Python for Data Science at PT Hacktivate

Welcome to my GitHub repository for the Independent Study program titled Python for Data Science at PT Hacktivate, held by the Ministry of Education, Culture, Research, and Technology. Throughout this program, I completed several assignments and final projects. I will briefly describe each assignment and final project below:

Assignments

Advanced Visualization - In this assignment, I used the London Crime data from Kaggle to create advanced visualizations using Python. I received a score of 100/100 for this assignment.
Inferential Statistics - In this assignment, I used the NYC Property Sales data from Kaggle to perform inferential statistics using Python. I received a score of 100/100 for this assignment.
Classification with Logistic Regression, KNN, SVM, Naive Bayes, Decision Tree, and Random Forest - In this assignment, I used the Bank Marketing data from the UCI Machine Learning Repository to perform classification using various algorithms including logistic regression, KNN, SVM, naive bayes, decision tree, and random forest. I received a score of 100/100 for this assignment.

Linear and Polynomial Regressions Predicting Prices - In this final project, I used the Uber and Lyft (Boston, MA) data from Kaggle to perform linear and polynomial regressions to predict prices. I received a score of 100/100 for this project.
Logistic Regression and SVM Predicting Rain - In this final project, I used the Rain in Australia data from Kaggle to build logistic regression and SVM models to predict if it will rain tomorrow. I received a score of 100/100 for this project.
Random Forest and Gradient Boosting Predicting Survival of Patients - In this final project, I used the Heart Failure Prediction data from Kaggle to build random forest and gradient boosting models to predict the survival of patients. I received a score of 100/100 for this project.
Factor Analysis and K-Means Clustering to Segment Credit Card Customers - In this final project, I used the Credit Card for Clustering data from Kaggle to perform factor analysis and k-means clustering to segment credit card customers. I received a score of 100/100 for this project.

I hope you find this repository helpful and feel free to reach out to me with any questions or comments. Thank you for visiting!

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