## Stock Market Data ----
# 1. Load and explore (through numerical and graphical summaries) the `Smarket` data (this is in `ISLR` package). 

## Logistic Regression ----
# 1. Fit a logistic regression model to predict `Direction` using `Lag1` through `Lag5` and `Volume`. Describe your results.
# 2. Create a confusion matrix for the training data.
# 3. What is the overall error rate of the model?
# 4. Create two data sets, `train` and `test` that correspond to the observations from 2001 to 2004 (`train`) and 2005 (`test`).
# 5. Repeat 1-3, but obtain the test confusion matrix and error rate.
# 6. Repeat 5, but with a model of `Direction` on `Lag1` and `Lag2` only.

## LDA ----
# 1. Fit a linear discriminant analysis model to the `train` data set you created in the previous section with `Direction` as the response and `Lag1` and `Lag2` as the predictors.
# 2. What are the values for $\hat{\pi}_1$ and $\hat{\pi}_2$?
# 3. Create a confusion matrix for the `test` data.
# 4. What is the test error rate?

## QDA ----
# 1. Fit a quadratic discriminant analysis model to the `train` data set you created in the previous section with `Direction` as the response and `Lag1` and `Lag2` as the predictors.
# 2. Create a confusion matrix for the `test` data.
# 3. What is the test error rate?
  
## KNN ----
# 1. Fit a KNN model with $K = 1$ (see the `class` library) to the `train` data set you created in the previous section with `Direction` as the response and `Lag1` and `Lag2` as the predictors.
# 2. Create a confusion matrix for the `test` data.
# 3. What is the test error rate?
# 4. Repeat 1.-3. with $K = 3$ and $K = 5$.

#Of all the models you fit today, which would you pick to predict values of `Direction` and why?