From a4151bda3c890bd3f29769e6fa54f55694153571 Mon Sep 17 00:00:00 2001 From: psavarmattas Date: Sat, 11 Feb 2023 13:24:35 +0530 Subject: [PATCH] Published --- README.MD | 13 +++++-------- main.ipynb | 4 ++-- 2 files changed, 7 insertions(+), 10 deletions(-) diff --git a/README.MD b/README.MD index bb7cb5f..101be36 100644 --- a/README.MD +++ b/README.MD @@ -1,7 +1,7 @@ # Cardiovascular Heart Disease Prediction Model (Stacking Model) -`Last Updated: 09th February 2023` +`Last Updated: 11th February 2023` -### **THIS RESEARCH IS BEING PUBLISHED** +### **THIS RESEARCH IS PUBLISHED PLEASE [CLICK HERE](https://ijrpr.com/uploads/V4ISSUE2/IJRPR9920.pdf) FOR READING THE PAPER** ## Dataset Information @@ -44,8 +44,6 @@ Points to keep in mind when working with a machine learning model 2. Pandas 3. Matplotlib 4. Scikit-Learn -5. Seaborn -6. Cufflinks ## Tools Needed: @@ -91,9 +89,8 @@ Points to keep in mind when working with a machine learning model 2. Pandas: Used to analyze the data. 3. OS Module: For working with files/directories. 4. matplotlib: Used for programmatic plot generation. -5. Seaborn: Used for statistical graphics. -6. Warnings: Used to control warnings in Python. -7. sklearn: These are simple and efficient tools for predictive data analsis. +5. Warnings: Used to control warnings in Python. +6. sklearn: These are simple and efficient tools for predictive data analsis. ### Data Exploration @@ -124,4 +121,4 @@ Points to keep in mind when working with a machine learning model 1. random: It generates random output. ## Acknowledgement -This data was possible because of the work of [Kuzak Dempsy](https://data.world/kudem). \ No newline at end of file +This dataset was provided by the work of [Kuzak Dempsy](https://data.world/kudem). \ No newline at end of file diff --git a/main.ipynb b/main.ipynb index b871786..b575e58 100644 --- a/main.ipynb +++ b/main.ipynb @@ -6,9 +6,9 @@ "metadata": {}, "source": [ "# Cardiovascular Heart Disease Prediction Model (Stacking Model)\n", - "`Last Updated: 09th February 2023`\n", + "`Last Updated: 11th February 2023`\n", "\n", - "### **THIS RESEARCH IS BEING PUBLISHED**\n", + "### **THIS RESEARCH IS PUBLISHED PLEASE [CLICK HERE](https://ijrpr.com/uploads/V4ISSUE2/IJRPR9920.pdf) FOR READING THE PAPER**\n", "\n", "### About this dataset:\n", "This dataset contains detailed information on the risk factors for cardiovascular disease. It includes information on age, gender, height, weight, blood pressure values, cholesterol levels, glucose levels, smoking habits and alcohol consumption of over 70 thousand individuals. Additionally it outlines if the person is active or not and if he or she has any cardiovascular diseases. This dataset provides a great resource for researchers to apply modern machine learning techniques to explore the potential relations between risk factors and cardiovascular disease that can ultimately lead to improved understanding of this serious health issue and design better preventive measures\n",