https://github.com/dvaser/heart-attact-analysis-prediction
DATA ANALYSIS
https://github.com/dvaser/heart-attact-analysis-prediction
classification data data-analysis data-visualization jupyter jupyter-notebook lineer-regresyon machine-learning python regression
Last synced: 2 months ago
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DATA ANALYSIS
- Host: GitHub
- URL: https://github.com/dvaser/heart-attact-analysis-prediction
- Owner: dvaser
- License: apache-2.0
- Created: 2024-09-16T17:46:29.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-09-16T17:58:57.000Z (9 months ago)
- Last Synced: 2025-04-05T20:43:13.730Z (2 months ago)
- Topics: classification, data, data-analysis, data-visualization, jupyter, jupyter-notebook, lineer-regresyon, machine-learning, python, regression
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/dvaser/heart-attact-analysis-prediction
- Size: 1.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PROJECT's KAGGLE LINK :
***https://www.kaggle.com/code/dvaser/heart-attact-analysis-prediction/***
# HEART ATTACK ANALYSIS & PREDICTION
## What will you learn from this project?
* EDA
* Missing Value Analysis
* Categoric and Numeric Features
* Standardization
* Box - Swarm - Cat - Correlation
* Outlier Detection
* ML Modelling and Tuning Machine Learning Model
## Introduction
* A heart attact, also called a myocardial infarction, happens when a part of the heart muscle doesn't get enough blood.
* The more time that passes without treatment to restore blood flow, the greater the damage to the heart muscle.
* Coronary artery disease (CAD) is the main cause of heart attack.
## Analysis Content
1. [Python Libraries](#1)
2. [Data Content](#2)
3. [Read & Analyse Data](#3)
4. [Missing Value Analysis](#4)
5. [Unique Value Analysis](#5)
6. [Categorical Feature Analysis](#6)
7. [Numeric Feature Analysis](#7)
8. [Standardization](#8)
9. [Box Plot Analysis](#9)
10. [Swarm Plot Analysis](#10)
11. [Cat Plot Analysis](#11)
12. [Correlation Analysis](#12)
13. [Outlier Detection](#13)
14. [Modelling](#14)
1. [Encoding Categorical Columns](#14.1)
2. [Scaling](#14.2)
3. [Train/Test Split](#14.3)
4. [Logistic Regression](#14.4)
5. [Logistic Regression Hyperparamater Tuning](#14.5)
15. [Conclusion](#15)
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## Data Content
* Age : Age of the patient
* Sex : Sex of the patient
* exang: exercise induced angina (1 = yes; 0 = no)
* ca: number of major vessels (0-3)
* cp : Chest Pain type chest pain type
* Value 1: typical angina
* Value 2: atypical angina
* Value 3: non-anginal pain
* Value 4: asymptomatic
* trtbps : resting blood pressure (in mm Hg)
* chol : cholestoral in mg/dl fetched via BMI sensor
* fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
* rest_ecg : resting electrocardiographic results
* Value 0: normal
* Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
* Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
* thalach : maximum heart rate achieved
* target : 0= less chance of heart attack 1= more chance of heart attack
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