I'm newbie at the Kaggle and I'm new to machine learning. I'll try to make this exploration interesting and detailed.

Content¶

• 1. Data analysis
  • 1.1. Expectations
  • 1.2. Loading and examination the dataset
  • 1.3. Data managing
• 2. Features analisys
  • 2.1. Passenger Class, Sex
  • 2.2. Age
  • 2.3. Parch, Sibsp
  • 2.4. Embarked
• 3. Predictions
  • 3.1. Data cleaning
  • 3.2. Results
• 4. Conclusion

1. Data analysis¶

1.1. Expectations¶

What I do expect from this analysis? I’ll create a model predicting a survival on the Titanic. And on the way to prediction I'll make illustrations for all found dependencies.
First of all, I want to understand what kind of variables do I have.
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In this post I want give a simplified explanation of what the linear regression model is and how to apply it for data predictions using python and some open python libraries (including scikit-learning).

Supervised learning is one of the major categories of Machine Learning algorithms. "Supervised" means we already have a dataset in which "correct answers" were given. For example, we have a stock data with open values and close values for a past few years, and we want to predict future values (prices or indexes). Supervised learning is subdivided into Regression problem and Classification problem. Regression problem means we're trying to predict a continuous value output (like predict stock value).