ModelSelection

In this repo i have:

• Clean the data
• Split the data into train, validation and test
• Fit an initial model and evaluate
• Tune the hyperparameter
• Evaluate the data into validation
• Select and evaluate final model on test set

Titanic.ipynb file is the foundation. Cleaning the data and Spliting data into train, val and test was done in that file. Then i have created five models :

• Logistic Regression
• Support Vector Machines
• Multilayer Perceptron
• Random Forest
• Boosting

Below are the brief introduction for each model

• Logistic Regression

  Regression is a statistical process for estimating the relationships among variables. This is often used to make a prediction about some outcome. Linear regression is one type of regression that is used when you have a continuous target variable.

• Support Vector Machines

  Support vector machines is a classifier that finds an optimal hyperplane that maximizes the margin between two classes.

• MLP

  Multi-layer perceptron is a classic feed-forward artificial neural network. This is one of the core components of some deep learning algorithms.

• RF

  A random forest is a machine learning technique that’s used to solve regression and classification problems. It utilizes ensemble learning, which is a technique that combines many classifiers to provide solutions to complex problems.

• Boosting

  Boosting combines the weak learners to form a strong learner, where a weak learner defines a classifier slightly correlated with the actual classification. In contrast to a weak learner, a strong learner is a classifier associated with the correct categories.

I have created five notebook for five model and train the model with the train data set and then i have found the best param or the best estimator for each model and best estimator was saved into json files of each model.

Best Estimator of each model

Comparision of algorithm

FinalModelSelection does this things :

• Evaluate all of our saved models on the validation set
• Select the best model based on performance on the validation set
• Evaluate that model on the holdout test set

How it does it

Notes:

• I have used google colab and i have mounted my drive so all the path of the files are related to my drive. Please change if you are using in the locally or your colab
• titanic.csv is the dataset which is used in the machine learning