Kaggle Master with Heart Attack Prediction Kaggle Project

Kaggle is Machine Learning & Data Science community. Become Kaggle master with real machine learning kaggle project

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Udemy

platform

English

language

Data Science

What you will learn

Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners.

Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detect

Machine learning describes systems that make predictions using a model trained on real-world data.

Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and ne

Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithm

Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources

Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.

Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems.

What is Kaggle?

Registering on Kaggle and Member Login Procedures

Getting to Know the Kaggle Homepage

Competitions on Kaggle

Datasets on Kaggle

Examining the Code Section in Kaggle

What is Discussion on Kaggle?

Courses in Kaggle

Ranking Among Users on Kaggle

Blog and Documentation Sections

User Page Review on Kaggle

Treasure in The Kaggle

Publishing Notebooks on Kaggle

What Should Be Done to Achieve Success in Kaggle?

First Step to the Project

Notebook Design to be Used in the Project

Examining the Project Topic

Recognizing Variables in Dataset

Required Python Libraries

Loading the Dataset

Initial analysis on the dataset

Examining Missing Values

Examining Unique Values

Separating variables (Numeric or Categorical)

Examining Statistics of Variables

Numeric Variables (Analysis with Distplot)

Categoric Variables (Analysis with Pie Chart)

Examining the Missing Data According to the Analysis Result

Numeric Variables – Target Variable (Analysis with FacetGrid)

Categoric Variables – Target Variable (Analysis with Count Plot)

Examining Numeric Variables Among Themselves (Analysis with Pair Plot)

Feature Scaling with the Robust Scaler Method for New Visualization

Creating a New DataFrame with the Melt() Function

Numerical - Categorical Variables (Analysis with Swarm Plot)

Numerical - Categorical Variables (Analysis with Box Plot)

Relationships between variables (Analysis with Heatmap)

Dropping Columns with Low Correlation

Visualizing Outliers

Dealing with Outliers

Determining Distributions of Numeric Variables

Transformation Operations on Unsymmetrical Data

Applying One Hot Encoding Method to Categorical Variables

Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms

Separating Data into Test and Training Set

Logistic Regression

Cross Validation for Logistic Regression Algorithm

Roc Curve and Area Under Curve (AUC) for Logistic Regression Algorithm

Hyperparameter Optimization (with GridSearchCV) for Logistic Regression Algorithm

Decision Tree Algorithm

Support Vector Machine Algorithm

Random Forest Algorithm

Hyperparameter Optimization (with GridSearchCV) for Random Forest Algorithm

Project Conclusion and Sharing

Screenshots

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