# Load necessary libraries
```{r}
library(survival)
library(glmnet)
library(dplyr)
library(ggplot2)
library(caret)

```

# I. Data management
## Load the Heart Disease dataset (Cleveland dataset)
```{r}
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data"
col_names <- c("age", "sex", "cp", "trestbps", "chol", "fbs", "restecg", "thalach", 
               "exang", "oldpeak", "slope", "ca", "thal", "num")

heart <- read.csv(url, header = FALSE, col.names = col_names)
```

## Question 1 Convert categorical variables to factors
```{r}
```

## Question 2 Simulate survival time
```{r}

```

## Question 3 Create a random train-test split
```{r}

```

# II. Models

## Question 1 Fit a CPH model using all covariates (except ca and thal)

```{r}
```

## Question 2 L1 Regularized-CPH
```{r}

```

## Question 3 Mix of L1 and L2 Regularization
```{r}

```

# III. Model evaluation

## Question 1 Evaluate each model evaluation with appropriate C-index
```{r}

```

# IV. Model predictions

## Question 1 Plot prediction curves
```{r}

```

# V. Now let's re-organise with another validation approach (For next time)
```{r}

```
