Linear Regresion with PyTorch

The dataset selected for this notebook is the Titanic dataset.

This dataset has several information where each row represents one person. The columns describe different attributes about the person including whether they survived, their age, their passenger-class, their sex, and the fare they paid. Finally the dataset is devided in training and evaluation data.

The training and evaluation data can be found in the following links:

• Training data: https://storage.googleapis.com/tf-datasets/titanic/train.csv
• Evaluation data: https://storage.googleapis.com/tf-datasets/titanic/eval.csv

This notebook is focused in the prediction of wheather a passenger survived or not, and it is build based on the following steps:

1. Install depencies
2. Import modules
3. Download and dataset analysis
4. Preparing the dataset for training
5. Creating a Linear Regression model
6. Training the model and fitting the data
7. Making predictions using the trained model
8. Saving the model
9. Commiting and uploading the notebook

This notebook is based on the concepts from the first two lectures and as part of the Assignment 2 of the course Deep Learning with PyTorch, which you can find in the following links:

• Assignment 2 - Linear Regression: click here
• Lecture 1: click here
• Lecture 2: click here
• PyTorch basics: https://jovian.ml/aakashns/01-pytorch-basics
• Linear Regression: https://jovian.ml/aakashns/02-linear-regression
• Logistic Regression: https://jovian.ml/aakashns/03-logistic-regression
• Linear regression (minimal): https://jovian.ml/aakashns/housing-linear-minimal
• Logistic regression (minimal): https://jovian.ml/aakashns/mnist-logistic-minimal

Step 1: Install dependencies

Step 2: Import Modules