Datascience_projects

Project in data science

Project 1:Titanic Survival Exploration with Decision Trees

Titanic survival data Explore by implementing a decision tree in sci-kit-learn

Project 2: Classification Models Evaluations

The aim of this workbook is to provide practice with metrics specifically related to classification problems. To this end, we will use the spam dataset.

Project 3: Regression Models Evaluations

The aim of this workbook is to provide practice with metrics specifically related to regression problems. To this end, we will use the Boston Housing dataset.

Project 4: Improving a model with Grid Search

In this mini-lab, we'll fit a decision tree model to some sample data. This initial model will overfit heavily. Then we'll use Grid Search to find better parameters for this model, to reduce the overfitting.

Project 5: Diabetes Case Study

In this mini-lab, we'll try to optimize a number of models using Randomized Grid Searching in this notebook.

Project 6: Find Donors for CharityML

Find Donors for CharityML with Kaggle: CharityML is a fictitious charity organization that provides financial support. In an effort to improve donor outreach effectiveness, I had to build an algorithm that best identifies potential donors. I evaluated and optimized several different supervised learners (AdaBoost, Decision Tree & Logistic Regression) to determine which algorithm will provide the highest donation yield.