9px;\n",
       " background-color: #2d2d2d;\n",
       " border-radius: 3px;\n",
       " border: none;\n",
       "}\n",
       "::-webkit-scrollbar-track {\n",
       " background: #2d2d2d;\n",
       " border: none;\n",
       " width: 11px;\n",
       " max-height: 9px;\n",
       "}\n",
       "::-webkit-scrollbar-thumb {\n",
       " border-radius: 2px;\n",
       " border: none;\n",
       " background: #49483e;\n",
       " background-clip: content-box;\n",
       " width: 11px;\n",
       "}\n",
       "HTML,\n",
       "body,\n",
       "div,\n",
       "dl,\n",
       "dt,\n",
       "dd,\n",
       "ul,\n",
       "ol,\n",
       "li,\n",
       "h1,\n",
       "h2,\n",
       "h3,\n",
       "h4,\n",
       "h5,\n",
       "h6,\n",
       "pre,\n",
       "code,\n",
       "form,\n",
       "fieldset,\n",
       "legend,\n",
       "input,\n",
       "button,\n",
       "textarea,\n",
       "p,\n",
       "blockquote,\n",
       "th,\n",
       "td,\n",
       "span,\n",
       "a {\n",
       " text-rendering: geometricPrecision;\n",
       " -webkit-font-smoothing: subpixel-antialiased;\n",
       " font-weight: 400;\n",
       "}\n",
       "div.input_area {\n",
       " background-color: #282828;\n",
       " background: #282828;\n",
       " padding-right: 1.2em;\n",
       " border: 0px;\n",
       " border-radius: 0px;\n",
       " border-top-right-radius: 4px;\n",
       " border-bottom-right-radius: 4px;\n",
       "}\n",
       "div.cell {\n",
       " padding: 0px;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border: medium solid #1e1e1e;\n",
       " border-radius: 4px;\n",
       " top: 0;\n",
       "}\n",
       "div.cell.selected {\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border: medium solid #1e1e1e;\n",
       " padding: 0px;\n",
       " border-radius: 5px;\n",
       "}\n",
       ".edit_mode div.cell.selected {\n",
       " padding: 0px;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border: medium solid #1e1e1e;\n",
       " border-radius: 5px;\n",
       "}\n",
       "div.cell.edit_mode {\n",
       " padding: 0px;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       "}\n",
       "div.CodeMirror-sizer {\n",
       " margin-left: 0px;\n",
       " margin-bottom: -21px;\n",
       " border-right-width: 16px;\n",
       " min-height: 37px;\n",
       " padding-right: 0px;\n",
       " padding-bottom: 0px;\n",
       " margin-top: 0px;\n",
       "}\n",
       "div.cell.selected:before,\n",
       ".edit_mode div.cell.selected:before,\n",
       "div.cell.selected:before,\n",
       "div.cell.selected.jupyter-soft-selected:before {\n",
       " background: #282828 !important;\n",
       " border: none;\n",
       " border-radius: 3px;\n",
       " position: absolute;\n",
       " display: block;\n",
       " top: 0px;\n",
       " left: 0px;\n",
       " width: 0px;\n",
       " height: 100%;\n",
       "}\n",
       "div.cell.text_cell.selected::before,\n",
       ".edit_mode div.cell.text_cell.selected:before,\n",
       "div.cell.text_cell.selected:before,\n",
       "div.cell.text_cell.selected.jupyter-soft-selected:before {\n",
       " background: #282828 !important;\n",
       " background-color: #282828 !important;\n",
       " border-color: #57564b !important;\n",
       "}\n",
       "div.cell.code_cell .input {\n",
       " border-left: 5px solid #282828 !important;\n",
       " border-radius: 3px;\n",
       " border-bottom-left-radius: 3px;\n",
       " border-top-left-radius: 3px;\n",
       "}\n",
       "div.cell.code_cell.selected .input {\n",
       " border-left: 5px solid #57564b !important;\n",
       " border-radius: 3px;\n",
       "}\n",
       ".edit_mode div.cell.code_cell.selected .input {\n",
       " border-left: 5px solid #33322b !important;\n",
       " border-radius: 3px;\n",
       "}\n",
       ".edit_mode div.cell.selected:before {\n",
       " height: 100%;\n",
       " border-left: 5px solid #33322b !important;\n",
       " border-radius: 3px;\n",
       "}\n",
       "div.cell.jupyter-soft-selected,\n",
       "div.cell.selected.jupyter-soft-selected {\n",
       " border-left-color: #33322b !important;\n",
       " border-left-width: 0px !important;\n",
       " padding-left: 7px !important;\n",
       " border-right-color: #33322b !important;\n",
       " border-right-width: 0px !important;\n",
       " background: #33322b !important;\n",
       " border-radius: 6px !important;\n",
       "}\n",
       "div.cell.selected.jupyter-soft-selected .input {\n",
       " border-left: 5px solid #282828 !important;\n",
       "}\n",
       "div.cell.selected.jupyter-soft-selected {\n",
       " border-left-color: #57564b;\n",
       " border-color: #1e1e1e;\n",
       " padding-left: 7px;\n",
       " border-radius: 6px;\n",
       "}\n",
       "div.cell.code_cell.selected .input {\n",
       " border-left: none;\n",
       " border-radius: 3px;\n",
       "}\n",
       "div.cell.selected.jupyter-soft-selected .prompt,\n",
       "div.cell.text_cell.selected.jupyter-soft-selected .prompt {\n",
       " top: 0;\n",
       " border-left: #282828 !important;\n",
       " border-radius: 2px;\n",
       "}\n",
       "div.cell.text_cell.selected.jupyter-soft-selected .input_prompt {\n",
       " border-left: none !important;\n",
       "}\n",
       "div.cell.text_cell.jupyter-soft-selected,\n",
       "div.cell.text_cell.selected.jupyter-soft-selected {\n",
       " border-left-color: #33322b !important;\n",
       " border-left-width: 0px !important;\n",
       " padding-left: 26px !important;\n",
       " border-right-color: #33322b !important;\n",
       " border-right-width: 0px !important;\n",
       " background: #33322b !important;\n",
       " border-radius: 5px !important;\n",
       "}\n",
       "div.cell.jupyter-soft-selected .input,\n",
       "div.cell.selected.jupyter-soft-selected .input {\n",
       " border-left-color: #33322b !important;\n",
       "}\n",
       "div.prompt,\n",
       ".prompt {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 9pt !important;\n",
       " font-weight: normal;\n",
       " color: #75715e;\n",
       " line-height: 170%;\n",
       " padding: 0px;\n",
       " padding-top: 4px;\n",
       " padding-left: 0px;\n",
       " padding-right: 1px;\n",
       " text-align: right !important;\n",
       " min-width: 11.5ex !important;\n",
       " width: 11.5ex !important;\n",
       "}\n",
       "div.prompt.input_prompt {\n",
       " font-size: 9pt !important;\n",
       " background-color: #282828;\n",
       " border-top: 0px;\n",
       " border-top-right-radius: 0px;\n",
       " border-bottom-left-radius: 0px;\n",
       " border-bottom-right-radius: 0px;\n",
       " padding-right: 3px;\n",
       " min-width: 11.5ex;\n",
       " width: 11.5ex !important;\n",
       "}\n",
       "div.cell.code_cell .input_prompt {\n",
       " border-right: 2px solid #49483e;\n",
       "}\n",
       "div.cell.selected .prompt {\n",
       " top: 0;\n",
       "}\n",
       ".edit_mode div.cell.selected .prompt {\n",
       " top: 0;\n",
       "}\n",
       ".edit_mode div.cell.selected .prompt {\n",
       " top: 0;\n",
       "}\n",
       ".run_this_cell {\n",
       " visibility: hidden;\n",
       " color: transparent;\n",
       " padding-top: 0px;\n",
       " padding-bottom: 0px;\n",
       " padding-left: 3px;\n",
       " padding-right: 12px;\n",
       " width: 1.5ex;\n",
       " width: 0ex;\n",
       " background: transparent;\n",
       " background-color: transparent;\n",
       "}\n",
       "div.code_cell:hover div.input .run_this_cell {\n",
       " visibility: visible;\n",
       "}\n",
       "div.cell.code_cell.rendered.selected .run_this_cell:hover {\n",
       " background-color: #1e1e1e;\n",
       " background: #1e1e1e;\n",
       " color: #57564b !important;\n",
       "}\n",
       "div.cell.code_cell.rendered.unselected .run_this_cell:hover {\n",
       " background-color: #1e1e1e;\n",
       " background: #1e1e1e;\n",
       " color: #57564b !important;\n",
       "}\n",
       "i.fa-step-forward.fa {\n",
       " display: inline-block;\n",
       " font: normal normal normal 9px \"FontAwesome\";\n",
       "}\n",
       ".fa-step-forward:before {\n",
       " content: \"\\f04b\";\n",
       "}\n",
       "div.cell.selected.jupyter-soft-selected .run_this_cell,\n",
       "div.cell.selected.jupyter-soft-selected .run_this_cell:hover,\n",
       "div.cell.unselected.jupyter-soft-selected .run_this_cell:hover,\n",
       "div.cell.code_cell.rendered.selected.jupyter-soft-selected .run_this_cell:hover,\n",
       "div.cell.code_cell.rendered.unselected.jupyter-soft-selected .run_this_cell:hover {\n",
       " background-color: #33322b !important;\n",
       " background: #33322b !important;\n",
       " color: #33322b !important;\n",
       "}\n",
       "div.output_wrapper {\n",
       " background-color: #232323;\n",
       " border: 0px;\n",
       " left: 0px;\n",
       " margin-bottom: 0em;\n",
       " margin-top: 0em;\n",
       " border-top-right-radius: 0px;\n",
       " border-top-left-radius: 0px;\n",
       "}\n",
       "div.output_subarea.output_text.output_stream.output_stdout,\n",
       "div.output_subarea.output_text {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 8.5pt !important;\n",
       " line-height: 150% !important;\n",
       " background-color: #232323;\n",
       " color: #cccccc;\n",
       " border-top-right-radius: 0px;\n",
       " border-top-left-radius: 0px;\n",
       " margin-left: 11.5px;\n",
       "}\n",
       "div.output_area pre {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 8.5pt !important;\n",
       " line-height: 151% !important;\n",
       " color: #cccccc;\n",
       " border-top-right-radius: 0px;\n",
       " border-top-left-radius: 0px;\n",
       "}\n",
       "div.output_area {\n",
       " display: -webkit-box;\n",
       "}\n",
       "div.output_html {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 8.5pt;\n",
       " color: #cccccc;\n",
       " background-color: #232323;\n",
       " background: #232323;\n",
       "}\n",
       "div.output_subarea {\n",
       " overflow-x: auto;\n",
       " padding: 1.2em !important;\n",
       " -webkit-box-flex: 1;\n",
       " -moz-box-flex: 1;\n",
       " box-flex: 1;\n",
       " flex: 1;\n",
       "}\n",
       "div.btn.btn-default.output_collapsed {\n",
       " background: #222222;\n",
       " background-color: #222222;\n",
       " border-color: #222222;\n",
       "}\n",
       "div.btn.btn-default.output_collapsed:hover {\n",
       " background: #1d1d1d;\n",
       " background-color: #1d1d1d;\n",
       " border-color: #1d1d1d;\n",
       "}\n",
       "div.prompt.output_prompt {\n",
       " font-family: monospace, monospace;\n",
       " font-weight: bold !important;\n",
       " background-color: #232323;\n",
       " color: transparent;\n",
       " border-bottom-left-radius: 4px;\n",
       " border-top-right-radius: 0px;\n",
       " border-top-left-radius: 0px;\n",
       " border-bottom-right-radius: 0px;\n",
       " min-width: 11.5ex !important;\n",
       " width: 11.5ex !important;\n",
       " border-right: 2px solid transparent;\n",
       "}\n",
       "div.out_prompt_overlay.prompt {\n",
       " font-family: monospace, monospace;\n",
       " font-weight: bold !important;\n",
       " background-color: #232323;\n",
       " border-bottom-left-radius: 2px;\n",
       " border-top-right-radius: 0px;\n",
       " border-top-left-radius: 0px;\n",
       " border-bottom-right-radius: 0px;\n",
       " min-width: 11.5ex !important;\n",
       " width: 11.5ex !important;\n",
       " border-right: 2px solid transparent;\n",
       " color: transparent;\n",
       "}\n",
       "div.out_prompt_overlay.prompt:hover {\n",
       " background-color: #49483e;\n",
       " box-shadow: none !important;\n",
       " border: none;\n",
       " border-bottom-left-radius: 2px;\n",
       " -webkit-border-: 2px;\n",
       " -moz-border-radius: 2px;\n",
       " border-top-right-radius: 0px;\n",
       " border-top-left-radius: 0px;\n",
       " min-width: 11.5ex !important;\n",
       " width: 11.5ex !important;\n",
       " border-right: 2px solid #49483e !important;\n",
       "}\n",
       "div.cell.code_cell .output_prompt {\n",
       " border-right: 2px solid transparent;\n",
       " color: transparent;\n",
       "}\n",
       "div.cell.selected .output_prompt,\n",
       "div.cell.selected .out_prompt_overlay.prompt {\n",
       " border-left: 5px solid #33322b;\n",
       " border-right: 2px solid #232323;\n",
       " border-radius: 0px !important;\n",
       "}\n",
       ".edit_mode div.cell.selected .output_prompt,\n",
       ".edit_mode div.cell.selected .out_prompt_overlay.prompt {\n",
       " border-left: 5px solid #33322b;\n",
       " border-right: 2px solid #232323;\n",
       " border-radius: 0px !important;\n",
       "}\n",
       "div.text_cell,\n",
       "div.text_cell_render pre,\n",
       "div.text_cell_render {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " line-height: 130% !important;\n",
       " color: #f8f8f0;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border-radius: 0px;\n",
       "}\n",
       "div .text_cell_render {\n",
       " padding: 0.4em 0.4em 0.4em 0.4em;\n",
       "}\n",
       "div.cell.text_cell .CodeMirror-lines {\n",
       " padding-top: .7em !important;\n",
       " padding-bottom: .4em !important;\n",
       " padding-left: .5em !important;\n",
       " padding-right: .5em !important;\n",
       " margin-top: .4em;\n",
       " margin-bottom: .3em;\n",
       "}\n",
       "div.cell.text_cell.unrendered div.input_area,\n",
       "div.cell.text_cell.rendered div.input_area {\n",
       " background-color: #282828;\n",
       " background: #282828;\n",
       " border: 0px;\n",
       " border-radius: 2px;\n",
       "}\n",
       "div.cell.text_cell .CodeMirror,\n",
       "div.cell.text_cell .CodeMirror pre {\n",
       " line-height: 170% !important;\n",
       "}\n",
       "div.cell.text_cell.rendered.selected {\n",
       " font-family: sans-serif;\n",
       " line-height: 170% !important;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border-radius: 0px;\n",
       "}\n",
       "div.cell.text_cell.unrendered.selected {\n",
       " font-family: sans-serif;\n",
       " line-height: 170% !important;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border-radius: 0px;\n",
       "}\n",
       "div.cell.text_cell.selected {\n",
       " font-family: sans-serif;\n",
       " line-height: 170% !important;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border-radius: 0px;\n",
       "}\n",
       ".edit_mode div.cell.text_cell.selected {\n",
       " font-family: sans-serif;\n",
       " line-height: 170% !important;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border-radius: 0px;\n",
       "}\n",
       "div.text_cell.unrendered,\n",
       "div.text_cell.unrendered.selected,\n",
       "div.edit_mode div.text_cell.unrendered {\n",
       " font-family: sans-serif;\n",
       " line-height: 170% !important;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border-radius: 0px;\n",
       "}\n",
       "div.cell.text_cell .prompt {\n",
       " border-right: 0;\n",
       " min-width: 11.5ex !important;\n",
       " width: 11.5ex !important;\n",
       "}\n",
       "div.cell.text_cell.rendered .prompt {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 9.5pt !important;\n",
       " font-weight: normal;\n",
       " color: #75715e !important;\n",
       " text-align: right !important;\n",
       " min-width: 14.5ex !important;\n",
       " width: 14.5ex !important;\n",
       " background-color: #282828;\n",
       " border-right: 2px solid #49483e;\n",
       " border-left: 4px solid #282828;\n",
       "}\n",
       "div.cell.text_cell.unrendered .prompt {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 9.5pt !important;\n",
       " font-weight: normal;\n",
       " color: #75715e !important;\n",
       " text-align: right !important;\n",
       " min-width: 14.5ex !important;\n",
       " width: 14.5ex !important;\n",
       " border-right: 2px solid #49483e;\n",
       " border-left: 4px solid #282828;\n",
       " background-color: #282828;\n",
       "}\n",
       "div.cell.text_cell.rendered .prompt {\n",
       " border-right: 2px solid #49483e;\n",
       "}\n",
       "div.cell.text_cell.rendered.selected .prompt {\n",
       " top: 0;\n",
       " border-left: 4px solid #57564b;\n",
       " border-right: 2px solid #49483e;\n",
       "}\n",
       "div.text_cell.unrendered.selected .prompt,\n",
       "div.text_cell.rendered.selected .prompt {\n",
       " top: 0;\n",
       " background: #282828;\n",
       " border-left: 4px solid #33322b;\n",
       " border-right: 2px solid #49483e;\n",
       "}\n",
       "div.rendered_html code {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 11pt;\n",
       " padding-top: 3px;\n",
       " padding-left: 2px;\n",
       " color: #f8f8f0;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       "}\n",
       "pre,\n",
       "code,\n",
       "kbd,\n",
       "samp {\n",
       " white-space: pre-wrap;\n",
       "}\n",
       ".well code,\n",
       "code {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 11pt !important;\n",
       " line-height: 170% !important;\n",
       " color: #f8f8f0;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " border-color: #282828;\n",
       "}\n",
       "kbd {\n",
       " padding: 1px;\n",
       " font-size: 11pt;\n",
       " font-weight: 800;\n",
       " color: #f8f8f0;\n",
       " background-color: transparent !important;\n",
       " border: 0;\n",
       " box-shadow: none;\n",
       "}\n",
       "pre {\n",
       " display: block;\n",
       " padding: 8.5px;\n",
       " margin: 0 0 9px;\n",
       " font-size: 12.0pt;\n",
       " line-height: 1.42857143;\n",
       " color: #f8f8f0;\n",
       " background-color: #282828;\n",
       " border: 1px solid #282828;\n",
       " border-radius: 2px;\n",
       "}\n",
       "div.rendered_html {\n",
       " color: #f8f8f0;\n",
       "}\n",
       ".rendered_html * + ul {\n",
       " margin-top: .4em;\n",
       " margin-bottom: .3em;\n",
       "}\n",
       ".rendered_html * + p {\n",
       " margin-top: .5em;\n",
       " margin-bottom: .5em;\n",
       "}\n",
       "div.rendered_html pre {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 11pt !important;\n",
       " line-height: 170% !important;\n",
       " color: #f8f8f0 !important;\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " max-width: 80%;\n",
       " border-radius: 0px;\n",
       " border-left: 3px solid #282828;\n",
       " max-width: 80%;\n",
       " border-radius: 0px;\n",
       " padding-left: 5px;\n",
       " margin-left: 6px;\n",
       "}\n",
       "div.text_cell_render pre,\n",
       "div.text_cell_render code {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 11pt !important;\n",
       " line-height: 170% !important;\n",
       " color: #f8f8f0;\n",
       " background: #1e1e1e;\n",
       " background-color: #1e1e1e;\n",
       " max-width: 80%;\n",
       " border-radius: 0px;\n",
       " border-left: none;\n",
       "}\n",
       "div.text_cell_render pre {\n",
       " border-left: 3px solid #49483e !important;\n",
       " max-width: 80%;\n",
       " border-radius: 0px;\n",
       " padding-left: 5px;\n",
       " margin-left: 6px;\n",
       "}\n",
       "div.text_cell_render h1,\n",
       "div.rendered_html h1,\n",
       "div.text_cell_render h2,\n",
       "div.rendered_html h2,\n",
       "div.text_cell_render h3,\n",
       "div.rendered_html h3,\n",
       "div.text_cell_render h4,\n",
       "div.rendered_html h4,\n",
       "div.text_cell_render h5,\n",
       "div.rendered_html h5 {\n",
       " font-family: sans-serif;\n",
       " margin: 0.4em .2em .3em .2em !important;\n",
       "}\n",
       ".rendered_html h1:first-child,\n",
       ".rendered_html h2:first-child,\n",
       ".rendered_html h3:first-child,\n",
       ".rendered_html h4:first-child,\n",
       ".rendered_html h5:first-child,\n",
       ".rendered_html h6:first-child {\n",
       " margin-top: 0.2em !important;\n",
       " margin-bottom: 0.2em !important;\n",
       "}\n",
       ".rendered_html h1,\n",
       ".text_cell_render h1 {\n",
       " color: #a6e22e !important;\n",
       " font-size: 200%;\n",
       " text-align: left;\n",
       " font-style: normal;\n",
       " font-weight: normal;\n",
       "}\n",
       ".rendered_html h2,\n",
       ".text_cell_render h2 {\n",
       " color: #a6e22e !important;\n",
       " font-size: 170%;\n",
       " font-style: normal;\n",
       " font-weight: normal;\n",
       "}\n",
       ".rendered_html h3,\n",
       ".text_cell_render h3 {\n",
       " color: #a6e22e !important;\n",
       " font-size: 140%;\n",
       " font-style: normal;\n",
       " font-weight: normal;\n",
       "}\n",
       ".rendered_html h4,\n",
       ".text_cell_render h4 {\n",
       " color: #a6e22e !important;\n",
       " font-size: 110%;\n",
       " font-style: normal;\n",
       " font-weight: normal;\n",
       "}\n",
       ".rendered_html h5,\n",
       ".text_cell_render h5 {\n",
       " color: #a6e22e !important;\n",
       " font-size: 100%;\n",
       " font-style: normal;\n",
       " font-weight: normal;\n",
       "}\n",
       "hr {\n",
       " margin-top: 8px;\n",
       " margin-bottom: 10px;\n",
       " border: 0;\n",
       " border-top: 1px solid #a6e22e;\n",
       "}\n",
       ".rendered_html hr {\n",
       " color: #a6e22e;\n",
       " background-color: #a6e22e;\n",
       " margin-right: 2em;\n",
       "}\n",
       "#complete > select > option:hover {\n",
       " background: rgba(93,92,82,.25);\n",
       " background-color: rgba(93,92,82,.25);\n",
       "}\n",
       "div#_vivaldi-spatnav-focus-indicator._vivaldi-spatnav-focus-indicator {\n",
       " position: absolute;\n",
       " z-index: 9999999999;\n",
       " top: 0px;\n",
       " left: 0px;\n",
       " box-shadow: none;\n",
       " pointer-events: none;\n",
       " border-radius: 2px;\n",
       "}\n",
       ".rendered_html tr,\n",
       ".rendered_html th,\n",
       ".rendered_html td {\n",
       " text-align: left;\n",
       " vertical-align: middle;\n",
       " padding: 0.42em 0.47em;\n",
       " line-height: normal;\n",
       " white-space: normal;\n",
       " max-width: none;\n",
       " border: none;\n",
       "}\n",
       ".rendered_html td {\n",
       " font-family: sans-serif !important;\n",
       " font-size: 9.3pt;\n",
       "}\n",
       ".rendered_html table {\n",
       " font-family: sans-serif !important;\n",
       " margin-left: 8px;\n",
       " margin-right: auto;\n",
       " border: none;\n",
       " border-collapse: collapse;\n",
       " border-spacing: 0;\n",
       " color: #cccccc;\n",
       " table-layout: fixed;\n",
       "}\n",
       ".rendered_html thead {\n",
       " font-family: sans-serif !important;\n",
       " font-size: 10.3pt !important;\n",
       " background: #1e1e1e;\n",
       " color: #cccccc;\n",
       " border-bottom: 1px solid #1e1e1e;\n",
       " vertical-align: bottom;\n",
       "}\n",
       ".rendered_html tbody tr:nth-child(odd) {\n",
       " background: #282828;\n",
       "}\n",
       ".rendered_html tbody tr {\n",
       " background: #202020;\n",
       "}\n",
       ".rendered_html tbody tr:hover:nth-child(odd) {\n",
       " background: #252525;\n",
       "}\n",
       ".rendered_html tbody tr:hover {\n",
       " background: #1e1e1e;\n",
       "}\n",
       ".rendered_html * + table {\n",
       " margin-top: .05em;\n",
       "}\n",
       "div.widget-area {\n",
       " background-color: #232323;\n",
       " background: #232323;\n",
       " color: #cccccc;\n",
       "}\n",
       "div.widget-area a {\n",
       " font-family: sans-serif;\n",
       " font-size: 12.0pt;\n",
       " font-weight: normal;\n",
       " font-style: normal;\n",
       " color: #f8f8f0;\n",
       " text-shadow: none !important;\n",
       "}\n",
       "div.widget-area a:hover,\n",
       "div.widget-area a:focus {\n",
       " font-family: sans-serif;\n",
       " font-size: 12.0pt;\n",
       " font-weight: normal;\n",
       " font-style: normal;\n",
       " color: #f8f8f0;\n",
       " background: rgba(93,92,82,.25);\n",
       " background-color: rgba(93,92,82,.25);\n",
       " border-color: transparent;\n",
       " background-image: none;\n",
       " text-shadow: none !important;\n",
       "}\n",
       "div.widget_item.btn-group > button.btn.btn-default.widget-combo-btn,\n",
       "div.widget_item.btn-group > button.btn.btn-default.widget-combo-btn:hover {\n",
       " background: #2c2c2c;\n",
       " background-color: #2c2c2c;\n",
       " border: 2px solid #2c2c2c !important;\n",
       " font-size: inherit;\n",
       " z-index: 0;\n",
       "}\n",
       "div.jupyter-widgets.widget-hprogress.widget-hbox {\n",
       " display: inline-table !important;\n",
       " width: 38% !important;\n",
       " margin-left: 10px;\n",
       "}\n",
       "div.jupyter-widgets.widget-hprogress.widget-hbox .widget-label,\n",
       "div.widget-hbox .widget-label,\n",
       ".widget-hbox .widget-label,\n",
       ".widget-inline-hbox .widget-label,\n",
       "div.widget-label {\n",
       " text-align: -webkit-auto !important;\n",
       " margin-left: 15px !important;\n",
       " max-width: 240px !important;\n",
       " min-width: 100px !important;\n",
       " vertical-align: text-top !important;\n",
       " color: #cccccc !important;\n",
       " font-size: 14px !important;\n",
       "}\n",
       ".widget-hprogress .progress {\n",
       " flex-grow: 1;\n",
       " height: 20px;\n",
       " margin-top: auto;\n",
       " margin-left: 12px;\n",
       " margin-bottom: auto;\n",
       " width: 300px;\n",
       "}\n",
       ".progress {\n",
       " overflow: hidden;\n",
       " height: 22px;\n",
       " margin-bottom: 10px;\n",
       " padding-left: 10px;\n",
       " background-color: #49483e !important;\n",
       " border-radius: 2px;\n",
       " -webkit-box-shadow: none;\n",
       " box-shadow: none;\n",
       " z-index: 10;\n",
       "}\n",
       ".progress-bar-danger {\n",
       " background-color: #e74c3c !important;\n",
       "}\n",
       ".progress-bar-info {\n",
       " background-color: #3498db !important;\n",
       "}\n",
       ".progress-bar-warning {\n",
       " background-color: #ff914d !important;\n",
       "}\n",
       ".progress-bar-success {\n",
       " background-color: #83a83b !important;\n",
       "}\n",
       ".widget-select select {\n",
       " margin-left: 12px;\n",
       "}\n",
       ".rendered_html :link {\n",
       " font-family: sans-serif;\n",
       " font-size: 100%;\n",
       " color: #a6e22e;\n",
       " text-decoration: underline;\n",
       "}\n",
       ".rendered_html :visited,\n",
       ".rendered_html :visited:active,\n",
       ".rendered_html :visited:focus {\n",
       " color: #acdf45;\n",
       "}\n",
       ".rendered_html :visited:hover,\n",
       ".rendered_html :link:hover {\n",
       " font-family: sans-serif;\n",
       " font-size: 100%;\n",
       " color: #97dc0b;\n",
       "}\n",
       "div.cell.text_cell a.anchor-link:link {\n",
       " font-size: inherit;\n",
       " text-decoration: none;\n",
       " padding: 0px 20px;\n",
       " visibility: none;\n",
       " color: rgba(0,0,0,.32);\n",
       "}\n",
       "div.cell.text_cell a.anchor-link:link:hover {\n",
       " font-size: inherit;\n",
       " color: #a6e22e;\n",
       "}\n",
       ".navbar-text {\n",
       " margin-top: 4px;\n",
       " margin-bottom: 0px;\n",
       "}\n",
       "#clusters > a {\n",
       " color: #a6e22e;\n",
       " text-decoration: underline;\n",
       " cursor: auto;\n",
       "}\n",
       "#clusters > a:hover {\n",
       " color: #ae81ff;\n",
       " text-decoration: underline;\n",
       " cursor: auto;\n",
       "}\n",
       "#nbextensions-configurator-container > div.row.container-fluid.nbext-selector > h3 {\n",
       " font-size: 17px;\n",
       " margin-top: 5px;\n",
       " margin-bottom: 8px;\n",
       " height: 24px;\n",
       " padding: 4px 0 4px 0;\n",
       "}\n",
       "div#nbextensions-configurator-container.container,\n",
       "#nbextensions-configurator-container.container {\n",
       " width: 100%;\n",
       " margin-right: auto;\n",
       " margin-left: auto;\n",
       "}\n",
       "div.nbext-selector > nav > .nav > li > a {\n",
       " font-family: sans-serif;\n",
       " font-size: 10.5pt;\n",
       " padding: 2px 5px;\n",
       "}\n",
       "div.nbext-selector > nav > .nav > li > a:hover {\n",
       " background: transparent;\n",
       "}\n",
       "div.nbext-selector > nav > .nav > li:hover {\n",
       " background-color: rgba(93,92,82,.25) !important;\n",
       " background: rgba(93,92,82,.25) !important;\n",
       "}\n",
       "div.nbext-selector > nav > .nav > li.active:hover {\n",
       " background: transparent !important;\n",
       " background-color: transparent !important;\n",
       "}\n",
       ".nav-pills > li.active > a,\n",
       ".nav-pills > li.active > a:active,\n",
       ".nav-pills > li.active > a:hover,\n",
       ".nav-pills > li.active > a:focus {\n",
       " color: #f8f8f2;\n",
       " background-color: rgba(93,92,82,.25) !important;\n",
       " background: rgba(93,92,82,.25) !important;\n",
       " -webkit-backface-visibility: hidden;\n",
       " -webkit-font-smoothing: subpixel-antialiased !important;\n",
       "}\n",
       "div.nbext-readme > .nbext-readme-contents > .rendered_html {\n",
       " font-family: sans-serif;\n",
       " font-size: 11.5pt;\n",
       " line-height: 145%;\n",
       " padding: 1em 1em;\n",
       " color: #f8f8f0;\n",
       " background-color: #282828;\n",
       " -webkit-box-shadow: none;\n",
       " -moz-box-shadow: none;\n",
       " box-shadow: none;\n",
       "}\n",
       ".nbext-icon,\n",
       ".nbext-desc,\n",
       ".nbext-compat-div,\n",
       ".nbext-enable-btns,\n",
       ".nbext-params {\n",
       " margin-bottom: 8px;\n",
       " font-size: 11.5pt;\n",
       "}\n",
       "div.nbext-readme > .nbext-readme-contents {\n",
       " padding: 0;\n",
       " overflow-y: hidden;\n",
       "}\n",
       "div.nbext-readme > .nbext-readme-contents:not(:empty) {\n",
       " margin-top: 0.5em;\n",
       " margin-bottom: 2em;\n",
       " border: none;\n",
       " border-top-color: rgba(148,204,114,.2);\n",
       "}\n",
       ".nbext-showhide-incompat {\n",
       " padding-bottom: 0.5em;\n",
       " color: #888888;\n",
       " font-size: 10.5pt;\n",
       "}\n",
       ".nbext-filter-menu.dropdown-menu > li > a:hover,\n",
       ".nbext-filter-menu.dropdown-menu > li > a:focus,\n",
       ".nbext-filter-menu.dropdown-menu > li > a.ui-state-focus {\n",
       " color: #f8f8f0 !important;\n",
       " background-color: rgba(93,92,82,.25) !important;\n",
       " background: rgba(93,92,82,.25) !important;\n",
       " border-color: rgba(93,92,82,.25) !important;\n",
       "}\n",
       ".nbext-filter-input-wrap > .nbext-filter-input-subwrap,\n",
       ".nbext-filter-input-wrap > .nbext-filter-input-subwrap > input {\n",
       " border: none;\n",
       " outline: none;\n",
       " background-color: transparent;\n",
       " padding: 0;\n",
       " vertical-align: middle;\n",
       " margin-top: -2px;\n",
       "}\n",
       "span.rendered_html code {\n",
       " background-color: transparent;\n",
       " color: #f8f8f0;\n",
       "}\n",
       "#nbextensions-configurator-container > div.row.container-fluid.nbext-selector {\n",
       " padding-left: 0px;\n",
       " padding-right: 0px;\n",
       "}\n",
       ".nbext-filter-menu {\n",
       " max-height: 55vh !important;\n",
       " overflow-y: auto;\n",
       " outline: none;\n",
       " border: none;\n",
       "}\n",
       ".nbext-filter-menu:hover {\n",
       " border: none;\n",
       "}\n",
       ".alert-warning {\n",
       " background-color: #232323;\n",
       " border-color: #232323;\n",
       " color: #f8f8f0;\n",
       "}\n",
       ".notification_widget.danger {\n",
       " color: #ffffff;\n",
       " background-color: #e74c3c;\n",
       " border-color: #e74c3c;\n",
       " padding-right: 5px;\n",
       "}\n",
       "#nbextensions-configurator-container > div.nbext-buttons.tree-buttons.no-padding.pull-right > span > button {\n",
       " border: none !important;\n",
       "}\n",
       "button#refresh_running_list {\n",
       " border: none !important;\n",
       "}\n",
       "mark,\n",
       ".mark {\n",
       " background-color: #282828;\n",
       " color: #f8f8f0;\n",
       " padding: .15em;\n",
       "}\n",
       "a.text-warning,\n",
       "a.text-warning:hover {\n",
       " color: #75715e;\n",
       "}\n",
       "a.text-warning.bg-warning {\n",
       " background-color: #1e1e1e;\n",
       "}\n",
       "span.bg-success.text-success {\n",
       " background-color: transparent;\n",
       " color: #a6e22e;\n",
       "}\n",
       "span.bg-danger.text-danger {\n",
       " background-color: #1e1e1e;\n",
       " color: #f92672;\n",
       "}\n",
       ".has-success .input-group-addon {\n",
       " color: #a6e22e;\n",
       " border-color: transparent;\n",
       " background: inherit;\n",
       " background-color: rgba(83,180,115,.10);\n",
       "}\n",
       ".has-success .form-control {\n",
       " border-color: #a6e22e;\n",
       " -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);\n",
       " box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);\n",
       "}\n",
       ".has-error .input-group-addon {\n",
       " color: #f92672;\n",
       " border-color: transparent;\n",
       " background: inherit;\n",
       " background-color: rgba(192,57,67,.10);\n",
       "}\n",
       ".has-error .form-control {\n",
       " border-color: #f92672;\n",
       " -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);\n",
       " box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);\n",
       "}\n",
       ".kse-input-group-pretty > kbd {\n",
       " font-family: monospace, monospace;\n",
       " color: #f8f8f0;\n",
       " font-weight: normal;\n",
       " background: transparent;\n",
       "}\n",
       ".kse-input-group-pretty > kbd {\n",
       " font-family: monospace, monospace;\n",
       " color: #f8f8f0;\n",
       " font-weight: normal;\n",
       " background: transparent;\n",
       "}\n",
       "div.nbext-enable-btns .btn[disabled],\n",
       "div.nbext-enable-btns .btn[disabled]:hover,\n",
       ".btn-default.disabled,\n",
       ".btn-default[disabled] {\n",
       " background: #2c2c2c;\n",
       " background-color: #2c2c2c;\n",
       " color: #f3f3e6;\n",
       "}\n",
       "label#Keyword-Filter {\n",
       " display: none;\n",
       "}\n",
       ".input-group .nbext-list-btn-add,\n",
       ".input-group-btn:last-child > .btn-group > .btn {\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border-color: #2f2f2f;\n",
       " border: 2px solid #2f2f2f;\n",
       "}\n",
       ".input-group .nbext-list-btn-add:hover,\n",
       ".input-group-btn:last-child > .btn-group > .btn:hover {\n",
       " background: #2a2a2a;\n",
       " background-color: #2a2a2a;\n",
       " border-color: #2a2a2a;\n",
       " border: 2px solid #2a2a2a;\n",
       "}\n",
       "#notebook-container > div.cell.code_cell.rendered.selected > div.widget-area > div.widget-subarea > div > div.widget_item.btn-group > button.btn.btn-default.dropdown-toggle.widget-combo-carrot-btn {\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border-color: #2f2f2f;\n",
       "}\n",
       "#notebook-container > div.cell.code_cell.rendered.selected > div.widget-area > div.widget-subarea > div > div.widget_item.btn-group > button.btn.btn-default.dropdown-toggle.widget-combo-carrot-btn:hover {\n",
       " background: #2a2a2a;\n",
       " background-color: #2a2a2a;\n",
       " border-color: #2a2a2a;\n",
       "}\n",
       ".ui-widget-content {\n",
       " background: #2f2f2f;\n",
       " background-color: #2f2f2f;\n",
       " border: 2px solid #2f2f2f;\n",
       " color: #f8f8f0;\n",
       "}\n",
       "div.collapsible_headings_toggle {\n",
       " color: rgba(93,92,82,.5) !important;\n",
       "}\n",
       "div.collapsible_headings_toggle:hover {\n",
       " color: #a6e22e !important;\n",
       "}\n",
       ".collapsible_headings_toggle .h1,\n",
       ".collapsible_headings_toggle .h2,\n",
       ".collapsible_headings_toggle .h3,\n",
       ".collapsible_headings_toggle .h4,\n",
       ".collapsible_headings_toggle .h5,\n",
       ".collapsible_headings_toggle .h6 {\n",
       " margin: 0.3em .4em 0em 0em !important;\n",
       " line-height: 1.2 !important;\n",
       "}\n",
       "div.collapsible_headings_toggle .fa-caret-down:before,\n",
       "div.collapsible_headings_toggle .fa-caret-right:before {\n",
       " font-size: xx-large;\n",
       " transition: transform 1000ms;\n",
       " transform: none !important;\n",
       "}\n",
       ".collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h1:after,\n",
       ".collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h2:after,\n",
       ".collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h3:after,\n",
       ".collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h4:after,\n",
       ".collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h5:after,\n",
       ".collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h6:after {\n",
       " position: absolute;\n",
       " right: 0;\n",
       " bottom: 20% !important;\n",
       " content: \"[\\002026]\";\n",
       " color: rgba(93,92,82,.5) !important;\n",
       " padding: 0.5em 0em 0em 0em !important;\n",
       "}\n",
       ".collapsible_headings_ellipsis .rendered_html h1,\n",
       ".collapsible_headings_ellipsis .rendered_html h2,\n",
       ".collapsible_headings_ellipsis .rendered_html h3,\n",
       ".collapsible_headings_ellipsis .rendered_html h4,\n",
       ".collapsible_headings_ellipsis .rendered_html h5,\n",
       ".collapsible_headings_ellipsis .rendered_html h6,\n",
       ".collapsible_headings_toggle .fa {\n",
       " transition: transform 1000ms !important;\n",
       " -webkit-transform: inherit !important;\n",
       " -moz-transform: inherit !important;\n",
       " -ms-transform: inherit !important;\n",
       " -o-transform: inherit !important;\n",
       " transform: inherit !important;\n",
       " padding-right: 0px !important;\n",
       "}\n",
       "#toc-wrapper {\n",
       " z-index: 90;\n",
       " position: fixed !important;\n",
       " display: flex;\n",
       " flex-direction: column;\n",
       " overflow: hidden;\n",
       " padding: 10px;\n",
       " border-style: solid;\n",
       " border-width: thin;\n",
       " border-right-width: medium !important;\n",
       " background-color: #1e1e1e !important;\n",
       "}\n",
       "#toc-wrapper.ui-draggable.ui-resizable.sidebar-wrapper {\n",
       " border-color: rgba(93,92,82,.25) !important;\n",
       "}\n",
       "#toc a,\n",
       "#navigate_menu a,\n",
       ".toc {\n",
       " color: #f8f8f0 !important;\n",
       " font-size: 11pt !important;\n",
       "}\n",
       "#toc li > span:hover {\n",
       " background-color: rgba(93,92,82,.25) !important;\n",
       "}\n",
       "#toc a:hover,\n",
       "#navigate_menu a:hover,\n",
       ".toc {\n",
       " color: #f8f8f2 !important;\n",
       " font-size: 11pt !important;\n",
       "}\n",
       "#toc-wrapper .toc-item-num {\n",
       " color: #a6e22e !important;\n",
       " font-size: 11pt !important;\n",
       "}\n",
       "input.raw_input {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 11pt !important;\n",
       " color: #f8f8f0;\n",
       " background-color: #282828;\n",
       " border-color: #252525;\n",
       " background: #252525;\n",
       " width: auto;\n",
       " vertical-align: baseline;\n",
       " padding: 0em 0.25em;\n",
       " margin: 0em 0.25em;\n",
       " -webkit-box-shadow: none;\n",
       " box-shadow: none;\n",
       "}\n",
       "audio,\n",
       "video {\n",
       " display: inline;\n",
       " vertical-align: middle;\n",
       " align-content: center;\n",
       " margin-left: 20%;\n",
       "}\n",
       ".cmd-palette .modal-body {\n",
       " padding: 0px;\n",
       " margin: 0px;\n",
       "}\n",
       ".cmd-palette form {\n",
       " background: #293547;\n",
       " background-color: #293547;\n",
       "}\n",
       ".typeahead-field input:last-child,\n",
       ".typeahead-hint {\n",
       " background: #293547;\n",
       " background-color: #293547;\n",
       " z-index: 1;\n",
       "}\n",
       ".typeahead-field input {\n",
       " font-family: sans-serif;\n",
       " color: #f8f8f0;\n",
       " border: none;\n",
       " font-size: 28pt;\n",
       " display: inline-block;\n",
       " line-height: inherit;\n",
       " padding: 3px 10px;\n",
       " height: 70px;\n",
       "}\n",
       ".typeahead-select {\n",
       " background-color: #293547;\n",
       "}\n",
       "body > div.modal.cmd-palette.typeahead-field {\n",
       " display: table;\n",
       " border-collapse: separate;\n",
       " background-color: #2b3850;\n",
       "}\n",
       ".typeahead-container button {\n",
       " font-family: sans-serif;\n",
       " font-size: 28pt;\n",
       " background-color: #2f2f2f;\n",
       " border: none;\n",
       " display: inline-block;\n",
       " line-height: inherit;\n",
       " padding: 3px 10px;\n",
       " height: 70px;\n",
       "}\n",
       ".typeahead-search-icon {\n",
       " min-width: 40px;\n",
       " min-height: 55px;\n",
       " display: block;\n",
       " vertical-align: middle;\n",
       " text-align: center;\n",
       "}\n",
       ".typeahead-container button:focus,\n",
       ".typeahead-container button:hover {\n",
       " color: #f8f8f0;\n",
       " background-color: #2a2a2a;\n",
       " border-color: #2a2a2a;\n",
       "}\n",
       ".typeahead-list > li.typeahead-group.active > a,\n",
       ".typeahead-list > li.typeahead-group > a,\n",
       ".typeahead-list > li.typeahead-group > a:focus,\n",
       ".typeahead-list > li.typeahead-group > a:hover {\n",
       " display: none;\n",
       "}\n",
       ".typeahead-dropdown > li > a,\n",
       ".typeahead-list > li > a {\n",
       " color: #f8f8f0;\n",
       " text-decoration: none;\n",
       "}\n",
       ".typeahead-dropdown,\n",
       ".typeahead-list {\n",
       " font-family: sans-serif;\n",
       " font-size: 13pt;\n",
       " color: #f8f8f0;\n",
       " background-color: #202937;\n",
       " border: none;\n",
       " background-clip: padding-box;\n",
       " margin-top: 0px;\n",
       " padding: 3px 2px 3px 0px;\n",
       " line-height: 1.7;\n",
       "}\n",
       ".typeahead-dropdown > li.active > a,\n",
       ".typeahead-dropdown > li > a:focus,\n",
       ".typeahead-dropdown > li > a:hover,\n",
       ".typeahead-list > li.active > a,\n",
       ".typeahead-list > li > a:focus,\n",
       ".typeahead-list > li > a:hover {\n",
       " color: #f8f8f0;\n",
       " background-color: #2b3850;\n",
       " border-color: #2b3850;\n",
       "}\n",
       ".command-shortcut:before {\n",
       " content: \"(command)\";\n",
       " padding-right: 3px;\n",
       " color: #75715e;\n",
       "}\n",
       ".edit-shortcut:before {\n",
       " content: \"(edit)\";\n",
       " padding-right: 3px;\n",
       " color: #75715e;\n",
       "}\n",
       "ul.typeahead-list i {\n",
       " margin-left: 1px;\n",
       " width: 18px;\n",
       " margin-right: 10px;\n",
       "}\n",
       "ul.typeahead-list {\n",
       " max-height: 50vh;\n",
       " overflow: auto;\n",
       "}\n",
       ".typeahead-list > li {\n",
       " position: relative;\n",
       " border: none;\n",
       "}\n",
       "div.input.typeahead-hint,\n",
       "input.typeahead-hint,\n",
       "body > div.modal.cmd-palette.in > div > div > div > form > div > div.typeahead-field > span.typeahead-query > input.typeahead-hint {\n",
       " color: #75715e !important;\n",
       " background-color: transparent;\n",
       " padding: 3px 10px;\n",
       "}\n",
       ".typeahead-dropdown > li > a,\n",
       ".typeahead-list > li > a {\n",
       " display: block;\n",
       " padding: 5px;\n",
       " clear: both;\n",
       " font-weight: 400;\n",
       " line-height: 1.7;\n",
       " border: 1px solid #202937;\n",
       " border-bottom-color: rgba(93,92,82,.5);\n",
       "}\n",
       "body > div.modal.cmd-palette.in > div {\n",
       " min-width: 750px;\n",
       " margin: 150px auto;\n",
       "}\n",
       ".typeahead-container strong {\n",
       " font-weight: bolder;\n",
       " color: #a6e22e;\n",
       "}\n",
       "#find-and-replace #replace-preview .match,\n",
       "#find-and-replace #replace-preview .insert {\n",
       " color: #ffffff;\n",
       " background-color: #57564b;\n",
       " border-color: #57564b;\n",
       " border-style: solid;\n",
       " border-width: 1px;\n",
       " border-radius: 0px;\n",
       "}\n",
       "#find-and-replace #replace-preview .replace .match {\n",
       " background-color: #f92672;\n",
       " border-color: #f92672;\n",
       " border-radius: 0px;\n",
       "}\n",
       "#find-and-replace #replace-preview .replace .insert {\n",
       " background-color: #a6e22e;\n",
       " border-color: #a6e22e;\n",
       " border-radius: 0px;\n",
       "}\n",
       ".jupyter-dashboard-menu-item.selected::before {\n",
       " font-family: 'FontAwesome' !important;\n",
       " content: '\\f00c' !important;\n",
       " position: absolute !important;\n",
       " color: #a6e22e !important;\n",
       " left: 0px !important;\n",
       " top: 13px !important;\n",
       " font-size: 12px !important;\n",
       "}\n",
       ".shortcut_key,\n",
       "span.shortcut_key {\n",
       " display: inline-block;\n",
       " width: 16ex;\n",
       " text-align: right;\n",
       " font-family: monospace;\n",
       "}\n",
       ".jupyter-keybindings {\n",
       " padding: 1px;\n",
       " line-height: 24px;\n",
       " border-bottom: 1px solid rgba(93,92,82,.25);\n",
       "}\n",
       ".jupyter-keybindings i {\n",
       " background: #282828;\n",
       " font-size: small;\n",
       " padding: 5px;\n",
       " margin-left: 7px;\n",
       "}\n",
       "div#short-key-bindings-intro.well,\n",
       ".well {\n",
       " background-color: #2f2f2f;\n",
       " border: 1px solid #2f2f2f;\n",
       " color: #f8f8f0;\n",
       " border-radius: 2px;\n",
       " -webkit-box-shadow: none;\n",
       " box-shadow: none;\n",
       "}\n",
       "#texteditor-backdrop {\n",
       " background: #1e1e1e;\n",
       " background-color: #1e1e1e;\n",
       "}\n",
       "#texteditor-backdrop #texteditor-container .CodeMirror-gutter,\n",
       "#texteditor-backdrop #texteditor-container .CodeMirror-gutters {\n",
       " background: #49483e;\n",
       " background-color: #49483e;\n",
       " color: #75715e;\n",
       "}\n",
       ".edit_app #menubar .navbar {\n",
       " margin-bottom: 0px;\n",
       "}\n",
       "#texteditor-backdrop #texteditor-container {\n",
       " padding: 0px;\n",
       " background-color: #282828;\n",
       " box-shadow: none;\n",
       "}\n",
       ".terminal-app {\n",
       " background: #1e1e1e;\n",
       "}\n",
       ".terminal-app > #header {\n",
       " background: #1e1e1e;\n",
       "}\n",
       ".terminal-app .terminal {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 11pt;\n",
       " line-height: 170%;\n",
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       " width: inherit;\n",
       " font-size: inherit;\n",
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       " line-height: 22px;\n",
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       "#notebook-container > div.cell.code_cell.rendered.selected > div.input > div.inner_cell > div.ctb_hideshow.ctb_show > div > div > button,\n",
       "#notebook-container > div.input > div.inner_cell > div.ctb_hideshow.ctb_show > div > div > button {\n",
       " font-size: 10pt;\n",
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       " width: inherit;\n",
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       " top: 0px;\n",
       " height: 8px;\n",
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       "}\n",
       "span.ansiblack,\n",
       ".ansi-black-fg {\n",
       " color: #282828;\n",
       "}\n",
       "span.ansiblue,\n",
       ".ansi-blue-fg,\n",
       ".ansi-blue-intense-fg {\n",
       " color: #66d9ef;\n",
       "}\n",
       "span.ansigray,\n",
       ".ansi-gray-fg,\n",
       ".ansi-gray-intense-fg {\n",
       " color: #888888;\n",
       "}\n",
       "span.ansigreen,\n",
       ".ansi-green-fg {\n",
       " color: #a6e22e;\n",
       "}\n",
       ".ansi-green-intense-fg {\n",
       " color: #888888;\n",
       "}\n",
       "span.ansipurple,\n",
       ".ansi-purple-fg,\n",
       ".ansi-purple-intense-fg {\n",
       " color: #ae81ff;\n",
       "}\n",
       "span.ansicyan,\n",
       ".ansi-cyan-fg,\n",
       ".ansi-cyan-intense-fg {\n",
       " color: #ae81ff;\n",
       "}\n",
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       "span.ansired,\n",
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       " overflow-x: visible;\n",
       " overflow-y: scroll;\n",
       " height: 400px;\n",
       " max-width: 800px;\n",
       "}\n",
       ".cm-s-ipython.CodeMirror {\n",
       " font-family: monospace, monospace;\n",
       " font-size: 11pt;\n",
       " background: #282828;\n",
       " color: #f8f8f0;\n",
       " border-radius: 2px;\n",
       " font-style: normal;\n",
       " font-weight: normal;\n",
       "}\n",
       ".cm-s-ipython div.CodeMirror-selected {\n",
       " background: #49483e;\n",
       "}\n",
       ".CodeMirror-gutters {\n",
       " border: none;\n",
       " border-right: 1px solid #49483e !important;\n",
       " background-color: #49483e !important;\n",
       " background: #49483e !important;\n",
       " border-radius: 0px;\n",
       " white-space: nowrap;\n",
       "}\n",
       ".cm-s-ipython .CodeMirror-gutters {\n",
       " background: #49483e;\n",
       " border: none;\n",
       " border-radius: 0px;\n",
       " width: 36px;\n",
       "}\n",
       ".cm-s-ipython .CodeMirror-linenumber {\n",
       " color: #75715e;\n",
       "}\n",
       ".CodeMirror-sizer {\n",
       " margin-left: 40px;\n",
       "}\n",
       ".CodeMirror-linenumber,\n",
       "div.CodeMirror-linenumber,\n",
       ".CodeMirror-gutter.CodeMirror-linenumberdiv.CodeMirror-gutter.CodeMirror-linenumber {\n",
       " padding-right: 1px;\n",
       " margin-left: 0px;\n",
       " margin: 0px;\n",
       " width: 26px !important;\n",
       " padding: 0px;\n",
       " text-align: right;\n",
       "}\n",
       ".CodeMirror-linenumber {\n",
       " color: #75715e;\n",
       "}\n",
       ".cm-s-ipython .CodeMirror-cursor {\n",
       " border-left: 2px solid #0095ff !important;\n",
       "}\n",
       ".cm-s-ipython span.cm-comment {\n",
       " color: #75715e;\n",
       " font-style: italic;\n",
       "}\n",
       ".cm-s-ipython span.cm-atom {\n",
       " color: #ae81ff;\n",
       "}\n",
       ".cm-s-ipython span.cm-number {\n",
       " color: #ae81ff;\n",
       "}\n",
       ".cm-s-ipython span.cm-property {\n",
       " color: #f8f8f0;\n",
       "}\n",
       ".cm-s-ipython span.cm-attribute {\n",
       " color: #f8f8f0;\n",
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       " color: #a6e22e;\n",
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       "}\n",
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       " background: rgba(249,38,114,.4);\n",
       "}\n",
       ".cm-s-ipython span.cm-tag {\n",
       " color: #ae81ff;\n",
       "}\n",
       ".cm-s-ipython span.cm-link {\n",
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       ".cm-s-ipython span.cm-quote {\n",
       " color: #e6db74;\n",
       "}\n",
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       " background-color: #49483e;\n",
       "}\n",
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       " font-weight: bold;\n",
       " background: rgba(249,38,114,.4) !important;\n",
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       " color: #a6e22e;\n",
       "}\n",
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       " font-weight: normal;\n",
       " color: #a6e22e !important;\n",
       " margin-top: 0.3em !important;\n",
       " margin-bottom: 0.3em !important;\n",
       "}\n",
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       " color: #f8f8f0 !important;\n",
       "}\n",
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       " color: #fd971f !important;\n",
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       " color: #75715e !important;\n",
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       " color: #e6db74;\n",
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       " color: #f92672;\n",
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       " color: #ae81ff;\n",
       "}\n",
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       " color: #ae81ff;\n",
       "}\n",
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       " color: #a6e22e;\n",
       "}\n",
       ".cm-s-default .cm-atom {\n",
       " color: #ae81ff;\n",
       "}\n",
       ".cm-s-default .cm-def {\n",
       " color: #a6e22e;\n",
       "}\n",
       ".CodeMirror-cursor {\n",
       " border-left: 2px solid #0095ff !important;\n",
       " border-right: none;\n",
       " width: 0;\n",
       "}\n",
       ".cm-s-default div.CodeMirror-selected {\n",
       " background: #49483e;\n",
       "}\n",
       ".cm-s-default .cm-selected {\n",
       " background: #49483e;\n",
       "}\n",
       ".MathJax_Display,\n",
       ".MathJax {\n",
       " border: 0 !important;\n",
       " font-size: 100% !important;\n",
       " text-align: center !important;\n",
       " margin: 0em !important;\n",
       " line-height: 2.25 !important;\n",
       "}\n",
       ".MathJax:focus,\n",
       "body :focus .MathJax {\n",
       " display: inline-block !important;\n",
       "}\n",
       ".MathJax:focus,\n",
       "body :focus .MathJax {\n",
       " display: inline-block !important;\n",
       "}\n",
       ".completions {\n",
       " position: absolute;\n",
       " z-index: 110;\n",
       " overflow: hidden;\n",
       " border: medium solid #49483e;\n",
       " box-shadow: none;\n",
       " line-height: 1;\n",
       "}\n",
       ".completions select {\n",
       " background: #282828;\n",
       " background-color: #282828;\n",
       " outline: none;\n",
       " border: none;\n",
       " padding: 0px;\n",
       " margin: 0px;\n",
       " margin-left: 2px;\n",
       " overflow: auto;\n",
       " font-family: monospace, monospace;\n",
       " font-size: 11pt;\n",
       " color: #f8f8f0;\n",
       " width: auto;\n",
       "}\n",
       "div#maintoolbar {\n",
       " display: none !important;\n",
       "}\n",
       "#header-container {\n",
       " display: none !important;\n",
       "}\n",
       "\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "        \"HTML-CSS\": {\n",
       "            /*preferredFont: \"TeX\",*/\n",
       "            /*availableFonts: [\"TeX\", \"STIX\"],*/\n",
       "            styles: {\n",
       "                scale: 100,\n",
       "                \".MathJax_Display\": {\n",
       "                    \"font-size\": \"100%\",\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "     </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For setting theme💖\n",
    "from jupyterthemes import get_themes\n",
    "import jupyterthemes as jt\n",
    "from jupyterthemes.stylefx import set_nb_theme\n",
    "set_nb_theme('monokai')\n",
    "# Themes : chesterish, grade3, gruvboxd, gruvboxl, manokai, oceans16, onedork, solarizedd, solarizedl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "a547a3bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing required tools.\n",
    "\n",
    "# Regular EDA (exploratory data analysis) and plotting libraries.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# to make plots appear inside the notebook.\n",
    "%matplotlib inline \n",
    "\n",
    "# Getting models from Scikit-Learn \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# For evaluating the model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47fece2",
   "metadata": {},
   "source": [
    "### ***Importing Data 💽***\n",
    "\n",
    "* ***There are many different kinds of ways to store data. The typical way of storing \"tabular data\", data similar to what you'd see in an Excel file is in `.csv` format. `.csv` stands for comma seperated values.***\n",
    "\n",
    "* ***Pandas has a built-in function to read `.csv` files called `read_csv()` which takes the file pathname of your `.csv` file. You'll likely use this a lot.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "aed89693",
   "metadata": {},
   "outputs": [
    {
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       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/heart-disease.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "bdead887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aaf8c0c",
   "metadata": {},
   "source": [
    "### ***Data Exploration (exploratory data analysis or EDA)***\n",
    "\n",
    "***Extracting more information from data.***\n",
    "\n",
    "***We're focusing on :***\n",
    " * ***1. Question which we are trying to solve.***\n",
    " * ***2. Type of data we have and dealing with different types data (e.g numerical and non-numerical).***\n",
    " * ***3. Dealing with missing data.***\n",
    " * ***4. The outliers.***\n",
    " * ***5. Adding more features inorder to extract more information from our data.***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "98c38ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "01f7f0b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <th>exang</th>\n",
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       "      <td>264</td>\n",
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       "      <td>0</td>\n",
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       "      <th>300</th>\n",
       "      <td>68</td>\n",
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       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
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       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "94e595f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of classes present of each\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "100d8ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, '1'), Text(1, 0, '0')])"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mqP0AgPiKWwgyMzP16KOPxnxm4MyZM5o8ebImTJgQr1EAAPeI6+cI1q1bpy1btmjs2LGaP3++fvjhB506dUplZWXxHAMAcA9X5O5qbZx8+eWXqqio0NWrVzVx4kStWbNGL7zwQjxHAADcI+4hAAAMLbxnEwCMIwQAYBwhMK62tlbTpk3TtWvXEj0KIIlL1ScCITCsoaFBxcXFCofDiR4FkMSl6hOFxWKDwuGwjh07pj179mjkyJH6888/df78eT3yCBciQ2ItWrRI06dPj3pL+TvvvKP6+nqdOnUqgZMNbzwjMKi6ulq7d+9WUVGRSktLEz0OIIlL1ScSITAoGAzq3LlzWr9+vTweT6LHAST17VL1eDDi+sliDA3jx49P9AhADCcvVY/+4RkBgCGBS9UnDj9ZAEMCl6pPHEIAYEjgUvWJQwgADAlcqj5xWCwGMGRwqfrEIAQAhoyXXnpJt2/fVkVFhY4fP66JEydqx44deu655xI92rDGJ4sBwDjWCADAOEIAAMYRAgAwjhAAgHGEAACMIwQAYBwhAADjCAEAGEcIAMC4/wW7Li5U3ULKTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['target'].value_counts().plot(kind='bar', color = ['cyan', 'crimson'], grid = True);\n",
    "plt.xticks(rotation = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "ea1b378c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data 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   trestbps  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   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "e7e603d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No missing values 😁\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "3f6a8cc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda51419",
   "metadata": {},
   "source": [
    "### ***Heart Disease frequency according to Sex***\n",
    "\n",
    "* If you want to compare two columns to each other, you can use the function `pd.crosstab(column_1, column_2)`. \n",
    "\n",
    "* This is helpful if you want to start gaining an intuition about how your independent variables interact with your dependent variables.\n",
    "\n",
    "* Let's compare our target column with the sex column. \n",
    "\n",
    "* Remember from our data dictionary, for the target column, 1 = heart disease present, 0 = no heart disease. And for sex, 1 = male, 0 = female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "593a5bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a3c6244",
   "metadata": {},
   "source": [
    "#### *There are 207 males and 96 females in our study.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "5cb9e69f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target,df.sex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee3e9e9",
   "metadata": {},
   "source": [
    "### ***What can we infer from this?***\n",
    "\n",
    "***Let's make a simple heuristic.\n",
    "Since there are about 100 women and 72 of them have a postive value of heart disease being present, we might infer, based on this one variable if the participant is a woman, there's a 75% chance she has heart disease.\n",
    "As for males, there's about 200 total with around half indicating a presence of heart disease. So we might predict, if the participant is male, 50% of the time he will have heart disease.\n",
    "Averaging these two values, we can assume, based on no other parameters, if there's a person, there's a 62.5% chance they have heart disease.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "44a7851a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e30633",
   "metadata": {},
   "source": [
    "### ***Creating a plot of crosstab***\n",
    "\n",
    "* You can plot the crosstab by using the `plot()` function and passing it a few parameters such as, `kind` (the type of plot you want), `figsize=(length, width)` (how big you want it to be) and `color=[colour_1, colour_2]` (the different colours you'd like to use).\n",
    "\n",
    "\n",
    "* Different metrics are represented best with different kinds of plots. In our case, a bar graph is great. We'll see examples of more later. And with a bit of practice, you'll gain an intuition of which plot to use with different variables.\n",
    "\n",
    "\n",
    "* We'll create the plot with `crosstab()` and `plot()`, then add some helpful labels to it with `plt.title()`, `plt.xlabel()` and more.\n",
    "\n",
    "\n",
    "* To add the attributes, you call them on `plt` within the same cell as where you make create the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "619b573b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vUoiNjTXrTTxz5ozGjRunbt26qXbt2ipbtqyefvppbdmyRRUrVtSNGzfk5+eXpWPdXf/58+fT9N5FR0friSeeyPI+Y2NjtXDhQlWuXFkdOnRIs75EiRJydXU1BbnUf6tPPPGEaZg61fbt2+Xi4mL6PTk5WaNGjVLhwoXVvn17zZkzR61bt1bz5s2zXCeQVdwKBbCw4sWLy9vbW6tXr9aZM2dMy5OSkjRq1Ci9/vrrSk5O1uXLlyX9Xy9equ3bt+vvv/82zYG7n9QAdW+vTlYkJSXpo48+kp2dnXr27JlhuyFDhujtt9826+GpWrWqHB0dTXX4+voqPj5eoaGhZtsuW7ZMQ4cO1c8//yzpzhCwu7u7Wci5evWqVq9eLen/epHGjBmjAQMG6Nq1a6Z2FStWVMGCBc2OKd3pfbnbli1b9Prrr2vt2rVZOyFZlNnjt2jRQpI0f/58s3ZLliwx+z016Nx7L7YNGzY80HEzK3Xu5L3v3apVqxQWFmYWdPz8/HT48GH98MMPqlixoqpWrZqlY91d/6xZs8yWb968WdHR0RkON//XPp2dnRUSEqLTp0+nWb9x40ZdunRJL774oiSpSZMmphru7kk8evSo+vfvr6+//tq0bN68efr99981cuRIDRkyRJUqVdJHH31kms4A5CR67gArMHr0aL322mvy9/dX165dVbhwYa1fv16//vqrhg0bJjc3Nzk7O6tkyZKaOXOmEhMT5eHhocOHD2v16tXKmzevWaDJSGo4Cg0N1fnz5/+zB+XQoUOmnqHk5GT9+++/CgsL07FjxzRy5Mj7/pEODg7W6NGj1aNHD7Vs2VKGYej7779XYmKiXn31VUlSp06dtHr1an3yySf6/fff9fTTT+vPP//U8uXLVa1aNb388suSpEaNGmnOnDkaMmSIGjZsqHPnzunbb7819VimvvagoCD17t1bAQEBat++vfLmzavNmzcrJiZGn332maQ7oeTFF1/U/PnzderUKTVo0EBxcXFaunSpSpYsqeDg4P88jw8js8d/9tln1aFDB82ZM0dnzpxRzZo1tX37du3Zs8dsf+XLl1e1atW0YsUKFShQQOXLl9emTZtMczmzetzMqlKlijp16qTFixfr7Nmzql+/vo4dO6Zly5apffv2ZhcTtW7dWuPHj1d4eLgGDx78UOdt0aJFOnPmjOrWrau///5boaGhKlOmTJoLLTKjYMGCGj9+vN588021bdtWfn5+qly5slJSUrR//36FhYWpadOmatOmjSTJ09NTgYGBWrx4seLj49W0aVPFx8dryZIlcnZ2Nl3ocvz4cU2bNk0NGzY0bfvBBx+oe/fuGjt2bLoX4wDZiXAHWAEfHx+FhoYqJCRECxYsUHJysipUqKDx48ebhoucnJw0e/ZsjR8/XosWLZJhGCpbtqxGjRql5ORkjR07VpGRkfL29s7wOPXr11erVq20detW7dmzR82bN7/vHK7ly5dr+fLlpuN7eHjoySef1KhRo/Tcc8/d9zV16tRJjo6OWrRokSZPnqyUlBR5e3trzpw5pgtNnJyctHDhQk2fPl3h4eH64YcfVLx4cXXt2lUDBw5U/vz5JUmDBw/W7du3tWHDBm3dulXFixdXgwYN1LNnT7Vp00Z79uxRs2bN1LBhQ3311VeaNWuWZsyYocTERD311FOaPHmy6Y+snZ2dvvzyS82dO1dr1qzR1q1b5e7urubNm2vIkCHpTtjPTlk5/tixY1W2bFmtWLFC4eHhqlOnjj788MM0N+WdOnWqxo8fr2XLlilPnjzy9fXVqFGj1KpVqwc6bmZ9/PHHKl++vFauXKktW7aoZMmSGjhwoHr16mXWrmjRoqpfv7527dqV5atk761/zpw5WrNmjbZs2aIiRYqoS5cuGjx4cJqh5sxq3ry5Vq1apQULFmjHjh1atWqV7O3t9eSTT2r06NF65ZVXzKYMvPvuu6pYsaKWLVumzz77TK6urqpdu7apdy4lJUXvvvuu7OzszOaS1q1bV+3atdP333+vNm3aPFBPI5BZdsb9ZqkCAKzK3r171b17d40bN87Us5kb9OrVS5cvX9bKlSstXQpg85hzBwDIUSdPntTPP/+cq8IokJsxLAsAyBE7duzQ6tWr9csvv8jNzU3t2rWzdEnAY4GeOwBAjsifP7927twpZ2dnTZ06VQUKFLB0ScBjgTl3AAAANoSeOwAAABtCuAMAALAhXFBxl0uXriklhVFqAABgvezt7eTm5pzhesLdXVJSDMIdAADI1RiWBQAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIVwtCwCADUlOTtK1a1eUmHhDKSm3LV0OssjBwVEuLoWUP3/Gtzr5L4Q7AABsRHJyki5ePKMCBVzl7u4hBwcH2dnZWbosZJJhGEpKSlR8/HnlyeMoR0enB9oPw7IAANiIa9euqEABV7m4FFKePHkIdrmMnZ2dnJzyydm5kBIS4h94P4Q7AABsRGLiDeXL9+DDebAO+fLlV1LSrQfennAHAICNSEm5LQcHB0uXgYdkb+/wUPMlCXcAANgQhmJzv4d9Dwl3AAAANoSrZQEAeAw4F3FWAXvL9ulcT0nRtQvXsrzd2LEfKixsXYbrp0yZrmefrfswpT20QYP6yMEhj778coZF65AId8ADcXN2VJ4C+SxdhlVJvn5Tl64lWboMABkoYG8vSw/YGvb2ynq0u6N48RL6+OPx6a6rUKHCgxdlgwh3wAPIUyCfjhd73tJlWJVK53ZKhDsAOcTR0VHe3tUtXUauQLgDAAA24YcfVmvFim8UF3dKRYsW00svvaxu3V4zXaAwduyHunw5XnXrNlBo6GJdunRRNWr46N13P9TPP+/WokXzdenSRVWtWl3vvDNaTzxRUpJ048YNLVgwRzt2bNWZM6fl6Ogkb+/qGjBgiJ588ql0a0lJSdGSJQu1bt33OnfurJ54oqRefTVQbdu2z/HzQLgDAAC5QnJycpplqU/hWLx4gWbPnqHOnbuqbt0GOnr0d82bN1Px8Zc0ePBQU/tDhw7qwoULGjr0bcXHX9KkSeM1eHBfOTnl1aBBQ3XlymV9+eVETZnyuSZMmCJJ+uST9xUZeVh9+w5UyZKldOpUrObOnamPPnpXixYtT/fq1okTxyksbJ1eey1YVat6a9++Pfrss7G6efOmOnZ8JedOkgh3AAAgF4iLO6UXXqiXZvnw4e+oadOW+vrreXr55U4aPPhNSVKdOvWUP38BTZ/+hTp16ioPDw9J0vXr1/TJJ+NVsmQpSdL27Vv1v//t1PLla1SqVGlJ0l9/RWnz5nBJUmJiom7evKmhQ99SkyZNJUk+PrV07VqCpk37QvHx8XJzczOrKSbmpNauXaMBA4aoa9dupnpSUm5r7tyZatu2vfLly7l524Q7AABg9YoXL6FPP/08zfISJZ5QZORh3bx5Uw0bNjLr3Xvuuec1deokRUT8otat/SRJbm7upmAnSe7u7ipc2M0U7CSpYMFCSkhIkCTlzZtXkyeHSJLOnTur2NgYxcSc1P/+t0vSnef53isi4hcZhqHnnnverJ6GDRtrxYpQHTkSqWeeqf0wp+O+CHcAAMDqOTo6qnLlqumuu3LlsiRp6NBB6a4/f/6c6b8LFCiQZv1/9aLt3fuzpk6dpJMn/1aBAs568smnlD//nf0YhpGm/eXLd+p59VX/DOo5f9/jPSzCHQAAyNWcnV0kSR999KlZD1yqokWLPfC+4+JOaeTI4WrcuIkmTPjCtP9Vq1Zq797/pbuNi8udeqZNm51ucEy9UCOn8IQKAACQq1WrVl2Ojo66cOG8KleuavpJTk7WrFnTH6qn7I8/jurWrUQFBgaZBcc9e+4Eu5SUtD13NWo8I0m6cuWKWT1nzpzW3LkzdePGzQeuJzPouQMAALla4cKF9cor3TRr1nQlJCSoRg0fnT79r2bPniEXFxdVqFDxgfft5VVZDg4O+uqrqerc+VXdunVLGzb8oJ9/vjPn7ubNG2m2efLJp9S0aQuNG/ex/vnnlDw9Kys6+rhmzZohL6/Kpos7cgrhDgAA5Hp9+gxQkSJFtHr1t1q8eIEKFiykunXrq2/fgcqbN+8D77d06TL68MOxmj9/tkaMeFMFCxZUtWreCgmZpcGD++rXXw+qfPm0T8gYPfojff31PH333UqdO3dG7u5F1LbtS+rVq9/DvMxMsTPSmwn4mLpwISHd7lXgXsWKufKEintUOrdT585dtXQZwGPt9OmT8vAol+663Pxs2cfR/d5Le3s7FSnikuG29NwBAPAYuHbh2gM/1xW5CxdUAAAA2BDCHQAAgA0h3AEAANgQwh0AAIANIdwBAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AALBqgwb1UcOGtTVoUJ8M2/TvH6yGDWtr3rxZmd5vw4a1tXDh3Owo0arw+DEAAB4Dbs6OylMgn0VrSL5+U5euJT3QtnZ2djp8+JAuXDivIkWKmq07e/aMIiMPZ0eJNoFwBwDAYyBPgXw6Xux5i9ZQ6dxO6QHDXeXKVXT8+DFt375VL7/cyWzd1q2bVaFCRZ08+Xc2VJn7MSwLAACsXoECLqpTp562bt2cZt1PP22Sr28zs2Vxcaf0ySfvqV27FmrcuK78/Jpr7NgPdeXKlQyPcflyvD77bIzatm0mX9/n1L9/sA4fPpTdLyXHEe4AAECu4OvbTIcPH9KlSxdNy06f/ldHj/6upk1bmJbdvHlTgwf3VUxMjIYNG6kpU6arY8cu2rgxTLNnz0h334mJiRoyZID+979d6tdvoMaM+UyurgX1xhsDdPTo7zn+2rITw7IAACBXaNiwkRwc8mj79q1q395fkrRlyyY99ZSXSpcuY2p38uTf8vB4Qu+997GeeKKkJOmZZ2rryJFIHToUke6+w8M36PjxvzRnzteqXLmqJKlevQbq3fs1zZo1XV98kX4otEb03AEAgFyhQAFn1a1bT1u3/mRa9tNPm9S0aXOzdl5elTVjxlyVKOGh2NgY/fzzbn3zzWKdPPm3kpPTn/N34MA+FStWXE8+6ank5GQlJycrJSVFDRo01KFDEUpKerC5gpZAzx0AAMg1mjRpprFjP1B8fLyuXUvQn3/+obFjP0/TbtmyJVq8eIEuX74sd/ciqly5ivLly68bN66nu9/Lly/r7NkzeuGFehmsj1fRosWy9bXkFMIdAADINVKHZnfu3Kb4+EuqVq26PDw8zNps3Pijpk37QgMGDFHr1n4qXLiwJOm9997Rn3/+ke5+XVxcVL58BY0e/VG66wsVKpx9LyKHEe4AAECuUaBAAdWtW1/btm3RpUsX1apV2zRtDh8+pMKFC+vVVwNNy65fv67Dhw/JySlvuvutWfMZ7dnzPxUtWsysh27OnK90+vS/GYY+a2Q1c+6OHj2qatWq6fTp02bLd+3aJX9/f9WoUUO+vr6aP39+mm1/++03BQYGysfHRw0bNtTkyZNz1dg4AADIPF/fpjpwYJ+OHftTTZo0TbO+atVqio+P14wZX+rgwQPauDFMAwf20sWLF3Tz5o1099m69UsqWrS43nhjgMLDNygiYr9CQqbo66/nqWTJUrKzs8vpl5VtrKLn7sSJE+rbt6+Sk5PNlkdERKhfv35q1aqVhgwZogMHDmjChAkyDEPBwcGSpJMnT6pHjx7y8fHRF198oePHj2vKlClKSEjQ+++/b4mXAwAActBzzzWSg4ODqlevoaJFi6ZZ36pVW/377z9av/4HffvtChUrVkz16zdUhw6dNGHCWMXEnFTZsuXMtilQoIBmzJijmTOnKSRkiq5fv66SJUtp6NC35O/f5VG9tGxhZxiGYamDJycna/ny5Zo0aZIcHR0VHx+v7du3m8bOe/TooevXr2vFihWmbT7//HOtWLFCu3fvlpOTk959913t3r1bGzdulJOTkyTpm2++0ZgxY7R161aVKFEi0/VcuJCglBSLnQ7kIsWKuVr8Tu/WptK5nTp37qqlywAea6dPn5SHR7l01+X2x489bu73Xtrb26lIEZcMt7Voz92BAwc0ceJEBQcHq0SJEho9erRpXWJiovbv36833njDbJsWLVpo7ty5ioiIUL169bR79241adLEFOwkqWXLlvroo49MQ7oAADzuLl1LeuBHfyF3seicu0qVKmnz5s0aNGiQHBwczNbFxsYqKSlJFSpUMFtertydFBsdHa0bN27o33//TdPG3d1dLi4uio6OztkXAAAAYGUs2nOX3jh5qqtX7wzvuLiYdzs6OztLkhISEjJsk9ouISEhu0oFAADIFazmatl7pU4FzOjqFHt7+/u2MQxD9vZW+/IAAAByhNWmH1dXV0lK0/uW+rurq6upxy69Hrrr16+b9gEAAPC4sNpwV7ZsWTk4OCgmJsZseervFSpUkLOzs0qUKKGTJ0+atblw4YISEhLSzMUDAACwdVYb7vLmzavatWtr48aNuvtuLeHh4XJ1dZW3t7ck6bnnntPWrVt169YtszYODg6qU6fOI68bAABLsuAdzpBNHvY9tNpwJ0n9+/dXRESEhg4dqu3bt+uLL77QvHnz1LdvX+XPn1+S1KtXL507d059+vTR1q1btWDBAo0bN06dO3dWyZIlLfwKAAB4dBwcHJWUlGjpMvCQkpJuycHhwa95tepwV79+fYWEhOj48eMaOHCg1q5dq7ffflu9e/c2talUqZLmz5+v69ev6/XXX9eCBQsUFBSkd99914KVAwDw6Lm4FFJ8/Hldu3ZVt28n04uXyxiGoVu3EhUff04uLoUfeD8WfUKFteEJFcgsnlCRFk+oAKxDUtItJSTEKynpllJSblu6HGSRg0MeubgUVv78zhm2seonVAAAgOzl6OgkN7fili4DFmTVw7IAAADIGsIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AAIANIdwBAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQni0LAICVcHN2VJ4C+SxdhlVJvn5Tl64lWbqMXIVwBwCAlchTIJ+OF3ve0mVYlUrndkqEuyxhWBYAAMCGEO4AAABsCOEOAADAhhDuAAAAbAjhDgAAwIYQ7gAAAGwI4Q4AAMCGEO4AAABsCOEOAADAhhDuAAAAbAjhDgAAwIYQ7gAAAGwI4Q4AAMCGEO4AAABsCOEOAADAhhDuAAAAbAjhDgAAwIYQ7gAAAGwI4Q4AAMCGEO4AAABsCOEOAADAhhDuAAAAbAjhDgAAwIYQ7gAAAGwI4Q4AAMCGEO4AAABsCOEOAADAhuSKcBcaGqpWrVqpZs2a8vPz0w8//GC2fteuXfL391eNGjXk6+ur+fPnW6hSAAAAy7L6cLd8+XJ9+OGHeuGFFzRjxgw1aNBAb731lsLCwiRJERER6tevnypWrKiQkBD5+flpwoQJmjdvnoUrBwAAePTyWLqA/7J69WrVrVtXI0aMkCQ1aNBAkZGR+uabb9SqVStNnTpVVatW1eeffy5JatSokZKTkzVz5kwFBgbKycnJkuUDAAA8Ulbfc5eYmChnZ2ezZYULF1Z8fLwSExO1f/9+NW/e3Gx9ixYtdOXKFUVERDzKUgEAACzO6sNd9+7dtXPnToWFhSkhIUE//vijtm3bpnbt2ik2NlZJSUmqUKGC2TblypWTJEVHR1uiZAAAAIux+mHZNm3aaM+ePXrjjTdMyzp06KBevXrp4MGDkiQXFxezbVJ7+hISEh5ZnQAAANbA6sNd//79dfDgQY0cOVJVq1bVr7/+qhkzZsjFxUWtW7eWJNnZ2aW7rb291XdMAgAAZCurDncRERHatWuXxo0bp5dfflmSVKdOHRUsWFDvv/++OnbsKCltD13q766uro+2YAAAAAuz6q6tf/75R5L0zDPPmC2vXbu2JOno0aNycHBQTEyM2frU3++diwcAAGDrrDrcpYazX375xWz5oUOHJEkVK1ZU7dq1tXHjRhmGYVofHh4uV1dXeXt7P7JaAQAArIFVD8tWq1ZNTZs21aeffqpr166pSpUqioyM1PTp09WoUSPVqFFD/fv3V1BQkIYOHaoOHTro4MGDmjdvnoYNG6b8+fNb+iUAAAA8UnbG3V1eVujWrVuaNm2afvjhB124cEGlSpVS27Zt1adPH9MNijdt2qSpU6cqOjpaJUqUUEBAgHr27JnlY124kKCUFKs+HbASxYq56nix5y1dhlWpdG6nzp27aukygFyN75a0+G5Jy97eTkWKuGS43urD3aNEuENm8QWcFl/AwMPjuyUtvlvS+q9wZ9Vz7gAAAJA1hDsAAAAbQrgDAACwIYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIXksXQAA4PHjXMRZBezpXwByAuEOAPDIFbC3l52li7BChqULgE3gf5sAAABsSKbD3ciRI/Xrr79muH7Pnj3q3bt3thQFAACAB5PpcLd69WrFxsZmuH7v3r3au3dvthQFAACAB5PhnLvY2Fi1bdtWt27dMi1766239NZbb2W4s+rVq2dvdQAAAMiSDMNdmTJl9P7772v//v0yDENr1qxRrVq1VKZMmTRt7e3t5e7urq5du+ZosQAAALi/+14t6+/vL39/f0lSXFycBgwYoPr16z+SwgAAAJB1mb4VyuLFi3OyDgAAAGSDLN3n7tixY1q3bp3Onz+v27dvp1lvZ2enTz/9NNuKAwAAQNZkOtz9+OOPevPNN5WSkpJhG8IdAACAZWU63E2fPl0lS5bU5MmTVblyZTk5OeVkXQAAAHgAmb7P3d9//60ePXro6aefJtgBAABYqUyHOw8PD928eTMnawEAAMBDynS4CwgI0NKlS3Xx4sWcrAcAAAAPIdNz7pKSkmRnZ6emTZuqdu3acnd3l52dnVkbLqgAAACwrEyHu0mTJpn+e8eOHem2IdwBAABYVqbD3R9//JGTdQAAACAbZHrOHQAAAKxfpnvuRo4cmal248aNe+BiAAAA8HAyHe5Wr1593/VFihSRu7v7QxcEAACAB/dQc+5SUlJ07tw5bdiwQbNmzdLEiROztTgAAABkzUPNubO3t1eJEiUUFBSkVq1aafz48dlVFwAAAB5Atl1QUbVqVR06dCi7dgcAAIAHkG3hbvv27XJ2ds6u3QEAAOABPPTVsrdu3VJUVJSOHz+u7t27Z1thAAAAyLqHvlrW3t5eRYsWVY8ePfTGG29kV10AAAB4ALniCRW//PKLJk+erCNHjsjV1VUtWrTQm2++aRoG3rVrl6ZMmaJjx46pSJEi6tatm3r27GmxegEAACwl0+HubufPn9c///wjR0dHlShRIkfvb3fo0CEFBQXJ19dXX331lU6ePKnJkyfr4sWLmjJliiIiItSvXz+1atVKQ4YM0YEDBzRhwgQZhqHg4OAcqwsAAMAaZSncRUZG6pNPPtHhw4fNlteoUUPvvvuuqlevnq3FSdLEiRNVs2ZNffnll7Kzs1ODBg2UkpKiBQsW6MaNG5o6daqqVq2qzz//XJLUqFEjJScna+bMmQoMDJSTk1O21wQAAGCtMn21bFRUlAIDA/Xnn3+qc+fOGjlypEaMGKFOnTopKipK3bt3119//ZWtxV28eFH79+9X165dZWdnZ1oeEBCgzZs3y97eXvv371fz5s3NtmvRooWuXLmiiIiIbK0HAADA2mW65+6LL76Qs7Ozli9frlKlSpmtGzBggDp27Khp06bpyy+/zLbi/vzzTxmGoUKFCumNN97Qtm3b5ODgoLZt22rkyJE6deqUkpKSVKFCBbPtypUrJ0mKjo5WvXr1sq0eAAAAa5fpnrv9+/fr1VdfTRPsJMnDw0Ndu3bV3r17s7W4ixcvSpLeeecdubm56auvvtLgwYP1/fff68MPP9TVq1clSS4uLmbbpV5okZCQkK31AAAAWLtM99zdunXrvjcpdnFx0c2bN7OlqFRJSUmSpGeeeUYffPCBJKl+/foyDEOfffaZOnfuLElmQ7Z3s7fPtns0AwAA5AqZTj9VqlTRunXrlJycnGZdUlKS1q5dK09Pz2wtLjVMNmrUyGx5w4YNZRiGfvvtN0lpe+hSf3d1dc3WegAAAKxdpsNdr1699Ntvv6lbt24KDw9XVFSUoqKiFBYWpm7duun333/P9nvLlS9fXtKdXsO7pfbolS5dWg4ODoqJiTFbn/r7vXPxAAAAbF2mh2WbNm2q9957TxMnTjR7EoVhGMqbN69GjBihli1bZmtxlSpVUqlSpbRhwwa9+uqrpuVbt25Vnjx55OPjo9q1a2vjxo167bXXTMOz4eHhcnV1lbe3d7bWAwAAYO2ydJ+7gIAAtWnTRv/73/8UFxcnwzBUunRpNWjQQIULF8724uzs7DR8+HC9+eabGj58uF5++WVFRkbqq6++UmBgoNzd3dW/f38FBQVp6NCh6tChgw4ePKh58+Zp2LBhyp8/f7bXBAAAYM3sDMMwLF3Ef9m8ebOmT59uerxYly5d1LdvX9MFE5s2bdLUqVMVHR2tEiVKKCAg4IGGiC9cSFBKitWfDliBYsVcdbzY85Yuw6pUOrdT585dtXQZyCWKFXNV+pfCPd4Mie+We/Ddkpa9vZ2KFHHJcH2Wwt2aNWu0e/dunTt3TikpKWl3Zmenr7/++sEqtQKEO2QW4S4tvoCRFYS79BHu0uK7Ja3/CneZHpadMmWKZs2aJUdHRxUpUoTbjAAAAFihTIe71atXq2HDhgoJCWEuGwAAgJXKdPdbQkKCWrRoQbADAACwYpkOd88//7z27NmTk7UAAADgIWV6WPa9995TUFCQhg0bpqZNm6pIkSLpPvbr2WefzdYCAQAAkHmZDnf//POPrl69qvXr12vDhg1p1huGITs7Ox09ejRbCwQAAEDmZTrcffzxx7py5YqCg4NVvnx55cmTpfsfAwAA4BHIdEL766+/NGjQIPXu3Tsn6wEAAMBDyPQFFR4eHtzbDgAAwMplOq316tVLX3/9tY4dO5aT9QAAAOAhZHpY9o8//pC9vb1eeukllSlTRkWLFpWDg4NZm9z++DEAAIDcLtPhbuvWrbK3t5eHh4eSkpL077//pmmT3q1RAAAA8OhkOtxt2bIlw3Vnz57V999/rzVr1mRHTQAAAHhAD3w/k6SkJP30009avXq1du/erdu3b3PBBQAAgIVlOdxFRkZq9erVWrduna5cuSLDMFS0aFH5+/urS5cuOVEjAAAAMilT4e7ChQv6/vvvtXr1ah07dsz0NApJGjx4sPr27ctNjQEAAKxAhoksOTlZW7Zs0apVq7Rr1y4lJyfLyclJjRs3VrNmzeTl5aWOHTuqcuXKBDsAAAArkWEqe/755xUfHy8XFxc1a9ZMzZo1U+PGjeXs7CxJiouLe2RFAgAAIHMyDHeXLl1SgQIF5Ofnp7p16+rZZ581BTsAAABYpwzD3cKFC7Vu3TqtW7dOoaGhsrOzU82aNdW8eXM1a9bsUdYIAACATMow3NWrV0/16tXT+++/r+3bt2vt2rXavn27IiIi9Nlnn6l8+fKys7PT9evXH2W9AAAAuA87wzCMzDZOSEhQeHi41q5dq19++cV0b7u6deuqY8eOatasmZycnHKy3hx14UKCUlIyfTrwGCtWzFXHiz1v6TKsSqVzO3Xu3FVLl4FcolgxV/FMo7QMie+We/Ddkpa9vZ2KFHHJcH2WLnN1cXGRv7+//P39de7cOa1fv15r167Vzz//rD179qhgwYLau3fvQxcNAACAB/PAj5QoVqyYevTooe+++07h4eEaMGCAChcunI2lAQAAIKuy5Xlh5cqV0+DBgxUeHp4duwMAAMAD4mGwAAAANoRwBwAAYEMIdwAAADaEcAcAAGBDCHcAAAA2hHAHAABgQwh3AAAANoRwBwAAYEMIdwAAADaEcAcAAGBDCHcAAAA2hHAHAABgQwh3AAAANoRwBwAAYEMIdwAAADaEcAcAAGBDcl24GzRokJo1a2a2bNeuXfL391eNGjXk6+ur+fPnW6g6AAAAy8pV4e7777/Xpk2bzJZFRESoX79+qlixokJCQuTn56cJEyZo3rx5FqoSAADAcvJYuoDMOnPmjMaOHSsPDw+z5VOnTlXVqlX1+eefS5IaNWqk5ORkzZw5U4GBgXJycrJEuQAAABaRa3ruRo8ereeee07169c3LUtMTNT+/fvVvHlzs7YtWrTQlStXFBER8ajLBAAAsKhcEe5Wrlyp33//Xe+9957Z8tjYWCUlJalChQpmy8uVKydJio6OfmQ1AgAAWAOrH5aNi4vTuHHjNG7cOLm7u5utu3r1qiTJxcXFbLmzs7MkKSEh4dEUCQAAYCWsuufOMAyNGjVKjRs3VosWLdJdL0l2dnbpbm9vb9UvDwAAINtZdc/d0qVLFRUVpbVr1yo5OVnS/wW65ORkubq6SkrbQ5f6e+p6AACAx4VVh7vw8HBdunRJDRs2TLOuWrVq+vDDD+Xg4KCYmBizdam/3zsXDwAAwNZZdbj76KOPdO3aNbNl06dP19GjRzVt2jSVLl1aYWFh2rhxo1577TXT8Gx4eLhcXV3l7e1tibIBAAAsxqrDXcWKFdMsK1y4sJycnFS9enVJUv/+/RUUFKShQ4eqQ4cOOnjwoObNm6dhw4Ypf/78j7pkAAAAi8r1VxzUr19fISEhOn78uAYOHKi1a9fq7bffVu/evS1dGgAAwCNn1T136Rk/fnyaZc2aNUvzvFkAAIDHUa7vuQMAAMD/IdwBAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AAIANIdwBAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AAIANIdwBAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AAIANIdwBAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AAIANIdwBAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AAIANIdwBAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AAIANyWPpAmDdnIs4q4A9/w8AAEBuQbjDfRWwt5edpYuwQoalCwAAIAN0yQAAANgQwh0AAIANIdwBAADYEKsPdykpKQoNDZWfn598fHzUtGlTjRs3TgkJCaY2u3btkr+/v2rUqCFfX1/Nnz/fghUDAABYjtVfUDF37lx98cUXCg4OVv369RUdHa2pU6fq2LFjmjdvniIiItSvXz+1atVKQ4YM0YEDBzRhwgQZhqHg4GBLlw8AAPBIWXW4MwxDc+fOVZcuXTRs2DBJUoMGDeTm5qahQ4fq6NGjmjp1qqpWrarPP/9cktSoUSMlJydr5syZCgwMlJOTkyVfAgAAwCNl1cOy165d00svvaS2bduaLa9YsaIk6a+//tL+/fvVvHlzs/UtWrTQlStXFBER8chqBQAAsAZWHe5cXFw0evRo1apVy2z55s2bJUlVq1ZVUlKSKlSoYLa+XLlykqTo6OhHUygAAICVsOpwl55ff/1Vs2fPVtOmTXX16lVJd0Lg3ZydnSXJ7KILAACAx0GuCncHDhxQr169VLp0aY0ZM0aGcec5AXZ26T9DwZ7HZgEAgMdMrkk/GzZsUFBQkJ544gktXLhQbm5ucnV1lZS2hy7199T1AAAAj4tcEe4WLFigN998UzVr1tTSpUtVvHhxSVLZsmXl4OCgmJgYs/apv987Fw8AAMDWWX24W7lypcaPH69WrVpp7ty5Zr1xefPmVe3atbVx40bTEK0khYeHy9XVVd7e3pYoGQAAwGKs+j53Fy5c0NixY1WqVCkFBAToyJEjZuvLli2r/v37KygoSEOHDlWHDh108OBBzZs3T8OGDVP+/PktVDkAAIBlWHW427lzp27cuKG4uDgFBASkWT9hwgS1a9dOISEhmjp1qgYOHKgSJUro7bffVs+ePS1QMQAAgGXZGXePZz7mLlxIUEoKp+NuxYq5Kv1rkR9vhqTjxZ63dBlWpdK5nTp37qqly0AuwXdL+vhuSYvvlrTs7e1UpIhLxusfYS0AAADIYYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhhDsAAAAbQrgDAACwIYQ7AAAAG0K4AwAAsCGEOwAAABtCuAMAALAhNhPu1q1bpzZt2ujpp59Wq1attGbNGkuXBAAA8MjZRLgLCwvT8OHD9dxzz2n69OmqU6eORowYoR9//NHSpQEAADxSeSxdQHaYPHmyWrVqpVGjRkmSnn/+eV2+fFlffvmlWrZsaeHqAAAAHp1c33MXGxurmJgYNW/e3Gx5ixYtdOLECcXGxlqoMgAAgEcv14e7EydOSJIqVKhgtrxcuXKSpOjo6EdeEwAAgKXk+mHZq1evSpJcXFzMljs7O0uSEhISMr0ve3u77CvMhpSzdAFWKk8ZD0uXYHX4N4Ss4LslfXy3pMV3i7n/Oh+5PtwZhiFJsrOzS3e5vX3mOyfd3JyzrzAb8relC7BS5SJWWroEq1OkiMt/NwL+v78tXYCV4rslLb5bsibXD8u6urpKSttDd+3aNbP1AAAAj4NcH+5S59rFxMSYLT958qTZegAAgMdBrg935cqVU+nSpdPc027jxo0qX768SpYsaaHKAAAAHr1cP+dOkgYOHKiRI0eqUKFCeuGFF7RlyxaFhYVpypQpli4NAADgkbIzUq88yOWWLVum+fPn699//1WZMmXUp08ftW/f3tJlAQAAPFI2E+4AAABgA3PuAAAA8H8IdwAAADaEcAcAAGBDCHdAFqxbt05t2rTR008/rVatWmnNmjWWLgmADTl69KiqVaum06dPW7oU5GKEOyCTwsLCNHz4cD333HOaPn266tSpoxEjRqS5xyIAPIgTJ06ob9++Sk5OtnQpyOW4WhbIpGbNmsnb29vs/olvvPGGoqKiFBYWZsHKAORmycnJWr58uSZNmiRHR0fFx8dr+/bt8vDwsHRpyKXouQMyITY2VjExMWrevLnZ8hYtWujEiROKjY21UGUAcrsDBw5o4sSJ6tmzp4YPH27pcmADCHdAJpw4cUJS2mcVlytXTpIUHR39yGsCYBsqVaqkzZs3a9CgQXJwcLB0ObABNvH4MSCnXb16VZLk4uJittzZ2VmSlJCQ8MhrAmAbihYtaukSYGPouQMyIXVqqp2dXbrL7e35pwQAsA78RQIywdXVVVLaHrpr166ZrQcAwNIId0AmpM61i4mJMVt+8uRJs/UAAFga4Q7IhHLlyql06dJp7mm3ceNGlS9fXiVLlrRQZQAAmOOCCiCTBg4cqJEjR6pQoUJ64YUXtGXLFoWFhZnd9w4AAEsj3AGZ9PLLL+vWrVuaP3++Vq5cqTJlyuizzz5T69atLV0aAAAmPKECAADAhjDnDgAAwIYQ7gAAAGwI4Q4AAMCGEO4AAABsCOEOAADAhhDuAAAAbAjhDgAAwIYQ7gArFhsbq0GDBqlOnTqqU6eO3n77bV28eDFHj3nq1Cl5eXnJ19dXN27cSLfNO++8Iy8vr2w93t0/VapUUa1atdSxY0ctXrxYt2/fNttm79698vLy0qpVq7KlhtysZ8+eeuedd3L8OKnn/O6fqlWrqk6dOgoICND333+fZptVq1bJy8tLe/fuzfH6APwfnlABWKlLly7ptdde061bt9SrVy/dvn1b8+bNU1RUlFauXCknJ6ccPX5cXJymT5+u4cOH5+hxUtWuXVudO3eWJKWkpCg+Pl47d+7UmDFjtGvXLs2YMUMODg6SpEqVKmnChAl65plnHklt1urLL7/U7t271aFDh0d2zGbNmqlZs2aSpOTkZF24cEGbN2/W22+/rYiICH300Uemts8++6wmTJigSpUqPbL6ABDuAKu1cOFCnT59WmvXrjX9caxRo4aCgoK0Zs0aUxDK6RratWunp556KsePVaZMGbVr185sWVBQkCZPnqxZs2Zp/vz56t27tySpaNGiado+ThITE/Xpp59q2bJlj/zYXl5eac59r169NGLECC1btkx169Y1PZKvTJkyKlOmzCOvEXjcMSwLWKn169erTp06Zr0eDRo0UIUKFbR+/focP/4LL7yg27dv68MPP5Qln1L4+uuvq0KFClqwYIGSk5MtVoe1OHPmjFq1aqXly5erT58+li5HkmRvb68PPvhAhQoV0pw5cyxdDvDYI9wBVujy5cuKjY1VtWrV0qyrVq2aIiMj77v9vXOj7v0JCQn5zxq8vb3VtWtX7d+/P1Nz227cuKFJkybJ19dX3t7e8vX11cSJEzOct5dZefLkUevWrXXhwgUdOXJEUvpz7vbt26eAgADVrl1bPj4+euWVV7Rly5Y0+1u1apXat2+v6tWrq169enrnnXd09uxZszYJCQmaNGmSWrZsqerVq8vHx0edO3fWTz/9ZNYuKipKwcHBqlevnmrUqKEOHTro22+/TXPMrVu36pVXXlGNGjX07LPPavDgwYqOjn6g83Hx4kU5OztrwYIFGjZsWKa3S29u470/DzOH0cXFRU2aNNGRI0d0/vx5SenPuQsPD5e/v798fHxUq1YtBQUF6cCBA2b7SklJ0fz589WyZUt5e3vr+eef15gxY5SQkGDW7ty5c/r444/14osvytvbW7Vq1VL37t3T7C87PxtAbsCwLGCFzpw5I0kqUaJEmnXFihVTQkKCrl69KldX13S3nzBhwn33n9mLIYYOHaqNGzfq888/l6+vr9zc3NJtd+vWLQUFBenQoUN6+eWX5e3trcOHD2vOnDk6cOCAFi1aJEdHx0wdMz2pw8J//PGHnn766TTrT5w4ob59+6pKlSoaOnSoJGnFihUaMGCAlixZotq1a0uSpk2bppCQELVo0UKdO3fWmTNntGTJEu3bt0/ffvut3N3dZRiG+vbtqyNHjqhbt24qW7asTp8+rWXLlmnw4MEKDw9XmTJldPHiRQUHB8vNzU39+/dX3rx5tX79er377rvKmzev/Pz8JN0JDKNGjVL9+vX11ltv6fLlywoNDVXnzp21YsUKVahQIUvn4sknn9QPP/wgOzu7LG3n7u7+n5+Lh53DmPo+RUVFqWjRomnW79u3T0OHDlWjRo3UqVMn3bhxQ0uWLFFQUJDWr19vGsJ99913tWbNGnXo0EE9evTQ8ePHFRoaqoiICIWGhipv3ry6efOmAgICdPXqVQUEBKhEiRL6+++/FRoaqj59+mj79u1ycXHJ1s8GkGsYAKxORESE4enpaaxYsSLNusmTJxuenp7G6dOnc+TYsbGxhqenpzF16lTDMAxj7dq1hqenpzFq1ChTmxEjRhienp6m37/55hvD09PTWLBggdm+5syZY3h6ehpLly79z+ONGDEiwza7d+82PD09jVmzZhmGYRh79uwxPD09je+++84wDMOYPXu24enpaVy4cMG0zcWLF43mzZsbixYtMgzDMGJiYozKlSsbEydONNt3VFSUUa1aNWPs2LGGYRjGoUOHDE9PTyM0NNSs3Y4dOwxPT09j/vz5hmEYxvr16w1PT0/j8OHDpjaJiYlGhw4dTMe4evWq8cwzzxhDhw4129fZs2eNZ5991hgwYECGrzmz/uvcZZfUc576uUjPihUrDE9PT2PdunWGYRjGd999Z3h6ehp79uwxDMMwPvjgA8PHx8dISUkxbfPHH38YzZs3N8LCwsyOc+/537lzp+Hp6WksXLjQMIz/O/87duwwaxcaGmp4enoa4eHhhmFk72cDyC0YlgWsUEpKyn+2sbfP+J/vxYsX7/uTlaHStm3bqkGDBvruu+/SDHel2rJli1xcXBQQEGC2vHv37nJxcUkznJlVSUlJ913v4eEhSfrkk09MQ9Zubm4KDw9XYGCgJGnTpk1KSUmRr6+v2bkoWrSoqlSpom3btkm6c9HKL7/8opdfftm0/9u3b5vek2vXrpkdc9KkSdq/f79u374tJycnrVq1yjRcunv3biUkJKhp06Zmx3RwcFC9evW0a9euRzaPMCUl5T8/F7du3XqoY6S+Txn1Knp4eOjatWsaM2aMjh8/LulOL3J4eLhatmwpSdq4caPs7OzUuHFjs9qqVq2qYsWKmd6n1q1b6+eff1bDhg1N+7+7/uvXr5uOKWXPZwPILRiWBayQs7OzpDtXRd4rdVlqm/TUr1//vvsfNGiQBg8enOl6PvjgA/n5+enDDz/U6tWr06w/deqUypQpk2bo1cnJSWXKlFFcXFymj5We+Ph4ScpwaKxly5batGmTNmzYoA0bNqhYsWJq3LixOnToYBp2i4mJkSS98sor6e7j7trz5MmjZcuWad++fTp58qRiYmJ08+ZNSTJdXPLMM88oMDBQS5Ys0c8//6zChQurYcOG8vPz0wsvvGB2zNThwPRcvHhRxYsXz+SZeHD//POPXnzxxfu2GTdunFmozarU9ymj4ftu3bpp165dWrJkiZYsWaLSpUurSZMm6tixoypXrizpzjkzDMN0Du919+fezs5Os2fP1sGDBxUTE6OYmBhTwEwN49n92QByA8IdYIVKliwp6c6E8XudPXtWBQsWVIECBTLcfsGCBffdf1ZvT1G+fHn17t1b06dP18KFC9OsN+5zNW1KSspD/3E8evSoJJkCwL0cHR01depURUVFadOmTdqxY4dWrVqlb7/9VsOGDVOfPn1Mf+y/+uor5cuXL8NjXblyRa+88opiY2P13HPPydfXV5UrV1apUqXUqVMns7ajR49W9+7dFR4erh07dig8PFzr1q1Tly5d9PHHH5uO+cknn6h06dLpHq9QoUJZPh8PolixYv/5uXjyyScf6hhHjx6VnZ1dhnM6XVxctGTJEh06dEibN2/Wjh07tHjxYi1dulQTJkyQn5+fUlJS5OzsrGnTpqW7j7x580q6cx/GLl266Pr162rYsKFat26tKlWqyDAMDRw40NQ+Oz8bQG5BuAOsUMGCBVW6dGn9/vvvadYdOXJE3t7e992+QYMG2V5T3759tW7dOk2fPl3Vq1c3W1eqVCkdOnRISUlJZkHu1q1bOnXqlKmH5EGkpKRo48aN8vDwSPfqYelOr9Q///yj2rVry8vLS4MGDdLp06f12muvad68eerTp49KlSolSXriiSdUpUoVs+1TJ99L0qJFi3T8+HEtXLjQrAc0IiLCbJvz58/rr7/+Uv369dW7d2/17t1bly5d0sCBA7VixQq99dZbpmO6u7uneU/27t2rlJSUHL8Zdaq8efPmyOciVUJCgnbt2iUfH58Me1ijo6N19epV1axZUzVr1tTw4cN17NgxBQQEaMGCBfLz81OpUqW0a9cueXt7q2DBgmbbh4eHq3DhwpLuXABx4cIFhYWFqXz58qY2a9euNdsmOz8bQG7BnDvASjVv3lw///yzaW6SJP3vf/9TdHS06Saxj1LevHn1/vvv6/r162keJ+Xr66uEhAQtXbrUbPk333yja9euZTjElhkzZsxQXFycgoODM5zLNXPmTPXo0cN0lbF0Z65ViRIlTHMTmzRpIkmaNWuWWU/j0aNH1b9/f3399deS/m9o8e5eLMMwtGTJEkkyzZFbtWqVevTood9++83Uzs3NTeXKlZOdnZ3s7e3VoEED5c2bV3PnzjWbN3jmzBkNGDBAEydOzPJVr9bIMAx9+umnun79unr16pVhuzFjxmjAgAGmeYuSVLFiRRUsWND0Pvn6+kq604t2ty1btuj11183hbf4+Hjlz5/f1Mst3fmfidQbO6c+si47PxtAbkHPHWClevfure+//149evRQz549lZiYqLlz56patWoWezpD6vDXhg0bzJZ36tRJq1ev1vjx4/Xnn3/K29tbkZGRWrVqlWrUqJFmODM9sbGxpueTGoahixcvateuXdq9e7eaNWumbt26Zbht6rNNAwIC1KVLFxUqVEh79uzR3r179frrr0uSPD09FRgYqMWLFys+Pl5NmzZVfHy8lixZImdnZw0ZMkSS1KhRIy1evFh9+/ZVx44dlZSUpLCwMEVGRsre3t4UTNq3b68FCxaoX79+6tq1q0qUKKHIyEjTLTycnZ3l7OysN998U+PGjVOXLl300ksvKTk5Wd98840SExM1YsQI02v4448/FBUVpeeeey7d24hYi6ioKNP7dPv2bZ0/f16bN2/Wr7/+qu7du993Xl9QUJB69+6tgIAAtW/fXnnz5tXmzZsVExOjzz77TJLUuHFjvfjii5o/f75OnTqlBg0aKC4uTkuXLlXJkiUVHBws6c77tGXLFvXt21ctW7bU1atXtWbNGtP8udT3KTs/G0BuYWfcb7IMAIs6ceKExo0bp/379ytfvnxq3Lix3n777Ry959apU6f04osvZnjRxdmzZ9WqVSslJCQoKirKtDwhIUHTp09XWFiYzp8/Lw8PD7Vp00b9+/e/7zym1OPdzcHBQcWKFVOZMmXUtm1bderUyfRcWenOkGb37t3NLgCIiIjQ9OnTdeTIESUkJKh8+fLq0qWLAgICTL1jhmEoNDRUy5YtU3R0tFxdXfXMM89oyJAhZo9YW7lypebPn6+4uDgVKlRI1apV0+DBg/Xee+8pKSnJ1Hv0119/aerUqTp48KDi4+NVqlQpvfTSS+rdu7fZcGtYWJgWLFigqKgo5cuXT9WqVdPAgQNVq1YtU5uQkBBNmzZNixYtUt26dTP1Xkl3rjbt0KGDxo8fn+ltHkTqOb+bo6OjihcvrgoVKsjf3z9Nj/KqVas0cuRIs9e0bds2zZo1S8ePH1diYqKeeuopBQUFqU2bNqbtkpKSNHfuXK1Zs0ZxcXFyd3dX/fr1NWTIEFNPnWEYmj17tlauXKkzZ86oaNGiqlmzpoYMGaJXXnlFNWvW1MyZMyVl72cDyA0IdwBgJQYMGKDevXvLx8fH0qUAyMWYcwcAVuDEiRP67bffHvqKVQCg5w4ArMC+fftkb2//UFcWA4BEuAMAALApDMsCAADYEMIdAACADSHcAQAA2BDCHQAAgA0h3AEAANgQwh0AAIAN+X/TetYXxgPSEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.target,df.sex).plot(kind='bar',\n",
    "                                  figsize=(10,6),\n",
    "                                  color=['cyan','crimson']);\n",
    "\n",
    "plt.title('Heart Disease Frequency for Sex')\n",
    "plt.xlabel('0 = No Disease, 1 = Disease')\n",
    "plt.ylabel('Amount')\n",
    "plt.legend(['Female','Male'])\n",
    "plt.grid()\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60332099",
   "metadata": {},
   "source": [
    "### ***Age Vs Max Heart Rate for Headrt Disease (thalach)***\n",
    "\n",
    "* Let's try combining a couple of independent variables, such as, `age` and `thalach` (maximum heart rate) and then comparing them to our target variable `heart disease`.\n",
    "\n",
    "\n",
    "* Because there are so many different values for `age` and `thalach`, we'll use a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "73d397c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Scatter with positive examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "           color='crimson')\n",
    "\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "           df.thalach[df.target==0],\n",
    "           color='darkcyan')\n",
    "\n",
    "# Adding some helpful info\n",
    "plt.title('Heart Disease in function of Age and Max Heart Rate')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Max Heart Rate (thalach)')\n",
    "plt.legend([\"Disease\", \"No Disease\"]);\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d472bf03",
   "metadata": {},
   "source": [
    "### ***What can we infer from this?***\n",
    "\n",
    "* It seems the younger someone is, the higher their max heart rate (dots are higher on the left of the graph) and the older someone is, the more green dots there are. But this may be because there are more dots all together on the right side of the graph (older participants).\n",
    "\n",
    "\n",
    "* Both of these are observational of course, but this is what we're trying to do, build an understanding of the data.\n",
    "\n",
    "\n",
    "* Let's check the age **distribution**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "2818bba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the distribution of the age column with a histogram\n",
    "df.age.plot.hist(color = 'crimson', grid =True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b550e6",
   "metadata": {},
   "source": [
    "***We can see it's a NORMAL DISTRIBUTION.***\n",
    "\n",
    "***(https://en.wikipedia.org/wiki/Normal_distribution) but slightly swaying to the right, which reflects in the scatter plot above.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aabd3de",
   "metadata": {},
   "source": [
    "### ***Heart Disease frequency per Chest Pain type***\n",
    "\n",
    "* Let's try another independent variable. This time, `cp` (chest pain).\n",
    "\n",
    "* We'll use the same process as we did before with `sex`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "047243e8",
   "metadata": {},
   "source": [
    "***cp - chest pain type***\n",
    "  * ***0: Typical angina: chest pain related decrease blood supply to the heart***\n",
    "  * ***1: Atypical angina: chest pain not related to heart***\n",
    "  * ***2: Non-anginal pain: typically esophageal spasms (non heart related)***\n",
    "  * ***3: Asymptomatic: chest pain not showing signs of disease*** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "4a7be9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp,df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "1a4437a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making the above data more visual\n",
    "pd.crosstab(df.cp,df.target).plot(kind='bar',\n",
    "                                 figsize=(10,6),\n",
    "                                 color=['cyan','crimson'])\n",
    "\n",
    "# Labelling\n",
    "plt.title('Heart Disease Frequency Per Chest Pain Type')\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No Disease\", \"Disease\"])\n",
    "plt.grid()\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f91effa",
   "metadata": {},
   "source": [
    "### ***What can we infer from this?***\n",
    "\n",
    "Remember from our data dictionary what the different levels of chest pain are.\n",
    "\n",
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "    \n",
    "It's interesting the atypical agina (value 1) states it's not related to the heart but seems to have a higher ratio of participants with heart disease than not.\n",
    "\n",
    "\n",
    "What does atypical agina even mean?\n",
    "\n",
    "At this point, it's important to remember, if your data dictionary doesn't supply you enough information, you may want to do further research on your values. This research may come in the form of asking a **subject matter expert** (such as a cardiologist or the person who gave you the data) or Googling to find out more.\n",
    "\n",
    "According to PubMed, it seems [even some medical professionals are confused by the term](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2763472/).\n",
    "\n",
    "> Today, 23 years later, “atypical chest pain” is still popular in medical circles. Its meaning, however, remains unclear. A few articles have the term in their title, but do not define or discuss it in their text. In other articles, the term refers to noncardiac causes of chest pain.\n",
    "\n",
    "Although not conclusive, this graph above is a hint at the confusion of defintions being represented in data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41ad260",
   "metadata": {},
   "source": [
    "### ***Make a correlation matrix***\n",
    "\n",
    "***Finally, we'll compare all of the independent variables in one hit.***\n",
    "\n",
    "***Why?***\n",
    "\n",
    "* Because this may give an idea of which independent variables may or may not have an impact on our target variable.\n",
    "\n",
    "\n",
    "* We can do this using `df.corr()` which will create a [**correlation matrix**](https://www.statisticshowto.datasciencecentral.com/correlation-matrix/) for us, in other words, a big table of numbers telling us how related each variable is the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "51235612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "5d8f8608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making  the correlation matrix more visual\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                annot = True,\n",
    "                linewidths = 0.5,\n",
    "                fmt = \".2f\",\n",
    "                cmap =None )\n",
    "\n",
    "bottom, top = ax.get_ylim()\n",
    "plt.yticks(rotation = 0)\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e83c4b0",
   "metadata": {},
   "source": [
    "#### ***Note :***\n",
    "\n",
    "***A higher positive value means a potential positive correlation (increase) and a higher negative value means a potential negative correlation (decrease).***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68eb0db3",
   "metadata": {},
   "source": [
    "### ***Before we model***\n",
    "\n",
    "Remember, we do exploratory data analysis (EDA) to start building an intuitition of the dataset.\n",
    "\n",
    "What have we learned so far? Aside from our basline estimate using `sex`, the rest of the data seems to be pretty distributed.\n",
    "\n",
    "So what we'll do next is **model driven EDA**, meaning, we'll use machine learning models to drive our next questions.\n",
    "\n",
    "**A few extra things to remember:***\n",
    "\n",
    "* Not every EDA will look the same, what we've seen here is an example of what you could do for structured, tabular dataset.\n",
    "* You don't necessarily have to do the same plots as we've done here, there are many more ways to visualize data, I encourage you to look at more.\n",
    "* We want to quickly find:\n",
    "    * Distributions (`df.column.hist()`)\n",
    "    * Missing values (`df.info()`)\n",
    "    * Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a235b2c7",
   "metadata": {},
   "source": [
    "## ***5. Modelling***\n",
    "\n",
    "***We've explored the data, now we'll try to use machine learning to predict our target variable based on the 13 independent variables.***\n",
    "\n",
    "***Remember our problem?***\n",
    "\n",
    "* >Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "That's what we'll be trying to answer.\n",
    "\n",
    "***And remember our evaluation metric?***\n",
    "\n",
    "* >If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursure this project.\n",
    "\n",
    "But before we build a model, we have to get our dataset ready."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "03066a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "7b755fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into X and y\n",
    "X = df.drop('target',axis = 1)\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "acf7b5d1",
   "metadata": {},
   "outputs": [
    {
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
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       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
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       "3        2   0     2  \n",
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       "..     ...  ..   ...  \n",
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       "300      1   2     3  \n",
       "301      1   1     3  \n",
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       "\n",
       "[303 rows x 13 columns]"
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  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "8b630421",
   "metadata": {},
   "outputs": [
    {
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  {
   "cell_type": "markdown",
   "id": "da7110d3",
   "metadata": {},
   "source": [
    "### ***Training and test data split***\n",
    "\n",
    "Now comes one of the most important concepts in machine learning, the **training/test split**.\n",
    "\n",
    "This is where you'll split your data into a **training set** and a **test set**.\n",
    "\n",
    "You use your training set to train your model and your test set to test it.\n",
    "\n",
    "The test set must remain separate from your training set.\n",
    "\n",
    "#### ***Why not use all the data to train a model?***\n",
    "\n",
    "Let's say you wanted to take your model into the hospital and start using it on patients. How would you know how well your model goes on a new patient not included in the original full dataset you had?\n",
    "\n",
    "This is where the test set comes in. It's used to mimic taking your model to a real environment as much as possible.\n",
    "\n",
    "And it's why it's important to never let your model learn from the test set, it should only be evaluated on it.\n",
    "\n",
    "To split our data into a training and test set, we can use Scikit-Learn's [`train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) and feed it our independent and dependent variables (`X` & `y`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "2d42dd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test set\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd30f051",
   "metadata": {},
   "source": [
    "> The `test_size` parameter is used to tell the `train_test_split()` function how much of our data we want in the test set.\n",
    "\n",
    "> A rule of thumb is to use 80% of your data to train on and the other 20% to test on. \n",
    "\n",
    "> For our problem, a train and test set are enough. But for other problems, you could also use a validation (train/validation/test) set or cross-validation (we'll see this in a second).\n",
    "\n",
    "> But again, each problem will differ. The post, [How (and why) to create a good validation set](https://www.fast.ai/2017/11/13/validation-sets/) by Rachel Thomas is a good place to go to learn more.\n",
    "\n",
    "***Let's look at our training data***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "d058e900",
   "metadata": {},
   "outputs": [
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
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       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "6c4a9e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132    1\n",
       "202    0\n",
       "196    0\n",
       "75     1\n",
       "176    0\n",
       "      ..\n",
       "188    0\n",
       "71     1\n",
       "106    1\n",
       "270    0\n",
       "102    1\n",
       "Name: target, Length: 242, dtype: int64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "38d6450b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ba569a",
   "metadata": {},
   "source": [
    "#### ***After splitting the data into training and test sets, we need to now build a machine learning model.***\n",
    "#### ***We'll train the data (find the patterns) on the training set.***\n",
    "#### ***And then we'll use the patterns on the test set.***\n",
    "#### ***We're going to implement three machine learning model :***\n",
    "#### ***1. Logistic Regression***\n",
    "#### ***2. K-Nearest Neighbours Classifier***\n",
    "#### ***3. Random Forest Classifier***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa93655",
   "metadata": {},
   "source": [
    "### ***Why these?***\n",
    "\n",
    "If we look at the [Scikit-Learn algorithm cheat sheet](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html), we can see we're working on a classification problem and these are the algorithms it suggests (plus a few more).\n",
    "\n",
    "| <img src=\"./images/sklearn-ml-map.png\" width=900/> | \n",
    "|:--:| \n",
    "| An example path we can take using the Scikit-Learn Machine Learning Map |\n",
    "\n",
    "\"Wait, I don't see Logistic Regression and why not use LinearSVC?\"\n",
    "\n",
    "Good questions. \n",
    "\n",
    "I was confused too when I didn't see Logistic Regression listed as well because when you read the Scikit-Learn documentation on it, you can see it's [a model for classification](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression).\n",
    "\n",
    "And as for LinearSVC, let's pretend we've tried it, and it doesn't work, so we're following other options in the map.\n",
    "\n",
    "For now, knowing each of these algorithms inside and out is not essential.\n",
    "\n",
    "Machine learning and data science is an iterative practice. These algorithms are tools in your toolbox.\n",
    "\n",
    "In the beginning, on your way to becoming a practioner, it's more important to understand your problem (such as, classification versus regression) and then knowing what tools you can use to solve it.\n",
    "\n",
    "Since our dataset is relatively small, we can experiment to find algorithm performs best.\n",
    "\n",
    "All of the algorithms in the Scikit-Learn library use the same functions, for training a model, `model.fit(X_train, y_train)` and for scoring a model `model.score(X_test, y_test)`. `score()` returns the ratio of correct predictions (1.0 = 100% correct).\n",
    "\n",
    "Since the algorithms we've chosen implement the same methods for fitting them to the data as well as evaluating them, let's put them in a dictionary and create a which fits and scores them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "68622ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a model dictionary\n",
    "models = {\"Logistic Regression\" : LogisticRegression(),\n",
    "         \"KNN\" : KNeighborsClassifier(),\n",
    "         \"Random Forest\" : RandomForestClassifier()}\n",
    "\n",
    "# Using a function to fit and evaluate the score of models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fitting and evaluating the give machine learning models.\n",
    "    models : a dictionary of different Scitkit-Learn machine learning models.\n",
    "    X_train : training data (no labels)\n",
    "    X_test : testing data (no labels)\n",
    "    y_train : training labels\n",
    "    y_test : \"testing labels\"\n",
    "    \n",
    "    \"\"\"\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "4fa0affc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\vedant\\coding stuff\\project\\heart-disease-prediction\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models = models,\n",
    "                            X_train = X_train,\n",
    "                            X_test = X_test,\n",
    "                            y_train = y_train,\n",
    "                            y_test = y_test)\n",
    "\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f2294d",
   "metadata": {},
   "source": [
    "### ***Model comparison***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "b1ce7cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index = [\"accuracy\"])\n",
    "model_compare.T.plot.bar(color = 'crimson', figsize = (7, 5));\n",
    "plt.xticks(rotation = 0)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832870a4",
   "metadata": {},
   "source": [
    "#### ****These things should be given more attention while working with a classification problem.****\n",
    "\n",
    "#### ***1. Hyperparameter tuning***\n",
    "#### ***2. Feature importance***\n",
    "#### ***3. Confusion matrix***\n",
    "#### ***4. Cross-validation***\n",
    "#### ***5. Precision***\n",
    "#### ***6. Recall***\n",
    "#### ***7. F1 score***\n",
    "#### ***8. Classification report***\n",
    "#### ***9. ROC curve***\n",
    "#### ***10. Area under the curve (AUC)***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4472d9bb",
   "metadata": {},
   "source": [
    "* **Hyperparameter tuning** - Each model you use has a series of dials you can turn to dictate how they perform. Changing these values may increase or decrease model performance.\n",
    "\n",
    "\n",
    "* **Feature importance** - If there are a large amount of features we're using to make predictions, do some have more importance than others? For example, for predicting heart disease, which is more important, sex or age?\n",
    "\n",
    "\n",
    "* [**Confusion matrix**](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) - Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line).\n",
    "\n",
    "\n",
    "* [**Cross-validation**](https://scikit-learn.org/stable/modules/cross_validation.html) - Splits your dataset into multiple parts and train and tests your model on each part and evaluates performance as an average. \n",
    "\n",
    "\n",
    "* [**Precision**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score) - Proportion of true positives over total number of samples. Higher precision leads to less false positives.\n",
    "\n",
    "\n",
    "* [**Recall**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score) - Proportion of true positives over total number of true positives and false negatives. Higher recall leads to less false negatives.\n",
    "\n",
    "\n",
    "* [**F1 score**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score) - Combines precision and recall into one metric. 1 is best, 0 is worst.\n",
    "\n",
    "\n",
    "* [**Classification report**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) - Sklearn has a built-in function called `classification_report()` which returns some of the main classification metrics such as precision, recall and f1-score.\n",
    "\n",
    "\n",
    "* [**ROC Curve**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_score.html) - [Receiver Operating Characterisitc](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) is a plot of true positive rate versus false positive rate.\n",
    "\n",
    "\n",
    "* [**Area Under Curve (AUC)**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html) - The area underneath the ROC curve. A perfect model achieves a score of 1.0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb74b8e9",
   "metadata": {},
   "source": [
    "## ***Hyperparameter Tuning (by hand)***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "7b5a48c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tuning KNN model\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neighbors\n",
    "neighbors = range(1,21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors = i)\n",
    "    \n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "21738305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "b08d8be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "4235ff1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data : 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label = \"Train score\")\n",
    "plt.plot(neighbors, test_scores, label = \"Test score\")\n",
    "plt.xticks(np.arange(1,21,2))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data : {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c737a52",
   "metadata": {},
   "source": [
    "### ***Hyperparameter tuning with RandomizedSearchCV***\n",
    "\n",
    "***We're about to tune :***\n",
    "* ***1. LogisticRegression()***\n",
    "* ***2. RandomForestClassifier()***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "38fe2646",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\" : np.logspace(-4, 4, 20),\n",
    "               \"solver\" : [\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\" : np.arange(10, 1000, 50),\n",
    "          \"max_depth\" : [None, 3, 5, 10],\n",
    "          \"min_samples_split\" : np.arange(2,20,2),\n",
    "          \"min_samples_leaf\" : np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85515b8",
   "metadata": {},
   "source": [
    "***We have created hyperparameter grid setup for each of our models, now we'll tune them using RandomizedSearchCV***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "f1ee2007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tuning LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                               param_distributions = log_reg_grid,\n",
    "                               cv = 5,\n",
    "                               n_iter = 20,\n",
    "                               verbose = True)\n",
    "\n",
    "# Fitting random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "11abe1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "747f85c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "1f164631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tuning RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                          param_distributions = rf_grid,\n",
    "                          cv = 5,\n",
    "                          n_iter = 20,\n",
    "                          verbose = True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "74231ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "38b1e06d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "9d091892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "762fab3f",
   "metadata": {},
   "source": [
    "### ***Three ways to tune the model :***\n",
    "#### ***1. By hand***\n",
    "#### ***2. RandomizedSearchCV***\n",
    "#### ***3. GridSearchCV***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee37f6c",
   "metadata": {},
   "source": [
    "### ***Hyperparameter tuning with GridSearchCV***\n",
    "\n",
    "***Trying to improve LogisticRegression model with GridSearchCV***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "5f49d817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\" : np.logspace(-4, 4, 30),\n",
    "               \"solver\" : [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                         param_grid = log_reg_grid,\n",
    "                         cv = 5,\n",
    "                         verbose = True)\n",
    "\n",
    "# Fit grid parameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "378cabaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "36a44d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "e2193fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76937b8f",
   "metadata": {},
   "source": [
    "### ***Evaluating our tunened machine learning classifier, beyond accuracy (using cross-validation)***\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "***To make comparisons and evaluate our trained model, first we need to make predictions***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "c6764ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with tune models\n",
    "y_preds = gs_log_reg.predict(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "98b3d249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec9e6b5",
   "metadata": {},
   "source": [
    "### ***ROC Curve and AUC Scores***\n",
    "\n",
    "***What's a ROC curve?***\n",
    "\n",
    "* It's a way of understanding how your model is performing by comparing the true positive rate to the false positive rate.\n",
    "\n",
    "In our case...\n",
    "\n",
    "> To get an appropriate example in a real-world problem, consider a diagnostic test that seeks to determine whether a person has a certain disease. A false positive in this case occurs when the person tests positive, but does not actually have the disease. A false negative, on the other hand, occurs when the person tests negative, suggesting they are healthy, when they actually do have the disease.\n",
    "\n",
    "Scikit-Learn implements a function `plot_roc_curve` which can help us create a ROC curve as well as calculate the area under the curve (AUC) metric.\n",
    "\n",
    "\n",
    "Reading the documentation on the [`plot_roc_curve`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_roc_curve.html) function we can see it takes `(estimator, X, y)` as inputs. Where `estiamator` is a fitted machine learning model and `X` and `y` are the data you'd like to test it on.\n",
    "\n",
    "\n",
    "In our case, we'll use the GridSearchCV version of our `LogisticRegression` estimator, `gs_log_reg` as well as the test data, `X_test` and `y_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "596f5650",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\vedant\\coding stuff\\project\\heart-disease-prediction\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function `plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: RocCurveDisplay.from_predictions or RocCurveDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting ROC curve and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg, X_test, y_test, color = 'crimson')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8c7402",
   "metadata": {},
   "source": [
    "### ***Confusion Matrix***\n",
    "> A confusion matrix is a visual way to show where your model made the right predictions and where it made the wrong predictions (or in other words, got confused).\n",
    "Scikit-Learn allows us to create a confusion matrix using [`confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) and passing it the true labels and predicted labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "be8aef14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "bea3b954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making our confusion matrix more visual\n",
    "sns.set_theme(font_scale = 1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using Seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True, # Annotate the boxes\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"Predicted label\") # predictions go on the x-axis\n",
    "    plt.ylabel(\"True label\") # true labels go on the y-axis \n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9740236f",
   "metadata": {},
   "source": [
    "***We can see the model gets confused (predicts the wrong label) relatively the same across both classes. In essence, there are 4 occasaions where the model predicted 0 when it should've been 1 (false negative) and 3 occasions where the model predicted 1 instead of 0 (false positive).***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d291030e",
   "metadata": {},
   "source": [
    "***After getting a ROC curve, an AUC metric and a confusion matrix, now we should get a classification report as well as cross-validated precision, recall and f1-score.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b01b30",
   "metadata": {},
   "source": [
    "### ***Classification report***\n",
    "\n",
    "> We can make a classification report using [`classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) and passing it the true labels as well as our models predicted labels. \n",
    "\n",
    "> A classification report will also give us information of the precision and recall of our model for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "f75f5995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08a55444",
   "metadata": {},
   "source": [
    "### ***Calculating evaluation metrics using cross-validation***\n",
    "\n",
    "> ***We'll evaluate precision, recall and f1-score of our model using cross-validation and to do so we'll be using `cross_val_score()`.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "56be68a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "e53c1140",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new classifier with best hyperparameters\n",
    "clf = LogisticRegression(C = 0.20433597178569418,\n",
    "                        solver = \"liblinear\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac78e70",
   "metadata": {},
   "source": [
    "#### ***1. Cross-validated : accuracy***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "a6945bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = cross_val_score(clf,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv = 5,\n",
    "                        scoring = \"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "d28eed93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cv_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f585b290",
   "metadata": {},
   "source": [
    "#### ***2. Cross-validated : precision***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "b0e63e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_precision = cross_val_score(clf,\n",
    "                              X,\n",
    "                              y,\n",
    "                              cv = 5,\n",
    "                              scoring = \"precision\")\n",
    "\n",
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09b312d",
   "metadata": {},
   "source": [
    "#### ***3. Cross-validated : recall***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "017424ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_recall = cross_val_score(clf,\n",
    "                              X,\n",
    "                              y,\n",
    "                              cv = 5,\n",
    "                              scoring = \"recall\")\n",
    "\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c7d790",
   "metadata": {},
   "source": [
    "#### ***4. Cross-validated : f1-score***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "d4acd737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_f1 = cross_val_score(clf, \n",
    "                        X, \n",
    "                        y, \n",
    "                        cv = 5, \n",
    "                        scoring = \"f1\")\n",
    "\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "c28a6b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating visualisation for cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                            \"Precision\": cv_precision,\n",
    "                            \"Recall\": cv_recall,\n",
    "                            \"F1\": cv_f1})\n",
    "\n",
    "cv_metrics[:1].T.plot.bar(title = \"Cross-Validated Metrics\", legend = False, color ='crimson');\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b470859",
   "metadata": {},
   "source": [
    "### ***Feature Importance***\n",
    "\n",
    "***Feature importance is used to find which features contributed most to the outcomes of the model and how did they contribute?***\n",
    "\n",
    "***Remember : Finding feature importance is different for each machine learning model.***\n",
    "\n",
    "> Feature importance is another way of asking, \"which features contributing most to the outcomes of the model?\"\n",
    "\n",
    "> Or for our problem, trying to predict heart disease using a patient's medical characterisitcs, which charateristics contribute most to a model predicting whether someone has heart disease or not?\n",
    "\n",
    "> Unlike some of the other functions we've seen, because how each model finds patterns in data is slightly different, how a model judges how important those patterns are is different as well. This means for each model, there's a slightly different way of finding which features were most important.\n",
    "\n",
    "> You can usually find an example via the Scikit-Learn documentation or via searching for something like \"[MODEL TYPE] feature importance\", such as, \"random forest feature importance\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "202fe34d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "86c6efb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding feature importance for LogisticRegression model\n",
    "\n",
    "# Fit an instance of LogisticRegression\n",
    "clf = LogisticRegression(C = 0.20433597178569418,\n",
    "                        solver = \"liblinear\")\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "5380729c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316728, -0.86044652,  0.6606704 , -0.01156993, -0.00166375,\n",
       "         0.04386107,  0.31275848,  0.02459362, -0.60413081, -0.56862803,\n",
       "         0.45051628, -0.63609898, -0.67663373]])"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "d1afb63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "5f867cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672806268220445,\n",
       " 'sex': -0.8604465226286001,\n",
       " 'cp': 0.6606703996492814,\n",
       " 'trestbps': -0.011569930743501303,\n",
       " 'chol': -0.001663745833540806,\n",
       " 'fbs': 0.043861067871676124,\n",
       " 'restecg': 0.3127584791782968,\n",
       " 'thalach': 0.02459361509185037,\n",
       " 'exang': -0.6041308102637141,\n",
       " 'oldpeak': -0.5686280255489925,\n",
       " 'slope': 0.4505162810238786,\n",
       " 'ca': -0.6360989756865822,\n",
       " 'thal': -0.67663372723561}"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "9a0d3121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index = [0])\n",
    "feature_df.T.plot.bar(title = \"Feature Importance\", legend = False, grid = True, color = 'crimson', figsize = (8, 4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "297b16d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "63d5675b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e38ac7",
   "metadata": {},
   "source": [
    "***slope - the slope of the peak exercise ST segment***\n",
    "   * ***0: Upsloping: better heart rate with excercise (uncommon)***\n",
    "   * ***1: Flatsloping: minimal change (typical healthy heart)***\n",
    "   * ***2: Downslopins: signs of unhealthy heart***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab1070b",
   "metadata": {},
   "source": [
    "### ***6. Experimentation***\n",
    "\n",
    "> Well we've completed all the metrics.\n",
    "We are able to put together a great report containing a confusion matrix, a handful of cross-valdated metrics such as precision, recall and F1 as well as which features contribute most to the model making a decision."
   ]
  }
 ],
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