Kaggle Master with Heart Attack Prediction Kaggle Project

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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What you'll 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