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Machine Learning Project: Heart Attack Prediction Analysis
Introduction to Machine Learning with Real Hearth Attack Prediction Project
1- First Step to the Hearth Attack Prediction Project (15:15)
FAQ about Machine Learning, Data Science
2- Notebook Design to be Used in the Project (14:16)
Project Link File - Hearth Attack Prediction Project, Machine Learning
3- Examining the Project Topic (10:00)
4- Recognizing Variables In Dataset (17:02)
First Organization
5- Required Python Libraries (8:40)
6- Loading the Statistics Dataset in Data Science (1:48)
7- Initial analysis on the dataset (12:21)
Preparation For Exploratory Data Analysis (EDA) in Data Science
8- Examining Missing Values (10:04)
9- Examining Unique Values (9:10)
10- Separating variables (Numeric or Categorical) (3:12)
11- Examining Statistics of Variables (18:12)
Exploratory Data Analysis (EDA) - Uni-variate Analysis
12- Numeric Variables (Analysis with Distplot): Lesson 1 (14:29)
13- Numeric Variables (Analysis with Distplot): Lesson 2 (3:57)
14- Categoric Variables (Analysis with Pie Chart): Lesson 1 (13:54)
15- Categoric Variables (Analysis with Pie Chart): Lesson 2 (15:39)
16- Examining the Missing Data According to the Analysis Result (10:09)
Exploratory Data Analysis (EDA) - Bi-variate Analysis
17- Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 1 (8:32)
18- Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 2 (7:30)
19- Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 1 (3:57)
20- Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 2 (12:56)
21- Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1 (4:56)
22- Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2 (6:54)
23- Feature Scaling with the Robust Scaler Method (9:00)
24- Creating a New DataFrame with the Melt() Function (11:22)
25- Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 1 (6:25)
26- Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 2 (11:10)
27- Numerical - Categorical Variables (Analysis with Box Plot): Lesson 1 (7:19)
28- Numerical - Categorical Variables (Analysis with Box Plot): Lesson 2 (7:44)
29- Relationships between variables (Analysis with Heatmap): Lesson 1 (6:04)
30- Relationships between variables (Analysis with Heatmap): Lesson 2 (12:31)
Preparation for Modelling in Machine Learning
31- Dropping Columns with Low Correlation (3:46)
32- Visualizing Outliers (8:31)
33- Dealing with Outliers – Trtbps Variable: Lesson 1 (9:57)
34- Dealing with Outliers – Trtbps Variable: Lesson 2 (10:53)
35- Dealing with Outliers – Thalach Variable (8:21)
36- Dealing with Outliers – Oldpeak Variable (7:50)
37- Determining Distributions of Numeric Variables (5:02)
38- Transformation Operations on Unsymmetrical Data (4:55)
39- Applying One Hot Encoding Method to Categorical Variables (5:24)
40- Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms (2:29)
41- Separating Data into Test and Training Set (7:04)
Modelling for Machine Learning
42- Logistic Regression (6:53)
43- Cross Validation (5:40)
44- Roc Curve and Area Under Curve (AUC) (8:17)
45- Hyperparameter Optimization (with GridSearchCV) (12:53)
46- Decision Tree Algorithm (5:05)
47- Support Vector Machine Algorithm (5:02)
48- Random Forest Algorithm (6:17)
49- Hyperparameter Optimization (with GridSearchCV) (10:53)
Conclusion
50- Project Conclusion and Sharing (3:31)
Extra
Machine Learning with Real Hearth Attack Prediction Project
40- Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms
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