Tools: Colab/Jupyter Notebook, GitHub
Algorithm Category: Univariate Classification
Purpose: Data Cleaning, Apply Algorithm, Apply GridSearchCV
Algorithm: Logistic Regression, Support Vector Machine Classification, Decison Tree Classification, K Nearest Neighbors Classification
Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn
Projects: Social Network Ads Purchase
Problem Description
Predict the user if they willing to puchase from the social network ads.
Problem Task
Target Cluster Datasets is about a determine the social media ad purchase based on following fields.
Problem Variables
There are two tables could be merged by ID
| Field | Description | Unit | dtype | Comments |
|---|---|---|---|---|
| Table 1 | Social_Network_Ads.csv | |||
| User ID | Each user have own identifier | Number | Continous | |
| Gender | Gender | Binary Category | ||
| Age | Age | Continous | ||
| EstimatedSalary | Salary earned by estimation | US Dollar | Continous | |
| Purchased | User social media ads purchsed history | Binary Category | Target Variable |
Reference:
Dateset:Original Dataset.csv
Train Processed Dataset:Train_X.csv,
Train_y.csv
Test Processed Dataset:Test_X.csv,
Test_y.csv
Demo:Jupyter Notebook/Colab Link
