Introduction & References used

#Introduction-&-References-used

Goal: Train a model with 891 training examples to predict the survival outcome (0,1) of 418 test examples

#Goal:-Train-a-model-with-891-training-examples-to-predict-the-survival-outcome-(0,1)-of-418-test-examples

1. Importing of Libraries and dataset

#1.-Importing-of-Libraries-and-dataset

2. Categorical Variable Analysis

#2.-Categorical-Variable-Analysis

3. Numerical Variable Analysis

#3.-Numerical-Variable-Analysis

4. Feature Engineering & Correlation

#4.-Feature-Engineering-&-Correlation

5. Transformation of test set

#5.-Transformation-of-test-set

6. Modeling & Predictions

#6.-Modeling-&-Predictions

- Created and used a logistic regression model from scratch via gradient descent

#--Created-and-used-a-logistic-regression-model-from-scratch-via-gradient-descent

- Applied SVM & Random Forest by using the sklearn library with some hyperparameter tuning

#--Applied-SVM-&-Random-Forest-by-using-the-sklearn-library-with-some-hyperparameter-tuning

- Applied a simple artifical neural network with Keras/Tensorflow

#--Applied-a-simple-artifical-neural-network-with-Keras/Tensorflow

7. References

#7.-References

- A - Z Machine Learning Udemy Course by Krill on Artifical Neural Network

#--A---Z-Machine-Learning-Udemy-Course-by-Krill-on-Artifical-Neural-Network
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1. Importing of libraries

#1.-Importing-of-libraries

2. Categorical Variable Analysis

#2.-Categorical-Variable-Analysis

2A. Pclass Variable

#2A.-Pclass-Variable

2B. Name Variable

#2B.-Name-Variable

2C. Sex Variable

#2C.-Sex-Variable

2D. Embarked Variable

#2D.-Embarked-Variable

3. Numerical Variables

#3.-Numerical-Variables

3A. Family Size Variable + 1 for himself

#3A.-Family-Size-Variable-+-1-for-himself

3B. Fare Variable

#3B.-Fare-Variable

3C: Age Variable

#3C:-Age-Variable

3D. Cabin Variable

#3D.-Cabin-Variable

4. Feature Selection / Correlation

#4.-Feature-Selection-/-Correlation

5. Transforming Kaggle test dataset

#5.-Transforming-Kaggle-test-dataset

6. Modeling and predictions

#6.-Modeling-and-predictions

6A. Creation and testing of a logistic regression model from scratch

#6A.-Creation-and-testing-of-a-logistic-regression-model-from-scratch

Sklearn Logistic Regression

#Sklearn-Logistic-Regression

6B. Sklearn Kernel SVM

#6B.-Sklearn-Kernel-SVM

6C. Sklearn Random Forest

#6C.-Sklearn-Random-Forest

6D. Artifical Neural Network

#6D.-Artifical-Neural-Network

Using Kaggle Test Dataset

#Using-Kaggle-Test-Dataset

SVM Kaggle Submission

#SVM-Kaggle-Submission