Kaggle Master with Heart Attack Prediction Kaggle Project
Monday, May 30, 2022
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