The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).
*The overall goal of the project is to Improve Your Score
- Objective: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
- Project Tools: Python (libraries: Numpy, Pandas, Scikit-learn) Data Visualization Tools: Matplotlib and Seaborn
- Data: Titanic Data Set from Kaggle
- Get Data
- Exploratory Data Analysis
- Data Cleaning
- Create a Logistic Regression
- Predict and Evaluate
- Objective: create a model that will predict whether or not they will click on an ad based off the features of that user.
- Project Tools: Python (libraries: Numpy, Pandas, Scikit-learn) Data Visualization Tools: Matplotlib and Seaborn
- In this project we will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement.
- Get Data
- Exploratory Data Analysis
- Logistic Regression
- Predict and Evaluate